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Associations between organic erectile dysfunction and the risk of herpes zoster and postherpetic neuralgia in men

Aging Male 2024年第1期27卷 2346310页

作者： Wang, Kuan-Hsun Hsieh, Wen-Che Lin, Heng-Jun Tsai, Fuu-Jen Hsu, Chao-Yu Department of Family Medicine Antai Medical Care Corporation Antai Tian-Sheng Memorial Hospital Pingtung Taiwan Department of Chinese Medicine Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan Management Office for Health Data Clinical Trial Research Center China Medical University Hospital Taichung Taiwan School of Chinese Medicine College of Chinese Medicine China Medical University Taichung Taiwan Department of Medical Research China Medical University Hospital Taichung Taiwan Division of Medical Genetics China Medical University Children’s Hospital Taichung Taiwan Department of Biotechnology and Bioinformatics Asia University Taichung Taiwan Department of Medical Education Ditmanson Medical Foundation Chia-Yi Christian Hospital Chia-Yi Taiwan Department of Artificial Intelligence and Healthcare Management Central Taiwan University of Science and Technology Taichung Taiwan Center for General Education National Taichung University of Science and Technology Taichung Taiwan Department of General Education National Chin-Yi University of Technology Taichung Taiwan

Background: Whether erectile dysfunction (ED) leads to considerable stress for affected men remains unclear? In this study, we investigated whether organic ED (OED) is associated with increased risks of herpes zoster (HZ) and postherpetic neuralgia (PHN). Methods: A representative subset of Taiwan’s National Health Insurance research database was employed for this study. Enrollees with OED from the years 2000 to 2018 were selected. To ensure comparability between the case and control groups, we implemented 1:1 propensity score matching based on age, index year, comorbidities, and medications. Results: The case group included 20,808 patients with OED, while the control group consisted of 20,808 individuals without OED. The OED group exhibited a significantly elevated risk of HZ (adjusted hazard ratio [aHR] = 1.74) and PHN (aHR = 1.56) compared to the non-OED group. Conclusions: Men experiencing OED seem to face elevated risks of HZ and PHN compared to those without OED. ED may serve as a warning sign for individuals at HZ risk. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Erectile dysfunction herpes zoster postherpetic neuralgia

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Prediction of Therapeutic Potential of CD19 CAR-T Cells for LBCL By Histone Mark Analyses of Core Epigenetic Programming

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Blood 2024年 144卷 2028-2028页

作者： Salvatore Fiorenza Yue Cao Ye Zheng Janaki Purushe Tamara Bock Erik L Kimble Derek Janssens Delaney Kirchmeier Alexandre V. Vinaud Hirayama Jordan Gauthier David G. Maloney Stanley R. Riddell Akira Nguyen-Shaw Steven Henikoff Jean Yang Cameron J. Turtle Faculty of Medicine and Health University of Sydney Balwyn North Australia Sydney Precision Data Science Centre University of Sydney Sydney Australia Bioinformatics and Computational Biology Department The University of Texas MD Anderson Cancer Center Houston TX Fred Hutchinson Seattle WA Faculty of Medicine and Health University of Sydney Camperdown Australia Fred Hutchinson Cancer Center Seattle WA Basic Sciences Division Van Andel Ins Seattle WA Clinical Research Division Fred Hutchinson Cancer Center Seattle WA Fred Hutch Cancer Center University of Washington Seattle WA Translational Science and Therapeutics Division Fred Hutchinson Cancer Center Seattle WA Basic Sciences Division Fred Hutchinson Cancer Center Seattle WA Sydney Precision Data Science Centre University of Sydney Camperdown Australia University of Sydney Sydney Australia

