Related Experiment Video
Updated: Sep 12, 2025
Author Spotlight: Investigating Immune Cell Dynamics in the Tumor Microenvironment — Challenges and Innovations in Cancer Prognosis
Published on: April 12, 2024
Leveraging diverse cell-death patterns to predict to predict prognosis and immunotherapy in hepatocellular carcinoma
Xiaoxiang Zhang1, Dongxiao Ding2, Dianqian Wang1
1Health Science Center, Ningbo University, Ningbo, 315800, Zhejiang, China.
Background:
Currently, liver hepatocellular carcinoma (LIHC) is characterized by high morbidity, rapid progression and early metastasis. Although many efforts have been made to improve the prognosis of LIHC, the situation is still dismal. Inability to initiate the process of programmed cell death (PCD) is closely associated with cancer progression, thus influencing patients' prognosis. In this study, our purpose was to construct PCD-related prognostic signature for LIHC patients.
Methods:
The list of PCD-related genes was obtained from GSEA gene sets. The gene set associated with survival time and survival status was screened by weighted correlation network analysis (WGCNA). Via Cox regression test and LASSO Cox regression model, prognostic signature was established and was then externally validated by ICGC-LIRI-JP dataset and GSE14520 dataset. The immune infiltration status and immune function of the signature were analyzed by ESTIMATE algorithm and ssGSEA algorithm. TIDE score, IPS and immune checkpoints expression and IC50 value were utilized to predict chemosensitivity and immunotherapy response. Moreover, GSE91061 dataset and PRJEB23709 dataset were enrolled to verify the predictive efficacy on immunotherapy response.
Results:
A total of 89 genes correlated with survival time and survival status were screened out from 1249 PCD-related genes. Next, the prognostic signature consisting of GLA, CLTA, CHGA, ERP29, MAPK3, CDK5, NLE1, STYXL1 and SFN was constructed. And high-risk patients were related to an adverse prognosis in TCGA-LIHC cohort and ICGC-LIRI-JP cohort. The prognostic signature also showed moderate to high predictive accuracy and was an independent prognostic indicator for LIHC. In general, low-risk patients exhibited higher StromalScore, immune cell infiltration levels, IPS, IPS-PD1 blocker, IPS-CTLA4 blocker, immune checkpoints expression and HLA-related genes expression while lower TIDE score, which indicated low-risk group tended to profit from ICI treatment. Furthermore, responders to ICI treatment had a lower riskscore in GSE91061 cohort, which showed similar result with ours.
Conclusions:
Our study developed a novel prognostic signature comprising of 9 PCD-related genes, which could stratify the risk and effectively predict the prognosis and the immunotherapy response of LIHC patients.
