KR20230055392A - Kit for diagnosing metastasis of cervical cancer - Google Patents

Abstract

The present invention relates to a biomarker for diagnosing the metastasis of cervical cancer and use thereof. More specifically, the present invention provides a biomarker composition comprising micro RNAs and mRNAs for diagnosing the metastasis of cervical cancer, a composition for diagnosing the metastasis of cervical cancer, a kit for diagnosing the metastasis of cervical cancer, and a method for providing information for diagnosing the metastasis of cervical cancer, wherein as a result of analyzing microRNA contained in exosomes isolated from the blood of cervical cancer patients receiving anticancer radiation before and after treatment, microRNA 16-1-3p (has-miR-16-1-3p), microRNA 15a-3p (has-miR-15a-3p), microRNA 6826-5p (has-miR-6826-5p), microRNA 605-5p (has-miR-605-5p), microRNA 6780a-5p (has-miR-6780a-5p), and micro RNA 6791-5p (has-miR-6791-5p) associated with the metastasis of cervical cancer were discovered and mRNA of family with sequence similarity 168 member A (FAM168A), mRNA of retinol-binding protein 3, interstitial (RBP3), and mRNA of complement C1q tumor necrosis factor-related protein 1 (C1QTNF1) were found to be associated.

Description

Kit for diagnosing metastasis of cervical cancer}

The present invention relates to biomarkers for diagnosing metastasis of cervical cancer and uses thereof.

Cervical cancer is a malignant tumor that frequently occurs in women, and more than 350,000 patients worldwide die from cervical cancer every year. Cervical cancer is divided into stages 0 to 4, and stage 0 cancer is also called carcinoma in situ, and stage 1 to 4 cancer is called invasive cervical cancer. It is known that carcinoma in situ develops between the ages of 35 and 40, about 10 years earlier than invasive cervical cancer. 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 group, those in their 50s, 60s, and 40s are in that order. The incidence rate is 3 times that of the United States, 2.5 times that of Japan, and 1/3 that of Brazil. This is 6%, which is higher than the 2% in the US, Japan, and Switzerland. Currently, the sexually transmitted infectious disease model is the most widely accepted model for the development of cervical cancer, and early onset sexual activity, multiple sexual partners, male factors, human papillomavirus (HPV) infection, and human immunodeficiency virus Infection is a known risk factor for the development of cervical cancer.

The recurrence rate after treatment of cervical cancer is 50% within 1 year, 25% within 2 years, and 15% within 3 years, and about 85% of patients recur within 3 years. In particular, the recurrence rate after radiation therapy for cervical cancer is also over 55%. The treatment of recurrent cervical cancer is determined differently depending on the type of treatment previously received and the condition of the patient. In general, if surgery was performed as the first treatment, radiation therapy is considered in case of recurrence, and surgery is considered in case of radiation therapy as the first treatment. If both treatments are unavailable, chemotherapy is administered. However, there is no standard treatment, and appropriate treatment is performed for each according to the recurrence site, but it is generally a treatment aimed at relieving symptoms rather than curing the disease.

According to a recent report, the 5-year progression-free survival period of cervical cancer patients with pelvic adductal metastasis (FIGO [International Federation of Gynecology and Obstetrics] stage IIIC1) and abdominal lymph node metastasis (FIGO stage IIIC2) showed a difference of 27%. there is. Therefore, determining the presence of metastasis to the abdominal lymph node is important for predicting the patient's treatment outcome. To diagnose this, both surgical evaluation of abdominal lymph nodes and evaluation using PET/CT or MRI are possible. (PET-CT sensitivity 85%, MRI sensitivity 66%). This is because there is no change in the fact that chemotherapy is performed despite the surgical cost and side effects. Radiation oncologists refer to tumor markers such as squamous cell carcinoma antigen or carcinoembryonic antigen in addition to images to determine whether to perform extended field radiotherapy (EBRT) involving abdominal lymph nodes, but there are always cases where the decision is not easy. Therefore, to solve these clinical problems, an accurate tool for predicting the degree of cancer metastasis is needed. Although the degree of metastasis can be simply expressed as a stage, a more meaningful higher-level approach is needed because it implies a complex biological meaning behind it.

On the other hand, exosomes are 40-150 nm extracellular vesicles that are secreted by cancer cells or other normal cells, and perform various physiological functions through cell-to-cell signal transmission, and are involved in cancer development, metastasis, 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 miRNAs can be considered as high-level regulators that can be considered at this point. In particular, because exosomes originating from normal cells as well as cancer cells are abundant in the blood, it is easy to understand 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 miRNAs and coding genes.

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

The present invention relates to a biomarker for diagnosing metastasis of cervical cancer and its use. The present inventors collect blood samples before and after treatment from cervical cancer patients undergoing chemotherapy and radiation, and from this, plasma exosome RNA is obtained. After isolation, microRNAs associated with metastasis of cervical cancer are detected through NGS analysis, and these are provided as biomarkers for diagnosing metastasis of cervical cancer.

Micro RNA 16-1-3p (hsa-miR-16-1-3p), micro RNA 15a-3p (hsa-miR-15a-3p) and micro RNA 6826-5p (hsa-miR-6826-5p) ) It provides a biomarker composition for diagnosing metastasis of cervical cancer comprising at least one selected from the group consisting of.

In addition, the present invention is micro RNA 16-1-3p (hsa-miR-16-1-3p), micro RNA 15a-3p (hsa-miR-15a-3p) and micro RNA 6826-5p (hsa-miR-6826 -5p) provides a composition for diagnosing metastasis of cervical cancer comprising an agent for measuring the expression level of one or more RNAs selected from the group consisting of.

