Combinatorial chemistry & high throughput screening · 2026
Identification of CD4+ T Cell-Related Biomarkers and Mechanisms for Hepatoma.
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Zhang Zhewei, Guo Liwen, Luo Jun, Zheng Jiaping, Zeng Hui
Аннотация
INTRODUCTION: The high incidence and recurrence of hepatoma necessitate better prognostic tools. Given the established cytotoxic role of CD4+ T cells, this study aimed to identify CD4+ T cell-related genes (TRGs) and their mechanisms in hepatoma.
METHODS: Transcriptome and clinical data from TCGA-LIHC were analyzed. TRGs were identified through differential analysis and univariate Cox regression analysis. Three machine learning algorithms (LASSO, Random Forest (RF), and xgboost) were used to select key biomarkers. A nomogram model was constructed and evaluated using Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC), and calibration curves. Functional enrichment and gene set enrichment (GSEA) analyses were performed. RT-qPCR analysis was performed to validate the biomarker expression.
RESULTS: Seventy-two TRGs were identified, with 38 exhibiting significant prognostic abilities. Four important biomarkers were identified, namely BMI1, CD4, GAPDH, and ZCRB1. The nomogram model based on the four biomarkers showed promising effectiveness in predicting survival in hepatoma, with an AUC value of 0.988. RT-qPCR confirmed the upregulation of BMI1, GAPDH, and ZCRB1, and downregulation of CD4 in tumor tissues.
DISCUSSION: The four TRGs are differentially expressed in hepatoma, and the nomogram model based on them shows promising predicting ability. These findings provide more prognostic insights and may guide therapeutic strategies.
CONCLUSION: The results confirm BMI1, CD4, GAPDH, and ZCRB1 as effective biomarkers for constructing a nomogram model with high predictive value for hepatoma survival.
METHODS: Transcriptome and clinical data from TCGA-LIHC were analyzed. TRGs were identified through differential analysis and univariate Cox regression analysis. Three machine learning algorithms (LASSO, Random Forest (RF), and xgboost) were used to select key biomarkers. A nomogram model was constructed and evaluated using Kaplan-Meier analysis, time-dependent receiver operating characteristic (ROC), and calibration curves. Functional enrichment and gene set enrichment (GSEA) analyses were performed. RT-qPCR analysis was performed to validate the biomarker expression.
RESULTS: Seventy-two TRGs were identified, with 38 exhibiting significant prognostic abilities. Four important biomarkers were identified, namely BMI1, CD4, GAPDH, and ZCRB1. The nomogram model based on the four biomarkers showed promising effectiveness in predicting survival in hepatoma, with an AUC value of 0.988. RT-qPCR confirmed the upregulation of BMI1, GAPDH, and ZCRB1, and downregulation of CD4 in tumor tissues.
DISCUSSION: The four TRGs are differentially expressed in hepatoma, and the nomogram model based on them shows promising predicting ability. These findings provide more prognostic insights and may guide therapeutic strategies.
CONCLUSION: The results confirm BMI1, CD4, GAPDH, and ZCRB1 as effective biomarkers for constructing a nomogram model with high predictive value for hepatoma survival.