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pmid:38412321
Unlocking the potential of senescence-related gene signature as a diagnostic and prognostic biomarker in sepsis: insights from meta-analyses, single-cell RNA sequencing, and in vitro experiments.
Jia Chen · Jinhong Si · Qiankun Li · Weihong Zhang · Jiahao He
Aging · 2024 doi:10.18632/aging.205574
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Experiment
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Analysis
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Execution
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Complete file
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In this study, 866 senescence-related genes were collected from CellAge. The training cohort, GSE65682, which included 42 control and 760 sepsis samples, was obtained from the Gene Expression Omnibus (GEO). Feature selection was performed using gene expression difference detection, LASSO analysis, random forest, and Cox regression. TGFBI and MAD1L1 were ultimately selected for inclusion in the multivariate Cox regression model. Clustering based on the expressions of TGFBI and MAD1L1 was significantly associated with sepsis characteristics and prognoses (all P < 0.05). The risk signature served as a reliable prognostic predictor across the GSE65682, GSE95233, and GSE4607 cohorts (pooled hazard ratio = 4.27; 95% confidence interval [CI] = 1.63-11.17). Furthermore, it also served as a robust classifier to distinguish sepsis samples from control cases across 14 cohorts (pooled odds ratio = 5.88; 95% CI = 3.54-9.77). Single-cell RNA sequencing analyses from five healthy controls and four sepsis subjects indicated that the risk signature could reflect the senescence statuses of monocytes and B cells; this finding was then experimentally validated in THP-1 and IM-9 cells in vitro (both P < 0.05). In all, a senescence-related gene signature was developed as a prognostic and diagnostic biomarker for sepsis, providing cut-in points to uncover underlying mechanisms and a promising clinical tool to support precision medicine.", "claims": [{"id": "claim-method", "statement": "To enhance the clarity of diagnosis and prognosis values, we utilized the meta package in R to combine odds ratios (ORs) and hazard ratios (HRs) obtained from several studies. 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