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.
Keywords: Bioinformatics; Biological science; Gene expression; Systems biology.