Chimeric antigen receptor-T cell (CAR-T) therapy has produced durable responses in a subset of patients with otherwise incurable B-lineage malignancies. Efforts are underway to identify genetic and epigenetic programs in CAR-T cells that influence product quality, thereby enabling development of next generation CAR-T products. The subset-of-origin (SOO) for CAR-T cell manufacturing and the effects of serial stimulation are key factors that might influence CAR-T quality. We hypothesized that serial stimulation might impart different effects on the epigenetic programming of CAR-T cells manufactured from distinct subsets; and that these epigenetic programs might associate with outcomes of clinical CAR-T therapy. We previously showed that sequencing of DNA bound to transcriptionally permissive (H3K4me2) and repressive (H3K27me3) histone methylation marks identifies numerous important differences in CAR-T cells from healthy donors (HD) and patients that were not apparent by RNA-seq. Here, we applied novel histone mark analyses to identify epigenetic programs that impact outcomes of large B cell lymphoma (LBCL) patients receiving CAR-T cell therapy. We first validated a novel in vitro model generating a roadmap of epigenetic and transcriptional programs during longitudinal serial stimulation of CAR T-cells manufactured from distinct SOO. H3K27me3 and H3K4me2 analyses and RNA-seq were performed on ex vivo isolated CD8+ Naïve (N), Central Memory (CM) and Effector Memory (EM) subsets from HD, on each SOO-derived CAR-T cell product after manufacturing, and prior to cycles of serial stimulation with a CD19+ lymphoma cell line. We first derived principal component analysis (PCA) plots from the 3 datasets (H3K4me2, H3K27me3, RNA-seq), each comprising 15 samples (3 SOO, 5 timepoints) from 7 HD. In PCA plots of histone mark data, we identified two epigenetic programmatic axes. One axis distinguished the SOO of CAR-T manufacturing. A second axis recapitulated the stimulation histor

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Corrigendum to “Best match graphs”

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Journal of Mathematical Biology 2021年第6期82卷 47-47页

作者： Schaller, David Geiß, Manuela Chávez, Edgar González Laffitte, Marcos López Sánchez, Alitzel Stadler, Bärbel M. R. Valdivia, Dulce I. Hellmuth, Marc Hernández Rosales, Maribel Stadler, Peter F. Max-Planck-Institute for Mathematics in the Sciences Inselstraße 22 Leipzig 04103 Germany Software Competence Center Hagenberg GmbH Softwarepark 21 Hagenberg 4232 Austria Centro de Física Aplicada y Tecnología Avanzada UNAM Juriquilla 76230 QRO Mexico Instituto de Matemáticas UNAM Juriquilla Blvd. Juriquilla 3001 Juriquilla Querétaro 76230 QRO Mexico Department of Computer Science Université de Sherbrooke 2500 Boul. de l’Université Sherbrooke J1K 2R1 Canada Centro de Investigación y de Estudios Avanzandos del IPN (CINVESTAV) Irapuato Unit Libramiento Norte Carretera Panamericana Irapuato-León Kilómetro 9.6 Irapuato 36821 Guanajuato Mexico Department of Mathematics Faculty of Science Stockholm University Stockholm SE - 106 91 Sweden Bioinformatics Group Department of Computer Science Interdisciplinary Center of Bioinformatics German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Leipzig Germany Leipzig Research Center for Civilization Diseases Leipzig University Härtelstraße 16-18 Leipzig 04107 Germany Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 Wien 1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa Fe 87501 NM United States

Two errors in the article Best Match Graphs (Geiß et al. in JMB 78: 2015–2057, 2019) are corrected. One concerns the tacit assumption that digraphs are sink-free, which has to be added as an additional precondition in Lemma 9, Lemma 11, Theorem 4. Correspondingly, Algorithm 2 requires that its input is sink-free. The second correction concerns an additional necessary condition in Theorem 9 required to characterize best match graphs. The amended results simplify the construction of least resolved trees for n-cBMGs, i.e., Algorithm 1. All other results remain unchanged and are correct as stated. © 2021, The Author(s).

关键词： Colored digraph Corrigendum Phylogenetic Combinatorics Rooted triples

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PO-454 Biological activities of novel brassinosteroid analogues in breast cancer cells

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ESMO Open 2018年 3卷 A200-A200页

作者： Andreeva, O. Savochka, O. Shcherbinin, D. Zhabinskii, V. Khripach, V. Scherbakov, A. N.N. Blokhin National Medical Research Center of Oncology of the Ministry of Health of the Russian Federation Laboratory of Molecular Endocrinology Moscow Russia Institute of Bioorganic Chemistry- National Academy of Sciences of Belarus Laboratory of Steroids Minsk Belarus Skolkovo Institute of Science and Technology Center for Data-Intensive Biomedicine and Biotechnology Moscow Russia Institute of Biomedical Chemistry Laboratory of Structure Bioinformatics Moscow Russia N.N. Blokhin National Medical Research Center of Oncology of the Ministry of Health of the Russian Federation Laboratory of OncoProteomics Moscow Russia

