KR20220064044A - Biomarkers for diagnosing early progression of cervical cancer, and uses thereof - Google Patents

Abstract

The present invention relates to a biomarker for diagnosing acute tumor response of cervical cancer in which, as a result of analyzing microRNAs contained in exosomes isolated from the blood before and after treatment of cervical cancer patients undergoing chemotherapy, it is discovered that microRNAs related to acute tumor response (AR) of cervical cancer are microRNA 92a-1-5p (hsa-miR-92a-1-5p), microRNA 3960 (hsa-miR-3960), microRNA 202-5p (hsa-miR-202-5p), microRNA 3928-5p (hsa-miR-3928-5p), and microRNA 574-3p (hsa-miR-574-3p), and the mRNA of Diablo homolog (DIABLO), the mRNA of Ubiquitin C (UBC), and the mRNA of SIN3 transcription regulator family member A (SIN3A) are related, and to uses thereof and, more specifically, to a biomarker composition for diagnosing acute tumor response of cervical cancer, to a composition for diagnosing acute tumor response of cervical cancer, to a kit for diagnosing acute tumor response of cervical cancer, and to a method for providing information for diagnosing acute tumor response of cervical cancer, wherein the compositions and the method include the microRNAs and the mRNAs.

Description

Biomarkers for diagnosing acute tumor response of cervical cancer and uses thereof {Biomarkers for diagnosing early progression of cervical cancer, and uses thereof}

The present invention relates to a biomarker for diagnosing an acute tumor response of cervical cancer and a use thereof.

Cervical cancer is a malignant tumor that occurs frequently in women, and more than 350,000 patients worldwide die every year from cervical cancer. Cervical cancer is divided into stages 0 to 4, stage 0 cancer is also called intraepithelial cancer, and stage 1 to 4 cancer is called invasive cervical cancer. It is known that intraepithelial cancer develops 10 years earlier than invasive cervical cancer at the age of 35-40 years, and invasive cervical cancer begins to increase after the age of 30, peaks at the age of 50, and then decreases rapidly. In Korea, the prevalence of cervical cancer is about 31 per 100,000 and the mortality rate is 6.8 per 100,000, showing no significant change over the past 10 years. When looking at the frequency of incidence and prevalence by age, they are in the order of 50s, 60s, and 40s. The incidence rate is three times that of the United States, 2.5 times that of Japan, and one-third that of Brazil. This is 6%, which is higher than the 2% in the US, Japan, and Switzerland. Currently, the sexually transmitted disease model is the most widely accepted for the development of cervical cancer, and early onset sexual activity, multiple sexual partners, male factors, human papillomavirus (HPV) infection, and human immunodeficiency virus (HIV) Infection is known to be a risk factor for cervical cancer.

The recurrence rate after treatment for cervical cancer is 50% within 1 year, 25% within 2 years, and 15% within 3 years, with about 85% of patients having recurrence within 3 years. In particular, the recurrence rate after radiation therapy for cervical cancer is over 55%. Treatment of recurrent cervical cancer is determined differently depending on the type of treatment received and the patient's condition. If both treatments are not possible, chemotherapy is performed. However, there is no standard treatment, and appropriate treatment is carried out depending on the site of relapse. In general, treatment aimed at alleviating symptoms rather than curing the disease.

Currently, the evaluation of acute treatment response in cervical cancer relies heavily on imaging tools, and tumor markers such as SCC ag and Cyfra21-1 are used as an adjunct, but it is insufficient to predict the response to radiotherapy. If this can be supplemented, it will be possible to contribute to the establishment of effective treatment strategies such as increase and decrease of additional radiation dose. In order to solve these clinical problems, an accurate tool for predicting radiotherapy response is needed. Although the clinical results are simple, they have complex biological meanings behind them, so a more meaningful, higher-level approach is needed.

On the other hand, exosomes are 40-150 nm extracellular vesicles that are secreted to cancer cells or other normal cells, and perform various physiological functions through cell-to-cell signal transduction. It is known to be involved in the regulation of immunity, inflammation and stress response. They contain specific non-coding/coding RNAs, and miRNAs that regulate several coding RNAs play a key role in regulating these functions. Therefore, exosome miRNA can be considered as a high-level regulator that can be considered at the present time. In particular, since exosomes originating from normal cells as well as cancer cells are abundant in the blood, it is easy to know the relationship between the physical response of cancer patients and cancer cells, and at the same time, it is possible to examine the relationship between miRNA and coding gene.

Korean Patent Registration No. 10-2096498 (Notice on May 27, 2020)

The present invention relates to a biomarker for diagnosing an acute tumor response of cervical cancer and a use thereof, and the present inventors collect blood samples before and after treatment among cervical cancer patients receiving chemotherapy and radiation, and plasma exosomes therefrom. After isolating RNA, microRNAs related to acute tumor response (AR) of cervical cancer are detected through NGS analysis, and these are provided as biomarkers for diagnosing acute tumor response of cervical cancer.

The present invention provides a biomarker composition for diagnosing an acute tumor response of cervical cancer comprising at least one selected from the group consisting of mRNA of microRNA 92a-1-5p (hsa-miR-92a-1-5p) and DIABLO (Diablo homolog) provides

In addition, the present invention comprises an agent for measuring the expression level of one or more RNAs selected from the group consisting of mRNA of micro RNA 92a-1-5p (hsa-miR-92a-1-5p), and DIABLO (Diablo homolog). Provided is a composition for diagnosing acute tumor response of cervical cancer.

In addition, the present invention provides a kit for diagnosing an acute tumor response of cervical cancer comprising the composition for diagnosing an acute tumor response.

In addition, the present invention relates to cervical cancer comprising measuring the mRNA expression level of microRNA 92a-1-5p (hsa-miR-92a-1-5p) or DIABLO (Diablo homolog) in a biological sample derived from a subject. To provide an informational method for diagnosing acute tumor response of

According to the present invention, as a result of analyzing microRNA contained in exosomes isolated from blood before and after treatment from cervical cancer patients receiving chemotherapy and radiation treatment, microRNA related to acute tumor response (AR) of cervical cancer RNAs: micro RNA 92a-1-5p (hsa-miR-92a-1-5p), micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), micro RNA 3928-5p (hsa-miR-3928-5p), and microRNA 574-3p (hsa-miR-574-3p) were discovered, DIABLO (Diablo homolog) mRNA, UBC (Ubiquitin C) mRNA, and SIN3A As it was confirmed that it is related to the mRNA of (SIN3 transcription regulator family member A), the microRNAs and mRNA can be used as a means of diagnosing an acute tumor response (AR) of cervical cancer, and treatment and surgery It is expected to be effectively used in clinical practice as a useful biomarker for predicting the prognosis.

