ArticleHeliyon2024
Gene signature developed based on programmed cell death to predict the therapeutic response and prognosis for liver hepatocellular carcinoma.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Synergistic Profiling of Programmed Cell Death and Immune Responses Identifies a Novel Prognostic Index for Cervical Cancer.ACS omega · 2026Article
- Construction and Validation of an N7-Methylguanosine-Related Prognostic Model for Acute Myeloid Leukemia.Blood and lymphatic cancer : targets and therapy · 2026Article
- Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach.Discover oncology · 2025Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The prognosis and therapeutic response of patients with liver hepatocellular carcinoma (LIHC) can be predicted based on programmed cell death (PCD) as PCD plays a crucial role during tumor progression. We developed a PCD-related gene signature to evaluate the therapeutic response and prognosis for patients with LIHC. Methods: Molecular subtypes of LIHC were classified using ConsensusClusterPlus according to the gene biomarkers related to PCD. To predict the prognosis of high- and low-risk LIHC patients, a risk model was established by LASSO regression analysis based on the prognostic genes. Functional enrichment analysis was conducted using clusterProfiler package, and relative abundance of immune cells was quantified applying CIBERSORT package. Finally, to determine drug sensitivity, oncoPredict package was employed. Results: PCD was correlated with the clinicopathologic features of LIHC. Then, we defined four molecular subtypes (C1-C4) of LIHC using PCD-related prognostic genes. Specifically, subtype C1 had the worst prognosis with enriched T cells regulatory (Tregs) and Macrophage_M0 and higher expression of T cell exhaustion markers, meanwhile, C1 also had a relatively higher TIDE score and metastasis potential. A risk model was established using 5 prognostic genes. High-risk patients tended to have higher expression of T cell exhaustion markers and TIDE score and unfavorable outcomes, and they were more sensitive to small molecule drug 5.Fluorouracil. Conclusion: A PCD-related gene signature was developed and verified to be able to accurately predict the prognosis and drug sensitivity of LIHC patients.
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