In addition, the present invention provides a kit for diagnosing cervical cancer metastasis comprising the composition for diagnosing metastasis.

In addition, the present invention is a micro RNA 16-1-3p (hsa-miR-16-1-3p), micro RNA 15a-3p (hsa-miR-15a-3p) or micro RNA 6826-5p in a biological sample derived from a subject Provided is a method for providing information for diagnosing metastasis of cervical cancer, including measuring the expression level of (hsa-miR-6826-5p).

According to the present invention, microRNA 16-1-3p related to metastasis of cervical cancer was analyzed as a result of analyzing microRNA contained in exosomes isolated from blood before and after treatment of cervical cancer patients undergoing chemotherapy. (hsa-miR-16-1-3p), micro RNA 15a-3p (hsa-miR-15a-3p), micro RNA 6826-5p (hsa-miR-6826-5p), the composition is micro RNA 605-5p ( hsa-miR-605-5p), microRNA 6780a-5p (hsa-miR-6780a-5p), and microRNA 6791-5p (hsa-miR-6791-5p) were discovered, and FAM168A (Family With Sequence Similarity 168 Member A) mRNA, RBP3 (Retinol-binding protein 3, interstitial) mRNA, and C1QTNF1 (Complement C1q tumor necrosis factor-related protein 1) mRNA were confirmed to be related. It can be used as a means of diagnosing metastasis and is expected to be efficiently used in clinical practice as a useful biomarker for predicting the prognosis after treatment and surgery.

1 is a diagram showing the mechanisms of microRNAs and mRNAs related to metastasis of cervical cancer.
FIG. 2 is a schematic diagram illustrating a treatment process, a blood sampling time, an MRI imaging time, and a follow-up observation process for cervical cancer patients who have undergone anticancer radiation therapy.
3 is a diagram showing the degree of cancer metastasis according to FIGO 2018 (The 2018 International Federation of Gynecology and Obstetrics) stages.
4 is a result of measuring the expression level of microRNA 15a-3p compared to microRNA 16-1-3p, which is related to the degree of metastasis of cervical cancer.
5 is a miRNA selection result through subgroup analysis of miRNAs (log2FC) having a significant association with the degree of metastasis of cervical cancer.
Figure 6 is the result of analyzing the correlation of miRNAs selected in Figure 5 above.
7 is a result of comparing miRNA parameters selected in FIG. 5 according to directionality.
8 is a result of network analysis by dividing the selected miRNA groups and miRNAs associated with them into network subgroups.
Figure 9 is the result of network analysis to understand the structure between the selected miRNA group and related mRNAs.
10 shows the results of screening related genes, including mRNA (A) highly correlated with each clinical factor and miRNA and mRNA (B) obtained through network analysis.
11 shows the results of IPA (Ingenuity pathway analysis) analysis of miRNAs and mRNAs related to metastasis of cervical cancer.
12 is a result of a Venn diagram of four function categories related to metastasis of cervical cancer.
13 is a result of network analysis of mRNAs and miRNAs selected in FIG. 12.
FIG. 14 is a result of analyzing log2FC values of the RNAs included in FIG. 13 with an IPA database and analyzing categories showing statistically significant differences according to metastasis of cervical cancer.
15 is a graph showing the relative ratios of categories selected for the main miRNAs associated with metastasis of cervical cancer.
16 is a graph showing significantly changed mRNAs and miRNAs according to changes in major miRNAs associated with metastasis of cervical cancer.
17 is a result of analyzing subgroups for cervical cancer metastasis targeting mRNAs.
Figure 18 is the result of analyzing the correlation of miRNAs selected in Figure 17 above.

The terms used in this specification have been selected from general terms that are currently widely used as much as possible while considering the functions in the present invention, but these may vary depending on the intention of a person skilled in the art, precedent, or the emergence of new technologies. In addition, in a specific case, there is also a term arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the 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, not simply the name of the term.

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

Numerical ranges are inclusive of the values defined therein. Every maximum numerical limitation given throughout this specification includes every lower numerical limitation, as if such lower numerical limitations were expressly written. Every minimum numerical limitation given throughout this specification includes every higher numerical limitation, as if such higher numerical limitations were expressly written. Every numerical limitation given throughout this specification will include every better numerical range within the broader numerical range, as if the narrower numerical limitations were expressly written.

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

To confirm that plasma exosomal miRNA is a biomarker that can predict metastasis of cervical cancer in patients who underwent chemotherapy, the present inventors tested plasma exosomal miRNA before and after radiation treatment (2 weeks). was analyzed to detect miRNAs showing clinical variables through fold change.

Micro RNA 16-1-3p (hsa-miR-16-1-3p), micro RNA 15a-3p (hsa-miR-15a-3p) and micro RNA 6826-5p (hsa-miR-6826-5p) ) It provides a biomarker composition for diagnosing metastasis of cervical cancer comprising at least one selected from the group consisting of.

When diagnosing cancer, when determining the stage of cancer progression according to the degree of spread of cancer cells, the TNM method examines whether cancer has spread to other organs in the body. Cervical cancer is a condition in which cancer is found in a site other than the cervix in a patient diagnosed with cervical cancer.

Whether or not cervical cancer has metastasized can predict prognosis or help determine whether to receive additional treatment. In particular, the presence or absence of metastases beyond the pelvis (stage IIIC2 or higher) greatly affects treatment outcomes. Although the degree of metastasis is being evaluated using various imaging tools at this point, there are not a few cases where it is difficult to determine that there is a lesion or metastasis that is difficult to observe on the image. If there is an additional tool that can help determine the degree of metastasis, it will be possible to provide more appropriate treatment to patients by determining whether to perform radiation treatment range or additional chemotherapy.

