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Predictive and prognostic value of excision repair cross-complementing group 1 in patients with advanced gastric cancer

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BJC reports 2024年第1期2卷 18页

作者： Yasuhide Yamada Kengo Nagashima Mizutomo Azuma Mitsuko Masutani Hitoshi Ichikawa Satoru Iwasa Naoki Takahashi Hidekazu Hirano Keisuke Kanato Nozomu Machida Takahiro Kinoshita Hiroaki Hata Hisato Kawakami Daisuke Takahari Narikazu Boku Yukinori Kurokawa Masanori Terashima Takaki Yoshikawa Shigeki Sekine Nobuyoshi Hiraoka Department of Medical Research National Center for Global Health and Medicine Tokyo 162-8655 Japan. yayamada@hosp.ncgm.go.jp. Biostatistics Unit Clinical and Translational Research Center Keio University Hospital Tokyo 160-8582 Japan. Department of Gastroenterology Kitasato University Hospital Sagamihara 252-0329 Japan. Molecular & Genomic Biomedicine Center for Bioinformatics and Molecular Medicine Nagasaki University Graduate School Nagasaki 852-8521 Japan. Department of Clinical Genomics National Cancer Center Research Institute Tokyo 104-0045 Japan. Department of Gastrointestinal Medical Oncology National Cancer Center Hospital Tokyo 104-0045 Japan. Department of Gastroenterology Saitama Cancer Center Saitama 362-0806 Japan. JCOG Data Center/ Operations Office National Cancer Center Tokyo 104-0045 Japan. Department of Gastroenterology Kanagawa Cancer Center Yokohama 241-8515 Japan. Department Gastric Surgery National Cancer Center Hospital East Chiba 241-8515 Japan. Department of Surgery Kyoto Medical Center Kyoto 612-0861 Japan. Department of Medical Oncology Kindai University Faculty of Medicine Osaka 577-8502 Japan. Department of Gastroenterological Chemotherapy Cancer Institute Hospital of JFCR Tokyo 135-8550 Japan. Department of Gastroenterological Surgery Osaka University Graduate School of Medicine Osaka 565-0871 Japan. Division of Gastric Surgery Shizuoka Cancer Center Shizuoka 411-8777 Japan. Department of Gastric Surgery National Cancer Center Hospital Tokyo 104-0045 Japan. Department of Pathology National Cancer Center Hospital Tokyo 104-0045 Japan.

BACKGROUND:To define the optimal chemotherapy regimen for each patient we therefore used tissue from patients to identify molecular prognostic or predictive biomarkers. METHODS:Endoscopic biopsy specimens from primary lesions and surgical specimens on a phase III trial in patients with unresectable advanced or recurrent gastric cancer treated with docetaxel with cisplatin plus S-1 (DCS) or cisplatin plus S-1 (CS), were collected. We measured the mRNA expression of ERCC1 and analyzed SNPs in GSTP1 and ERCC1. RESULTS:Low ERCC1 expression was associated with favorable prognosis for overall survival, OS by multivariable analysis (P = 0.001). There were significant interactions between the two treatment arms of DCS and CS, and ERCC1 mRNA expression. In patients with low ERCC1 expression of a favorable prognosis, DCS therapy was inferior to CS (P = 0.046). In addition to GSTP1 rs1695 (HR 1.728), ERCC1 rs3212980, ERCC1 rs2298881, ERCC1 rs3212964 with high expression of ERCC1 mRNA were associated with significantly worse prognosis with regard to OS. CONCLUSIONS:ERCC1 mRNA is an independent prognostic factor and predictive marker that can be used to guide the addition of docetaxel. The SNPs of ERCC1 and GSTP1 could be also prognostic or predictive factors.

