AUTHOR=Yin Tingting , Yin Yin , Qu Lin TITLE=Exploration of shared gene signature with development of pre-eclampsia and cervical cancer JOURNAL=Frontiers in Genetics VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.972346 DOI=10.3389/fgene.2022.972346 ISSN=1664-8021 ABSTRACT=

Background: The relationship between pre-eclampsia (PE) and cervical cancer (CC) has drawn more attention recently, while little is known about the shared pathogenesis of CC and PE. In the present research, we aimed to generate the shared gene network as well as the prognostic model to reveal the development of CC and PE.

Methods: The transcription data of CC and PE patients were obtained and enrolled into weighted gene co-expression network (WGCNA) analysis. Disease-specific modules in CC and PE were determined to discover the shared genes. The expression patterns of genes at protein level were examined by HPA database. Further, LASSO penalty regression and Cox analysis were applied to create a prognostic signature based on the shared genes, with survival curves and ROC plots employed to confirm the predictive capacity. To uncover the function roles and pathways involved in signature, gene set enrichment analysis (GSEA) was conducted. Finally, the immune infiltration status in CC was depicted using CIBERSORT algorithms.

Results: WGCNA determined three hub modules between CC and PE. A total of 117 shared genes were obtained for CC and PE and mainly enriched in cell proliferation, regulation of cell development and neuron differentiation. Then, we created a robust prognostic model based on the 10 shared genes by performing stepwise Cox analyses. Our proposed model presented a favorable ability in prognosis forecast and was correlated with the infiltration of immunocytes including B cells, macrophages and T cells. GSEA disclosed that high-risk group was involved in cancer-related pathways.

Conclusion: The present project identified the shared genes to uncover the pathogenesis of CC and PE and further proposed and validated a prognostic signature to accurately forecast the clinical outcomes of CC patients.