The microRNA 16-1-3p (hsa-miR-16-1-3p) is composed of the nucleotide sequence represented by SEQ ID NO: 1, and the microRNA 15a-3p (hsa-miR-15a-3p)) is the sequence It consists of the nucleotide sequence represented by SEQ ID NO: 2, and the micro RNA 6826-5p (hsa-miR-6826-5p) consists of the nucleotide sequence represented by SEQ ID NO: 3.

The biomarker composition includes micro RNA 605-5p (hsa-miR-605-5p), micro RNA 6780a-5p (hsa-miR-6780a-5p), micro RNA 6791-5p (hsa-miR-6791-5p), Add one or more selected from the group consisting of FAM168A (Family With Sequence Similarity 168 Member A) mRNA, RBP3 (Retinol-binding protein 3, interstitial) mRNA, and C1QTNF1 (Complement C1q tumor necrosis factor-related protein 1) mRNA can be included with

The microRNA 605-5p (hsa-miR-605-5p) consists of the nucleotide sequence represented by SEQ ID NO: 4, and the microRNA 6780a-5p (hsa-miR-6780a-5p) is represented by SEQ ID NO: 5 nucleotide sequence, the microRNA 6791-5p (hsa-miR-6791-5p) consists of the nucleotide sequence represented by SEQ ID NO: 6, and the mRNA of FAM168A (Family With Sequence Similarity 168 Member A) is SEQ ID NO: 7, the RBP3 (Retinol-binding protein 3, interstitial) mRNA is composed of the nucleotide sequence represented by SEQ ID NO: 8, and the C1QTNF1 (Complement C1q tumor necrosis factor-related protein 1) mRNA consists of the nucleotide sequence represented by SEQ ID NO: 9.

In addition, the present invention is micro RNA 16-1-3p (hsa-miR-16-1-3p), micro RNA 15a-3p (hsa-miR-15a-3p) and micro RNA 6826-5p (hsa-miR-6826 -5p) provides a composition for diagnosing metastasis of cervical cancer comprising an agent for measuring the expression level of one or more RNAs selected from the group consisting of.

Agents 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 that is the starting point of DNA synthesis, and refers to oligonucleotides synthesized for the purpose of diagnosis, DNA sequencing, and the like. The primers may be synthesized and used in a length of 15 to 30 base pairs, but may vary depending on the purpose of use, and may be modified by methylation, capping, or the like by a known method.

The probe refers to a nucleic acid capable of specifically binding to an mRNA having a length of several to several hundred bases prepared through enzymatic chemical separation and purification or synthesis. The presence or absence of mRNA can be confirmed by labeling a radioactive isotope or an enzyme, and it can be designed and modified using a known method.

The diagnostic composition includes micro RNA 605-5p (hsa-miR-605-5p), micro RNA 6780a-5p (hsa-miR-6780a-5p), micro RNA 6791-5p (hsa-miR-6791-5p), FAM168A Expression of one or more RNAs selected from the group consisting of (Family With Sequence Similarity 168 Member A) mRNA, RBP3 (Retinol-binding protein 3, interstitial) mRNA, and C1QTNF1 (Complement C1q tumor necrosis factor-related protein 1) mRNA It may further include an agent to measure the level.

In addition, the present invention provides a kit for diagnosing cervical cancer metastasis comprising the composition for diagnosing metastasis.

The kit consists of one or more other component compositions, solutions or devices suitable for the assay method. For example, the kit includes 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 DNA polymerase, a dNTP mixture, a PCR buffer and water to perform PCR. may be a kit. The PCR buffer may contain KCl, Tris-HCl and MgCl2. In addition, the kit may additionally include components necessary for conducting electrophoresis that can confirm whether or not the PCR product is amplified.

In addition, the kit may be a kit including essential elements required to perform RT-PCR. The RT-PCR kit contains, in addition to each pair of primers specific for a marker gene, 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), sterilized water, etc. may be included. It may also include a primer pair specific to a gene used as a quantitative control.

In addition, the present invention is a micro RNA 16-1-3p (hsa-miR-16-1-3p), micro RNA 15a-3p (hsa-miR-15a-3p) or micro RNA 6826-5p in a biological sample derived from a subject Provided is a method for providing information for diagnosing metastasis of cervical cancer, including measuring the expression level of (hsa-miR-6826-5p).

The biological sample is blood or plasma-derived exosomes.

The expression level was measured 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 (microarray), and can be measured through one or more methods selected from the group consisting of northern blotting (northern blotting).

The method of providing information includes micro RNA 605-5p (hsa-miR-605-5p), micro RNA 6780a-5p (hsa-miR-6780a-5p), and micro RNA 6791-5p (hsa-miR-605-5p) in a biological sample derived from a subject. -6791-5p), FAM168A (Family With Sequence Similarity 168 Member A) mRNA, RBP3 (Retinol-binding protein 3, interstitial) mRNA, and C1QTNF1 (Complement C1q tumor necrosis factor-related protein 1) mRNA Measuring the expression level of one or more RNAs selected from; may further include.

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

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

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

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

[Example]

1. Patients and specimens

Patients who received chemotherapy after being diagnosed with cervical cancer were included. The Institutional Review Board of Ajou University Hospital obtained informed consent from the donors and approved this study.

The stage of cervical cancer was set based on the FIGO 2018 (The 2018 International Federation of Gynecology and Obstetrics) stage, and the degree of cancer metastasis was fully reflected in each stage group, and this was used as the clinical end point.

As shown in Figure 2, the treatment process, the time of blood collection, the time of MRI imaging, and the follow-up observation process were performed for 28 cervical cancer patients who had undergone chemoradiation therapy.

