Identification and Construction of a R-loop Mediated Diagnostic Model and Associated Immune Microenvironment of COPD through Machine Learning and Single-Cell Transcriptomics

Inflammation, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 11, 2025

Chronic obstructive pulmonary disease (COPD) is a prevalent chronic inflammatory airway with high incidence and significant burden. R-loops, functional chromatin structure formed during transcription, are closely associated inflammation due to its aberrant formation. However, the role of R-loop regulators (RLRs) in COPD remains unclear. Utilizing both bulk transcriptome data single-cell RNA sequencing data, we assessed diverse expression patterns RLRs lung tissues patients. A lower score was found patients neutrophils. 12 machine learning algorithms (150 combinations) identified 14 hub (CBX8, EHD4, HDLBP, KDM6B, NFAT5, NLRP3, NUP214, PAFAH1B3, PINX1, PLD1, POLB, RCC2, RNF213, VIM) COPD. RiskScore based on two distinct subtypes. Patient groups at risk (low scores) had higher immune increase neutrophils their microenvironment compared low-risk groups. PD-0325901 QL-X-138 represent prospective treatments for high-risk score) (high Finally, RT-PCR experiments confirmed differences 8 (EHD4, mice tissue. R-loops significantly contribute development constructing predictive models may provide crucial insight into personalized treatment strategies

Language: Английский

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A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery

BMC Infectious Diseases, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 14, 2025

Sepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used clinic do not have the characteristics of rapid specific growth decline after effective treatment. Machine learning has shown great potential early diagnosis, subtype analysis, accurate treatment prognosis evaluation sepsis. Gene expression matrices from GSE13904 GSE26440 were combined into training model quality control standardization. Then, intersection genes obtained by crossing screened differentially expressed (DEGs) module strongest correlation WGCNA analysis. 113 machine algorithms to build diagnosis model. Then CIBERSORT algorithm is analyze relationship between change core gene immune response Construct nomogram, DCA CIC further verify reliability The molecular compounds interacting key searched Traditional Chinese Medicine Active Compound Library (TCMACL). We 405 DEGs, including 334 up-regulated 71 down-regulated genes. 308 MEturquoise analysis DEGs for subsequent GO KEGG enrichment showed that sepsis was mainly related bacterial infection. are applied construct screen 22 hub Four four (CD177, GNLY, ANKRD22, IFIT1) through PPI network constructed Subsequently, diagnostic proved good predictive value CIC. Finally, (Dieckol, Grosvenorine Tellimagrandin II) out as drugs. combinated can distinguish patients. At same time, therapeutic docking.

Language: Английский

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Inflammation molecular network alterations in a depressive-like primate model

Journal of Affective Disorders, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

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Identification of biomarkers associated with programmed cell death in liver ischemia–reperfusion injury: insights from machine learning frameworks and molecular docking in multiple cohorts

Frontiers in Medicine, Journal Year: 2025, Volume and Issue: 12

Published: March 14, 2025

Liver ischemia-reperfusion injury (LIRI) is a major reason for liver that occurs during surgical procedures such as hepatectomy and transplantation cause of graft dysfunction after transplantation. Programmed cell death (PCD) has been found to correlate with the degree LIRI plays an important role in treatment LIRI. We aim comprehensively explore expression patterns mechanism action PCD-related genes find novel molecular targets early prevention first compared profiles, immune biological function profiles control samples. Then, potential mechanisms differentially expressed were explored by functional enrichment analysis. The hub further screened applying multiple machine learning methods Cytoscape. GSEA, GSVA, correlation analysis, transcription factor prediction, ceRNA network single-cell analysis revealed regulatory gene Finally, therapeutic agents based on CMap database docking technology. Forty-seven associated PCD identified LIRI, showed they involved regulation TNF signaling pathway well hydrolase activity. By utilizing methods, 11 model identified. ROC curves confusion matrix from six cohorts illustrate superior diagnostic value our model. MYC was target BMS-536924 PF-431396 This study characterizes identifies one core molecule, providing new strategy

Language: Английский

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Identifying depression with mixed features: the potential value of eye-tracking features

Frontiers in Neurology, Journal Year: 2025, Volume and Issue: 16

Published: March 19, 2025

Objectives To investigate the utility of eye-tracking features as a neurobiological marker for identifying depression with mixed (DMF), psychiatric disorder characterized by presence depressive symptoms alongside subsyndromal manic features, thereby complicating both diagnosis and therapeutic intervention. Methods A total 93 participants were included, comprising 41 patients major (MDD), whom 20 classified DMF, 52 healthy controls (HC). Eye-tracking collected using an infrared-based device, evaluated clinical scales including Montgomery-Åsberg Depression Rating Scale (MADRS), Young Mania (YMRS), Brief Psychiatric (BPRS). Performance extreme gradient boosting (XGBoost) model based on demographic characteristics was compared that created after adding ocular movement data. Results Significant differences observed in certain between MDD, HC, particularly orienting saccades overlapping saccades. Incorporating into XGBoost enhanced predictive accuracy evidenced increase area under curve (AUC) from 0.571 to 0.679 ( p < 0.05), representing 18.9% improvement. This suggests notable enhancement model’s ability distinguish DMF other groups. The velocity task completion time during free viewing identified significant factors. Conclusion especially time, hold potential non-invasive biomarkers identification DMF. integration these parameters machine learning significantly improved diagnosis, offering promising approach enhancing decision-making settings.

Language: Английский

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Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: April 1, 2025

Language: Английский

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Shared genetic architecture of gastroesophageal reflux disease and age related phenotypes

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: May 1, 2025

Increasing age is a risk factor of gastroesophageal reflux disease. This study aims to uncover the shared genetic architecture disease (GERD) and age-related phenotypes. Based on publicly available GWAS statistics, this genome-wide pleiotropic association research was performed with multiple approaches sequentially explore associations from single-nucleotide polymorphism (SNP) gene levels, reveal underlying etiology between GERD featured mechanisms phenotypes, including frailty index (FI), telomere length (TL), longevity, parental lifespan (PL). Strong were observed. A set loci genes identified by PLACO, FUMA, Bayesian colocalization additional MAGMA analysis. Our provided strong evidence correlation several especially (FI) brought novel insight into aging.

Language: Английский

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Multiclass Classification of Mental Health Disorders Using XGBoost-HOA Algorithm

SN Computer Science, Journal Year: 2024, Volume and Issue: 5(8)

Published: Dec. 12, 2024

Language: Английский

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