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Exploration of dark chemical genomics space via portal learning: Applied to targeting the undruggable genome and COVId-19 anti-infective polypharmacology

arXiv

引用

arXiv 2021年

作者： Cai, Tian Xie, Li Chen, Muge Liu, Yang He, di Zhang, Shuo Mura, Cameron Bourne, philip E. Xie, Lei Ph.D. Program in Computer Science The Graduate Center The City University of New York New York10016 United States Department of Computer Science Hunter College The City University of New York New York10065 United States Master Program in Computer Science Courant Institute of Mathematical Sciences New York University United States School of Data Science Department of Biomedical Engineering University of Virginia VA22903 United States Helen and Robert Appel Alzheimer’s Disease Research Institute Feil Family Brain & Mind Research Institute Weill Cornell Medicine Cornell University New York10021 United States

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOd) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods, thereby allowing us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific *** Codes 68T07 © 2021, CC BY.

关键词： Genes

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Exploration of dark Chemical Genomics Space via Portal Learning: Applied to Targeting the Undruggable Genome and COVId-19 Anti-Infective Polypharmacology

Research Square

引用

Research Square 2021年

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning, to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOd) problem in statistical machine learning. Here, we have implemented Portal Learning to predict chemical-protein interactions on a genome-wide scale. Systematic studies demonstrate that Portal Learning can effectively assign ligands to unexplored gene families (unknown functions), versus existing state-of-the-art methods. Compared with AlphaFold2-based protein-ligand docking, Portal Learning significantly improved the performance by 79% in PR-AUC and 27% in ROC-AUC, respectively. The superior performance of Portal Learning allowed us to target previously "undruggable" proteins and design novel polypharmacological agents for disrupting interactions between SARS-CoV-2 and human proteins. Portal Learning is general-purpose and can be further applied to other areas of scientific inquiry. © 2021, CC BY.

关键词： Coronavirus

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Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

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Nucleic Acids Research 2024年第3期52卷 E17页

作者： Chang, Lan-Yun Lee, Meng-Zhan Wu, Yujia Lee, Wen-Kai Ma, Chia-Liang Chang, Jun-Mao Chen, Ciao-Wen Huang, Tzu-Chun Lee, Chia-Hwa Lee, Jih-Chin Tseng, Yu-Yao Lin, Chun-Yu Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 300 Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan Department of Food Science Nutrition and Nutraceutical Biotechnology Shih Chien University Taipei 104 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Cancer and Immunology Research Center National Yang Ming Chiao Tung University Taipei 112 Taiwan Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (dEG) list (or each of its dEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVId-19-related cells and patient samples, Gscore provided a means for studying how dEGs are implicated in COVId-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual dEGs, dEG lists, and genome-wide expression profiles based on existing biological knowledge. © 2024 Oxford University Press. All rights reserved.

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Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

arXiv

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arXiv 2023年

作者： Takaba, Kenichiro Pulido, Iván Behara, Pavan Kumar Cavender, Chapin E. Friedman, Anika J. Henry, Michael M. Macdermott-Opeskin, Hugo Iacovella, Christopher R. Nagle, Arnav M. Payne, Alexander Matthew Shirts, Michael R. Mobley, david L. Chodera, John d. Wang, Yuanqing Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Pharmaceuticals Research Center Advanced Drug Discovery Asahi Kasei Pharma Corporation Shizuoka410-2321 Japan Center for Neurotherapeutics Department of Pathology and Laboratory Medicine University of California IrvineCA92697 United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California 9500 Gilman Drive La Jolla San DiegoCA92093 United States Department of Chemical and Biological Engineering University of Colorado Boulder BoulderCO80309 United States Open Molecular Software Foundation DavisCA95618 United States Department of Bioengineering University of California Berkeley BerkeleyCA94720 United States Department of Pharmaceutical Sciences University of California IrvineCA92697 United States Tri-Institutional Ph.D. Program in Chemical Biology Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University New YorkNY10004 United States

The development of reliable and extensible molecular mechanics (MM) force fields—fast, empirical models characterizing the potential energy surface of molecular systems—is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest. © 2023, CC BY.

