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      Integrative analysis of cancer multimodality data identifying COPS5 as a novel biomarker of diffuse large B-cell lymphoma

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          Abstract

          Preventing, diagnosing, and treating diseases requires accurate clinical biomarkers, which remains challenging. Recently, advanced computational approaches have accelerated the discovery of promising biomarkers from high-dimensional multimodal data. Although machine-learning methods have greatly contributed to the research fields, handling data sparseness, which is not unusual in research settings, is still an issue as it leads to limited interpretability and performance in the presence of missing information. Here, we propose a novel pipeline integrating joint non-negative matrix factorization (JNMF), identifying key features within sparse high-dimensional heterogeneous data, and a biological pathway analysis, interpreting the functionality of features by detecting activated signaling pathways. By applying our pipeline to large-scale public cancer datasets, we identified sets of genomic features relevant to specific cancer types as common pattern modules (CPMs) of JNMF. We further detected COPS5 as a potential upstream regulator of pathways associated with diffuse large B-cell lymphoma (DLBCL). COPS5 exhibited co-overexpression with MYC, TP53, and BCL2, known DLBCL marker genes, and its high expression was correlated with a lower survival probability of DLBCL patients. Using the CRISPR-Cas9 system, we confirmed the tumor growth effect of COPS5, which suggests it as a novel prognostic biomarker for DLBCL. Our results highlight that integrating multiple high-dimensional data and effectively decomposing them to interpretable dimensions unravels hidden biological importance, which enhances the discovery of clinical biomarkers.

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          Most cited references30

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          GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis

          Abstract Introduced in 2017, the GEPIA (Gene Expression Profiling Interactive Analysis) web server has been a valuable and highly cited resource for gene expression analysis based on tumor and normal samples from the TCGA and the GTEx databases. Here, we present GEPIA2, an updated and enhanced version to provide insights with higher resolution and more functionalities. Featuring 198 619 isoforms and 84 cancer subtypes, GEPIA2 has extended gene expression quantification from the gene level to the transcript level, and supports analysis of a specific cancer subtype, and comparison between subtypes. In addition, GEPIA2 has adopted new analysis techniques of gene signature quantification inspired by single-cell sequencing studies, and provides customized analysis where users can upload their own RNA-seq data and compare them with TCGA and GTEx samples. We also offer an API for batch process and easy retrieval of the analysis results. The updated web server is publicly accessible at http://gepia2.cancer-pku.cn/.
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            Causal analysis approaches in Ingenuity Pathway Analysis

            Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. Availability: The causal analytics tools ‘Upstream Regulator Analysis', ‘Mechanistic Networks', ‘Causal Network Analysis' and ‘Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). Supplementary information: Supplementary material is available at Bioinformatics online.
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              A new initiative on precision medicine.

              President Obama has announced a research initiative that aims to accelerate progress toward a new era of precision medicine, with a near-term focus on cancers and a longer-term aim to generate knowledge applicable to the whole range of health and disease.
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                Author and article information

                Contributors
                URI : https://loop.frontiersin.org/people/2613755/overviewRole: Role:
                Role: Role: Role:
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                URI : https://loop.frontiersin.org/people/1065055/overviewRole: Role: Role:
                URI : https://loop.frontiersin.org/people/2529550/overviewRole: Role:
                URI : https://loop.frontiersin.org/people/1357254/overviewRole: Role:
                URI : https://loop.frontiersin.org/people/991531/overviewRole: Role:
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                21 June 2024
                2024
                : 15
                : 1407765
                Affiliations
                [1] 1 Department of Computational Biology and Medical Science , The University of Tokyo , Kashiwa, Japan
                [2] 2 The Institute of Medical Science , The University of Tokyo , Tokyo, Japan
                Author notes

                Edited by: Shoba Ranganathan, Macquarie University, Australia

                Reviewed by: Wanwei Zhang, Columbia University, United States

                Prashanth N. Suravajhala, Amrita Vishwa Vidyapeetham University, India

                *Correspondence: Kenta Nakai, knakai@ 123456ims.u-tokyo.ac.jp
                Article
                1407765
                10.3389/fgene.2024.1407765
                11224480
                64929698-1e2f-48e5-ae1f-4f91e8953796
                Copyright © 2024 Dai, Li, Yamamoto, Goyama, Loza, Park and Nakai.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 27 March 2024
                : 03 June 2024
                Funding
                The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by JST SPRING (JPMJSP2108 to YD) and AMED (23ck0106XXXh000X to SG).
                Categories
                Genetics
                Original Research
                Custom metadata
                Computational Genomics

                Genetics
                biomarker discovery,diffuse large b-cell lymphoma,joint non-negative matrix factorization,multi-omics,pathway analysis

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