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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.


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The normalized expression levels of seven cross-cancer gene signatures across 12 types of cancer and normal samples.
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f2: The normalized expression levels of seven cross-cancer gene signatures across 12 types of cancer and normal samples.

Mentions: A gene signature denotes a set of genes that are significantly differentially expressed between cancer and normal samples. We call those pathways/gene sets significantly altered in multiple cancer types as cross-cancer gene signatures while those disrupted in just one cancer type as cancer-specific gene signatures. We performed gene set association analysis using all gene sets generated by gene clustering; the results are shown in Supplementary Table S3. We identified 20, 7, 7, 6, 7, 15, 30, and 1 significant gene sets for BLCA, BRCA, COAD, HNSC, LIHC, LUAD, LUSC, and KICH, respectively. No significant associations were found for KIRC, KIRP, PRAD, and THCA. Among 46 significant gene sets, seven are cross-cancer gene signatures whose expression levels were significantly altered in at least four cancer types (Fig. 2), the false discovery rates (FDRs) of these gene sets for each cancer type are shown in Table 2. In order to gain biological insights into these gene sets, we performed three types of pathway enrichment analyses, GO analysis, KEGG analysis, and Pathway Commons analysis, and disease association analysis for genes of each of these gene sets. The results of these analyses are shown in Supplementary Table S4. Interestingly, we found that these seven cross-cancer gene signatures are all closely related to cell cycle regulation, as we expected. Gene set CLUSTER2556 is significant in BLCA, COAD, and LUSC. There are 9 significant gene sets shared by two cancer types. Gene set CLUSTER242 is shared by LIHC and LUSC, and the remaining 8 gene sets are shared by LUAD and LUSC. LUAD and LUSC are more similar to one another than other cancer types possibly because they are both lung cancers.


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)

The normalized expression levels of seven cross-cancer gene signatures across 12 types of cancer and normal samples.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: The normalized expression levels of seven cross-cancer gene signatures across 12 types of cancer and normal samples.
Mentions: A gene signature denotes a set of genes that are significantly differentially expressed between cancer and normal samples. We call those pathways/gene sets significantly altered in multiple cancer types as cross-cancer gene signatures while those disrupted in just one cancer type as cancer-specific gene signatures. We performed gene set association analysis using all gene sets generated by gene clustering; the results are shown in Supplementary Table S3. We identified 20, 7, 7, 6, 7, 15, 30, and 1 significant gene sets for BLCA, BRCA, COAD, HNSC, LIHC, LUAD, LUSC, and KICH, respectively. No significant associations were found for KIRC, KIRP, PRAD, and THCA. Among 46 significant gene sets, seven are cross-cancer gene signatures whose expression levels were significantly altered in at least four cancer types (Fig. 2), the false discovery rates (FDRs) of these gene sets for each cancer type are shown in Table 2. In order to gain biological insights into these gene sets, we performed three types of pathway enrichment analyses, GO analysis, KEGG analysis, and Pathway Commons analysis, and disease association analysis for genes of each of these gene sets. The results of these analyses are shown in Supplementary Table S4. Interestingly, we found that these seven cross-cancer gene signatures are all closely related to cell cycle regulation, as we expected. Gene set CLUSTER2556 is significant in BLCA, COAD, and LUSC. There are 9 significant gene sets shared by two cancer types. Gene set CLUSTER242 is shared by LIHC and LUSC, and the remaining 8 gene sets are shared by LUAD and LUSC. LUAD and LUSC are more similar to one another than other cancer types possibly because they are both lung cancers.

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