Identification and validation of a novel nine-gene prognostic signature of stem cell characteristic in hepatocellular carcinoma

J Appl Genet. 2024 Mar 5. doi: 10.1007/s13353-024-00850-7. Online ahead of print.ABSTRACTCurrently, cancer stem cells (CSCs) are regarded as the most promising target for cancer therapy due to their close association with tumor resistance, invasion, and recurrence. Thus, identifying CSCs-related genes and constructing a prognostic risk model associated with CSCs may be crucial for hepatocellular carcinoma (HCC) therapy. Xena Browser was used to download gene expression profiles and clinical data, while MSigDB was used to obtain genes associated with CSCs. Firstly, the non-negative matrix factorization (NMF) algorithm was used to cluster the HCC samples based on CSCs-related genes. To evaluate the predictive performance of the risk model, the receiver operating characteristic curves (ROC) and Kaplan-Meier analysis were used. The R package "rms" was used to construct the final nomogram based on risk scores and clinical characteristics. Based on 449 CSCs-related genes, a total of 588 HCC samples from TCGA-LIHC and ICGC-LIRI_JP were classified into four molecular subtypes with marked differences in survival and mRNA stemness index (mRNAsi) between subtypes. Univariate Cox regression, multivariate Cox regression, and LASSO regression analyses were performed on a total of 1417 differentially expressed genes (DEGs) between subtypes, and a nine-gene prognostic model was constructed with TTK, ST6GALNAC4, SPP1, SGCB, MEP1A, HTRA1, CD79A, C6, and ATP2A3. In both the training and testing...
Source: J Appl Genet - Category: Genetics & Stem Cells Authors: Source Type: research