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Peer-reviewedCC BY 4.0International Journal of Genomics

Prognostic Modeling of Lysosomal Cell Death Signatures in Lung Adenocarcinoma: A Machine Learning Framework for Survival Outcome Prediction

  • Runzhe Wang1,2
  • Qingtao Zhao3
  • Shicheng Liu3
  • Lingxin Kong3
  • Dahu Ren3
  • Jianghong Yan4
  • Shuo Yang4
  • Guochen Duan1,3
4 affiliations
  1. 1Hebei Medical University Shijiazhuang China hebmu.edu.cn
  2. 2Study Office of Cardiac Surgery The Second Hospital of Hebei Medical University Shijiazhuang China hebmu.edu.cn
  3. 3Study Office of Thoracic Surgery Hebei General Hospital Shijiazhuang China hebmu.edu.cn
  4. 4Hebei Children′s Hospital Shijiazhuang China
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Abstract

Background: Lung cancer remains a leading cause of cancer‐related mortality worldwide. This study provides a comprehensive characterization of the transcriptomic landscape, cellular composition, and immune architecture of lung adenocarcinoma using multiomics approaches.

Methods: Gene expression data were obtained from GEO (GSE131907) and TCGA‐LUAD (n = 526). LRCD gene sets were curated from GeneCards, MSigDB, and published literature. Analyses included differential expression profiling, single‐cell RNA sequencing (GSE131907, n = 11,376 cells), GSEA, and cell‐cell communication mapping. A LASSO‐based prognostic model with 10‐fold cross‐validation was built and validated across three independent cohorts using ROC and Kaplan–Meier analyses.

Results: Fourteen prognostically relevant genes were identified, including protective factors (BTK, CLNK, CTSG; HR < 1) and risk factors (CTSV, HMOX1; HR > 1). Single‐cell analysis profiled 11,376 cells from one primary tumor and one normal lung sample. The prognostic model achieved moderate‐to‐good discrimination (AUC : 0.545–0.692) and effectively stratified patients by risk. Model genes correlated significantly with immune cell infiltration and checkpoint expression across risk groups, and functional enrichment highlighted immune trafficking, cytokine signaling, and cell adhesion pathways.

Conclusions: This multiomics study identifies reliable LRCD‐based prognostic markers and offers mechanistic insights into lung adenocarcinoma biology, with potential utility for clinical risk stratification.

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Cite

BibTeXRIS
BibTeX
@article{wang2026prognostic,
  title = {{Prognostic Modeling of Lysosomal Cell Death Signatures in Lung Adenocarcinoma: A Machine Learning Framework for Survival Outcome Prediction}},
  author = {Runzhe Wang and Qingtao Zhao and Shicheng Liu and Lingxin Kong and Dahu Ren and Jianghong Yan and Shuo Yang and Guochen Duan},
  journal = {International Journal of Genomics},
  year = {2026},
  month = oct,
  doi = {10.1155/ijog/8237477},
  url = {https://doi.org/10.1155/ijog/8237477},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Prognostic Modeling of Lysosomal Cell Death Signatures in Lung Adenocarcinoma: A Machine Learning Framework for Survival Outcome Prediction
AU  - Runzhe Wang
AU  - Qingtao Zhao
AU  - Shicheng Liu
AU  - Lingxin Kong
AU  - Dahu Ren
AU  - Jianghong Yan
AU  - Shuo Yang
AU  - Guochen Duan
PY  - 2026
DA  - 2026/10/30
JO  - International Journal of Genomics
DO  - 10.1155/ijog/8237477
UR  - https://doi.org/10.1155/ijog/8237477
AB  - Background: Lung cancer remains a leading cause of cancer‐related mortality worldwide. This study provides a comprehensive characterization of the transcriptomic landscape, cellular composition, and immune architecture of lung adenocarcinoma using multiomics approaches.  Methods: Gene expression data were obtained from GEO (GSE131907) and TCGA‐LUAD (n = 526). LRCD gene sets were curated from GeneCards, MSigDB, and published literature. Analyses included differential expression profiling, single‐cell RNA sequencing (GSE131907, n = 11,376 cells), GSEA, and cell‐cell communication mapping. A LASSO‐based prognostic model with 10‐fold cross‐validation was built and validated across three independent cohorts using ROC and Kaplan–Meier analyses.  Results: Fourteen prognostically relevant genes were identified, including protective factors (BTK, CLNK, CTSG; HR < 1) and risk factors (CTSV, HMOX1; HR > 1). Single‐cell analysis profiled 11,376 cells from one primary tumor and one normal lung sample. The prognostic model achieved moderate‐to‐good discrimination (AUC : 0.545–0.692) and effectively stratified patients by risk. Model genes correlated significantly with immune cell infiltration and checkpoint expression across risk groups, and functional enrichment highlighted immune trafficking, cytokine signaling, and cell adhesion pathways.  Conclusions: This multiomics study identifies reliable LRCD‐based prognostic markers and offers mechanistic insights into lung adenocarcinoma biology, with potential utility for clinical risk stratification.
SN  - 2314-436X
SN  - 2314-4378
ER  -