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Polygenic risk modeling for prediction of epithelial ovarian cancer risk (vol 30, pg 349, 2021)

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EUROPEAN JOURNAL OF HUMAN GENETICS 2022年第5期30卷 630-631页

作者： Dareng, Eileen O. Tyrer, Jonathan P. Barnes, Daniel R. Jones, Michelle R. Yang, Xin Aben, Katja K. H. Adank, Muriel A. Agata, Simona Andrulis, Irene L. Anton-Culver, Hoda Antonenkova, Natalia N. Aravantinos, Gerasimos Arun, Banu K. Augustinsson, Annelie Balmana, Judith Bandera, Elisa V. Barkardottir, Rosa B. Barrowdale, Daniel Beckmann, Matthias W. Beeghly-Fadiel, Alicia Benitez, Javier Bermisheva, Marina Bernardini, Marcus Q. Bjorge, Line Black, Amanda Bogdanova, Natalia V. Bonanni, Bernardo Borg, Ake Brenton, James D. Budzilowska, Agnieszka Butzow, Ralf Buys, Saundra S. Cai, Hui Caligo, Maria A. Campbell, Ian Cannioto, Rikki Cassingham, Hayley Chang-Claude, Jenny Chanock, Stephen J. Chen, Kexin Chiew, Yoke-Eng Chung, Wendy K. Claes, Kathleen B. M. Colonna, Sarah Cook, Linda S. Couch, Fergus J. Daly, Mary B. Dao, Fanny Davies, Eleanor de la Hoya, Miguel de Putter, Robin Dennis, Joe DePersia, Allison Devilee, Peter Diez, Orland Ding, Yuan Chun Doherty, Jennifer A. Domchek, Susan M. Dork, Thilo du Bois, Andreas Durst, Matthias Eccles, Diana M. Eliassen, Heather A. Engel, Christoph Evans, Gareth D. Fasching, Peter A. Flanagan, James M. Fortner, Renee T. Machackova, Eva Friedman, Eitan Ganz, Patricia A. Garber, Judy Gensini, Francesca Giles, Graham G. Glendon, Gord Godwin, Andrew K. Goodman, Marc T. Greene, Mark H. Gronwald, Jacek Hahnen, Eric Haiman, Christopher A. Hakansson, Niclas Hamann, Ute Hansen, Thomas V. O. Harris, Holly R. Hartman, Mikael Heitz, Florian Hildebrandt, Michelle A. T. Hogdall, Estrid Hogdall, Claus K. Hopper, John L. Huang, Ruea-Yea Huff, Chad Hulick, Peter J. Huntsman, David G. Imyanitov, Evgeny N. Isaacs, Claudine Jakubowska, Anna James, Paul A. Janavicius, Ramunas Jensen, Allan Johannsson, Oskar Th. John, Esther M. Jones, Michael E. Kang, Daehee Karlan, Beth Y. Karnezis, Anthony Kelemen, Linda E. Khusnutdinova, Elza Kiemeney, Lambertus A. Kim, Byoung-Gie Kjaer, Susanne K. Komenaka, Ian Kupryjanczyk, Jolanta Kurian, Allison W. Kwong, Ava Lambrechts, Diether Larson, Melissa C. Lazaro, Conxi L University of Cambridge Centre for Cancer Genetic Epidemiology Department of Public Health and Primary Care Cambridge UK University of Cambridge Centre for Cancer Genetic Epidemiology Department of Oncology Cambridge UK Center for Bioinformatics and Functional Genomics Cedars-Sinai Medical Center Los Angeles CA USA Radboud University Medical Center Radboud Institute for Health Sciences Nijmegen The Netherlands Netherlands Comprehensive Cancer Organisation Utrecht The Netherlands The Netherlands Cancer Institute—Antoni van Leeuwenhoek hospital Family Cancer Clinic Amsterdam The Netherlands Veneto Institute of Oncology IOV—IRCCS Immunology and Molecular Oncology Unit Padua Italy Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital Fred A. Litwin Center for Cancer Genetics Toronto ON Canada University of Toronto Department of Molecular Genetics Toronto ON Canada University of California Irvine Department of Epidemiology Genetic Epidemiology Research Institute Irvine CA USA N.N. Alexandrov Research Institute of Oncology and Medical Radiology Minsk Belarus ‘Agii Anargiri’ Cancer Hospital Athens Greece University of Texas MD Anderson Cancer Center Department of Breast Medical Oncology Houston TX USA Lund University Department of Cancer Epidemiology Clinical Sciences Lund Sweden Vall d’Hebron Institute of Oncology Hereditary cancer Genetics Group Barcelona Spain University Hospital of Vall d’Hebron Department of Medical Oncology Barcelona Spain Rutgers Cancer Institute of New Jersey Cancer Prevention and Control Program New Brunswick NJ USA Landspitali University Hospital Department of Pathology Reykjavik Iceland University of Iceland BMC (Biomedical Centre) Faculty of Medicine Reykjavik Iceland University Hospital Erlangen Friedrich-Alexander-University Erlangen-Nuremberg Department of Gynecology and Obstetrics Comprehensive Cancer Center ER-EMN Erlangen Germany Vanderbilt University School of Medicine Division of Epidemiology Department

