Integrating microRNA expression, miRNA-mRNA regulation network and signal pathway: a novel strategy for lung cancer biomarker discovery

Abstract

Background Since there are inextricably connections among molecules in the biological networks, it would be a more efficient and accurate research strategy to screen microRNA (miRNA) markers combining with miRNA-mRNA regulatory networks. The independent regulation mode is more “fragile” and “influential” than the co-regulation mode. miRNAs can be used as biomarkers if they can independently regulate hub genes with important roles in the PPI network, simultaneously the expression products of the regulated hub genes play important roles in the signaling pathways of related tissue diseases. Methods We collected miRNA expression of non-small cell lung cancer (NSCLC) from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Volcano plot and signal-to-noise ratio (SNR) methods were used to obtain significant differentially expressed (SDE) miRNAs from the TCGA database and GEO database, respectively. A human miRNA-mRNA regulatory network was constructed and the number of genes uniquely targeted (NOG) by a certain miRNA was calculated. The area under the curve (AUC) values were used to screen for clinical sensitivity and specificity. The candidate markers were obtained using the criteria of the top five maximum AUC values and NOG ≥ 3. The protein–protein interaction (PPI) network was constructed and independently regulated hub genes were obtained. Gene Ontology (GO) analysis and KEGG pathway analysis were used to identify genes involved in cancer-related pathways. Finally, the miRNA which can independently regulate a hub gene and the hub gene can participate in an important cancer-related pathway was considered as a biomarker. The AUC values and gene expression profile analysis from two external GEO datasets as well as literature validation were used to verify the screening capability and reliability of this marker. Results Fifteen SDE miRNAs in lung cancer were obtained from the intersection of volcano plot and SNR based on the GEO database and the TCGA database. Five miRNAs with the top five maximum AUC values and NOG ≥ 3 were screened out. A total of 61 hub genes were obtained from the PPI network. It was found that the hub gene GTF2F2 was independently regulated by miR-708-5p. Further pathway analysis indicated that GTF2F2 participates in protein expression by binding with polymerase II, and it can regulate transcription and accelerate tumor growth. Hence, miR-708-5p could be used as a biomarker. The good screening capability and reliability of miR-708-5p as a lung cancer marker were confirmed by AUC values and gene expression profiling of external datasets, and experimental literature. The potential mechanism of miR-708-5p was proposed. Conclusions This study proposes a new idea for lung cancer marker screening by integrating microRNA expression, regulation network and signal pathway. miR-708-5p was identified as a biomarker using this novel strategy. This study may provide some help for cancer marker screening.

A Novel Strategy for Identifying NSCLC MicroRNA Biomarkers and Their Mechanism Analysis Based on a Brand-New CeRNA-Hub-FFL Network

“…In biological networks, the independent regulation model is an important complement to a synergistic effect and is more impervious to the dataset [ 15 ]. There are some meaningful studies based on this model [ 16 , 64 ], but this model does not consider the biological functions of the target genes. We argue that if the miRNA target gene is highly correlated with the disease, then the miRNA will be more directly and definitively involved in the progression of this disease.…”

“…The independent regulation model is more “fragile” and “influential” than the co-regulation model. MiRNAs can be used as biomarkers if they can independently regulate hub genes with important roles in the protein–protein interaction (PPI) network [ 16 ]. Some investigators have well demonstrated the generality and predictive ability of the independent regulation model in complex diseases, such as prostate cancer [ 17 ], colorectal cancer [ 18 ], and so on [ 19 ].…”

Overview of MicroRNAs as Diagnostic and Prognostic Biomarkers for High-Incidence Cancers in 2021

“…Nie et al analyzed the miRNAs expression profiles of lung cancer by bioinformatics analysis and discovered that miRNA-708-5p was significantly up-regulated in NSCLC tissues. To understand its sensitivity and specificity, a receiver operating characteristic (ROC) curve was plotted, and the area under the curve (AUC) was ≥0.9, indicating that miRNA-708-5p exhibited a good diagnostic value for NSCLC [ 40 ]. In addition, carcinoembryonic antigen (CEA) as a diagnostic marker for tumor has been widely used in clinical practice, but it has some shortcomings such as poor specificity.…”

Cell Behavior of Non-Small Cell Lung Cancer Is at EGFR and MicroRNAs Hands

“…Furthermore, an enrichment analysis explored 33 aberrantly expressed miRNAs in LADC and LSCC, of which miR-25-3p was a prospective prognostic biomarker in NSCLC by regulating TGFβ and EGFR signaling [8, [101][102][103][104][105]. It has been recently reported that six differentially expressed miRNAs, including miR-31-5p, -5p, miR-708-5p, miR-451a, miR-30a-5p, and miR-126-3p, were notably associated with overall survival [9, 70,[106][107][108][109][110]. High expression levels of miR-31 and miR-21 were associated with poor prognosis, but better and prolonged survival was linked with elevated expression of miR-126, miR-708, miR-30a, and miR-451 in NSCLC patients [109,110].…”

“…It has been recently reported that six differentially expressed miRNAs, including miR-31-5p, -5p, miR-708-5p, miR-451a, miR-30a-5p, and miR-126-3p, were notably associated with overall survival [9, 70,[106][107][108][109][110]. High expression levels of miR-31 and miR-21 were associated with poor prognosis, but better and prolonged survival was linked with elevated expression of miR-126, miR-708, miR-30a, and miR-451 in NSCLC patients [109,110]. The expression analysis and miRNA-hub gene network identified EGFR, phosphatase tensin homolog (PTEN), STAT3, vascular endothelial growth factor-A (VEGFA), transforming protein RhoA (RHOA), catenin beta 1 (CTNNB1), T53, and KRAS as possible target genes for these six miRNAs [9, 70,[106][107][108].…”