Prognostic role of dysregulated circRNAs in patients with non-small cell lung cancer: a meta-analysis.

BACKGROUND: Lung cancer is the leading cause of cancer incidence and mortality. Non-small cell lung cancer (NSCLC) accounts for the vast majority of lung cancer, which lacks comprehensive prognostic biomarkers to predict the prognosis of patients. This research was performed to assess the potential prognostic role of circular RNAs (circRNAs) in patients with NSCLC. METHODS: We searched the following databases: PubMed, Web of Science, Embase, and Ovid MEDLINE(R) up to May 20, 2019 to identify studies which explored the association between circRNAs and NSCLC. Newcastle-Ottawa Scale (NOS) was applied to assess the quality of the included studies. Pooled hazard ratios (HRs) and the corresponding 95% confidence interval (CI) were calculated to assess the prognostic value of circRNAs in patients with NSCLC. Subgroup analyses were performed to explain heterogeneity among the included studies. Publication bias was estimated using Begg's funnel plot. Sensitivity analysis was performed to test the stability of pooled results. RESULTS: A total of 19 eligible studies including 1,650 NSCLC patients were included in this research. Pooled results indicated that the up-regulated expression of circRNAs was significantly associated with worse prognosis of patients with NSCLC (HR =2.08, 95% CI: 1.81-2.40). CONCLUSIONS: Our finding indicated that circRNAs could serve as prognostic biomarkers in patients with NSCLC. However, further large-scale prospective studies about the clinical significance of circRNAs are of great need in order to obtain conclusive results.

Broad- and narrow-sense validity performance of three polygenic risk score methods for prostate cancer risk assessment.

BACKGROUND: Several polygenic risk score (PRS) methods are available for measuring the cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). Their performance in predicting risk at the individual level has not been well studied. METHODS: We compared the performance of three PRS methods for prostate cancer risk assessment in a clinical trial cohort, including genetic risk score (GRS), pruning and thresholding (P + T), and linkage disequilibrium prediction (LDpred). Performance was evaluated for score deciles (broad-sense validity) and score values (narrow-sense validity). RESULTS: A training process was required to identify the best P + T model (397 SNPs) and LDpred model (3 011 362 SNPs). In contrast, GRS was directly calculated based on 110 established risk-associated SNPs. For broad-sense validity in the testing population, higher deciles were significantly associated with higher observed risk; Ptrend was 7.40 × 10-11 , 7.64 × 10-13 , and 7.51 × 10-10 for GRS, P + T, and LDpred, respectively. For narrow-sense validity, the calibration slope (1 is best) was 1.03, 0.77, and 0.87, and mean bias score (0 is best) was 0.09, 0.21, and 0.10 for GRS, P + T, and LDpred, respectively. CONCLUSIONS: The performance of GRS was better than P + T and LDpred. Fewer and well-established SNPs of GRS also make it more feasible and interpretable for genetic testing at the individual level.

Systematic evaluation of cancer-specific genetic risk score for 11 types of cancer in The Cancer Genome Atlas and Electronic Medical Records and Genomics cohorts.

BACKGROUND: Genetic risk score (GRS) is an odds ratio (OR)-weighted and population-standardized method for measuring cumulative effect of multiple risk-associated single nucleotide polymorphisms (SNPs). We hypothesize that GRS is a valid tool for risk assessment of most common cancers. METHODS: Utilizing genotype and phenotype data from The Cancer Genome Atlas (TCGA) and Electronic Medical Records and Genomics (eMERGE), we tested 11 cancer-specific GRSs (bladder, breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, prostate, renal, and thyroid cancer) for association with the respective cancer type. Cancer-specific GRSs were calculated, for the first time in these cohorts, based on previously published risk-associated SNPs using the Caucasian subjects in these two cohorts. RESULTS: Mean cancer-specific GRS in the population controls of eMERGE approximated the expected value of 1.00 (between 0.98 and 1.02) for all 11 types of cancer. Mean cancer-specific GRS was consistently higher in respective cancer patients than controls for all 11 types of cancer (P < 0.05). When subjects were categorized into low-, average-, and high-risk groups based on cancer-specific GRS (<0.5, 0.5-1.5, and >1.5, respectively), significant dose-response associations of higher cancer-specific GRS with higher OR of respective type of cancer were found for nine types of cancer (P-trend  < 0.05). More than 64% subjects in the population controls of eMERGE can be classified as high risk for at least one type of these cancers. CONCLUSION: Validity of GRS for predicting cancer risk is demonstrated for most types of cancer. If confirmed in larger studies, cancer-specific GRS may have the potential for developing personalized cancer screening strategy.