Polygenic risk scores (PRS) for epithelial ovarian cancer (EOC) have the potential to improve risk stratification. Joint estimation of Single Nucleotide Polymorphism (SNP) effects in models could improve predictive performance over standard approaches of PRS construction. Here, we implemented computationally efficient, penalized, logistic regression models (lasso, elastic net, stepwise) to individual level genotype data and a Bayesian framework with continuous shrinkage, “select and shrink for summary statistics” (S4), to summary level data for epithelial non-mucinous ovarian cancer risk prediction. We developed the models in a dataset consisting of 23,564 non-mucinous EOC cases and 40,138 controls participating in the Ovarian Cancer Association Consortium (OCAC) and validated the best models in three populations of different ancestries: prospective data from 198,101 women of European ancestries; 7,669 women of East Asian ancestries; 1,072 women of African ancestries, and in 18,915 BRCA1 and 12,337 BRCA2 pathogenic variant carriers of European ancestries. In the external validation data, the model with the strongest association for non-mucinous EOC risk derived from the OCAC model development data was the S4 model (27,240 SNPs) with odds ratios (OR) of 1.38 (95% CI: 1.28–1.48, AUC: 0.588) per unit standard deviation, in women of European ancestries; 1.14 (95% CI: 1.08–1.19, AUC: 0.538) in women of East Asian ancestries; 1.38 (95% CI: 1.21–1.58, AUC: 0.593) in women of African ancestries; hazard ratios of 1.36 (95% CI: 1.29–1.43, AUC: 0.592) in BRCA1 pathogenic variant carriers and 1.49 (95% CI: 1.35–1.64, AUC: 0.624) in BRCA2 pathogenic variant carriers. Incorporation of the S4 PRS in risk prediction models for ovarian cancer may have clinical utility in ovarian cancer prevention programs.

关键词： Clinical genetics Genetic markers Risk factors Biomedicine general Human Genetics bioinformatics Gene Expression Cytogenetics

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data for Genomics in Healthcare: The Potential of Next-Generation Sequencing in Developing Precision Medicine

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IOP Conference Series: Earth and Environmental Science 2021年第1期794卷

作者： Dafa Dwi Antara Michael Yahezkiel Lie Muhammad Darisza Nicola Kertapati Ivan Valentino Bharuno Mahesworo Ika Nurlaila Haryono Soeparno Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia 11480

Recent advances in genomics on healthcare technology have opened the path towards more precise treatments by analysing patients' genome using a high throughput DNA sequencing technology called Next Generation Sequencing (NGS). The information obtained is further used to determine a more effective treatment path for patients based on their genetic profiles. It can also be employed to predict the risk of patients developing certain diseases. Despite particular difficulties it might create in the first phase of settling the integrative frameworks down, a successful combination of NGS and Big data technology would offer a huge improvement in terms of the effectiveness of treatments and medicine administration for the patients. This paper elucidates a general overview of how NGS-based DNA sequencing makes precision medicine possible and describes the use of Big data for genomics in healthcare-wise.

