Explainable Graph Neural Network for Alzheimer's Disease And Related Dementias Risk Prediction

Alzheimer's disease and related dementias (ADRD) ranks as the sixth leading cause of death in the US, underlining the importance of accurate ADRD risk prediction. While recent advancement in ADRD risk prediction have primarily relied on imaging analysis, yet not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. Our goal is to utilize Graph Neural Networks (GNNs) with claims data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative method to evaluate relationship importance and its influence on ADRD risk prediction, ensuring comprehensive interpretation. We employed Variationally Regularized Encoder-decoder Graph Neural Network (VGNN) for estimating ADRD likelihood. We created three scenarios to assess the model's efficiency, using Random Forest and Light Gradient Boost Machine as baselines. We further used our relation importance method to clarify the key relationships for ADRD risk prediction. VGNN surpassed other baseline models by 10% in the area under the receiver operating characteristic. The integration of the GNN model and relation importance interpretation could potentially play an essential role in providing valuable insight into factors that may contribute to or delay ADRD progression. Employing a GNN approach with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data

OSlms: A Web Server to Evaluate the Prognostic Value of Genes in Leiomyosarcoma

The availability of transcriptome data and clinical annotation offers the opportunity to identify prognosis biomarkers in cancer. However, efficient online prognosis analysis tools are still lacking. Herein, we developed a user-friendly web server, namely Online consensus Survival analysis of leiomyosarcoma (OSlms), to centralize published gene expression data and clinical datasets of leiomyosarcoma (LMS) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). OSlms comprises of a total of 268 samples from three independent datasets, and employs the Kaplan Meier survival plot with hazard ratio (HR) and log rank test to estimate the prognostic potency of genes of interests for LMS patients. Using OSlms, clinicians and basic researchers could determine the prognostic significance of genes of interests and get opportunities to identify novel potential important molecules for LMS. OSlms is free and publicly accessible at http://bioinfo.henu.edu.cn/LMS/LMSList.jsp

Letter: hepatocellular carcinoma risk in patients with non‐selective beta blockers—authors’ reply

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/170219/1/apt16598_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/170219/2/apt16598.pd

High KRT8 Expression Independently Predicts Poor Prognosis for Lung Adenocarcinoma Patients

Keratin 8 (KRT8), a type II basic intermediate filament (IF) protein, is essential for the development and metastasis of various cancers. In this study, by analyzing RNA-seq data from the Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we have determined the expression profile of KRT8, and assessed its prognostic significance and the possible mechanism underlying the dysregulation. Our results showed that KRT8 mRNA expression was significantly up-regulated in both LUAD and LUSC tissues compared with normal lung tissues. The high KRT8 expression group for LUAD patients significantly reduced overall survival (OS) and recurrence-free survival (RFS). Univariate and multivariate analysis revealed that KRT8 expression was an independent prognostic indicator for poor OS and RFS in LUAD patients. However, KRT8 expression had no prognostic value in terms of OS and RFS for LUSC. By exploring DNA copy number alterations (CNAs) of the KRT8 gene in LUAD, we found that DNA low copy gain (+1 and +2) was associated with elevated KRT8 mRNA expression. From the above findings, we have deduced that KRT8 is aberrantly expressed in LUAD tissues and that its expression might independently predict poor OS and RFS for LUAD patients, but not for LUSC patients

Gene Expression Profiling Reveals Distinct Molecular Subtypes of Esophageal Squamous Cell Carcinoma in Asian Populations

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide, particularly in Asian populations, and responds poorly to conventional therapy. Subclassification of ESCCs by molecular analysis is a powerful strategy in extending conventional clinicopathologic classification, improving prognosis and therapy. Here we identified two ESCC molecular subtypes in Chinese population using gene expression profiling data and further validated the molecular subtypes in two other independent Asian populations (Japanese and Vietnamese). Subtype I ESCCs were enriched in pathways including immune response, while genes overexpressed in subtype II ESCCs were mainly involved in ectoderm development, glycolysis process, and cell proliferation. Specifically, we identified potential ESCC subtype-specific diagnostic markers (FOXA1 and EYA2 for subtype I, LAMC2 and KRT14 for subtype II) and further validated them in a fourth Asian cohort. In addition, we propose a few subtype-specific therapeutic targets for ESCC, which may guide future ESCC clinical treatment when further validated

OSmfs: An Online Interactive Tool to Evaluate Prognostic Markers for Myxofibrosarcoma

