Dataset Information

Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548 Normal Tissue Controls across 12 TCGA Cancer Types.

ABSTRACT: The Cancer Genome Atlas (TCGA) has accrued RNA-Seq-based transcriptome data for more than 4000 cancer tissue samples across 12 cancer types, translating these data into biological insights remains a major challenge. We analyzed and compared the transcriptomes of 4043 cancer and 548 normal tissue samples from 21 TCGA cancer types, and created a comprehensive catalog of gene expression alterations for each cancer type. By clustering genes into co-regulated gene sets, we identified seven cross-cancer gene signatures altered across a diverse panel of primary human cancer samples. A 14-gene signature extracted from these seven cross-cancer gene signatures precisely differentiated between cancerous and normal samples, the predictive accuracy of leave-one-out cross-validation (LOOCV) were 92.04%, 96.23%, 91.76%, 90.05%, 88.17%, 94.29%, and 99.10% for BLCA, BRCA, COAD, HNSC, LIHC, LUAD, and LUSC, respectively. A lung cancer-specific gene signature, containing SFTPA1 and SFTPA2 genes, accurately distinguished lung cancer from other cancer samples, the predictive accuracy of LOOCV for TCGA and GSE5364 data were 95.68% and 100%, respectively. These gene signatures provide rich insights into the transcriptional programs that trigger tumorigenesis and metastasis, and many genes in the signature gene panels may be of significant value to the diagnosis and treatment of cancer.

SUBMITTER: Peng L

PROVIDER: S-EPMC4544034 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548 Normal Tissue Controls across 12 TCGA Cancer Types.

Peng Li L Bian Xiu Wu XW Li Di Kang DK Xu Chuan C Wang Guang Ming GM Xia Qing You QY Xiong Qing Q

Scientific reports 20150821

The Cancer Genome Atlas (TCGA) has accrued RNA-Seq-based transcriptome data for more than 4000 cancer tissue samples across 12 cancer types, translating these data into biological insights remains a major challenge. We analyzed and compared the transcriptomes of 4043 cancer and 548 normal tissue samples from 21 TCGA cancer types, and created a comprehensive catalog of gene expression alterations for each cancer type. By clustering genes into co-regulated gene sets, we identified seven cross-canc ...[more]

PMID: 26292924

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Project description:BackgroundMicroRNAs (miRNAs) are frequently deregulated in various types of cancer. While antisense oligonucleotides are used to block oncomiRs, delivery of tumour-suppressive miRNAs holds great potential as a potent anti-cancer strategy. Here, we aim to determine, and functionally analyse, miRNAs that are lowly expressed in various types of tumour but abundantly expressed in multiple normal tissues.MethodsThe miRNA sequencing data of 14 cancer types were downloaded from the TCGA dataset. Significant differences in miRNA expression between tumor and normal samples were calculated using limma package (R programming). An adjusted p value < 0.05 was used to compare normal versus tumor miRNA expression profiles. The predicted gene targets were obtained using TargetScan, miRanda, and miRDB and then subjected to gene ontology analysis using Enrichr. Only GO terms with an adjusted p < 0.05 were considered statistically significant. All data from wet-lab experiments (cell viability assays and flow cytometry) were expressed as means ± SEM, and their differences were analyzed using GraphPad Prism software (Student's t test, p < 0.05).ResultsBy compiling all publicly available miRNA profiling data from The Cancer Genome Atlas (TCGA) Pan-Cancer Project, we reveal a small set of tumour-suppressing miRNAs (which we designate as 'normomiRs') that are highly expressed in 14 types of normal tissues but poorly expressed in corresponding tumour tissues. Interestingly, muscle-enriched miRNAs (e.g. miR-133a/b and miR-206) and miRNAs from DLK1-DIO3 locus (e.g. miR-381 and miR-411) constitute a large fraction of the normomiRs. Moreover, we define that the CCCGU motif is absent in the oncomiRs' seed sequences but present in a fraction of tumour-suppressive miRNAs. Finally, the gain of function of candidate normomiRs across several cancer cell types indicates that miR-206 and miR-381 exert the most potent inhibition on multiple cancer types in vitro.ConclusionOur results reveal a pan-cancer set of tumour-suppressing miRNAs and highlight the potential of miRNA-replacement therapies for targeting multiple types of tumour.

Dataset Information

Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548 Normal Tissue Controls across 12 TCGA Cancer Types.

Publications

Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548 Normal Tissue Controls across 12 TCGA Cancer Types.

Similar Datasets

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