Subtype-specific analysis of gene co-expression networks and immune cell profiling reveals high grade serous ovarian cancer subtype linkage to variable immune microenvironment

High-grade serous ovarian cancer (HGSOC) is marked by significant molecular diversity, presenting a major clinical challenge due to its aggressive nature and poor prognosis. This study aims to deepen the understanding of HGSOC by characterizing mRNA subtypes and examining their immune microenvironment (TIME) and its role in disease progression. Using transcriptomic data and an advanced computational pipeline, we investigated four mRNA subtypes: immunoreactive, differentiated, proliferative, and mesenchymal, each associated with distinct gene expression profiles and clinical behaviors. We performed differential expression analysis among mRNA subtypes using DESeq2 and conducted Weighted Gene Co-Expression Network Analysis (WGCNA) to identify co-expressed gene modules related to clinical traits, e.g., age, survival, and subtype classification. Gene Ontology (GO) analysis highlighted key pathways involved in tumor progression and immune evasion. Additionally, we utilized TIMER 2.0 to assess immune cell infiltration across different HGSOC subtypes, providing insights into the interplay between tumor immune microenvironment (TIME). Our findings show that the immunoreactive subtype, particularly the M3 module-associated network, was marked by high immune cell infiltration, including M1 (p < 0.0001) and M2 macrophages (p < 0.01), and Th1 cells (p < 0.01) along with LAIR-1 expression (p = 1.63e-101). The M18 module exhibited strong B cell signatures (p = 6.24e-28), along with significant FCRL5 (adj. p = 3.09e-30) and IRF4 (adj. p = 3.09e-30) coexpression. In contrast, the M5 module was significantly associated with the mesenchymal subtype, along with fibroblasts (p < 0.0001). The proliferative subtype was characterized by M15 module-driven cellular growth and proliferation gene expression signatures, along with significant ovarian stromal cell involvement (p < 0.0001). Our study reveals the complex interplay between mRNA subtypes and suggests genes contributing to molecular subtypes, underscoring the important clinical implications of mRNA subtyping in HGSOC.