WO2019088709A2 - Information providing method for predicting ovarian cancer prognosis by using nc886 gene - Google Patents

Abstract

The present invention relates to a composition which is capable of diagnosing, preventing or treating ovarian cancer on the basis of an nc886 gene expression level. The present invention also relates to an information providing method for predicting the prognosis of ovarian cancer patients by confirming an ovarian cancer subtype and an nc886 gene expression level in ovarian cancer patients. Herein, the information providing method includes an information providing method for identifying the nc886 gene expression level with expression patterns of other genes related to nc886. The present invention also relates to a use of a composition for predicting an ovarian cancer prognosis, the composition comprising an agent for measuring the nc886 gene expression level. Therefore, the present invention can exhibit an effect of preventing or treating ovarian cancer by inhibiting nc886 gene expression and can diagnose ovarian cancer or predict the prognosis of ovarian cancer patients by measuring the nc886 gene expression level. In addition, the present invention analyzes the nc886 expression level to predict ovarian cancer metastasis or resistance to an anticancer agent and thus is effective as an information providing method for predicting an ovarian cancer prognosis.

Description

Methods for the prediction of ovarian cancer prognosis using NC886 gene

This application claims priority from Korean Patent Application No. 10-2017-0144330, filed on October 31, 2017, the entire contents of which are incorporated herein by reference.

The present invention relates to a composition capable of diagnosing, preventing or treating ovarian cancer based on the expression level of nc886 gene.

The present invention also relates to a method for providing information for predicting the prognosis of ovarian cancer patients by confirming the expression level of ovarian cancer subtype and nc886 gene in ovarian cancer patients. Herein, the information providing method includes an information providing method for identifying the expression level of the nc886 gene as an expression pattern of other genes related to nc886.

The present invention also relates to the use of a composition comprising an agent for measuring the level of expression of the nc886 gene for ovarian cancer prognosis prediction.

Transforming growth factor-β (TGF-β) and microRNA (miRNA) pathways are most important for reprogramming gene expression in cancer. TGF-β, a cytokine that regulates a large number of target genes through SMAD transcription factors (TFs), has two seemingly opposite roles in cancer. TGF-β plays a role of carcinogenesis by promoting epithelial-mesenchymal transition (EMT) and fibrosis as malignant tumor progresses, although it is a tumor suppressor that suppresses cancer cell proliferation at the initial stage. miRNAs, non-coding RNAs (ncRNAs) that are processed by Dicer enzymes inhibit expression of messenger RNA (mRNA). Expression of several miRNAs such as miR-21, miR-155 and miR-17-92a-1 clusters is increased in some malignant tumors, but expression of most miRNAs is inhibited in cancer. The overall down-regulation of miRNAs promotes tumorigenesis as evidenced by short hairpin RNA (shRNA) -mediated knockdown or improved tumor formation upon a single allele loss of Dicer.

Like most other cancers, TGF-β and miRNA play an important role in the development, progression, and recurrence of ovarian cancer. Most ovarian cancers are derived from the ovarian surface epithelium (OSE) and the tubal epithelium. Epithelial tumors are composed of heterogeneous tumors and are classically classified into four major histological types (serous, mucinous, endometrioid, and clear cell). The most common type of ovarian cancer is the high grade tubal epithelium, which represents a high frequency of TP53 mutations. Mucinous, endometriosis, and low grade serous cancers are characterized by KRAS, ERBB2, BRAF, and PTEN mutations. It is also divided into four molecular types (immune-reative, differentiated, proliferative, and mesenchymal) for the purpose of diagnosis of ovarian cancer. Recent studies have integrated the mRNA / miRNA expression profile to classify ovarian cancer patients into two subtypes. According to this classification, the overall expression level of the miRNA target gene is determined in ovarian cancer subtypes (hereinafter referred to as " iM / fibrosis ") named as "integrated mesenchymal (iM) subtype" or "fibrosis" subtype . On the other hand, miRNA target genes are suppressed in other ovarian cancer subtypes called "integrated epithelial (iE) subtypes" or "oxidative stress" subtypes (hereinafter "iE / The reason why the TGF-beta and miRNA pathways are reversed according to the subtype of ovarian cancer is not studied or disclosed.

nc886 is a long ncRNA of 101 nucleotides transcribed by RNA Polymerase III (Pol III). An interesting feature of the nc886 genome region is the presence of strong CpG islands. Since Pol III activity is generally elevated in tumorigenesis, it is expected that nc886 levels will increase in cancer cells. However, nc886 has been shown to be sexually silent after some of the malignant tumors.

[Prior Art Literature]

[Non-Patent Document]

1. Inman GJ. Switching TGFbeta from a tumor suppressor to a tumor promoter. Curr Opin Genet Dev 21 , 93-99 (2011).

2. Yang D , et al. Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer. Cancer Cell 23, 186-199 (2013) .

3. Mateescu B , et al. miR-141 and miR-200a act on ovarian tumorigenesis by controlling oxidative stress response. Nat Med 17 , 1627-1635 (2011).

4. Lee K , et al. Precursor miR-886, a novel noncoding RNA repressed in cancer, associates with PKR and modulates its activity. RNA 17 , 1076-1089 (2011).

5. Park JL , et al. Epigenetic regulation of RNA polymerase III transcription in early breast tumorigenesis. Oncogene , (2017) Aug 28. doi: 10.1038 / onc.2017.285.

Accordingly, the present inventors confirmed that the expression of nc886 gene in ovarian cancer, particularly iM / fibrosis subtype, is increased, and the prognosis of ovarian cancer patients can be predicted according to the expression level of nc886. Respectively.

Accordingly, an object of the present invention is to provide a pharmaceutical composition for preventing or treating ovarian cancer, which comprises an antisense oligonucleotide which inhibits the expression of nc886 gene.

Another object of the present invention is to provide a composition for diagnosing ovarian cancer of the iM / fibrosis subtype, which comprises an agent for measuring the expression level of the nc886 gene.

Yet another object of the present invention is to provide a method for diagnosing ovarian cancer, comprising the steps of: i) identifying ovarian cancer subtypes of ovarian cancer patients;

ii) obtaining a biological sample in the ovarian cancer patient of step i);

iii) confirming the expression pattern of the nc886 gene of the sample obtained in the step ii); And

iv) classifying the ovarian cancer patient of step ii) according to the expression pattern of nc886 identified in the step iii) into the nc886 highly positive group or the nc886 lowly expressed group;

The present invention provides a method for providing information for predicting the prognosis of a patient suffering from ovarian cancer.

Yet another object of the present invention is to provide a method for screening for gene expression data, comprising: i) obtaining gene expression data in A2780_vector, SKOV3_vector, OSE80PC_nc886-kd, SKOV3_nc886, OSE80PC_control-kd, A2780_nc886 and SKOV3_TGF-

ii) analyzing the data obtained in step i) to obtain a pattern;

iii) integrating the pattern obtained in step ii) with a Bayesian compound covariate predictor (BCCP) algorithm to form a classifier; And

iv) classifying the gene expression data of patients with ovarian cancer into nc886 or nc886 low expression groups by the classifier formed in step iii);

The present invention provides a method for providing information for predicting the prognosis of a patient suffering from ovarian cancer.

Yet another object of the present invention is to provide a use for a composition for predicting ovarian cancer prognosis of the iM / fibrosis subtype of a composition comprising an agent for measuring the expression level of the nc886 gene.

In order to accomplish the above object, the present invention can provide a pharmaceutical composition for preventing or treating ovarian cancer comprising an antisense oligonucleotide which inhibits the expression of nc886 gene.

According to a preferred embodiment of the present invention, the ovarian cancer may be an ovarian cancer of the iM / fibrosis subtype.

The present invention can also provide a composition for diagnosing ovarian cancer of the iM / fibrosis subtype comprising the agent for measuring the expression level of the nc886 gene.

The present invention also provides a method of treating ovarian cancer, comprising: i) obtaining a biological sample in an ovarian cancer patient;

ii) identifying the expression pattern of the nc886 gene of the sample obtained in the step i); And

iii) classifying the ovarian cancer patient of step i) according to the expression pattern of nc886 identified in the step ii) into the nc886 highly positive group or the nc886 lowly expressed group;

The present invention provides a method for providing information for predicting the prognosis of a patient suffering from ovarian cancer.

According to a preferred embodiment of the present invention, the class of step iii) is ADAP2, ADM, ADM2, AHR, AK2P2, ALPP, ALS2CR14, ALS2CR4, ANXA2, ASS1, BCYRN1, C5orf13, C6orf170, C9orf130, CALD1, CCBE1, CCDC125 , CD68, CDKN1A, CENTA1, CEP27, CTGF, CYCSL1, CYR61, DDIT4, DENR, DHRS2, DOPEY2, DTWD2, EIF2AK4, ENO2, FAM153B, FAM40B, FAM72D, FAM73A, FLJ21986, FRMD6, FSTL1, GOS2, GDF15, GNAI1, GPT2 , HCFC1R1, HSCB, IER5L, IGFBP5, IL20RB, JAK1, KLHL28, LAMA5, LEP, MARCKS, MOAP1, NLRP8, NR4A2, NUBPL, PALLD, PCDHB9, PCK2, PDE4C, PIP5K2B, PLA2G2D, PNPT1, PPP1R10, PRO1853, PTPLA, PTPLAD2 RAB32, RASSF6, RAXL1, RBM3, RBM47, RHBDL2, RN5S9, RNU11, RNU1-3, RNU1-5, RNU1A3, RNU1F1, RNU1G2, RUNDC2C, SDHALP1, SEL1L3, SEMA3B, SLC3A2, SLC4A5, SLC5A8, SNAI2, SNORD3A, SNORD3C , SNOR3D, SPARC, SSTR2, SYAP1, TAF9L, TAGLN, TAP1, TAX1BP3, TGFB1I1, THBS1, TIPARP, TMEM106A, TMEM191A, TMEM47, TNFSF14, TPM1, TRIB1, TRIB3, TRIM6, TUBB6, UBE2L6, UGCG, USP49, VPS41, ZNF223 , ZNF682, ZNF773 and ZNF786 May be based on the expression pattern of the group.

According to a preferred embodiment of the present invention, the information providing method comprises: iv) judging that the prognosis is poor when classified as the nc886 high incidence group as a result of the classification in the step iii); As shown in FIG.

According to a preferred embodiment of the present invention, the ovarian cancer may be an ovarian cancer of the iM / fibrosis subtype.

According to a preferred embodiment of the present invention, the poor prognosis may be indicative of cancer cell metastasis, resistance to chemotherapeutic agents, and death of the patient.

According to a preferred embodiment of the present invention, the anticancer agent may be paclitaxel.

Also, the present invention provides a method for screening for gene expression, comprising: i) obtaining gene expression data in A2780_vector, SKOV3_vector, OSE80PC_nc886-kd, SKOV3_nc886, OSE80PC_control-kd, A2780_nc886 and SKOV3_TGF-?

ii) analyzing the data obtained in step i) to obtain a pattern;

iii) integrating the pattern obtained in step ii) with a Bayesian compound covariate predictor (BCCP) algorithm to form a classifier; And

iv) classifying the gene expression data of patients with ovarian cancer into nc886 or nc886 low expression groups by the classifier formed in step iii);

The present invention provides a method for providing information for predicting the prognosis of a patient suffering from ovarian cancer.

