Interferon-stimulated gene subtypes as key indicators of immune landscape and survival outcomes in ovarian cancer

Abstract

Purpose: Ovarian cancer (OV) remains the most lethal gynecological malignancy, underscoring the critical need for robust prognostic biomarkers to enhance patient outcomes. In this study, we classified OV patients by their interferon-stimulated gene (ISG) expression profiles and investigated the associations between these subtypes, the immune microenvironment, and survival outcomes.

Methods: We employed consensus clustering in the TCGA-OV cohort (n = 376) to classify patients into ISG-related subgroups. Survival analysis, differential gene expression (DESeq), KEGG and GSEA pathway enrichment analyses, genomic variation assessments, immune cell profiling using the CIBERSORT algorithm, and TIDE analysis were conducted in the TCGA-OV cohort. In addition, immune checkpoint marker expressions were assessed using data from the TCGA-OV cohort and multiplex immunofluorescence (mIF) staining on an independent cohort (n = 80).

Results: Two distinct ISG subtypes were identified: ISG Cluster A and ISG Cluster B. Patients in ISG Cluster B exhibited significantly improved overall survival (OS) (p = 0.0442). A total of 328 dysregulated genes were identified, with Cluster B showing overexpression of immune-related genes and enhanced involvement in immune signaling pathways. ISG Cluster B also presented higher tumor mutation burden (TMB) and an enriched immune profile, including M1 macrophages and CD8 + T cells. TIDE analysis indicated a more favorable response to immune checkpoint inhibitors in this cluster, corroborated by high expressions of PD-L1 and ISG15, which were associated with prolonged OS.

Conclusions: Our findings demonstrate that ISG-related subtypes are significantly associated with the immune microenvironment and survival outcomes in OV. The biomarkers identified in this study have the potential to inform precision therapy development, thereby enhancing treatment efficacy and personalized care for OV patients.

Keywords: Immune microenvironment; Interferon stimulated genes; Ovarian cancer.

Fig. 1

Identification of Two ISG-Related Molecular Subtypes. A Consensus clustering indicates K = 2 as the optimal subgroup distinction. B Optimal clustering stability is achieved at K = 2, as demonstrated in consensus clustering. C Kaplan-Meier survival analysis comparing OS between ISG Cluster A and Cluster B

Fig. 2

Analysis of Differentially Expressed Genes and Functional Pathways in ISG Molecular Subtypes. A Volcano plot highlights DEGs between ISG Cluster A and Cluster B, applying a threshold of |log2 Fold Change| > 1 and P < 0.05 in the TCGA-OV cohort. B Bar plot illustrates KEGG pathway enrichment between ISG subtypes, with bar length representing -log10(p-adjusted value). C, D GSEA delineates distinct signaling pathways active in ISG Cluster A versus Cluster B

Fig. 3

Genomic Variations within ISG-Related Molecular Subtypes. A Analysis of somatic mutations across ISG-related subgroups in the TCGA-OV dataset. B Comparison of differential somatic mutations between ISG-related subgroups. C TMB comparison across ISG-related subgroups, illustrating genomic instability differences. The p values in (C) were calculated by unpaired two-tailed Student’s t test. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns, no significance

Fig. 4

Immune Landscape of ISG Molecular Subtypes. A CIBERSORT-derived estimates of 22 immune cell types within ISG-related subtypes, showing median values and interquartile ranges. B Comparison of stromal and immune scores between ISG-related subtypes, indicating variations in tumor microenvironment composition. C Expression differences in ITGAX, CD8A, and CD80 between ISG-related subtypes, highlighting immune marker disparities. The p values in A were calculated by Kruskal-Wallis test. The p values in B, C were calculated by unpaired two-tailed Student’s t test. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns, no significance

Fig. 5

Predictive Value of ISG-Related Molecular Subtypes for Immunotherapy Efficacy. A TIDE analysis comparing ISG-related subtypes, suggesting differential immunotherapy outcomes. B Expression levels of CD274, CTLA4, PDCD1, and LAG3 differentiate ISG-related subtypes, implicating immune checkpoint regulation. C Comparison of CD274, CTLA4, PDCD1, and LAG3 expression between ISG15^low and ISG15^high subgroups within the TCGA-OV cohort. D Immunohistochemistry validates ISG15 and PD-L1 expression patterns in ovarian cancer samples. Scale bars, 50 μm. E Multiplex immunofluorescence staining shows PD-L1 expression differences between ISG15^low and ISG15^high subtypes in OV tissues. F Multiplex immunofluorescence staining shows the correlation between PD-L1 and ISG15 expression in OV tissues. G Kaplan-Meier curves illustrate overall survival differences between ISG15^highPD-L1^high and ISG15^lowPD-L1^low subtypes in OV, assessed by multiplex immunofluorescence staining. The p values in A, B were calculated by unpaired two-tailed Student’s t test. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns, no significance