MiRNA biomarker for diagnosis or auxiliary diagnosis of lung adenocarcinoma and application of miRNA biomarker

Abstract

The invention discloses a miRNA biomarker for diagnosis or auxiliary diagnosis of lung adenocarcinoma and application of the miRNA biomarker, and relates to the technical field of biological medicines. The invention relates to an application of a reagent for detecting target miRNA (micro Ribonucleic Acid) in a sample in preparation of a product for lung adenocarcinoma diagnosis or auxiliary diagnosis. The target miRNA comprises 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. According to the invention, eight key miRNAs are screened out, an 8-miRNA risk scoring model 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 is constructed, and a research result shows that the constructed 8-miRNA risk scoring model has the potential of predicting the distant metastasis of a patient suffering from 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 an application thereof. Background Art

[0002] At present, 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). Adenocarcinoma is the most common histological subtype of NSCLC. The survival of NSCLC patients depends on the stage at diagnosis. Relevant studies have shown that among the 5-year survival rates of NSCLC, the survival rate of stage I-II is relatively high, and the survival rate of stage III-IV is relatively low. After distant metastasis occurs in NSCLC patients, the survival rate of patients decreases significantly.

[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 stage I, stage II, and some stage IIIA NSCLC patients; 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 regulates gene expression at the post-transcriptional level by regulating the stability of mRNA or inducing mRNA degradation. However, under certain conditions, miRNA can also promote the translation of target mRNA. MiRNA is encoded by only 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 that provides publicly available cancer genome data. The TCGA database contains a large amount of miRNA sequencing data of NSCLC patients. In this regard, the inventor believes that how to use the miRNA sequencing data in the TCGA database to establish miRNA markers for predicting distant metastasis of lung adenocarcinoma is a technical problem that technicians in this field urgently need to solve.

[0006] The information disclosed in this background technology section is only intended to enhance the understanding of the overall background of the invention and should not be regarded as an acknowledgment 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 view of the above technical problems, the embodiments of the present invention provide a miRNA biomarker for diagnosing or assisting in the diagnosis of lung adenocarcinoma and its application 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 miRNA includes 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 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; the nucleotide sequence of hsa-miR-6784-5p is shown in SEQ ID NO.8 and the nucleotide sequence of hsa-miR-784-5p is shown in 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; 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 miRNA; 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 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; the nucleotide sequence of hsa-miR-6784-5p is shown in SEQ ID NO.8 and the nucleotide sequence of hsa-miR-784-5p is shown in 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 screens out 8 key miRNAs and constructs 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, which has good performance in predicting distant metastasis of lung adenocarcinoma (AUC: 0.831), and is found to be 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 of 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 for the 8-miRNA marker construction;

[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 12 DEMs;

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

[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 distinguishing DM group from NDM group by miR-490-5p;

[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 to distinguish 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 technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments 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 creative work are within the scope of protection of the present invention.

[0039] In view of the above technical problems, the embodiments of the present invention provide a miRNA biomarker for diagnosing or assisting in the diagnosis of lung adenocarcinoma and its application 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: 1) samples with miRNA sequencing data and clinical information; 2) samples with prognostic information. Exclusion criteria: unclear stage of samples and overall survival of 0. Finally, a total of 344 lung adenocarcinoma samples were included in the study, including 23 cases in stage IV, as the distant metastasis group, i.e., DM group, and 321 cases in stages I-III, as the non-distant metastasis group, i.e., NDM group.

[0043] 2. Screening of DEMs and construction of miRNA risk scoring model

[0044] The edgeR package of R software (version 4.3.1) was used for differential analysis. The fold change (FC) of each miRNA was calculated. The DEM was screened with the criteria of |log2 FC|>1 and P<0.05. The key miRNAs were screened from the DEM by LASSO regression, and a miRNA risk score model for predicting distant metastasis of lung adenocarcinoma was constructed by multivariate logistic regression analysis (risk score formula: β1×miRNA1EXP+β2×miRNA2EXP+...+βn×miRNAnEXP. β is the regression coefficient of the corresponding miRNA, and miRNAnEXP is the expression level of the corresponding miRNA). The receiver operating characteristic (ROC) curve was drawn to verify the predictive performance of the model. The patients were divided into high-risk group and low-risk group according to the optimal cutoff value of the ROC curve. The Kaplan-Meier curve was drawn, and the survival difference between the high-risk group and the low-risk group was evaluated by the Log-rank test. The risk score model and clinical variables were included in univariate and multivariate logistic regression analysis to verify the effect 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 target genes of miRNAs were predicted using the miRWalk and TargetScan databases. Overlapping target genes were identified using Venn diagrams. Then, the target genes were subjected to gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis 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 the hub gene.

