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

Peng L, Bian XW, Li DK, Xu C, Wang GM, Xia QY, Xiong Q - Sci Rep (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqing 400715, China.

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.

No MeSH data available.


Related in: MedlinePlus

Two possible carcinogenic mechanisms.(1) gene expression aberrations in cell cycle-associated pathways can directly lead to carcinogenesis, these pathways are cross-cancer gene signatures altered across a range of cancer types; (2) gene expression aberrations in organ-specific pathways can indirectly lead to carcinogenesis by interacting with cell cycle-associated pathways, these pathways are cancer-specific gene signatures altered in a single cancer type. Stars represent driver mutations that can alter the expression levels of their target genes.
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f1: Two possible carcinogenic mechanisms.(1) gene expression aberrations in cell cycle-associated pathways can directly lead to carcinogenesis, these pathways are cross-cancer gene signatures altered across a range of cancer types; (2) gene expression aberrations in organ-specific pathways can indirectly lead to carcinogenesis by interacting with cell cycle-associated pathways, these pathways are cancer-specific gene signatures altered in a single cancer type. Stars represent driver mutations that can alter the expression levels of their target genes.

Mentions: The cell cycle lies at the core of cancer1617. In normal cells, the cell cycle is controlled by a series of signaling pathways by which a cell grows, replicates its DNA and divides. In cancers, as a result of mutations, this regulatory process malfunctions, resulting in uncontrolled cell proliferation that leads to carcinogenesis1819. From the perspective of pathway, we hypothesize that there may be two potential carcinogenic mechanisms, as illustrated in Fig. 1: (1) one or more driver mutations are within a cell cycle-associated pathway, altering its expression pattern and consequently leading to cancer; (2) one or more driver mutations lie in an organ/tissue-specific pathway or other pathways not related to cell cycle, which interacts with a cell cycle-associated pathway, alters its expression pattern, and ultimately results in cancer. Since the deregulation of cell cycle is a common characteristic shared by multiple cancer types, we expected that the expression of cell cycle-associated pathways would be altered across a range of cancers. By analyzing and comparing the transcriptome data of 12 cancer types, we can test this hypothesis.


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

Peng L, Bian XW, Li DK, Xu C, Wang GM, Xia QY, Xiong Q - Sci Rep (2015)

Two possible carcinogenic mechanisms.(1) gene expression aberrations in cell cycle-associated pathways can directly lead to carcinogenesis, these pathways are cross-cancer gene signatures altered across a range of cancer types; (2) gene expression aberrations in organ-specific pathways can indirectly lead to carcinogenesis by interacting with cell cycle-associated pathways, these pathways are cancer-specific gene signatures altered in a single cancer type. Stars represent driver mutations that can alter the expression levels of their target genes.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4544034&req=5

f1: Two possible carcinogenic mechanisms.(1) gene expression aberrations in cell cycle-associated pathways can directly lead to carcinogenesis, these pathways are cross-cancer gene signatures altered across a range of cancer types; (2) gene expression aberrations in organ-specific pathways can indirectly lead to carcinogenesis by interacting with cell cycle-associated pathways, these pathways are cancer-specific gene signatures altered in a single cancer type. Stars represent driver mutations that can alter the expression levels of their target genes.
Mentions: The cell cycle lies at the core of cancer1617. In normal cells, the cell cycle is controlled by a series of signaling pathways by which a cell grows, replicates its DNA and divides. In cancers, as a result of mutations, this regulatory process malfunctions, resulting in uncontrolled cell proliferation that leads to carcinogenesis1819. From the perspective of pathway, we hypothesize that there may be two potential carcinogenic mechanisms, as illustrated in Fig. 1: (1) one or more driver mutations are within a cell cycle-associated pathway, altering its expression pattern and consequently leading to cancer; (2) one or more driver mutations lie in an organ/tissue-specific pathway or other pathways not related to cell cycle, which interacts with a cell cycle-associated pathway, alters its expression pattern, and ultimately results in cancer. Since the deregulation of cell cycle is a common characteristic shared by multiple cancer types, we expected that the expression of cell cycle-associated pathways would be altered across a range of cancers. By analyzing and comparing the transcriptome data of 12 cancer types, we can test this hypothesis.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Silkworm Genome Biology, Southwest University, Chongqing 400715, China.

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.

No MeSH data available.


Related in: MedlinePlus