ABSTRACTIntroductionBrassinosteroids (BS) are plant steroid hormones playing an important roles in various physiological processes and being of particular interest because of their antiproliferative activities towards human cancer cells. The study was dedicated to the synthesis of novel BS analogues and to the analysis of their effects on breast cancer (BC) cells and normal epithelium. Material and methodsWe developed an approach for the synthesis of novel secasterol derivatives with variations in the B cycle and side chain. Six new compounds, BS2-BS7, were obtained. Biological experiments were performed on the MCF-7 BC and MCF-10A normal breast epithelium cell cultures. The cytotoxicity was assessed by MTT test. Dock6 and Autodock Vina were used for molecular docking. Binding energies were evalueted using MM_PBSA approach on the 15 ns trajectories of MD simulation (Gromacs 5.1). Reporter assays were performed in steroid-free conditions. The MCF-7 BC cells were transfected with plasmids containing sequences for ERα- and AP-1-driven luciferase expression together with β-galactosidase plasmid to control the transfection efficacy. After transfection cells were treated with BS or vehicle control; then in 9–24 hour the cells were collected for subsequent analysis of luciferase and β-galactosidase activities. ERα expression was measured by immunoblotting. Results and discussionsEvaluation of BS toxicity against MCF-7 revealed that compounds with cholestane/ergostane based side chain inhibited cell growth with IC50 values of 12.7–23.6 μmol. (22 R,23 R,24 R)−22,23-Dihydroxy-24-methyl-B-homo-7-oxa-5α-cholest-2-en-6-one ( BS4) was chosen as the lead compound with low toxicity in MCF-10A normal epithelium and high activity against BC. ERα was found to be a presumable target of BS4. BS/ERα complex structures were obtained using molecular docking software. The best BS4 conformation was found to match 17β-estradiol conformation in ERα ligand-binding domain and had the best bindin

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Proteomics Standards Initiative's ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms

arXiv

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arXiv 2021年

作者： LeDuc, Richard D. Deutsch, Eric W. Binz, Pierre-Alain Fellers, Ryan T. Cesnik, Anthony J. Klein, Joshua A. Van Den Bossche, Tim Gabriels, Ralf Yalavarthi, Arshika Perez-Riverol, Yasset Carver, Jeremy Bittremieux, Wout Kawano, Shin Pullman, Benjamin Bandeira, Nuno Kelleher, Neil L. Thomas, Paul M. Vizcaíno, Juan Antonio National Resource for Translational and Developmental Proteomics Northwestern University EvanstonIL60611 United States Institute for Systems Biology SeattleWA98109 United States Clinical Chemistry Service Lausanne University Hospital Lausanne1011 Switzerland Department of Genetics Stanford University StanfordCA94305 United States Chan Zuckerberg Biohub 499 Illinois St San FranciscoCA94158 United States SciLifeLab School of Engineering Sciences in Chemistry Biotechnology and Health KTH-Royal Institute of Technology Stockholm 113 51 SolnaSE-171 21 Sweden Program for Bioinformatics Boston University BostonMA02215 United States VIB-UGent Center for Medical Biotechnology VIB Technologiepark 75-FSVM II Ghent9052 Belgium Department of Biomolecular Medicine Faculty of Medicine and Health Sciences Ghent University Ghent9000 Belgium Cambridge HinxtonCB10 1SD United Kingdom La Jolla CA92093 United States La Jolla CA92093 United States La Jolla CA92093 United States Toyama University of International Studies Toyama Higashikuromaki 6 5-1 Toyama930-1292 Japan Database Center for Life Science Joint Support-Center for Data Science Research Research Organization of Information and Systems Chiba Kashiwa277-0871 Japan

It is important for the proteomics community to have a standardized manner to represent all possible variations of a protein or peptide primary sequence, including natural, chemicallyinduced and artifactualmodifications. The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) in collaboration with several members of the Consortium for Top-Down Proteomics (CTDP) has developed a standard notation called ProForma 2.0, which is a substantial extension of the original ProForma notation developed by the CTDP. ProForma 2.0 aims to unify the representation of proteoforms and peptidoforms. ProForma 2.0 supports use cases needed for bottom-up and middle-/top-down proteomics approaches and allows the encoding of highly modified proteins and peptides using a humanand machine-readable string. ProForma 2.0 can be used to represent protein modifications in a specified or ambiguous location, designated by mass shifts, chemical formulas, or controlled vocabulary terms, including cross-links (natural and chemical), and atomic isotopes. Notational conventions are based on public controlled vocabularies and ontologies. The most up-to-date full specification document and information about software implementations are available at http://***/proforma. © 2021, CC0.

