Current medicinal chemistry · 2026
HAMP, G6PD, and CCL18 as Potential Molecular Markers to Guide Treatment Decisions in Anti-PD-1/PD-L1 Immunotherapy for Hepatocellular Carcinoma.
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Huang Yamei, Tong Huanjun, Xin Qi, Shi Yuanfei
Аннотация
INTRODUCTION: This study explored the role of programmed death receptor 1 (PD-1) and programmed death-ligand 1 (PD-L1) in hepatocellular carcinoma (HCC) and established a gene-based model for predicting response to PD-1/PD-L1 treatment.
MATERIALS AND METHODS: The anti-PD-1/PD-L1 treatment data of HCC were derived from the GEO dataset. Differentially expressed genes (DEGs) were analyzed using the DESeq2 package. LASSO regression was performed using the glmnet package. Immune composition was confirmed using CIBERSORTx. The PyCaret package was employed to predict the therapeutic response to anti-PD-1/PD-L1 treatment.
RESULTS: The HAMP, G6PD, and CCL18 genes influenced HCC progression and response to anti-PD-1/PD-L1. A Support Vector Machine (SVM) model was developed using gene expression data from three genes to predict the response of HCC patients to anti- PD-1/PD-L1 therapy. The model performed well on validation datasets, effectively distinguishing responders from non-responders.
DISCUSSION: This study identified novel molecular markers and developed a predictive tool for predicting the efficacy of anti-PD-1/PD-L1 therapy in patients with HCC.
CONCLUSION: A novel SVM model comprising three genes was developed for predicting the treatment response in HCC patients.
MATERIALS AND METHODS: The anti-PD-1/PD-L1 treatment data of HCC were derived from the GEO dataset. Differentially expressed genes (DEGs) were analyzed using the DESeq2 package. LASSO regression was performed using the glmnet package. Immune composition was confirmed using CIBERSORTx. The PyCaret package was employed to predict the therapeutic response to anti-PD-1/PD-L1 treatment.
RESULTS: The HAMP, G6PD, and CCL18 genes influenced HCC progression and response to anti-PD-1/PD-L1. A Support Vector Machine (SVM) model was developed using gene expression data from three genes to predict the response of HCC patients to anti- PD-1/PD-L1 therapy. The model performed well on validation datasets, effectively distinguishing responders from non-responders.
DISCUSSION: This study identified novel molecular markers and developed a predictive tool for predicting the efficacy of anti-PD-1/PD-L1 therapy in patients with HCC.
CONCLUSION: A novel SVM model comprising three genes was developed for predicting the treatment response in HCC patients.