According to a preferred embodiment of the present invention, the gene is selected from the group consisting of ADAP2, ADM, ADM2, AHR, AK2P2, ALPP, ALS2CR14, ALS2CR4, ANXA2, ASS1, BCYRN1, C5orf13, C6orf170, C9orf130, CALD1, CCBE1, CCDC125, CD68, CDKN1A , CENTA1, CEP27, CTGF, CYCSL1, CYR61, DDIT4, DENR, DHRS2, DOPEY2, DTWD2, EIF2AK4, ENO2, FAM153B, FAM40B, FAM72D, FAM73A, FLJ21986, FRMD6, FSTL1, GOS2, GDF15, GNAI1, GPT2, HCFC1R1, HSCB , IER5L, IGFBP5, IL20RB, JAK1, KLHL28, LAMA5, LEP, MARCKS, MOAP1, NLRP8, NR4A2, NUBPL, PALLD, PCDHB9, PCK2, PDE4C, PIP5K2B, PLA2G2D, PNPT1, PPP1R10, PRO1853, PTPLA, PTPLAD2, RAB32, RASSF6 , RAXL1, RBM3, RBM47, RHBDL2, RN5S9, RNU11, RNU1-3, RNU1-5, RNU1A3, RNU1F1, RNU1G2, RUNDC2C, SDHALP1, SEL1L3, SEMA3B, SLC3A2, SLC4A5, SLC5A8, SNAI2, SNORD3A, SNORD3C, SNORD3D, SPARC , SSTR2, SYAP1, TAF9L, TAGLN, TAP1, TAX1BP3, TGFB1I1, THBS1, TIPARP, TMEM106A, TMEM191A, TMEM47, TNFSF14, TPM1, TRIB1, TRIB3, TRIM6, TUBB6, UBE2L6, UGCG, USP49, VPS41, ZNF223, ZNF682, ZNF773 And ZNF786 Which can either be one or more genes that are chosen.

According to a preferred embodiment of the present invention, the information providing method comprises the steps of: (v) predicting metastasis of ovarian cancer cells, resistance to chemotherapeutic agents and death of ovarian cancer patients when classified as nc886 high-risk group in step iv) Judging that it is bad;

As shown in FIG.

According to a preferred embodiment of the present invention, the anticancer agent may be paclitaxel.

In addition, the present invention can provide a use for prediction of ovarian cancer prognosis of a composition comprising an agent for measuring the level of expression of nc886 gene.

Accordingly, the present invention can prevent or treat ovarian cancer by inhibiting the expression of nc886 gene, and can diagnose ovarian cancer by measuring the expression level of nc886 gene or predict the prognosis of ovarian cancer patients.

In addition, the present invention is effective as an information providing method for ovarian cancer prognosis prediction by analyzing the expression level of nc886 to predict the ovarian cancer metastasis or resistance to an anticancer agent.

[Figure 1] shows the expression levels of nc886 and TGFBI in ovarian cancer cell lines.

[Figure 2] shows the result of northern hybridization after treating SKOV3 and A2780 cells with 10M of 5-Aza-2'deoxycytidine (AzadC). It was confirmed that the expression of nc886 in each cell line was increased by treatment with AzadC.

[Figure 3] shows the scatter plot of nc886 and TGFBI expression values obtained from 25 patients with ovarian cancer at Cheil Hospital.

[Figure 4] shows the results of qRT-PCR analysis of the amount of TGFBI and SMAD5 expressed by Northern hybridization after the TGF-beta treatment of SKOV3 cells and the expression level of nc886. Expression of nc886 and TGFBI was significantly increased by TGF-β treatment, but SMAD5 expression was slightly increased. On the other hand, SKOV3 cells transfected to express DNA methyl-transferase (DNMT1) showed decreased expression of nc886 in spite of TGF-β treatment.

5 shows the genome region of nc886 of chromosome 5, the degree of methylation of the CpG site of nc886, and the heat map of EpiTYPER data. The arrow indicates the transfer direction. All symbols in the magnified view (nc886 RNA, wavy lines, CpG islands, blue bars, EpiTYPER areas, dark magenta bars) were drawn at precise scales with nt coordinates computed based on the 5 'end of nc886 + The CpG positions (vertical bars) measured by EpiTYPER and pyrosequencing were designated dark magenta and purple, respectively.

FIG. 6 shows the quantification of nc886 expression levels in each cell line after northern hybridization. it was confirmed that the cell line transfected to express nc886 and the OSE80PC cell line stably expressed nc886. 'SKOV3_TGF-β' means SKOV3_ vector cells treated with TGF-β.

[Fig. 7] shows the cell adhesion assay results of each cell line. Image, and quantification graph, and the graph shows the mean and standard deviation of pentaplicate. The expression of nc886 or TGF-β treatment increased cell adhesion.

[Fig. 8] shows the results of cell migration analysis of each cell line. Image, and quantification graph, and the graph shows the mean and standard deviation of pentaplicate. The expression of nc886 or TGF-β treatment increased the cell migration capacity.

[Figure 9] shows the results of cell invasion analysis of each cell line. Image, and quantification graph, and the graph shows the mean and standard deviation of pentaplicate. The expression of nc886 or TGF-β treatment increased cell penetration ability.

[Fig. 10] shows the migration ability of a cell line when SKOV3 cell line is treated with TGF-beta and / or nc886 kd. TGF-β treatment increased the cell migration capacity, but knockdown (kd) of nc886 inhibited the effect. Representative image, quantification graph, and northern hybridization results.

[Figure 11] represents the cell viability calculated as the MTT value, plotted against the concentration of paclitaxel. The percentage of apoptotic cells at the half lethal dose (IC50, ㎛) and the half dose lethal dose concentration appeared to the right. It was confirmed that nc886 and TGF-β produced resistance to paclitaxel.

[Figure 12] shows the experimental plan for orthotopic implantation of ovarian cancer cells in nude mice and the number of mice showing ovarian cancer cell metastasis in each organ. When nc886 and TGF-β were treated, it was confirmed that ovarian cancer cells metastasized to other organs.

[Figure 13] shows a gene whose expression level is significantly changed according to ectopic expression of nc886 (" nc886_exp ") or TGF-beta treatment. Of these, 273 genes were identical, and the fc value was log2 scale.

[Fig. 14] shows a scatter plot comparing fc values of 5221 genes modified by nc886 (x axis) and TGF-beta (y axis).

[Fig. 15] shows nc886 and SNORD38B northern hybridization results as karyotype markers, and nc886 was mainly expressed in cytoplasm.

[Fig. 16] is a scattergram comparing the fc value of 2,636 genes with the expression of nc886-kd (x axis) and nc886 (y axis).

[Figure 17] is a heat map showing unsupervised hierarchical clustering of genes associated with 1024 genes and 118 nc886 and expression levels. Selection criteria for each gene are shown on the right. The corresponding expression level, which is the array value associated with the median value of the sample, is displayed in green to red (see the color bar below for scales). Seven experiments that were performed three times in each experiment showed that nc886 expression levels by nc886-low and nc886-high (blue bars and red bars at the top, respectively) according to each manipulation (ectopic expression by nc886 kd and TGF-β treatment) Group. The blue / red color signature (nc886-low / -high) was used in all bar graphs and plots.

[Figure 18] shows the results of qRT-PCR of some genes whose expression level is related to nc886. The expression level of the genes was increased according to nc886 expression or TGF-β treatment.

[Fig. 19] shows the results of acid scattering on the Biocarta Z-score, Biocart pathway with the highest inhibition (Z-score cutoff = -4) by TGF-beta treatment, and Western blotting or Northern blotting for each cell line. The red data are the NF-κB-related pathways (IL1R_PATHWAY, NFKB_PATHWAY, NTHI_PATHWAY, CD40_PATHWAY), and the size marker's kilodalton (kD) is shown on the right. In other words, it was confirmed that the regulation of the gene associated with nc886 can not be explained by the PKR / NF-κB pathway.

[Figure 20] shows the rank distribution of MIR (top) and TFT (transcription factors target; bottom) at nc886-kd (left) and TGF-beta treatment (right).

[Figure 21] shows the scatter plot for the Z score of the MIR or TFT of nc886 kd and TGF- [beta] treatment on the left. The top five candidate nc886-related miRNAs were selected and data points highlighted in red. The heat map on the right shows clustering of genes that are highly modified in nc886 kd and TGF-β treatment. One cluster contains 397 candidate miRNA target genes cross-compared with five miRNAs to directly target CNN, PDCD6 and ZEB2.

[Figure 22] shows a tactical miRNA qRT-PCR analysis including a normalized control group, small nuclear RNA U6 (U6 snRNA).

[Figure 23] shows a workflow for identifying miRNAs and target genes related to nc886 / TGF-beta in ovarian cancer. When making lists for miRNAs and target mRNAs, nc886 has a direct effect on miRNAs, so we gave larger weightings to "nc886_kd" and "TGF-β" sequence data than "nc886_exp". In stable cell lines expressing nc886, their pathway would be reduced by their long-term secondary effects. For the candidate miRNA target, 397 genes that decreased in nc886_kd and increased in TGF-β were selected (p <0.05 in both experiments). We did not apply the fc cutoff because the ability of single miRNAs to regulate individual target mRNAs is not strong. The MIR Z score calculated from the frequency of replication of the miRNA database (miRbase: www.mirbase.org/) was considered when choosing more important miRNAs.

24 shows the expression of 13 candidate miRNA target genes with recognition sites for two or more miRNAs (yellow highlighted boxes) among five miRNAs selected as genes associated with nc886 and the expression patterns of four selected genes among them as qRT -PCR shows the result of verification. The expression level of the genes decreased at nc886-kd but increased with TGF-β treatment.

[Figure 25] shows cleaved mutations with the nc886-related top 12 proteins and Dicer domains determined through mass spectrometry.

[Figure 26] shows Western blotting results and nc886-binding assay results.

[Fig. 27] shows the results of in vitro treatment and analysis of nc886 and pre-miR-200c together with an appropriate amount of FLAG-Dicer (WT) and FLAG-Dicer purified by FLAG IP. Maturation of the pre-miRNAs shown was visualized by Northern hybridization. Each bar in the graph corresponds to the corresponding band. Pre-miRNAs, mature miRNA and degradation products were quantified and plotted.

[Figure 28] (left) of SKOV3_nc886 cells transfected with pcDNA3.1-FLAG-Dicer (WT). Cells were harvested 24 hours after transfection and Western blotting (right) detected Dicer using anti-Dicer antibody. QRT-PCR results of nc886 (left) 48 hours after transfection of Dicer target siRNA and Western blotting results of Dicer (right).

Figure 29 shows the expression of miR-mimic (miR-124-3p, -183-5p, -203a-3p, -200c-3p and -19b-3p) transfected into OSE80PC cells 24 hours after cell harvesting for RNA preparation. 3p) or qRT-PCR results of four genes due to non-microbial transfection. MRNA expression of the three genes (CNN3, PDCD6, ZEB2) that are not PRKCA is inhibited by miRNA mimic, indicating that PRKCA is not regulated by these miRNAs. Represents the 3'-untranslated region of the candidate miRNA target gene. This region was cloned into a plasmid downstream of the firefly luciferase (Pp) open reading frame. A perfectly complementary target site, artificially designed as a positive control, was also replicated. Luciferase was tested for transfection with miRNA-mimic or control-mimic with the plasmid. Analysis was performed 24 hours after transfection of the luciferase plasmid. Relative luciferase values (y-axis) were calculated from several standardizations. First, the value from Pp was normalized to the Renilla luciferase (Rr) value from co-transfected pRL-SV40. In each plasmid, the Pp / Rr value of the negative control mimic was set to one. Mean and standard deviation were calculated from three samples. The 3'-untranslated region with the miRNA target region in all three genes is sufficient to confer miRNA mimetic inhibition in the luciferase assay, demonstrating that CNN3, PDCD6 and ZEB2 are direct miRNA targets in the TGF-beta / nc886 pathway did.

[Figure 30] shows the results of miRNA qRT-PCR, representative images and quantification graphs as a result of western blotting analysis of cell adhesion according to Dicer-kd for 24 hours.

[Fig. 31] shows cell migration analysis according to ectopic expression of Dicer. Western blot results for anti-FLAG antibodies show representative images and quantification plots.

