Microarray data analysis on gene and miRNA expression to identify biomarkers in non-small cell lung cancer

Abstract

Background: The aim of this study was to gain further investigation of non-small cell lung cancer (NSCLC) tumorigenesis and identify biomarkers for clinical management of patients through comprehensive bioinformatics analysis. Methods: miRNA and mRNA microarray datasets were downloaded from GEO (Gene Expression Omnibus) database under the accession number GSE102286 and GSE101929, respectively. Genes and miRNAs with differential expression were identified in NSCLC samples compared with controls, respectively. The interaction between differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs) was predicted, followed by functional enrichment analysis, and construction of miRNA-gene regulatory network, protein-protein interaction (PPI) network, and competing endogenous RNA (ceRNA) network. Through comprehensive bioinformatics analysis, we anticipate to find novel therapeutic targets and biomarkers for NSCLC. Results: A total of 123 DEmiRs (5 up-and 118 down-regulated miRNAs) and 924 DEGs (309 up-and 615 down-regulated genes) were identified. These genes and miRNAs were significantly involved in different pathways including adherens junction, relaxin signaling pathway, and axon guidance. Furthermore, hsa-miR-9-5p, has-miR-196a-5p and hsa-miR-31-5p, as well as hsa-miR-1, hsa-miR-218-5p and hsa-miR-135a-5p were shown to have higher degree in the miRNA-gene regulatory network and ceRNA network, respectively. Furthermore, BIRC5 and FGF2, as well as RTKN2 and SLIT3 were hubs in the PPI network and ceRNA network, respectively. Conclusion: Several pathways (adherens junction, relaxin signaling pathway, and axon guidance) miRNAs (hsa-miR-9-5p, has-miR-196a-5p, hsa-miR-31-5p, hsa-miR-1, hsa-miR-218-5p and hsa-miR-135a-5p) and genes (BIRC5, FGF2, RTKN2 and SLIT3) may play important roles in the pathogenesis of NSCLC.

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

“…So far, many lung cancer studies based on gene arrays have been conducted by different researchers, forming a series of gene expression datasets. By integrating multiple datasets, key genes involved in the progression and prognosis of lung cancer can be fully identified ( Jin et al, 2020a ; Jin et al, 2020b ; Wu et al, 2020 ). We analyzed GEO datasets from the Chinese lung population and used bioinformatics to discover possible biomarkers of lung cancer.…”

Bioinformatics approach to identify common gene signatures of patients with coronavirus 2019 and lung adenocarcinoma

“…miRBNA regulates diverse oncological processes, including proliferation, cell survival, apoptosis, tumour metastasis, and growth. Among these miRNAs, 12 (Wang et al 2017 ; Dong et al 2018 ; Wu et al 2019 ; Jin et al 2020 ; Li et al 2020 ; Mokhlesi and Talkhabi 2020 ; Yu et al 2020b ; Zhou et al 2020 ; Wei et al 2021 ) are involved in the oncogenesis of NSCLC, and four (Chuang et al 2015 ; Gao et al 2019 ; Deng et al 2021 ; Xu et al 2021 ) contribute to the pathogenesis of other cancers. Eleven TF-genes are included in TF-gene interaction network.…”

Identification of a ZC3H12D-regulated competing endogenous RNA network for prognosis of lung adenocarcinoma at single-cell level

“…We chose the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm [ 36 , 37 ] or Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP) [ 38 ] to reduce the dimensionality of the quality-controlled scRNA-seq data of normal lung cell from mouse [ 39 ]. Moreover, scRNA-seq data from lung cell atlas of human [ 40 ] and lung cancer brain metastases [ 41 ] of human were re-analyzed through the UCSC cell browser ( https://cells.ucsc.edu/ ) [ 42 ].…”