KR102602134B1 - Method of providing information for diagnosing metastasis of cervical cancer - Google Patents

Abstract

The present invention relates to a biomarker for diagnosing metastasis of cervical cancer and its use. More specifically, as a result of analyzing microRNA contained in exosomes isolated from the blood of cervical cancer patients receiving anticancer radiation treatment before and after treatment, the uterus Micro RNA 16-1-3p (has-miR-16-1-3p), micro RNA 15a-3p (has-miR-15a-3p), micro RNA 6826-5p (has- (miR-6826-5p), micro RNA 605-5p (has-miR-605-5p), and micro RNA 6780a-5p (has-miR-6780a-5p), micro RNA 6791-5p (has-miR-6791- 5p) was discovered, and it was found to be related to the mRNA of FAM168A (Family With Sequence Similarity 168 Member A), the mRNA of RBP3 (Retinol-binding protein 3, interstitial), and the mRNA of C1QTNF1 (Complement C1q tumor necrosis factor-related protein 1). As confirmed, a biomarker composition for diagnosing cervical cancer metastasis including the micro RNAs and mRNA, a composition for diagnosing cervical cancer metastasis, a kit for diagnosing cervical cancer metastasis, and a method of providing information for diagnosing cervical cancer metastasis are provided.

Description

{Method of providing information for diagnosing metastasis of cervical cancer}

The present invention relates to a biomarker for diagnosing metastasis of cervical cancer and its use.

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. Stage 0 cancer is also called intraepithelial cancer, and stage 1 to 4 cancer is called invasive cervical cancer. Intraepithelial cancer develops around the age of 35 to 40, about 10 years earlier than invasive cervical cancer, and invasive cervical cancer is known to start increasing after the age of 30, peak in the 50s, and then show a sharp decline. In Korea, the prevalence level of cervical cancer is about 31 per 100,000 people and the mortality rate is about 6.8 per 100,000 people, showing no significant changes over the past 10 years. Looking at the frequency of incidence and prevalence by age, it is in the order of those in their 50s, 60s, and 40s. The incidence rate is 3 times that of the United States, 2.5 times that of Japan, and 1/3 that of Brazil. Mortality due to cervical cancer accounts for a proportion of cancer mortality. This 6% is higher than the around 2% in the United States, Japan, and Switzerland. Currently, the sexually transmitted infectious disease model is most widely accepted for the development of cervical cancer, with early onset sexual activity, multiple sexual partners, male factors, human papillomavirus (HPV) infection, and human immunodeficiency virus. Infections are known to be risk factors for cervical cancer.

The recurrence rate of cervical cancer after treatment is approximately 50% within 1 year, 25% within 2 years, and 15% within 3 years, with approximately 85% of patients experiencing recurrence within 3 years. In particular, the recurrence rate after radiation treatment for cervical cancer also exceeds 55%. Treatment for recurrent cervical cancer is determined differently depending on the type of treatment previously received and the patient's condition. In general, if surgery was the primary treatment, radiation therapy is considered in case of recurrence, and if radiation therapy was the primary treatment, surgery is considered. If both treatments are not possible, chemotherapy is performed. However, there is no standard treatment, and appropriate treatment is administered depending on the site of recurrence. In general, the treatment is aimed at alleviating symptoms rather than curing the disease.

According to a recent report, there is a 27% difference in the 5-year progression-free survival time between patients with intrapelvic cervical cancer metastasis (FIGO [International Federation of Gynecology and Obstetrics] stage IIIC1) and abdominal lymph node metastasis (FIGO stage IIIC2). there is. Therefore, determining the presence of abdominal lymph node metastasis is important to predict the patient's treatment outcome. To diagnose this, both surgical evaluation of the abdominal lymph nodes and evaluation methods using PET/CT or MRI are possible. However, rather than invasively evaluating the abdominal lymph nodes of patients expected to be at the chemotherapy stage, imaging and tumor markers are used despite low sensitivity. In most cases, it relies on (PET-CT sensitivity 85%, MRI sensitivity 66%). This is because chemotherapy remains the same even though it involves surgical costs and side effects. Radiation oncologists refer to tumor markers such as squamous cell carcinoma antigen or carcinoembryonic antigen in addition to images to decide 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 is needed to predict the degree of cancer metastasis. The degree of metastasis can be expressed simply in terms of stage, but it has complex biological meaning behind it, so a more meaningful, high-level approach is needed.