[0049] 5. Statistical methods

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

[0051] 2. Experimental results:

[0052] 1. Clinical characteristics of patients

[0053] The flowchart of this study is shown in Figure 1A. Data of 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 with lung adenocarcinoma DM group with distant metastasis and 321 patients with lung adenocarcinoma NDM group without distant metastasis. 2213 miRNAs were detected in each sample. After excluding those with extremely low expression (80% of the samples had an expression of 0), the remaining 545 miRNAs were used. The RPM (reads per million) value of miRNA sequencing data was used for subsequent analysis. The clinical information of the patients was also downloaded from the TCGA database. The specific clinical information included age at diagnosis, gender, smoking history, T stage, lymph node status, and survival status. The clinical information of 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 according to 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 according to the ROC curve. With 1.944 as the cutoff point, the patients were divided into a high-risk group (≥1.944, n = 284) and a low-risk group (<1.944, n = 60). The Kaplan-Meier method and Log-rank test were used for survival analysis. The results showed that the high-risk group had a worse prognosis than the low-risk group (p = 0.003) ( Figure 3 ).

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

[0064] The miRNA markers and clinical characteristics (age, sex, smoking history, T stage, N stage) were included in univariate and multivariate logistic regression analysis to verify the effect of the constructed 8-miRNA markers (high risk and low risk) on metastasis. In the 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 and included in the multivariate analysis. Multivariate logistic analysis showed that the 8-miRNA marker (OR = 15.62, P < 0.001) was an independent risk factor for distant metastasis of 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 3664 target genes. Then, enrichment analysis was performed on the 3664 target genes to clarify the biological functions of the target genes.

[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 A, B). KEGG pathway analysis results showed that the target genes were significantly enriched in tumor pathways, herpes simplex virus type Ⅰ 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 usually associated with tumor progression, indicating 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 Cytoscape string APP plug-in was used to construct the PPI network of the above target genes, and then the cytoHubba plug-in was used to screen the top 24 hub genes (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). 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, and 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 of lung adenocarcinoma. According to the risk score, patients were divided into high-risk and low-risk groups, and the high-risk group had a worse prognosis.

[0078] We also analyzed the target genes of 8 miRNAs, and the studies showed that the target genes may be involved in a variety of pathways, including tumor pathways, 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, cAMP signaling pathway, etc. In addition, we also screened out 24 hub genes. Therefore, the 8-miRNA marker we constructed has the potential to predict distant metastasis of lung adenocarcinoma.

[0079] The embodiments described above are only descriptions of the preferred implementation modes of the present invention, and are not intended to limit the scope of the present invention. Without departing from the design 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 protection scope 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 diagnosis or auxiliary diagnosis of lung adenocarcinoma, characterized in that: 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.

2. Use of the reagent for detecting target miRNA in a sample according to claim 1 in preparing a product for diagnosis or auxiliary diagnosis 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 diagnosis or auxiliary diagnosis of lung adenocarcinoma, characterized in that: The target miRNA is used as a biomarker for predicting distant metastasis of lung adenocarcinoma.

4. Use of the reagent for detecting target miRNA in a sample according to claim 2 in the preparation of a product for diagnosis or auxiliary diagnosis of lung adenocarcinoma, characterized in that: Products for diagnosis or auxiliary diagnosis of lung adenocarcinoma include primers and probes for detecting target miRNA.

5. A biomarker composition for predicting distant metastasis of lung adenocarcinoma, characterized in that: The biomarker composition includes: 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. A kit for diagnosing or assisting in the diagnosis of lung adenocarcinoma, characterized in that: The kit is used to detect target miRNA; the target miRNA includes 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.

7. The kit for diagnosing or assisting in the diagnosis of lung adenocarcinoma according to claim 6, 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; the nucleotide sequence of hsa-miR-6784-5p is shown in SEQ ID NO.8 and the nucleotide sequence of hsa-miR-784-5p is shown in SEQ ID NO.

8.

8. The kit for diagnosing or assisting in the diagnosis of lung adenocarcinoma according to claim 6, characterized in that: The kit includes primers and probes for detecting target miRNA.

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