Identification of differentially expressed genes in small and non-small cell lung cancer based on meta-analysis of mRNA

Abstract

Lung cancer has the lowest survival rate spread globally resulting in a large number of deaths. This is attributed to insufficient measures such as lack of early detection and chemoresistance in the patients. It can be subdivided into two histological groups: Non-Small-Cell Lung Cancer (NSCLC), which is most prevalent (85% of all lung cancers) but less destructive; and Small-Cell Lung Cancer (SCLC), which is intermittently metastatic and less prevalent (15% of all lung cancers). The present study deals with the analysis of gene expression of two subtypes to identify the Differentially Expressed Genes (DEGs). For this study, we selected two datasets from the Omnibus database, which included 50 non-small cell lung cancer samples, 31 small cell lung cancer samples, and 48 samples from normal lung tissue. After DEGs identification using the meta-analysis approach, they were then subjected to further analysis following p-value adjustment via the Benjamini-Hochberg method. We identified 440 overexpressed and 489 underexpressed genes in NSCLC, and 489 overexpressed and 525 underexpressed genes in SCLC, compared with normal lung tissues. Furthermore, we identified 3 overlapping genes between upregulated DEGs in NSCLC and downregulated DEGs in SCLC; and 8 overlapping genes between upregulated DEGs in SCLC and downregulated DEGs in NSCLC. Accordingly, a Protein-Protein Interaction (PPI) network of the overlapping genes was generated, which contained a total of 261 genes, of which the top five were TRIM29, ANK3, CSTA, FGG, and AGR2. These five candidate genes reported herein may prove to be potential therapeutic targets.

Transcriptomic analysis delineates potential signature genes and miRNAs associated with the pathogenesis of asthma

“…GEOquery package 47 in R was used to download the CEL files which contained unprocessed mRNA expression profiles. Unprocessed expression data from CEL files were read as an affy-object using the affy module 48 followed by background correction, quantile normalization, and log 2 transformation using Robust Multiarray Average (RMA) method in R. Also, the probe IDs were mapped to their corresponding HGNC (HUGO Gene Nomenclature Committee) gene symbol(s) 49 using hgu133plus2.db package available in R. Relative expression values across genes mapping to more than one probe IDs were averaged 50,51 .…”

Unravelling host-pathogen interactions: ceRNA network in SARS-CoV-2 infection (COVID-19)

“…The 18 infected samples were studied at different time points of Day1, Day2, Day3, Day5, Day14, and Day28, respectively. The relative expression levels of a gene symbol mapping to multiple probeset IDs were averaged and the probeset IDs with no corresponding gene symbol were eliminated ( Ahmad et al, 2019 , Shriwash et al, 2019 , Singh et al, 2020 ). A three-stage screening protocol was employed to screen the significant differentially expressed genes (DEGs) between two infection status groups, i.e., uninfected vs infected.…”

Unravelling the Role of miR-20b-5p, CCNB1, HMGA2 and E2F7 in Development and Progression of Non-Small Cell Lung Cancer (NSCLC)

“…Probe IDs were mapped to their corresponding HUGO Gene Nomenclature Committee (HGNC) gene symbol(s) using the GPL570 annotation file and hgu133plus2.db package available in R v4.0.2. Expression values of duplicate gene symbols mapping to multiple probe IDs were averaged across the samples [20,21] in both the datasets followed by p-value computation of all unique genes using two-sample paired t-test function. Common genes from both the datasets were identified using the intersect function.…”