A degradome-related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma based on machine learning procedure
- Ziqing Deng 1, Qian Feng 2, Dan Zhao 3, Zhihao Huang 4
- Ziqing Deng 1, Qian Feng 2, Dan Zhao 3
- 1Department of General Surgery, Nanchang People's Hospital, Nanchang, China.
- 2Department of Emergency, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
- 3Department of Critical Care Medicine, The Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine, Nanchang, China.
- 4Department of General Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
- 0Department of General Surgery, Nanchang People's Hospital, Nanchang, China.
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed a prognostic degradome-based signature (DPS) to predict outcomes for stomach adenocarcinoma (STAD) patients. A low DPS score indicates better immunotherapy response and prognosis, guiding personalized treatment strategies.
Area Of Science
- Oncology
- Bioinformatics
- Cancer Genomics
Background
- Stomach adenocarcinoma (STAD) presents high invasiveness, heterogeneity, morbidity, and mortality.
- The degradome, a key class of cellular enzymes, significantly influences cellular activity and carcinogenesis.
- Existing prognostic models for STAD require refinement to improve patient outcomes.
Purpose Of The Study
- To develop and validate a prognostic degradome-based prognostic signature (DPS) for stomach adenocarcinoma (STAD).
- To investigate the association of the DPS with immune infiltration, immunotherapy response, and drug sensitivity.
- To establish an optimal predictive model for STAD prognosis and treatment guidance.
Main Methods
- An integrative machine learning approach utilizing 10 methods was employed on TCGA and multiple GEO datasets (GSE15459, GSE26253, GSE62254).
- The Enet [alpha=0.5] method was identified as optimal for developing the prognostic degradome-based prognostic signature (DPS).
- The DPS performance was evaluated against clinical factors (age, sex, stage) and assessed for its correlation with immune markers and drug efficacy.
Main Results
- The developed DPS demonstrated stable and powerful performance in predicting STAD clinical outcomes, outperforming age, sex, and clinical stage.
- Low DPS scores correlated with increased immune cell infiltration (B cells, CD8+ T cells), higher cytolytic and co-stimulation scores, and better immunotherapy response indicators.
- High DPS scores were associated with lower IC50 values for chemotherapy and targeted therapy drugs, suggesting potential treatment guidance.
Conclusions
- An optimal prognostic degradome-based prognostic signature (DPS) for STAD was successfully developed.
- The DPS serves as an independent risk factor, capable of predicting prognosis and guiding risk stratification for STAD patients.
- The DPS has the potential to inform personalized treatment strategies, including immunotherapy and chemotherapy selection for STAD.
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