Epilepsy and long-term risk of arrhythmias.

BACKGROUND AND AIMS: Previous evidence has mainly supported transient changes in cardiac function during interictal or peri-ictal phases in people with epilepsy, but the long-term risk of cardiac arrhythmias is poorly described. This study aimed to assess the long-term association of epilepsy with cardiac arrhythmias, considering the potential role of genetic predisposition and antiseizure medications (ASMs) in any associations observed. METHODS: This population-based study evaluated UK Biobank data for individuals recruited between 2006 and 2010. Cox proportional hazards models and competing risk models were used to examine the association of epilepsy history with the long-term incidence risk of cardiac arrhythmias and arrhythmias subtypes. Polygenic risk scores (PRS) were calculated to investigate the effect of genetic susceptibility. The role of ASMs was also evaluated by integrating observational and drug target Mendelian randomization (MR) evidence. RESULTS: The study included 329 432 individuals, including 2699 people with epilepsy. Compared with those without epilepsy, people with epilepsy experienced an increased risk of all cardiac arrhythmias [hazard ratio (HR) 1.36, 95% confidence interval (CI) 1.21-1.53], atrial fibrillation (HR 1.26, 95% CI 1.08-1.46), and other cardiac arrhythmias (HR 1.56, 95% CI 1.34-1.81). The associations were not modified by genetic predisposition as indicated by PRS. Competing and sensitivity analyses corroborated these results. Individuals with epilepsy using ASMs, especially carbamazepine and valproic acid, were at a higher risk for cardiac arrhythmias. This observation was further supported by drug target MR results (PSMR < .05 and PHEIDI > .05). CONCLUSION: This study revealed the higher risk of cardiac arrhythmias persists long term in people with epilepsy, especially among those using carbamazepine and valproic acid. These findings highlight the need for regular heart rhythm monitoring and management in people with epilepsy in order to reduce the risk of further cardiovascular complications.

Identification of tumour-infiltrating myeloid subsets associated with overall survival in lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) is a primary subtype of lung cancer with limited therapeutic options and poor prognosis, and tumour-infiltrating myeloid cells (TIMs) are key regulators of LUSC. However, the correlation between the abundance of TIM subtypes and clinical outcomes of LUSC remains unexplored. This study aimed to develop and validate a prognostic model for low- and high-risk patients with LUSC based on myeloid cell microenvironments. TIM markers in the tumoural (T) and stromal (S) regions were quantified using immunohistochemistry for 502 LUSC patients. L1-penalized Cox regression was used to develop a myeloid survival score (MSS) model based on the training cohort, followed by validation in distinct cohorts from multiple centres. RNA sequencing and immunostaining were used to examine the mechanisms of myeloid cells in LUSC progression and predict potential drug targets and therapeutic agents. Of the 12 myeloid markers, CD163T, CD163S, and S100A12T were highly associated with overall survival (OS) in LUSC patients. The MSS of the three myeloid signatures accurately categorized LUSC patients into risk categories, with an observable difference in OS between the training and validation cohorts. Tumours with high MSS were associated with enhanced antioxidative ability and hedgehog signalling and a shift to a more pro-tumorigenic microenvironment, accompanied by a reduced tumour cell immunogenicity and increased CD8+ T cell exhaustion patterns. Additionally, in high-risk patients, potential drug targets and compounds regulating hedgehog signalling were identified. Our study provides the first prognostic myeloid signature for LUSC, which may help advance precision medicine. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

A Metabolic Gene Signature to Predict Breast Cancer Prognosis.

Background: The existing metabolic gene signatures for predicting breast cancer outcomes only focus on gene expression data without considering clinical characteristics. Therefore, this study aimed to establish a predictive risk model combining metabolic enzyme genes and clinicopathological characteristics to predict the overall survival in patients with breast cancer. Methods: Transcriptomics and corresponding clinical data for patients with breast cancer were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differentially expressed metabolic genes between tumors and normal tissues were identified in the TCGA dataset (training dataset). A prognostic model was then built using univariate and multifactorial Cox proportional hazards regression analyses in the training dataset. The capability of the predictive model was then assessed using the receiver operating characteristic in both datasets. Pathway enrichment analysis and immune cell infiltration were performed using Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Ontology (GO) enrichment and CIBERSORT algorithm, respectively. Results: In breast cancer and normal tissues, 212 metabolic enzyme genes were differentially expressed. The predictive model included four factors: age, stage, and expression of SLC35A2 and PLA2G10. Patients with breast cancer were classified into high- and low-risk groups based on the model; the high-risk group had a significantly poorer overall survival rate than the low-risk group. Furthermore, the two risk groups showed different activation of pathways and alterations in the properties of tumor microenvironment-infiltrating immune cells. Conclusion: We developed a powerful model to predict prognosis in patients with breast cancer by combining the gene expression of metabolic enzymes with clinicopathological characteristics.