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Abstract Background Numerous meta-analyses have explored the association between the triglyceride-glucose (TyG) index and diverse health outcomes, yet the comprehensive assessment of the scope, validity, and quality of this evidence remains incomplete. Our aim was to systematically review and synthesise existing meta-analyses of TyG index and health outcomes and to assess the quality of the evidence. Methods A thorough search of PubMed, EMBASE, and Web of Science databases was conducted from their inception through to 8 April 2024. We assessed the quality of reviews using A Measurement Tool to Assess Systematic Reviews (AMSTAR) and the certainty of the evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) system. This study was registered with PROSPERO (CRD: 42024518587). Results Overall, a total of 95 associations from 29 meta-analyses were included, investigating associations between TyG index and 30 health outcomes. Of these, 83 (87.4%) associations were statistically significant (P

Background: Ambient air pollution might serve as a prognostic factor for ovarian cancer (OC) survival, yet the relationships between plant-based diet indices (PDIs) and OC survival remain unclear. We aimed to investigate the associations of comprehensive air pollution and PDIs with OC survival and explored the effects of air pollution-diet interactions. Methods: The present study encompassed 658 patients diagnosed with OC. The overall plant-based diet index (PDI), the healthful PDI (hPDI), and the unhealthful PDI (uPDI) were evaluated by a self-reported validated food frequency questionnaire. In addition, an air pollution score (APS) was formulated by summing the concentrations of particulate matter with a diameter of 2.5 microns or less, ozone, and nitrogen dioxide. Cox proportional hazard models were applied to calculate hazard ratios (HRs) and 95 % confidence intervals (CIs). The potential interactions of APS with PDIs in relation to overall survival (OS) were assessed on both multiplicative and additive scales. Results: Throughout a median follow-up of 37.60 (interquartile: 24.77–50.70) months, 123 deaths were confirmed. Comparing to the lowest tertiles, highest uPDI was associated with lower OS of OC (HR = 2.06, 95 % CI = 1.30, 3.28; P-trend

Abstract Platinum-based chemotherapy remains one of the major choices for treatment of ovarian cancer (OC). However, primary or acquired drug resistance severely impairs their efficiency, thereby causing chemotherapy failure and poor prognosis. SH3 domain containing ring finger 2 (SH3RF2) has been linked to the development of cancer. Here we find higher levels of SH3RF2 in the tumor tissues from cisplatin-resistant OC patients when compared to those from cisplatin-sensitive patients. Similarly, cisplatin-resistant OC cells also express higher levels of SH3RF2 than normal OC cells. Through in vitro and in vivo loss-of-function experiments, SH3RF2 is identified as a driver of cisplatin resistance, as evidenced by increases in cisplatin-induced cell apoptosis and DNA damage and decreases in cell proliferation induced by SH3RF2 depletion. Mechanistically, SH3RF2 can directly bind to the RNA-binding protein mRNA processing factor (RBPMS). RBPMS has been reported as an inhibitor of cisplatin resistance in OC. As a E3 ligase, SH3RF2 promotes the K48-linked ubiquitination of RBPMS to increase its proteasomal degradation and activator protein 1 (AP-1) transactivation. Impairments in RBPMS function reverse the inhibitory effect of SH3RF2 depletion on cisplatin resistance. Collectively, the SH3RF2-RBPMS-AP-1 axis is an important regulator in cisplatin resistance and inhibition of SH3RF2 may be a potential target in preventing cisplatin resistance.

Abstract Clear cell carcinoma (CCC), endometrioid carcinoma (EC), and serous carcinoma (SC) are the major histological subtypes of epithelial ovarian cancer (EOC), whose differences in carcinogenesis are still unclear. Here, we undertake comprehensive proteomic profiling of 80 CCC, 79 EC, 80 SC, and 30 control samples. Our analysis reveals the prognostic or diagnostic value of dysregulated proteins and phosphorylation sites in important pathways. Moreover, protein co-expression network not only provides comprehensive view of biological features of each histological subtype, but also indicates potential prognostic biomarkers and progression landmarks. Notably, EOC have strong inter-tumor heterogeneity, with significantly different clinical characteristics, proteomic patterns and signaling pathway disorders in CCC, EC, and SC. Finally, we infer MPP7 protein as potential therapeutic target for SC, whose biological functions are confirmed in SC cells. Our proteomic cohort provides valuable resources for understanding molecular mechanisms and developing treatment strategies of distinct histological subtypes.

