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Exploring the genomic and transcriptomic profiles of glycemic traits and drug repurposing

Journal of Biomedical Science 2025年第1期32卷 1-14页

作者： Lin, Min-Rou Tsai, Cheng-Lin Liao, Cai-Sian Wei, Chun-Yu Chou, Wan-Hsuan Hsiao, Tzu-Hung Chang, Wei-Chiao Department of Clinical Pharmacy School of Pharmacy Taipei Medical University Taipei Taiwan Bioinformatics Program Institute of Statistical Science Taiwan International Graduate Program Academia Sinica Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program National Taiwan University Taipei Taiwan Core Laboratory of Neoantigen Analysis for Personalized Cancer Vaccine Office of R&D Taipei Medical University Taipei Taiwan Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Public Health Fu Jen Catholic University New Taipei City Taiwan Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan Master Program in Clinical Genomics and Proteomics School of Pharmacy Taipei Medical University Taipei Taiwan Integrative Research Center in Critical Care Wan Fang Hospital Taipei Medical University Taipei Taiwan

Type 2 diabetes is an increasingly prevalent metabolic disorder with moderate to high heritability. Glycemic indices are crucial for diagnosing and monitoring the disease. Previous genome-wide association study (GWAS) have identified several risk loci associated with type 2 diabetes, but data from the Taiwanese population remain relatively sparse and primarily focus on type 2 diabetes status rather than glycemic trait levels. We conducted a comprehensive genome-wide meta-analysis to explore the genetics of glycemic traits. The study incorporated a community-based cohort of 145,468 individuals and a hospital-based cohort of 35,395 individuals. The study integrated genetics, transcriptomics, biological pathway analyses, polygenic risk score calculation, and drug repurposing for type 2 diabetes. This study assessed hemoglobin A1c and fasting glucose levels, validating known loci (FN3K, SPC25, MTNR1B, and FOXA2) and discovering new genes, including MAEA and PRC1. Additionally, we found that diabetes, blood lipids, and liver- and kidney-related traits share genetic foundations with glycemic traits. A higher PRS was associated with an increased risk of type 2 diabetes. Finally, eight repurposed drugs were identified with evidence to regulate blood glucose levels, offering new avenues for the management and treatment of type 2 diabetes. This research illuminates the unique genetic landscape of glucose regulation in Taiwanese Han population, providing valuable insights to guide future treatment strategies for type 2 diabetes.

关键词： Drug repurposing Fasting glucose Genome-wide association study Glycemic traits Hemoglobin A1c Polygenic risk score Transcriptome-wide association study Type 2 diabetes

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Impact of substance use disorders on critical care management and health outcomes in septic adolescents

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Annals of Intensive Care 2025年第1期15卷 1-9页

作者： Markus, Havell Ceneviva, Gary D. Thomas, Neal J. Krawiec, Conrad Pennsylvania State University College of Medicine P.O. Box 850 500 University Drive Hershey 17033-0850 PA United States Program in Bioinformatics and Genomics Pennsylvania State University Huck Life Sciences Building University Park 16802 PA United States Medical Scientist Training Program Pennsylvania State University College of Medicine 500 University Drive Hershey 17033 PA United States Pediatric Critical Care Medicine Department of Pediatrics Penn State Hershey Children’s Hospital P.O. Box 850 500 University Drive Hershey 17033-0850 PA United States Department of Public Health Sciences Pennsylvania State University College of Medicine 500 University Drive Hershey 17033-0850 PA United States

