Unveiling membrane transporter proteins-related genes for predicting the prognosis of hepatocellular carcinoma patients.
[BACKGROUND] Prior research has indicated that membrane transporter proteins may play a role in tumorigenesis, progression, and mechanisms of drug resistance in hepatocellular carcinoma (HCC) by affec
APA
Xiao Y, Zhang G, Zhao H (2026). Unveiling membrane transporter proteins-related genes for predicting the prognosis of hepatocellular carcinoma patients.. Discover oncology, 17(1). https://doi.org/10.1007/s12672-026-04554-8
MLA
Xiao Y, et al.. "Unveiling membrane transporter proteins-related genes for predicting the prognosis of hepatocellular carcinoma patients.." Discover oncology, vol. 17, no. 1, 2026.
PMID
41678042
Abstract
[BACKGROUND] Prior research has indicated that membrane transporter proteins may play a role in tumorigenesis, progression, and mechanisms of drug resistance in hepatocellular carcinoma (HCC) by affecting the transport of drugs, nutrients, and metabolites. Therefore, a comprehensive elucidation of the role of membrane transporter protein-related genes (MTPRGs) in the outcome of those suffering with HCC is essential.
[METHODS] The bulk RNA data/survival information/clinical records of HCC patients were retrieved from the UCSC Xena and the Gene Expression Omnibus databases. Through combining differential analysis, the protein-protein interaction construction, univariate COX regression analysis, and least absolute shrinkage and selection operator (LASSO), the membrane transporter protein-related prognostic genes were identified, and the prognostic model was subsequently formulated. The relation between risk score and immune infiltration, clinical features, and chemotherapy was further evaluated. The expression of prognostic genes was afterwards assessed by in vitro tests.
[RESULTS] Through a comprehensive analysis, three membrane transporter protein-related prognostic genes (SLC1A5, SLC38A1, and GPD1L) for HCC patients were identified. Moreover, the prognostic model demonstrated robust predictive efficacy for predictions of individuals with HCC. Additionally, risk score had significant associations with immune infiltration, clinical features, and chemotherapy. Furthermore, SLC1A5, SLC38A1, and GPD1L were all highly expressed in the HCC samples.
[CONCLUSION] Our research generated a risk model incorporating MTPRGs which shown strong predictive effectiveness for the prognosis of HCC patients, which will offer novel insights to guide subsequent therapy strategies.
[GRAPHICAL ABSTRACT] [Image: see text]
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1007/s12672-026-04554-8.
[METHODS] The bulk RNA data/survival information/clinical records of HCC patients were retrieved from the UCSC Xena and the Gene Expression Omnibus databases. Through combining differential analysis, the protein-protein interaction construction, univariate COX regression analysis, and least absolute shrinkage and selection operator (LASSO), the membrane transporter protein-related prognostic genes were identified, and the prognostic model was subsequently formulated. The relation between risk score and immune infiltration, clinical features, and chemotherapy was further evaluated. The expression of prognostic genes was afterwards assessed by in vitro tests.
[RESULTS] Through a comprehensive analysis, three membrane transporter protein-related prognostic genes (SLC1A5, SLC38A1, and GPD1L) for HCC patients were identified. Moreover, the prognostic model demonstrated robust predictive efficacy for predictions of individuals with HCC. Additionally, risk score had significant associations with immune infiltration, clinical features, and chemotherapy. Furthermore, SLC1A5, SLC38A1, and GPD1L were all highly expressed in the HCC samples.
[CONCLUSION] Our research generated a risk model incorporating MTPRGs which shown strong predictive effectiveness for the prognosis of HCC patients, which will offer novel insights to guide subsequent therapy strategies.
[GRAPHICAL ABSTRACT] [Image: see text]
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1007/s12672-026-04554-8.
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