Integrated bioinformatics analysis for differentially expressed genes and signaling pathways identification in gastric cancer

Abstract

Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

Investigation of single nucleotide polymorphisms in differentially expressed genes and proteins reveals the genetic basis of skeletal muscle growth differences between Tibetan and Large White pigs

“…A c c e p t e d A r t i c l e analysis using the DAVID database (https://david.ncifcrf.gov/tools.jsp) [18], a widely recognized platform for high-throughput gene function analysis. Again, the organism was specified as "Sus scrofa," and we adhered to the default settings for the other parameters.…”

“…This was achieved using the g:Profiler tool (available at https://biit.cs.ut.ee/gprofiler/orth) [17]; the organism was set to "Sus scrofa", the statistical domain scope was set to "only annotated genes", the significance threshold was set to "Benjamini-Hochberg FDR" , and the user threshold was set to "0.05". Furthermore, we performed a KEGG pathway analysis using the DAVID database (https://david.ncifcrf.gov/tools.jsp) [18]. For this analysis, the organism was again set to "Sus scrofa", and the default settings were maintained for other parameters.…”

Oxidative stress and autophagy-mediated immune patterns and tumor microenvironment infiltration characterization in gastric cancer

“…Genes that correspond to multiple probes should all be selected for the average expression value of that gene. We normalized the matrix data and removed the batch effect using the "limma" and "sva" packages in R [27][28][29]. Besides, 1495 OS-related genes (Relevance score > 5) were obtained from the GeneCards database.…”

Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages

“…At present, many bioinformatic studies about GC have been published, but the differentially expressed genes (DEGs) and signaling pathways revealed by different studies were not consistent. The study by Yang and Gong (2021) showed that OLFM4, IGF2BP3, CLDN1, and MMP1 were the most significantly upregulated DEGs, which significantly enriched in negative regulation of growth, fatty acid binding, and cellular response to zinc ions. In a bioinformatics analysis conducted by Xu et al (2021), the expressions of ITGB1 and alpha-2 collagen type I (COL1A2) were significantly increased in GC tissues, and 63 characteristic DEGs were mainly involved in regulating extracellular matrix (ECM)-receptor interactions and the PI3K-Akt signaling pathway.…”

“…Most published experimental and bioinformatic studies included GC specimens with unclear tumor stages, making it impossible to accurately analyze the DEGs and signaling pathways throughout GC development (Yang et al, 2021;Xu et al, 2021). In this study, therefore, we retrieved three microarray datasets containing gene data with definite GC stages and then divided them into the early stage (ES) group and the late stage (LS) group.…”