1 is a diagram showing the mechanisms of microRNAs and mRNA involved in the acute tumor response (AR) of cervical cancer.
2 is a schematic diagram illustrating a treatment process, a blood collection time, an MRI scan time, and a follow-up procedure for cervical cancer patients who have undergone chemotherapy and radiation therapy.
3 is an MRI photograph of a primary site for measuring a radiation treatment response to a tumor.
4 is a result of miRNA selection through subgroup analysis of miRNAs (log2FC) having a significant correlation with the acute tumor response (AR) of cervical cancer.
5 is a result of analyzing the correlation between the miRNAs selected in FIG. 4 .
6 is a result of network analysis of selected miRNA groups and their associated miRNAs.
7 is a result of network analysis that identifies the structure between the selected miRNA group and the associated mRNA.
8 is a result of screening related genes including mRNA (A) highly related to each clinical element and miRNA and mRNA (B) obtained through network analysis.
9 is a result of IPA (Ingenuity Pathway Analysis) analysis of miRNAs and mRNAs involved in the acute tumor response (AR) of cervical cancer.
10 is a result of drawing a Venn diagram of four function categories related to an acute tumor response (AR) of cervical cancer.
11 is a result of network analysis of the mRNA and miRNA reselected in FIG. 10 .
12 is a result of analyzing the categories showing statistically significant differences according to whether or not the acute tumor response (AR) of cervical cancer by analyzing the log2FC values of the RNAs included in FIG. 11 using the IPA database.
13 is a graph showing the relative ratio of categories selected for the main miRNA related to the acute tumor response (AR) of cervical cancer.
14 is a graph showing the significantly changed mRNA and miRNA according to the change of the main miRNA related to the acute tumor response (AR) of cervical cancer.
15 is a result of subgroup analysis for the acute tumor response (AR) of cervical cancer targeting the mRNA of FIG. 11 .
16 is a result of analyzing the correlation between the miRNAs selected in FIG. 16 .

The terms used in this specification have been selected as currently widely used general terms as possible while considering the functions in the present invention, but these may vary depending on the intention or precedent of a person skilled in the art, the emergence of new technology, and the like. In addition, in a specific case, there is a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the corresponding invention. Therefore, the term used in the present invention should be defined based on the meaning of the term and the overall content of the present invention, rather than the name of a simple term.

Unless defined otherwise, all terms used herein, including technical and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

Numerical ranges are inclusive of the values defined in that range. Every maximum numerical limitation given throughout this specification includes all lower numerical limitations as if the lower numerical limitation were expressly written. Every minimum numerical limitation given throughout this specification includes all higher numerical limitations as if the higher numerical limitation were expressly written. Any numerical limitation given throughout this specification shall include all numerical ranges within the broader numerical range, as if the narrower numerical limitation were expressly written down.

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

In order to confirm that plasma exosome miRNA is a biomarker that can predict the acute tumor response (AR) of cervical cancer in patients undergoing chemotherapy, before/after radiation treatment (2 weeks) ) liver plasma exosome miRNAs were analyzed and miRNAs showing clinical variables were detected through fold change.

Provided is a biomarker composition for diagnosing acute tumor response of cervical cancer, comprising at least one selected from the group consisting of this microRNA 92a-1-5p (hsa-miR-92a-1-5p) and DIABLO (Diablo homolog) mRNA do.

The acute tumor response (AR) of cervical cancer is to the extent that the tumor rapidly decreases according to radiation therapy, and the higher the value, the lower the bad treatment response, meaning a good treatment response. If the treatment response can be predicted, it will be possible to increase the treatment efficiency to control the tumor while minimizing the damage to normal tissues caused by radiation treatment by adjusting the radiation dose to be irradiated for each patient. It may provide a clue for the development of therapeutic agents to improve it.

The micro RNA 92a-1-5p (hsa-miR-92a-1-5p) consists of the nucleotide sequence shown in SEQ ID NO: 1, and the mRNA of DIABLO (Diablo homolog) is the nucleotide sequence shown in SEQ ID NO: 2 is done

The biomarker composition includes micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), micro RNA 3928-5p (hsa-miR-3928-5p), micro RNA 574- It may further include one or more selected from the group consisting of 3p (hsa-miR-574-3p), UBC (Ubiquitin C) mRNA, and SIN3A (SIN3 transcription regulator family member A) mRNA.

The micro RNA 3960 (hsa-miR-3960) consists of the nucleotide sequence shown in SEQ ID NO: 3, and the micro RNA 202-5p (hsa-miR-202-5p) consists of the nucleotide sequence shown in SEQ ID NO: 4 and the microRNA 3928-5p (hsa-miR-3928-5p) is composed of the nucleotide sequence shown in SEQ ID NO: 5, and the micro RNA 574-3p (hsa-miR-574-3p) is shown in SEQ ID NO: 6 consists of the nucleotide sequence shown, the UBC (Ubiquitin C) mRNA consists of the nucleotide sequence shown in SEQ ID NO: 7, and the SIN3A (SIN3 transcription regulator family member A) mRNA is the nucleotide sequence shown in SEQ ID NO: 8 is made of

In addition, the present invention comprises an agent for measuring the expression level of one or more RNAs selected from the group consisting of mRNA of micro RNA 92a-1-5p (hsa-miR-92a-1-5p), and DIABLO (Diablo homolog). Provided is a composition for diagnosing acute tumor response of cervical cancer.

The agent for measuring the expression level may be sense and antisense primers or probes that complementarily bind to the microRNA.

The primer is a short gene sequence serving as a starting point of DNA synthesis, and refers to an oligonucleotide synthesized for use in diagnosis, DNA sequencing, and the like. The primers may be synthesized and used with a length of typically 15 to 30 base pairs, but may vary depending on the purpose of use, and may be modified by methylation, capping, etc. by a known method.