As shown in Figure 3, FIGO stage (2018) IB-IIIC1 patients (18 patients) have lesions only in the pelvis, and IIIC2, IVA stage patients (7 patients) have lesions in the abdominal lymph nodes. In the IVA patient, there was a lesion in the abdominal lymph node and a cervical tumor invaded the bladder. In stage IVB patients (3 patients), 1 patient had metastasis to the left supraclavicular region, 1 patient had metastasis to the left lower lobe of the lung, and 1 patient had metastasis to the right supraclavicular region and left axilla. Among them, the patient with metastasis to the left lung had no abdominal lymph node metastasis and only pelvic lymph node metastasis was confirmed. For patients with metastasis to the abdominal lymph nodes, radiation therapy was performed to the abdominal lymph nodes along with the pelvis, and for IVB patients, local radiation therapy was administered simultaneously to the metastasized area during the treatment period. The clinical results of the patients are shown in Table 1 below.

2. Dataset construction for exosomal miRNAs and mRNAs

Before radiation treatment and during the second week of radiation treatment, 5-10 cc of blood was collected from the patients. Plasma was separated from the collected blood, and additionally 3-5 cc of plasma samples from 29 people stored in the human body material storage were distributed and used in the experiment. Exosomes were isolated from plasma samples, and Next Generation Sequencing (NGS) analysis was performed. Samples with low expression levels from plasma were excluded from analysis.

2-1. Small RNA library construction and sequencing

Plasma was mixed with Exo2D RNA solution (Exosomeplus) to separate exosomes. Exosome RNA from plasma-derived exosomes was extracted using the 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 determined using the Agilent RNA 6000 Pico Kit and Small RNA Kit on 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. Input RNA was first polyadenylated to provide priming sequences for 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 the addition of non-template nucleotides bound by SMRT smRNA Oligos enriched with locked nucleic acid (LNA) technology. In the template conversion step, PrimeScript RT used the SMART smRNA Oligo as a template to add a second adapter sequence to the 3' end of each first-strand cDNA molecule. After this, full-length Illumina adapters containing index sequences for sample multiplexing were added during PCR amplification. Forward PCR Primer binds to the sequence added by SMART smRNA Oligo, while Reverse PCR Primer binds to the sequence added by 3’smRNA dT Primer.

The amplified library was purified on a 6% Novex TBE-PAGE gel (Thermo Fisher, MA), and a fraction of 138 bp (cDNA 18 bp or more + adapter 120 bp or more) or more was excised. The resulting library cDNA molecules contained the sequences required for clustering on Illumina flow cells.

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 the 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 calculation were performed using modules of the Illumina pipeline.

2-2. Adapter trimming

Raw sequence reads of small RNAs from different experimental samples were preprocessed with miRDeep2 and analyzed. Adapter trimming was performed using the cutadapt program to remove miRNA-attached adapter sequences if present during the construction of the smRNA library. The first 3nt of all reads were trimmed to remove extra bases inserted during the SMART template-switching activity process. The adapter sequence and all 3' of the adapter were also removed. If a read matched at least the first 5 bp of the 3' adapter sequence, the sequence was considered to be indeed an adapter sequence and then the read was trimmed. 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

Clusters were formed by collecting reads treated with adapter sequences to minimize sequence uniqueness and computational intensity. This cluster contains reads with 100% matching sequence IDs and read lengths, and provisional cluster IDs and number of retained reads were provided.

2-4. Ribosomal RNA filtering

Most RNA components are known as rRNA. Reads were aligned and matched to the 45S pre-rRNA and mitochondrial rRNA of Homo sapiens to eliminate the effect of high amounts of rRNA.

2-5. miRNA reads

Sequence alignment and detection of microRNAs was performed using the miRDeep2 software algorithm. Before sequence alignment, the Homo sapiens reference genome release hg19 was retrieved from the UCSC Genome Browser and indexed using Bowtie (1.1.2) to align sequencing reads to the reference sequence. Precursor miRNAs obtained from miRBase v21 were aligned with the reference sequence. The miRDeep2 algorithm is based on the miRNA biogenesis model, aligning reads to potential hairpin structures in a manner consistent with Dicer treatment and assigning a score indicating the likelihood that a hairpin is a bona fide miRNA precursor.

2-6. miRNA and other RNA category proportions

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 assumed non-parametric. The NGS data included small RNA, non-mRNA, and mRNA, and among them, miRNA and mRNA were used for analysis. Of these, 14 or more patients with a read count of 0 were excluded from the analysis. 586 miRNAs and 15324 mRNAs were used for analysis. Values of all transcript data were used as log2 fold change (log2FC) values of read count values 2 weeks after the start of radiation treatment compared to read count values of plasma exosomes before treatment. For each of the 28 patients, the read count value before radiation treatment was used as a control, and the change in read count value 2 weeks after radiation treatment 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 selecting biomarkers were carried out in the following steps.

Stage I

miRNAs capable of predicting clinical end points were selected, and their correlations were analyzed (Figs. 4, 4 and 5). 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 FIG. 4, it was confirmed whether there were values without expression among the miR-16-1-3p (log2FC) values, replacing the expression in the cluster gene. As a result of regular conversion of the expression level RPM [reads per million] before radiation treatment and an association with miR-16-1-3p, miR-15a-3p, a cluster gene, showed the highest association. Based on this result, when the miR-16-1-3p (log2 FC) value could not be measured, it was replaced with miR-15a-3p (log2FC) and used in future analyses. It was marked as miR-16-1-3p [or15a-3p].

As shown in FIG. 5, there is a significant association (|R|>0 & |R |>0) miRNAs were selected through subgroup analysis of miRNAs (log2FC). Based on the Adjusted R value, miRNAs that were consistently included were selected when all possible combinations were calculated.