Background: Adult septic patients with substance abuse disorder (SUD) are at increased risk of poor outcomes, but the impact on adolescents is unknown. We aimed to determine if pre-existing SUD is associated with increased adverse outcomes and critical care resources in critically ill adolescents hospitalized with sepsis. We hypothesize that SUD is associated with increased risk of adverse outcomes and usage of critical care resources in this adolescent patient population. Methods: This was a retrospective cohort study utilizing the TriNetX© electronic health record (EHR) database, which consists of EHR from participating healthcare organizations predominantly in the United States. Critically ill adolescents with sepsis aged 12–21 years were divided into two groups (SUD history and no-SUD history). Data related to demographics, diagnostic, procedural, and medication codes were analyzed. The primary outcomes were organ dysfunction, critical care therapies, and all-cause 1-year mortality. Results: We included 5,436 critically ill adolescents with sepsis [730 (13.43%) SUD history and 4706 (86.57%) no-SUD history]. SUD history was associated with increased odds of organ dysfunction (adjusted odds ratio [aOR] 1.84;95% confidence interval [CI] 1.56–2.16;p < 0.001), vasoactive/inotropic drug usage (aOR 1.29;95% CI 1.10–1.52;p = 0.002), mechanical ventilation (aOR 2.19;95% CI 1.85–2.59;p < 0.001), but not mortality (aOR 1.03;95% CI 0.76–1.41;p = 0.83). Conclusions: Our retrospective analysis suggests history of SUD in critically ill septic adolescent patients is associated with increased utilization of critical care resources and organ dysfunction. Further study is needed to determine if substance abuse represents a potentially modifiable risk factor for critical illness in adolescent patients. © The Author(s) 2025.

关键词： Adolescent Critical care Electronic health records Substance use disorders Young adult

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Detection of Gene Fusions and Rearrangements in Formalin-Fixed, Paraffin-Embedded Solid Tumor Specimens Using High-Throughput Chromosome Conformation Capture

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Journal of Molecular Diagnostics 2025年第5期27卷 346-359页

作者： Galbraith, Kristyn Wu, Jamin Sikkink, Kristin Mohamed, Hussein Reid, Derek Perez-Arreola, Michelle Belton, Jon-Matthew Nomikou, Sofia Melnyk, Shadi Yang, Yiying Liechty, Benjamin L. Jour, George Tsirigos, Aristotelis Hermel, David J. Beck, Alyssa Sigal, Darren Dahl, Nathan A. Vibhakar, Rajeev Schmitt, Anthony Snuderl, Matija Department of Pathology NYU Langone Health and NYU Grossman School of Medicine New York New York United States Arima Genomics Inc. Carlsbad California United States Department of Pathology Weill Cornell School of Medicine New York New York United States Division of Precision Medicine Department of Medicine NYU School of Medicine New York New York United States Applied Bioinformatics Laboratories NYU School of Medicine New York New York United States Scripps Clinic/Scripps Cancer Center La Jolla California United States Moores Cancer Center University of California San Diego San Diego California United States Morgan Adams Foundation Pediatric Brain Tumor Research Program Department of Pediatrics University of Colorado School of Medicine Aurora Colorado United States Brain and Spine Tumor Center Laura and Isaac Perlmutter Cancer Center NYU Langone Health New York New York United States

Chromosomal structural variants (SVs) are major contributors to cancer development. Although multiple methods exist for detecting SVs, they are limited in throughput, such as fluorescent in situ hybridization and targeted panels, and use RNA, which degrades in formalin-fixed, paraffin-embedded (FFPE) blocks and is unable to detect SVs that do not produce a fusion transcript. High-throughput chromosome conformation capture (Hi-C) is a DNA-based next-generation sequencing (NGS) method that preserves the spatial conformation of the genome, capturing long-range genetic interactions and SVs. Herein, a retrospective study analyzing 71 FFPE specimens from 10 different solid tumors was performed. Results showed high concordance (98%) with clinical fluorescent in situ hybridization and RNA NGS in detecting known SVs. Furthermore, Hi-C provided insight into the mechanism of SV formation, including chromothripsis and extrachromosomal DNA, and detected rearrangements between genes and regulatory regions, all of which are undetectable by RNA NGS. Lastly, SVs were detected in 71% of cases in which previous clinical methods failed to identify a driver. Of these, 14% were clinically actionable based on current medical guidelines, and an additional 14% were not in medical guidelines but involve targetable biomarkers. Current data suggest that Hi-C is a robust and accurate method for genome-wide SV analyses from FFPE tissue and can be incorporated into current clinical NGS workflows. © 2025 Association for Molecular Pathology and American Society for Investigative Pathology

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A multi-omic approach implicates novel protein dysregulation in post-traumatic stress disorder