Figure 32 shows the expression levels (leveled microarray values on the x axis) of each of the three genes (FRMD6, TAGLN, TPM1) in a population of ovarian cancer patients (GSE9891, n = 285, (N = 25, Example 9) in ovarian cancer patients (Cheil Hospital and Women's Healthcare Center, n = 25) using qRT-PCR Three genes (FRMD6, TAGLN, TPM1) and nc886 were measured. Both have a significant positive correlation and support that FRMD6, TAGLN and TPM1 are good representative genes of the 118 gene markers and that the 118 gene signature can be used as a proxy marker for the expression of nc886.

[Figure 33] shows a schematic diagram of the parameters of the prediction model. BCCP: Bayesian Compound Covariate Predictor, LOOCV: leave-one-out cross-validation. The cutoff for Bayesian probability is nc886 high> 0.7 or nc886 low <0.3.

[Figure 34] shows Kaplan-Meier plots for overall survival (OS) and recurrence-free survival (RFS). A total of 285 patients were stratified into two groups as predicted by the BCCP algorithm. The P value was generated by the log-rank test, and the + sign represents the censored data.

[Figure 35] is a matrix table showing the drug sensitivity of two groups of ovarian cancer patients; Of the 285 patients, 243 were excluded except for the undetermined subtype (n = 33) and the patients without chemotherapy data (n = 9). P values were determined by χ ² - test. In addition, ROC analysis was performed for the nc886 signal value of ovarian cancer patients treated with chemotherapy. The Bayesian probability for the nc886 signal was used to identify patients resistant to chemotherapy. AUC: area under the curve, CI: confidential interval.

[Figure 36] shows that the red / green letter characterizes pro- / anti- tumor formation. The bold highlighted part represents a function that has been proven to be regulated by nc 886 in the present invention.

Hereinafter, terms of the present invention will be described.

The term " antisense " of the present invention is also referred to as an antisense oligomer, and includes a sequence of a nucleotide base capable of hybridizing with a target sequence in RNA by Watson-Crick base pair formation to form mRNA and RNA: oligomer heterodimers in the target sequence, Means an oligomer having a backbone between subunits. The antisense can have an exact sequence complement or approximate complement to the target sequence, block or inhibit the translation of the mRNA, and alter the processing of the mRNA producing splice variants of the mRNA. Accordingly, the antisense of the present invention may preferably be an antisense oligomer complementary to the polynucleotide of the nc886 gene of the present invention. The antisense can be used in a manner that prevents or suppresses the expression of a carcinogen gene by administration to a subject in a conventional manner. For example, a method of mixing an antisense oligodeoxynucleotide with a poly-L-lysine derivative by electrostatic attraction and administering the mixture to a vein of a subject (JS Kim et al., J controlled Release 53, 175-182 , 1998) may be used but are not particularly limited thereto.

The term " prognosis " of the present invention means a prognosis for a medical cause (e.g., long-term viability, disease-free survival rate, etc.) and includes a positive prognosis (positive prognosis) or a negative prognosis (negative prognosis) (Eg, recurrence, tumor growth, metastasis, drug resistance, etc.), positive prognosis may include disease progression such as a disease-free state, improvement or stabilization of disease such as tumor degeneration, . In particular, the poor prognosis of ovarian cancer patients in the present invention indicates that metastasis of ovarian cancer cells occurs (Example 3, [Figure 12]), resistance to ovarian cancer is present and ovarian cancer patients may die (Example 9, [Figure 34] and [Figure 35]).

The term " prediction " of the present invention means a presumption of medical judgment, and it is an object of the present invention to provide a method for diagnosing ovarian cancer in a patient diagnosed with ovarian cancer (disease progression, improvement, Drug resistance) in advance.

The term " agent for measuring the expression level of a gene " of the present invention means a molecule that can be used for confirming the expression level of the biomarker gene of the present invention, preferably a primer pair specifically binding to the gene, Probes or antisense nucleotides.

Hereinafter, the present invention will be described in more detail.

As described above, in the prior art, it has been confirmed that the TGF-β pathway is involved in relation to the prognosis of cancer, but the relation with the nc886 gene has not been disclosed. In particular, the relevance of nc886 in relation to ovarian cancer metastasis and drug resistance has not been studied.

Since the nc886 gene according to the present invention is induced by TGF-beta in ovarian cancer, particularly iM / fibrosis subtype, it can prevent or treat ovarian cancer using miRNA pathway, and its expression level The diagnosis of ovarian cancer or its prognosis can be predicted.

Accordingly, the present invention relates to a pharmaceutical composition for preventing or treating ovarian cancer comprising an antisense oligonucleotide inhibiting the expression of the nc886 gene or a pharmaceutical composition for inhibiting the expression of the iM / fibrosis subtype comprising the agent for measuring the expression level of the nc886 gene A composition for diagnosing ovarian cancer is provided.

The ovarian cancer cell line, especially the iM / fibrosis subtype ovarian cancer cell line, has a high expression level of nc886 gene, and when the expression level of nc886 gene is high, it can be predicted that the prognosis of ovarian cancer patient is bad.

The ovarian cancer of the iM / fibrosis subtype is an ovarian cancer which strongly expresses the mesenchymal-associated miRNAs (Prior Art 2), miR-200a and negative (ovarian cancer).

The substance that inhibits the expression of the nc886 gene may be an antisense oligonucleotide, an aptamer, an siRNA, or a shRNA specifically binding to the nc886 gene, but is preferably an antisense oligonucleotide. The sequence may be 3'-UAGAGACACGACCCCAAGCU-5 ', and the sequence of the nc886 gene is the same as that of SEQ ID NO: 1.

The present invention also provides a method of treating ovarian cancer, comprising: i) obtaining a biological sample in an ovarian cancer patient;

ii) confirming the expression level of the nc886 gene of the sample obtained in the step i); And

iii) classifying the ovarian cancer patient of step i) according to the expression level of nc886 identified in the step ii) into the nc886 highly immunized group or the nc886 low expressed group;

The present invention provides a method for providing information for predicting the prognosis of ovarian cancer patients.

The "biological sample" obtained from the ovarian cancer patient may be an ovarian-derived tissue, cell, whole blood, serum, plasma, saliva, sputum, cerebrospinal fluid or urine of an ovarian cancer patient, preferably ovarian cancer tumor tissue.

In the step iii) of the present invention, classification into the nc886 high-incidence group or the nc886 low-expression group can be classified based on the expression pattern of the genes whose expression levels vary according to the expression of nc886. The expression pattern of a total of 1024 genes confirmed in the present invention can be used, but more specifically, the expression pattern of the following 118 genes can be analyzed and used as a classification standard; ADL2, ADM2, AHR, AK2P2, ALPP, ALS2CR14, ALS2CR4, ANXA2, ASS1, BCYRN1, C5orf13, C6orf170, C9orf130, CALD1, CCBE1, CCDC125, CD68, CDKN1A, CENTA1, CEP27, CTGF, CYCSL1, CYR61, IGFBP5, IL20RB, JAK1, KLHL28, LAMA5, IGFBP5, IGFBP5, IL20RB, DENR, DHRS2, DOPEY2, DTWD2, EIF2AK4, ENO2, FAM153B, FAM40B, FAM72D, FAM73A, FLJ21986, FRMD6, FSTL1, G0S2, GDF15, GNAI1, GPT2, LIP, MARCKS, MOAP1, NLRP8, NR4A2, NUBPL, PALLD, PCDHB9, PCK2, PDE4C, PIP5K2B, PLA2G2D, PNPT1, PPP1R10, PRO1853, PTPLA, PTPLAD2, RAB32, RASSF6, RAXL1, RBM3, RBM47, RHBDL2, RN5S9, RNU1-3, RNU1-5, RNU1A3, RNU1F1, RNU1G2, RUNDC2C, SDHALP1, SEL1L3, SEMA3B, SLC3A2, SLC4A5, SLC5A8, SNAI2, SNORD3A, SNORD3C, SNORD3D, SPARC, SSTR2, SYAP1, TAF9L, TAGLN, TAP1, TAX1BP3, Which is selected from the group consisting of 118 genes of TGFB1I1, THBS1, TIPARP, TMEM106A, TMEM191A, TMEM47, TNFSF14, TPM1, TRIB1, TRIB3, TRIM6, TUBB6, UBE2L6, UGCG, USP49, VPS41, ZNF223, ZNF682, ZNF773 and ZNF786 Expression of one or more genes L More preferably the expression patterns of all of the FRMD6, TAGLN, SNAI2, CALD1, CTGF and TPM1 genes are preferably based on the expression patterns of all of the 118 genes, and more preferably, It is most preferable to be based on the reference.

Iv) judging that the prognosis is poor if it is classified as the nc886 high incidence group as a result of the classification in the step iii); As shown in FIG.

The ovarian cancer of the present invention is preferably an ovarian cancer of the iM / fibrosis subtype, but is not limited thereto.

The anticancer agent of the present invention is preferably paclitaxel, but it is not limited as long as it is an anticancer agent capable of treating ovarian cancer.

The present invention also provides a method for screening for gene expression, comprising: i) obtaining gene expression data in A2780_vector, SKOV3_vector, OSE80PC_nc886-kd, SKOV3_nc886, OSE80PC_control-kd, A2780_nc886 and SKOV3_TGF-?

ii) analyzing the data obtained in step i) to obtain a pattern;

iii) integrating the pattern obtained in step ii) with a Bayesian compound covariate predictor (BCCP) algorithm to form a classifier; And

iv) classifying the gene expression data of patients with ovarian cancer into nc886 or nc886 low expression groups by the classifier formed in step iii);

The present invention provides a method for providing information for predicting the prognosis of ovarian cancer patients.

The A2780_vector, SKOV3_vector, and OSE80PC_nc886-kd cell lines are low cell line expressions of nc886, and SKOV3_nc886, OSE80PC_control-kd, A2780_nc886 and SKOV3_TGF-β cell lines are high cell line expressions of nc886 (FIG. The patterns of the genes expressed in these cells are analyzed to obtain data which can serve as basis for judging whether the expression level of nc886 is high or not. Gene expression data can be generated using the Affymetrix microarray platform (U133 v2.0), but can be used as a tool commonly used in the art to generate gene expression data. The generated gene expression data can be analyzed using BRB-ArrayTools, but it can be used as a means commonly used in the art to generate gene expression data.

Then, the pattern of the genes may be integrated into a BCCP (Bayesian compound covariate predictor) algorithm to form a classifier as a criterion for determining whether the expression level of nc886 is high. The BCCP algorithm can determine whether the sample belongs to a specific subgroup, p <0.05 for three experiments, and fold change> 1.3. In addition to the BCCP algorithm, it can be used as a means commonly used in the art to analyze patterns of genes.

When the classifier is used, the degree of expression of nc886 in a specific ovarian cancer patient and further the prognosis of the patient can be predicted (Example 9).

In the step iv) of the present invention, classification into the nc886 high expression group or the nc886 low expression group can be classified based on the expression pattern of genes whose expression levels vary according to the expression of nc886. The expression pattern of a total of 1024 genes confirmed in the present invention can be used, but more specifically, the expression pattern of the following 118 genes can be analyzed and used as a classification standard; ADL2, ADM2, AHR, AK2P2, ALPP, ALS2CR14, ALS2CR4, ANXA2, ASS1, BCYRN1, C5orf13, C6orf170, C9orf130, CALD1, CCBE1, CCDC125, CD68, CDKN1A, CENTA1, CEP27, CTGF, CYCSL1, CYR61, IGFBP5, IL20RB, JAK1, KLHL28, LAMA5, IGFBP5, IGFBP5, IL20RB, DENR, DHRS2, DOPEY2, DTWD2, EIF2AK4, ENO2, FAM153B, FAM40B, FAM72D, FAM73A, FLJ21986, FRMD6, FSTL1, G0S2, GDF15, GNAI1, GPT2, LIP, MARCKS, MOAP1, NLRP8, NR4A2, NUBPL, PALLD, PCDHB9, PCK2, PDE4C, PIP5K2B, PLA2G2D, PNPT1, PPP1R10, PRO1853, PTPLA, PTPLAD2, RAB32, RASSF6, RAXL1, RBM3, RBM47, RHBDL2, RN5S9, RNU1-3, RNU1-5, RNU1A3, RNU1F1, RNU1G2, RUNDC2C, SDHALP1, SEL1L3, SEMA3B, SLC3A2, SLC4A5, SLC5A8, SNAI2, SNORD3A, SNORD3C, SNORD3D, SPARC, SSTR2, SYAP1, TAF9L, TAGLN, TAP1, TAX1BP3, Which is selected from the group consisting of 118 genes of TGFB1I1, THBS1, TIPARP, TMEM106A, TMEM191A, TMEM47, TNFSF14, TPM1, TRIB1, TRIB3, TRIM6, TUBB6, UBE2L6, UGCG, USP49, VPS41, ZNF223, ZNF682, ZNF773 and ZNF786 Expression of one or more genes L More preferably the expression patterns of all of the FRMD6, TAGLN, SNAI2, CALD1, CTGF and TPM1 genes are preferably based on the expression patterns of all of the 118 genes, and more preferably, It is most preferable to be based on the reference.