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Seeing or believing in hyperplexed spatial proteomics via antibodies: New and old biases for an image-based technology

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Biological imaging 2024年 4卷 e10页

作者： Maddalena M Bolognesi Lorenzo Dall'Olio Amy Maerten Simone Borghesi Gastone Castellani Giorgio Cattoretti Istituto di Bioimmagini e Fisiologia Molecolare - CNR Milan Italy. National Biodiversity Future Center (NBFC) Palermo Italy. Laboratorio di Data Science and Bioinformatics IRCCS Istituto delle Scienze Neurologiche di Bologna - AUSL BO Ospedale Bellaria Bologna Italy. Department of in vitro Toxicology and Dermato-Cosmetology Vrije Universiteit Brussel Jette Belgium. Department of Mathematics and Applications University of Milano Bicocca Milan Italy. Department of Experimental Diagnostic and Specialty Medicine University of Bologna Bologna Italy. Pathology Department of Medicine and Surgery Universitá di Milano-Bicocca Monza Italy.

Hyperplexed in-situ targeted proteomics via antibody immunodetection (i.e., >15 markers) is changing how we classify cells and tissues. Differently from other high-dimensional single-cell assays (flow cytometry, single-cell RNA sequencing), the human eye is a necessary component in multiple procedural steps: image segmentation, signal thresholding, antibody validation, and iconographic rendering. Established methods complement the human image evaluation, but may carry undisclosed biases in such a new context, therefore we re-evaluate all the steps in hyperplexed proteomics. We found that the human eye can discriminate less than 64 out of 256 gray levels and has limitations in discriminating luminance levels in conventional histology images. Furthermore, only images containing visible signals are selected and eye-guided digital thresholding separates signal from noise. BRAQUE, a hyperplexed proteomic tool, can extract, in a marker-agnostic fashion, granular information from markers which have a very low signal-to-noise ratio and therefore are not visualized by traditional visual rendering. By analyzing a public human lymph node dataset, we also found unpredicted staining results by validated antibodies, which highlight the need to upgrade the definition of antibody specificity in hyperplexed immunostaining. Spatially hyperplexed methods upgrade and supplant traditional image-based analysis of tissue immunostaining, beyond the human eye contribution.

关键词： dimensionality reduction algorithms flow cytometry human vision image analysis multiplex staining

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Perceptual and technical barriers in sharing and formatting metadata accompanying omics studies

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Cell Genomics 2025年第5期5卷 100845-100845页

作者： Huang, Yu-Ning Munteanu, Viorel Love, Michael I. Ronkowski, Cynthia Flaire Deshpande, Dhrithi Wong-Beringer, Annie Corbett-Detig, Russell Dimian, Mihai Moore, Jason H. Garmire, Lana X. Reddy, T.B.K. Butte, Atul J. Robinson, Mark D. Eskin, Eleazar Abedalthagafi, Malak S. Mangul, Serghei Department of Clinical Pharmacy Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles 90089 CA United States Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau 2045 Moldova Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava 720229 Romania Department of Genetics University of North Carolina at Chapel Hill Chapel Hill 27514 NC United States Department of Biostatistics University of North Carolina at Chapel Hill Chapel Hill 27514 NC United States Department of Biomolecular Engineering University of California Santa Cruz Santa Cruz 95064 CA United States Genomics Institute University of California Santa Cruz Santa Cruz 95064 CA United States Department of Computers Electronics and Automation Stefan cel Mare University of Suceava Suceava 720229 Romania Department of Computational Biomedicine Cedars-Sinai Medical Center Los Angeles 90069 CA United States Department of Computational Medicine and Bioinformatics University of Michigan Medical School Ann Arbor 48105 MI United States US Department of Energy Joint Genome Institute Lawrence Berkeley National Laboratory Berkeley 94720 CA United States Bakar Computational Health Sciences Institute University of California San Francisco (UCSF) San Francisco 94143 CA United States Center for Data-Driven Insights and Innovation University of California Oakland Oakland 94607 CA United States SIB Swiss Institute of Bioinformatics and Department of Molecular Life Sciences University of Zurich Zurich 8057 Switzerland Department of Computer Science University of California Los Angeles Los Angeles CA United States Department of Computational Medicine University of California Los Angeles Los Angeles CA United States Department of Human Genetics University of California Los Angeles Los Angeles CA United Stat