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Genome medicine 2025年第1期17卷 1-19页

作者： Wang, Jiawei Liu, Yujing Li, Hongyu Nguyen, Tuan P. Soto-Vargas, John Lee Wilson, Rashaun Wang, Weiwei Lam, TuKiet T. Zhang, Chi Lin, Chen Wolf, Erika J. Williamson, Douglas E. Ursano, Robert Stein, Thor DiSano, Krista Schnurr, Paula Scott, William K. Pierce, Meghan Montalvo-Ortiz, Janitza Noller, Crystal Miller, Mark W. Marx, Brian McKee, Ann Logue, Mark W. Keane, Terence M. Labadorf, Adam T. Kaye, Alfred Huber, Bertrand R. Hoffman, Ellen Davis, David A. Cruz, Dianne A. Che, Alicia Benedek, David Alvarez, Victor E. Lewis, David A. Glausier, Jill Holtzheimer, Paul E. Friedman, Matthew J. Williams, Kenneth R. Picciotto, Marina R. Nairn, Angus C. Krystal, John H. Duman, Ronald S. Young, Keith A. Zhao, Hongyu Girgenti, Matthew J. Program of Computational Biology & Bioinformatics Yale University New Haven 06511 CT United States Department of Psychiatry Yale School of Medicine New Haven 06511 CT United States Department of Biostatistics Yale School of Public Health New Haven 06510 CT United States NIDA Neuroproteomics Center Yale School of Medicine New Haven 06511 CT United States Department of Molecular Biophysics and Biochemistry Yale School of Medicine New Haven 06511 CT United States Keck MS & Proteomics Resource Yale School of Medicine New Haven 06510 CT United States Department of Psychiatry University of Pittsburgh Pittsburgh 15213 PA United States Department of Psychiatry Geisel School of Medicine at Dartmouth Lebanon 03756 NH United States Central Texas Veterans Health Care System Research Service Temple 76504 TX United States Department of Psychiatry and Behavioral Sciences Texas A & M University School of Medicine Bryan 77807 TX United States National Center for PTSD United States Department of Veterans Affairs White River Junction 05009 VT United States

Background : Post-traumatic stress disorder (PTSD) is a common and disabling psychiatric disorder. PTSD involves multiple brain regions and is often comorbid with other psychiatric disorders, such as major depressive disorder (MDD). Recent genome-wide association studies (GWASs) have identified many PTSD risk loci and transcriptomics studies of postmortem brain have found differentially expressed genes associated with PTSD cases. In this study, we integrated genome-wide measures across modalities to identify convergent molecular effects in the PTSD brain. Methods: We performed tandem mass spectrometry (MS/MS) on a large cohort of donors (N = 66) in two prefrontal cortical areas, dorsolateral prefrontal cortex (DLPFC), and subgenual prefrontal cortex (sgPFC). We also coupled the proteomics data with transcriptomics and microRNA (miRNA) profiling from RNA-seq and small-RNA sequencing, respectively for the same cohort. Additionally, we utilized published GWAS results of multiple psychiatric disorders for integrative analysis. Results: We found differentially expressed proteins and co-expression protein modules disrupted by PTSD. Integrative analysis with transcriptomics and miRNA data from the same cohort pointed to hsa-mir-589 as a regulatory miRNA responsible for dysregulation of neuronal protein networks for PTSD, including the gamma-aminobutyric acid (GABA) vesicular transporter, SLC32A1. In addition, we identified significant enrichment of risk genes for other psychiatric disorders, such as autism spectrum disorder (ASD) and major depressive disorder (MDD) within PTSD protein co-expression modules, suggesting shared molecular pathology. Conclusions: We integrated genome-wide measures of mRNA and miRNA expression and proteomics profiling from PTSD, MDD, and control (CON) brains to identify convergent and divergent molecular processes across genomic modalities. We substantially expand the number of differentially expressed genes and proteins in PTSD and identify dow

关键词： Major depressive disorder MicroRNAs Multi-omics Prefrontal cortex PTSD

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Bile acid synthesis impedes tumor-specific T cell responses during liver cancer

