Construction of a Diagnostic Model and Drug Prediction for Postischemic Stroke Cognitive Impairment Based on Machine Learning Screening of Lactate Metabolism- and Pyroptosis-Related Genes.

Overview

1. Department of Traditional Chinese Medicine, Shandong Provincial Third Hospital, Shandong University, Jinan, China, sdu.edu.cn
2. Department of Internal Medicine, Jinan Municipal Government Hospital, Jinan, China
3. General Outpatient Clinic, Jinan Second People′s Hospital, Jinan, China
4. Department of Pharmacy, Jinan Second People′s Hospital, Jinan, China
5. The First Clinical School of Medicine, Shandong University of Chinese Medicine, Jinan, China, sdutcm.edu.cn
6. Department of Internal Medicine, Jinan Second People′s Hospital, Jinan, China

Abstract

Reliable molecular biomarkers for poststroke cognitive impairment (PSCI) remain limited. Using publicly available bulk transcriptomic and single‐cell RNA‐seq datasets from GEO, we investigated lactate metabolism– and pyroptosis‐related signatures and developed a diagnostic model. Differential expression analysis, KEGG pathway enrichment, and weighted gene coexpression network analysis (WGCNA) were performed, followed by multialgorithm feature selection (LASSO, SVM‐RFE, and random forest). A logistic regression classifier was trained in the discovery cohort and externally validated in an independent cohort. Glycolysis/lactate metabolism, HIF‐1 signaling, and NOD‐like receptor‐related pathways were enriched in PSCI‐associated samples, and key coexpression modules were strongly correlated with ischemic injury traits. Cross‐model consensus identified LDHA, GSDMD, and CASP1 as hub genes, yielding an AUC of 0.912 (95% bootstrap CI: 0.841–0.983) in the training cohort and 0.885 (95% bootstrap CI: 0.798–0.972) in the validation cohort. Immune deconvolution and scRNA‐seq validation suggested increased proinflammatory microglia‐associated signals, with relatively higher LDHA expression in microglia than in neurons; cell–cell communication analysis highlighted inflammatory interactions including IL1B–IL1R1. Connectivity map (CMap) analysis nominated candidate compounds, and molecular docking predicted favorable binding between oxamate and LDHA (binding energy = −9.5 kcal/mol). Collectively, these findings propose a compact LDHA/GSDMD/CASP1 biomarker panel for PSCI diagnosis and provide hypothesis‐generating therapeutic leads that warrant further experimental validation.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• geo:GSE223580, at NCBI GEO; found in the text, “2.1. Data Sources and Preprocessing”

Other data links

• ncbi.nlm.nih.gov/geo, NCBI; found in the text, “2.1. Data Sources and Preprocessing”

Data Availability Statement

The datasets analyzed in this study are publicly available in the Gene Expression Omnibus (GEO) repository under accession numbers GSE223580 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE223580), GSE137482 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137482), and GSE174574 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE174574). No new datasets were generated during this study.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 2, 28 September 2026

• Issue: n/a → 1

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 8 keywords, 10 MeSH terms, 36 references.

Cite

This paper

Ge, S., Zhang, Q., Liu, N., Zheng, X., Xu, H., & Zhang, L. (2026). Construction of a Diagnostic Model and Drug Prediction for Postischemic Stroke Cognitive Impairment Based on Machine Learning Screening of Lactate Metabolism- and Pyroptosis-Related Genes. Human mutation, 2026(1), 2963117. https://doi.org/10.1155/humu/2963117

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