关键词： Quantum chemistry

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OpenMM 8: Molecular dynamics Simulation with Machine Learning Potentials

arXiv

引用

arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, philip Wang, Yuanqing Zhang, Ivy Chodera, John d. de Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUdA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CdK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Association of thyroid cancer with human papillomavirus infections

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Scientific Reports 2024年第1期14卷 1-7页

作者： Yang, Tzong-Hann Hung, Shih-Han Cheng, Yen-Fu Chen, Chin-Shyan Lin, Herng-Ching Department of Otorhinolaryngology Taipei City Hospital Taipei Taiwan Department of Speech Language and Audiology National Taipei University of Nursing and Health Taipei Taiwan Department of Otolaryngology-Head and Neck Surgery School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Center of General Education University of Taipei Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan Department of Otolaryngology School of Medicine Taipei Medical University Taipei 110 Taiwan Department of Otolaryngology Wan Fang Hospital Taipei 110 Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan Department of Medical Research Taipei Veterans General Hospital Taipei Taiwan Department of Otolaryngology-Head and Neck Surgery Taipei Veterans General Hospital Taipei Taiwan Department of Economics National Taipei University New Taipei City Taiwan School of Health Care Administration College of Management Taipei Medical University Taipei Taiwan Research Center of Sleep Medicine Taipei Medical University Hospital Taipei Taiwan

While Human Papillomavirus (HPV) particles have been detected in a small proportion of benign thyroid nodules or thyroid cancer cases, a role of HPV in these thyroid conditions has not been established. This study aims to investigate the association of HPV infection with thyroid cancer (TC) using a nationwide population-based study. We retrieved data for this case–control study from Taiwan's Longitudinal Health Insurance database 2010. The study sample included 3062 patients with TC and 9186 propensity-scored matched controls. We employed multivariate logistic regression models to quantitatively evaluate the association of TC with HPV infections after taking age, sex, monthly income, geographic location and urbanization level of the patient's residence, diabetes, hypertension, and hyperlipidemia into considerations. Chi-squared test revealed that there was a significant difference in the prevalence of prior HPV infections between patients with TC and controls (15.3% vs. 7.6%, p < 0.001). The adjusted odds ratio of prior HPV infections for patients with TC was 2.199 (95% CI = 1.939–2.492) relative to controls. The adjusted ORs of prior HPV infections for patients with TC was similar for males and females. Our research suggests a significant link between HPV infection and the development of TC. © 2023, The Author(s).

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The Western and Chinese exercise training for blood pressure reduction among hypertensive patients: An overview of systematic reviews

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Journal of clinical hypertension (Greenwich, Conn.) 2024年第12期26卷 1327-1341页

作者： Kelvin Tsoi Amy Lam Joshua Tran Ziyu Hao Karen Yiu Yook-Chin Chia Yuda Turana Saulat Siddique Yuqing Zhang Hao-Min Cheng Ji-Guang Wang Kazuomi Kario The Jockey Club School of Public Health and Primary Care Faculty of Medicine The Chinese University of Hong Kong Hong Kong Hong Kong. Stanley Ho Big Data Decision Analytics Research Centre The Chinese University of Hong Kong Hong Kong Hong Kong. Department of Medicine and Therapeutics Faculty of Medicine The Chinese University of Hong Kong Hong Kong Hong Kong. Department of Medical Sciences School of Medical and Life Sciences Sunway University Bandar Sunway Malaysia. Department of Primary Care Medicine Faculty of Medicine University of Malaya Kuala Lumpur Malaysia. Department of Neurology School of Medicine and Health Sciences Atma Jaya Catholic University of Indonesia Jakarta Indonesia. Punjab Medical Center Lahore Pakistan. Divisions of Hypertension and Heart Failure Fu Wai Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China. Center for Evidence-based Medicine Taipei Veterans General Hospital Taipei Taiwan. Ph.D. Program of Interdisciplinary Medicine (PIM) National Yang Ming Chiao Tung University College of Medicine Taipei Taiwan. Institute of Public Health National Yang Ming Chiao Tung University College of Medicine Taipei Taiwan. Institute of Health and Welfare Policy National Yang Ming Chiao Tung University College of Medicine Taipei Taiwan. Department of Cardiovascular Medicine the Shanghai Institute of Hypertension Shanghai Key Laboratory of Hypertension State Key Laboratory of Medical Genomics National Research Centre for Translational Medicine Ruijin Hospital Shanghai Jiaotong University School of Medicine Shanghai China. Division of Cardiovascular Medicine Department of Medicine Jichi Medical University School of Medicine Tochigi Japan.

