ArticleScientific reports2026
Identification of macrophage-enriched genes in ovarian cancer by single-cell RNA sequencing and establishment of a prognosis model.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Ovarian cancer (OC) is the most lethal gynecologic malignancy, presenting insidious onset and lacking specific biomarkers. Tumor-associated macrophages (TAMs) modulate immunity via M1/M2 plasticity, yet their heterogeneity and pro-metastatic mechanisms remain unclear. Using scRNA-seq, we compared immune profiles of OC and normal tissues, extracted TAM modules with hdWGCNA, and intersected them with DEGs to screen candidate genes Univariate/multivariate Cox and LASSO refined a seven-gene prognosis model (EPB41L2, AAK1, PRPF38B, RB1, GPR34, CLEC12A, BRD2) and established a nomogram. We also analyzed the DEGs' functional enrichment, immune signature, immune infiltration profile, and drug sensitivity. Unique immune cell infiltration landscape and gene expression profiles in the tumor microenvironment in OC were revealed. Based on the median risk score, OC patients were assigned to high-risk (HR) and low-risk (LR) groups, and the latter had far longer survival time than the former (P < 0.0001), which was validated in GEO (P = 0.018, P = 0.0063). The HR group had significantly increased Dysfunction Score (P < 0.01), MSI Score (P < 0.01), ESTIMATEScore (P = 0.045), and StromalScore (P = 0.0031) rather than TumorPurity Score (P = 0.045). The seven-gene prognosis model possesses a certain capability to predict survival rates and correlates with drug sensitivity and immunotherapy response. Identified immunoregulatory targets provide a theoretical basis for TAM-targeted treatments.
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