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Science (New York, N.Y.) 2025年第6730期387卷 192-201页

作者： Varanasi, Siva Karthik Chen, Dan Liu, Yingluo Johnson, Melissa A. Miller, Cayla M. Ganguly, Souradipta Lande, Kathryn LaPorta, Michael A. Hoffmann, Filipe Araujo Mann, Thomas H. Teneche, Marcos G. Casillas, Eduardo Mangalhara, Kailash C. Mathew, Varsha Sun, Ming Jensen, Isaac J. Farsakoglu, Yagmur Chen, Timothy Parisi, Bianca Deota, Shaunak Havas, Aaron Lee, Jin Chung, H Kay Schietinger, Andrea Panda, Satchidananda Williams, April E. Farber, Donna L. Dhar, Debanjan Adams, Peter D. Feng, Gen-Sheng Shadel, Gerald S. Sundrud, Mark S. Kaech, Susan M. NOMIS Center for Immunobiology and Microbial Pathogenesis Salk Institute for Biological Studies La Jolla CA United States Department of Pathology School of Medicine University of California San Diego La Jolla CA United States Sanford Burnham Prebys Medical Discovery Institute La Jolla CA United States Department of Medicine School of Medicine University of California San Diego CA United States Razavi Newman Integrative Genomics and Bioinformatics Core Facility Salk Institute for Biological Studies La Jolla CA United States Department of Microbiology and Immunology Columbia University Irving Medical Center New York NY USA Department of Surgery Columbia University Irving Medical Center New York NY USA Regulatory Biology Laboratory Salk Institute for Biological Studies La Jolla CA United States Immunology Program Memorial Sloan Kettering Cancer Center New York NY USA Immunology and Microbial Pathogenesis Program Weill Cornell Graduate School of Medical Sciences New York NY USA Parker Institute for Cancer Immunotherapy New York NY USA Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth Hanover NH USA Department of Medicine Geisel School of Medicine at Dartmouth Hanover NH USA Center for Digestive Health Dartmouth Health NH United States Dartmouth Cancer Center Dartmouth Health NH United States

The metabolic landscape of cancer greatly influences antitumor immunity, yet it remains unclear how organ-specific metabolites in the tumor microenvironment influence immunosurveillance. We found that accumulation of primary conjugated and secondary bile acids (BAs) are metabolic features of human hepatocellular carcinoma and experimental liver cancer models. Inhibiting conjugated BA synthesis in hepatocytes through deletion of the BA-conjugating enzyme bile acid-CoA:amino acid N-acyltransferase (BAAT) enhanced tumor-specific T cell responses, reduced tumor growth, and sensitized tumors to anti-programmed cell death protein 1 (anti-PD-1) immunotherapy. Furthermore, different BAs regulated CD8+ T cells differently;primary BAs induced oxidative stress, whereas the secondary BA lithocholic acid inhibited T cell function through endoplasmic reticulum stress, which was countered by ursodeoxycholic acid. We demonstrate that modifying BA synthesis or dietary intake of ursodeoxycholic acid could improve tumor immunotherapy in liver cancer model systems.

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Early-Life Risks of Central Precocious Puberty

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Endocrine Practice 2025年

作者： Yin, Wei-Jou Hsu, Jhih-Wei Chen, Chun-Chang Su, Emily Chia-Yu Wang, San-Yuan Chen, Yan-Jen Chen, Yang-Ching College of Medicine Taipei Medical University Taipei Taiwan Department of Family Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Master Program in Clinical Genomics and Proteomics Department of Pharmaceutical Sciences Taipei Medical University Taipei Taiwan Department of Family Medicine Wan Fang Hospital Taipei Medical University Taipei Taiwan Nutrition Research Center Taipei Medical University Hospital Taipei Taiwan

