Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy

Lin Zhang, Yexiang Lin, Kaiyue Wang, Lifeng Han, Xue Zhang, Xiumei Gao, Zheng Li, Houliang Zhang, Jiashun Zhou, Heshui Yu*, Xuebin Fu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Introduction: Machine learning (ML) has gained intensive popularity in various fields, such as disease diagnosis in healthcare. However, it has limitation for single algorithm to explore the diagnosing value of dilated cardiomyopathy (DCM). We aim to develop a novel overall normalized sum weight of multiple-model MLs to assess the diagnosing value in DCM. Methods: Gene expression data were selected from previously published databases (six sets of eligible microarrays, 386 samples) with eligible criteria. Two sets of microarrays were used as training; the others were studied in the testing sets (ratio 5:1). Totally, we identified 20 differently expressed genes (DEGs) between DCM and control individuals (7 upregulated and 13 down-regulated). Results: We developed six classification ML methods to identify potential candidate genes based on their overall weights. Three genes, serine proteinase inhibitor A3 (SERPINA3), frizzled-related proteins (FRPs) 3 (FRZB), and ficolin 3 (FCN3) were finally identified as the receiver operating characteristic (ROC). Interestingly, we found all three genes correlated considerably with plasma cells. Importantly, not only in training sets but also testing sets, the areas under the curve (AUCs) for SERPINA3, FRZB, and FCN3 were greater than 0.88. The ROC of SERPINA3 was significantly high (0.940 in training and 0.918 in testing sets), indicating it is a potentially functional gene in DCM. Especially, the plasma levels in DCM patients of SERPINA3, FCN, and FRZB were significant compared with healthy control. Discussion: SERPINA3, FRZB, and FCN3 might be potential diagnosis targets for DCM, Further verification work could be implemented.

Original languageEnglish (US)
Article number1044443
JournalFrontiers in Cardiovascular Medicine
Volume9
DOIs
StatePublished - Jan 11 2023

Keywords

  • FCN3
  • FRZB
  • SERPINA3
  • diagnosis value
  • dilated cardiomyopathy
  • machine learning

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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