Immunogenic Cell Death-relevant Molecular Patterns, PrognosticGenes, and Implications for Immunotherapy in Ovarian Cancer

Background: Ovarian cancer (OV) is one of the deadliest gynecologic cancers, and approximately 75% of serous ovarian cancer [SOC] patients are diagnosed at advanced stages due to the lack of effective biomarkers. Objective: Immunogenic cell death (ICD) has been investigated in many comprehensive studies, and the role of ICD in ovarian cancer and its impact on immunotherapy is not yet known. Method: The NMF clustering analysis was employed to categorize OV samples into different subgroups. Survival, mutation, and CNV analyses were performed in these clusters. ESTIMATE, CIBERSORT, TIDE, and drug sensitivity analyses [based on GDSC] were also performed on the subtypes. Then, differentially expressed immunogenic cell death genes (DE-ICDGs) in OV were obtained by crossing the DEGs between cluster 3 vs cluster 1, DEGs from the TCGA-GTEx dataset, and DEGs from the GSE40595 dataset. Functional enrichment analysis of DE-ICDGs was then performed. The signature genes related to the prognosis of OV in three OV datasets were excavated by drawing Kaplan-Meier curves. Finally, quantitative real-time PCR [qRT-PCR] was performed to verify the expression trends of the signature genes. Results: The NMF clustering analysis categorized OV samples into three distinct groups according to the expression levels of ICDGs, with differential analysis indicating that Cluster 3 represented the subgroup with high ICD expression. Mutation and CNV analysis did not differ significantly between clusters, but Amp and Del';s numbers did. Immuno- infiltration analysis revealed that cluster 3 showed significant differences from cluster 1 and cluster 2. Immunotherapy and drug sensitivity analysis showed differences in immunotherapy and chemotherapy sensitivity between the clusters. The DEGs in cluster3 vs. cluster1, TCGA-GTEx dataset and GSE40595 dataset were intersected to obtain a total of 71 DE-ICDGs, and functional enrichment result suggested that the DE-ICDGs were significantly correlated with inflammatory response, complement system and positive regulation of cytokine production. 2 DE-ICDGs (FN1 and LUM) were identified that were associated with OV prognosis and were validated significantly down-regulated in the SOC group with PCR. Conclusion: We identified ICD-associated subtypes of OV and mined 2 OV prognostic genes (FN1 and LUM) associated with ICD, which may have important implications for OV prognosis and therapy. conclusion: We identified ICD-associated subtypes of OV and mined 2 OV prognostic genes (FN1 and LUM) associated with ICD by analyzing transcriptome data and clinical data of OV from public databases, which may have important implications for OV prognosis and therapy.