The above information providing method of the present invention is characterized in that, when vcn86 is classified as a high-risk group in step iv), the prognosis of the ovarian cancer cell is judged to be poor by predicting metastasis of cancer cells, resistance to chemotherapy, and death of ovarian cancer patients step; As shown in FIG.

The anticancer agent of the present invention is preferably paclitaxel, but it is not limited as long as it is an anticancer agent capable of treating ovarian cancer.

The present invention also provides a use for predicting ovarian cancer prognosis of a composition comprising an agent that measures the level of expression of the nc886 gene. Since the nc886 gene and the agent for measuring the expression level are the same as those used in the above composition or information providing method, description thereof will be replaced by the description above. nc886 expression level is measured and it is predicted that the prognosis of the ovarian cancer patient is poor when it is separated into the nc886 high incidence group. The poor prognosis is because ovarian cancer cells are metastasized, resistant to anticancer drugs such as paclitaxel, Of the population can die. The expression level of the nc886 gene can be directly measured, and the expression pattern of the next 118 genes related to the expression of the nc886 gene can be analyzed and used as a classification standard; ADL2, ADM2, AHR, AK2P2, ALPP, ALS2CR14, ALS2CR4, ANXA2, ASS1, BCYRN1, C5orf13, C6orf170, C9orf130, CALD1, CCBE1, CCDC125, CD68, CDKN1A, CENTA1, CEP27, CTGF, CYCSL1, CYR61, IGFBP5, IL20RB, JAK1, KLHL28, LAMA5, IGFBP5, IGFBP5, IL20RB, DENR, DHRS2, DOPEY2, DTWD2, EIF2AK4, ENO2, FAM153B, FAM40B, FAM72D, FAM73A, FLJ21986, FRMD6, FSTL1, G0S2, GDF15, GNAI1, GPT2, LIP, MARCKS, MOAP1, NLRP8, NR4A2, NUBPL, PALLD, PCDHB9, PCK2, PDE4C, PIP5K2B, PLA2G2D, PNPT1, PPP1R10, PRO1853, PTPLA, PTPLAD2, RAB32, RASSF6, RAXL1, RBM3, RBM47, RHBDL2, RN5S9, RNU1-3, RNU1-5, RNU1A3, RNU1F1, RNU1G2, RUNDC2C, SDHALP1, SEL1L3, SEMA3B, SLC3A2, SLC4A5, SLC5A8, SNAI2, SNORD3A, SNORD3C, SNORD3D, SPARC, SSTR2, SYAP1, TAF9L, TAGLN, TAP1, TAX1BP3, Which is selected from the group consisting of 118 genes of TGFB1I1, THBS1, TIPARP, TMEM106A, TMEM191A, TMEM47, TNFSF14, TPM1, TRIB1, TRIB3, TRIM6, TUBB6, UBE2L6, UGCG, USP49, VPS41, ZNF223, ZNF682, ZNF773 and ZNF786 Expression of one or more genes L More preferably the expression patterns of all of the FRMD6, TAGLN, SNAI2, CALD1, CTGF and TPM1 genes are preferably based on the expression patterns of all of the 118 genes, and more preferably, It is most preferable to be based on the reference.

Hereinafter, the present invention will be described in more detail with reference to Examples. It is to be understood by those skilled in the art that these examples are for illustrative purposes only and that the scope of the present invention is not construed as being limited by these examples.

[Example 1]

<1-1> Cell lines, antibodies and other reagents

The ovarian cell line used in the present invention is as described in [Table 1] below. SKOV3- and A2780-derived cell lines stably expressing nc886 were prepared as follows: Briefly, cells were transfected with nc886 expression plasmids (Table 3), treated with antibiotics (depending on the plasmid, puromycin, Or G418), single colonies were isolated and nc886 expression was confirmed by Northern hybridization. Table 2 shows stable cell lines prepared and used in the present invention, and [Table 3] and [Table 4] show information of plasmids used in the production of the cell lines of Table 2 above.

In the case of TGF-beta (R & D Systems, Minneapolis, MN), 10 ng / ml was treated for 96 hours. Specifically, fresh TGF-β was supplemented daily by changing the culture medium for 96 hours, unless otherwise specified. The source of the antibody and reagent used in the present invention is the same as that of the prior art 4 unless otherwise specified.

name	Characteristic	source	nc886 level
primary OSE	primary ovarian surface epithelial cells	ScienCell Research Laboratories	++
IOSE-80PC (OSE80PC)	immortalized ovarian surface epithelial cells	Dr. Nelly Auersperg (University of British Columbia, Vancouver, British Columbia, Canada)	+++++
MPSC1 (JH514)	low grade ovarian serous adenocarcinoma	Dr. Ie-Ming Shih (John Hopkins Medical Institutions, Baltimore, MD, USA)	+++
HeyA8	high grade ovarian adenocarcinoma	Dr. Anil K. Sood (MD Anderson Cancer Center, Houston, TX, USA)	+++++++
OVCA5	high grade ovarian adenocarcinoma	Dr. Anil K. Sood (MD Anderson Cancer Center, Houston, TX, USA)	+++++++++
OVCA420	ovarian adenocarcinoma	Dr. Anil K. Sood (MD Anderson Cancer Center, Houston, TX, USA)	+
OVCAR-3	high grade ovarian adenocarcinoma	Dr. Ie-Ming Shih (John Hopkins Medical Institutions, Baltimore, MD, USA)	+++
A2780	ovarian adenocarcinoma	Dr. Ie-Ming Shih (John Hopkins Medical Institutions, Baltimore, MD, USA)	-
SKOV3	ovarian adenocarcinoma	Dr. Ie-Ming Shih (John Hopkins Medical Institutions, Baltimore, MD, USA)	-
OVCA433	ovarian adenocarcinoma	Dr. Anil K. Sood (MD Anderson Cancer Center, Houston, TX, USA)	-
OVCA432	ovarian adenocarcinoma	Dr. Anil K. Sood (MD Anderson Cancer Center, Houston, TX, USA)	-
IGROV-1	ovarian adenocarcinoma	Dr. Anil K. Sood (MD Anderson Cancer Center, Houston, TX, USA)	-
BG-1	ovarian adenocarcinoma	Dr. Kenneth S. Korach (National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA)	-

Stable cell line	Parent cell line	The plasmid used	Remarks
SKOV3_vector	SKOV3	pLKO.1 - TRC	Used for functional and mRNA analysis
SKOV3_nc886	SKOV3	pLKO.1-886 (102T)
A2780_vector	A2780	pCAGGS-GFP
A2780_nc886	A2780	pCAGGS-GFP / 886
SKOV3-GFP (vector)	SKOV3	pCAGGS-GFP	Used for functional analysis
SKOV3-GFP (nc886)	SKOV3	pCAGGS-GFP / 886
A2780-pLKO (vector)	A2780	pLKO.1 - TRC
A2780-pLKO (nc886)	A2780	pLKO.1-886 (102T)

Patient sample

25 patients with malignant ovarian cancer were sampled at Cheil Hospital and Women 's Health Care Center. The tissue samples used in this study were approved by all patients who were allowed to collect / use the tissue and by Cheil General Hospital & Women's Healthcare Center (CGH-IRB-2011-68). Surgical staging was performed according to the guidelines of the International Gynecology and Obstetrics Society (FIGO). 100 milligrams of patient tissue was added to 1 ml of TRIzol reagent (Invitrogen, Carlsbad, CA), homogenized using an Automill machine (Tokken, Chiba, Japan) and the total RNA prepared according to the manufacturer's instructions. RNA concentration and purity were determined using NanoDrop ND-1000 (NanoDrop, Wilmington, DE) prior to qRT-PCR.

Methylation analysis

Pyisoquencing was performed as described in the prior art 5. For EpiTPYER analysis, 2 의 of genomic DNA was treated with bisulfite according to manufacturer's instructions (Qiagen, Leipzig, Germany) using EpiTect Plus Bisulfite Kit. PCR primers were designed using EpiDesigner (Sequenom, http://www.epidesigner.com) and the sequence information is summarized in Table 4 below. The PCR conditions for EpiTPYER are as follows; 15 minutes at 94 占 (initial denaturation); 45 cycles of 94 ° C for 20 seconds, 56 to 62 ° C for 30 seconds, and 72 ° C for 60 seconds (amplification); 72 [deg.] C for 3 min (final extension). EpiTYPER was performed using EpiTYPER Reagent and SpectroCHIP Set (Agena Bioscience, San Diego, Calif.) And data were analyzed using EpiTYPERTM ver 1.2 (Agena Bioscience) heat map).

Measurement and analysis of RNA and mRNA sequences

Separation of the total / nuclear / cytoplasmic RNA, northern hybridization and qRT-PCR were carried out as described in the prior art 4 using the primers shown in Table 4 above. Each band of Northern blot was quantified with AlphaView software 2.0.1.1 (Alpha Innotech, San Jose, Calif.). miRNA was measured by TaqMan microRNA assay (Applied Biosystems, Waltham, Mass.). All analyzes were performed 3 times, except where otherwise indicated and Student's t-test was applied to assess its significance. Results with a p-value of less than 0.05 were considered significant. In some drawings where the difference is obvious, the p value is not displayed.

mRNA analysis was performed using the TotalPrep ™ RNA amplification kit and the HumanHT-12 v4.0 Expression BeadChip kit (Illumina, San Diego, Calif.). The degree of correlation is expressed as the Pearson's r value in scatter plots for the array fc values of pairs of samples and the significance of the linear regression model is shown in the F-statistics using R software (version 2.6.1) Based p-value. &Lt; / RTI &gt;

Plasmid DNA, RNA and transfection

The plasmid DNA in this study is summarized in Table 3 above. To prepare nc886 expression plasmids and luciferase sensor plasmids, PCR-amplified DNA fragments from genomic DNA were inserted into the indicated vector. PCR was performed with the amfiFusion High Fidelity PCR Master Mix (GenDepot, Barker, TX) using the primers listed in Table 4 above.

siDicer and siPKR are Invitrogen's Stealth RNAi ™ siRNA. siRNA sequences are available upon request. siRNA was transfected at 40 nM for various times as indicated in the figure legend. In the nc886-kd experiment, the sequences of nc886 targeted and non-targeted control antialligos designated as "anti-nc886" and "anti-control" in this study are 3'-UAGAGACACGACCCCAAGCU-5 'and 3'-CCGACCGAAATCGAGUCGCC-5', respectively. Anti- oligo was transfected at 100 nM. A custom RNA double stranded miRNA mimic of ST Pharm (Siheung, Korea) was transfected for 24 h at 10 nM. Small RNAs (siRNA, anti-oligos and miRNA mimetics) were transfected with Lipofectamine ™ RNAiMAX reagent (Invitrogen). Lipofectamine ™ 2000 reagent (Invitrogen) was used when plasmid DNA (alone or in combination with small RNA) was transfected.

