An AI-derived inflammation-metabolism ecosystem signature reveals ATP2A2 as a microenvironment-remodeling driver of immune evasion in lung adenocarcinoma.
- 1Department of Respiratory and Critical Care Medicine, Tianjin Chest Hospital, Tianjin University, Tianjin, China.
- 2Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin, China.
- 3Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin University, Tianjin, China.
Abstract
Chronic inflammation and metabolic dysregulation are increasingly recognized as interconnected drivers of tumorigenesis and immune evasion, yet their combined prognostic and mechanistic relevance in lung adenocarcinoma (LUAD) remains poorly defined. A curated 117-gene set representing systemic inflammation-metabolism dysregulation was obtained from MSigDB. Core prognostic genes were identified through differential expression analysis and univariate Cox regression. Using TCGA-LUAD as the training cohort and six independent GEO cohorts for external validation, 101 machine learning algorithm combinations were systematically evaluated to construct a robust prognostic model. Model performance was assessed by C-index, time-dependent ROC, and PCA. Immune characteristics were analyzed using TCIA, ESTIMATE, and TIMER-based deconvolution. ATP2A2, identified as the gene most strongly correlated with risk score, underwent multi-cohort transcriptomic and immunohistochemical validation, in vitro functional characterization, and mechanistic investigation via IOBR-integrated immune profiling. Single-cell RNA sequencing and multiplex immunofluorescence provided cellular-resolution verification of microenvironmental associations. The RSF + SuperPC model achieved optimal performance and effectively stratified patients into high- and low-risk groups with significantly divergent survival across all seven cohorts. High-risk tumors showed reduced immune/stromal infiltration and elevated tumor purity. ATP2A2 was consistently overexpressed in tumor tissue, correlated with poor differentiation, advanced stage, and shorter survival, and predicted immunotherapy resistance. Mechanistically, high ATP2A2 expression was associated with suppressed immune cell infiltration, attenuated chemokine signaling, impaired cancer-immunity cycle activity, and an "immune-desert" phenotype confirmed by single-cell and spatial analyses. This study establishes a validated inflammation-metabolism-based prognostic signature in LUAD and identifies ATP2A2 as a key driver linking tumor aggressiveness to immunosuppressive microenvironment remodeling, positioning it as a promising prognostic biomarker and potential therapeutic target for overcoming immunotherapy resistance.
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BibTeX
@article{hu2026aiderived,
title = {{An AI-derived inflammation-metabolism ecosystem signature reveals ATP2A2 as a microenvironment-remodeling driver of immune evasion in lung adenocarcinoma.}},
author = {Xiujuan Hu and Guoxin Zhang and Yue Zhang and Yong Zhao and Fang Zhou and Guanhua Li},
journal = {Frontiers in cell and developmental biology},
year = {2026},
month = oct,
doi = {10.3389/fcell.2026.1971779},
url = {https://europepmc.org/article/MED/42952829},
note = {CC BY},
}
RIS
TY - JOUR
TI - An AI-derived inflammation-metabolism ecosystem signature reveals ATP2A2 as a microenvironment-remodeling driver of immune evasion in lung adenocarcinoma.
AU - Xiujuan Hu
AU - Guoxin Zhang
AU - Yue Zhang
AU - Yong Zhao
AU - Fang Zhou
AU - Guanhua Li
PY - 2026
DA - 2026/10/14
JO - Frontiers in cell and developmental biology
DO - 10.3389/fcell.2026.1971779
UR - https://europepmc.org/article/MED/42952829
AB - Chronic inflammation and metabolic dysregulation are increasingly recognized as interconnected drivers of tumorigenesis and immune evasion, yet their combined prognostic and mechanistic relevance in lung adenocarcinoma (LUAD) remains poorly defined. A curated 117-gene set representing systemic inflammation-metabolism dysregulation was obtained from MSigDB. Core prognostic genes were identified through differential expression analysis and univariate Cox regression. Using TCGA-LUAD as the training cohort and six independent GEO cohorts for external validation, 101 machine learning algorithm combinations were systematically evaluated to construct a robust prognostic model. Model performance was assessed by C-index, time-dependent ROC, and PCA. Immune characteristics were analyzed using TCIA, ESTIMATE, and TIMER-based deconvolution. ATP2A2, identified as the gene most strongly correlated with risk score, underwent multi-cohort transcriptomic and immunohistochemical validation, in vitro functional characterization, and mechanistic investigation via IOBR-integrated immune profiling. Single-cell RNA sequencing and multiplex immunofluorescence provided cellular-resolution verification of microenvironmental associations. The RSF + SuperPC model achieved optimal performance and effectively stratified patients into high- and low-risk groups with significantly divergent survival across all seven cohorts. High-risk tumors showed reduced immune/stromal infiltration and elevated tumor purity. ATP2A2 was consistently overexpressed in tumor tissue, correlated with poor differentiation, advanced stage, and shorter survival, and predicted immunotherapy resistance. Mechanistically, high ATP2A2 expression was associated with suppressed immune cell infiltration, attenuated chemokine signaling, impaired cancer-immunity cycle activity, and an "immune-desert" phenotype confirmed by single-cell and spatial analyses. This study establishes a validated inflammation-metabolism-based prognostic signature in LUAD and identifies ATP2A2 as a key driver linking tumor aggressiveness to immunosuppressive microenvironment remodeling, positioning it as a promising prognostic biomarker and potential therapeutic target for overcoming immunotherapy resistance.
SN - 2296-634X
ER -