The probe refers to a nucleic acid capable of specifically binding to mRNA having a length of several bases to several hundred bases produced through enzymatic chemical separation or purification or synthesis. The presence or absence of mRNA can be checked by labeling radioactive isotopes or enzymes, and it can be designed and modified by a known method.

The diagnostic composition includes micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), micro RNA 3928-5p (hsa-miR-3928-5p), and micro RNA 574-3p (hsa-miR-574-3p), UBC (Ubiquitin C) mRNA, and SIN3A (SIN3 transcription regulator family member A) mRNA of one or more RNA selected from the group consisting of mRNA to further include an agent for measuring the level of expression can

In addition, the present invention provides a kit for diagnosing an acute tumor response of cervical cancer comprising the composition for diagnosing an acute tumor response.

The kit consists of one or more other component compositions, solutions or devices suitable for the assay method. For example, the kit includes a genomic DNA derived from a sample to be analyzed, a primer set specific for the marker gene of the present invention, an appropriate amount of a DNA polymerase, a dNTP mixture, a PCR buffer, and water in order to perform PCR. It may be a kit. The PCR buffer may contain KCl, Tris-HCl and MgCl2. In addition, components necessary for performing electrophoresis that can confirm whether or not the PCR product is amplified may be additionally included in the kit.

In addition, the kit may be a kit including essential elements necessary for performing RT-PCR. In addition to each primer pair specific for a marker gene, the RT-PCR kit includes a test tube or other suitable container, reaction buffer, deoxynucleotides (dNTPs), enzymes such as Taq-polymerase and reverse transcriptase, DNase, RNase inhibitors, DEPC -Water (DEPC-water), sterile water, etc. may be included. It can also include a pair of primers specific for a gene used as a quantitative control.

In addition, the present invention relates to cervical cancer comprising measuring the mRNA expression level of microRNA 92a-1-5p (hsa-miR-92a-1-5p) or DIABLO (Diablo homolog) in a biological sample derived from a subject. To provide an informational method for diagnosing acute tumor response of

The biological sample is exosomes derived from blood or plasma.

The expression level is determined by next generation sequencing (NGS), polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), real-time polymerase chain reaction (Real-time PCR), RNase protection assay (RNase protection assay; RPA), microarray, and northern blotting (northern blotting) can be measured by at least one method selected from the group consisting of.

The providing method includes micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), and micro RNA 3928-5p (hsa-miR-3928-5p) in a biological sample derived from a subject. , Micro RNA 574-3p (hsa-miR-574-3p), UBC (Ubiquitin C) mRNA, and SIN3A (SIN3 transcription regulator family member A) mRNA of one or more RNA selected from the group consisting of measuring the expression level step; may be further included.

The subject may be a patient receiving radiation therapy for treating cervical cancer.

The diagnosis, in a broad sense, means judging the actual condition of a patient's disease in all aspects. The content of the judgment is the disease name, etiology, disease type, severity, detailed mode of the disease, and the presence or absence of complications. The prognosis refers to prediction of disease progression and recovery, and refers to a prospect or a preliminary evaluation.

The method of providing information for diagnosis provides objective basic information necessary as a preliminary step for diagnosis or prognosis prediction, and a doctor's clinical judgment or opinion is excluded.

Hereinafter, the present invention will be described in more detail through examples. These examples are only for illustrating the present invention, and it will be apparent to those of ordinary skill in the art that the scope of the present invention is not to be construed as being limited by these examples.

[Example]

1. Patient and Specimen

The subjects were patients diagnosed with cervical cancer who received chemotherapy and radiation therapy. The Institutional Review Board of Ajou University Hospital obtained informed consent from the donor and approved this study.

Acute treatment response was defined as the value obtained by dividing the tumor size on the imaging test taken at the time of cancer diagnosis by the tumor size on the imaging test taken at the 4th week of the start of radiotherapy. This was used as a clinical end point. The scope of evaluation of tumor size was limited to the pelvis and abdominal lymph nodes. The size of the cervical tumor was measured using a radiotherapy treatment planning system (eclipse) on a T2 weighted MRI image or a diffusion weighted MRI image. Lymph node size was measured by CT or T2 weighted MRI image. All measurements were made using images before treatment and at the 4th week of starting chemotherapy, and the types of images used before and during treatment were the same.

As shown in FIG. 2 , the treatment process, blood collection time, MRI imaging time, and follow-up procedure were performed for 28 cervical cancer patients who had undergone chemotherapy.

As shown in FIG. 3 , in order to measure the radiation treatment response to the tumor, the volume of the tumor in the primary site was measured using MRI, and the lymph node metastasis lesion was measured by CT or MRI. The scope for measuring tumor volume was limited to the pelvis and abdominal lymph nodes. The clinical results of the patients are shown in Table 1 below.

2. Dataset configuration for exosome miRNA and mRNA

Before radiotherapy and at the second week of radiotherapy, 5-10cc of blood was collected from the patients. Plasma was separated from the collected blood, and 3-5 cc of plasma samples from 29 people stored in the human-derived storage facility were used for the experiment. Exosomes were isolated from plasma samples, and NGS (Next Generation Sequencing) analysis was performed. Samples with low expression levels from plasma were excluded from the analysis.

2-1. Small RNA library construction and sequencing

Plasma was mixed with Exo2D RNA solution (Exosomeplus) to separate exosomes. Exosomal RNA of plasma-derived exosomes was extracted using miRNeasy Serum / Plasma Kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. The concentration of extracted RNA was calculated by Quant-IT RiboGreen (Invitrogen). The size of RNA was confirmed using the Agilent RNA 6000 Pico Kit and Small RNA Kit in an Agilent 2100 Bioanalyzer (Agilent Technologies, Boblingen, Germany).

