A miRNA biomarker for diagnosing or assisting in the diagnosis of lung adenocarcinoma and its application

Abstract

The present invention discloses a miRNA biomarker for diagnosing or assisting the diagnosis of lung adenocarcinoma and its application, relating to the field of biomedicine. The invention also discloses a reagent for detecting target miRNAs in a sample and its application in preparing a product for diagnosing or assisting the diagnosis of lung adenocarcinoma. The target miRNAs include hsa-miR-3136-5p, hsa-miR-519d-5p, hsa-miR-381-5p, hsa-miR-490-5p, hsa-miR-377-5p, hsa-miR-320e, hsa-miR-2355-5p, and hsa-miR-6784-5p. The present invention screened out 8 key miRNAs and constructed an 8-miRNA risk scoring model including hsa-miR-3136-5p, hsa-miR-519d-5p, hsa-miR-381-5p, hsa-miR-490-5p, hsa-miR-377-5p, hsa-miR-320e, hsa-miR-2355-5p, and hsa-miR-6784-5p. The results showed that the 8-miRNA risk scoring model we constructed has the potential to predict distant metastasis in patients with lung adenocarcinoma.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to a miRNA biomarker for diagnosing or assisting in the diagnosis of lung adenocarcinoma and its application. Background Art

[0002] Lung cancer is a malignant tumor with a high mortality rate. Lung cancer is divided into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC), with adenocarcinoma being the most common histological subtype of NSCLC. The survival of NSCLC patients depends on the stage at diagnosis. Studies have shown that the 5-year survival rate for NSCLC is relatively high in stages I-II and relatively low in stages III-IV. The survival rate of NSCLC patients decreases significantly after distant metastasis develops.

[0003] However, due to the lack of specific symptoms in the early stages of NSCLC and the lack of early diagnostic methods, more than half of patients have already developed distant metastasis at the time of diagnosis, and the overall median survival is only 7-12 months. Currently, radical surgical resection is the standard treatment for patients with stage I, stage II, and some stage IIIA NSCLC; however, even after undergoing radical surgery, 30%-55% of patients still experience recurrence and metastasis, leading to treatment failure. Therefore, it is necessary to find new molecular biomarkers to predict the occurrence of distant metastasis of NSCLC in order to guide subsequent treatment and thus improve the prognosis of NSCLC patients.

[0004] miRNA is a non-coding short-chain RNA composed of 19-24 nucleotides, which usually binds to the 3′-untranslated region (3′-UTR) of the target mRNA, negatively regulating gene expression at the post-transcriptional level by regulating the stability of the mRNA or inducing the degradation of the mRNA. However, under certain conditions, miRNA can also promote the translation of the target mRNA. MiRNA is only encoded by about 3% of human genes, but they can regulate about 30% of human protein-coding genes. Therefore, they can regulate a variety of biological functions including metabolism, growth, development, immunity, etc. Related studies have confirmed that miRNA has great potential as a biomarker in the diagnosis and prognosis of various diseases.

[0005] TCGA is a public database providing publicly available cancer genomic data. The TCGA database contains a large amount of miRNA sequencing data from NSCLC patients. The inventors believe that using the miRNA sequencing data in the TCGA database to establish miRNA markers for predicting distant metastasis of lung adenocarcinoma is a technical problem that urgently needs to be solved by those skilled in the art.

[0006] The information disclosed in this background technology section is only intended to enhance understanding of the overall background of the invention and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to a person skilled in the art. Summary of the Invention

[0007] In response to the above technical problems, embodiments of the present invention provide a miRNA biomarker for diagnosing or assisting in the diagnosis of lung adenocarcinoma and its application, so as to solve the problems raised in the above background technology.

[0008] A reagent for detecting target miRNA in a sample is used in the preparation of a product for diagnosis or auxiliary diagnosis of lung adenocarcinoma, wherein the target miRNAs include hsa-miR-3136-5p, hsa-miR-519d-5p, hsa-miR-381-5p, hsa-miR-490-5p, hsa-miR-377-5p, hsa-miR-320e, hsa-miR-2355-5p, and hsa-miR-6784-5p.