Meanwhile, exosomes are extracellular vesicles of 40-150 nm that are secreted by cancer cells or other normal cells. They perform various physiological functions through signal transmission between cells and play a role in the development, metastasis, and development of cancer. 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 the present time. In particular, because blood is rich in exosomes originating from not only cancer cells but also normal cells, it is easy to understand the relationship between the physical responses 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 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 from cervical cancer patients receiving chemotherapy before and after treatment, and extract plasma exosome RNA from these. After isolation, micro RNAs related to cervical cancer metastasis are detected through NGS analysis, and these are used as biomarkers for the diagnosis of cervical cancer metastasis.

The present invention provides 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 biomarker composition for diagnosing metastasis of cervical cancer, including one or more selected from the group consisting of.

In addition, the present invention provides 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 composition for diagnosing metastasis of cervical cancer, including an agent for measuring the expression level of one or more RNAs selected from the group consisting of.

Additionally, the present invention provides a kit for diagnosing cervical cancer metastasis, including the composition for diagnosing metastasis.

In addition, the present invention provides 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. Provides a method of providing information for diagnosing metastasis of cervical cancer, including the step of measuring the expression level of (hsa-miR-6826-5p).

According to the present invention, as a result of analyzing microRNA contained in exosomes isolated from the blood before and after treatment of cervical cancer patients receiving chemotherapy, microRNA 16-1-3p is associated with metastasis of cervical cancer. (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), and micro RNA 6780a-5p (hsa-miR-6780a-5p), micro RNA 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 to the above-mentioned micro RNAs and mRNA in cervical cancer. It can be used as a means of diagnosing metastasis and is expected to be used efficiently in clinical practice as a useful biomarker for predicting the prognosis after treatment and surgery.

Figure 1 is a diagram showing the mechanisms of micro RNAs and mRNA involved in metastasis of cervical cancer.
Figure 2 is a schematic diagram explaining the treatment process, blood collection time, MRI imaging time, and follow-up process for cervical cancer patients who underwent anti-cancer radiation therapy.
Figure 3 is a diagram showing the degree of cancer metastasis according to FIGO 2018 (The 2018 International Federation of Gynecology and Obstetrics) stages.
Figure 4 shows the results of measuring the expression level of micro RNA 15a-3p compared to micro RNA 16-1-3p, which is related to the degree of metastasis of cervical cancer.
Figure 5 shows the results of selection of miRNAs through subgroup analysis of miRNAs (log2FC) that have a significant correlation with the degree of metastasis of cervical cancer.
Figure 6 shows the results of analyzing the correlation between the selected miRNAs in Figure 5.
Figure 7 shows the results of comparing the selected miRNA variables in Figure 5 according to direction.
Figure 8 shows the results of network analysis by dividing the selected group of miRNAs and their associated miRNAs into network subgroups.
Figure 9 shows the results of network analysis identifying the structure between the selected group of miRNAs and related mRNAs.
Figure 10 shows the results of selecting related genes, including mRNA (A), which is highly related to each clinical factor, and miRNA and mRNA (B) obtained through network analysis.
Figure 11 shows the results of IPA (Ingenuity pathway analysis) analysis of miRNAs and mRNAs related to metastasis of cervical cancer.
Figure 12 is the result of drawing a Venn diagram of four function categories related to metastasis of cervical cancer.
Figure 13 shows the results of network analysis of the mRNAs and miRNAs selected in Figure 12.
Figure 14 shows the results of analyzing the log2FC values of the RNAs included in Figure 13 using the IPA database to analyze categories showing statistically significant differences depending on whether cervical cancer metastasis.
Figure 15 is a graphical representation of the relative proportions of selected categories for major miRNAs related to metastasis of cervical cancer.
Figure 16 is a graph showing significantly changed mRNAs and miRNAs according to changes in main miRNAs related to metastasis of cervical cancer.
Figure 17 shows the results of subgroup analysis of cervical cancer metastasis targeting mRNAs.
Figure 18 shows the results of analyzing the correlation between the selected miRNAs in Figure 17.

The terms used in this specification are general terms that are currently widely used as much as possible while considering the function in the present invention, but this may vary depending on the intention or precedent of a person skilled in the art, the emergence of new technology, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the relevant invention. Therefore, the terms 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 simply the name of the term.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the technical field to which the present invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless clearly defined in the present application, should not be interpreted in an ideal or excessively formal sense. No.

