Current Genomics - Volume 26, Issue 1, 2025

Lung adenocarcinoma, the predominant subtype of lung cancer, presents a significant challenge to public health due to its notably low five-year survival rate. Recent epidemiological data highlights a concerning trend: patients with pulmonary adenocarcinoma and comorbid diabetes exhibit substantially elevated mortality rates compared to those without diabetes, suggesting a potential link between hyperinsulinemia in diabetic individuals and accelerated progression of pulmonary adenocarcinoma. Insulin Receptor (IR) is a tyrosine-protein kinase on the cell surface, and its over-expression is considered the pathological hallmark of hyperinsulinemia in various cancer cell types. Research indicates that IR can translocate to the nucleus of lung adenocarcinoma cells to promote their proliferation, but its precise molecular targets remain unclear. This study aims to silence IRs in lung adenocarcinoma cells and identify key genes within the ERK pathway that may serve as potential molecular targets for intervention.

Gene expression data from lung adenocarcinoma and para cancer tissues were retrieved from the Gene Expression Omnibus (GEO) database and assessed through "pheatmap", GO annotation, KEGG analysis, R calculations, Cytoscape mapping, and Hub gene screening. Significant genes were visualized using the ggplot2 tool to compare expression patterns between the two groups. Additionally, survival analysis was performed using the R "survminer" and "survival" packages, along with the R "pathview" package for pathway visualization. Marker genes were identified and linked to relevant signaling pathways. Validation was conducted utilizing real-time quantitative polymerase chain reaction and immunoblotting assays in an A549 lung cancer cell model to determine the roles of these marker genes in associated signaling cascades.

The study examined 58 lung adenocarcinoma samples and paired para-neoplastic tissues. Analysis of the GSE32863 dataset from GEO revealed 1040 differentially expressed genes, with 421 up-regulated and 619 down-regulated. Visualization of these differences identified 172 significant alterations, comprising 141 up-regulated and 31 down-regulated genes. Functional enrichment analysis using Gene Ontology (GO) revealed 56 molecular functions, 77 cellular components, and 816 biological processes. KEGG analysis identified 17 strongly enriched functions, including cytokine interactions and tumor necrosis factor signaling. Moreover, the ERK signaling pathway was associated with four Hub genes (FGFR4, ANGPT1, TEK, and IL1B) in cellular biological processes. Further validation demonstrated a positive correlation between IL-1B expression in the ERK signaling pathway and lung cancer through real-time fluorescence quantitative enzyme-linked reaction with immunoblotting assays.

In IR-silenced lung adenocarcinoma, the expression of the IL-1B gene exhibited a positive correlation with the ERK signaling pathway.

Currently, macaques are used as animal models for human disease in biomedical research. There are two macaques species widely used as animal models, i.e., cynomolgus macaques and rhesus macaques. These two primates distribute widely, and their natural habitats are different. Cynomolgus macaques distribute in tropical climates, while rhesus macaques mostly distribute in relatively cold environments, and cynomolgus macaques have a common frostbite problem during winter when they are transferred to cold environments.

In order to explore the molecular mechanisms underlying the temperature adaptation in macaques, genetic analysis and natural selection tests were performed. Based on the analysis of heat shock protein genes, DNAJC22, DNAJC28, and HSF5 showed positive selection signals. To these 3 genes, the significantly differential expression had been confirmed between cynomolgus macaques and Chinese rhesus macaques.

Molecular evolution analysis showed that mutations of DNAJC22, DNAJC28, and HSF5 in Chinese rhesus macaques could enable them to gain the ability to rapidly regulate body temperature. The heat shock proteins provided an important function for Chinese rhesus macaques, allowing them to adapt to a wide range of temperatures and spread widely. The selection time that was estimated suggested that the cold adaptation of Chinese rhesus macaques coincided with the time that the modern human populations migrated northward from tropic regions to relatively cold regions, and the selection genes were similar.

This study elucidated the evolutionary history of cynomolgus macaques and rhesus macaques from molecular adaptation. Furthermore, it provided an evolutionary perspective to reveal the different distribution and adaptation of macaques. Cynomolgus macaques is an ideal biomedical animal model to mimic human natural frostbite.

The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches.

This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets.

Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes.

The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks.

The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 top-ranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.