[0009] Preferably, the nucleotide sequence of hsa-miR-3136-5p is shown as SEQ ID NO.1; the nucleotide sequence of hsa-miR-519d-5p is shown as SEQ ID NO.2; the nucleotide sequence of hsa-miR-381-5p is shown as SEQ ID NO.3; the nucleotide sequence of hsa-miR-490-5p is shown as SEQ ID NO.4; the nucleotide sequence of hsa-miR-377-5p is shown as SEQ ID NO.5; the nucleotide sequence of hsa-miR-320e is shown as SEQ ID NO.6; the nucleotide sequence of hsa-miR-2355-5p is shown as SEQ ID NO.7; the nucleotide sequence of hsa-miR-6784-5p is shown as SEQ ID NO.8, and the nucleotide sequence of hsa-miR-784-5p is shown as SEQ ID NO.8.

[0010] Among them, the nucleotide sequence of SEQ ID NO.1 is CTGACTGAATAGGTAGGGTCATT; the nucleotide sequence of SEQ ID NO.2 is CCTCCAAAGGGAAGCGCTTTCTGTT; the nucleotide sequence of SEQ ID NO.3 is AGCGAGGTTGCCCTTTGTATAT; the nucleotide sequence of SEQ ID NO.4 is CCATGGATCTCCAGGTGGGT; the nucleotide sequence of SEQ ID NO.5 is AGAGGTTGCCCTTGGTGAATTC; the nucleotide sequence of SEQ ID NO.6 is AAAGCTGGGTTGAGAAGG; the nucleotide sequence of SEQ ID NO.7 is ATCCCCAGATACAATGGACAA; and the nucleotide sequence of SEQ ID NO.8 is GCCGGGGCTTTGGGTGAGGG.

[0011] Preferably, the target miRNA is used as a biomarker for predicting distant metastasis of lung adenocarcinoma.

[0012] Preferably, the product for diagnosis or auxiliary diagnosis of lung adenocarcinoma includes primers and probes for detecting target miRNA.

[0013] A biomarker composition for predicting distant metastasis of lung adenocarcinoma, the biomarker composition comprising: hsa-miR-3136-5p, hsa-miR-519d-5p, hsa-miR-381-5p, hsa-miR-490-5p, hsa-miR-377-5p, hsa-miR-320e, hsa-miR-2355-5p, and hsa-miR-6784-5p.

[0014] A kit for diagnosing or assisting in the diagnosis of lung adenocarcinoma, the kit being used to detect target miRNAs; the target miRNAs include hsa-miR-3136-5p, hsa-miR-519d-5p, hsa-miR-381-5p, hsa-miR-490-5p, hsa-miR-377-5p, hsa-miR-320e, hsa-miR-2355-5p, and hsa-miR-6784-5p.

[0015] Preferably, the nucleotide sequence of hsa-miR-3136-5p is shown as SEQ ID NO.1; the nucleotide sequence of hsa-miR-519d-5p is shown as SEQ ID NO.2; the nucleotide sequence of hsa-miR-381-5p is shown as SEQ ID NO.3; the nucleotide sequence of hsa-miR-490-5p is shown as SEQ ID NO.4; the nucleotide sequence of hsa-miR-377-5p is shown as SEQ ID NO.5; the nucleotide sequence of hsa-miR-320e is shown as SEQ ID NO.6; the nucleotide sequence of hsa-miR-2355-5p is shown as SEQ ID NO.7; the nucleotide sequence of hsa-miR-6784-5p is shown as SEQ ID NO.8, and the nucleotide sequence of hsa-miR-784-5p is shown as SEQ ID NO.8.

[0016] Preferably, the kit comprises primers and probes for detecting target miRNA.