关键词： Mass spectrometry

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Correction to: Effect of early tumor response on the health-related quality of life among patients on second-line chemotherapy for advanced gastric cancer in the ABSOLUTE trial

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Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association 2021年第2期24卷 477-478页

作者： Kazumasa Fujitani Kohei Shitara Atsuo Takashima Keisuke Koeda Hiroki Hara Norisuke Nakayama Shuichi Hironaka Kazuhiro Nishikawa Yutaka Kimura Kenji Amagai Hisashi Hosaka Yoshito Komatsu Ken Shimada Ryohei Kawabata Hideki Ohdan Yasuhiro Kodera Masato Nakamura Takako Eguchi Nakajima Yoshinori Miyata Toshikazu Moriwaki Tetsuya Kusumoto Kazuo Nishikawa Kazuhiro Ogata Masashi Shimura Satoshi Morita Wasaburo Koizumi Department of Surgery Osaka General Medical Center 3-1-56 Bandaihigashi Sumiyoshi-ku Osaka 558-0056 Japan. fujitani@gh.opho.jp. Department of Gastrointestinal Oncology National Cancer Center Hospital East Kashiwa Japan. Gastrointestinal Medical Oncology Division National Cancer Center Hospital Tokyo Japan. Department of Medical Safety Science Iwate Medical University Morioka Japan. Department of Gastroenterology Saitama Cancer Center Ina-machi Japan. Department of Gastroenterology Kanagawa Cancer Center Yokohama Japan. Clinical Trial Promotion Department Chiba Cancer Center Chiba Japan. Department of Medical Oncology and Hematology Faculty of Medicine Oita University Oita Japan. Department of Surgery National Hospital Organization Osaka National Hospital Osaka Japan. Department of Surgery Sakai City Medical Center Sakai Japan. Department of Gastroenterology Ibaraki Prefectural Central Hospital Kasama Japan. Division of Gastroenterology Gunma Prefectural Cancer Center Ohta Japan. Division of Cancer Chemotherapy Hokkaido University Hospital Cancer Center Sapporo Japan. Division of Medical Oncology Department of Internal Medicine Showa University Northern Yokohama Hospital Yokohama Japan. Department of Surgery Osaka Rosai Hospital Osaka Japan. Department of Gastroenterological and Transplant Surgery Graduate School of Biomedical and Health Sciences Hiroshima University Hiroshima Japan. Department of Gastroenterological Surgery Nagoya University Graduate School of Medicine Nagoya Japan. Aizawa Comprehensive Cancer Center Aizawa Hospital Nagano Japan. Department of Clinical Oncology St. Marianna University School of Medicine Kawasaki Japan. Kyoto Innovation Center for Next Generation Clinical Trials and iPS Cell Therapy Kyoto University Hospital Kyoto Japan. Department of Medical Oncology Saku Central Hospital Advanced Care Center Saku Japan. Division of Gastroenterology Faculty of Medicine University of Tsukuba Tsukuba Japan. Department of Gast

The article Effect of early tumor response on the health-related quality of life among patients.

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bioinformatics Pathway Analysis Pipeline for NGS Transcriptome Profile data on Nasopharyngeal Carcinoma

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

作者： Digdo Sudigyo Gisti Rahmawati Dicka W. Setiasari Risky H. Poluan Tjeng Wawan Cenggoro Arif Budiarto Alam Ahmad Hidayat Sri R. Indrasari Afiahayati Sofia M. Haryana Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Study Program of Biotechnology Gadjah Mada University Yogyakarta Indonesia 55281 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Faculty of Medicine Public Health and Nursing Gadjah Mada University Yogyakarta Indonesia 55281 Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Gadjah Mada University Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

Next-Generation Sequencing (NGS)-based genomics data have a huge potential to be used in transcriptomic profiling of Nasopharyngeal Carcinoma (NPC) to study the biosynthesis mechanism behind it. The high dimensionality of NGS data is the main challenge in performing the data analysis to extract useful information. In this workflow pipeline, memory-efficient Linux-based software such as HISAT2 and HTSeq are utilized to process the raw NGS data. Furthermore, Differential Expression Gene (DEG) list can be obtained by performing advanced analysis to the aligned Ribonucleic Acid (RNA) sequence using the edgeR protocol. This DEG list is one of the main inputs of biological pathway analysis that can be done in DAVID and PANTHER web-based software. Both tools generate a different pathway result related to inflammation.