The numerical range includes the values defined in the range above. Every maximum numerical limit given throughout this specification includes all lower numerical limits as if the lower numerical limit were explicitly written out. Every minimum numerical limit given throughout this specification includes every higher numerical limit as if such higher numerical limit was clearly written. All numerical limits given throughout this specification will include all better numerical ranges within the broader numerical range, as if the narrower numerical limits were clearly 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 analyzed plasma exosomal miRNA before and after radiotherapy (2 weeks). were analyzed to detect miRNAs showing clinical variables through fold change.

The present invention provides 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 biomarker composition for diagnosing metastasis of cervical cancer, including one or more selected from the group consisting of.

When diagnosing cancer, the stage of cancer progression is determined based on the extent to which cancer cells have spread. The TNM method is used to determine whether cancer has spread to other organs of the body. Metastasis of cervical cancer refers to the spread of cervical cancer cells into the lymph fluid. This refers to a condition in which cancer is discovered in a site other than the cervix in a patient diagnosed with cervical cancer, as it travels through the bloodstream to other organs and proliferates there.

Whether cervical cancer has metastasized can help predict prognosis or determine whether to require additional treatment. In particular, the presence of metastases beyond the pelvis (stage IIIC2 or higher) greatly affects treatment outcome. At present, the degree of metastasis is being evaluated using various imaging tools, but there are many cases in which it is difficult to determine that metastasis has occurred or lesions that are difficult to observe on images. If there were a tool that could additionally help determine the degree of metastasis, it would be possible to provide more appropriate treatment to patients by determining the extent of radiation therapy or whether to administer additional chemotherapy.

The micro RNA 16-1-3p (hsa-miR-16-1-3p) consists of the base sequence shown in SEQ ID NO: 1, and the micro RNA 15a-3p (hsa-miR-15a-3p)) has the sequence It consists of a nucleotide sequence represented by number 2, and the micro micro RNA 6826-5p (hsa-miR-6826-5p) consists of a 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. It can be included as .

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

In addition, the present invention provides 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 composition for diagnosing metastasis of cervical cancer, including 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 bind complementary to the micro RNA.

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

The probe refers to a nucleic acid that can specifically bind to mRNA of a few bases to hundreds of bases in length, produced through enzyme-chemical separation purification or synthesis. The presence or absence of mRNA can be confirmed by labeling it with a radioactive isotope or enzyme, and it can be designed, modified and used by known methods.

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), and FAM168A. Expression of one or more RNAs selected from the group consisting of mRNA of (Family With Sequence Similarity 168 Member A), mRNA of RBP3 (Retinol-binding protein 3, interstitial), and mRNA of C1QTNF1 (Complement C1q tumor necrosis factor-related protein 1) An agent whose level is measured may additionally be included.

Additionally, the present invention provides a kit for diagnosing cervical cancer metastasis, including the composition for diagnosing metastasis.

The kit consists of one or more different component compositions, solutions or devices suitable for the analytical method. For example, in order to perform PCR, the kit includes genomic DNA derived from the sample to be analyzed, a primer set specific for the marker gene of the present invention, an appropriate amount of DNA polymerase, dNTP mixture, PCR buffer, and water. It could be a kit. The PCR buffer solution may contain KCl, Tris-HCl, and MgCl2. In addition, the kit may additionally include components required to perform electrophoresis to check whether amplification of the PCR product has occurred.

Additionally, the kit may be a kit containing essential elements required to perform RT-PCR. In addition to each primer pair specific for the marker gene, the RT-PCR kit contains test tubes or other suitable containers, reaction buffer, deoxynucleotides (dNTPs), enzymes such as Taq-polymerase and reverse transcriptase, DNase, RNase inhibitors, and DEPC. -Can include DEPC-water, sterilized water, etc. It may also include a pair of primers specific to the gene used as a quantitative control.

In addition, the present invention provides 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. Provides a method of providing information for diagnosing metastasis of cervical cancer, including the step of measuring the expression level of (hsa-miR-6826-5p).

The biological sample is blood or plasma derived exosomes.

The expression level was 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), and RNase protection assay. It can be measured using one or more methods selected from the group consisting of (RNase protection assay (RPA)), microarray, and northern blotting.

