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Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data.

ABSTRACT: As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Blood transcriptome fingerprints for the 16 reference cohorts can be accessed interactively via: https://drinchai.shinyapps.io/BloodGen3Module/ .

SUBMITTER: Altman MC

PROVIDER: S-EPMC8289976 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data.

Altman Matthew C MC Rinchai Darawan D Baldwin Nicole N Toufiq Mohammed M Whalen Elizabeth E Garand Mathieu M Syed Ahamed Kabeer Basirudeen B Alfaki Mohamed M Presnell Scott R SR Khaenam Prasong P Ayllón-Benítez Aaron A Mougin Fleur F Thébault Patricia P Chiche Laurent L Jourde-Chiche Noemie N Phillips J Theodore JT Klintmalm Goran G O'Garra Anne A Berry Matthew M Bloom Chloe C Wilkinson Robert J RJ Graham Christine M CM Lipman Marc M Lertmemongkolchai Ganjana G Bedognetti Davide D Thiebaut Rodolphe R Kheradmand Farrah F Mejias Asuncion A Ramilo Octavio O Palucka Karolina K Pascual Virginia V Banchereau Jacques J Chaussabel Damien D

Nature communications 20210719 1

As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunologica ...[more]

PMID: 34282143

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Project description:IntroductionCirrhosis is one of the most important risk factors for development of hepatocellular carcinoma (HCC). Recent studies have shown that removal or well control of the underlying cause could reduce but not eliminate the risk of HCC. Therefore, it is important to elucidate the molecular mechanisms that drive the progression of cirrhosis to HCC.Materials and methodsMicroarray datasets incorporating cirrhosis and HCC subjects were identified from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were determined by GEO2R software. Functional enrichment analysis was performed by the clusterProfiler package in R. Liver carcinogenesis-related networks and modules were established using STRING database and MCODE plug-in, respectively, which were visualized with Cytoscape software. The ability of modular gene signatures to discriminate cirrhosis from HCC was assessed by hierarchical clustering, principal component analysis (PCA), and receiver operating characteristic (ROC) curve. Association of top modular genes and HCC grades or prognosis was analyzed with the UALCAN web-tool. Protein expression and distribution of top modular genes were analyzed using the Human Protein Atlas database.ResultsFour microarray datasets were retrieved from GEO database. Compared with cirrhotic livers, 125 upregulated and 252 downregulated genes in HCC tissues were found. These DEGs constituted a liver carcinogenesis-related network with 272 nodes and 2954 edges, with 65 nodes being highly connected and formed a liver carcinogenesis-related module. The modular genes were significantly involved in several KEGG pathways, such as "cell cycle," "DNA replication," "p53 signaling pathway," "mismatch repair," "base excision repair," etc. These identified modular gene signatures could robustly discriminate cirrhosis from HCC in the validation dataset. In contrast, the expression pattern of the modular genes was consistent between cirrhotic and normal livers. The top modular genes TOP2A, CDC20, PRC1, CCNB2, and NUSAP1 were associated with HCC onset, progression, and prognosis, and exhibited higher expression in HCC compared with normal livers in the HPA database.ConclusionOur study revealed a highly connected module associated with liver carcinogenesis on a cirrhotic background, which may provide deeper understanding of the genetic alterations involved in the transition from cirrhosis to HCC, and offer valuable variables for screening and surveillance of HCC in high-risk patients with cirrhosis.

Dataset Information

Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data.

Publications

Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data.

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