Bioinformatic analysis reveals lysosome-related biomarkers and molecular subtypes in preeclampsia: novel insights into the pathogenesis of preeclampsia

Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data

Introduction

The combination of next-generation sequencing technology and Cancer Genome Atlas (TCGA) data provides unprecedented opportunities for the discovery of cancer subtypes. Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researchers can more accurately identify different cancer subtypes and reveal their molecular heterogeneity.

Methods

In this paper, we propose the SMMSN (Self-supervised Multi-fusion Strategy Network) model for the discovery of cancer subtypes. SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. In response to the problem of lack label information in multi-omics data, SMMSN propose to use dual self-supervise method to cluster cancer subtypes from the integrated data.

Results

We conducted experiments on three labeled and five unlabeled multi-omics datasets to distinguish potential cancer subtypes. Kaplan Meier survival curves and other results showed that SMMSN can obtain cancer subtypes with significant differences.

Discussion

In the case analysis of Glioblastoma Multiforme (GBM) and Breast Invasive Carcinoma (BIC), we conducted survival time and age distribution analysis, drug response analysis, differential expression analysis, functional enrichment analysis on the predicted cancer subtypes. The research results showed that SMMSN can discover clinically meaningful cancer subtypes.

Unveiling an oxidative stress-linked diagnostic signature and molecular subtypes in preeclampsia: novel insights into pathogenesis

Preeclampsia (PE) is a complex pregnancy disorder characterized by hypertension and organ dysfunction, affecting both maternal and fetal health. Oxidative stress has been implicated in the pathogenesis of PE, but the underlying molecular mechanisms remain poorly understood. In this study, we aimed to identify a diagnostic signature and molecular subtypes associated with oxidative stress in PE to gain novel insights into its pathogenesis. The ssGSEA algorithm evaluated oxidative stress-related pathway scores using transcriptional data from the GSE75010 dataset. Oxidative stress-related genes (ORGs) were co lected from these pathways, and hub ORGs associated with PE were identified using the LASSO and logistic regression models. A nomogram prediction model was constructed using the identified ORGs. Consensus clustering identified two molecular subgroups related to oxidative stress, labeled as C1 and C2, with unique immune characteristics and inflammatory pathway profiles. Seventy ORGs associated with oxidative stress, ce l death, and inflammation-related pathways were identified in PE. EGFR, RIPK3, and ALAD were confirmed as core ORGs for PE biomarkers. The C1 and C2 subgroups exhibited distinct immune characteristics and inflammatory pathway profiles. This study provides novel insights into the role of oxidative stress in PE pathogenesis. A diagnostic signature and molecular subtypes associated with oxidative stress were identified, which may improve understanding, diagnosis, and management of PE.

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

BACKGROUND

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

This study identified GBA, OCRL, TLR7 and HEXB as potential lysosome-related diagnostic biomarkers shared between preeclampsia and cancers.