[0017] The embodiments of the present invention provide a miRNA biomarker for diagnosing or assisting in the diagnosis of lung adenocarcinoma and its application, which have the following beneficial effects: the present invention screened out 8 key miRNAs and constructed an 8-miRNA risk scoring model including hsa-miR-3136-5p, hsa-miR-519d-5p, hsa-miR-381-5p, hsa-miR-490-5p, hsa-miR-377-5p, hsa-miR-320e, hsa-miR-2355-5p, and hsa-miR-6784-5p. The model has good performance in predicting distant metastasis of lung adenocarcinoma (AUC: 0.831), and it was found that it is an independent risk factor for distant metastasis of lung adenocarcinoma. Compared with the low-risk group, the high-risk group has a worse prognosis; the research results show that the 8-miRNA risk scoring model we constructed has the potential to predict distant metastasis in patients with lung adenocarcinoma. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A is a flow chart of the present invention;

[0019] Figure 1 B is a volcano plot of differentially expressed miRNAs between lung adenocarcinoma (LUAD) tissues with distant metastasis (DM) and without distant metastasis (NDM);

[0020] Figure 2 Results were constructed for the 8-miRNA marker;

[0021] in, Figure 2 A is the parameter λ selected by 10-fold cross validation in the least absolute shrinkage and selection operator (LASSO) analysis; Figure 2 B is the LASSO regression coefficient spectrum of the 12 DEMs;

[0022] Figure 3 The diagnostic ability results of the 8-miRNA markers are shown;

[0023] in, Figure 3 A is the receiver operating characteristic (ROC) curve of miR-3136-5p distinguishing DM group from NDM group;

[0024] Figure 3 B is the ROC curve of miR-519d-5p distinguishing DM group from NDM group;

[0025] Figure 3 C is the ROC curve of miR-381-5p distinguishing DM group from NDM group;

[0026] Figure 3 D is the ROC curve for miR-490-5p distinguishing DM group from NDM group;

[0027] Figure 3 E is the ROC curve of miR-377-5p distinguishing DM group from NDM group;

[0028] Figure 3 F is the ROC curve of miR-320e distinguishing DM group from NDM group;

[0029] Figure 3 G is the ROC curve of miR-2355-5p distinguishing DM group from NDM group;

[0030] Figure 3 H is the ROC curve of miR-6784-5p distinguishing DM group from NDM group;

[0031] Figure 3 I is the ROC curve of 8-miRNA markers for distinguishing DM group from NDM group;

[0032] Figure 3 J is the result of Kaplan-Meier survival analysis;

[0033] Figure 4 The Venn diagram of target genes of eight miRNAs;

[0034] in, Figure 4 A is miR-3136-5p; Figure 4 B is miR-519d-5p; Figure 4 C is miR-381-5p; Figure 4 D is miR-490-5p; Figure 4 E is miR-377-5p; Figure 4 F is miR-320e; Figure 4 G is miR-2355-5p; Figure 4 H is miR-6784-5p;

[0035] Figure 5 is the gene ontology (GO) analysis diagram of overlapping target genes; Figure 5 A is a bar graph; Figure 5 B is a bubble chart;

[0036] Figure 6 is the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis map of overlapping target genes; Figure 6 A is a bar graph; Figure 6 B is a bubble chart;

[0037] Figure 7 This is the protein-protein interaction (PPI) network diagram of 24 hub genes. DETAILED DESCRIPTION

[0038] The following is a clear and complete description of the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] In response to the above technical problems, embodiments of the present invention provide a miRNA biomarker for diagnosing or assisting in the diagnosis of lung adenocarcinoma and its application, so as to solve the problems raised in the above background technology.

[0040] 1. Experimental methods:

[0041] 1. Data download and processing

[0042] miRNA sequencing data and clinical information were downloaded from the TCGA database. Inclusion criteria included: 1) samples with miRNA sequencing data and clinical information; 2) samples with prognostic information. Exclusion criteria included samples with unclear stage and overall survival (OS). A total of 344 lung adenocarcinoma samples were included in the study, including 23 stage IV patients who served as the distant metastasis group (DM group), and 321 stage I-III patients who served as the non-distant metastasis group (NDM group).