Hypertension remains the world's leading cause of premature death. Interventions such as exercise, diet modification, and pharmacological therapy remain the mainstay of hypertension treatment. Numerous systematic reviews and meta-analyses demonstrated the effectiveness of western exercises, such as aerobic exercise and resistance exercise, in reducing blood pressure in hypertensive patients. There is recently emerging evidence of blood pressure reduction with Chinese exercises, such as Tai Chi, Baduanjin, and Qigong. The current overview of systematic reviews aims to evaluate the quality and descriptively summarize the evidence for the effectiveness of western and Chinese exercises for hypertension management. Thirty-nine systematic reviews were included in this overview, with 15 of those being on Chinese exercise. Evidence suggests that exercise training, regardless of Western or Chinese exercise, generally reduced both systolic and diastolic blood pressure. High-intensity intermittent training did not further reduce blood pressure when compared to moderate-intensity continuous training. Conflicting results on the effectiveness of blood pressure reduction when comparing Chinese and Western exercise training were observed. This suggests the comparable effectiveness of Chinese exercise training, in particularly Tai Chi, to general or aerobic exercise training in terms of blood pressure reduction. The Chinese exercise modality and intensity may be more suitable for the middle-aged and elderly population.

关键词： Antihypertensive Therapy Exercise/Hypertension Lifestyle Modification/Hypertension Non‐pharmacological Therapy Western and Chinese exercise

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Prevalence of hearing loss, tinnitus, vertigo and sudden deafness among patients with polymyositis and dermatomyositis

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Scientific Reports 2024年第1期14卷 1-7页

作者： Hung, Shih-Han Xirasagar, Sudha dang, Luong Huu Viet-Nhi, Nguyen-Kieu Cheng, Yen-Fu Chen, Chin-Shyan Lin, Herng-Ching Department of Otolaryngology School of Medicine Taipei Medical University Taipei 110 Taiwan Department of Otolaryngology Wan Fang Hospital Taipei 110 Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan Department of Health Services Policy and Management Arnold School of Public Health University of South Carolina Columbia SC United States Department of Otolaryngology Faculty of Medicine University of Medicine and Pharmacy at Ho Chi Minh City Ho Chi Minh City Viet Nam Department of Medical Research Taipei Veterans General Hospital Taipei Taiwan Department of Otolaryngology-Head and Neck Surgery Taipei Veterans General Hospital Taipei Taiwan Department of Otolaryngology-Head and Neck Surgery School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan Department of Economics National Taipei University New Taipei City Taiwan School of Health Care Administration College of Management Taipei Medical University Taipei Taiwan Research Center of Sleep Medicine Taipei Medical University Hospital Taipei Taiwan

Little is known about a possible association of autoimmune inner ear disease among patients diagnosed with polymyositis (PM)/dermatomyositis (dM). This study aimed to explore differences in the prevalence of inner ear symptoms among patients with and without PM/dM using a nationwide population-based dataset. data for this study were retrieved from the Taiwan National Health Insurance Research database. The study sample included 1622 patients diagnosed with PM/dM and 8109 propensity-score matched comparison patients without PM/dM. We performed multivariate logistic regressions to calculate odds ratios (ORs) and 95% confidence interval (CI) for tinnitus, hearing loss, sudden deafness, and vertigo among patients with PM/dM versus comparison patients. Chi-square tests showed statistically significant differences between patients with PM/dM and comparison patients in the prevalence of tinnitus (16.1% vs. 12.7%, p < 0.001), non-conductive hearing loss (9.2% vs. 6.8%, p < 0.001), and vertigo (14.4% vs. 11.1%, p < 0.001). The adjusted ORs for tinnitus, non-conductive hearing loss, and vertigo, respectively, were 1.332 (95% CI = 1.147–1.547), 1.399 (95% CI = 1.154–1.696), and 1.374 (95% CI = 1.173–1.611) for patients with PM/dM when compared to comparison patients. Our study finds that patients with PM/dM have higher prevalence rates of tinnitus, non-conductive hearing loss, and vertigo than comparison patients. © The Author(s) 2024.