The above 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) from a biological sample derived from a subject. -6791-5p), a group consisting of mRNA for FAM168A (Family With Sequence Similarity 168 Member A), mRNA for RBP3 (Retinol-binding protein 3, interstitial), and mRNA for C1QTNF1 (Complement C1q tumor necrosis factor-related protein 1) It may further include measuring the expression level of one or more RNAs selected from.

The subject may be a patient receiving radiation therapy to treat cervical cancer.

In a broad sense, the above diagnosis means judging the actual condition of the patient's disease in all aspects. The contents of the judgment include the name of the disease, etiology, type, severity, detailed conditions of the disease, and the presence or absence of complications. The prognosis refers to a prediction regarding the progression and recovery of the disease, and refers to an outlook or preliminary evaluation.

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

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 skilled in the art that the scope of the present invention is not to be construed as limited by these examples.

[Example]

1. Patients and specimens

Patients who received chemotherapy and radiation therapy after being diagnosed with cervical cancer were eligible. The Institutional Review Board of Ajou University Hospital obtained informed consent from 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 each stage group sufficiently reflected the degree of cancer metastasis, which was used as the clinical end point.

As shown in Figure 2, the treatment process, blood collection time, MRI imaging time, and follow-up process were conducted for 28 cervical cancer patients who underwent anti-cancer radiation therapy.

As shown in Figure 3, patients with FIGO stage (2018) IB-IIIC1 (18 patients) have lesions only in the pelvis, and patients with stage IIIC2 and IVA (7 patients) have lesions in abdominal lymph nodes. In this case, the IVA patient had lesions in the abdominal lymph nodes and the cervical tumor invaded the bladder. Among stage IVB patients (3 patients), 1 patient had metastasis to the left upper clavicle, 1 patient had a single lesion metastasized to the left lower lobe of the lung, and 1 patient had metastasis to the right upper clavicle and left axilla. Among these, the patient with metastasis to the left lung did not have abdominal lymph node metastasis and only pelvic lymph node metastasis was confirmed. In cases of metastasis to the abdominal lymph nodes, radiation therapy was administered to the abdominal lymph nodes along with the pelvis, and for IVB patients, local radiation therapy was administered to the metastatic area simultaneously during the treatment period. The clinical results of the above patients are shown in Table 1 below.

2. Construction of dataset for exosomal miRNA and mRNA

Before radiation treatment and during the second week of radiation treatment, 5-10 cc of blood was collected from patients. Plasma was separated from the collected blood, and additionally, 3-5 cc of plasma samples from 29 people stored in the human derivatives storage were distributed and used in experiments. 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

Exosomes were isolated by mixing plasma with Exo2D RNA solution (Exosomeplus). Exosomal 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 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. Input RNA was first polyadenylated to provide a priming sequence for oligo (dT) primers. cDNA synthesis was primed by a 3'smRNA dT Primer that incorporated an adapter sequence at the 5' end of each RNA template, followed by the addition of non-template nucleotides bound by SMRT smRNA Oligo enhanced by 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. After this, full-length Illumina adapters containing the index sequence for sample multiplexing during PCR amplification were added. Forward PCR Primer bound to the sequence added by SMART smRNA Oligo, while Reverse PCR Primer bound 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 more than 138 bp (cDNA 18 bp or more + adapter 120 bp or more) was excised. The resulting library cDNA molecules contained the sequences required for clustering on an Illumina flow cell.

The library was gel purified and verified for 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 Platforms) and verified using 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, generating 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 obtained from different experimental samples were preprocessed with miRDeep2 and analyzed. Adapter trimming used the cutadapt program to remove adapter sequences to which miRNAs were attached during the construction of the smRNA library. The first 3 nt of all reads were trimmed to remove additional bases inserted during the SMART template-switching activity process. The adapter sequence and all 3' sides of the adapter were also removed. If a read matched more than the first 5 bp of the 3' adapter sequence, the sequence was considered to actually be an adapter sequence and was then trimmed from the read. For analysis, only trimmed reads of at least 18bp 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 processed with adapter sequences were collected to form clusters. This cluster contains reads with 100% match by sequence ID and read length, and a provisional cluster ID and number of retained reads were provided.

2-4. Ribosomal RNA filtering

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

2-5. miRNA read

Sequence alignment and detection of microRNAs were performed using the miRDeep2 software algorithm. Before performing 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. Precursor miRNAs obtained from miRBase v21 were aligned with the reference sequence. The miRDeep2 algorithm is based on the miRNA biogenesis model and aligned reads to potential hairpin structures in a manner consistent with Dicer processing and assigned a score indicating the likelihood that the hairpin is a true miRNA precursor.