Metadata, or “data about data,” is essential for organizing, understanding, and managing large-scale omics datasets. It enhances data discovery, integration, and interpretation, enabling reproducibility, reusability, and secondary analysis. However, metadata sharing remains hindered by perceptual and technical barriers, including the lack of uniform standards, privacy concerns, study design limitations, insufficient incentives, inadequate infrastructure, and a shortage of trained personnel. These challenges compromise data reliability and obstruct integrative meta-analyses. Addressing these issues requires standardization, education, stronger roles for journals and funding agencies, and improved incentives and infrastructure. Looking ahead, emerging technologies such as artificial intelligence and machine learning may offer promising solutions to automate metadata processes, increasing accuracy and scalability. Fostering a collaborative culture of metadata sharing will maximize the value of omics data, accelerating innovation and scientific discovery. © 2025 The Authors

关键词： barriers in metadata sharing practices data metadata metadata completeness

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Integrated querying and version control of context-specific biological networks

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database 2020年第2020期2020卷 baaa018-baaa018页

作者： Cowman, Tyler Coşkun, Mustafa Grama, Ananth Koyutürk, Mehmet Department of Computer and Data Sciences Case Western Reserve University Cleveland 44106 OH United States Center for Proteomics and Bioinformatics Case Western Reserve University Cleveland 44106 OH United States Department of Computer Engineering Abdullah Gül University Kayseri 38080 Turkey Department of Computer Science Purdue University West Lafayette 47906 IN United States

Motivation: Biomolecular data stored in public databases is increasingly specialized to organisms, context/pathology and tissue type, potentially resulting in significant overhead for analyses. These networks are often specializations of generic interaction sets, presenting opportunities for reducing storage and computational cost. Therefore, it is desirable to develop effective compression and storage techniques, along with efficient algorithms and a flexible query interface capable of operating on compressed data structures. Current graph databases offer varying levels of support for network integration. However, these solutions do not provide efficient methods for the storage and querying of versioned networks. Results: We present VerTIoN, a framework consisting of novel data structures and associated query mechanisms for integrated querying of versioned context-specific biological networks. As a use case for our framework, we study network proximity queries in which the user can select and compose a combination of tissue-specific and generic networks. Using our compressed version tree data structure, in conjunction with state-of-the-art numerical techniques, we demonstrate real-time querying of large network databases. Conclusion: Our results show that it is possible to support flexible queries defined on heterogeneous networks composed at query time while drastically reducing response time for multiple simultaneous queries. The flexibility offered by VerTIoN in composing integrated network versions opens significant new avenues for the utilization of ever increasing volume of context-specific network data in a broad range of biomedical applications. © 2020 The Author(s) 2020. Published by Oxford University Press.

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TMOD-04. IMAGE-BASED DRUG RESPONSE PROFILING FROM PEDIATRIC TUMOR CELL SPHEROIDS USING PATIENT-BY-PATIENT DEEP TRANSFER LEARNING

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Neuro-Oncology 2021年第SUPPLEMENT_1期23卷 i36-i36页

作者： Berker, Yannick ElHarouni, Dina Peterziel, Heike Oehme, Ina Schlesner, Matthias Witt, Olaf Oppermann, Sina Hopp Children’s Cancer Center Heidelberg (KiTZ) Heidelberg Germany Translational Pediatric Pharmacology Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Clinical Bioinformatics Pediatric Neurooncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Clinical Cooperation Unit Pediatric Oncology German Cancer Research Center (DKFZ) and German Cancer Consortium (DKTK) Heidelberg Germany Biomedical Informatics Data Mining and Data Analytics University of Augsburg Augsburg Germany

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Whole-genome sequencing of 1,171 elderly admixed individuals from Brazil (vol 13, 1004, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-11页