As shown in Figure 6, after obtaining the directionality through multiple linear regression combinations of the miRNA variables, miR-605-5p+miR-6780a-5p+miR-6791-5p+miR-6826- Values were obtained by adding and subtracting in the order of 5p-miR-16-1-3p [or 15a-3p]. A higher value was associated with a higher stage, and the difference between IIB-IIIC1 and IIIC2-IVA was particularly remarkable.

As shown in Figure 7, miR-16-1-3p [or 15a-3p] (log2FC) and miR-605-5p+miR-6780a-5p+miR-6791-5p+miR When compared with -6826-5p (log2FC), the number of former decreases and the increase in the latter shows that there are more cases of metastasis outside the pelvis.

Phase II

Network analysis was performed by dividing the main miRNA groups and miRNAs associated with them into network subgroups. The influence of main miRNA groups and subgroups was described according to clinical end points.

Network analysis uses the Igraph package - prim algorithm, and in all networks, the red edge is visualized as a positive direction and the blue edge as a negative direction. In the miRNA group stratification, group A refers to the main miRNA group, and group B refers to miRNA groups related to several main miRNA groups.

As shown in FIG. 8, as a result of network analysis using miRNAs related to stages of cervical cancer, the level of each miRNA increased in Stage IB-IIIC1 and Stage IIIC2-IVB containing the main miRNA. Numbers and decreased numbers were compared (increase: log2FC>1.5, decrease: log2FC <-1.5). The A level significantly increased or decreased in the direction shown in FIG. 7, which means that miRNAs of level A independently contribute to the stage. In particular, when miR-16-1-3p [or 15a-3p] of stage IIIC2-IVB decreased, B-level miRNAs were significantly increased, which was assumed to be the lower miRNAs. When miR-605-5p of stage IIIC2-IVB increased, B level miRNAs tended to increase.

III stage

Through network analysis, the structure of the entire miRNA-mRNA was identified, including the main miRNA group, related mRNAs, and miRNAs included in stage II. The shortest pathway connecting major miRNAs was described, and the positions of subgroup miRNAs with clinical relevance were identified on the stage II network. Network analysis was visualized under the same conditions as in step II above.

As shown in Figure 9, a network was constructed by combining (|R| >0.6) mRNAs associated with A-level miRNAs and miRNAs in figure3D, and the shortest distance on A-level miRNAs (arrows) was indicated by a red line. miR-6780a-5p, miR-6826-5p, miR-605-5p, miR-6791-5p, and miR-16-1-3p[or15a-3p] are linked centrally to PLCXD3 (star). (F5, FAM168A, ACIN1, CMTR2, CCDC88B, RNF216, ARHGEF2, ALDH2, PLCXD3, FCHO2, ATE1, SMAD9, ADCY10, RBP3, connected through 15 or more PERP) Figure 3D shows that part of the B level is observed on the network. It can be seen that the structure of miRNA-mRNA is appropriate.

Stage IV

mRNAs related to clinical end points and miRNAs and mRNAs included in the stage III network were selected to be included (FIG. 10), and significant disease/function from the existing IPA (Ingenuity Pathway Analysis) database Function categories and subcategories were extracted (FIG. 11).

As shown in FIG. 10 , relevant genes including mRNA (A) highly correlated with each clinical factor and miRNA and mRNA (B) obtained through network analysis were selected.

As shown in FIG. 11, 30 significant function categories were selected by Ingenuity Pathway Analysis (IPA) analysis of miRNAs and mRNAs related to cervical cancer stages. Among the selected 30 categories, cancer, cellular movement, cell to cell signaling and interaction, and cell death and survival were selected. Metastasis of tumor cell lines, cell movement of myeloid cells, cell movement of endothelial cells, and migration of nerve cells into specific subcategories of these of neurons), migration of phagocytes, activation of blood platelets, apoptosis of endothelial cells, and necrosis of epithelial cells. .

V stage

Disease/function categories that are likely to be associated with significance and clinical end points are selected, and venn diagrams are shown using mRNAs and miRNAs corresponding to each group (FIG. 12). ). Through the Venn diagram shown, mRNAs and miRNAs commonly associated with cancer and specific functions were re-selected.

As shown in FIG. 12, a Venn diagram of four function categories selected in relation to the metastasis stage was drawn, and mRNA and miRNA of overlapping items with the cancer region were selected.

VI level

Reselected mRNAs and miRNAs were formed into a network using the short pathway of the main miRNA shown in FIG. 9 . Network analysis was visualized under the same conditions as in step II above.

As shown in FIG. 13, a network was formed using a short pathway including miRNAs and mRNAs selected in FIG. 12 and the main miRNAs described in FIG. 9, and changes in each main miRNA (log2FC>1.5 (up), log2FC < Statistically significantly changed mRNAs and miRNAs according to -1.5 (down), others - no change) (red numbers mean decrease, blue numbers mean increase). Major miRNAs appeared to be associated in the direction of miR-16-1-3p[or15a-3p] -> miR-6780a-5p -> miR-6826-5p -> miR-6791-5p <-> miR605-5p. In particular, the increase in miR-6826-5p was significantly associated with the increase in miR-605-5p. Changes in miR-605-5p appeared to be associated with the most mRNA changes.

VII level

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

As shown in FIG. 14, log2FC values of the RNAs included in the network of FIG. 13 were all analyzed in the IPA database for each patient, z scores were obtained for each category, and then metastasis outside the pelvis was determined (Stage IB-IIIC1 vs. StageIIIC2- IVB) As a result of describing items showing statistically significant differences, migration/survival of endothelial cells/neuronal cells/epithelial cells/myeloid cells, and activation of blood cells (blood cell) activation) was associated with the degree of metastasis of cervical cancer.