2-6. Proportion of miRNA 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 visualizations were performed using R 4.0.2 version (www.r-project.org), and all comparative analyzes were assumed to be non-parametric. The NGS data included small RNA, non-mRNA, and mRNA, and among these, miRNA and mRNA were used for analysis. Among these, cases where the read count was 0 in more than 14 patients were excluded from the analysis. There were 586 cases of miRNA and 15324 cases of mRNA used in the analysis. The values of all transcriptome data used the log2 fold change (log2FC) value of the read count values 2 weeks after the start of radiation treatment compared to the 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 the 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 that could predict clinical end points were selected, and their correlations were analyzed (Figures 4, 4, and 5). Correlation analysis consisted of 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, among the miR-16-1-3p (log2FC) values, there were values with no expression, so it was confirmed whether it was replacing the expression in the cluster gene. As a result of normalizing the expression level RPM [reads per million] before radiation treatment to examine the correlation with miR-16-1-3p, the cluster gene, miR-15a-3p, showed the highest correlation. Based on this result, in cases where the value of miR-16-1-3p (log2 FC) could not be measured, it was replaced with miR-15a-3p (log2FC) and used in future analysis. It was designated as miR-16-1-3p [or15a-3p].

As shown in Figure 5, there was a significant correlation (|R|>0 & |R) with the stage of cervical cancer (IB, IIB-IIIC1, IIIC2-IVA, IVB) and whether it metastasized (IB-IIIC1 vs. IIIC-IVB). |>0) The miRNAs were selected through subgroup analysis of the miRNAs (log2FC). When all possible combinations of cases were calculated based on the Adjusted R value, consistently included miRNAs were selected.

As shown in Figure 6, after obtaining directionality through multiple linear regression combination of the above-mentioned miRNA variables, each miRNA (log2FC) values were divided into miR-605-5p+miR-6780a-5p+miR-6791-5p+miR-6826- The value was obtained by adding and subtracting in that order: 5p-miR-16-1-3p[or 15a-3p]. Larger values were associated with higher disease stages, and in particular, the difference between IIB-IIIC1 and IIIC2-IVA was significant.

As shown in Figure 7, according to the direction of the above-mentioned miRNA variables, 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), it shows that the former decreases and as the latter increases, there are more cases of metastasis outside the pelvis.

Stage II

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

Network analysis used the Igraph package - prim algorithm, and in all networks, red edges indicated positive directions and blue edges indicated negative directions. For miRNA group stratification, A group refers to the main miRNA group, and B group refers to the miRNA group associated with several major miRNA groups.

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

Stage III

Through network analysis, the overall structure of the 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 locations of clinically relevant subgroup miRNAs were identified in the level 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 the miRNAs in figure3D, and the shortest distance on the A level miRNAs (arrows) was indicated by a red line. miR-6780a-5p,miR-6826-5p,miR-605-5p,miR-6791-5p,miR-16-1-3p[or15a-3p] are connected around PLCXD3 (star). (Connected through more than 15 F5, FAM168A, ACIN1, CMTR2, CCDC88B, RNF216, ARHGEF2, ALDH2, PLCXD3, FCHO2, ATE1, SMAD9, ADCY10, RBP3, and PERP) In Figure 3D, part of the B level is observed on the network. This shows that the structure of the miRNA-mRNA is appropriate.

Stage IV

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

As shown in Figure 10, related genes were selected, including mRNA (A) that was highly related to each clinical element and miRNA and mRNA (B) obtained through network analysis.

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

Stage V

Diseases/function categories with a degree of significance and possible association with clinical end points were selected, and a Vendiagram was drawn using the mRNA and miRNA corresponding to each group (Figure 12 ). Using the Venn diagram shown, mRNAs and miRNAs commonly associated with cancer and specific functions were re-selected.

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

Stage VI

The re-selected mRNAs and miRNAs were formed into a network using the short pathway of the main miRNA in Figure 9. Network analysis was visualized under the same conditions as in step II above.

As shown in Figure 13, a network was formed using a short pathway including the selected miRNAs and mRNAs in Figure 12 and the main miRNAs described in Figure 9, and the change in each main miRNA (log2FC>1.5 (up), log2FC< -1.5 (down), others - no change), showing statistically significant changes (red numbers mean decrease, blue numbers mean increase). The main miRNAs were found to be related 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.