Cell adhesion assay

MeT5A cells from a human mesothelial cell line (American Type Culture Collection, Manassas, VA) were plated on flat 96-well plates (10 ⁶ cells per well) and allowed to adhere overnight to form a mesocarp layer. After experimental manipulations (e. G. Ectopic expression of nc886 and TGF-beta treatment), ovarian cells were isolated by trypsinization and washed with phosphate buffered saline (PBS) and probed with 10 [mu] M CellTraker (Invitrogen) for 45 min at 37 [ CellTraker ^™ - labeled cells were washed with 0.1% fetal bovine serum (FBS) and RPMI 1640 medium to remove free dye and stained (10 ⁵ cells / well) on the middle layer. Immediately after sowing, the fluorescence of each well was read by omega (BMG Labtech, Ortenberg, Germany) and the start time for adhering to the medium layer was shown. After removal by gentle washing and aspiration at the indicated time points (60 and 120 minutes), the fluorescence of each well was imaged using Scion Image Software (Scion Corp., Frederick, Md.) To quantify the adherent cells Pixel.

Cell migration and invasion assay

Cell migration assays were performed in a Boyden chamber with a polycarbonate filter (pore size 8-μm) without polyvinylpyrrolidone (PVP). The filter was thoroughly washed with PBS and dried just before use. The same Boyden chamber was used for cell penetration analysis, but a polycarbonate filter pre-coated with Matrigel at a concentration of 1 g / ml was used. Cells were trypsinized and resuspended in RPMI 1640 containing 1% FBS and added to the upper chamber in the presence or absence of TGF-beta (5 ng / ml) for 24 or 36 hours. The same RPMI 1640 medium containing 5% FBS was added to the bottom chamber. Cells transferred to the lower surface of the membrane (or invading through the Matrigel) were fixed with methanol for 10 minutes and stained with 0.05% crystal violet for 30 minutes. After removing residual cells from the upper chamber using a cotton swab, the cells at the bottom of the filter were counted with an inverted microscope. All experiments were done in triplicate and at least 5 fields per filter were calculated.

Drug resistance

Cells were treated with paclitaxel (A.G. Scientific, San Diego, Calif.) At the indicated appropriate concentrations. On the second day after treatment, cell viability was measured by MTT assay. Cell death percentages were determined by double staining of annexin V and propidium iodide (PI), which measure the exposure of phosphatidylserine to the outside of the cell membrane, and cell membrane integrity during cell death was excluded. This was performed according to the manufacturer's protocol using the Annexin-V FITC Apoptosis Detection Kit (BioBud, Seongnam, Korea). The stained cells were analyzed by FACS cater-plus flow cytometer.

The preparation of the S100 fraction to identify interacting proteins and the pull-down of nc886,

Culturing the cells in a 15cm culture dish OSE80PC (dish) and were harvested from about 10 ⁹ cells in 30 dishes. Biotinylated synthetic nc886 was made with the MEGAscript® T7 transcription kit (Ambion, Waltham, MA) using biotin-16-UTP (Roche Applied Science, Indianapolis, IN). As a control, a biotinylated oligoribonucleotide (GE Dharmacon, Lafayette, CO) having the tRNA sequence (5'-gaagcggggucucuuauuu-3 ') was synthesized and used in parallel. Streptavidin magnetic beads were purchased from New England Biolabs (Ipswich, Mass.). Prior to binding to the nc886-beads, the S100 fraction was thawed, incubated at room temperature (RT) for 2 hours, and super-centrifuged (40,000 rpm at 40,000 rpm for 15 minutes on a Beckman TLA 100.3 rotor). The supernatant ("clear S100 fraction") was preincubated with empty beads for 30 minutes at 4 ° C before incubation with experimental beads containing nc886 or tRNA fragments.

In vitro  Dicer processing analysis

As a source of partially purified Dicer, 293T cells were transfected with a plasmid expressing FLAG-Dicer ("pcDNA3.1-FLAG-Dicer (WT)" in [Table 3]). Approximately 10 ⁷ cells were suspended in 1 ml Buffer D [20 mM HEPES-KOH (pH 7.4), 100 mM KCl, 0.2 mM EDTA, 5% glycerol, 0.2 mM phenylmethylsulfonyl fluoride (PMSF), 0.5 mM DTT] -and-thaw and FLAG-Dicer were purified by pulling down with Anti-FLAG® M2 magnetic beads (Sigma-Aldrich, St. Louis, Mo.). The purified Dicer-FLAG-beads were resuspended in 200 μl of Buffer D and the slurry was frozen in aliquots. The miRNA precursor, nc886 and vtRNA1-1 (ncRNA is similar to nc886 but different from nc886) was synthesized with the MEGAscript® T7 transcription kit.

10 ㎕ Dicer processing of the reaction mixture is analyzed (the amount of labeled and nc886 or vtRNA1-1 number as a competitor) FLAG-Dicer bead slurry, 50 ng of the miRNA precursor of _{5 ㎕, 6 mM MgCl 2,} 0.7 mM ATP, 30 mM Creatine phosphate, and 20 ng / [mu] l creatine kinase. When a suitable amount of FLAG-Dicer bead slurry was used, the control slurry purified from 293T cells that had not been transfected through the same dissolution and pulldown process was added in parallel to adjust the total volume of the slurry to 5 μl. The mixture was incubated at 37 DEG C for 60 minutes. During the incubation, the slurry was resuspended in the reaction mixture every 10 minutes. The reaction was terminated by the addition of 10 [mu] l of RNA gel-loading buffer (95% formamide, 18 mM EDTA, 0.025% sodium dodecyl sulfate). The reaction products were separated on a 15% denaturing polyacrylamide gel and Northern hybridized using probes for each precursor (Table 4).

nc886 -Dicer binding analysis

Binding assays were performed using an in vitro Dicer process that included 0.001% Nonidet P-40, 3.2 mM Ribonucleoside-Vanadyl Complex (New England Biolabs) and 0.24 unit / ㎕ RNase Inhibitor (New England Biolabs) and excluded ATP, creatine phosphate and creatine kinase RTI ID = 0.0 &gt; 4 C &lt; / RTI &gt; for 10 minutes. After washing five times with buffer D, the bound RNA was eluted with RNase-free H ₂ O by heating the reaction at 75 ° C for 5 minutes. The eluted RNA was subjected to qRT-PCR of nc886 and vtRNA1-1 using the primers listed in Table 4 above.

Genome and Clinical Data Analysis

Gene expression and clinical data are available from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). The clinical relevance of nc886 was tested using gene expression data from ovarian cancer patients (GSE9891, n = 285). Gene expression data were generated using an Affymetrix microarray platform (U133 v2.0). All data were normalized using robust multi-array averaging. All patients underwent cytoreduction and platinum-based chemotherapy. BRB-ArrayTools was used primarily to statistically analyze gene expression data, and all other statistical analyzes were performed in the R language environment (http://www.r-project.org). Cluster analysis was performed using Cluster and Treeview 55. The development and validation of prediction models based on gene expression signatures and the estimation of prediction accuracy were performed as follows: The expression pattern (training set) of the 118 genes obtained from the cell line was determined using a Bayesian compound covariate predictor (BCCP) Algorithms have been combined to form a classifier. This algorithm estimates the probability that a particular sample belongs to a subgroup. The computational error rate of this training set was estimated by Leave-One-Out-Cross-Validation (LOOCV) classification and the classifier was applied directly to the gene expression data (test set) of ovarian cancer patients. Kaplan-Meier plots and log rank tests were used to predict patients' prognosis. To assess the usefulness of the SOH signal for predicting tolerance to adjuvant chemotherapy, we used a receiver-operating characteristic (ROC) curve analysis. For the ROC curve, the area under the curve (AUC) was calculated, ranging from 0.5 (for informative predictive markers) to 1 (for perfect predictive markers). The confidence interval (CI) of the AUC was calculated using the bootstrap method. P values less than 0.05 were considered statistically significant, and all tests were two-tailed.

Methylation specificity - high resolution melting (MS- HRM )

Bisulfite treatment and PCR reactions for MS-HRM were performed as described in the EpiTPYER assay. PCR primers were listed in Table 4 and amplicons were set in the range of nt -424 to +558 (-424 to -254, -294 to -141, -162 to -15, -40, +68, +45 to +207, +230 to +317, +293 to +443 and +469 to +558). Melting curves of MS-HRM were obtained on a LightCycler® 480 Instrument II and analyzed using GeneScanning software (Roche, Basel, Switzerland).

Chromatin Immunoprecipitation ( ChIP ) analysis

ChIP was performed using the ChIP Assay Kit (EMD Millipore, Billerica, Mass.) According to the manufacturer's protocol. Briefly, formaldehyde (final concentration 1%) - fixed cells were harvested and lysed. The chromatin was sonicated to obtain 300-500 nucleotide DNA fragments and then immunoprecipitated at 4 ° C for 24 hours using anti-SMAD4 antibody (sc-73599 from Santa Cruz Biotechnology, Dallas, TX). The antibody-chromatin complex was pulled with salmon sperm DNA-protein G-agarose beads. After cross-linking was reversed, DNA (immunoprecipitation and injection) was purified using a Fragment DNA purification kit (Intron Biotechnology, Seoul, Korea) and PCR amplified using the primers shown in Table 4.

orientation  transplantation( Orthotopic  implantation)

BALB / c thymic nude mice (Orient Bio, Seongnam, Korea) were used. All rats were 20-25 grams of females and were kept free of food and water under normal laboratory conditions. (Ovarian cells ("SKOV3_vector" and "SKOV3_nc886") were induced by inoculating 5 × 10 ⁶ cells (100 μl of PBS) into the abdominal cavity of each mouse (20 in total, n = 10 for each cell line). One-half of the mice were treated with TGF-β (1 μg per kg of body weight) from the fifth week after inoculation, and the remaining half were treated with vehicle PBS via peritoneal injection. Processing was done three times a week (Monday, Wednesday, Friday). At 8 weeks after inoculation, mice were sacrificed and dissected, and the number and location of individual tumor nodules were confirmed and recorded.

Luciferase  analysis

Luciferase assays were performed using the Dual-Luciferase® Reporter Assay System (Promega, Madison, Wis.). OSE80PC cells (96-well plate) were transfected with 5 ng of pRL-SV40 combined with 0.5 ng of the indicated sensor plasmid (Table 3) and miRNA mimic or control mimic RNA per well. Twenty-four hours after transfection, cells were harvested for luciferase analysis according to the manufacturer's instructions. The luciferase value was normalized and plotted as described in the prior art document 1.

Mass spectrophotometer

Sample preparation and nano LC-MS / MS analysis: After the streptavidin pulldown and subsequent washing steps, the washed beads were boiled in 30 μl of 1X NUPAGE® LDS sample buffer (Invitrogen, Carlsbad, Calif.) And analyzed by SDS-PAGE (NuPAGE 10% Bis-Tris gel, Invitrogen). The eluted proteins were visualized with Coomassie brilliant blue stain and cut into 6 gel pieces according to their molecular size. Each gel piece was de-stained and in-gel digested using trypsin (T9600, GenDepot, Barker, Tex.). The trypsin peptide was resuspended in 10 μl loading solution (5% methanol containing 0.1% formic acid) and loaded onto a Nano LC 1000 system (Thermo Fisher Scientific, Waltham, Calif.) Coupled to an LTQ Orbitrap Elite ™ mass spectrometer (Thermo Fisher Scientific) MA) in a nanoflow LC-MS / MS assay. The peptide was loaded onto a 2 cm x 100 mu m pre-column of Reprosil-Pur Basic C18 (1.9 mu m, Dr. Maisch GmbH, Germany). The pre-column was switched in series with a 50 mm x 150 쨉 m analytical column packed with Reprosil-Pur Basic C18 equilibrated in 0.1% formic acid / water. The peptides were eluted using a 75 min discontinuous gradient of 4-26% acetonitrile / 0.1% formic acid at a flow rate of 600 nl / min. The eluted peptides were directly electrosprayed into an LTQ Orbitrap Elite mass spectrometer operating in a data-dependent acquisition mode to obtain the splitting spectra of the top 50 strongest ions.

