Neutrophils of MDSCs in ovarian cancer are negatively related to Immunotherapy and prognosis: a multi-omics-based consensus clustering

Background: A plateau has been reached for conventional therapies in ovarian cancer as there is no definitive increase in overall survival. It is necessary to distinguish molecular subtypes based on multi-omics integration to take more targeted strategies to improve prognosis and increase therapeutic efficacy. Methods: : A total of 202 patients with mRNA, miRNA, DNA methylation, somatic mutation, and Reverse Phase Protein Array (RPPA) data were assembled into one multi-omics dataset and then developed a model via MOVICS. A total of 1028 OV samples with raw microarray data and clinicopathological information were obtained from the Gene Expression Omnibus (GEO) as a validation cohort. Then, a series of immune infiltration analyses were performed by GSVA, TIDE, EaSIeR, and ESTIMATE package. Lastly, Metascape was conducted to unravel activated signaling pathways, and the ChAMP package was used to identify potential biomarkers. Results: : 202 OV samples retrieved from the multi-omics TCGA cohort were categorized into two subgroups by MOVICS. Drug sensitivity analysis revealed that targeted therapy and immunotherapy are possibly considered as preferable approaches to prolong the survival of such patients as C2 while platinum-based drugs to C1. Furthermore, immune infiltration analysis revealed that there is a negative correlation between myeloid and neutrophil cells (MDSC) and prognosis and response to immunotherapy in C1. Immune-associated pathways were mainly enriched in C1 and metabolic-related pathways in C2. CRISPLD2, SLC12A8, STARD8, and DCHS1 were considered as potential targets to possibly improve the outcome by disrupting the immunosuppressive immunocytes by integration of DNA methylation and transcriptome expression. Conclusion: : The molecular subtypes based on integrated multi-omics provided novel insights into the molecular basis of immune recognition and immune regulation of cancer cells for predicting the prognosis of ovarian cancer and potential clinical therapeutic targets for OV.