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image of Mining of Targeted Therapeutic Drugs for Hepatocellular Carcinoma based on Programmed Cell Death-related Features and Construction of an Imaging Histology Diagnostic Model

Abstract

Introduction

The programmed cell death (PCD) is crucial in inhibiting cancer cell proliferation and enhancing anti-tumor immune responses. Mining targeted therapeutics for liver hepatocellular carcinoma (LIHC) based on PCD genes and revealing their molecular mechanisms are essential for the development of effective clinical treatments for LIHC.

Methods

Key genes associated with PCD characteristics of LIHC were identified in cancer genome mapping by the weighted gene co-expression network analysis (WGCNA). In this study, the performance and clinical value of key genes were evaluated by the receiver operating characteristic curve (ROC). The relative expressions of genes related to PCD in LIHC cells were measured employing QRT-PCR. The practical regulation of PCD-correlated key genes on the migration and invasion levels of LIHC cells was assessed by transwell and wound healing assays. Functional and pathway characterization of gene sets was performed by Gene Set Enrichment Analysis (GSEA). CIBERSORT was used to assess immune cell infiltration in the samples. DSigDB and AutoDock tools were used for molecular docking of key genes and downstream targeted drugs. Impact omics characterization of the samples was determined by the nomogram.

Results

Three genes, CAMK4, CD200R1, and KCNA3, were screened as key PCD-related genes in LIHC. Cellular experiments verified that CD200R1 knockdown repressed the migration and invasion in LIHC cells. GSEA showed that these three genes were enriched for cytokine release, apoptosis, and other pathways. In immune profiling, we revealed that the three genes were related to the infiltration of immune cells such as CD4+ memory T cells and CD8+ T cells. Molecular docking predicted potential drugs for the three biomarkers, among which CAMK4 was tightly bound to GSK1838705A and had the highest AUC value in the ROC curve. In addition, we constructed a nomogram to accurately assess the imaging features of LIHC.

Discussion

This study provided a new strategy for precision treatment of LIHC by screening key genes associated with PCD in LIHC (CAMK4, CD200R1, and KCNA3), revealing their roles in the regulation of the tumor immune microenvironment and predicting potential target drugs, as well as constructing a diagnostic model based on imaging histology; however, the study did not delve deeper into the long-range drug-target interaction mechanism and lacked molecular dynamics simulation validation, which limited the comprehensiveness of the results.

Conclusion

This study identified key genes associated with PCD in LIHC, revealed its immunoregulatory mechanism, and predicted potential target drugs, providing new ideas for precision treatment and diagnosis of LIHC.

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2025-07-08
2025-09-14

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Mining of Targeted Therapeutic Drugs for Hepatocellular Carcinoma based on Programmed Cell Death-related Features and Construction of an Imaging Histology Diagnostic Model
Bentham Science Publishers ; https://doi.org/10.2174/0115680266397340250701110954
/content/journals/ctmc/10.2174/0115680266397340250701110954
/content/journals/ctmc/10.2174/0115680266397340250701110954
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  • Article Type:     Research Article
Keywords: radiomediography ; molecular docking ; liver hepatocellular carcinoma ; weighted correlation network analysis ; Programmed cell death ; Gene Set Enrichment Analysis

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