Stage VIII

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

As shown in FIG. 15, the network of FIG. 13 is 50% of RNAs related to cellular movement, 30% of RNAs related to cell death and survival, and 30% of RNAs related to cell death and survival. to cell signaling and interaction) were 20%. The RNAs significantly changed according to the changes of miR-605-5p and miR-6826-5p were similar to the functional distribution of the overall RNAs. In the case of miR-6780a-5p, the proportion of cell to cell signaling and interaction was high, and in the case of miR-6791-5p, RNAs related to cell death and survival importance was high. In the case of miR-16-1-3p [or 15a-3p], cellular movement was about 30% and cell death and survival was about 70%, but no specific subcategory was included. wasn't doing

Stage IX

Significantly changed mRNAs and miRNAs were analyzed according to changes in the main miRNAs.

As shown in FIG. 16, changes in mRNA and miRNA according to changes in the main miRNA were analyzed. Changes in the same direction are shown in red, changes in the opposite direction are shown in blue, and changes exceeding 1.5 log2FC are shown in shade. CM stands for cellular movement, CCSI stands for cell to cell signaling and interaction, and CDS stands for cell death and survival. The subcategories are: Metastasis of tumor cell line (Meta), Cell movement of myeloid cells (CM (m)), Cell movement of endothelial cells (Cell movement of endothelial cells (CM (e)), migration of neurons (CM (n)), migration of phagocytes (CM (p)), Cellular movement-others (CM (o)), activation of blood platelets (CCSI (p)), cell to cell signaling and interaction-others (CCSI (o)), apoptosis of endothelial cells ( Apoptosis of endothelial cells (CDS (en)), Necrosis of epithelial tissue (CDS (ep)), and Cell death and survival -others (CDS (o)).

X stage

Using the mRNAs obtained in FIG. 13, mRNAs capable of predicting clinical end points were extracted, and the clinical significance between them and the main miRNAs was analyzed.

As shown in FIG. 17, the extracted mRNAs were analyzed for subgroups of metastasis stage of cervical cancer. When all possible combinations of cases were calculated based on the Adjusted R value, mRNAs that were continuously included were selected. FAM168A, RBP3 and C1QTNF1 were selected, and FAM168A belonged to miR-16-1-3p [or 15a-3p] community, RBP3 belonged to miR-6826-5p, and C1QTNF1 belonged to miR-6791-5p.

As shown in FIG. 18, the RBP3-C1QTNF1-FAM168A (log2FC) value was significantly higher as the number of stages increased. By subgroup analysis using FAM168A, RBP3, C1QTNF1, miR-605-5p, miR-6780a-5p, miR-6791-5p, miR-6826-5p, miR-16-1-3p [or 15a-3p] As miR-605-5p+RBP3-miR-16-1-3p[or 15a-3p]-C1QTNF1 (log2FC) increased, the difference according to the stage became larger.

