Multiomics identification of programmed cell death-related characteristics for nonobstructive azoospermia based on a 675-combination machine learning computational framework.

Background: Abnormal programmed cell death (PCD) plays a central role in spermatogenic dysfunction. However, the molecular mechanisms and biomarkers of PCD in patients with nonobstructive azoospermia (NOA) remain unclear.

Methods: The genetic conditions of NOA patients were analysed using bulk transcriptomic, single-cell transcriptomic, single nucleotide polymorphism (SNP), and clinical data from multiple centres.

Cross-talk between oxidative stress and lipid metabolism regulators reveals molecular clusters and immunological characterization in polycystic ovarian syndrome.

Background: Changes in the oxidative stress and lipid metabolism (OSLM) pathways play important roles in polycystic ovarian syndrome (PCOS) pathogenesis and development. Consequently, a systematic analysis of genes related to OSLM was conducted to identify molecular clusters and explore new biomarkers that are helpful for the diagnostic of PCOS.

Methods: Gene expression and clinical data from 22 PCOS women and 14 normal women were obtained from the GEO database (GSE34526, GSE95728, and GSE106724).

Identification of RNMT as an immunotherapeutic and prognostic biomarker: From pan-cancer analysis to lung squamous cell carcinoma validation.

Background: Dysregulation of RNA guanine-7 methyltransferase (RNMT) plays a crucial role in the tumor progression and immune responses. However, the detailed role of RNMT in pan-cancer is still unknown.

Methods: Bulk transcriptomic data of pan-cancer were obtained from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Cancer Cell Line Encyclopedia (CCLE) databases.