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Hybrid feature selection to identify important single nucleotide polymorphism for asthma classification

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AIP Conference Proceedings 2024年第1期2991卷

作者： Wan Ni Wong Weng Howe Chan Hui Wen Nies Kauthar Mohd Daud Artificial Intelligence and Bioinformatics Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia UTM Big Data Centre Ibnu Sina Institute for Scientific and Industrial Research Universiti Teknologi Malaysia 81310 Skudai Johor Malaysia Center for Artificial Intelligence Technology Faculty of Information Science & Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia

Single nucleotide polymorphism (SNP), being the most common genetic variation, is found to be responsible for traits and complex diseases such as asthma. Polygenic risk scoring (PRS) and machine learning (ML) are the current two common types of genetic risk prediction models. PRS employs statistical association testing to identify alleles with risk. However, it was found not performing well in unraveling the SNP-SNP interactions and attaining low predictive value. In contrast, ML algorithms are more robust in detecting disease associated SNPs, resulting in an improvement in prediction performance. The challenge remains as the high dimensionality nature of SNP tends to cause overfitting problems and impact the classifier performance. This paper introduces a two-stage hybrid feature selection that integrates Mutual Information and Random Forest Recursive Feature Elimination for identifying informative SNPs from 176,287 SNPs in a dataset of 128 asthmatic and non-asthmatic samples. The reduced samples based on the identified informative SNPs are then used with Support Vector Machine to train a classification model. The model from this paper outperforms other ML models with an average accuracy, precision, recall and area under the curve of 98.91%, 98.36%, 99.72% and 0.99 respectively. Additional analysis shows that the genes associated with the top selected SNPs, namely Ring Finger Protein 217 (RNF217) and A-kinase-anchoring protein 6 (AKAP6) are correlated with respiratory diseases. This shows that the proposed method can identify important SNPs that are able to differentiate between asthmatic and non-asthmatic samples.

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Prediction of oral food challenge outcomes via ensemble learning

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Informatics in Medicine Unlocked 2023年 36卷

作者： Zhang, Justin Lee, Deborah Jungles, Kylie Shaltis, Diane Najarian, Kayvan Ravikumar, Rajan Sanders, Georgiana Gryak, Jonathan Department of Electrical and Computer Engineering University of Michigan Ann Arbor MI United States Department of Internal Medicine University of Michigan Ann Arbor MI United States Department of Pediatrics University of Michigan Ann Arbor MI United States Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI United States Michigan Institute for Data Science University of Michigan Ann Arbor MI United States Department of Emergency Medicine University of Michigan Ann Arbor MI United States Department of Computer Science and Engineering University of Michigan Ann Arbor MI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann Arbor MI United States Mary H. Weiser Food Allergy Center University of Michigan Ann Arbor MI United States Department of Computer Science Queens College City University of New York New York NY United States

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values ≥89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study. © 2022 The Author(s)

关键词： Ensemble learning Learning Using Concave and Convex Kernels Machine learning Oral food challenges SHAP

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Genomics and epidemiology for gastric adenocarcinomas (GE4GAC): a Brazilian initiative to study gastric cancer

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Applied Cancer research 2019年第1期39卷 1-4页

