Transcriptomic analysis reveals potential biomarkers for early-onset pre-eclampsia using integrative bioinformatics and LASSO based approach

被引：1

作者：

Palanisamy, Tamil Barathi ^{[1
]}

Arumugam, Mohanapriya ^{[1
]}

机构：

[1] Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Tamil Nadu, Vellore

来源：

Computers in Biology and Medicine | 2025年 / 192卷

关键词：

Biomarkers; Functional enrichment; High-throughput sequencing; Pre-eclampsia; Receiver operating characteristic curve; Regression analysis; Regulatory networks;

D O I：

10.1016/j.compbiomed.2025.110203

中图分类号：

学科分类号：

摘要：

Pre-eclampsia (PE) is a severe vascular disorder during pregnancy, significantly affecting maternal and fetal health worldwide. However, the exact molecular mechanism of its pathophysiology remains unclear, highlighting the need for reliable early diagnostic methods. Our primary aim of this study was to identify key genes (KGs) that may affect the outcome of patients with PE via integrated bioinformatics analysis. We analysed a gene expression dataset from the national center for biotechnology information (NCBI) sequence read archive (SRA) database and performed standard preprocessing steps, including quality assessment, trimming, genome alignment, and feature counts. Following this, normalization and differentially expressed genes (DEGs) were performed using Deseq2, which identified 781 DEGs were identified comprising 457 upregulated and 324 downregulated genes. Identified DEGs were significantly enriched in the cytokine interaction pathway and cellular calcium ion homeostasis. PPI network analysis revealed eight KGs (CXCL8, GAPDH, MMP9, SPP1, PTGS2, LEP, FGF7, and FGF10). These KGs were further found to be regulated by ten transcription factors (TFs), among which NF-kB1 and RELA consistently interact with all the KGs, and four microRNAs (miRNAs) such as hsa-mir-335-5p, has-mir-16a-5p, has-let-7b-5p, and has-mir-204-5p. The least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation (CV) confirmed all eight KGs may act as potential biomarkers based on their coefficients. Among these, GAPDH, SPP1, FGF7, and FGF10 emerged as novel biomarkers. Additionally, receiver operating characteristic (ROC) curve analysis for these novel biomarkers showed an area under the curve (AUC) of 0.869, demonstrating strong discriminatory power between the healthy and EOPE groups. The drug-gene interaction was performed by DrugMap database revealed an important interaction of GAPDH and FGF7 with FDA-approved drugs, indicating their therapeutic significance in PE. This analysis also facilitates drug repurposing for PE treatment. © 2025 Elsevier Ltd

引用

共 78 条

[1]

Baumwell S., Karumanchi S.A., Pre-eclampsia: clinical manifestations and molecular mechanisms, Nephron Clin. Pract., 106, pp. c72-c81, (2007)

[2]

Mutter W.P., Karumanchi S.A., Molecular mechanisms of preeclampsia, Microvasc. Res., 75, pp. 1-8, (2008)

[3]

Rana S., Lemoine E., Granger J.P., Karumanchi S S.A., Preeclampsia: pathophysiology challenges and perspectives, Circ. Res., 124, pp. 1094-1112, (2019)

[4]

Sibai B., Dekker B.G., Kupferminc M., Pre-eclampsia, Lancet, 4, pp. 785-799, (2005)

[5]

Ives C.W., Sinkey R., Rajapreyar I., Tita A.T.N., Oparil S., Preeclampsia-pathophysiology and clinical presentations, JACC state-of-the-art review, J. Am. Coll. Cardiol., 76, pp. 1690-1702, (2020)

[6]

Burton G.J., Redman C.W., Roberts J.M., Moffett A., Pre-eclampsia: pathophysiology and clinical implications, BMJ, 366, (2019)

[7]

Staff A.C., The two-stage placental model of preeclampsia: an update, J. Reprod. Immunol., 134, pp. 1-10, (2019)

[8]

Yong H.E., Melton P.E., Johnson M.P., Freed K.A., Kalionis B., Murthi P., Et al., Genome-wide transcriptome directed pathway analysis of maternal pre-eclampsia susceptibility genes, PLoS One, 10, (2015)

[9]

Gao Y., Wu Z., Liu S., Chen Y., Zhao G., Lin H.P., Identification of KGs in the pathogenesis of preeclampsia via bioinformatic analysis and experimental verification, Front. Endocrinol., 14, (2023)

[10]

Nagalakshmi U., Wang Z., Waern K., Shou C., Raha D., Gerstein M., Snyder M M., The transcriptional landscape of the yeast genome defined by RNA sequencing, Science, 320, pp. 1344-1349, (2008)

← 1 2 3 4 5 6 7 8 →