Lysosome-related biomarkers in preeclampsia and cancers: Machine learning and bioinformatics analysis

Lysosomes serve as regulatory hubs, and play a pivotal role in human diseases. However, the precise functions and mechanisms of action of lysosome-related genes remain unclear in preeclampsia and cancers. This study aimed to identify lysosome-related biomarkers in preeclampsia, and further explore the biomarkers shared between preeclampsia and cancers. We obtained GSE60438 and GSE75010 datasets from the Gene Expression Omnibus database, pre-procesed them and merged them into a training cohort. The limma package in R was used to identify the differentially expressed mRNAs between the preeclampsia and normal control groups. Differentially expressed lysosome-related genes were identified by intersecting the differentially expressed mRNAs and lysosome-related genes obtained from Gene Ontology and GSEA databases. Gene Ontology annotations and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed using the DAVID database. The CIBERSORT method was used to analyze immune cell infiltration. Weighted gene co-expression analyses and three machine learning algorithm were used to identify lysosome-related diagnostic biomarkers. Lysosome-related diagnostic biomarkers were further validated in the testing cohort GSE25906. Nomogram diagnostic models for preeclampsia were constructed. In addition, pan-cancer analysis of lysosome-related diagnostic biomarkers were identified by was performed using the TIMER, Sangebox and TISIDB databases. Finally, the Drug-Gene Interaction, TheMarker and DSigDB Databases were used for drug-gene interactions analysis. A total of 11 differentially expressed lysosome-related genes were identified between the preeclampsia and control groups. Three molecular clusters connected to lysosome were identified, and enrichment analysis demonstrated their strong relevance to the development and progression of preeclampsia. Immune infiltration analysis revealed significant immunity heterogeneity among different clusters. GBA, OCRL, TLR7 and HEXB were identified as lysosome-related diagnostic biomarkers with high AUC values, and validated in the testing cohort GSE25906. Nomogram, calibration curve, and decision curve analysis confirmed the accuracy of predicting the occurrence of preeclampsia based on OCRL and HEXB. Pan-cancer analysis showed that GBA, OCRL, TLR7 and HEXB were associated with the prognosis of patients with various tumors and tumor immune cell infiltration. Twelve drugs were identified as potential drugs for the treatment of preeclampsia and cancers. This study identified GBA, OCRL, TLR7 and HEXB as potential lysosome-related diagnostic biomarkers shared between preeclampsia and cancers.