作者： Bartelli, Thais F. Senda de Abrantes, Lais L. Freitas, Helano C. Thomas, Andrew M. Silva, Jordana M. Albuquerque, Gabriela E. Araújo, Luiza F. Branco, Gabriela P. de Amorim, Maria G. Serpa, Marianna S. Takenaka, Isabella K. T. M. Souza, Deborah T. Monção, Lucas O. Moda, Bruno S. Valieris, Renan Defelicibus, Alexandre Borges, Rodrigo Drummond, Rodrigo D. Alves, Francisco I. A. Santos, Monize N. P. Bobrovnitchaia, Irina G. Elhaik, Eran Coelho, Luiz G. V. Khayat, André Demachki, Samia Assumpção, Paulo P. Santiago, Karina M. Torrezan, Giovana T. Carraro, Dirce M. Peres, Stela V. Calsavara, Vinícius F. Burbano, Rommel Nóbrega, Calebe R. Baladão, Graziela P. P. Pereira, Ana C. C. Gatti, Camila M. Fagundes, Marcela A. Araújo, Marília S. Miranda, Tayana V. Barbosa, Monica S. Cardoso, Daniela M. M. Carneiro, Lilian C. Brito, Alexandre M. Ramos, Amanda F. P. L. Silva, Lucas L. L. Pontes, Jaqueline C. Tiengo, Tatiane Arantes, Paola E. Santana, Vilma Cordeiro, Milena Sant’Ana, Rosane O. Andrade, Hanna B. Anaissi, Ana K. M. Sampaio, Sara V. Abdallah, Emne A. Chinen, Ludmilla T. D. Braun, Alexcia C. Flores, Bianca C. T. Mello, Celso A. L. Claro, Laura C. L. Sztokfisz, Claudia Z. Altamirano, Carlos C. Carter, David R. F. Jesus, Victor H. F. Riechelmann, Rachel Medina, Tiago Gollob, Kenneth J. Martins, Vilma R. Setúbal, João C. Pelosof, Adriane G. Coimbra, Felipe J. Costa-Jr, Wilson L. Silva, Israel T. Nunes, Diana N. Curado, Maria P. Dias-Neto, Emmanuel Lab of Medical Genomics A.C.Camargo Cancer Center Sao Paulo Brazil International Research Center A.C.Camargo Cancer Center Sao Paulo Brazil Clinical Oncology Department A.C. Camargo Cancer Center São Paulo Brazil Department CIBIO University of Trento Trento Italy Biochemistry Department Chemistry Institute Universidade de São Paulo São Paulo Brazil Lab. of Bioinformatics and Computational Biology A.C.Camargo Cancer Center Sao Paulo Brazil Data Science group International Research Center A.C.Camargo Cancer Center Sao Paulo Brazil Department of Animal & Plant Sciences University of Sheffield Sheffield UK Instituto Alfa de Gastroenterologia Hospital das Clínicas Universidade Federal de Minas Gerais Belo Horizonte Brazil Instituto de Ciências Biológicas Universidade Federal do Pará Belém Brazil Núcleo de Pesquisas em Oncologia Universidade Federal do Pará Belém Brazil Lab. de Genômica e Biologia Molecular A.C.Camargo Cancer Center Sao Paulo Brazil Epidemiology and Statistics Group A.C.Camargo Cancer Center Sao Paulo Brazil Laboratório de Biologia Molecular Hospital Ophir Loyola Belém Brazil Universidade Federal de Goiás IPTESP – Instituto de Patologia Tropical e Saúde Pública Goiânia Brazil Department of Abdominal Surgery Hospital Araujo Jorge Goiânia Brazil Instituto de Saúde Coletiva da Universidade Federal da Bahia Salvador Brazil Instituto do Cancer do Ceará Fortaleza Brazil Department of Pathology A.C.Camargo Cancer Center São Paulo Brazil Endoscopy sector A.C.Camargo Cancer Center São Paulo Brazil Instituto Nacional de Enfermedades Neoplásicas Surquillo Peru Dept. of Biological and Medical Sciences Faculty of Health and Life Sciences Oxford Brookes University Oxford UK Translational Immuno-oncology Group A.C.Camargo Cancer Center Sao Paulo Brazil Tumor Biology and Biomarkers Group A.C.Camargo Cancer Center Sao Paulo Brazil Department of Abdominal Surgery A.C.Camargo Cancer Center São Paulo Brazil

Gastric cancer (GC) is the fifth most common type of cancer worldwide with high incidences in Asia, Central, and South American countries. This patchy distribution means that GC studies are neglected by large research centers from developed countries. The need for further understanding of this complex disease, including the local importance of epidemiological factors and the rich ancestral admixture found in Brazil, stimulated the implementation of the GE4GAC project. GE4GAC aims to embrace epidemiological, clinical, molecular and microbiological data from Brazilian controls and patients with malignant and pre-malignant gastric disease. In this letter, we summarize the main goals of the project, including subject and sample accrual and current findings.

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