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      Integrative proteogenomic characterization of hepatocellular carcinoma across etiologies and stages

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          Abstract

          Proteogenomic analyses of hepatocellular carcinomas (HCC) have focused on early-stage, HBV-associated HCCs. Here we present an integrated proteogenomic analysis of HCCs across clinical stages and etiologies. Pathways related to cell cycle, transcriptional and translational control, signaling transduction, and metabolism are dysregulated and differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels. We describe candidate copy number-driven driver genes involved in epithelial-to-mesenchymal transition, the Wnt-β-catenin, AKT/mTOR and Notch pathways, cell cycle and DNA damage regulation. The targetable aurora kinase A and CDKs are upregulated. CTNNB1 and TP53 mutations are associated with altered protein phosphorylation related to actin filament organization and lipid metabolism, respectively. Integrative proteogenomic clusters show that HCC constitutes heterogeneous subgroups with distinct regulation of biological processes, metabolic reprogramming and kinase activation. Our study provides a comprehensive overview of the proteomic and phophoproteomic landscapes of HCCs, revealing the major pathways altered in the (phospho)proteome.

          Abstract

          Proteogenomic analyses of hepatocellular carcinomas (HCC) have focused on early-stage, HBV-associated tumours and lacked information about the phosphoproteome. Here, the authors present a comprehensive HCC proteogenomics and phosphoproteomics study in patient samples from multiple etiologies and stages.

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          Most cited references104

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                markus.heim@unibas.ch
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                4 May 2022
                4 May 2022
                2022
                : 13
                : 2436
                Affiliations
                [1 ]GRID grid.410567.1, Department of Biomedicine, , University Hospital Basel, University of Basel, ; Basel, Switzerland
                [2 ]GRID grid.410567.1, Institute of Medical Genetics and Pathology, , University Hospital Basel, University of Basel, ; Basel, Switzerland
                [3 ]GRID grid.5734.5, ISNI 0000 0001 0726 5157, Department for BioMedical Research (DBMR), , University of Bern, ; Bern, Switzerland
                [4 ]GRID grid.419765.8, ISNI 0000 0001 2223 3006, SIB Swiss Institute of Bioinformatics, ; Lausanne, Switzerland
                [5 ]GRID grid.6612.3, ISNI 0000 0004 1937 0642, Biozentrum, , University of Basel, ; Basel, Switzerland
                [6 ]GRID grid.410567.1, Department of Gastroenterology and Hepatology, , University Hospital Basel, ; Basel, Switzerland
                [7 ]GRID grid.417728.f, ISNI 0000 0004 1756 8807, Department of Pathology, , Humanitas Clinical and Research Center, IRCCS, ; Milan, Italy
                [8 ]GRID grid.452490.e, Department of Biomedical Sciences, , Humanitas University, ; Milan, Italy
                Author information
                http://orcid.org/0000-0002-6100-0026
                http://orcid.org/0000-0002-7292-2261
                http://orcid.org/0000-0001-7596-1250
                http://orcid.org/0000-0002-1863-1869
                http://orcid.org/0000-0002-5611-2699
                http://orcid.org/0000-0002-9314-5318
                http://orcid.org/0000-0002-3149-2381
                http://orcid.org/0000-0002-4444-977X
                http://orcid.org/0000-0002-2005-5266
                http://orcid.org/0000-0003-2686-2939
                http://orcid.org/0000-0002-7523-4894
                Article
                29960
                10.1038/s41467-022-29960-8
                9068765
                35508466
                0164d7b0-fcef-4010-a198-b85e12606b10
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 10 May 2021
                : 9 April 2022
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                proteomics,cancer genomics,hepatocellular carcinoma,tumour heterogeneity,phosphoproteins

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