Database search and data validation: The MS / MS spectra obtained were retrieved from the Proteome Discoverer 1.3 interface (Thermo Fisher Scientific) for the target-decoy human Refseq database using the Mascot algorithm (Mascot 2.3, Matrix Science). The precursor mass tolerance was limited to within 20ppm, and the fragment mass tolerance was 0.5 daltons and allowed a maximum of 2 missing cuts. Oxidation, protein N-terminal acetylation and dynamic deformation of the breakdown were allowed. The peptides identified in the mascot result file have been validated with a 5% false discover rate (FDR) and are subject to manual verification. The number of recovered peptides was used to calculate the amount of protein present to compare the relative amounts between different conditions of the sample.

[Example 2]

nc886  Expression TGF lt; / RTI &gt;

To determine the expression level of nc886 in ovarian cancer cell lines (Table 1), nc886 was quantified after northern hybridization, and TGFBI was measured by qRT-PCR and expressed as a relative value to primary OSE. nc886 expression was higher in ovarian surface epithelial cells than in ovarian surface epithelial cells in OSE80PC, MPSC1, HeyA8 and OVCA5, while nc886 expression was silenced in A2780, SKOV3, OVCA433, OVCA432, IGROV-1 and BG-1. The reason for the silencing of nc886 expression was due to CpG DNA and methylation, which was treated with 5-Aza-2'deoxycytidine (AzadC), a post-translational modification that resulted in DNA demethylation restoring nc886 expression to SKOV3 and A2780 cells Followed by cell culture (Fig. 2).

The expression of nc886 and TGFBI in ovarian cancer patients was positively correlated (Pearson's r value = +0.5903; Fig. 3). To determine whether nc886 and TGFBI were simultaneously regulated by TGF-β, SKOV3 cells were treated with TGF-β. As a result, nc886 and TGFBI were significantly increased, and expression of SMAD5 was increased but weak (Fig. 4). In addition, the induction of nc886 by TGF-β was nullified by the ectopic expression of DNA methyl-transferase (DNMT1) (FIG. 4). SKOV3 cells were transfected with DNMT1 expression plasmid (and vector control) 48 hours after treatment with 10 ng / ml of TGF-beta, and after 24 hours of harvesting the cells, the expression level of nc886 was measured by qRT-PCR Respectively.

We hypothesized that the induction of nc886 by TGF-β is mediated through postmortem mechanisms of hypomethylation. First, methylation-sensitive high resolution lysis (MS-HRM) analysis was performed on several PCR segments in bisulfite-treated genomic DNA to investigate the 1000 nt region. Similar to AzadC treatment, TGF-β treatment alters the dissolution profile similar to that of low-methylated DNA (nt -424 to -254, all nt numbers +1 at the 5 'end of the nc886 transcript). Second, individual CpG sites in the -165 to + 493 nt region were investigated by EpiTYPER analysis. Treatment of TGF- [beta] and AzadC for 96 hours induced a hypomethylation of all measurable CpG sites (Figure 5, dark vertical bars at the top). Table 5 below shows the methylation value of the CpG region in the EpiTYPER assay (raw data of the heat map in FIG. 5). These data were confirmed by the results of fatigue sequencing in which the degree of methylation of three CpG sites in the nc886 promoter region was measured after treatment of SKOV3 cells with AzadC or TGF-? For 96 hours.

CpG sites *	SKOV3
	untreat	TGF -β	AzadC
-127 & -120	0.89	0.57	0.52
-113 & -109	0.91	0.58	0.53
-85 & -79	0.95	0.57	0.50
-61	0.91	0.64	0.59
-46	1.00	0.74	0.61
-15	0.76	0.47	0.38
-1 & +5	0.88	0.52	0.42
+72	0.94	0.69	0.64
+79 & +81	0.92	0.69	0.61
+253 & +257	0.96	0.64	0.69
+359 & +361	0.99	0.55	0.73
+368	0.96	0.69	0.74
+380	0.96	0.71	0.68
+418	0.94	0.62	0.57
+467	0.97	0.43	0.51

When SMAD4 TF was knocked down using small interfering RNA (siRNA), nc886 induction by TGF-β was not impaired. In the ChIP experiment, TGF-β induced SMAD4 binding in the nc886 genome region Did not do it. It was also confirmed that the induction was not due to a change in the number of genome copies. In conclusion, the present inventors confirmed that nc886 is an ncRNA induced by TGF-β through a posterior mechanism.

[Example 3]

nc886  In cell phenotype TGF -β.

To test the functional significance of nc886 in the TGF-beta pathway, SKOV3 and A2780-derived ovarian cancer cell lines stably expressing nc886 were generated. To confirm that the phenotype of the stable cell clone is not a coincidence, we transfected two different nc886 expression plasmids (Table 4) to create various stable cell lines. The expression level of ectopic nc886 was not higher than the endogenous expression level of primary OSE cells or OSE80PC cells and was similar to that of TGF-β-induction (FIG. 6), confirming that the phenotype of the stable cell clone was not an artificial result.

Since TGF-β enhances ovarian cancer metastasis, we examined the role of nc886, an ncRNA induced by TGF-β. Ectopic expression of nc886 in SKOV3 and A2780 cells promoted adhesion to mesothelial cells and migration and invasion ([Figure 7] - [Figure 9]) similar to TGF-β treatment. Data from in vitro assays were supported by in vivo experiments. SKOV3_vector or SKOV3_nc886 cells were inoculated intraperitoneally into BALB / c thymic nude mice and treated with TGF-? Or phosphate buffered saline as a vehicle control via intraperitoneal injection. As a result, it was confirmed that when nc886 was expressed and TGF-β was treated, SKOV3 cells were transferred to distant organs (FIG. 12).

In addition, the TGF-beta pathway may exhibit multiple effects, so it may be desirable to use an antisense oligonucleotide (anti-nc886 or nc886-kd) as well as a control anti-oligo ("anti- Or "control-kd") to evaluate the role of nc886. However, since nc886 is an inhibitor of protein kinase R (PKR), PKR at nc886-kd is activated and eventually kills cells. Thus, siRNA was transfected prior to nc886-kd of SKOV3_nc886 cells to inhibit PKR. This showed no difference in cell proliferation and migration compared to control siRNA (data not shown). When SKOV3 cell line was treated with TGF-β for 96 hours, transfected with siPKR for 24 hours and transfected with nc886 kd for 18 hours, the migration of SKOV3 cells was stimulated by TGF-β treatment, kd, while nc886-kd did not affect cell migration upon vehicle treatment (Fig. 10). This confirms that anti-nc886 does not exhibit nonspecific side effects. All these data show that nc886 plays an important role in TGF-β induced ovarian cancer metastasis.

Paclitaxel has been used as a front-line chemotherapeutic for ovarian cancer patients. Therefore, MTT analysis was performed to examine the effect of nc886 and / or TGF-β on cell viability in the treatment of paclitaxel. The cytotoxicity of paclitaxel was significantly alleviated by nc886 expression or TGF-beta treatment (Fig. 11). When SKOV3_nc886 cells were treated with TGF-β, paclitaxel (IC50 = 3.972 μM), which was almost 100 times higher than paclitaxel concentration, was required to induce a cytotoxic effect comparable to SKOV3_vector (IC50 = 0.048 μM) without TGF-β . The MTT data were verified by annexin V-FITC staining analysis. Paclitaxel treatment enriched the annexin V-positive (apoptosis) cells. When paclitaxel was treated at each IC50 concentration, the fraction of apoptotic cells was similar between the four experimental sets (nc886 and / or TGF-?). In the absence of paclitaxel, nc886 and / or TGF-β elicited significant differences in MTT levels or basal levels of apoptosis. In summary, paclitaxel induces apoptosis of ovarian cancer cells, while nc886 / TGF-beta confers resistance to paclitaxel.

[Example 4]

nc886  Of gene expression TGF -β-mediated Reprogramming  Imitate.

To determine whether nc886 mimics gene expression expression mediated by TGF- [beta], global gene expression by mRNA microarrays was measured. We performed microarrays from three samples to calculate the p value, and significantly evaluated the genes considered to be p < 0.05, and further selected these genes by a multiple of the expression value (fc). In SKOV3 cells, 1196 genes modified by TGF-β treatment were identified (fc cutoff = 0.5). In the same analysis for nc886 expression, 380 genes were transformed ("SKOV3_nc886" versus "SKOV3_vector"). The intersection of these two sets of genes showed that the vast majority of the nc886 target genes (273 of 380) were also TGF-beta target genes (Figure 13). In order to investigate the correlation between nc886 expression and TGF-beta treatment, 5221 genes with a p value of less than 0.05 were selected in both sets and all of them were plotted without fc cleavage. There was a strong positive correlation between these sets (Pearson's r value = +0.7478 and p <2.2e-16; Fig. 14). nc886 induced a gene expression pattern similar to that of TGF-s in SKOV3 cells, and this similarity accounts for the analogy of tumor phenotypes shown in [Figure 7] to [Figure 9].

To confirm the nc886-dependent gene expression signature, immortal OSE80PC cells were transfected with 100 nM of nc886 target anti-oligo ("anti-nc886") or non-target anti-control ("anti-control"). Cells were harvested 48 hours after transfection for nuclear / cytosolic fractionation. Two sets of experiments were added with nc886-kd in OSE80PC cells and nc886 expression in A2780 cells. Pair comparisons were performed with four data sets (SKOV3_TGF-β, SKOV3_nc886, A2780_nc886, and OSE80PC_nc886-kd) to determine whether there is a gene expression correlation. We selected 2636 genes that were significantly (p <0.05) changed in both SKOV3_nc886 and OSE80PC_nc886-kd, plotted all 2636 genes (without fc cutoff), and found a statistically significant negative correlation (Pearson's r value = -0.3123 and p <2.2e-16; Fig. 16). Similarly, gene expression of "OSE80PC_nc886-kd" is inversely correlated with that of "SKOV3_TGF-β" or "A2780_nc886", confirming that there is a positive correlation between "A2780_nc886" and "SKOV3_TGF-β" or "SKOV3_nc886" Respectively.

We also performed unchecked hierarchical clustering of all experimental sets. We chose 1024 genes from at least one experiment that diverged (1024x108) from the median value. The heat map of the 1024 gene was clearly divided into two groups of three nc886-low samples versus four nc886-high samples according to nc886 levels (Fig. 17, left). We wanted to identify a smaller subset of the nc886 signature gene that could serve as a surrogate marker of nc886 expression to be used later in subsequent clinical data analysis. For this, 118 genes (fc> 1.3 in at least 3 experiments) were selected in the heat map showing clustering of the nc886-low and nc886-high samples and the expression pattern of the samples was shown (Fig. Among these 118 genes, six genes (FRMD6, TAGLN, SNAI2, CALD1, CTGF, and TPM1) that were most increased by TGF-β treatment were selected for qRT-PCR verification. Their expression was induced by TGF-β or nc886 but decreased by nc886-kd (FIG. 18). Thus, we confirmed that cell nc886 can mimic TGF-β-mediated reprogramming of gene expression.

[Example 5]

Of ovarian cancer gene expression nc886  The control mechanism PKR  It is not through a route.

We started by analyzing our sequence data for the molecular database (MSigDB: http://software.broadinstitute.org/gsea/msigdb/) to reveal molecular mechanisms that altered the gene expression pattern of nc886. For example, in the case of Biocarta path analysis, there are 217 sets defined a priori. Each of the 217 sets contains dozens of genes in a given Biocarta pathway. Z scores were calculated (positive and negative values, respectively) indicating whether the expression of the gene in the pathway set was overall increased or decreased. 217 Biocarta pathway Z-scores were determined and plotted in "SKOV3_TGF-β" and "SKOV3_nc886", and a strong positive correlation was observed. As a result, a negative correlation was found between "OSE80PC_nc886-kd" and "SKOV3_TGF- (Fig. 19).