10 ng of RNA isolated from each sample was used to construct a sequencing library with the SMARTer smRNA-Seq kit for Illumina according to the manufacturer's protocol. The input RNA was first polyadenylated to provide priming sequences for the oligo (dT) primers. cDNA synthesis was primed by a 3'smRNA dT Primer incorporating an adapter sequence at the 5' end of each RNA template, followed by addition of non-template nucleotides bound by SMRT smRNA Oligo enhanced with locked nucleic acid (LNA) technology. In the template conversion step, PrimeScript RT used SMART smRNA Oligo as a template to add a second adapter sequence to the 3' end of each first strand cDNA molecule. Afterwards, a full-length Illumina adapter containing an index sequence for sample multiplexing during PCR amplification was added. Forward PCR Primer bound to sequences added by SMART smRNA Oligo, whereas Reverse PCR Primer bound to sequences added by 3'smRNA dT Primer.

The amplified library was purified on a 6% Novex TBE-PAGE gel (Thermo Fisher, MA) to excise fractions greater than 138 bp (cDNA 18 bp or more + adapter 120 bp or more). The resulting library cDNA molecules contained the sequences required for clustering in the Illumina flow cell.

Libraries were gel purified and validated by checking size, purity and concentration on an Agilent Bioanalyzer. Libraries were quantified using qPCR according to the qPCR Quantification Protocol Guide (KAPA Library Quantificatoin Kit for Illumina Sequecing Platform) and validated using a TapeStation D1000 ScreenTape (Agilent Technologies, Waldbronn, Germany). Libraries were pooled in equimolar amounts and sequenced on an Illumina HiSeq 2500 (Illumina, San Diego, USA) instrument to generate 51 base reads. Image decomposition and quality value calculations were performed using modules in the Illumina pipeline.

2-2. Adapter trimming

The raw sequence reads of small RNAs obtained from different experimental samples were pretreated with miRDeep2 and analyzed. Adapter trimming was removed if there was an adapter sequence to which miRNA was attached during the smRNA library construction process using the cutadapt program. The first 3nts of all reads were trimmed to remove additional bases inserted during the SMART template-switching activity process. The adapter sequence and all 3' of the adapter were also removed. If the read matched the first 5 bp or more of the 3' adapter sequence, the sequence was considered to be in fact the adapter sequence and then trimmed from the read. For analysis, only trimmed reads of at least 18 bp were considered reliable. The remaining adapter sequences were classified as non-adapter reads. In this analysis, trimmed and non-adapter reads were combined and considered processed reads for downstream analysis.

2-3. Clustering

To minimize sequence uniqueness and computational intensity, reads treated with adapter sequences were collected to form clusters. This cluster contained reads with 100% matching sequence ID and read length, and a temporary cluster ID and number of retained reads were provided.

2-4. Ribosomal RNA filtering

Most RNA constructs are known as rRNA. To eliminate the effect of large amounts of rRNA, reads were aligned and matched to 45S pre-rRNA and mitochondrial rRNA of Homo sapiens .

2-5. miRNA read

Sequence alignment and detection of microRNAs was performed using the miRDeep2 software algorithm. Prior to sequence alignment, the Homo sapiens reference genome release hg19 was searched in the UCSC genome browser and indexed using Bowtie (1.1.2) to align sequencing reads to the reference sequence. The precursor miRNA obtained from miRBase v21 was aligned with the reference sequence. The miRDeep2 algorithm, based on the miRNA biogenesis model, aligned reads to potential hairpin structures in a manner consistent with Dicer processing and assigned a score indicating the likelihood that the hairpin was a true miRNA precursor.

2-6. ratio of miRNAs and other RNA categories

Clustered reads were sequentially aligned to the reference genome, miRBase v21, and the non-coding RNA database RNAcentral release 10.0 to identify known miRNAs and other types of RNA.

All data and visualization were performed using R 4.0.2 version (www.r-project.org), and all comparative analyzes were assumed to be nonparametric. The NGS data included small RNA, non-mRNA, and mRNA, of which miRNA and mRNA were used for analysis. Among these, 14 or more cases with a read count of 0 were excluded from the analysis. 586 cases of miRNA and 15324 cases of mRNA were used for the analysis. For all transcript data values, the log2 fold change (log2FC) value of the read count values 2 weeks after the start of radiotherapy compared to the read count values of plasma exosomes before treatment was used. For each of 28 patients, the read count value before radiotherapy was used as a control, and the change in the read count value 2 weeks after radiotherapy was calculated as a log2 fold change (log2FC) value. TMM normalization was performed using edgeR and log2FC values were obtained.

3. Analysis process and results

The analysis process and results for screening biomarkers were performed in the following steps.

Step I

MiRNAs capable of predicting a clinical end point were selected, and their correlation was analyzed ( FIGS. 4 and 4 ). Correlation analysis was performed in the following three steps.

Step 1: Hmisc package - rcorr function - pearson's correlation

Step 2: Leaps package - regsubsets function - exhaustive algorithm (set max variable 10 or 7)

Step 3: Mean comparison (Wincoxon rank sum test or Kruskal-Wallis test) and boxplots

As shown in Figure 4, miRNAs were selected through subgroup analysis of miRNAs (log2FC) having a significant association (|R| >0.4) with the acute tumor response (AR) of cervical cancer. . When all possible combinations of cases were calculated based on the Adjusted R value, miRNAs that were continuously included were selected.

As shown in FIG. 5 , after obtaining directionality by multiple linear regression combination of selected miRNA variables, each miRNA (log2FC) value was simply calculated by miR-3928-3p+miR-92a-1-5p+miR-574-3p+miR The value was obtained by adding and subtracting in the order of -3960-miR-202-5p. It was found that the higher the value, the higher the correlation with the acute tumor response.

Stage II

The main miRNA group and their associated miRNAs were divided into network subgroups and network analysis was performed. The influence of major miRNA groups and subgroups according to clinical end points was described.

For network analysis, Igraph package - prim algorithm was used, and in all networks, the red edge was visualized in the positive direction and the blue edge in the negative direction.

In miRNA group stratification, group A means the main miRNA group, and group B means the miRNA group associated with several major miRNA groups.

As shown in FIG. 6 , miRNAs related to acute tumor response (AR) were divided into subgroups and analyzed as a network. For each community including the main miRNA, good AR (<0.2) and pool AR (≥ 0.2) were displayed, and the number of increased and decreased levels of each miRNA was compared (increase: log2FC>1.5, decrease: log2FC <-1.5) ). Except for miR-574-3p, A level was significantly increased or decreased in the same direction as in FIG. 5 , and it was found that level A miRNAs each independently contributed to AR. It can be seen that miR-574-3p had relatively little influence. There was no difference in B level miRNAs according to the AR group. Therefore, it can be seen that B level miRNAs included in the current network have little relevance to A level.

