Therapeutic Implications of Targeting Pyroptosis in Cardiac-related Etiology of Heart Failure.

pdf/starting-the-journey-understanding-the-roles-of-complement-29vyb47y97.pdf

Starting the Journey: Understanding the Roles of Complement Proteins in Liver Diseases Through Mendelian Randomization.

This study explored the causal relationships among primary sclerosing cholangitis (PSC), ulcerative colitis (UC), and hepatobiliary cancer (HBC) by using bidirectional two-sample, two-step Mendelian randomization (MR) analysis. Genetic variants associated with PSC and UC from the FinnGen research database were used for instrumental variable-based analyses. Mediation analyses were conducted to examine the role of PSC and UC in HBC risk. The findings revealed a causal effect of genetic predisposition to UC on PSC risk (inverse-variance-weighted [IVW] analysis odds ratio [OR] 1.145, p < 0.001), whereas no reverse causality was observed. Although UC showed no direct causal effect on HBC risk, genetic susceptibility to PSC significantly increased the risk of HBC (IVW analysis OR = 1.855, p < 0.001). Mediation analysis further identified PSC as a significant mediator amplifying the causal effect of UC on HBC risk (effect size = 0.083). These results established a causal link between genetic susceptibility to UC and increased risk of PSC, and highlighted the critical role of PSC in mediating the impact of UC on HBC risk. 

The mediating role of primary sclerosing cholangitis in the association between ulcerative colitis and hepatobiliary cancer investigated through Mendelian randomization

Incidence of Post-therapeutic Malignancies in Benign and Malignant Gastric Diseases, Human Leukocyte Antigen-restricted Human Endogenous Retrovirus Gene-derived Peptides, and Survival

Amino acid transporters play pivotal roles in cancer biology, including in urological cancers. Among them, L-type amino acid transporter 1 (LAT1), alanine-serine-cysteine transporter 2 (ASCT2), and cystine-glutamate transporter (xCT) have garnered significant attention due to their involvement in various aspects of tumor progression and response to therapy. This review focuses on elucidating the regulation and functions of these amino acid transporters in urological cancers, including prostate, bladder, and renal cancers. Understanding the intricate regulatory mechanisms governing these amino acid transporters is essential for developing effective therapeutic strategies. Furthermore, exploring their interactions with signaling pathways and microenvironmental cues in the context of urological cancers may uncover novel therapeutic vulnerabilities. This comprehensive overview highlights the importance of amino acid transporters, particularly LAT1, ASCT2, and xCT, in urological cancers and underscores the potential of their inhibitors as therapeutic targets for improving patient outcomes.

The Regulation and Function of the Amino Acid Transporters LAT1, ASCT2, xCT in Urological Cancers

Abstract Objective This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs). Methods Sixty-one medical characteristics (including demography information, laboratory measurements, and complicating disease) readily available from EMRs were retained for clinical analysis. These characteristics further aided the development of prediction models by using seven ML algorithms [light gradient-boosting machine (LightGBM), support vector machine (SVM), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), decision tree, multilayer perceptron (MLP), and logistic regression]. The performance of this model was assessed using the area under the receiver operating characteristic curve (AUC). Shapley additive explanation (SHAP) was further applied to interpret the findings of the best-performing model. Results The LightGBM model exhibited the highest AUC (0.849; 95% CI, 0.814–0.883). Additionally, the SHAP plot per the LightGBM depicted that age, heart failure, hypertension, glucose, serum creatinine, indirect bilirubin, serum uric acid, and amylase can help identify PLHIV who were at a high or low risk of developing CHD. Conclusion This study developed a CHD risk prediction model for PLHIV utilizing ML techniques and EMR data. The LightGBM model exhibited improved comprehensive performance and thus had higher reliability in assessing the risk predictors of CHD. Hence, it can potentially facilitate the development of clinical management techniques for PLHIV care in the era of EMRs. 

Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning

Human endogenous retroviruses (HERVs) are the remnants of ancient retroviral infections integrated into the human genome. Although most HERVs are silenced or rendered inactive by various regulatory mechanisms, they retain the potential to influence the nearby genes. We analyzed the regulatory map of 91 HERV-Ks on neighboring genes in human breast cancer and investigated the impact of HERV-Ks on the tumor microenvironment (TME) and prognosis of breast cancer. Nine RNA-seq datasets were obtained from GEO and NCBI SRA. Differentially expressed genes and HERV-Ks were analyzed using DESeq2. Validation of high-risk prognostic candidate genes using TCGA data. These included Overall survival (multivariate Cox regression model), immune infiltration analysis (TIMER), tumor mutation burden (maftools), and drug sensitivity analysis (GSCA). A total of 88 candidate genes related to breast cancer prognosis were screened, of which CD48, SLAMF7, SLAMF1, IGLL1, IGHA1, and LRRC8A were key genes. Functionally, these six key genes were significantly enriched in some immune function-related pathways, which may be associated with poor prognosis for breast cancer (p = 0.00016), and the expression levels of these genes were significantly correlated with the sensitivity of breast cancer treatment-related drugs. Mechanistically, they may influence breast cancer development by modulating the infiltration of various immune cells into the TME. We further experimentally validated these genes to confirm the results obtained from bioinformatics analysis. This study represents the first report on the regulatory potential of HERV-K in the neighboring breast cancer genome. We identified three key HERV-Ks and five neighboring genes that hold promise as novel targets for future interventions and treatments for breast cancer. 

HERVK-mediated regulation of neighboring genes: implications for breast cancer prognosis

Objective Prostatitis is a common disease of the male genitourinary system, which seriously disturbs the physical and mental health of male patients. It is related to many factors such as living habits, age, and race, but the etiology has not been fully elucidated. This study investigated whether there is a causal relationship between clinical biochemical indicators (i.e., intermediate phenotype) and prostatitis through Mendelian randomization. The subjects of the study were prostatitis patients and related SNPs in the Guangxi Fangchenggang health examination cohort. Methods According to the requirements of Mendelian randomization (MR), the single nucleotide polymorphisms (SNPs) related to prostatitis patients and 29 common SNPs related to clinical biochemical indicators were analyzed by linkage disequilibrium, and the calculated SNPs were selected. Finally, the related SNPs were analyzed by Mendelian randomization method. Results 15 biochemical indicators such as complement C4, FOL, CRP, HCY, and estradiol have shared chronic prostatitis SNP sites, and five qualified SNPs were finally screened for complement C4. Finally, complement C4 was obtained by Mendelian randomization method (P = 0.039), which was statistically significant. The other 28 clinical endophenotypes were all negative. Conclusion The results show that there was a causal relationship between complement C4 and prostatitis, and the more consistent SNP is rs2075799.

/pdf/investigating-causal-relations-between-genetic-related-1r9xbqro.pdf

Investigating Causal Relations between Genetic-Related Intermediate Endophenotype and Risk of Chronic Prostatitis: Mendelian Randomization Study

Prognostic Implications and Immune Infiltration in the Nod-like receptor signaling pathway: A Comprehensive Analysis across Pan-cancer

Abstract Background Few studies are focusing on the mechanism of erastin acts on prostate cancer (PCa) cells, and essential ferroptosis-related genes (FRGs) that can be PCa therapeutic targets are rarely known. Methods In this study, in vitro assays were performed and RNA-sequencing was used to measure the expression of differentially expressed genes (DEGs) in erastin-induced PCa cells. A series of bioinformatic analyses were applied to analyze the pathways and DEGs. Results Erastin inhibited the expression of SLC7A11 and cell survivability in LNCaP and PC3 cells. After treatment with erastin, the concentrations of malondialdehyde (MDA) and Fe 2+ significantly increased, whereas the glutathione (GSH) and the oxidized glutathione (GSSG) significantly decreased in both cells. A total of 295 overlapping DEGs were identified under erastin exposure and significantly enriched in several pathways, including DNA replication and cell cycle. The percentage of LNCaP and PC3 cells in G1 phase was markedly increased in response to erastin treatment. For four hub FRGs, TMEFF2 was higher in PCa tissue and the expression levels of NRXN3 , CLU , and UNC5B were lower in PCa tissue. The expression levels of SLC7A11 and cell survivability were inhibited after the knockdown of TMEFF2 in androgen-dependent cell lines (LNCaP and VCaP) but not in androgen-independent cell lines (PC3 and C4-2). The concentration of Fe 2+ only significantly increased in TMEFF2 downregulated LNCaP and VCaP cells. Conclusion TMEFF2 might be likely to develop into a potential ferroptosis target in PCa and this study extends our understanding of the molecular mechanism involved in erastin-affected PCa cells. 

Identification of ferroptosis related genes and pathways in prostate cancer cells under erastin exposure

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