In the 217 Biocarta pathway, nc886 was an inhibitor of PKR, an activator of NF-κB, and TGF-β inhibited NF-κB, thus confirming the NF-κB pathway. In fact, NF-κB and related pathways (IL1R_PATHWAY, NFKB_PATHWAY, NTHI_PATHWAY, CD40_PATHWAY) were evaluated as the most inhibited pathway in TGF-β treated SKOV3 cells. Thus, a plausible mechanism is that nc886 induced by TGF- [beta] inhibits the activity of PKR and consequently NF-kB. However, nc886 did not have a significant effect on NF-κB and related pathways. None of these 4 pathways showed a significant Z score (> +3 or <-3) for nc886-kd or expression. In the case of "OSE80PC_nc886-kd", PKR was actually activated in the form of phospho-PKR (the active form of PKR), resulting in phosphorylation of its substrate eIF2α. However, NF-κB was not activated, suggesting that PKR / NF-κB binding is irrelevant in OSE80PC cells. In addition, in the PKR expression data of ovarian cancer cell lines, the phospho-PKR band (where nc886 was reduced or silenced) was not stronger than the basal expression of OSE80PC cells (where nc886 expression was high). Similarly, the phospho-eIF2? Band was not seen in any sample. All these data suggest that PKR activation is blocked by natural cellular PKC inhibitors other than nc886 in ovarian cancer cells and the role of nc886 in ovarian cancer metastasis can not be attributed to PKR activation.

[Example 6]

nc886 miRNA  Suppress the path.

Since nc886 is exclusively localized to the cytoplasm (Fig. 15), there will be little direct role of nuclear events such as TF activity and chromatin remodeling. Therefore, we investigated the effect of nc886 on gene expression.

An interesting pattern was found in the miRNA target gene set (hereinafter "MIRs") while examining other gene sets in MSigDB. In each of the 221 MIR collections, each set contains a gene with the target site of the miRNA seed sequence. As with the Biocarta pathway, the overall expression of the gene in a given MIR set yielded a Z score. A positive (or negative) MIR Z score indicates that the target gene is abundant (or depleted) and therefore the activity / level of the miRNA is low or high. The number of MIR sets is less than the number of miRNAs because different miRNAs can have the same seed sequence.

As a result, it was confirmed that nc886 and TGF-β influence the overall MIR pattern. The MIR Z score was calculated for each pair of experiments and the 221 values were sorted from the lowest value to the highest value and the data plotted (Figure 20). In the case of "OSE80PC_nc886-kd", the Z-score distribution was shifted down with the MIR set at position 190 where x-intercept was. This indicates that 189 MIRs have been depleted but only 31 MIRs have been abundant. That is, the activity / level of most miRNAs increased at nc886kd. For TGF-β treatment (and nc886-high), the pattern was reversed: 27 and 193 MIRs were depleted and enriched. For comparison, the same analysis was performed on the TF target gene set (referred to as " TFT " in MSigDB). There are 615 TFT sets, each of which has a target gene for TF. We plotted 615 TFTs in the same way. Some TFTs were abundant and other TFTs were depleted. In contrast to the biased distribution of MIR, the overall ranking distribution is relatively balanced. The tendency of MIRs for positive values also appeared in ectopic expression of nc886 in SKOV3 and A2780 (data not shown); However, the size was lower in TGF-β treatment. It was assumed that the cell line was stable and that the direct effect of nc886 on MIR was diffused during the long - term culture. Therefore, further analysis focused on nc886-kd and TGF-β treatment. Overall, nc886 levels were positive for MIR, indicating that nc886 inhibits the miRNA pathway.

The MIR Z-scores of nc886-kd and TGF-β are shown in the scatter plot. For comparison, the TFT Z-score was plotted in the same manner. In the MIR scatter plot, most data points are confined to the second quadrant, showing that MIR generally decreases and increases by nc886-kd and TGF-β. Importantly, there is a strong negative correlation between nc886-kd and TGF-β (Pearson's r = -0.5927 <-0.5, indicating a "strong correlation"). In the same analysis, TFT was also statistically significant (p = 2.783e-10) but showed a weak negative correlation (Pearson's r = -0.2509 between -0.3 and -0.1, indicating a "weak correlation"). When comparing pairs from four experimental sets ("SKOV3_TGF-β", "SKOV3_nc886", "A2780_nc886" and "OSE80PC_nc886-kd"), the MIR correlation was stronger than the TFT correlation. The scatter plot between "SKOV3_TGF-β" and "SKOV3_nc886" and between SKOV3_nc886 and OSE80PC_nc886-kd showed positive and negative correlations, respectively.

The MIR profile was examined to identify miRNAs most affected by TGF-beta / nc886. The MIR was classified according to the sum of the Z scores in the four experimental sets. Richly replicated miRNAs were selected from the top-level MIR-associated miRNAs (based on miRbase, http://www.mirbase.org/), and finally 5 miRNAs (miR-124-3p, -183-5p , -203a-3p, -200c-3p and -19b-3p) were selected and they were shown to inhibit ovarian cancer cell motility. Expression levels of three mature miRNAs (miR-124-3p, -200c-3p and -203a-3p) among the five miRNAs were measured using the miR-Taqman PCR assay. All of these were increased by nc886-kd, but were reduced in TGF-beta treated or nc886 stable cell lines consistent with the MIR profile (Fig. 22). The increase / decrease of mature miRNAs can not be attributed to the transcription of the primary miRNAs because the primary / precursor miRNAs are relatively unaffected.

Since miRNA function inhibits the expression of target mRNA, miRNA target genes were searched in the nc886 / TGF-beta pathway in ovarian cancer (Fig. 23). We considered 1552 array probes that were initially significantly altered (p <0.05) in both nc886-kd and TGF-β treatments and selected 477 probes that decreased in both nc886-kd and TGF-β. The effect of miRNA on the target mRNA is usually mild, so no fc cutoff was applied. When 397 mRNA genes of miRNA target candidates, probes for probes overlapping ncRNA and tin-free genes are removed, 397 mRNA genes are generated. We found that when the MIR gene sets the top five miRNAs we are interested in (miR-124-3p, -183-5p, -203a-3p, -200c-3p and 19b-3p; Were significantly enriched in the 397 genes. For example, 552 genes are listed in the MIR set of miR-124-3p (designated miR-124a in MSigDB), which is calculated as ~ 2.8% of the 19,313 protein coding genes in the human genome. When analyzing 397 genes for this MIR set, 25 genes (6.3%, p <0.001) were targeted by miR-124-3p. This concentration was present in all four miRNAs and supports the hypothesis that nc886-kd / TGF-β modulates the miRNA pathway and affects gene expression patterns.

The genes targeted by one or more miRNAs were selected to further probe the expected miRNA target. This is because several miRNAs that inhibit a single gene are apparently more inhibited than single miRNAs through cooperative or simple weighting. As a result, it was confirmed that over 40 miRNAs targeted 13 genes, some of which were known to be associated with ovarian cancer (Fig. 24). Among them, CNN3, PDCD6, PRKCA, and ZEB2 were selected as additional test subjects. qRT-PCR measurements confirmed that their expression was reduced by nc886-kd and increased by TGF-beta (Figure 24), consistent with the sequence data. miRNA-mimic (a mixture of miR-124-3p, -183-5p, -203a-3p, -200c-3p and -19b-3p) was tested in OSE80PC cells (nc886- miRNAs are low). The levels of all three genes except PRKCA were reduced by miRNA mimetics (Figure 29). Next, it was confirmed whether CNN3, PDCD6 and ZEB2 were direct targets by performing luciferase analysis with a sensor plasmid having a 3'-untranslated region (UTR) of these genes (Fig. 29). All three sensor plasmids were direct target genes in response to miRNA mimetics (Figure 29).

However, considering that nc886 regulates key portions of hundreds of miRNAs, and that each miRNA has some effect on hundreds of targets, nc886 is thought to inhibit total miRNA activity.

[Example 7]

nc886  By interacting with Dicer miRNA  It inhibits maturation.

Unlike most regulated ncRNAs that recognize target DNA or RNA and regulate gene expression, nc886 appears to interact by interacting with proteins and modulating their activity, as seen in the nc886 / PKR case. Therefore, the target protein of nc886 was identified in ovarian cancer. To do this, we used in vitro biotinylated nc886 to search for proteins that interact with nc886 in the soluble cytoplasmic extract of OSE80PC cells (S100 fraction). Mass spectrometric analysis revealed that several proteins including EIF2AK2 (aka PKR), ACTG2, KRT28, ADAR, PKM, DHX9, HARS2, ILF3, KRT13, DICER1, TAGLN and STAU1 (mass spectroscopic score> 10, Respectively. In addition to the effect of nc886 on MIR, Dicer was intended to identify this because it is a multi-domain enzyme that converts precursor miRNA to mature miRNA (Fig. 25). Dicer-nc886 interaction was verified by immunoprecipitation (IP) of FLAG-tagged Dicer. nc886 is present in the FLAG-Dicer IP complex but not in the negative control (compared to WT (wild type) and FLAG in [Figure 25]). The specificity of the interaction was further confirmed by comparison with the paralog ncRNA, vtRNA1-1, for nc886. vtRNA1-1 is similar to nc886 in length (99 nt vs. 101 nt) and sequence (38 identical nts when 6 consecutive identical nucleotides are calculated). Nevertheless, these ncRNAs bind differently than Dicer. Wild-type and mutant Dicer ("pcDNA3.1-FLAG-Dicer-" series) or pcDNA3.1-FLAG vectors were transfected with 293T cells. After FLAG-IP, the minor portion of the beads was resuspended in SDS-PAGE gel loading buffer and heated for 2 minutes to elute the bound protein detected by FLAG Western Blot. The remaining major portion was used for binding analysis to calculate the% input RNA from the 2 ^{- ΔCt} value. As a result, nc886 was present in FLAG-Dicer IP complex at a significantly higher level than vtRNA1-1 (WT in [Fig. 26]). When assessing the role of nc886, vtRNA1-1 shares most of its other characteristics, such as intracellular abundance, cytoplasmic location, and transcription by Pol III, thus requiring optimal control. However, vtRNA1-1 was not significant for expression of the tumor or miRNA target gene (data not shown), which is in contrast to nc886. It is assumed that Dicer binds to nc886 and vtRNA1-1, respectively, because it is proficient and incomplete.

The nc886-Dicer interaction was demonstrated by testing truncation mutations (Figure 25). The mechanism of Dicer interaction for pre-miRNA has been intensively studied in many literature. The PAZ (PIWI-AGO-ZWILLE) domain recognizes the end of the pre-miRNA and plays an important role in determining its position in the RNase III catalytic domain ("RIIIDa" and "RIIIDb" in [FIG. 25]). The ATPase / helicase domain ("Helicase" in [Figure 25]) interacts with the pre-miRNA loop and facilitates the processing of some pre-miRNAs. The role of DUF283 (a domain of unknown function) and double-stranded RNA binding domain ("dsRBD" in [Figure 25]) in pre-miRNA binding is less clear. The nc886 interaction in FLAG-IP data decreases in the absence of helicase domains (WT and ΔDUF comparisons) and further decreases (ΔDUF and ΔPAZ comparisons) when the PAZ domain is deleted. In the ΔPAZ, the nc886 signal was above the background level (FLAG) but could not be distinguished from the vtRNA1-1 signal. Thus, it has been shown that the helicase and PAZ domains contribute to the specific recognition of nc886.