[0043] 2. Screening of DEMs and Construction of miRNA Risk Scoring Model

[0044] Differential analysis was performed using the "edgeR" package in R software (version 4.3.1). The fold change (FC) of each miRNA was calculated, and the DEM was selected using the criteria of |log2 FC| > 1 and P < 0.05. Key miRNAs were identified from the DEM using LASSO regression. Multivariate logistic regression analysis was then used to construct a miRNA risk score model for predicting distant metastasis of lung adenocarcinoma (risk score formula: β1 × miRNA1EXP + β2 × miRNA2EXP + ... + βn × miRNAnEXP, where β represents the regression coefficient of the corresponding miRNA and miRNAnEXP represents the expression level of the corresponding miRNA). Receiver operating characteristic (ROC) curves were plotted to verify the predictive performance of the model. Patients were divided into high-risk and low-risk groups using the optimal cutoff value of the ROC curve. Kaplan-Meier curves were plotted, and the survival difference between the high-risk and low-risk groups was evaluated using the log-rank test. The risk score model and clinical variables were included in univariate and multivariate logistic regression analysis to verify the impact of the risk score model on distant metastasis. In the univariate analysis, variables with P < 0.1 were included in the multivariate analysis.

[0045] 3. Target gene prediction and GO and KEGG enrichment analysis

[0046] The miRWalk and TargetScan databases were used to predict miRNA target genes. Overlapping target genes were identified using a Venn diagram. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was then performed on target genes using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tool.

[0047] 4. Construct protein-protein interaction (PPI) network and screen hub genes

[0048] The string APP plug-in of Cytoscape software was used to construct the target gene PPI network and visualize the network, and the cytoHubba plug-in was used to screen hub genes.

[0049] 5. Statistical methods

[0050] R4.3.1 software was used for data and image processing, and P < 0.05 was considered statistically significant.

[0051] 2. Experimental results:

[0052] 1. Clinical characteristics of patients

[0053] The flow chart of this study can be found in Figure 1A. Data from 513 patients with lung adenocarcinoma were downloaded from the TCGA database. Patients with unclear stage and overall survival of 0 were excluded. A total of 344 patients with lung adenocarcinoma were included in the study, including 23 patients in the DM group with distant metastasis and 321 patients in the NDM group without distant metastasis. 2213 miRNAs were detected in each sample. Excluding those with extremely low expression levels (80% of samples had an expression level of 0), the remaining 545 miRNAs were analyzed using the RPM (reads per million) values ​​of the miRNA sequencing data. Clinical information for the patients was also downloaded from the TCGA database, including age at diagnosis, sex, smoking history, T stage, lymph node status, and survival status. Clinical information for the patients is shown in Table 1.

[0054] Table 1 Clinical characteristics of patients

[0055]

[0056] Abbreviations: NDM = non-distant metastasis; DM = distant metastasis; SD = standard deviation.

[0057] 2. DEM screening

[0058] The edgeR package of R software (version 4.3.1) was used to perform differential analysis between the DM and NDM groups. A total of 12 DEMs were identified, all of which were upregulated ( Figure 1 B).

[0059] 3. Construction of miRNA markers

[0060] LASSO regression analysis was performed on the 12 DEMs, and 10-fold cross validation was used to screen out 8 key miRNAs from the 12 DEMs for model construction: hsa-miR-3136-5p, hsa-miR-519d-5p, hsa-miR-381-5p, hsa-miR-490-5p, hsa-miR-377-5p, hsa-miR-320e, hsa-miR-2355-5p, hsa-miR-6784-5p ( Figure 2 ).

[0061] The above 8 miRNAs were then subjected to multivariate logistic regression analysis, and the risk score of each patient was calculated based on the regression coefficients of the 8 miRNAs.

[0062] The ROC curve was drawn based on the risk score, and the area under the curve (AUC) was 0.831 (95% CI: 0.73-0.933, p < 0.0001), indicating that the 8-miRNA risk score model has good performance in predicting distant metastasis. The optimal cutoff value was determined to be 1.944 based on the ROC curve. With 1.944 as the cutoff point, patients were divided into a high-risk group (≥1.944, n = 284) and a low-risk group (<1.944, n = 60). Survival analysis was performed using the Kaplan-Meier method and the Log-rank test. The results showed that the high-risk group had a worse prognosis than the low-risk group (p = 0.003) ( Figure 3 ).

