Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer

Abstract

Background: Lung cancer is the most common malignant tumor, and it has a high mortality rate. However, the study of miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer (NSCLC) is insufficient. Therefore, this study explored the differential expression of mRNA and miRNA in the plasma of NSCLC patients.

Methods: The Gene Expression Omnibus (GEO) database was used to download microarray datasets, and the differentially expressed miRNAs (DEMs) were analyzed. We predicted transcription factors and target genes of the DEMs by using FunRich software and the TargetScanHuman database, respectively. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for GO annotation and KEGG enrichment analysis of downstream target genes. We constructed protein-protein interaction (PPI) and DEM-hub gene networks using the STRING database and Cytoscape software. The GSE20189 dataset was used to screen out the key hub gene. Using The Cancer Genome Atlas (TCGA) and UALCAN databases to analyze the expression and prognosis of the key hub gene and DEMs. Then, GSE17681 and GSE137140 datasets were used to validate DEMs expression. Finally, the receiver operating characteristic (ROC) curve was used to verify the ability of the DEMs to distinguish lung cancer patients from healthy patients.

Results: Four upregulated candidate DEMs (hsa-miR199a-5p, hsa-miR-186-5p, hsa-miR-328-3p, and hsa-let-7d-3p) were screened from 3 databases, and 6 upstream transcription factors and 2253 downstream target genes were predicted. These genes were mainly enriched in cancer pathways and PI3k-Akt pathways. Among the top 30 hub genes, the expression of KLHL3 was consistent with the GSE20189 dataset. Except for let-7d-3p, the expression of other DEMs and KLHL3 in tissues were consistent with those in plasma. LUSC patients with high let-7d-3p expression had poor overall survival rates (OS). External validation demonstrated that the expression of hsa-miR-199a-5p and hsa-miR-186-5p in peripheral blood of NSCLC patients was higher than the healthy controls. The ROC curve confirmed that the DEMs could better distinguish lung cancer patients from healthy people.

Conclusion: The results showed that miR-199a-5p and miR-186-5p may be noninvasive diagnostic biomarkers for NSCLC patients. MiR-199a-5p-KLHL3 may be involved in the occurrence and development of NSCLC.

Keywords: Bioinformatics; Non-small cell lung cancer; miRNA-mRNA regulatory network; microRNA.

Fig. 1

Flow chart of the construction of the miRNA-mRNA network of lung cancer patients

Fig. 2

Heat and volcano maps of DEMs in lung cancer and normal plasma samples. A, B. GSE24709 dataset. C, D. GSE31568 dataset. E, F. GSE61741 dataset. Red indicates higher expression, and green indicates lower expression. G. Venn diagram of the expression levels of the DEMs in the 3 datasets. H. Log fold change heat map of the candidate DEMs

Fig. 3

The upstream transcription factors and downstream target genes of the DEMs. A-B. FunRich prediction of potential upstream transcription factors of the candidate DEMs. C-F. miRNA-target genes network diagram of the 4 DEMs. C. hsa-let-7d-3p. D. hsa-miR-186-5p. E. hsa-miR-199a-5p. F. hsa-miR-328-3p

Fig. 4

GO annotation analysis of the DEMs target genes in biological processes, cell components, and molecular functions. A, C, E. Bar plots. B, D, F. Bubble charts

Fig. 5

KEGG pathway enrichment analysis of the DEMs target genes. A. Bar plots. B. Bubble charts

Fig. 6

PPI and DEM-hub gene networks. A. PPI network of the top 30 hub genes of the DEMs. B. miRNA-hub gene regulatory network

Fig. 7

Identification of the mRNA expression levels of the top 30 hub genes in the GSE20189 dataset. A. NEDD4. B. SKP1. C. FBXO9. D. CUL3. E. KLHL11. F. FBXL5. G. BTRC. H. CCNF. I. ATG7. J. SMURF2. K. ITCH. L. KLHL3. M. ASB7. N. UBE2B. O. ARIH2. P. UBE2K. Q. UBE2Q1. R. UBAC1. S. UBR2. **p<0.01, ****p<0.0001

Fig. 8

Expression levels of the DEMs and KLHL3 in NSCLC tissues. A-D. hsa-miR-199a-5p. E-H. hsa-miR-186-5p. I-L. hsa-miR-328-3p. M-P. hsa-let-7d-3p. Q-T. KLHL3. U. KLHL3 protein expression in lung cancer tissue and normal tissue was verified by using the Human Protein Atlas database. Sample size (8 LUAD, 4 LUSC, 3 normal). *p<0.05, ***p<0.001, ****p<0.0001

Fig. 9

Overall survival analysis of the DEMs and KLHL3 in NSCLC patients. A-B. hsa-miR-199a-5p. C-D. hsa-miR-186-5p. E-F. hsa-miR-328-3p. G-H. hsa-let-7d-3p. I-J. KLHL3

Fig. 10

Progression free survival analysis of the DEMs and KLHL3 in NSCLC patients. A-B. hsa-let-7d-3p. C-D. hsa-miR-186-5p. E-F. hsa-miR-199a-5p. G-H. hsa-miR-328-3p. I-J. KLHL3

Fig. 11

Nomogram and ROC curve for the prognostic value of hsa-let-7D-3p. A. Nomogram. B. ROC curve

Fig. 12

External validation and ROC analysis of the 4 DEMs. A-H. External validation of the 4 DEMs. A. hsa-miR-199a-5p (GSE17681). B. hsa-miR-186a-5p (GSE17681). C. hsa-miR-199a-5p (GSE137140). D. hsa-186-5p (GSE137140). E. hsa-let-7d-3p (GSE176181). F. hsa-miR-328-3p (GSE176181). G. hsa-let-7d-3p (GSE137140). H. hsa-328-3p (GSE137140). I-J. ROC analysis of the 4 DEMs. I. hsa-miR-328-3p. J. hsa-let-7d-3p. K. hsa-miR-186-5p. L. hsa-miR-199a-5p. *p<0.05, ****p<0.0001