Dataset Information

Dual Regulatory Mechanisms of Expression and Mutation Involving Metabolism-Related Genes FDFT1 and UQCR5 during CLM.

ABSTRACT: Colorectal cancer (CRC) is the third most common cancer worldwide, and liver metastasis presents a major cause of CRC-associated death. Extensive genomic analysis has provided valuable insight into the pathogenesis and progression of CRC; however, a comprehensive proteogenomic characterization of CRC liver metastasis (CLM) has yet to be reported. Here, we analyzed the proteomes of 44 paired normal colorectal tissues and CRC tissues with or without liver metastasis, as well as analyzed genomics of CRC characterized previously by The Cancer Genome Atlas (TCGA) to conduct integrated proteogenomic analyses. We identified a total of 2,170 significantly deregulated proteins associated with CLM, 14.88% of which were involved in metabolic pathways. The mutated peptide number was found to have potential prognosis value, and somatic variants revealed two metabolism-related genes UQCR5 and FDFT1 that frequently mutated only in the liver metastatic cohort and displayed dysregulated protein abundance with biological function and clinical significance in CLM. Proteogenomic characterization and integrative and comparative genomic analysis provides functional context and prognostic value to annotate genomic abnormalities and affords a new paradigm for understanding human colon and rectal cancer liver metastasis.

SUBMITTER: Ma YS

PROVIDER: S-EPMC6579909 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Dual Regulatory Mechanisms of Expression and Mutation Involving Metabolism-Related Genes FDFT1 and UQCR5 during CLM.

Ma Yu-Shui YS Wu Zhi-Jun ZJ Zhang Hong-Wei HW Cai Bo B Huang Tao T Long Hui-Deng HD Xu Hong H Zhao Yong-Zhong YZ Yin Yu-Zhen YZ Xue Shao-Bo SB Li Liu L Liu Cheng-Lin CL Xie Ru-Ting RT Tian Lin-Lin LL Liu Ji-Bin JB Wu Xu-Ming XM Fu Da D

Molecular therapy oncolytics 20190522

Colorectal cancer (CRC) is the third most common cancer worldwide, and liver metastasis presents a major cause of CRC-associated death. Extensive genomic analysis has provided valuable insight into the pathogenesis and progression of CRC; however, a comprehensive proteogenomic characterization of CRC liver metastasis (CLM) has yet to be reported. Here, we analyzed the proteomes of 44 paired normal colorectal tissues and CRC tissues with or without liver metastasis, as well as analyzed genomics o ...[more]

PMID: 31236441

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Project description:A lot of studies indicated that aberrant expression of long non-coding RNA genes (lncRNAs) is closely related to human diseases. Identifying disease-related lncRNAs (disease lncRNAs) is critical for understanding the pathogenesis and etiology of diseases. Most of the previous methods focus on prioritizing the potential disease lncRNAs based on shallow learning methods. The methods fail to extract the deep and complex feature representations of lncRNA-disease associations. Furthermore, nearly all the methods ignore the discriminative contributions of the similarity, association, and interaction relationships among lncRNAs, disease, and miRNAs for the association prediction. A dual convolutional neural networks with attention mechanisms based method is presented for predicting the candidate disease lncRNAs, and it is referred to as CNNLDA. CNNLDA deeply integrates the multiple source data like the lncRNA similarities, the disease similarities, the lncRNA-disease associations, the lncRNA-miRNA interactions, and the miRNA-disease associations. The diverse biological premises about lncRNAs, miRNAs, and diseases are combined to construct the feature matrix from the biological perspectives. A novel framework based on the dual convolutional neural networks is developed to learn the global and attention representations of the lncRNA-disease associations. The left part of the framework exploits the various information contained by the feature matrix to learn the global representation of lncRNA-disease associations. The different connection relationships among the lncRNA, miRNA, and disease nodes and the different features of these nodes have the discriminative contributions for the association prediction. Hence we present the attention mechanisms from the relationship level and the feature level respectively, and the right part of the framework learns the attention representation of associations. The experimental results based on the cross validation indicate that CNNLDA yields superior performance than several state-of-the-art methods. Case studies on stomach cancer, lung cancer, and colon cancer further demonstrate CNNLDA's ability to discover the potential disease lncRNAs.

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Dataset Information

Dual Regulatory Mechanisms of Expression and Mutation Involving Metabolism-Related Genes FDFT1 and UQCR5 during CLM.

Publications

Dual Regulatory Mechanisms of Expression and Mutation Involving Metabolism-Related Genes FDFT1 and UQCR5 during CLM.

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