Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma

Abstract

Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities.

Integrating TCGA and Single-Cell Sequencing Data for Hepatocellular Carcinoma: A Novel Glycosylation (GLY)/Tumor Microenvironment (TME) Classifier to Predict Prognosis and Immunotherapy Response

“…Sha et al showed that glycosyltransferase (GT)-based GT score was associated with GT expression and neuroblastoma (NB) prognosis, disialoganglioside phenotype, and immune infiltration, providing new evidence for predicting NB prognosis and response to immunotherapy [ 39 ]. By using in silico approaches, Caputo et al identified hub genes and transcription factors including AURKA , CCNB1 , CDK1 , RRM2 , and TOP2A as promising candidates for potential drug testing [ 54 ]. Huang et al found that POLE2 , GABARAPL1 , PIK3R1 , NDC80 , and TPX2 play critical roles as potential biomarkers to enhance the immunotherapeutic role of PD-L1 inhibitors [ 55 ].…”

Bioinformatics‐based identification of hepatocellular carcinoma‐associated hub genes and assessment of the restorative effect of tannic acid in rat liver exposed to monosodium glutamate

“…To compare the mRNA expression differences of hub genes between HCC cancerous liver tissues and corresponding adjacent non‐tumor liver tissues (histologically normal, adjacent to the tumor but beyond the observed aberrations) 42 , 43 and to evaluate their prognostic value, the Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer‐pku.cn/ , accessed on 12 May 2024) platform 44 and the University of ALabama CANcer (UALCAN; https://ualcan.path.uab.edu/ , accessed on 12 May 2024) interactive web portal 45 was used. The correlation between mRNA expression of hub genes and the clinical stage of liver cancer patients, as well as protein expression encoded by hub genes, were also analyzed by UALCAN.…”