[0063] The predictive value of 4.8-miRNA markers for distant metastasis of lung adenocarcinoma

[0064] Univariate and multivariate logistic regression analyses were performed to verify the impact of the constructed 8-miRNA signature (high-risk vs. low-risk) on metastasis. In univariate analysis, age (OR = 0.96, P = 0.075), T stage (OR = 2.48, P = 0.072), N stage (OR = 2.55, P = 0.051), and risk score (OR = 14.39, P < 0.001) were associated with metastasis. Multivariate logistic analysis revealed that the 8-miRNA signature (OR = 15.62, P < 0.001) was an independent risk factor for distant metastasis in lung adenocarcinoma (Table 2).

[0065] Table 2 Logistic regression analysis

[0066]

[0067] Abbreviations: OR = odds ratio; CI = confidence interval

[0068] 5. Target gene prediction and enrichment analysis

[0069] The target genes of the above 8 miRNAs were predicted using the miRWalk and TargetScan databases. hsa-miR-377-5p has 1021 overlapping target genes, hsa-miR-381-5p has 174 overlapping target genes, hsa-miR-490-5p has 690 overlapping target genes, hsa-miR-519d-5p has 646 overlapping target genes, hsa-miR-3136-5p has 585 overlapping target genes, hsa-miR-320e has 602 overlapping target genes, hsa-miR-2355-5p has 807 overlapping target genes, and hsa-miR-6784-5p has 444 overlapping target genes ( Figure 4 The eight miRNAs have a total of 3,664 target genes. We then performed enrichment analysis on these 3,664 target genes to clarify their biological functions.

[0070] GO and KEGG enrichment analysis was performed on 3664 target genes using the DAVID database. As shown in the figure, biological process (BP) analysis showed that these target genes were mainly involved in biological processes such as transcriptional regulation and positive regulation, RNA polymerase II promoter transcriptional regulation and positive regulation, intracellular signal transduction, cell proliferation positive regulation, nervous system development, and protein phosphorylation. The results of cellular component (CC) analysis showed that these target genes were mainly enriched in the nucleus, cytoplasm, and nucleoplasm. The results of molecular function (MF) analysis showed that the target genes were mainly enriched in protein binding, metal ion binding, RNA polymerase II transcription factor active sequence-specific DNA binding, RNA polymerase II core promoter proximal region sequence-specific DNA binding, and transcription factor active sequence-specific DNA binding ( Figure 5 KEGG pathway analysis showed that the target genes were significantly enriched in the tumor pathway, herpes simplex virus type 1 infection, PI3K-Akt signaling pathway, MAPK signaling pathway, Ras signaling pathway, human cytomegalovirus infection, calcium signaling pathway, proteoglycan, endocytosis, adhesion, and cAMP signaling pathways, which are often associated with tumor progression. This suggests that the miRNAs we screened play a potential role in the progression of lung adenocarcinoma ( Figure 6 A, B).

[0071] 6. PPI network construction and hub gene screening

[0072] The PPI network of the target genes was constructed using the Cytoscape string APP plug-in, and the top 24 hub genes were screened using the cytoHubba plug-in (Table 3, Figure 7), among which hsa-miR-377-5p was associated with 10 hub genes (RPF2, RBM28, UTP6, KRR1, GNL3L, LSG1, UTP23, RRP8, NLE1, RBM34), hsa-miR-490-5p was associated with 3 hub genes (BMS1, RBM28, NLE1), hsa-miR-519d-5p was associated with 4 hub genes (RPF2, WDR12, RSL24D1, RCL1), hsa-miR-32 0e was associated with four hub genes (RBM28, KRR1, DDX17, and DNTTIP2), hsa-miR-2355-5p was associated with four hub genes (BMS1, NOP9, KRR1, and DDX51), and hsa-miR-6784-5p was associated with five hub genes (NAT10, DDX31, RRP15, SURF6, and POLR1A). However, hsa-miR-381-5p and hsa-miR-3136-5p were not associated with the above hub genes.

[0073] Table 324 Hub genes of eight miRNAs in protein-protein interaction (PPI) network

[0074]

[0075]

[0076] In summary, we first downloaded high-throughput miRNA data from The Cancer Genome Atlas (TCGA) database and analyzed the data using bioinformatics analysis methods, including the edgeR package in R language, Kaplan-Meier curve and Log-rank method, as well as a variety of online analysis tools.