关键词： dermatomyositis Epidemiology Inner ear disorders Polymyositis Tinnitus

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A descriptive study of variable discretization and cost-sensitive logistic regression on imbalanced credit data

arXiv

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arXiv 2018年

作者： Zhang, Lili Ray, Herman Priestley, Jennifer Tan, Soon Analytics and Data Science Ph.D. Program Kennesaw State University United States Analytics and Data Science Institute Kennesaw State University United States Ermas Consulting Inc. United States

Training classification models on imbalanced data tends to result in bias towards the majority class. In this paper, we demonstrate how variable discretization and cost-sensitive logistic regression help mitigate this bias on an imbalanced credit scoring dataset, and further show the application of the variable discretization technique on the data from other domains, demonstrating its potential as a generic technique for classifying imbalanced data beyond credit socring. The performance measurements include ROC curves, Area under ROC Curve (AUC), Type I Error, Type II Error, accuracy, and F1 score. The results show that proper variable discretization and cost-sensitive logistic regression with the best class weights can reduce the model bias and/or variance. From the perspective of the algorithm, cost-sensitive logistic regression is beneficial for increasing the value of predictors even if they are not in their optimized forms while maintaining monotonicity. From the perspective of predictors, the variable discretization performs better than cost-sensitive logistic regression, provides more reasonable coefficient estimates for predictors which have nonlinear relationships against their empirical logit, and is robust to penalty weights on misclassifications of events and non-events determined by their apriori proportions. Copyright © 2018, The Authors. All rights reserved.

关键词： Cost benefit analysis

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Pruritus and Its Association With Cancer and Mortality in dermatomyositis and Polymyositis: A Nationwide Cohort Study in Taiwan From 2005 to 2022

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dermatologic Therapy 2025年第1期2025卷

作者： der-Jr Huang Yu-Hsuan Joni Shao Yi-Hsien Shih Woan-Ruoh Lee Ling-Ya Huang Yu-Min Kuo Quoc Thao Trang pham Hao-Jui Weng Department of Dermatology Taipei Medical University-Shuang Ho Hospital New Taipei City Taiwan shh.org.tw Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan tmu.edu.tw Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan tmuh.org.tw Health Data Analytics and Statistics Center Office of Data Science Taipei Medical University Taipei Taiwan tmu.edu.tw Department of Dermatology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan tmu.edu.tw Graduate Institute of Medical Sciences College of Medicine Taipei Medical University Taipei Taiwan tmu.edu.tw Division of Allergy Immunology and Rheumatology Department of Internal Medicine National Taiwan University Hospital and National Taiwan University Taipei Taiwan ntu.edu.tw Graduate Institute of Clinical Medicine College of Medicine National Taiwan University Taipei Taiwan ntu.edu.tw International Ph.D. Program in Cell Therapy and Regenerative Medicine College of Medicine Taipei Medical University Taipei Taiwan tmu.edu.tw Department of Dermatology Faculty of Medicine University of Medicine and Pharmacy at Ho Chi Minh City Ho Chi Minh City Vietnam hcmut.edu.vn Graduate Institute of Clinical Medicine College of Medicine Taipei Medical University Taipei Taiwan medcol.mw

Background: Pruritus is the most common initial symptom reported by patients with dermatomyositis (dM) and polymyositis (PM). However, there is limited data regarding the impact of pruritus on cancer and mortality in patients with dM and PM. In this study, we aimed to investigate how pruritus is associated with cancer and mortality in patients with dM and PM. Methods: This nationwide, population-based retrospective cohort study included adult dM and PM patients from Taiwan’s National Health Insurance Research database between 2005 and 2022. Sex- and age-matched pruritic patients, identified by over 6 weeks of antipruritic medication use, and nonpruritic patients were analyzed. The primary outcome was cancer occurrence or all-cause mortality. The association between pruritus and these outcomes was estimated using Cox proportional hazards models. Results: Among 919 matched pairs of pruritic and nonpruritic patients, cancer was observed in 19.96% in the long-term pruritic group (LPG), 14.63% in the short-term pruritic group (SPG), and 10.34% in the non-pruritic group (NPG) ( p < 0.0001). All-cause mortality was documented as 30.37% in the LPG, 29.69% in the SPG, and 37.76% in the NPG ( p < 0.0001). After adjusting for sex, age, and other comorbidities, pruritus was associated with an increased risk of cancer (hazard ratio (HR) 1.708, 95% confidence interval (CI) 1.229–2.374) and a lower risk of all-cause mortality (HR 0.483, 95% CI 0.409–0.569). Conclusion: This population-based study revealed that pruritus appeared to be associated with increased risks of cancer and decreased all-cause mortality. Thus, pruritus may serve as a pragmatic factor for risk stratification and tailored treatment strategies in dM and PM. Cancer screening, particularly for nasopharyngeal and breast cancers in East Asian populations, is recommended for patients with dM or PM, especially those presenting with pruritus. Meanwhile, patients without pruritus may require vigilant management for pote

关键词： cancer dermatomyositis idiopathic inflammatory myopathies mortality polymyositis pruritus

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