Identifying the programmed cell death index of hepatocellular carcinoma for prognosis and therapy response improvement by machine learning: a bioinformatics analysis and experimental validation

Yuanxin Shi; Yunxiang Feng; Peng Qiu; Kai Zhao; Xiangyu Li; Zhengdong Deng; Jianming Wang

doi:10.3389/fimmu.2023.1298290

Identifying the programmed cell death index of hepatocellular carcinoma for prognosis and therapy response improvement by machine learning: a bioinformatics analysis and experimental validation

Front Immunol. 2023 Dec 19:14:1298290. doi: 10.3389/fimmu.2023.1298290. eCollection 2023.

Authors

Yuanxin Shi¹, Yunxiang Feng¹, Peng Qiu¹, Kai Zhao¹, Xiangyu Li¹, Zhengdong Deng², Jianming Wang^{1

3}

Affiliations

¹ Department of Biliary and Pancreatic Surgery/Cancer Research Center Affiliated Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Department of Pediatric Surgery, Tongji Hospital of Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
³ Affiliated Tianyou Hospital, Wuhan University of Science and Technology, Wuhan, China.

Abstract

Background: Despite advancements in hepatocellular carcinoma (HCC) treatments, the prognosis for patients remains suboptimal. Cumulative evidence suggests that programmed cell death (PCD) exerts crucial functions in HCC. PCD-related genes are potential predictors for prognosis and therapeutic responses.

Methods: A systematic analysis of 14 PCD modes was conducted to determine the correlation between PCD and HCC. A novel machine learning-based integrative framework was utilized to construct the PCD Index (PCDI) for prognosis and therapeutic response prediction. A comprehensive analysis of PCDI genes was performed, leveraging data including single-cell sequencing and proteomics. GBA was selected, and its functions were investigated in HCC cell lines by in vitro experiments.

Results: Two PCD clusters with different clinical and biological characteristics were identified in HCC. With the computational framework, the PCDI was constructed, demonstrating superior prognostic predictive efficacy and surpassing previously published prognostic models. An efficient clinical nomogram based on PCDI and clinicopathological factors was then developed. PCDI was intimately associated with immunological attributes, and PCDI could efficaciously predict immunotherapy response. Additionally, the PCDI could predict the chemotherapy sensitivity of HCC patients. A multilevel panorama of PCDI genes confirmed its stability and credibility. Finally, the knockdown of GBA could suppress both the proliferative and invasive capacities of HCC cells.

Conclusion: This study systematically elucidated the association between PCD and HCC. A robust PCDI was constructed for prognosis and therapy response prediction, which would facilitate clinical management and personalized therapy for HCC.

Keywords: hepatocellular carcinoma; immunotherapy; machine learning; prognostic model; programmed cell death.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Apoptosis / genetics
Carcinoma, Hepatocellular* / genetics
Carcinoma, Hepatocellular* / therapy
Computational Biology
Humans
Liver Neoplasms* / genetics
Liver Neoplasms* / therapy
Machine Learning
Prognosis

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The work was supported by the National Natural Science Foundation of China (No. 81874062, No. 82072730).