[0077] Compared with the NDM group, a total of 12 DEMs were identified in the DM group. We constructed an 8-miRNA biomarker, which was confirmed to have good performance in predicting distant metastasis and was found to be an independent risk factor for distant metastasis in lung adenocarcinoma. Based on the risk score, patients were divided into high-risk and low-risk groups, with the high-risk group having a worse prognosis.

[0078] We also analyzed the target genes of the eight miRNAs, revealing that these target genes may be involved in a variety of pathways, including tumorigenesis, herpes simplex virus type 1 infection, PI3K-Akt signaling, MAPK signaling, Ras signaling, human cytomegalovirus infection, calcium signaling, proteoglycans, endocytosis, adhesion, and cAMP signaling. Furthermore, we identified 24 hub genes. Therefore, our constructed eight-miRNA signature has the potential to predict distant metastasis in lung adenocarcinoma.

[0079] The embodiments described above are merely descriptions of preferred implementations of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should fall within the scope of protection determined by the claims of the present invention.

Claims

1. Use of a reagent for detecting target miRNA in a sample in the preparation of a product for predicting distant metastasis of lung adenocarcinoma, characterized in that: The target miRNA consists of hsa-miR-3136-5p, hsa-miR-519d-5p, hsa-miR-381-5p, hsa-miR-490-5p, hsa-miR-377-5p, hsa-miR-320e, hsa-miR-2355-5p and hsa-miR-6784-5p.

2. Use of the reagent for detecting target miRNA in a sample according to claim 1 in preparing a product for predicting distant metastasis of lung adenocarcinoma, characterized in that: The nucleotide sequence of hsa-miR-3136-5p is shown in SEQ ID NO.1; the nucleotide sequence of hsa-miR-519d-5p is shown in SEQ ID NO.2; the nucleotide sequence of hsa-miR-381-5p is shown in SEQ ID NO.3; the nucleotide sequence of hsa-miR-490-5p is shown in SEQ ID NO.4; the nucleotide sequence of hsa-miR-377-5p is shown in SEQ ID NO.5; the nucleotide sequence of hsa-miR-320e is shown in SEQ ID NO.6; the nucleotide sequence of hsa-miR-2355-5p is shown in SEQ ID NO.7; and the nucleotide sequence of hsa-miR-6784-5p is shown in SEQ ID NO.

8.

3. Use of the reagent for detecting target miRNA in a sample according to claim 2 in the preparation of a product for predicting distant metastasis of lung adenocarcinoma, characterized in that: The product for predicting distant metastasis of lung adenocarcinoma includes primers and probes for detecting target miRNA.

4. A biomarker composition for predicting distant metastasis of lung adenocarcinoma, characterized in that: The biomarker composition consists of hsa-miR-3136-5p, hsa-miR-519d-5p, hsa-miR-381-5p, hsa-miR-490-5p, hsa-miR-377-5p, hsa-miR-320e, hsa-miR-2355-5p and hsa-miR-6784-5p.

5. A kit for predicting distant metastasis of lung adenocarcinoma, characterized in that: The kit includes primers and probes for detecting target miRNA; the target miRNA consists of hsa-miR-3136-5p, hsa-miR-519d-5p, hsa-miR-381-5p, hsa-miR-490-5p, hsa-miR-377-5p, hsa-miR-320e, hsa-miR-2355-5p and hsa-miR-6784-5p.

6. The kit for predicting distant metastasis of lung adenocarcinoma according to claim 5, characterized in that The nucleotide sequence of hsa-miR-3136-5p is shown in SEQ ID NO.1; the nucleotide sequence of hsa-miR-519d-5p is shown in SEQ ID NO.2; the nucleotide sequence of hsa-miR-381-5p is shown in SEQ ID NO.3; the nucleotide sequence of hsa-miR-490-5p is shown in SEQ ID NO.4; the nucleotide sequence of hsa-miR-377-5p is shown in SEQ ID NO.5; the nucleotide sequence of hsa-miR-320e is shown in SEQ ID NO.6; the nucleotide sequence of hsa-miR-2355-5p is shown in SEQ ID NO.7; and the nucleotide sequence of hsa-miR-6784-5p is shown in SEQ ID NO.8.

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