Integrative Multiomics Evaluation of IIDH1 Metabolic Enzyme as a Candidate Oncogene That is Correlated with Poor Prognosis and Immune Infiltration in Prostate Adenocarcinoma.
Chen-Yueh WenKuan-Hao TsuiChiung-Hung ChangYi-Han ChiuShu-Chuan Amy LinChing-Yu ChuGiou-Teng YiangPublished in: Journal of oncology (2022)
Mutations in the isocitrate dehydrogenase gene (IDH1) are involved in the progression of tumors. Although IDH1 has a role in various tumors, its clinical relevance and its expression in response to the immune response have not been investigated in prostate adenocarcinoma (PRAD). In the present study, we investigated the utility of IDH1 as a prognostic biomarker for PRAD by analyzing IDH1 mRNA expression and its association with patient survival and immune cell infiltration. IDH1 mRNA expression was significantly higher in PRAD tissue than in normal tissue, and Kaplan-Meier survival analysis showed that IDH1 expression was significantly associated with poor prognosis in PRAD patients. To elucidate the mechanisms involved, the correlation between IDH1 expression and the level of immune cell infiltration, in particular of immunosuppressive cells such as CD8+ T-cells, CD4+ T-cells, and macrophages, was further analyzed by single-cell RNA sequencing. We also screened a pharmacogenetic database for IDH1-specific drugs that inhibited high expression in PRAD. In the present study, we used a combination of databases to identify a significant correlation between IDH1 expression and cellular infiltration and to explain the mechanism by which IDH1 confers poor prognosis in PRAD, thus demonstrating the relevance of IDH1 expression as a prognostic biomarker with clinical utility in PRAD patients.
Keyphrases
- poor prognosis
- long non coding rna
- low grade
- wild type
- end stage renal disease
- single cell
- immune response
- prostate cancer
- chronic kidney disease
- ejection fraction
- squamous cell carcinoma
- high grade
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- emergency department
- machine learning
- rectal cancer
- gene expression
- deep learning
- oxidative stress
- genome wide
- high throughput
- toll like receptor
- artificial intelligence
- locally advanced
- high resolution
- free survival
- single molecule