文献类型：期刊文献

英文题名：Identification and multi-layered validation of seven diagnostic biomarkers for dilated cardiomyopathy via integrative machine learning, single-cell transcriptomics, and Mendelian randomization

作者：Li, Jingwei[1];Song, Zhongyang[2,3];Wang, Guanwei[1];Chen, Yuchan[4];Cheng, Jiamiao[1];Zhang, Zhiming[5]

第一作者：李佳蔚

通信作者：Zhang, ZM[1]

机构：[1]Gansu Univ Chinese Med, Coll Clin Tradit Chinese Med, Lanzhou, Peoples R China;[2]Gansu Univ Tradit Chinese Med, Affiliated Hosp, Dept Oncol, Lanzhou, Peoples R China;[3]Gansu Inst Cardiovasc Dis, Lanzhou, Peoples R China;[4]Gansu Univ Chinese Med, Coll Integrated Tradit Chinese & Western Med, Lanzhou, Peoples R China;[5]Gansu Prov Hosp Tradit Chinese Med, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Prov Hosp Tradit Chinese Med, Lanzhou, Peoples R China.

年份：2026

卷号：14

外文期刊名：FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001799058600001)】；

基金：The author(s) declared that financial support was received for this work and/or its publication. This research was supported by the National Natural Science Foundation (Grant Nos. 81660730 and 82560862), Gansu Provincial Science and Technology Program Project (Grant No. 24YFFA067), Longyuan Program for Young Innovation and Entrepreneurship Talents (Grant No. 2024QNGR53), Gansu Province Fourth Batch of Longyuan Youth Talents Funding Support (Grant No. 001174001), and Project of the Gansu Provincial Administration of Traditional Chinese Medicine (Grant No. GZKG-2024-60).

语种：英文

外文关键词：co-expression-based functional importance score; diagnostic biomarkers; dilated cardiomyopathy; immune microenvironment; machine learning; Mendelian randomization; single-cell RNA sequencing; WGCNA

摘要：Background Dilated cardiomyopathy (DCM) is the most common non-ischemic cardiomyopathy and a major cause of heart failure, but disease-specific molecular biomarkers remain limited. This study aimed to identify and prioritize tissue-level, disease-responsive candidate biomarkers for DCM using an integrative multi-omics bioinformatics framework.Methods Bulk myocardial transcriptomic data from GSE57338 were used as the discovery cohort, and GSE26887, GSE42955, and GSE79962 served as external microarray validation cohorts. GSE116250 was used for independent RNA-seq validation. Differentially expressed genes were intersected with WGCNA hub genes to define candidate genes. Four machine-learning algorithms, including LASSO, random forest, SVM-RFE, and XGBoost, were applied to identify core diagnostic candidates. Tissue-level model performance was evaluated by ROC analysis, calibration assessment, nomogram visualization, and decision curve analysis. Orthogonal validation was performed using GTEx, HPA, and snRNA-seq data. Immune infiltration, bidirectional Mendelian randomization, and CellOracle-based GRN analysis with a co-expression-based functional importance score were used as hypothesis-generating analyses. The workflow explicitly separated diagnostic performance, localization evidence, and exploratory mechanistic context in myocardial tissue.Results Integration of 309 DEGs and 2,093 WGCNA hub genes yielded 270 candidates. Seven candidates-HMGN2, AQP3, SERPINA3, FREM1, HMOX2, CSDC2, and TUBA3E-were selected by at least three algorithms. In the discovery cohort, the RF model achieved an AUC of 0.985 and the logistic model achieved a C-statistic of 0.993; however, these estimates were interpreted as potentially optimistic upper bounds because feature selection was not nested within cross-validation. External validation showed uneven robustness: SERPINA3, HMOX2, FREM1, and HMGN2 were consistently supported across microarray and RNA-seq cohorts, whereas AQP3, CSDC2, and TUBA3E were exploratory. GTEx, HPA, and snRNA-seq supported cardiac expression and cell-type localization, including cardiomyocyte enrichment of CSDC2/HMOX2 and fibroblast enrichment of FREM1. MR and GRN analyses suggested disease-responsive rather than disease-driving biology, including possible heart failure-associated AQP3 downregulation and a putative PPARGC1A-CSDC2/HMOX2 regulatory context.Conclusion This study identifies seven prioritized, predominantly disease-responsive tissue-level molecular candidates for DCM. These findings provide candidates and testable hypotheses for future translational research, rather than disease-driving therapeutic targets or a directly applicable clinical test.