Objectives: To investigate the factors in early life that may contribute to central precocious puberty (CPP). Methods: The study utilized data from the Taiwan Puberty Longitudinal Study, including 2241 children under pubertal assessment and a questionnaire of risk factors. We analyzed associations using the Fitting Generalized Linear Models in R (R Core Team, 2023), with R studio (Posit, 2023) version 4.3.1. Results: Among the 2241 children examined, 745 had CPP. Overall, higher gestational weight gain (GWG) increased the risk of CPP (odds ratio [OR]: 1.03, 95% CI: 1.01-1.05);while higher GWG served as a protective factor in females (OR: 0.97, 95% CI: 0.95-0.99). Maternal gestational diabetes mellitus (GDM) also increased the CPP risk, particularly in males (OR: 2.66, 95% CI: 1.00-7.25). Longer exclusive breastfeeding was linked to lower CPP risk overall (OR: 0.96, 95% CI: 0.93-1.00) but was not significant when analyzed by gender. Conclusions: Higher maternal GWG was associated with an increased risk of CPP overall, while GWG became negatively associated with CPP, only in females. Maternal GDM was linked to a higher risk of CPP, particularly in males. The duration of exclusive breastfeeding was inversely correlated with CPP risk, but this effect was not significant when analyzed by gender. © 2025 AACE

关键词： central precocious puberty early-life risk factors pediatrics

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ITIH4 alleviates OVA-induced asthma by regulating lung-gut microbiota

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Molecular medicine 2025年第1期31卷 1-13页

作者： Liu, Yi-Hsuan Lee, Yueh-Lun Han, Chia-Li Lo, Yu-Chun Liao, Zih-An Shih, Yu-Syuan Lin, Yi-Wen Peng, Syue-Wei Lee, Kang-Yun Ho, Shu-Chuan Wu, Sheng-Ming Lin, Cheng-Wei Chung, Kian Fan Chang, Jer-Hwa Chuang, Hsiao-Chi School of Respiratory Therapy College of Medicine Taipei Medical University Taipei Taiwan Department of Microbiology and Immunology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Master Program in Clinical Genomics and Proteomics College of Pharmacy Taipei Medical University Taipei Taiwan The Ph.D. Program for Neural Regenerative Medicine College of Medical Science and Technology Taipei Medical University Taipei Taiwan Division of Pulmonary Medicine Department of Internal Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Division of Pulmonary Medicine Department of Internal Medicine Shuang Ho Hospital Taipei Medical University New Taipei City Taiwan Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei Taiwan Department of Biochemistry and Molecular Cell Biology Taipei Medical University Taipei Taiwan National Heart and Lung Institute Imperial College London London UK Division of Pulmonary Medicine Department of Internal Medicine Wan Fang Hospital Taipei Medical University Taipei Taiwan Cell Physiology and Molecular Image Research Center Wan Fang Hospital Taipei Medical University Taipei Taiwan

Inter-alpha-trypsin inhibitor heavy chain 4 (ITIH4), a Type 2 acute phase protein, is critical for resolving inflammation and promoting tissue repair. While its role in chronic respiratory diseases is recognized, its effects on asthma remain unclear. This study investigated the effects of ITIH4 on the modulation of lung and gut microbiota, the attenuation of allergic inflammation, and the improvement of respiratory outcomes in an asthma mouse model. Six-week-old male Balb/c mice were divided into five groups: control, ITIH4, ovalbumin (OVA), and two OVA + ITIH4 treatment groups at different doses. Lung function and oxygen saturation were measured, and bronchoalveolar lavage fluid (BALF) was analyzed for white blood cell counts and cytokines. Lung and gut microbiota were profiled using 16 S rRNA gene sequencing, and short-chain fatty acids (SCFAs) were measured using gas chromatography-mass spectrometry (GC-MS). Proteomic profiling of intestinal tissues was conducted to identify ITIH4-associated signaling pathways. ITIH4 administration significantly mitigated OVA-induced asthma symptoms by reducing weight loss, airway resistance, and tissue damping (p < 0.05). Histological analysis showed decreased airway wall thickening and lung injury scores (p < 0.05). ITIH4 also lowered BALF eosinophils and lymphocytes, IgE, and Th2 cytokines (IL-4, IL-5, and IL-13) (p < 0.05). ITIH4 treatment modulated microbiome composition, enriching Gram-positive taxa (Nocardioidaceae and Acholeplasmataceae) and depleting Gram-negative Helicobacteraceae (p < 0.05). SCFAs correlated with microbiome alterations, notably reduced 4-methylpentanoic acid levels (p < 0.05). Proteomic analysis revealed a dose-dependent activation of granzyme A signaling and suppression of metabolic and solute transport pathways. ITIH4 ameliorates asthma symptoms by modulating lung and gut microbiota, dampening Th2-driven inflammation, and restoring mucosal immune balance. These findings support IT