Having described specific parts of the present invention in detail above, it is clear to those skilled in the art 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 metastasis of cervical cancer, and uses its <130> ADP-2023-0214 <160> 9 <170> KoPatentIn 3.0 <210> 1 <211> 22 <212> DNA <213> artificial sequence <220> <223> hsa-miR-16-1-3p <400> 1 ccaguauuaa cugugcugcu ga 22 <210> 2 <211> 22 <212> DNA <213> artificial sequence <220> <223> hsa-miR-15a-3p <400> 2 caggccauau ugugcugccu ca 22 <210> 3 <211> 23 <212> DNA <213> artificial sequence <220> <223> hsa-miR-6826-5p <400> 3 ucaauaggaa agagguggga ccu 23 <210> 4 <211> 23 <212> DNA <213> artificial sequence <220> <223> hsa-miR-605-5p <400> 4 uaaaucccau ggugccuucu ccu 23 <210> 5 <211> 23 <212> DNA <213> artificial sequence <220> <223> hsa-miR-6780a-5p <400> 5 uuggggaggga agacagcugg aga 23 <210> 6 <211> 22 <212> DNA <213> artificial sequence <220> <223> hsa-miR-6791-5p <400> 6 ccccuggggc ugggcaggcg ga 22 <210> 7 <211> 735 <212> DNA <213> artificial sequence <220> <223> FAM168A mRNA <400> 7 atgaaccctg tttacagccc cgtgcagcct ggggctcctt atggcaaccc taagaacatg 60 gcctacacgg gttaccccac agcctatcca gcagctgccc ctgcctacaa tcccagcctg 120 taccccacca atagtcccag ttatgctcca gagtttcagt tcctgcattc agcttatgca 180 actctgctga tgaaacaggc ctggccacag aactcgtctt cctgtggcac tgaaggcacc 240 ttccacctcc cagtggacac cgggaccgag aaccgaactt accaagcatc ctctgcggct 300 ttcagatata ctgcggggac accatacaag gtcccaccga cccagagtaa cactgctcca 360 cccccctact ccccatcacc caacccctat cagacggcca tgtatccaat cagaagtgcc 420 tacccccagc agaatctgta tgcccaggga gcctactaca cacagccggt gtatgctgcc 480 cagcctcatg tcatccacca taccacggtc gtccagccca acagcattcc ctctgctatc 540 tacccagcac ctgttgccgc cccgaggacc aacggtgtgg ccatgggcat ggtggcaggc 600 accaccatgg caatgtcagc aggtaccctg ctgactacac cccagcacac ggcgattggg 660 gcacaccctg tctccatgcc aacatatagg gcccaaggaa cccctgcgta cagctacgtg 720 cccccacact ggtaa 735 <210> 8 <211> 3744 <212> DNA <213> artificial sequence <220> <223> RBP3 mRNA <400> 8 atgatgagag aatgggttct gctcatgtcc gtgctgctct gtggcctggc tggccccaca 60 cacctgttcc agccaagcct ggtgctggac atggccaagg tcctcttgga taactactgc 120 ttcccggaga acctgctggg catgcaggaa gccatccagc aggccatcaa gagccatgag 180 attctgagca tctcagaccc gcagacgctg gccagtgtgc tgacagccgg ggtgcagagc 240 tccctgaacg atcctcgcct ggtcatctcc tatgagccca gcacccccga gcctccccca 300 caagtcccag cactcaccag cctctcagaa gaggaactgc ttgcctggct gcaaaggggc 360 ctccgccatg aggttctgga gggtaatgtg ggctacctgc gggtggacag cgtcccgggc 420 caggaggtgc tgagcatgat gggggagttc ctggtggccc acgtgtgggg gaatctcatg 480 ggcacctccg ccttagtgct ggatctccgg cactgcacag gaggccaggt ctctggcatt 540 ccctacatca tctcctacct gcacccaggg aacaccatcc tgcacgtgga cactatctac 600 aaccgcccct ccaacaccac cacggagatc tggaccttgc cccaggtcct gggagaaagg 660 tacggtgccg acaaggatgt ggtggtcctc accagcagcc agaccagggg cgtggccgag 720 gacatcgcgc acatccttaa gcagatgcgc agggccatcg tggtgggcga gcggactggg 780 ggaggggccc tggacctccg gaagctgagg ataggcgagt ctgacttctt cttcacggtg 840 cccgtgtcca ggtccctggg gccccttggt ggaggcagcc agacgtggga gggcagcggg 900 gtgctgccct gtgtggggac tccggccgag caggccctgg agaaagccct ggccatcctc 960 actctgcgca gcgcccttcc aggggtagtc cactgcctcc aggaggtcct gaaggactac 1020 tacacgctgg tggaccgtgt gcccaccctg ctgcagcact tggccagcat ggacttctcc 1080 acggtggtct ccgaggaaga tctggtcacc aagctcaatg ccggcctgca ggctgcgtct 1140 gaggatccca ggctcctggt gcgagccatc gggcccacag aaactccttc ttggcccgcg 1200 cccgacgctg cagccgaaga ctcaccaggg gtggccccag agttgcctga ggacgaggct 1260 atccggcaag cactggtgga ctctgtgttc caggtgtcgg tgctgccagg caatgtgggc 1320 tacctgcgct tcgatagttt tgctgacgcc tccgtcctgg gtgtgttggc cccatatgtc 1380 ctgcgccagg tgtgggagcc gctacaggac acggagcacc tcatcatgga cctgcgccac 1440 aaccctggag ggccatcctc tgctgtgccc ctgctcctgt cctacttcca gggccctgag 1500 gccggccccg tgcacctctt caccacctat gatcgccgca ccaacatcac gcaggagcac 1560 ttcagccaca tggagctccc gggcccacgc tacagcaccc aacgtggggt gtatctgctc 1620 accagccacc gcaccgccac ggccgcggag gagttcgcct tccttatgca gtcgctgggc 1680 tgggccacac tggtaggtga gatcaccgcg ggcaacctgc tgcacacccg cacggtgccg 1740 ctgctggaca cacccgaagg cagcctcgcg ctcaccgtgc cggtcctcac cttcatcgac 1800 aatcacggcg aggcctggct gggtggtgga gtggtgcccg atgccatcgt gctggccgag 1860 gaggccctgg acaaagccca ggaagtgctg gagttccacc aaagcctggg ggccttggtg 1920 gagggcacag ggcacctgct ggaggcccac tatgctcggc cagaggtcgt ggggcagacc 1980 agtgccctcc tgcgggccaa gctggcccag ggcgcctacc gcacagctgt ggacttggag 2040 tctctggcct ctcagctcac agcagacctc caggaggtgt ctggggacca ccgcttgcta 2100 gtgttccaca gccctggcga gctggtggta gaggaagcac ccccaccacc ccctgctgtc 2160 ccctctccag aggagctcac ctaccttatt gaggccctgt tcaagacaga ggtgctgccc 2220 ggccagctgg gctacctgcg ttttgacgcc atggctgaac tggagacagt gaaggccgtg 2280 gggccacagc tggtgcggct ggtatggcaa cagctggtgg acacggctgc gctggtgatc 2340 gacctgcgct acaaccctgg cagctactcc acggccatcc cgctgctctg ctcctacttc 2400 tttgaggcag agccccgcca gcacctgtat tctgtctttg acagggccac ctcaaaagtc 2460 acggaggtgt ggaccttgcc ccaggtcgcc ggccagcgct acggctcaca caaggacctc 2520 tacatcctga tgagccacac cagtggctct gcggccgagg cctttgcaca caccatgcag 2580 gacctgcagc gggccacggt cattggggag cccacggccg gaggcgcact ctctgtggggc 2640 atctaccagg tgggcagcag ccccttatat gcatccatgc ccacccagat ggccatgagt 2700 gccaccacag gcaaggcctg ggacctggct ggtgtggagc ccgacatcac tgtgcccatg 2760 agcgaagccc tttccatagc ccaggacata gtggctctgc gtgccaaggt gcccacggtg 2820 ctgcagacgg ccgggaagct ggtggctgat aactatgcct ctgccgagct gggggccaag 2880 atggccacca aactgagcgg tctgcagagc cgctactcca gggtgacctc agaagtggcc 2940 ctagccgaga tcctgggggc tgacctgcag atgctctccg gagacccaca cctgaaggca 3000 gcccatatcc ctgagaatgc caaggaccgc attcctggaa ttgtgcccat gcagatccct 3060 tcccctgaag tatttgaaga gctgatcaag ttttccttcc acactaacgt gcttgaggac 3120 aacattggct acttgaggtt tgacatgttt ggggacggtg agctgctcac ccaggtctcc 3180 aggctgctgg tggagcacat ctggaagaag atcatgcaca cggatgccat gatcatcgac 3240 atgaggttca acatcggtgg ccccacatcc tccattccca tcttgtgctc ctacttcttt 3300 gatgaaggcc ctccagttct gctggacaag atctacagcc ggcctgatga ctctgtcagt 3360 gaactctgga cacacgccca ggttgtaggt gaacgctatg gctccaagaa gagcatggtc 3420 attctgacca gcagtgtgac ggccggcacc gcggaggagt tcacctatat catgaagagg 3480 ctgggccggg ccctggtcat tggggaggtg accagtgggg gctgccagcc accacagacc 3540 taccacgtgg atgacaccaa cctctacctc actatcccca cggcccgttc tgtggggggcc 3600 tcggatggca gctcctggga aggggtgggg gtgacacccc atgtggttgt ccctgcagaa 3660 gaggctctcg ccagggccaa ggagatgctc cagcacaacc agctgagggt gaagcggagc 3720 ccaggcctgc aggaccacct gtag 3744 <210> 9 <211> 846 <212> DNA <213> artificial sequence <220> <223> C1QTNF1 mRNA <400> 9 atgggctccc gtggacaggg actcttgctg gcgtactgcc tgctccttgc ctttgcctct 60 ggcctggtcc tgagtcgtgt gccccatgtc cagggggaac agcaggagtg ggaggggact 120 gaggagctgc cgtcgcctcc ggaccatgcc gagagggctg aagaacaaca tgaaaaatac 180 aggcccagtc aggaccaggg gctccctgct tcccggtgct tgcgctgctg tgaccccggt 240 acctccatgt acccggcgac cgccgtgccc cagatcaaca tcactatctt gaaaggggag 300 aagggtgacc gcggagatcg aggcctccaa gggaaatatg gcaaaacagg ctcagcaggg 360 gccaggggcc acactggacc caaagggcag aagggctcca tgggggcccc tggggagcgg 420 tgcaagagcc actacgccgc cttttcggtg ggccggaaga agcccatgca cagcaaccac 480 tactaccaga cggtgatctt cgacacggag ttcgtgaacc tctacgacca cttcaacatg 540 ttcaccggca agttctactg ctacgtgccc ggcctctact tcttcagcct caacgtgcac 600 acctggaacc agaaggagac ctacctgcac atcatgaaga acgaggagga ggtggtgatc 660 ttgttcgcgc aggtgggcga ccgcagcatc atgcaaagcc agagcctgat gctggagctg 720 cgagagcagg accaggtgtg ggtacgcctc tacaagggcg aacgtgagaa cgccatcttc 780 agcgaggagc tggacaccta catcaccttc agtggctacc tggtcaagca cgccaccgag 840 ccctag 846