Stage III

Through network analysis, the structure of the entire miRNA-mRNA, including the main miRNA group, associated mRNAs, and miRNA included in step II, was identified. The shortest pathway linking major miRNAs was described, and the locations of clinically relevant subgroup miRNAs were identified on the network of stage II. Network analysis was visualized under the same conditions as in step II.

As shown in FIG. 7 , a network was constructed by combining (|R| >0.6) mRNAs associated with A level miRNAs and selected miRNAs, and the shortest distance between A level miRNAs (arrows) is indicated by a red line. It had a long distance of 25 or more, and it was not an organic connection structure as it was connected in a straight line, and since miRNA of B level was not found in the current network, the current network is not suitable as a miRNA-mRNA structure that connects level A.

Stage IV

Selected to include mRNAs related to the clinical end point and miRNAs and mRNAs included in the network of stage III (FIG. 8), and significant disease/function from the existing IPA (Ingenuity Pathway Analysis) database Function categories and subcategories were extracted (FIG. 9).

As shown in FIG. 8 , related genes were selected, including mRNAs (A) highly correlated with each clinical element, and miRNAs and mRNAs (B) obtained through network analysis.

As shown in FIG. 9 , 30 significant function categories were selected by IPA (Ingenuity Pathway Analysis) analysis with miRNAs and mRNAs related to acute tumor response (AR). Among the 30 research hypothetical areas, RNA damage and repair, cancer, cell death and survival, and DNA Replication, Recombination , and Repair) was selected. As specific subcategories of the above items, DNA metabolism (Metabolism of DNA), DNA degradation (Degradation of DNA), DNA repair (DNA replication), DNA fragmentation (Fragmentation of DNA), apoptosis of tumor cell line (Apoptosis of tumor) cell lines), and Repair of DNA.

Step V

A disease/function category with a degree of significance and a possibility of association with a clinical end point was selected, and a Venn diagram using mRNA and miRNA corresponding to each group was shown (FIG. 10). ). Through the Venn diagram shown, mRNAs and miRNAs commonly associated with cancer and specific functions were reselected.

As shown in FIG. 10 , a Venn diagram of four function categories selected related to acute tumor response (AR) was drawn, and mRNA and miRNA of items overlapped with the cancer region were selected.

Step VI

Reselected mRNA and miRNAs were formed into a network using the short pathway of the main miRNA of FIG. 7 . Network analysis was visualized under the same conditions as in step II.

As shown in FIG. 11 , a network was formed using the miRNAs and mRNAs selected in FIG. 10 and main miRNAs. The figure above is a network in which the case where the correlation between edges does not become 0.4 is removed, and the figure below is a network in which the case where the correlation does not become 0.6 is removed. Except for miR-92a-1-5p, miR-3928-3p, and miR-564-3p, all mRNAs were significantly changed according to changes in miR-202-5p and miR-3960. . Groups centered on miR-202-5p and miR-3960 and DIABLO are positively linked with EGF via miR-574-3p. Only changes in miR-92a-1-5p significantly changed DIABLO.

Step VII

The log2FC values of RNAs constituting the network of stage VI were uploaded to the IPA database for each 28 patients, and z-scores of significant disease/function categories were obtained. Only items for which a significant z-score was obtained in all patients were selected, and items with statistically significant differences according to clinical end points were analyzed.

As shown in FIG. 12 , all log2FC values of RNAs included in the network of FIG. 11 (upper) were analyzed in the IPA database for each patient to obtain z scores for each category, and then as poor and good responses based on Acute tumor response 0.2 As a result of describing items showing statistically significant differences when divided, apoptosis accompanied by DNA fragmentation and increase in DNA metabolism according to cell growth were associated with good AR.

Stage VIII

Changes in the ratio of RNAs for the selected subcategories to the main miRNA were analyzed for the RNA groups that were significantly changed in all networks.

As shown in Figure 13, the network of Figure 11 (top) was apoptosis associated with DNA fragmentation in about 10%, and in 60-65% of RNA damage / repair (RNA damage / repair) and It was associated with DNA metabolism, the other 25-30% were cell death and survival of other types. Their composition was the same as the specific gravity of mRNAs that moved according to the changes in miR-202-5p and miR-3690, and DIABLO associated with miR-92a-1-5p was the key to DNA fragmentation and apoptosis. seems to play a role.

Step IX

mRNAs and miRNAs that were significantly changed according to changes in the main miRNA were analyzed.

As shown in FIG. 14 , mRNA changes according to changes in miR-202-5p and miR-3690 were significantly changed by both miRNAs, UBC (miR-202-5p), and SIN3A (miR-3960). has been included Changes in the same direction are indicated in red color, changes in the opposite direction are indicated in blue color, and changes exceeding 1.5 log2FC are indicated by shades. Each label indicates that DRR stands for DNA Replication, Recombination, and Repair, RDR stands for RNA damage and repair, DNA(r) stands for DNA replication, DNA (d) is DNA degradation, DRR(o) is DNA Replication, Recombination, and Repair-others, and Apop(f) is apoptosis-DNA fragmentation. DNA fragmentation), Apop(o) stands for Apoptosis - others, and non-Apop stands for Non-apoptosis.

Step X

Using the mRNA obtained in FIG. 11, mRNAs capable of predicting a clinical end point were extracted, and the clinical significance between them and the main miRNAs was analyzed. Correlation analysis was performed in the same step as step I above.

As shown in FIG. 15 , a subgroup analysis was performed on the mRNA constituting FIG. 11 for acute tumor response (AR). When all possible combinations were calculated based on the Adjusted R value, mRNAs that were continuously included were selected. DIABLO, SIN3A, UBC, and RPL41 were screened.

As shown in FIG. 16 , as the DIBLO+UBC+SIN3A-RPL41 (log2FC) value increased, the acute tumor response was significantly improved. The direction of RPL41 is consistent with miR-3960 in network analysis, but in the opposite direction in multiple linear regression.

As the specific parts of the present invention have been described in detail above, for those of ordinary skill in the art, it is clear that these specific descriptions are only preferred embodiments, and the scope of the present invention is not limited thereby. Do. That is, the substantial scope of the present invention is defined by the appended claims and their equivalents.

