ABSTRACT
Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. However, there are still no reliable biomarkers for the prognosis of this disease. This study aims to construct a robust protein-based prognostic prediction model for GC patients. The protein expression data and clinical information of GC patients were downloaded from the TCPA and TCGA databases, and the expressions of 218 proteins in 352 GC patients were analyzed using bioinformatics methods. Additionally, Kaplan-Meier (KM) survival analysis and univariate and multivariate Cox regression analysis were applied to screen the prognosis-related proteins for establishing the prognostic prediction risk model. Finally, five proteins, including NDRG1_pT346, SYK, P90RSK, TIGAR, and XBP1, were related to the risk prognosis of gastric cancer and were selected for model construction. Furthermore, a significant trend toward worse survival was found in the high-risk group (p = 1.495 × 10-7). The time-dependent ROC analysis indicated that the model had better specificity and sensitivity compared to the clinical features at 1, 2, and 3 years (AUC = 0.685, 0.673, and 0.665, respectively). Notably, the independent prognostic analysis results revealed that the model was an independent prognostic factor for GC patients. In conclusion, the robust protein-based model based on five proteins was established, and its potential benefits in the prognostic prediction of GC patients were demonstrated.
ABSTRACT
IMPORTANCE: The effect of coronavirus disease 2019 (COVID-19) vaccination on fertility warrants clarification in women undergoing assisted reproductive treatment. OBJECTIVE: To study the association between female COVID-19 vaccination and outcomes of assisted reproductive treatment. DATA SOURCES: PubMed, Embase, the Web of Science, Cochrane Library, and medRxiv and bioRxiv were searched for eligible studies from December 1, 2019, to November 30, 2022, with no language restrictions. STUDY SELECTION AND SYNTHESIS: Observational studies comparing assisted reproductive outcomes between women with and without COVID-19 vaccination were included. The pooled estimates were calculated using the random-effects models as mean differences (MDs), standardized MDs, or odds ratios with 95% confidence intervals (CIs). Heterogeneity was evaluated using the I2 statistic. MAIN OUTCOMES: The number of oocytes retrieved and clinical pregnancy rate. RESULTS: Twenty-one cohort studies involving a total of 19,687 treatment cycles were included. In a comparison of the vaccinated vs. unvaccinated groups, the pooled MD for oocyte number was -0.06 (95% CI, -0.51 to 0.39; I2 = 0), and the pooled odds ratio for clinical pregnancy was 0.95 (95% CI, 0.85-1.05; I2 = 0). Similarly, there were no statistically significant adverse effects identified in other outcomes determined a priori, including 4 cycle characteristics, 6 laboratory parameters, and 3 pregnancy indicators. Most results were consistently unchanged in subgroup and sensitivity analyses, with no evidence of publication bias according to Egger's test. CONCLUSION AND RELEVANCE: Our work did not find significant differences in assisted reproductive outcomes between vaccinated and unvaccinated women. However, more data are warranted to confirm the safety of COVID-19 vaccination for assisted reproductive treatment and in female fertility in general.
