SERPINH1 functions as a multifunctional regulator to promote the malignant progression of cervical cancer

by Qian Liu, Yuanhao Peng, Wenbin Liu, Xiangjian Luo

Cervical cancer remains the second leading cause of female cancer mortality worldwide, with metastasis representing a critical therapeutic challenge. This study systematically reveals the key role of SERPINH1 (Serpin Family H Member 1) as a hub regulator of malignant progression in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Through analysis of TCGA-CESC datasets, we identified that high SERPINH1 expression is significantly correlated with poor prognosis and contributes to tumor progression by promoting cell proliferation, invasion, and metastatic phenotypes. In vitro experiments validated these findings, demonstrating that SERPINH1 overexpression markedly enhanced the proliferation, invasion, and metastasis of cervical cancer cells, whereas its knockdown substantially inhibited these processes. Furthermore, based on the SERPINH1-related differentially expressed genes, a prognostic risk model was constructed, successfully identifying PLOD1, ITGA5, and ESM1 as core collaborative genes affecting patient prognosis. Overall, our findings underscore the multiple functions of SERPINH1 as a hub for cervical cancer metastasis regulation, suggesting its potential as a promising biomarker for tailoring strategies in metastasis patients of CESC.

Association between hyperlipidemia and nephrolithiasis: A comprehensive bioinformatics analysis deciphering the potential common denominator pathogenesis

by Zhikai Su, Zhenjie Ling, Haoqiang Chen, Lei Hu, Songtao Xiang, Qian Li, Jianfu Zhou

Objective

Evidence suggests that nephrolithiasis and hyperlipidemia are linked. The study is designed to identify diagnostic biomarkers for nephrolithiasis in conjunction with hyperlipidemia using bioinformatics analysis, while exploring the potential common denominator pathogenesis.

Methods

The NCBI Gene Expression Omnibus (GEO) database provided separate datasets for nephrolithiasis and hyperlipidemia. We employed the R limma package to detect differentially expressed genes (DEGs), which were subsequently analyzed for enrichment using Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Immune cell infiltration was analyzed by the CIBERSORT method. The WGCNA-R package clustered genes with similar expression profiles, followed by an analysis of the associations between the modules and specific traits or phenotypes. The STRING database was utilized to establish a protein-protein interaction (PPI) network and key functional modules, which were then analyzed using Cytoscape software. Diagnostic genes for both diseases were screened from core hub genes using least absolute shrinkage and selection operator (Lasso) regression. Subsequently, we generated receiver operating characteristic (ROC) curves to validate the predictive ability of these diagnostic genes for diagnosing nephrolithiasis in combination with hyperlipidemia. Lastly, the Network Analyst platform facilitated the construction of transcription factor-gene (TF-gene) and TF-miRNA regulatory networks.

Results

Based on datasets of nephrolithiasis and hyperlipidemia, we identified 167 DEGs and 74 hub genes through WGCNA. Using PPI networks and machine learning techniques, we recognized three frequently diagnostic genes (HSP90AB1, HSPA5, and STUB1), which demonstrated high diagnostic validity. The functional enrichment of these three diagnostic genes primarily involved pathways related to cellular metabolism.

Conclusions

Our study identified three candidate diagnostic genes that can predict nephrolithiasis in conjunction with hyperlipidemia, providing a solid foundation for further exploration into the pathogenesis of nephrolithiasis and hyperlipidemia.