<110> Ajou University <120> Biomarkers for diagnosing early progression of cervical cancer, and uses it <130> ADP-2020-0420 <160> 8 <170> KoPatentIn 3.0 <210> 1 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-92a-1-5p <400> 1 agguugggau cgguugcaau gcu 23 <210> 2 <211> 720 <212> DNA <213> Artificial Sequence <220> <223> DIABLO mRNA <400> 2 atggcggctc tgaagagttg gctgtcgcgc agcgtaactt cattcttcag gtacaagacag 60 tgtttgtgtg ttcctgttgt ggctaacttt aagaagcggt gtttctcaga attgataaga 120 ccatggcaca aaactgtgac gattggcttt ggagtaaccc tgtgtgcggt tcctattgca 180 cagaaatcag agcctcattc ccttagtagt gaagcattga tgaggagagc agtgtctttg 240 gtaacagata gcacctctac ctttctctct cagaccacat atgcgttgat tgaagctatt 300 actgaatata ctaaggctgt ttatacctta acttctcttt accgacaata tacaagttta 360 cttgggaaaa tgaattcaga ggaggaagat gaagtgtggc aggtgatcat aggagccaga 420 gctgagatga cttcaaaaca ccaagagtac ttgaagctgg aaaccacttg gatgactgca 480 gttggtcttt cagagatggc agcagaagct gcatatcaaa ctggcgcaga tcaggcctct 540 ataaccgcca ggaatcacat tcagctggtg aaactgcagg tggaagaggt gcaccagctc 600 tcccggaaag cagaaaccaa gctggcagaa gcacagatag aagagctccg tcagaaaaca 660 caggaggaag gggaggagcg ggctgagtcg gagcaggagg cctacctgcg tgaggattga 720 720 <210> 3 <211> 20 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-3960 <400> 3 ggcggcggcg gaggcggggg 20 <210> 4 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-202-5p <400> 4 uuccuaugca uauacuucuu ug 22 <210> 5 <211> 23 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-3928-5p <400> 5 ugaagcucua agguuccgcc ugc 23 <210> 6 <211> 22 <212> DNA <213> Artificial Sequence <220> <223> hsa-miR-574-3p <400> 6 cacgcucaug cacacaccca ca 22 <210> 7 <211> 2058 <212> DNA <213> Artificial Sequence <220> <223> UBC mRNA <400> 7 atgcagatct tcgtgaagac tctgactggt aagaccatca ccctcgaggt tgagcccagt 60 gacaccatcg agaatgtcaa ggcaaagatc caagataagg aaggcatccc tcctgaccag 120 cagaggctga tctttgctgg aaaacagctg gaagatgggc gcaccctgtc tgactacaac 180 atccagaaag agtccaccct gcacctggtg ctccgtctca gaggtgggat gcaaatcttc 240 gtgaagacac tcactggcaa gaccatcacc cttgaggtcg agcccagtga caccatcgag 300 aacgtcaaag caaagatcca ggacaaggaa ggcattcctc ctgaccagca gaggttgatc 360 tttgccggaa agcagctgga agatgggcgc accctgtctg actacaacat ccagaaagag 420 tctaccctgc acctggtgct ccgtctcaga ggtgggatgc agatcttcgt gaagaccctg 480 actggtaaga ccatcaccct cgaggtggag cccagtgaca ccatcgagaa tgtcaaggca 540 aagatccaag ataaggaagg cattcctcct gatcagcaga ggttgatctt tgccggaaaa 600 cagctggaag atggtcgtac cctgtctgac tacaacatcc agaaagagtc caccttgcac 660 ctggtactcc gtctcagagg tgggatgcaa atcttcgtga agacactcac tggcaagacc 720 atcacccttg aggtcgagcc cagtgacact atcgagaacg tcaaagcaaa gatccaagac 780 aaggaaggca ttcctcctga ccagcagagg ttgatctttg ccggaaagca gctggaagat 840 gggcgcaccc tgtctgacta caacatccag aaagagtcta ccctgcacct ggtgctccgt 900 ctcagaggtg ggatgcagat cttcgtgaag accctgactg gtaagaccat cactctcgaa 960 gtggagccga gtgacaccat tgagaatgtc aaggcaaaga tccaagacaa ggaaggcatc 1020 cctcctgacc agcagaggtt gatctttgcc ggaaaacagc tggaagatgg tcgtaccctg 1080 tctgactaca acatccagaa agagtccacc ttgcacctgg tgctccgtct cagaggtggg 1140 atgcagatct tcgtgaagac cctgactggt aagaccatca ctctcgaggt ggagccgagt 1200 gacaccattg agaatgtcaa ggcaaagatc caagacaagg aaggcatccc tcctgaccag 1260 cagaggttga tctttgctgg gaaacagctg gaagatggac gcaccctgtc tgactacaac 1320 atccagaaag agtccaccct gcacctggtg ctccgtctta gaggtgggat gcagatcttc 1380 gtgaagaccc tgactggtaa gaccatcact ctcgaagtgg agccgagtga caccattgag 1440 aatgtcaagg caaagatcca agacaaggaa ggcatccctc ctgaccagca gaggttgatc 1500 tttgctggga aacagctgga agatggacgc accctgtctg actacaacat ccagaaagag 1560 tccaccctgc acctggtgct ccgtcttaga ggtgggatgc agatcttcgt gaagaccctg 1620 actggtaaga ccatcactct cgaagtggag ccgagtgaca ccattgagaa tgtcaaggca 1680 aagatccaag acaaggaagg catccctcct gaccagcaga ggttgatctt tgctgggaaa 1740 cagctggaag atggacgcac cctgtctgac tacaacatcc agaaagagtc caccctgcac 1800 ctggtgctcc gtctcagagg tgggatgcaa atcttcgtga agaccctgac tggtaagacc 1860 atcaccctcg aggtggagcc cagtgacacc atcgagaatg tcaaggcaaa gatccaagat 1920 aaggaaggca tccctcctga tcagcagagg ttgatctttg ctgggaaaca gctggaagat 1980 ggacgcaccc tgtctgacta caacatccag aaagagtcca ctctgcactt ggtcctgcgc 2040 ttgagggggg gtgtctaa 2058 <210> 8 <211> 3822 <212> DNA <213> Artificial Sequence <220> <223> SIN3A mRNA <400> 8 atgaagcggc gtttggatga ccaggagtca ccggtgtatg cagcccagca gcgtcggatc 60 cctggcagca cagaggcttt tcctcaccag caccgggtgc ttgcccctgc ccctcctgtg 120 