关键词： Airway hyperresponsiveness Inflammation Microbiome Short-chain fatty acid Th2

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An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment

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Cell genomics 2025年第4期5卷 100820-100820页

作者： Wang, Lida Markus, Havell Chen, Dieyi Chen, Siyuan Zhang, Fan Gao, Shuang Khunsriraksakul, Chachrit Chen, Fang Olsen, Nancy Foulke, Galen Jiang, Bibo Carrel, Laura Liu, Dajiang J. Department of Public Health Sciences Pennsylvania State University College of Medicine Hershey 17033 PA United States Bioinformatics and Genomics PhD Program Pennsylvania State University College of Medicine Hershey 17033 PA United States Institute for Personalized Medicine Pennsylvania State University College of Medicine Hershey 17033 PA United States Department of Biochemistry and Molecular Biology Pennsylvania State University College of Medicine Hershey 17033 PA United States Department of Medicine Penn State University College of Medicine Hershey 17033 PA United States Department of Dermatology Penn State University College of Medicine Hershey 17033 PA United States

Most variants identified from genome-wide association studies (GWASs) are non-coding and regulate gene expression. However, many risk loci fail to colocalize with expression quantitative trait loci (eQTLs), potentially due to limited GWAS and eQTL analysis power or cellular heterogeneity. Population-scale single-cell RNA-sequencing (scRNA-seq) datasets are emerging, enabling mapping of eQTLs in different cell types (sc-eQTLs). Compared to eQTL data from bulk tissues (bk-eQTLs), sc-eQTL datasets are smaller. We propose a joint model of bk-eQTLs as a weighted sum of sc-eQTLs (JOBS) from constituent cell types to improve power. Applying JOBS to One1K1K and eQTLGen data, we identify 586% more eQTLs, matching the power of 4× the sample sizes of OneK1K. Integrating sc-eQTLs with GWAS data creates an atlas for 14 immune-mediated disorders, colocalizing 29.9% or 32.2% more loci than using sc-eQTL or bk-eQTL alone. Extending JOBS, we develop a drug-repurposing pipeline and identify novel drugs validated by real-world data. © 2025 The Author(s)

关键词： autoimmune disease bulk eQTL colocalization drug repurposing GWAS scRNAseq sequencing design single cell eQTL transcriptome

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Ibaqpy: A scalable Python package for baseline quantification in proteomics leveraging SDRF metadata

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Journal of proteomics 2025年 317卷

作者： Zheng, Ping Audain, Enrique Webel, Henry Dai, Chengxin Klein, Joshua Hitz, Marc-Phillip Sachsenberg, Timo Bai, Mingze Perez-Riverol, Yasset Chongqing Key Laboratory of Big Data for Bio Intelligence Chongqing University of Posts and Telecommunications Chongqing China Institute of Medical Genetics University Medicine Oldenburg Carl von Ossietzky University Oldenburg Germany The Novo Nordisk Foundation Center for Biosustainability Technical University of Denmark Lyngby Denmark State Key Laboratory of Proteomics Beijing Proteome Research Center National Center for Protein Sciences (Beijing) Beijing Institute of Life Omics Beijing China Program for Bioinformatics Boston University Boston United States Department of Computer Science Applied Bioinformatics University of Tübingen Tübingen Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Tübingen Germany European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Cambridge United Kingdom