Stage VII

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

As shown in Figure 14, all log2FC values of RNA included in the network of Figure 13 were analyzed in the IPA database for each patient, a z score was obtained for each category, and then according to the presence or absence of metastasis outside the pelvis (Stage IB-IIIC1 vs. StageIIIC2- IVB) The results of describing the items showing statistically significant differences include migration/survival of endothelial cells/neuronal cells/epithelial cells/myeloid cells, and blood cell activation. activation) was associated with the degree of metastasis of cervical cancer.

Stage VIII

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

As shown in Figure 15, the network in Figure 13 contains 50% RNAs associated with cellular movement, 30% RNAs associated with cell death and survival, and 50% RNAs associated with cell death and survival, and 50% RNAs associated with cell death and survival. RNAs related to cell signaling and interaction were 20%. RNAs that changed significantly according to changes in miR-605-5p and miR-6826-5p were similar to the overall functional distribution of 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, the proportion of RNAs related to cell death and survival was high. The proportion was high. In the case of miR-16-1-3p[or 15a-3p], cellular movement accounted for 30% and cell death and survival accounted for approximately 70%, but specific subcategories were not included at all. wasn't doing it

Stage IX

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

As shown in Figure 16, changes in mRNA and miRNA according to changes in the main miRNA were analyzed. Changes in the same direction are indicated in red, changes in the opposite direction are indicated in blue, and changes exceeding 1.5 log2FC are indicated 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 level

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

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

As shown in Figure 18, the RBP3- C1QTNF1- FAM168A (log2FC) value was significantly higher as the stage increased. Subgroup analysis was performed using FAM168A, RBP3, C1QTNF1, miR-605-5p, miR-6780a-5p, miR-6791-5p, miR-6826-5p, and miR-16-1-3p[or 15a-3p] As miR-605-5p+RBP3-miR-16-1-3p[or 15a-3p]-C1QTNF1 (log2FC) increased, the difference depending on the stage became larger.

As the specific parts of the present invention have been described in detail above, it is clear to those skilled in the art that these specific techniques are merely preferred embodiments and do not limit the scope of the present invention. do. That is, the practical 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 of that <130> ADP-2023-0216 <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 uugcugccu 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 uugggaggga 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 gcaccccccga 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 tgcggggccaa 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 acgggaggtgt ggaccttgcc ccaggtcgcc ggccagcgct acggctcaca caaggacctc 2520 tacatcctga tgagccacac cagtggctct gcggccgagg cctttgcaca caccatgcag 2580 gacctgcagc gggccacggt cattggggag cccacggccg gaggcgcact ctctgtgggc 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 gcggagggagt tcacctatat catgaagagg 3480 ctgggccggg ccctggtcat tggggaggtg accagtgggg gctgccagcc accacagacc 3540 taccacgtgg atgacaccaa cctctacctc actatcccca cggcccgttc tgtgggggcc 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)

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-3p) in subject-derived biological samples. A method of providing information for the diagnosis of metastasis of cervical cancer, comprising measuring the expression level of 6826-5p). The method of claim 1, wherein the biological sample is blood- or plasma-derived exosomes. The method of claim 1, wherein the expression level is determined by next generation sequencing (NGS), polymerase chain reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), or real-time polymerase chain reaction (Real-time polymerase chain reaction). A method of providing information, characterized in that it is measured through one or more methods selected from the group consisting of PCR), RNase protection assay (RPA), microarray, and northern blotting. The method of claim 1, wherein the method of providing information is performed by collecting micro RNA 605-5p (hsa-miR-605-5p), micro RNA 6780a-5p (hsa-miR-6780a-5p), and micro RNA 6791 from a biological sample derived from a subject. -5p (hsa-miR-6791-5p), mRNA for FAM168A (Family With Sequence Similarity 168 Member A), mRNA for RBP3 (Retinol-binding protein 3, interstitial), and C1QTNF1 (Complement C1q tumor necrosis factor-related protein 1). ) A method of providing information for the diagnosis of metastasis of cervical cancer, further comprising measuring the expression level of one or more RNAs selected from the group consisting of mRNA. The method of claim 1, wherein the subject is a patient receiving radiation therapy to treat cervical cancer.