tatgaagcag tgtctgagac catgcagtca gctacgggaa ttcagtactc tgtaacaccc 180 agctaccagg tttcagccat gccacagagc tccggcagtc atgggcccgc tatagcagca 240 gttcatagca gccatcatca cccaacagcg gtgcagcccc acggaggcca ggtggtccag 300 agtcatgctc atccagcccc accagttgca ccagtgcagg gacagcagca atttcagagg 360 ctgaaggtgg aggatgcgct atcttatctt gaccaggtga agctgcagtt tggtagtcag 420 cctcaggtct acaatgattt ccttgacatc atgaaggaat ttaaatctca gagcatcgac 480 accccaggag tgattagtcg tgtgtcccag ctattcaaag gccaccccga tctgataatg 540 ggattcaaca ccttcttgcc ccctggctac aaaattgagg tgcaaaccaa tgacatggtg 600 aatgtgacaa ctcctggcca ggttcatcag attcccaccc atggcatcca gccacagcct 660 caaccaccac cccaacatcc ttcccagcct tcagcccagt cagccccagc tcctgcccag 720 ccagctcctc agccccccacc tgccaaagtc agcaagccct cccaactgca agcacatact 780 ccggccagtc agcagactcc cccacttcca ccgtatgcat ccccacgttc tccgccggtc 840 cagcctcaca caccagtgac aatctcgttg ggaacggccc catccttgca gaacaatcaa 900 cctgtggagt ttaatcatgc catcaactat gttaataaga tcaagaacag atttcagggc 960 caaccagaca tctacaaagc attcctggag attttgcaca catatcagaa agagcagaga 1020 aatgccaagg aagctggagg aaactacact ccagcattga cagagcagga ggtgtatgcc 1080 caggttgctc gtctctttaa aaaccaggaa gatttgttgt cagagtttgg acaattccta 1140 ccagatgcca acagctccgt gcttttaagc aaaacaactg ctgagaaggt tgattctgtg 1200 agaaatgatc atggaggcac tgtcaagaag ccccaactga acaacaagcc gcagaggccc 1260 agccagaatg gctgccagat ccgcagacat cctacaggaa ccacccctcc agttaagaag 1320 aaacccaaac tgctcaatct gaaggattct tctatggcag atgccagcaa acatggtggt 1380 ggaacagaat cgttattttt tgataaggtc cgaaaggctc ttcggagtgc agaagcctac 1440 gaaaatttcc tacgctgtct tgttattttt aaccaggagg tgatctctcg tgctgagctt 1500 gtgcaactag tctctccttt cctggggaaa ttccctgagt tgtttaattg gtttaaaaac 1560 tttctgggct ataaggagtc tgtacatctg gaaacttatc caaaggagcg agccacagag 1620 ggcattgcta tggagataga ttatgcttct tgtaaacgat tgggctccag ctatcgagcc 1680 ttaccaaaga gttaccagca gcccaagtgt acaggacgga ctcctctctg taaagaggtt 1740 ttaaatgata cctgggtttc cttcccttcg tggtctgagg actctacctt tgtgagttcc 1800 aagaagactc aatatgaaga acatatttat cgttgtgaag atgaacgctt tgagcttgat 1860 gtagttttag agaccaatct ggcaacaatc cgggttctgg aagcaataca gaagaagctt 1920 tcccgcttgt ctgctgaaga acaagccaaa tttcgcttgg acaacaccct tgggggcaca 1980 tcagaagtca tccatagaaa agcactccag aggatatatg ctgataaagc agctgacatc 2040 attgatggtc tgagaaagaa tccctccatt gctgttccaa ttgtccttaa aaggttgaag 2100 atgaaagagg aagaatggcg agaagctcag agaggcttta acaaagtatg gcgagaacaa 2160 aatgagaaat actacttgaa gtctctggac caccagggga tcaactttaa acagaatgac 2220 accaaggtcc tgaggtctaa gagcttactc aatgagattg agagtatcta tgatgagagg 2280 caagagcagg ctacggagga gaatgctggt gtacctgttg gcccacacct ctcacttgcg 2340 tatgaagaca aacaaatact ggaagatgct gctgctctga ttatccacca tgtgaagagg 2400 cagacaggca ttcagaagga ggacaaatat aagataaaac aaatcatgca tcattttatt 2460 ccagatttgc tctttgccca aagaggtgat ctctcagatg tggaggaaga ggaagaagaa 2520 gagatggatg tagatgaagc cacaggggca gttaagaagc acaatggtgt tggggggcagt 2580 ccccctaagt ccaagttact gtttagtaac acagcagctc aaaaattaag aggaatggat 2640 gaagtataca acctcttcta tgtcaacaac aactggtata tttttatgcg actgcaccag 2700 attctctgcc tgaggctgct acggatttgt tcccaagccg aacggcaaat tgaagaagaa 2760 aaccgagaga gagaatggga acgggaagtg ctgggcataa agcgagacaa gagtgacagc 2820 cctgccattc agctacgtct caaagaacct atggatgttg atgtagaaga ttattaccca 2880 gctttcctgg acatggtgcg gagcctgctg gatggcaaca tagactcatc acagtatgaa 2940 gattcactga gagagatgtt caccattcat gcctacattg cctttaccat ggacaaactg 3000 atccagagca ttgtcagaca gctgcagcat atcgtgagtg atgagatctg tgtgcaggtg 3060 actgaccttt acctggcaga aaataataat ggggccaccg gaggccagct gaacacacag 3120 aactcaagga gcctcctgga gtcaacgtat cagcggaaag ctgagcagct aatgtcagat 3180 gagaattgct ttaagcttat gtttattcag agccaaggcc aggtccagct gactattgag 3240 cttctggaca cagaagagga gaattcggat gaccctgtgg aagcagagcg ctggtcagac 3300 tacgtggagc gatacatgaa ttcagatact acctcgcctg agcttcgtga acacttagca 3360 cagaaaccag tatttctccc caggaatcta cggcggatcc ggaagtgtca acgtggtcga 3420 gagcagcagg aaaaggaagg gaaggaagga aacagcaaga agaccatgga gaatgtggat 3480 agtctggata agctggagtg tagattcaag ctgaattcct acaagatggt gtatgtgatc 3540 aaatcagagg actatatgta tcggaggacc gccctgctcc gggctcatca gtcccatgag 3600 cgtgtaagca agcgtctaca tcagagattc caggcctggg tagataaatg gaccaaggag 3660 catgtgcccc gtgaaatggc agcagagacc agcaagtggc tcatgggtga ggggctggag 3720 ggcctggtgc cctgtaccac cacctgtgat acagagaccc tgcattttgt gagcattaac 3780 aagtatcgtg tcaaatacgg cacagtattc aaagcccctt aa 3822

