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Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:

2025 04

Page:

557-566

Research Field:

Publishing date:

Info

Title:

Combining bulk and single-cell sequencing data to develop a prognostic model of immunogenic cell death in hepatocellular carcinoma based on machine learning

Author(s):

DU Yanan; WANG Yixuan; XIE Li; LI Rutian

Department of Oncology,Nanjing Drum Tower Hospital,the Affiliated Hospital of Nanjing University Medical School,Jiangsu Nanjing 210008£¬China.

Keywords:

hepatocellular carcinoma; machine learning; immunogenic cell death; prognosis; weighted gene co-expression network analysis

PACS:

R735.7

DOI:

10.3969/j.issn.1672-4992.2025.04.003

Abstract:

Objective:To develop a new prognostic model of immunogenic cell death related signatures (ICDRS) for hepatocellular carcinoma (HCC) patients.Methods:RNA-seq expression data and related clinical information were downloaded from TCGA,GEO,and ICGC databases,respectively.Immunogenic cell death (ICD) related genes were screened using AddModuleScore,single-sample gene set enrichment analysis£¨ssGSEA£©and weighted gene co-expression network analysis£¨WGCNA£©.Based on ICD related genes,10 machine learning algorithms and 79 combinations were used to construct a consistent ICDRS prognosis model.We used receiver operating characteristic (ROC) curve to evaluate the prognostic model and univariate and multivariate regression analyses to determine the independent prognostic value of ICDRS.The pattern of genomic variants,tumor mutational burden£¨TMB£©,immune cell infiltration and immune evasion in different risk groups were assessed.Finally,the sensitivity to drugs in different risk groups was also assessed.Results:Through a series of analysis,we constructed a consistent ICDRS model containing six ICD related genes,including YWHAB,EHD1,TUBA4A,SRI,SLC16A3,FYN.The model assigned all patients to either a high-risk group or a low-risk group,and the prognosis of patients in the high-risk group was worse than that in the low-risk group.The model had good performance in predicting the prognosis and clinical transformation of HCC,and ICDRS was an independent prognostic factor for OS in HCC patients.In addition,there was no significant difference in TMB between the two groups,but compared with the higher-risk group,patients in the low-risk group had more abundant immune cell infiltration and lower TIDE scores,suggesting that the low-risk group may have a better response to immunotherapy.We also found that the high-risk group had higher sensitivity to drugs such as Oxaliplatin,Mitoxantrone and Sorafenib,while the low-risk group benefited more from drugs such as Cediranib,Vinblastine and Docetaxel.Conclusion:In this study,ICD related genes were combined to construct a prognostic model of HCC using bioinformatics and machine learning.It can help clinicians choose personalized treatment strategies for HCC patients with different risks.

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Memo

Memo:

National Natural Science Foundation of China(No.82272852£©£»¹ú¼Ò×ÔÈ»¿Æѧ»ù½ð×ÊÖúÏîÄ¿£¨±àºÅ£º82272852£©

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