A study on construction of a prognosis model for liver cancer based on analgesic targets and screening therapeutic drugs

• Published in: Genes & genomics Vol. 46; no. 7; pp. 831 - 850
• Main Authors: Jiang, Xueyan, Ping, Yaodong, Chen, Yuan, Zhu, Benben, Fu, Rong, Hao, Yiwei, Fan, Lei
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.07.2024; Springer Nature B.V; 한국유전학회
• Subjects: Acetaminophen; Analgesics; Analgesics - pharmacology; Analgesics - therapeutic use; Animal Genetics and Genomics; Biomarkers, Tumor - genetics; Biomedical and Life Sciences; Cancer therapies; Clinical significance; Drug screening; Female; Functional morphology; Gene Expression Profiling; Gene Expression Regulation, Neoplastic - drug effects; Gene set enrichment analysis; Genes; Genomes; Genomic analysis; Hepatocellular carcinoma; Human Genetics; Humans; Immunosuppressive agents; Information processing; Life Sciences; Liver cancer; Liver diseases; Liver Neoplasms - drug therapy; Liver Neoplasms - genetics; Male; Medical prognosis; Microbial Genetics and Genomics; Molecular modelling; p53 Protein; Plant Genetics and Genomics; Prognosis; Research Article; Survival analysis; Transcriptome; Transcriptomes; Transcriptomics; 생물학; Liver cancer; Prognosis model; Analgesic target; Aanalgesics; Bioinformatics
• ISSN: 1976-9571; 2092-9293; 2092-9293
• DOI: 10.1007/s13258-024-01515-9

Background Liver cancer is one of the most malignant liver diseases in the world, and the 5-year survival rate of such patients is low. Analgesics are often used to cure pain prevalent in liver cancer. The expression changes and clinical significance of the analgesic targets (ATs) in liver cancer have not been deeply understood. Objective The purpose of this study is to clarify the expression pattern of ATs gene in liver cancer and its clinical significance. Through the comprehensive analysis of transcriptome data and clinical parameters, the prognosis model related to ATs gene is established, and the drug information sensitive to ATs is mined. Methods The study primarily utilized transcriptomic data and clinical information from liver cancer patients sourced from The Cancer Genome Atlas (TCGA) database. These data were employed to analyze the expression of ATs, conduct survival analysis, gene set variation analysis (GSVA), immune cell infiltration analysis, establish a prognostic model, and perform other bioinformatic analyses. Additionally, data from liver cancer patients in the International Cancer Genome Consortium (ICGC) were utilized to validate the accuracy of the model. Furthermore, the impact of analgesics on key genes in the prognostic model was assessed using data from the Comparative Toxicogenomics Database (CTD). Results The study investigated the differential expression of 58 ATs genes in liver cancer compared to normal tissues. Patients were stratified based on ATs expression, revealing varied survival outcomes. Functional enrichment analysis highlighted distinctions in spindle organization, centrosome, and spindle microtubule functions. Prognostic modeling identified low TP53 expression as protective, while elevated CCNA2, NEU1, and HTR2C levels posed risks. Commonly used analgesics, including acetaminophen and others, were found to influence the expression of these genes. These findings provide insights into potential therapeutic strategies for liver cancer and shed light on the molecular mechanisms underlying its progression. Conclusions The collective analysis of gene signatures associated with ATs suggests their potential as prognostic predictors in hepatocellular carcinoma patients. These findings not only offer insights into cancer therapy but also provide novel avenues for the development of indications for analgesics.