Prognostic and predictive value of pathohistological features in gastric cancer and identification of SLITRK4 as a potential biomarker for gastric cancer

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Nov. 25, 2024

The aim of this study was to develop a quantitative feature-based model from histopathologic images assess the prognosis patients with gastric cancer. Whole slide image (WSI) H&E-stained histologic specimens cancer Cancer Genome Atlas were included and randomly assigned training test groups in 7:3 ratio. A systematic preprocessing approach employed as well non-overlapping segmentation method that combined patch-level prediction multi-instance learning integrate features across images. Subjects categorized into high- or low-risk based on median risk score derived model, significance stratification assessed using log-rank test. In addition, combining transcriptomic data other large cohort studies, we further searched for genes associated pathological their prognostic value. total 165 training, 26 integrated through learning, each process generating 11 probabilistic 2 predictive labeling features. We applied 10-fold Lasso-Cox regression achieve dimensionality reduction these accuracy verified Kaplan-Meyer (KM) curves consistency index 0.741 set 0.585 set. Deep learning-based resultant supervised pathohistological have potential superior patients, transforming pixels an effective labor-saving tool optimize clinical management patients. Also, SLITRK4 identified marker

Language: Английский

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A comprehensive analysis of genes associated with hypoxia and cuproptosis in pulmonary arterial hypertension using machine learning methods and immune infiltration analysis: AHR is a key gene in the cuproptosis process

Frontiers in Medicine, Journal Year: 2024, Volume and Issue: 11

Published: Sept. 26, 2024

Background Pulmonary arterial hypertension (PAH) is a serious condition characterized by elevated pulmonary artery pressure, leading to right heart failure and increased mortality. This study investigates the link between PAH genes associated with hypoxia cuproptosis. Methods We utilized expression profiles single-cell RNA-seq data of from GEO database genecad. Genes related cuproptosis were identified. After normalizing data, differential gene was analyzed control groups. performed clustering analyses on cuproptosis-related constructed weighted co-expression network (WGCNA) identify key linked subtype scores. KEGG, GO, DO enrichment conducted for hypoxia-related genes, protein–protein interaction (PPI) created using STRING. Immune cell composition differences examined SingleR Seurat used scRNA-seq analysis, PCA t-SNE dimensionality reduction. hub across clusters built diagnostic model LASSO random forest, optimizing parameters 10-fold cross-validation. A total 113 combinations 12 machine learning algorithms employed evaluate accuracy. GSEA pathway analysis AHR FAS , Nomogram assess risk impact. also correlation immune types Spearman correlation. Results identified several PPI networks illustrated relationships among these infiltration highlighting associations monocytes, macrophages, CD8 T cells. The FGF2 emerged as markers, forming robust (NaiveBayes) an AUC 0.9. Conclusion potential biomarkers PAH, influencing proliferation inflammatory responses, thereby offering new insights prevention treatment.

Language: Английский

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Identification of novel key genes and signaling pathways in hypertrophic cardiomyopathy: evidence from bioinformatics and next generation sequencing data analysis

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 15, 2024

Abstract Hypertrophic cardiomyopathy (HCM) is a global health problem characterized by left ventricle become thick and stiff with effect of indication including chest pain, fluttering, fainting shortness breath. In this investigation, we aimed to identify diagnostic markers analyzed the therapeutic potential essential genes. Next generation sequencing (NGS) dataset GSE180313 was obtained from Gene Expression Omnibus (GEO) database used differentially expressed genes (DEGs) in HCM. DEGs were screened using DESeq2 Rbioconductor tool. Then, Ontology (GO) REACTOME pathway enrichment analyses performed. Moreover, protein-protein interaction (PPI) network constructed, module analysis Next, miRNA-hub gene regulatory TF-hub constructed analyzed. Finally, values hub assessed receiver operating characteristic (ROC) curve analysis. By performing analysis, total 958 (479 up regulated 479 down genes) successfully identified GSE180313, respectively. GO revealed that functions signaling pathways significantly enriched response stimulus, multicellular organismal process, metabolism extracellular matrix organization. The FN1, SOX2, TUBA4A, RPS2, TUBA1C, ESR1, SNCA, LCK, PAK1 APLNR might be associated gens FN1 TPM3, together corresponding predicted miRNAs (e.g., hsa-mir-374a-5p hsa-miR-8052), SH3KBP1 ESR1 TFs (e.g PRRX2 STAT3) found correlated This investigation could serve as basis for further understanding molecular pathogenesis targets

Language: Английский

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Decoding the anti-hypertensive mechanism of α-mangostin based on network pharmacology, molecular docking and experimental validation

Molecular Medicine, Journal Year: 2024, Volume and Issue: 30(1)

Published: Nov. 26, 2024

Abstract Background Hypertension is a leading risk factor for disability and deaths worldwide. Evidence indicates that alpha-mangostin(α-MG) can reduce blood pressure improve target organ damage. Nonetheless, its pharmacological targets potential mechanisms of action remain inadequately elucidated. Method We used SwissTargetPrediction to identify α-MG’s drug DisGeNET, GeneCards, CTD, GEO databases hypertension-related targets, then determined antihypertensive therapeutic α-MG by intersecting these targets. GO functional enrichment analysis, KEGG pathway disease association analysis were conducted using the DAVID database R package “clusterprofile”, visualized with Cytoscape software. The binding affinity identified was confirmed through molecular docking Autodock Vina v.1.2.2 impact on genes validated an Angiotensin II-induced hypertensive mouse model RT-qPCR. Results A total 51 109 821 Furthermore, 10 cellular component terms, top 20 enriched biological processes, functions, pathways related effects documented. Molecular studies indicated strong HSP90AA1 domain. In Ang mice aorta, treatment effectively reversed aberrant mRNA expression TNF, HSP90AA1, NFKB1, PPARG, SIRT1, PTGS2, RELA. Conclusion Our analyses showed RELA might be hypertension, laying groundwork further investigation into clinical uses.

Language: Английский

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