Myxofibrosarcoma is a complex genetic disease with poor prognosis. However, more effective biomarkers that forebode poor prognosis in Myxofibrosarcoma remain to be determined. Herein, utilizing gene expression profiling data and clinical follow-up data of Myxofibrosarcoma cases in three independent cohorts with a total of 128 Myxofibrosarcoma samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we constructed an easy-to-use web tool, named Online consensus Survival analysis for Myxofibrosarcoma (OSmfs) to analyze the prognostic value of certain genes. Through retrieving the database, users generate a Kaplan–Meier plot with log-rank test and hazard ratio (HR) to assess prognostic-related genes or discover novel Myxofibrosarcoma prognostic biomarkers. The effectiveness and availability of OSmfs were validated using genes in ever reports predicting the prognosis of Myxofibrosarcoma patients. Furthermore, utilizing the cox analysis data and transcriptome data establishing OSmfs, seven genes were selected and considered as more potentially prognostic biomarkers through overlapping and ROC analysis. In conclusion, OSmfs is a promising web tool to evaluate the prognostic potency and reliability of genes in Myxofibrosarcoma, which may significantly contribute to the enrichment of novelly potential prognostic biomarkers and therapeutic targets for Myxofibrosarcoma

Dynamic Prognosis Prediction for Patients on DAPT After Drug‐Eluting Stent Implantation: Model Development and Validation

Background The rapid evolution of artificial intelligence (AI) in conjunction with recent updates in dual antiplatelet therapy (DAPT) management guidelines emphasizes the necessity for innovative models to predict ischemic or bleeding events after drug‐eluting stent implantation. Leveraging AI for dynamic prediction has the potential to revolutionize risk stratification and provide personalized decision support for DAPT management. Methods and Results We developed and validated a new AI‐based pipeline using retrospective data of drug‐eluting stent‐treated patients, sourced from the Cerner Health Facts data set (n=98 236) and Optum's de‐identified Clinformatics Data Mart Database (n=9978). The 36 months following drug‐eluting stent implantation were designated as our primary forecasting interval, further segmented into 6 sequential prediction windows. We evaluated 5 distinct AI algorithms for their precision in predicting ischemic and bleeding risks. Model discriminative accuracy was assessed using the area under the receiver operating characteristic curve, among other metrics. The weighted light gradient boosting machine stood out as the preeminent model, thus earning its place as our AI‐DAPT model. The AI‐DAPT demonstrated peak accuracy in the 30 to 36 months window, charting an area under the receiver operating characteristic curve of 90% [95% CI, 88%–92%] for ischemia and 84% [95% CI, 82%–87%] for bleeding predictions. Conclusions Our AI‐DAPT excels in formulating iterative, refined dynamic predictions by assimilating ongoing updates from patients' clinical profiles, holding value as a novel smart clinical tool to facilitate optimal DAPT duration management with high accuracy and adaptability

Pharmacological boosting of cGAS activation sensitizes chemotherapy by enhancing antitumor immunity

Summary: Enhancing chemosensitivity is one of the largest unmet medical needs in cancer therapy. Cyclic GMP-AMP synthase (cGAS) connects genome instability caused by platinum-based chemotherapeutics to type I interferon (IFN) response. Here, by using a high-throughput small-molecule microarray-based screening of cGAS interacting compounds, we identify brivanib, known as a dual inhibitor of vascular endothelial growth factor receptor and fibroblast growth factor receptor, as a cGAS modulator. Brivanib markedly enhances cGAS-mediated type I IFN response in tumor cells treated with platinum. Mechanistically, brivanib directly targets cGAS and enhances its DNA binding affinity. Importantly, brivanib synergizes with cisplatin in tumor control by boosting CD8+ T cell response in a tumor-intrinsic cGAS-dependent manner, which is further validated by a patient-derived tumor-like cell clusters model. Taken together, our findings identify cGAS as an unprecedented target of brivanib and provide a rationale for the combination of brivanib with platinum-based chemotherapeutics in cancer treatment

Activation of Peroxisome Proliferator-Activated Receptor γ Contributes to the Survival of T Lymphoma Cells by Affecting Cellular Metabolism

Peroxisome proliferator-activated receptor γ (PPARγ) is a metabolic regulator involved in maintaining glucose and fatty acid homeostasis. Besides its metabolic functions, the receptor has also been implicated in tumorigenesis. Ligands of PPARγ induce apoptosis in several types of tumor cells, leading to the proposal that these ligands may be used as antineoplastic agents. However, apoptosis induction requires high doses of ligands, suggesting the effect may not be receptor-dependent. In this report, we show that PPARγ is expressed in human primary T-cell lymphoma tissues and activation of PPARγ with low doses of ligands protects lymphoma cells from serum starvation-induced apoptosis. The prosurvival effect of PPARγ was linked to its actions on cellular metabolic activities. In serum-deprived cells, PPARγ attenuated the decline in ATP, reduced mitochondrial hyperpolarization, and limited the amount of reactive oxygen species (ROS) in favor of cell survival. Moreover, PPARγ regulated ROS through coordinated transcriptional control of a set of proteins and enzymes involved in ROS metabolism. Our study identified cell survival promotion as a novel activity of PPARγ. These findings highlight the need for further investigation into the role of PPARγ in cancer before widespread use of its agonists as anticancer therapeutics