Promoter hypomethylation and overexpression of TSTD1 mediate poor treatment response in breast cancer

Thiosulphate sulfurtransferase: Biological roles and therapeutic potential

Mitochondria are central to eukaryotic cell function, driving energy production, intermediary metabolism, and cellular homeostasis. Dysregulation of mitochondrial function often results in oxidative stress, a hallmark of numerous diseases, underscoring the critical need for maintaining mitochondrial integrity. Among mitochondrial enzymes, thiosulfate sulfurtransferase (TST) has emerged as a key regulator of sulfur metabolism, redox balance, and Fe-S protein maintenance. Beyond its well-known role in cyanide detoxification, TST facilitates hydrogen sulfide (H2S) metabolism by catalyzing the transfer of sulfur from persulfides (R-SSH) to thiosulfate (S2O32-), promoting H2S oxidation and preventing its toxic accumulation. Additionally, TST contributes to the thiol-dependent antioxidant system by regulating reactive sulfur species and sustaining mitochondrial functionality through its role in sulfide-driven bioenergetics. This review highlights the biochemical and therapeutic significance of TST in mitochondrial and cellular health, emphasizing its protective roles in diseases associated with oxidative stress and mitochondrial dysfunction. Dysregulation of TST has been implicated in diverse pathologies, including specific metabolic disorders, neurological diseases, cardiovascular conditions, kidney dysfunction, inflammatory bowel disease, and cancer. These associations underline TST's potential as a biomarker and therapeutic target. Therapeutic strategies to activate the TST pathway are explored, with a focus on sodium thiosulfate (STS), novel small molecule (Hit 2), and recombinant hTST protein. STS, an FDA-approved compound, has demonstrated antioxidant and anti-inflammatory effects across multiple preclinical models, mitigating oxidative damage and improving mitochondrial integrity. A slow-release oral formulation of STS is under development, offering promise for expanding its clinical applications. Small molecule activators like Hit 2 and hTST protein have shown efficacy in enhancing mitochondrial respiration and reducing oxidative stress, though both reagents need further in vitro and in vivo investigations. Despite promising advancements, TST-based therapies remain underexplored. Future research should focus on leveraging TST's interplay with pathways like NRF2 signaling, investigating its broader protective roles in cellular health, and developing targeted interventions. Enhancing TST activity represents an innovative therapeutic approach for addressing mitochondrial dysfunction, oxidative stress, and their associated pathologies, offering new hope for the treatment of diseases associated with mitochondrial dysfunction.

Crosstalk of methylation and tamoxifen in breast cancer (Review)

Tamoxifen is a widely used anti‑estrogen drug in the endocrine therapy of breast cancer (BC). It blocks estrogen signaling by competitively binding to estrogen receptor α (ERα), thereby inhibiting the growth of BC cells. However, with the long‑term application of tamoxifen, a subset of patients with BC have shown resistance to tamoxifen, which leads to low overall survival and progression‑free survival. The molecular mechanism of resistance is mainly due to downregulation of ERα expression and abnormal activation of the PI3K/AKT/mTOR signaling pathway. Moreover, the downregulation of targeted gene expression mediated by DNA methylation is an important regulatory mode to control protein expression. In the present review, methylation and tamoxifen are briefly introduced, followed by a focus on the effect of methylation on tamoxifen resistance and sensitivity. Finally, the clinical application of methylation for tamoxifen is described, including its use as a prognostic indicator. Finally, it is hypothesized that when methylation is used in combination with tamoxifen, it could recover the resistance of tamoxifen.

Obesity-associated epigenetic alterations and the obesity-breast cancer axis

Both breast cancer and obesity can regulate epigenetic changes or be regulated by epigenetic changes. Due to the well-established link between obesity and an increased risk of developing breast cancer, understanding how obesity-mediated epigenetic changes affect breast cancer pathogenesis is critical. Researchers have described how obesity and breast cancer modulate the epigenome individually and synergistically. In this review, the epigenetic alterations that occur in obesity, including DNA methylation, histone, and chromatin modification, accelerated epigenetic age, carcinogenesis, metastasis, and tumor microenvironment modulation, are discussed. Delineating the relationship between obesity and epigenetic regulation is vital to furthering our understanding of breast cancer pathogenesis.

Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine

Personalized interventions are deemed vital given the intricate characteristics, advancement, inherent genetic composition, and diversity of cardiovascular diseases (CVDs). The appropriate utilization of artificial intelligence (AI) and machine learning (ML) methodologies can yield novel understandings of CVDs, enabling improved personalized treatments through predictive analysis and deep phenotyping. In this study, we proposed and employed a novel approach combining traditional statistics and a nexus of cutting-edge AI/ML techniques to identify significant biomarkers for our predictive engine by analyzing the complete transcriptome of CVD patients. After robust gene expression data pre-processing, we utilized three statistical tests (Pearson correlation, Chi-square test, and ANOVA) to assess the differences in transcriptomic expression and clinical characteristics between healthy individuals and CVD patients. Next, the recursive feature elimination classifier assigned rankings to transcriptomic features based on their relation to the case-control variable. The top ten percent of commonly observed significant biomarkers were evaluated using four unique ML classifiers (Random Forest, Support Vector Machine, Xtreme Gradient Boosting Decision Trees, and k-Nearest Neighbors). After optimizing hyperparameters, the ensembled models, which were implemented using a soft voting classifier, accurately differentiated between patients and healthy individuals. We have uncovered 18 transcriptomic biomarkers that are highly significant in the CVD population that were used to predict disease with up to 96% accuracy. Additionally, we cross-validated our results with clinical records collected from patients in our cohort. The identified biomarkers served as potential indicators for early detection of CVDs. With its successful implementation, our newly developed predictive engine provides a valuable framework for identifying patients with CVDs based on their biomarker profiles.