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Updated: Jul 25, 2025
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
Published on: March 1, 2024
Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune
Lilian Bai1, Yanyan Guo1, Junxing Gong2
1Shanghai Key Laboratory of Embryo Original Diseases, The International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Insights
Preeclampsia biomarkers LEP, SASH1, RAB6C, and FLT1 were identified, offering potential diagnostic and therapeutic targets. These genes are linked to placental immune cell infiltration, advancing understanding of preeclampsia pathology.
Area of Science:
- Reproductive biology
- Immunology
- Genetics
Background:
- Preeclampsia poses significant risks to both mother and child.
- Understanding the placental immune microenvironment is crucial for preeclampsia treatment.
- Identifying characteristic genes can elucidate preeclampsia's pathological mechanisms.
Purpose of the Study:
- To screen for differential genes in preeclampsia.
- To identify potential biomarkers for preeclampsia diagnosis and treatment.
- To analyze immune cell infiltration in the placental microenvironment.
Main Methods:
- Differential gene screening using the limma package.
- Enrichment analyses (GO, KEGG, DO, GSEA).
- Biomarker identification via LASSO, SVM-RFE, and Random Forest algorithms.
- Immune cell infiltration analysis using CIBERSORT.
- Gene expression validation using RT-qPCR.
Main Results:
- Identified 73 differential genes related to reproductive development and hormone transport.
- KEGG analysis highlighted cytokine-cytokine receptor interactions and IL-17 signaling pathways.
- LEP, SASH1, RAB6C, and FLT1 identified as potential placental biomarkers associated with immune cells.
Conclusions:
- Differentially expressed genes in preeclampsia are linked to inflammatory responses.
- LEP, SASH1, RAB6C, and FLT1 show promise as diagnostic and therapeutic targets for preeclampsia.
- These biomarkers are associated with immune cell infiltration, contributing to understanding preeclampsia's pathophysiology.
Abstract:
Introduction: Preeclampsia is a disease that affects both the mother and child, with serious consequences. Screening the characteristic genes of preeclampsia and studying the placental immune microenvironment are expected to explore specific methods for the treatment of preeclampsia and gain an in-depth understanding of the pathological mechanism of preeclampsia. Methods: We screened for differential genes in preeclampsia by using limma package. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, disease ontology enrichment, and gene set enrichment analyses were performed. Analysis and identification of preeclampsia biomarkers were performed by using the least absolute shrinkage and selection operator regression model, support vector machine recursive feature elimination, and random forest algorithm. The CIBERSORT algorithm was used to analyze immune cell infiltration. The characteristic genes were verified by RT-qPCR. Results: We identified 73 differential genes, which mainly involved in reproductive structure and system development, hormone transport, etc. KEGG analysis revealed emphasis on cytokine-cytokine receptor interactions and interleukin-17 signaling pathways. Differentially expressed genes were dominantly concentrated in endocrine system diseases and reproductive system diseases. Our findings suggest that LEP, SASH1, RAB6C, and FLT1 can be used as placental markers for preeclampsia and they are associated with various immune cells. Conclusion: The differentially expressed genes in preeclampsia are related to inflammatory response and other pathways. Characteristic genes, LEP, SASH1, RAB6C, and FLT1 can be used as diagnostic and therapeutic targets for preeclampsia, and they are associated with immune cell infiltration. Our findings contribute to the pathophysiological mechanism exploration of preeclampsia. In the future, the sample size needs to be expanded for data analysis and validation, and the immune cells need to be further validated.
