CCOC s a relatively rare subtype of ovarian cancer with high degree of resistance to standard chemotherapy. Little is known about the underlying molecular mechanisms, and it remains a challenge to predict its prognosis after chemotherapy. We analyzed the proteome of CCOC tissue samples from two independent cohorts using DIA-MS. A total of 8697 proteins were characterized in the first cohort (H1 cohort, 32 patients, 35 FFPE samples) and 9409 proteins in the second cohort (H2 cohort, 24 patients, 28 FF samples). After bioinformatics analysis, we narrowed our focus to 15 proteins significantly correlated with RFS in both cohorts. These proteins are mainly involved in DNA damage response, extracellular matrix, and mitochondrial metabolism. We further developed a 13-protein model to predict the prognosis of patients with CCOC in H2 cohort, and validated the model in the H1 cohort in both DIA and PRM data. Finally, we verified the modulated pathways from our CCOC proteomic dataset in several published CCOC transcriptome and proteome datasets. Taken together, this study presents a CCOC proteomic data resource and a promising 13-protein panel which could potentially predict the recurrence and survival of CCOC.

Background: Observational studies have linked various exposures to ovarian cancer (OC) risk, but the findings are potential subject to reverse causation and confounding. Herein, we performed the Mendelian randomization (MR) study to evaluate potential causal associations of 100 factors with risk of OC and six common histotypes. Methods: Two-sample MR analyses were applied to data from the genome wide association study summary results comprising a total of 25,509 women with epithelial OC and 40,941 controls of European descent in the Ovarian Cancer Association Consortium. Genetic instrumental variables associated with influencing factors were selected. Inverse-variance weighted method was used as the primary analysis, and the MR assumptions were evaluated in sensitivity analyses. MR-PRESSO method was applied for the detection and correction of potential horizontal pleiotropy. Findings: OC and six histotypes were considered in this study. Of 100 known and suspected influencing factors, 7 lifestyle factors, 12 dietary factors, 4 reproductive factors, 12 body size factors, 3 comobidities, and 7 biomarkers were significantly associated with risk of OC. Among them, 26, 9, 25, 19, 5, 13, and 22 factors were associated with the risk of OC, clear cell OC, endometrioid OC, high grade serous OC, low grade serous OC, mucinous OC, and low malignant potential OC respectively. Interpretation: Our study adds to current knowledge on the causal effect of known and suspected influencing factors on OC and six histotypes. Further investigation is needed to better understand potential pathways or mechanisms of these factors.