Intensity-based absolute quantification (iBAQ) is essential in proteomics as it allows for the assessment of a protein's absolute abundance in various samples or conditions. However, the computation of these values for increasingly large-scale and high-throughput experiments, such as those using DIA, TMT, or LFQ workflows, poses significant challenges in scalability and reproducibility. Here, we present ibaqpy (https://***/bigbio/ibaqpy), a Python package designed to compute iBAQ values efficiently for experiments of any scale. Ibaqpy leverages the Sample and Data Relationship Format (SDRF) metadata standard to incorporate experimental metadata into the quantification workflow. This allows for automatic normalization and batch correction while accounting for key aspects of the experimental design, such as technical and biological replicates, fractionation strategies, and sample conditions. Designed for large-scale proteomics datasets, ibaqpy can also recompute iBAQ values for existing experiments when an SDRF is available. We showcased ibaqpy's capabilities by reanalyzing 17 public proteomics datasets from ProteomeXchange, covering HeLa cell lines with 4921 samples and 5766 MS runs, quantifying a total of 11,014 proteins. In our reanalysis, ibaqpy is a key component in automating reproducible quantification, reducing manual effort and making quantitative proteomics more accessible while supporting FAIR principles for data reuse. Significance: proteomics studies often rely on intensity-based absolute quantification (iBAQ) to assess protein abundance across various biological conditions. Despite its widespread use, computing iBAQ values at scale remains challenging due to the increasing complexity and volume of proteomics experiments. Existing tools frequently lack metadata integration, limiting their ability to handle experimental design intricacies such as replicates, fractions, and batch effects. Our work introduces ibaqpy, a scalable Python package that lev

关键词： Big data bioinformatics Data integration proteomics Quantification

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Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers

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Clinical Breast Cancer 2025年

作者： Lin, Neil Abbas-Aghababazadeh, Farnoosh Su, Jie Wu, Alison J. Lin, Cherie Shi, Wei Xu, Wei Haibe-Kains, Benjamin Liu, Fei-Fei Kwan, Jennifer Y.Y. Institute of Medical Science Temerty Faculty of Medicine University of Toronto Toronto Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Princess Margaret Bioinformatics and Computational Genomics Laboratory University Health Network Toronto Canada Biostatistics Division Princess Margaret Cancer Centre Toronto Canada Research Institute Princess Margaret Cancer Centre Toronto Canada Department of Computer Science University of Toronto Toronto Canada Ontario Institute for Cancer Research Toronto Canada Vector Institute for Artificial Intelligence Toronto Canada Department of Radiation Oncology Temerty Faculty of Medicine University of Toronto Toronto Canada Department of Medical Biophysics Temerty Faculty of Medicine University of Toronto Toronto Canada Radiation Medicine Program Princess Margaret Cancer Centre Toronto Canada

Background: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare costs. Early identification of patients at risk for severe RD can facilitate preventive management and reduce severe symptoms. This study evaluated the utility of subjective and objective factors, such as patient-reported outcomes (PROs) and serum cytokines, for predicting RD in breast cancer patients. The performance of machine learning (ML) and logistic regression-based models were compared. Patients and Methods: Data from 147 breast cancer patients who underwent radiotherapy was analyzed to develop prognostic models. ML algorithms, including neural networks, random forest, XGBoost, and logistic regression, were employed to predict clinically significant Grade 2+ RD. Clinical factors, PROs, and cytokine biomarkers were incorporated into the risk models. Model performance was evaluated using nested cross-validation with separate loops for hyperparameter tuning and calculating performance metrics. Results: Feature selection identified 18 predictors of Grade 2+ RD including smoking, radiotherapy boost, reduced motivation, and the cytokines interleukin-4, interleukin-17, interleukin-1RA, interferon-gamma, and stromal cell-derived factor-1a. Incorporating these predictors, the XGBoost model achieved the highest performance with an area under the curve (AUC) of 0.780 (95% CI: 0.701-0.854). This was not significantly improved over the logistic regression model, which demonstrated an AUC of 0.714 (95% CI: 0.629-0.798). Conclusion: Clinical risk factors, PROs, and serum cytokine levels provide complementary prognostic information for predicting severe RD in breast cancer patients undergoing radiotherapy. ML and logistic regression models demonstrated comparable performance for predicting clinically significant RD with AUC>0.70. ©

关键词： Cancer survivorship Oncology Predictive modeling Radiotherapy Skin toxicity

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