Overexpression of FRAT1 protein is closely related to triple-negative breast cancer

Computational pipeline predicting cell death suppressors as targets for cancer therapy

Identification of promising targets for cancer therapy is a global effort in precision medicine. Here, we describe a computational pipeline integrating transcriptomic and vulnerability responses to cell-death inducing drugs, to predict cell-death suppressors as candidate targets for cancer therapy. The prediction is based on two modules; the transcriptomic similarity module to identify genes whose targeting results in similar transcriptomic responses of the death-inducing drugs, and the correlation module to identify candidate genes whose expression correlates to the vulnerability of cancer cells to the same death-inducers. The combined predictors of these two modules were integrated into a single metric. As a proof-of-concept, we selected ferroptosis inducers as death-inducing drugs in triple negative breast cancer. The pipeline reliably predicted candidate genes as ferroptosis suppressors, as validated by computational methods and cellular assays. The described pipeline might be used to identify repressors of various cell-death pathways as potential therapeutic targets for different cancer types.

Causal Relationship between Immune Cells and Gynecological Cancers through Bidirectional and Multivariable Mendelian Randomization Analyses

Background: Evidence suggests potential associations between gynecological malignancies and various immune cell chemicals and systems. However, establishing a causal relationship remains uncertain. Methods: This work employed Wald ratio for one single-nucleotide polymorphism (SNP) or the inverse-variance weighted method (IVW) for multiple SNPs to conduct bidirectional two-sample Mendelian randomization (MR) analysis by utilizing genome-wide association study (GWAS) data. We employed supplementary methods, including MR-Egger and weighted median methods, to detect and correct for the influence of horizontal pleiotropy. In addition, we also use colocalization analysis for further validation. Results: In IVW analysis, increases in relative count of circulating CD11c+ HLA-DR++ conventional dendritic cells (cDC) were associated with an elevated risk of breast cancer (OR [95% CI], 1.1295 [1.0632-1.2000], P = 8.044 × 10-5), while elevated levels of HLA-DR on plasmacytoid dendritic cells (DC) and HLA-DR on DC were protective against breast cancer. In addition, actual count of CD39+ resting Treg AC was also shown to be causally associated with the development of ovarian cancer, whereas a high relative count of CD28+ CD45RA- CD8+ T cells reduced the risk of cervical cancer. Sensitivity analysis revealed almost no evidence of bias in the current study. Multivariable MR (MVMR) analyses further confirmed a direct impact of the CD11c+ HLA-DR++ cDC immune phenotype on breast cancer. Colocalization analysis showed the lead SNP, rs780094, suggesting HLA-DR GWAS shared a common genetic mechanism with breast cancer. Conclusions: The MR study identified significant causal relationships between multiple immunophenotypes and breast cancer, aiming to provide clinicians with some reference for cancer prediction and explore further potential associations between immune phenotypes and gynecologic tumors.