What this means
This study aimed to investigate the associations of senescence-associated secretory phenotype (SASP)-related genes with the tumor immune microenvironment (TIME) and clinical outcomes in lung adenocarcinoma (LUAD) and to develop and evaluate a prognostic signature for risk stratification and exploratory treatment-response prediction. Non-negative matrix factorization (NMF) was conducted to identify distinct molecular subtypes.
A plain-English summary is on its way. Until then, this is the start of the authors' own summary.
- Who it was tested in
- People
- Kind of research
- A study
The science
- Original title
- Machine learning-derived intracellular protein-based prognostic model reflecting a senescence-associated secretory phenotype-driven immunosuppressive microenvironment in lung adenocarcinoma.
- Authors
- Chang X, Jiang L, Zhang L et al.
- Journal
- PeerJ
- Published
- Oct 25, 2026
- Study type
- Study · in humans
- PMID
- 42927379 ↗
Abstract
Background
This study aimed to investigate the associations of senescence-associated secretory phenotype (SASP)-related genes with the tumor immune microenvironment (TIME) and clinical outcomes in lung adenocarcinoma (LUAD) and to develop and evaluate a prognostic signature for risk stratification and exploratory treatment-response prediction.
What they did
Non-negative matrix factorization (NMF) was conducted to identify distinct molecular subtypes. Least absolute shrinkage and selection operator (LASSO), Random Forest, and extreme gradient boosting (XGBoost) were utilized to identify core prognostic SASP genes. The expression patterns of selected SASP-related genes were explored using multi-omics datasets, LUAD cell lines, and clinical specimens.
What they found
Cluster 1 was associated with significantly shorter overall survival (OS), enhanced senescence pathway activity, and computationally inferred immunosuppressive and immune-exclusion features. Five intracellular SASP regulators-ANGPTL4, CDKN2A, H2AX, MMP1, and CCL20-were selected through machine learning to construct the SASP Protein Regulator Score (SASP-PRS). A high SASP-PRS was associated with the activation of senescence/SASP and cell-cycle pathways, suppression of antitumor immune signatures, and distinct immune-checkpoint and chemokine expression profiles. A higher SASP-PRS was also associated with advanced disease stage, poorer OS, predicted lower responsiveness to immunotherapy, lower predicted sensitivity to erlotinib, and higher predicted sensitivity to selected chemotherapeutic agents. The nomogram incorporating the SASP-PRS demonstrated discriminatory ability and calibration for predicting 1-, 3-, and 5-year OS in the analyzed LUAD cohorts. These findings suggest that SASP-related gene-expression patterns are associated with LUAD molecular subtypes, senescence signaling, computationally inferred immune features, and treatment-related outcomes. The SASP-PRS may serve as a potential biomarker for prognostic stratification in LUAD. However, its utility for treatment-response assessment remains exploratory. Prospective external validation and functional studies are required to establish its clinical applicability and clarify the mechanisms linking SASP-related networks to the LUAD immune microenvironment.
The authors' abstract, grouped under plain headings from its own section labels.
Chang X, Jiang L, Zhang L et al. Machine learning-derived intracellular protein-based prognostic model reflecting a senescence-associated secretory phenotype-driven immunosuppressive microenvironment in lung adenocarcinoma. PeerJ. 2026. doi:10.7717/peerj.21835. PMID: 42927379. https://pubmed.ncbi.nlm.nih.gov/42927379/