Assuming that nc886-Dicer interaction induced damaged miRNA processing based on the fact that mature miRNA levels are increased / decreased when nc886 is low / high (Fig. 22), we attempted to identify this hypothesis with miRNA processing assays. Specifically, competitor (unlabeled nc886 or vtRNA1-1) was added to the processing reaction by the indicated amount. In addition to the untreated pre-miRNA bands, the treated mature miRNA bands were quantified to calculate the percentage of the processing indicated at the bottom [= mature miRNA / (mature miRNA + pre-miRNA)]. As a result, miR-124-1 and -200c precursors were efficiently processed into mature miRNAs by FLAG-purified Dicer. Importantly, nc886 efficiently inhibits miRNA maturation when added to the processing assay as compared to vtRNA1-1 (Figure 27). This data is in good agreement with the binding data (Figure 26), so it is likely that nc886 binds to Dicer and as a result, it can titrate Dicer from the true miRNA precursor. Notably, nc886 not only binds but is degraded by Dicer (Figure 27). Degradation of nc886 by Dicer also occurred in ovarian cancer cells as shown by our experiments, indicating that ectopic expression of Dicer and kd result in reduced expression levels and increased expression levels of nc886, respectively (Fig. 28). These data suggest that there is an important feedback mechanism to control miRNA as well as the intracellular levels of nc886.

The degradation of nc886 was distinguished from real miRNA processing. Unlike a single discrete band of mature miRNA size (22 nts) of true pre-miRNA, Dicer degrades nc886 into multiple bands in a wide range of 25-80 nts (Figure 27). nc886 barely produced mature miRNA in this assay consistent with mature miR-886 detectable in whole cell RNA. The degree of nc886 degradation is quantitatively less than the amount of pre-miR-200c treatment (Fig. 27). Canonical pre-miRNAs have a nearly complete duplex stem with a 2 nt 3'-overhang end structure. This region is recognized by the PAZ domain which correctly positions the cleavage site in the catalytic core. The nc886 lacks such structural features to account for less efficient wobble cutting. Although nc886 is a weaker substrate than the true pre-miRNA, nc886 is expected to be able to compete with Dicer because it is highly abundant in cells (10 ⁵ copies / cell). nc886 interacts physically with Dicer, acts as a pseudo-substrate, and inhibits the miRNA pathway by titrating away from miRNA precursors.

[Example 8]

nc886  The phenotype is due to Dicer inhibition.

The functional significance of Dicer inhibition in the TGF-beta / nc886 pathway was then assessed. SKOV3 vector cells were treated with TGF-beta for 96 hours, transfected with pcDNA3.1-FLAG-Dicer (WT) for 24 hours and analyzed. Dicer kd decreased mature miRNA levels and enhanced cell adhesion, similar to that performed by TGF-β and nc886 (FIG. 30). More importantly, ectopic expression of Dicer attenuated cell migration stimulated by TGF-beta or nc886 (Figure 31). The inhibitory effect of Dicer on cell migration was not nonspecific toxic effect because PARP level was the same and ectopic expression of Dicer did not inhibit SKOV3_vector. This clearly demonstrated that Dicer inhibition is an important event in the role of TGF-β / nc886 in promoting cell metastatic potential.

[Example 9]

nc886  In ovarian cancer patients, chemotherapy resistance (chemical resistance), poor prognosis Be relevant .

We sought to investigate the clinical relevance of nc886. A very specific gene expression signature (nc886 signature of 118 genes, Figure 17) was identified for nc886. This was verified in another small group (25 in First Hospital) collected with available RNA prior to applying the signature to population gene expression data of ovarian cancer patients (GSE9891, n = 285). For this, three genes (FRMD6, TAGLN and TPM1) among the 118 genes were selected and confirmed to be correlated with the 118 gene signature of the GSE9891 group (FIG. 32). The three genes (FRMD6, TAGLN, TPM1) were selected for the following reasons; First, nc886 was altered in our model of inhibiting the miRNA pathway to increase miRNA target genes ("nc886_kd" decreased and "nc886_exp" or "TGF-β" increased). Second, they were one of the most regulated genes by nc886 and TGF-β (8th, 9th, and 17th when summarizing fold changes in 4 experimental pairs). Third, their expression was confirmed by qRT-PCR. Then, qRT-PCR was performed in the first hospital group (n = 25) to directly compare these three genes with nc886 and confirm a strong positive correlation ([Fig.32], three Pearson's r values> +0.5) . This data used this method to cross-compare the expression data of ovarian cancer patients (group GSE9891, [Figure 33]), as the 118 gene signature represents a good proxy measure for nc886. When these patients were stratified according to the nc886 signal, overall survival and recurrence-free survival of patients with high nc886 expression were significantly lower than those of low nc886 patients (log rank test p <0.001, [Fig. 34]). As secondary chemotherapy treatment information was available and nc886 promoted drug resistance (Fig. 11), the association of nc886 with the chemical resistance test showed that most of the chemically resistant patients (15 of 18 or 83% of the nc886-high subtype (AUC: 70.2%, p = 0.002, [Fig. 35]), while nc886 was highly predictive of resistance to chemotherapy in the ROC (Receiver-Operating Characteristic) ), nc886 could direct the clinical outcome of patients with ovarian cancer after treatment, and as shown in Table 6, in the univariate and multivariate Cox regression analysis, the nc886 subtype was significantly associated with overall survival, In summary, clinical data are in good agreement with our cell culture data (Fig. 11 and [Fig. 35]), indicating that novel TGF-beta target genes and miRNAs Regulator Nc886 of the tumor can be a major predisposing factor for ovarian cancer shows comprehensively that it can be the target of future chemotherapy.

Therefore, the present invention can prevent or treat ovarian cancer by inhibiting the expression of nc886 gene, and can diagnose ovarian cancer by measuring the expression level of nc886 gene.

In addition, the present invention is effective as an information providing method for ovarian cancer prognosis prediction by analyzing the expression level of nc886 to predict the ovarian cancer metastasis or resistance to an anticancer agent.

SEQ ID No. 1 of the present invention represents the sequence of nc886.

Claims (14)

1. A pharmaceutical composition for preventing or treating ovarian cancer, comprising an antisense oligonucleotide which inhibits the expression of nc886 gene.
2. The pharmaceutical composition for preventing or treating ovarian cancer according to claim 1, wherein the ovarian cancer is ovarian cancer of the iM / fibrosis subtype.
3. A composition for diagnosing ovarian cancer of the iM / fibrosis subtype comprising an agent for measuring the expression level of the nc886 gene.
4. i) obtaining a biological sample in an ovarian cancer patient;

   ii) confirming the expression level of the nc886 gene of the sample obtained in the step i); And

   iii) classifying the ovarian cancer patient of step i) according to the expression level of nc886 identified in the step ii) into the nc886 highly immunized group or the nc886 low expressed group;

   A method for providing information for predicting the prognosis of ovarian cancer patients.
5. The method according to claim 4, wherein the class of step iii) is selected from the group consisting of ADAP2, ADM, ADM2, AHR, AK2P2, ALPP, ALS2CR14, ALS2CR4, ANXA2, ASS1, BCYRN1, C5orf13, C6orf170, C9orf130, CALD1, CCBE1, CCDC125, CD68, CDKN1A , CENTA1, CEP27, CTGF, CYCSL1, CYR61, DDIT4, DENR, DHRS2, DOPEY2, DTWD2, EIF2AK4, ENO2, FAM153B, FAM40B, FAM72D, FAM73A, FLJ21986, FRMD6, FSTL1, GOS2, GDF15, GNAI1, GPT2, HCFC1R1, HSCB , IER5L, IGFBP5, IL20RB, JAK1, KLHL28, LAMA5, LEP, MARCKS, MOAP1, NLRP8, NR4A2, NUBPL, PALLD, PCDHB9, PCK2, PDE4C, PIP5K2B, PLA2G2D, PNPT1, PPP1R10, PRO1853, PTPLA, PTPLAD2, RAB32, RASSF6 , RAXL1, RBM3, RBM47, RHBDL2, RN5S9, RNU11, RNU1-3, RNU1-5, RNU1A3, RNU1F1, RNU1G2, RUNDC2C, SDHALP1, SEL1L3, SEMA3B, SLC3A2, SLC4A5, SLC5A8, SNAI2, SNORD3A, SNORD3C, SNORD3D, SPARC , SSTR2, SYAP1, TAF9L, TAGLN, TAP1, TAX1BP3, TGFB1I1, THBS1, TIPARP, TMEM106A, TMEM191A, TMEM47, TNFSF14, TPM1, TRIB1, TRIB3, TRIM6, TUBB6, UBE2L6, UGCG, USP49, VPS41, ZNF223, ZNF682, ZNF773 And ZNF786. How to provide information for predicting the prognosis of ovarian cancer, it characterized in that the expression patterns of one or more genes comprising a reference.
6. 4. The method of claim 4, further comprising: iv) determining that the prognosis is poor if it is classified as a nc886 high incidence group as a result of the classification in step iii);

   The method comprising the steps of: (a) determining a prognosis of an ovarian cancer patient;
7. [Claim 5] The method according to claim 4, wherein the ovarian cancer is an ovarian cancer of the iM / fibrosis subtype.
8. [Claim 7] The method according to claim 6, wherein the poor prognosis means that the cancer cell is metastasized, the presence of resistance to an anticancer agent, and the patient's death.
9. 9. The method according to claim 8, wherein the anticancer agent is paclitaxel.
10. i) obtaining gene expression data in A2780_vector, SKOV3_vector, OSE80PC_nc886-kd, SKOV3_nc886, OSE80PC_control-kd, A2780_nc886 and SKOV3_TGF-? cell lines;

    ii) analyzing the data obtained in step i) to obtain a pattern;

    iii) integrating the pattern obtained in step ii) with a Bayesian compound covariate predictor (BCCP) algorithm to form a classifier; And

    iv) classifying the gene expression data of patients with ovarian cancer into nc886 or nc886 low expression groups by the classifier formed in step iii);

    A method for providing information for predicting the prognosis of ovarian cancer patients.
11. 11. The method of claim 10, wherein the gene is selected from the group consisting of ADAP2, ADM, ADM2, AHR, AK2P2, ALPP, ALS2CR14, ALS2CR4, ANXA2, ASS1, BCYRN1, C5orf13, C6orf170, C9orf130, CALD1, CCBE1, CCDC125, CD68, CDKN1A, , CTGF, CYCSL1, CYR61, DDIT4, DENR, DHRS2, DOPEY2, DTWD2, EIF2AK4, ENO2, FAM153B, FAM40B, FAM72D, FAM73A, FLJ21986, FRMD6, FSTL1, GOS2, GDF15, GNAI1, GPT2, HCFC1R1, HSCB, IER5L, IGFBP5 , IL20RB, JAK1, KLHL28, LAMA5, LEP, MARCKS, MOAP1, NLRP8, NR4A2, NUBPL, PALLD, PCDHB9, PCK2, PDE4C, PIP5K2B, PLA2G2D, PNPT1, PPP1R10, PRO1853, PTPLA, PTPLAD2, RAB32, RASSF6, RAXL1, RBM3 RBM47, RHBDL2, RN5S9, RNU11, RNU1-3, RNU1-5, RNU1A3, RNU1F1, RNU1G2, RUNDC2C, SDHALP1, SEL1L3, SEMA3B, SLC3A2, SLC4A5, SLC5A8, SNAI2, SNORD3A, SNORD3C, SNORD3D, SPARC, SSTR2, SYAP1 , TAF9L, TAGLN, TAP1, TAX1BP3, TGFB1I1, THBS1, TIPARP, TMEM106A, TMEM191A, TMEM47, TNFSF14, TPM1, TRIB1, TRIB3, TRIM6, TUBB6, UBE2L6, UGCG, USP49, VPS41, ZNF223, ZNF682, ZNF773 and ZNF786 One or more selected from the group Wherein the method comprises the steps of:
12. 11. The method according to claim 10, further comprising: v) judging that the prognosis is poor by predicting metastasis of ovarian cancer cells, resistance to chemotherapy, and death of ovarian cancer patients when classified as nc886 high-grade group in step iv);

    The method comprising the steps of: (a) determining a prognosis of an ovarian cancer patient;
13. 13. The method according to claim 12, wherein the anticancer agent is paclitaxel.
14. Use of a composition comprising an agent that measures the level of expression of the nc886 gene for the prognosis of ovarian cancer.

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