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

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

Yin Bi  1   2   3 , Ting Li  1   2   3 , Shun Zhang  4 , Yihua Yang  1   2   3 , Mingyou Dong  1   5
Affiliations

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

Yin Bi et al. Heliyon. .

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.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Above is the comprehensive workflow diagram illustrating the steps of our research.
Fig. 2
Fig. 2
The visualization illustrates the count of shared differentially expressed genes in two datasets, COVID-19 (GSE171110) and RSA (GSE165004). (A) A comparison of the number of differentially expressed genes was performed between the COVID-19 and RSA datasets. (B) The graph illustrating the differential expression of genes in COVID-19 datasets using a volcano plot. (C) A volcano plot was created to illustrate the differentially expressed genes in the RSA dataset. (D) A Venn diagram was constructed to demonstrate the overlap of differentially expressed genes between the COVID-19 and RSA datasets.
Fig. 3
Fig. 3
Functional enrichment analysis was performed for the common differentially expressed genes. (A) Bubble graphs were generated to display the results of the GO enrichment analysis. (B) A circle diagram was created to visualize the GO enrichment analysis. (C) Bar graphs were used to present the findings of the KEGG enrichment analysis. (D) A circle diagram was constructed to illustrate the results of the KEGG enrichment analysis.
Fig. 4
Fig. 4
PPI-network analysis reveals common differentially expressed genes between COVID-19 and RSA.
Fig. 5
Fig. 5
The figure shows the top 10 hub genes.
Fig. 6
Fig. 6
Expression validation of hub genes between control and RSA groups. (A) RPS27A, (B) RPL5, (C) RPS8, (D) RPL4, (E) RPS2, (F) RPL30, (G) RPL23A, (H) RPL31, (I) RPL26, (J) RPL37A, *p < 0.05, **p < 0.01, ***p < 0.001.
Fig. 7
Fig. 7
The top 10 TFs were ranked based on their P values and their interactions with common differentially expressed genes.
Fig. 8
Fig. 8
The top ten miRNAs were ranked according to the most significant difference in p-values.
Fig. 9
Fig. 9
Ten potentially effective drugs were screened.
See this image and copyright information in PMC

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