Identification and Validation of Key Genes Associated With Systemic Sclerosis-Related Pulmonary Hypertension

Abstract

Systemic sclerosis-associated with pulmonary arterial hypertension (SSc-PAH) is still a major cause of SSc related deaths. Early diagnosis and prompt treatment are crucial to reduce the mortality of patients with SSc-PAH. To screen the candidate biomarkers and potential therapeutic targets for SSc-PAH, we analyzed the data set (GSE33463 and GSE19617) for confirming key genes in peripheral blood mononuclear cells from SSc-PAH patients. A total of 105 SSc patients from gene expression omnibus (GEO) were included as discovery cohort (n = 69) and duplication cohort (n = 36) for screening hub genes by weighted gene co-expression network analysis (WGCNA). Furthermore, an independent validation cohort (n = 40), including healthy controls, SSc and SSc-PAH patients, was used for further validation by quantitative real-time polymerase chain reaction. The results showed that four key genes, including IFIT2, IFIT3, RSAD2, and PARP14, may serve as potential biomarkers in SSc-PAH. Also, they could be independent risk factors for SSc-PAH. In conclusion, the four key genes can be expected to become the potential therapeutic targets and early biomarkers for accurate therapy and diagnosis of SSc-PAH in the future, which also provides promising insights into the pathogenesis of SSc-PAH at the molecular level.

ECM2 and GLT8D2 in human pulmonary artery hypertension: fruits from weighted gene co-expression network analysis

“…Through the construction of weighted coexpression networks and the measuring of correlation patterns among each gene, WGCNA is being increasingly used to explore the functionality of genes at a systemic level (43). In the field of PAH, the WGCNA algorithm has been used to explore the hub genes in systemic sclerosis associated with PAH or IPAH based on samples of peripheral blood mononuclear cells (PBMCs) (44)(45)(46). A PBMC is any blood cell with a round nucleus, such as a lymphocyte, monocyte, or macrophage, that produces a selective response of the immune system and PBMCs are the principal cells in immunity (47).…”

Integrative Analysis of Genomics and Transcriptome Data to Identify Regulation Networks in Female Osteoporosis

“…In contrast to previous studies based on protein-protein interaction (PPI) networks, we employed WGCNA to aggregate genes with common expression characteristics into modules. This systemic biology method helped free-scale gene co-expression networks to identify associations without previous PPI knowledge (Zheng et al, 2020). The WGCNA co-expression networks revealed three gene modules consisting of 40 lncRNAs and 16 mRNAs, which were significantly related to the level of BMD.…”

<p>Identifying a Potential Key Gene, TIMP1, Associated with Liver Metastases of Uveal Melanoma by Weight Gene Co-Expression Network Analysis</p>

“…Cells (2×10 3 /well) were seeded into 96-well plates with 0.2 mL cell suspension. Cells of each well were cultivated with Cell proliferation was assessed at four time points (24,48,72, and 96 hours) by analyzing the absorbance at 450 nm by a microplate reader (Bio-Rad, USA).…”

“…Recently, WGCNA has been widely applied to identify the important genes involved in disease processes, such as pancreatic cancer, 21 clear cell renal cell carcinoma, 22 endometrial carcinoma, 23 and systemic sclerosis-related pulmonary hypertension. 24 In this study, the WGCNA algorithm was used to analyze the microarray data downloaded from public datasets about UM metastasis to explore the metastasis-related genes. The genes may help to predict the malignant potential and prognosis of UM.…”