Identification and verification of diagnostic biomarkers based on mitochondria-related genes related to immune microenvironment for preeclampsia using machine learning algorithms

Front Immunol. 2024 Jan 8:14:1304165. doi: 10.3389/fimmu.2023.1304165. eCollection 2023.

Abstract

Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality worldwide. Preeclampsia is linked to mitochondrial dysfunction as a contributing factor in its progression. This study aimed to develop a novel diagnostic model based on mitochondria-related genes(MRGs) for preeclampsia using machine learning and further investigate the association of the MRGs and immune infiltration landscape in preeclampsia. In this research, we analyzed GSE75010 database and screened 552 DE-MRGs between preeclampsia samples and normal samples. Enrichment assays indicated that 552 DE-MRGs were mainly related to energy metabolism pathway and several different diseases. Then, we performed LASSO and SVM-RFE and identified three critical diagnostic genes for preeclampsia, including CPOX, DEGS1 and SH3BP5. In addition, we developed a novel diagnostic model using the above three genes and its diagnostic value was confirmed in GSE44711, GSE75010 datasets and our cohorts. Importantly, the results of RT-PCR confirmed the expressions of CPOX, DEGS1 and SH3BP5 were distinctly increased in preeclampsia samples compared with normal samples. The results of the CIBERSORT algorithm revealed a striking dissimilarity between the immune cells found in preeclampsia samples and those found in normal samples. In addition, we found that the levels of SH3BP5 were closely associated with several immune cells, highlighting its potential involved in immune microenvironment of preeclampsia. Overall, this study has provided a novel diagnostic model and diagnostic genes for preeclampsia while also revealing the association between MRGs and immune infiltration. These findings offer valuable insights for further research and treatment of preeclampsia.

Keywords: diagnostic model; immune microenvironment; machine learning; mitochondria-related genes; preeclampsia.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Biomarkers
  • DNA, Mitochondrial
  • Female
  • Humans
  • Machine Learning
  • Mitochondria / genetics
  • Pre-Eclampsia* / diagnosis
  • Pre-Eclampsia* / genetics
  • Pregnancy

Substances

  • DNA, Mitochondrial
  • Biomarkers

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was funded by the National Natural Science Foundation of China (No. 81701532), the key research and development foundation of Shannxi province (No. 2022KW-22) and foundation of Commision of Shannxi provincial health & family planning (No. 2018D055), Yangling Benzhen Charitable Foundation and Buchang Foundation(No.BC2017-08), the Clinical Research Award of the First Affiliated Hospital of Xi’an Jiaotong University (No.XJTU1AF-CRF-2020-026), Institutional Foundation of The First Affiliated Hospital of Xi’an Jiaotong University(No.2021ZXY-13).