Identification of hub genes and potential molecular mechanisms in gastric cancer by integrated bioinformatics analysis

Abstract

ObjectiveGastric cancer (GC) is the fourth most common cause of cancer-related deaths in the world. In the current study, we aim to identify the hub genes and uncover the molecular mechanisms of GC.MethodsThe expression profiles of the genes and the miRNAs were extracted from the Gene Expression Omnibus database. The identification of the differentially expressed genes (DEGs), including miRNAs, was performed by the GEO2R. Database for Annotation, Visualization and Integrated Discovery was used to perform GO and KEGG pathway enrichment analysis. The protein–protein interaction (PPI) network and miRNA-gene network were constructed using Cytoscape software. The hub genes were identified by the Molecular Complex Detection (MCODE) plugin, the CytoHubba plugin and miRNA-gene network. Then, the identified genes were verified by Kaplan–Meier plotter database and quantitative real-time PCR (qRT-PCR) in GC tissue samples.ResultsA total of three mRNA expression profiles (, and ) were downloaded from the Gene Expression Omnibus (GEO) database, including 69, 20 and 27cases separately. A total of 120 overlapped upregulated genes and 246 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, collagen catabolic process, collagen fibril organization and cell adhesion. In addition, three KEGG pathways were significantly enriched, including ECM-receptor interaction, protein digestion and absorption, and the focal adhesion pathways. In the PPI network, five significant modules were detected, while the genes in the modules were mainly involved in the ECM-receptor interaction and focal adhesion pathways. By combining the results of MCODE, CytoHubba and miRNA-gene network, a total of six hub genes including COL1A2, COL1A1, COL4A1, COL5A2, THBS2 and ITGA5 were chosen. The Kaplan–Meier plotter database confirmed that higher expression levels of these genes were related to lower overall survival, except for COL5A2. Experimental validation showed that the rest of the five genes had the same expression trend as predicted.ConclusionIn conclusion, COL1A2, COL1A1, COL4A1, THBS2 and ITGA5 may be potential biomarkers and therapeutic targets for GC. Moreover, ECM-receptor interaction and focal adhesion pathways play significant roles in the progression of GC.

Peer Review #2 of "Integrated bioinformatics analysis of As, Au, Cd, Pb and Cu heavy metal responsive marker genes through Arabidopsis thaliana GEO datasets (v0.1)"

“…Moreover, a reduced transcriptome approach to monitor potential effects by environmental toxicants at genome-scale using zebrafish embryo test was also developed, whose reliability was assessed by RNA-ampliseq technology to identify DEGs (Wang et al 2018). Furthermore, previous study have usually focused on the aspects of cancer diagnosis and treatment using integrated bioinformatics analyses, with few other related research (Cao et al 2018, Zhan et al 2018.…”

Effect of X-rays on transcript expression of rat brain microvascular endothelial cells: role of calcium signaling in X-ray-induced endothelium damage

“…Most previously acquired biological networks have been found to be subject to scale-free attribution [25]. Therefore, we used the molecular complex detection (MCODE) in Cytoscape software (version 3.6.0) to screen modules with MCODE scores ≥ 3 and nodes ≥ 3 in the PPI network [26]. We used topology analysis to analyze the connectivity of nodes in the PPI network so as to obtain a higher degree of important nodes (central proteins) [27].…”

PENK inhibits osteosarcoma cell migration by activating the PI3K/Akt signaling pathway

“…A minimum required interaction score of ≥ 0.4 was selected as the cutoff value. The Molecular Complex Detection (MCODE) Cytoscape software plugin (version 3.7.1) was used to generate subclusters in the PPI network [16]. The advanced options were set as follows: degree cutoff = 2, node score cutoff = 0.2, and k-core = 2.…”

“…The concentration and purity of total RNA was determined by measuring the absorbance at 260 and 280 nm (A260/A280). Single-strand cDNA was synthesized by reverse transcription with a Superscript II Reverse Transcriptase Kit and oligo dT (12)(13)(14)(15)(16)(17)(18) primers (Invitrogen, Carlsbad, CA, USA) and amplified by RT-PCR with iQ SYBR Green Supermix (Bio-Rad, Hercules, CA, USA). Quantitative PCR (qPCR) was performed using SYBR Green PCR Master Mix (Applied Biosystems) following the manufacturer's instructions.…”