Bioinformatics-based analysis of the dialog between COVID-19 and RSA

Yin Bi; Ting Li; Shun Zhang; Yihua Yang; Mingyou Dong

doi:10.1016/j.heliyon.2024.e30371

Bioinformatics-based analysis of the dialog between COVID-19 and RSA

Heliyon. 2024 Apr 27;10(9):e30371. doi: 10.1016/j.heliyon.2024.e30371. eCollection 2024 May 15.

Authors

Yin Bi^{1

2

3}, Ting Li^{1

2

3}, Shun Zhang⁴, Yihua Yang^{1

2

3}, Mingyou Dong^{1

5}

Affiliations

¹ Guangxi Reproductive Medical Center, The First Affiliated Hospital of Guangxi Medical University, Nanning, 530000, China.
² Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Guangxi Medical University, Nanning, 530000, China.
³ The Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Guangxi Medical University, Ministry of Education, Nanning, 530000, China.
⁴ Department of Reproductive Medical Center, The Affiliated Hospital of Guilin Medical University, Guilin, Guangxi 541001, China.
⁵ The Key Laboratory of Molecular Pathology (For Hepatobiliary Diseases) of Guangxi, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, 533000, China.

Abstract

Pregnant women infected with SARS-CoV-2 in early pregnancy may face an increased risk of miscarriage due to immune imbalance at the maternal-fetal interface. However, the molecular mechanisms underlying the crosstalk between COVID-19 infection and recurrent spontaneous abortion (RSA) remain poorly understood. This study aimed to elucidate the transcriptomic molecular dialog between COVID-19 and RSA. Based on bioinformatics analysis, 307 common differentially expressed genes were found between COVID-19 (GSE171110) and RSA (GSE165004). Common DEGs were mainly enriched in ribosome-related and cell cycle-related signaling pathways. Using degree algorithm, the top 10 hub genes (RPS27A, RPL5, RPS8, RPL4, RPS2, RPL30, RPL23A, RPL31, RPL26, RPL37A) were selected from the common DEGs based on their scores. The results of the qPCR were in general agreement with the results of the raw letter analysis. The top 10 candidate drugs were also selected based on P-values. In this study, we provide molecular markers, signaling pathways, and small molecule compounds that may associate COVID-19. These findings may increase the accurate diagnosis and treatment of COVID-19 patients.

Keywords: COVID-19; Central hub genes; Common differentially expressed genes; Recurrent spontaneous abortion; Small molecular compounds.