Claims (5)

RNA of microRNA 16-1-3p (hsa-miR-16-1-3p), microRNA 15a-3p (hsa-miR-15a-3p) and microRNA 6826-5p (hsa-miR-6826-5p) A kit for diagnosing metastasis of cervical cancer comprising an agent for measuring the expression level of. The kit for diagnosing metastasis of cervical cancer according to claim 1, wherein the agent for measuring the expression level is sense and antisense primers or probes that complementarily bind to the microRNA. The method of claim 1, wherein the composition for diagnosis is micro RNA 605-5p (hsa-miR-605-5p), micro RNA 6780a-5p (hsa-miR-6780a-5p), micro RNA 6791-5p (hsa-miR-5p) 6791-5p), FAM168A (Family With Sequence Similarity 168 Member A) mRNA, RBP3 (Retinol-binding protein 3, interstitial) mRNA, and C1QTNF1 (Complement C1q tumor necrosis factor-related protein 1) mRNA in the group consisting of A kit for diagnosing metastasis of cervical cancer, further comprising an agent for measuring the expression level of one or more selected RNAs. The method of claim 1, wherein the microRNA 16-1-3p (hsa-miR-16-1-3p) consists of the nucleotide sequence represented by SEQ ID NO: 1, and the microRNA 15a-3p (hsa-miR-15a -3p)) is composed of the nucleotide sequence represented by SEQ ID NO: 2, and the micro RNA 6826-5p (hsa-miR-6826-5p) is composed of the nucleotide sequence represented by SEQ ID NO: 3. Cervical cancer, characterized in that Kit for diagnosis of metastasis of The method of claim 3, wherein the microRNA 605-5p (hsa-miR-605-5p) consists of the nucleotide sequence represented by SEQ ID NO: 4, and the microRNA 6780a-5p (hsa-miR-6780a-5p) is It consists of the nucleotide sequence represented by SEQ ID NO: 5, and the micro RNA 6791-5p (hsa-miR-6791-5p) consists of the nucleotide sequence represented by SEQ ID NO: 6, and the FAM168A (Family With Sequence Similarity 168 Member A ) mRNA consists of the nucleotide sequence represented by SEQ ID NO: 7, the RBP3 (Retinol-binding protein 3, interstitial) mRNA consists of the nucleotide sequence represented by SEQ ID NO: 8, and the C1QTNF1 (Complement C1q tumor necrosis factor A kit for diagnosing metastasis of cervical cancer, characterized in that the mRNA of -related protein 1) consists of the nucleotide sequence represented by SEQ ID NO: 9.

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