Clinical Significance of a Novel Vasculogenic Mimicry-Based Prognostic Model in Hepatocellular Carcinoma

Yifan Zeng; Shuwen Jiang; Zhuoqi Lou; Lin Chen; Yongtao Zhang; Liya Pan; Qingmiao Shi; Bing Ruan

doi:10.2174/0109298673298862240510073543

Clinical Significance of a Novel Vasculogenic Mimicry-Based Prognostic Model in Hepatocellular Carcinoma

Curr Med Chem. 2024 May 16. doi: 10.2174/0109298673298862240510073543. Online ahead of print.

Authors

Yifan Zeng¹, Shuwen Jiang¹, Zhuoqi Lou¹, Lin Chen¹, Yongtao Zhang¹, Liya Pan¹, Qingmiao Shi¹, Bing Ruan¹

Affiliation

¹ State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, National Medical Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou City, 310003, China.

PMID: 38757323
DOI: 10.2174/0109298673298862240510073543

Abstract

Background: Vasculogenic mimicry, a novel neovascularization pattern of aggressive tumors, is associated with poor clinical outcomes.

Objective: The aim of this research was to establish a new model, termed VC score, to predict the prognosis, Tumor Microenvironment (TME) components, and immunotherapeutic response in Hepatocellular Carcinoma (HCC).

Methods: The expression data of the public databases were used to develop the prognostic model. Consensus clustering was performed to confirm the molecular subtypes with ideal clustering efficacy. The high- and low-risk groups were stratified utilizing the VC score. Various methodologies, including survival analysis, single-sample Gene Set Enrichment Analysis (ssGSEA), Tumor Immune Dysfunction and Exclusion scores (TIDE), Immunophenoscore (IPS), and nomogram, were utilized for verification of the model performance and to characterize the immune status of HCC tissues. GSEA was performed to mine functional pathway information.

Results: The survival and immune characteristics varied between the three molecular subtypes. A five-gene signature (TPX2, CDC20, CFHR4, SPP1, and NQO1) was verified to function as an independent predictive factor for the prognosis of patients with HCC. The high-risk group exhibited lower Overall Survival (OS) rates and higher mortality rates in comparison to the low-risk group. Patients in the low-risk group were predicted to benefit from immune checkpoint inhibitor therapy and exhibit increased sensitivity to immunotherapy. Enrichment analysis revealed that signaling pathways linked to the cell cycle and DNA replication processes exhibited enrichment in the high-risk group.

Conclusions: The VC score holds the potential to establish individualized treatment plans and clinical management strategies for patients with HCC.

Keywords: Hepatocellular carcinoma; immunotherapy.; machine learning; prognostic prediction model; tumor microenvironment; vasculogenic mimicry.