Summary: Background: Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time. Methods: The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022324611. Findings: Thirty-four eligible studies were identified, of which twenty-eight studies were included in the meta-analysis with a pooled SE of 88% (95%CI: 85–90%), SP of 85% (82–88%), and AUC of 0.93 (0.91–0.95). Analysis for different algorithms revealed a pooled SE of 89% (85–92%) and SP of 88% (82–92%) for machine learning; and a pooled SE of 88% (84–91%) and SP of 84% (80–87%) for deep learning. Acceptable diagnostic performance was demonstrated in subgroup analyses stratified by imaging modalities (Ultrasound, Magnetic Resonance Imaging, or Computed Tomography), sample size (≤300 or >300), AI algorithms versus clinicians, year of publication (before or after 2020), geographical distribution (Asia or non Asia), and the different risk of bias levels (≥3 domain low risk or < 3 domain low risk). Interpretation: AI algorithms exhibited favorable performance for the diagnosis of OC through medical imaging. More rigorous reporting standards that address specific challenges of AI research could improve future studies. Funding: This work was supported by the Natural Science Foundation of China (No. 82073647 to Q-JW and No. 82103914 to T-TG), LiaoNing Revitalization Talents Program (No. XLYC1907102 to Q-JW), and 345 Talent Project of Shengjing Hospital of China Medical University (No. M0268 to Q-JW and No. M0952 to T-TG).

Epidemiological studies have suggested that dietary acid load (DAL) might be related to the risk and prognosis of cancer, whereas the evidence is contentious. Several high-quality observational studies have been published following a prior systematic review with only one study included. Consequently, we conducted an updated systematic review and meta-analysis to comprehensively investigate the relationship between DAL and cancer risk and prognosis. A systematic literature search was conducted in the PubMed, Embase, and Web of Science databases from inception to 26 October 2021. Summary relative risks (RRs) with 95% CIs were calculated using a random-effects model. Publication bias, subgroup, meta-regression, and sensitivity analyses were also conducted. Ten observational studies (six cohorts and four case–control studies) with 227,253 participants were included in this systematic review and meta-analysis. The summary RRs revealed a statistically significant associations between DAL and cancer risk (RR = 1.58, 95% CI = 1.23–2.05, I2 = 71.9%, n = 7) and prognosis (RR = 1.53, 95% CI = 1.10–2.13, I2 = 77.1%, n = 3). No evidence of publication bias was observed in the current analysis. Positive associations were observed in most subgroup analyses stratified by predefined factors, including region, study design, study quality, study population, participants’ gender, age of participants, cancer type, DAL assessment indicator, and adjustment of potential confounding parameters. No evidence of heterogeneity between subgroups was indicated by meta-regression analyses. The high DAL might be associated with an increased risk of cancer, as well as a poor prognosis of cancer. More high-quality prospective studies are warranted to further determine the associations between DAL and risk and prognosis for specific cancers.

The ovarian cancer (OC) follow-up study (OOPS) is an on-going hospital-based large prospective longitudinal cohort study aimed to explore the relationship between pre/post-diagnostic biological, clinical, environmental, and lifestyle factors with focus on the diet and OC prognosis (including drug resistance, relapse, and mortality). Patients recruited during the baseline survey were between 18 and 79 years old, with histologically confirmed OC diagnosis. Their follow-up and medical treatment were conducted at the gynecological oncology ward at Shengjing Hospital of China Medical University, Shenyang, China after 2015. A total of 703 OC patients made up the final OOPS study population. The follow-up stage was conducted in both passive and active modes. In the passive mode, the follow-up was performed by linkage to the Liaoning Providence Center for Disease Control and Prevention every 6 months to obtain health outcome results. The status of lifestyle factors was re-estimated using the same measurements as those in the baseline survey. OC participants in the OOPS study completed a questionnaire and anthropometric examinations. In addition, biological specimens were collected during the baseline survey, which included blood, urine, and stool samples that were stored for further use. This article is intended to serve as an introduction to this project and to provide details for investigators who may be carry out related analysis.

Background and AimsEpidemiological evidence on the relation between one-carbon metabolism (OCM) micronutrients intake and ovarian cancer (OC) survival are limited and conflicting. We evaluated the aforementioned associations in a prospective cohort-the Ovarian Cancer Follow-Up Study.MethodsA total of 635 newly diagnosed OC patients aged 18–79 y were enrolled in the present study. Dietary intake related to one-carbon metabolism, including methionine, vitamins B2, B3, B6, B9, B12, choline, and betaine, was assessed using a validated 111-item food frequency questionnaire. Deaths were ascertained until March 31, 2021, via medical records and active follow-up. Cox proportional hazards regression model was used to evaluate the hazard ratios (HRs) and 95% confidence intervals (CIs) for these aforementioned associations.ResultsDuring a median follow-up of 37.2 months (interquartile: 24.7–50.2 months), 114 deaths were identified. We observed an improved survival with the highest compared with the lowest tertile of dietary vitamin B6 (HR = 0.52, 95%CI: 0.32–0.84, P-trend