作者： Naslavsky, Michel S. Scliar, Marilia O. Yamamoto, Guilherme L. Wang, Jaqueline Yu Ting Zverinova, Stepanka Karp, Tatiana Nunes, Kelly Ceroni, Jose Ricardo Magliocco de Carvalho, Diego Lima da Silva Simoes, Carlos Eduardo Bozoklian, Daniel Nonaka, Ricardo dos Santos Brito Silva, Nayane da Silva Souza, Andreia de Souza Andrade, Heloisa Passos, Marilia Rodrigues Silva Castro, Camila Ferreira Bannwart Mendes-Junior, Celso T. Mercuri, Rafael L. V. Miller, Thiago L. A. Buzzo, Jose Leonel Rego, Fernanda O. Araujo, Nathalia M. Magalhaes, Wagner C. S. Mingroni-Netto, Regina Celia Borda, Victor Guio, Heinner Rojas, Carlos P. Sanchez, Cesar Caceres, Omar Dean, Michael Barreto, Mauricio L. Lima-Costa, Maria Fernanda Horta, Bernardo L. Tarazona-Santos, Eduardo Meyer, Diogo Galante, Pedro A. F. Guryev, Victor Castelli, Erick C. Duarte, Yeda A. O. Passos-Bueno, Maria Rita Zatz, Mayana Human Genome and Stem Cell Research Center University of São Paulo São Paulo SP Brazil Department of Genetics and Evolutionary Biology Biosciences Institute University of São Paulo São Paulo SP Brazil Hospital Israelita Albert Einstein São Paulo SP Brazil Instituto da Criança Faculdade de Medicina da Universidade de São Paulo São Paulo SP Brazil Orthopedic Research Labs Boston Children’s Hospital and Department of Genetics Harvard Medical School Boston MA USA Laboratório DASA São Paulo Brazil Laboratory of Genome Structure and Ageing European Research Institute for the Biology of Ageing University Medical Center Groningen Groningen Netherlands São Paulo State University (UNESP) Molecular Genetics and Bioinformatics Laboratory School of Medicine Botucatu State of São Paulo Brazil São Paulo State University (UNESP) Department of Pathology School of Medicine Botucatu State of São Paulo Brazil Departamento de Química Faculdade de Filosofia Ciências e Letras de Ribeirão Preto Universidade de São Paulo Ribeirão Preto São Paulo Brazil Centro de Oncologia Molecular Hospital Sirio-Libanes São Paulo Brazil Department of Biochemistry Institute of Chemistry University of São Paulo São Paulo São Paulo Brazil Bioinformatics Graduate program University of São Paulo São Paulo Brazil Departamento de Genética Ecologia e Evolução Instituto de Ciências Biológicas Universidade Federal de Minas Gerais Belo Horizonte MG Brazil Núcleo de Ensino e Pesquisa Instituto Mário Penna Belo Horizonte MG Brazil Laboratorio de Biotecnologia y Biologia Molecular Instituto Nacional de Salud Lima Peru Universidad de Huánuco Huánuco Peru Division of Cancer Epidemiology and Genetics National Cancer Institute Bethesda MD USA Instituto de Saúde Coletiva Universidade Federal da Bahia Salvador BA 40110-040 Brazil Center for Data and Knowledge Integration for Health Institute Gonçalo Muniz Fundação Oswaldo Cruz Salvador BA Brazil Instituto de Pesquisas René Rachou Fundação Os

As whole-genome sequencing (WGS) becomes the gold standard tool for studying population genomics and medical applications, data on diverse non-European and admixed individuals are still scarce. Here, we present a high-coverage WGS dataset of 1,171 highly admixed elderly Brazilians from a census-based cohort, providing over 76 million variants, of which ~2 million are absent from large public databases. WGS enables identification of ~2,000 previously undescribed mobile element insertions without previous description, nearly 5 Mb of genomic segments absent from the human genome reference, and over 140 alleles from HLA genes absent from public resources. We reclassify and curate pathogenicity assertions for nearly four hundred variants in genes associated with dominantly-inherited Mendelian disorders and calculate the incidence for selected recessive disorders, demonstrating the clinical usefulness of the present study. Finally, we observe that whole-genome and HLA imputation could be significantly improved compared to available datasets since rare variation represents the largest proportion of input from WGS. These results demonstrate that even smaller sample sizes of underrepresented populations bring relevant data for genomic studies, especially when exploring analyses allowed only by WGS.

