Variants in the Signaling Protein TSAd are Associated with Susceptibility to Ovarian Cancer in BRCA1/2 Negative High Risk Families

Bioinformatics analysis and validation of genes related to paclitaxel's anti-breast cancer effect through immunogenic cell death

Research indicated that Paclitaxel (PTX) can induce immunogenic cell death (ICD) through immunogenic modulation. However, the combination of PTX and ICD has not been extensively studied in breast cancer (BRCA). The TCGA-BRCA and GSE20685 datasets were enrolled in this study. Samples from the TCGA-BRCA dataset were consistently clustered based on selected immunogenic cell death-related genes (ICD-RGs). Next, candidate genes were obtained by overlapping differentially expressed genes (DEGs) between BRCA and normal groups, intersecting genes common to DEGs between cluster1 and cluster2 and hub module genes, and target genes of PTX from five databases. The univariate Cox algorithm and the least absolute shrinkage and selection operator (LASSO) were performed to obtain biomarkers and build a risk model. Following observing the immune microenvironment in differential risk subgroups, single-gene gene set enrichment analysis (GSEA) was carried out in all biomarkers. Finally, the expression of biomarkers was analyzed. Enrichment analysis showed that 626 intersecting genes were linked with inflammatory response. Further five biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified and a risk model was built. The model's performance was validated using GSE20685 dataset. Furthermore, the biomarkers were enriched with adaptive immune response. Lastly, the experimental results indicated that the alterations in IL18, SH2D2A, and CHI3L1 expression after treatment matched those in the public database. In this study, Five PTX-ICD-related biomarkers (CHI3L1, IL18, PAPLN, SH2D2A, and UBE2L6) were identified to aid in predicting BRCA treatment outcomes.

A Single Nucleotide Polymorphism in Catalase Is Strongly Associated with Ovarian Cancer Survival

Ovarian cancer is the deadliest of all gynecologic cancers. Recent evidence demonstrates an association between enzymatic activity altering single nucleotide polymorphisms (SNP) with human cancer susceptibility. We sought to evaluate the association of SNPs in key oxidant and antioxidant enzymes with increased risk and survival in epithelial ovarian cancer. Individuals (n = 143) recruited were divided into controls, (n = 94): healthy volunteers, (n = 18), high-risk BRCA1/2 negative (n = 53), high-risk BRCA1/2 positive (n = 23) and ovarian cancer cases (n = 49). DNA was subjected to TaqMan SNP genotype analysis for selected oxidant and antioxidant enzymes. Of the seven selected SNP studied, no association with ovarian cancer risk (Pearson Chi-square) was found. However, a catalase SNP was identified as a predictor of ovarian cancer survival by the Cox regression model. The presence of this SNP was associated with a higher likelihood of death (hazard ratio (HR) of 3.68 (95% confidence interval (CI): 1.149–11.836)) for ovarian cancer patients. Kaplan-Meier survival analysis demonstrated a significant median overall survival difference (108 versus 60 months, p<0.05) for those without the catalase SNP as compared to those with the SNP. Additionally, age at diagnosis greater than the median was found to be a significant predictor of death (HR of 2.78 (95% CI: 1.022–7.578)). This study indicates a strong association with the catalase SNP and survival of ovarian cancer patients, and thus may serve as a prognosticator.