Claims (15)

A biomarker composition for diagnosing an acute tumor response of cervical cancer, comprising at least one selected from the group consisting of mRNA of microRNA 92a-1-5p (hsa-miR-92a-1-5p) and DIABLO (Diablo homolog). The method of claim 1, wherein the micro RNA 92a-1-5p (hsa-miR-92a-1-5p) consists of the nucleotide sequence shown in SEQ ID NO: 1, and the mRNA of DIABLO (Diablo homolog) is SEQ ID NO: 2 A biomarker composition comprising a nucleotide sequence represented by According to claim 1, wherein the biomarker composition is micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), micro RNA 3928-5p (hsa-miR-3928-5p) ), microRNA 574-3p (hsa-miR-574-3p), UBC (Ubiquitin C) mRNA, and SIN3A (SIN3 transcription regulator family member A) mRNA to further include one or more selected from the group consisting of A biomarker composition characterized. The method of claim 3, wherein the micro RNA 3960 (hsa-miR-3960) consists of the nucleotide sequence represented by SEQ ID NO: 3, and the micro RNA 202-5p (hsa-miR-202-5p) is represented by SEQ ID NO: 4 consists of the nucleotide sequence shown, and the micro RNA 3928-5p (hsa-miR-3928-5p) consists of the nucleotide sequence shown in SEQ ID NO: 5, and the micro RNA 574-3p (hsa-miR-574-3p) ) consists of the nucleotide sequence shown in SEQ ID NO: 6, the mRNA of UBC (Ubiquitin C) consists of the nucleotide sequence shown in SEQ ID NO: 7, and the mRNA of SIN3A (SIN3 transcription regulator family member A) is SEQ ID NO: A biomarker composition comprising the nucleotide sequence represented by 8. An acute tumor of cervical cancer comprising an agent measuring the expression level of one or more RNAs selected from the group consisting of microRNA 92a-1-5p (hsa-miR-92a-1-5p) and DIABLO (Diablo homolog) mRNA A composition for diagnosing a reaction. [Claim 6] The composition for diagnosing an acute tumor response of cervical cancer according to claim 5, wherein the agent for measuring the expression level is a sense and antisense primer or probe that complementarily binds to the microRNA. The method of claim 5, wherein the diagnostic composition is micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), micro RNA 3928-5p (hsa-miR-3928-5p) , Micro RNA 574-3p (hsa-miR-574-3p), UBC (Ubiquitin C) mRNA, and SIN3A (SIN3 transcription regulator family member A) mRNA of one or more RNA selected from the group consisting of measuring the expression level A composition for diagnosing acute tumor response of cervical cancer, characterized in that it further comprises an agent. The method of claim 5, wherein the microRNA 92a-1-5p (hsa-miR-92a-1-5p) consists of the nucleotide sequence shown in SEQ ID NO: 1, and the mRNA of DIABLO (Diablo homolog) is SEQ ID NO: 2 A composition for diagnosing acute tumor response of cervical cancer, characterized in that it consists of a nucleotide sequence represented by The method of claim 7, wherein the micro RNA 3960 (hsa-miR-3960) consists of the nucleotide sequence shown in SEQ ID NO: 3, and the micro RNA 202-5p (hsa-miR-202-5p) is shown in SEQ ID NO: 4 consists of the nucleotide sequence shown, and the micro RNA 3928-5p (hsa-miR-3928-5p) consists of the nucleotide sequence shown in SEQ ID NO: 5, and the micro RNA 574-3p (hsa-miR-574-3p) ) consists of the nucleotide sequence shown in SEQ ID NO: 6, the mRNA of UBC (Ubiquitin C) consists of the nucleotide sequence shown in SEQ ID NO: 7, and the mRNA of SIN3A (SIN3 transcription regulator family member A) is SEQ ID NO: A composition for diagnosing acute tumor response of cervical cancer, characterized in that it consists of the nucleotide sequence represented by 8. A kit for diagnosing an acute tumor response of cervical cancer comprising the composition for diagnosing an acute tumor response according to any one of claims 5 to 9. Acute tumor response diagnosis of cervical cancer, comprising measuring the mRNA expression level of microRNA 92a-1-5p (hsa-miR-92a-1-5p) or DIABLO (Diablo homolog) in a biological sample derived from a subject How to provide information for The method of claim 11, wherein the biological sample is blood or plasma-derived exosomes. The method of claim 11, wherein the expression level is next generation sequencing (NGS), polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), real-time polymerase chain reaction (Real-time) PCR), RNase protection assay (RPA), microarray, and Northern blotting (northern blotting), characterized in that measured through one or more methods selected from the group consisting of information providing method. The method of claim 11, wherein the providing method comprises micro RNA 3960 (hsa-miR-3960), micro RNA 202-5p (hsa-miR-202-5p), and micro RNA 3928-5p (hsa- miR-3928-5p), micro RNA 574-3p (hsa-miR-574-3p), UBC (Ubiquitin C) mRNA, and SIN3A (SIN3 transcription regulator family member A) mRNA at least one RNA selected from the group consisting of Measuring the expression level of; Information providing method for diagnosing acute tumor response of cervical cancer, characterized in that it further comprises. The method of claim 11, wherein the subject is a patient receiving radiation therapy for treating cervical cancer.

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