关键词： data publication and archiving Haplotypes Mobile elements Rare variants Structural variation

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Association of Risk Factors For Venous Thromboembolism and Overall Survival in Lung Cancer

Blood Vessels, Thrombosis & Hemostasis

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Blood Vessels, Thrombosis & Hemostasis 2025年

作者： Thomas E. Plate Asaad Trabolsi Rachel S. Kronenfeld Leticia E. Campoverde Dan Morgenstern-Kaplan Alyssa J. Mercadel Michael Caballero Wei Zhao Gerald A. Soff Sylvester Comprehensive Cancer Center University of Miami Miller School of Medicine Miami Fl USA Hematology Oncology Fellowship Program Jackson Memorial Health System/ University of Miami Miami Fl USA Division of Pediatric Hematology/Oncology University of Miami/Jackson Memorial Hospital Miami FL Internal Medicine Residency program division of internal medicine Department of Medicine University of Miami/ Jackson Memorial Hospital Miami Division of Gynecologic Oncology Department of Obstetrics Gynecology and Reproductive Sciences University of Miami Leonard M. Miller School of Medicine Miami Data Science and Engineering UHealth Information Technology University of Miami Miami FL Biostatistics and Bioinformatics Shared Resource Sylvester Comprehensive Cancer Center University of Miami Miami FL Division of Hematology Department of Medicine University of Miami Miller School of Medicine Miami Fl USA

Venous thromboembolism (VTE) is a frequent complication in patients with lung cancer, but the risk factors and incidence in different lung cancer subtypes have not been fully characterized. Despite multiple studies supporting the use of VTE prophylaxis in patients with cancer at increased risk of VTE by the Khorana score (KS), routine use of VTE prophylaxis is uncommon in clinical practice for these patients. This study further characterizes the risk factors and incidence of VTE and in patients with lung cancer at a university cancer center. Further, we assessed the association of KS and its individual components with overall survival in this same group of patients with lung cancer. Utilizing natural language processing and human review to detect thrombotic events in the electronic medical record, a 12-month incidence of 10.1% was identified in the 632 lung cancer patients analyzed. Significant risk factors included age <60 years and white blood cell count (WBC) ≥ 11× 10 9 /L, but KS itself was not significantly associated with VTE. The median overall survival was 12 months with VTE. The KS, age ≥ 60 years, stage III-IV, WBC ≥ 11× 10 9 /L, Hgb <10 gm/dL, BMI, surgery and VTE were identified as significant predictors of death. These findings warrant further validation, as the KS and two of its individual components in this study of lung cancer were significantly associated with reduced overall survival.

关键词： Lung cancer thrombosis Khorana Score cancer associated thrombosis

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Correction to: Spatiotemporal expression profle of novel and known small RNAs throughout rice plant development focussing on seed tissues

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BMC genomics 2022年第1期23卷 141页

作者： Anikó Meijer Tim De Meyer Klaas Vandepoele Tina Kyndt Department of Biotechnology Ghent University Ghent Belgium. Department of Data Analysis and Mathematical Modelling Ghent University Ghent Belgium. Department of Plant Biotechnology and Bioinformatics Ghent University Ghent Belgium. Klaas.Vandepoele@psb.vib-ugent.be. VIB Center for Plant Systems Biology Ghent Belgium. Klaas.Vandepoele@psb.vib-ugent.be. Bioinformatics Institute Ghent Ghent University Ghent Belgium. Klaas.Vandepoele@psb.vib-ugent.be. Department of Biotechnology Ghent University Ghent Belgium. Tina.Kyndt@UGent.be.

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