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Prognostic 7-SLC-Gene Signature Identified via Weighted Gene Co-Expression Network Analysis for Patients with Hepatocellular Carcinoma.
- Source :
- Journal of Oncology; 2/17/2023, p1-21, 21p
- Publication Year :
- 2023
-
Abstract
- Background. Solute carrier (SLC) proteins play an important role in tumor metabolism. But SLC-associated genes' prognostic significance in hepatocellular carcinoma (HCC) remained elusive. We identified SLC-related factors and developed an SLC-related classifier to predict and improve HCC prognosis and treatment. Methods. From the TCGA database, corresponding clinical data and mRNA expression profiles of 371 HCC patients were acquired, and those of 231 tumor samples were derived from the ICGC database. Genes associated with clinical features were filtered using weighted gene correlation network analysis (WGCNA). Next, univariate LASSO Cox regression studies developed SLC risk profiles, with the ICGC cohort data being used in validation. Result. Univariate Cox regression analysis revealed that 31 SLC genes P < 0.05 were related to HCC prognosis. 7 (SLC22A25, SLC2A2, SLC41A3, SLC44A1, SLC48A1, SLC4A2, and SLC9A3R1) of these genes were applied in developing a SLC gene prognosis model. Samples were classified into the low-andhigh-risk groups by the prognostic signature, with those in the high-risk group showing a significantly worse prognosis (P < 0.001 in the TCGA cohort and P = 0.0068 in the ICGC cohort). ROC analysis validated the signature's prediction power. In addition, functional analyses showed enrichment of immune-related pathways and different immune status between the two risk groups. Conclusion. The 7-SLC-gene prognostic signature established in this study helped predict the prognosis, and was also correlated with the tumor immune status and infiltration of different immune cells in the tumor microenvironment. The current findings may provide important clinical indications for proposing a novel combination therapy consists of targeted anti-SLC therapy and immunotherapy for HCC patients. [ABSTRACT FROM AUTHOR]
- Subjects :
- STATISTICS
DATABASES
MEDICAL information storage & retrieval systems
VASCULAR cell adhesion molecule-1
REGRESSION analysis
GENE expression
CELLULAR signal transduction
COMPARATIVE studies
DESCRIPTIVE statistics
GENE expression profiling
MESSENGER RNA
PREDICTION models
DATA analysis software
RECEIVER operating characteristic curves
CARRIER proteins
HEPATOCELLULAR carcinoma
PROPORTIONAL hazards models
IMMUNOTHERAPY
Subjects
Details
- Language :
- English
- ISSN :
- 16878450
- Database :
- Complementary Index
- Journal :
- Journal of Oncology
- Publication Type :
- Academic Journal
- Accession number :
- 161966882
- Full Text :
- https://doi.org/10.1155/2023/4364654