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Identification and Diagnostic Potential of Pyroptosis-Related Genes in Endometriosis: A Novel Bioinformatics Analysis

Teng, P.; Li, W.; Ding, C.; Gu, K.; Luo, X.; Su, C.; Ni, G.; Ly, Y.; Ding, J.

2024-09-24 bioinformatics
10.1101/2024.09.23.614461 bioRxiv
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ObjectiveThis study aimed to identify and analyze potential signatures of pyroptosis-related genes in EMs. MethodsTranscriptomic datasets related to endometriosis were retrieved from the GEO databases (GSE7305, GSE7307, and GSE11691). Differential gene expression analysis was performed to identify pyroptosis-related differentially expressed genes (PRDEGs) by intersecting DEGs with a curated list of PRGs. Various bioinformatics tools were employed to explore the biological functions and pathways associated with PRDEGs. ResultsWe identified 26 PRDEGs from combined datasets and constructed an EMs diagnostic model using LASSO regression based on pyroptosis scores. The model included 5 DEGs: KIF13B, BAG6, MYO5A, HEATR, and AK055981. Additionally, 21 Key Module Genes (KMGs) were identified, leading to the classification of 3 distinct EMs subtypes. These subtypes were analyzed for immune cell infiltration, revealing a complex immune landscape in EMs. ConclusionsThis study reveals pyroptosis crucial role in EMs and offers a novel diagnostic model based on pyroptosis-related genes. Modulating pyroptosis may provide a new therapeutic approach for managing EMs.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.