Integrative Multi-Omics Analysis Identifies IL18R1 as a Circulating Prognostic Biomarker for Risk Stratification in
Shengjuan Hu1, Sicong Li2, Yiyuan Cui2
1Department of Hematology and Oncology, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, China.
None:
Background: Small cell lung cancer (SCLC) carries a dismal prognosis with limited biomarkers for risk stratification. This study aimed to identify circulating prognostic biomarkers. Methods: We prioritized SCLC risk-associated genes using Summary-data-based Mendelian Randomization of pQTL/eQTL, differential expression, and weighted gene co-expression network analysis. Five machine learning approaches were compared to develop a diagnostic model based on ACE, AGER, and IL18R1, trained on GSE149507 and validated in GSE60052. We conducted single-cell transcriptomic analysis using public datasets (GSE150766 and GSE279570) and peripheral blood mononuclear cells (PBMCs) from our extensive-stage cohort. Finally, prioritizing the lead candidate IL18R1, we enrolled a prospective clinical cohort to assess its prognostic utility. A LASSO-Cox prognostic model incorporating plasma IL18R1 and clinical variables was internally validated (7:3 split) for progression-free survival (PFS) prediction. Results: Integrative multi-omics identified ACE, AGER, and IL18R1 as SCLC-protective genes. Elastic Net machine learning identified a two-gene predictive signature (AGER and IL18R1) with robust diagnostic accuracy. Single-cell RNA sequencing revealed the predominant downregulation of ACE, AGER, and IL18R1 in T cells and alveolar type II cells from SCLC patients. PBMC analysis further supported IL18R1 downregulation in CD8+ T cells, NK cells, and dendritic cells. In an independent prospective cohort (n = 300), lower plasma IL18R1 levels were independently associated with shorter PFS (HR = 0.997 per unit increase; 95% CI: 0.995-0.999; and p = 0.003), with time-dependent AUCs of 0.77-0.86. Performance in limited-stage disease was inconsistent and requires further validation. A prognostic model incorporating plasma IL18R1 and 11 clinical parameters stratified patients into distinct risk groups (HR = 5.19), showing a strong discriminative ability in extensive-stage SCLC. Conclusions: We identified ACE, AGER, and IL18R1 as protective factors against SCLC progression. Integration of plasma IL18R1 with clinical parameters provides a prognostic tool for extensive-stage SCLC.
