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Transcriptional response profiles of paired tumor-normal samples offer novel perspectives in pan-cancer analysis.


ABSTRACT: Both tumor and adjacent normal tissues are valuable in cancer research. Transcriptional response profiles represent the changes of gene expression levels between paired tumor and adjacent normal tissues. In this study, we performed a pan-cancer analysis based on the transcriptional response profiles from 633 samples across 13 cancer types. We obtained two interesting results. Using consensus clustering method, we characterized ten clusters with distinct transcriptional response patterns and enriched pathways. Notably, head and neck squamous cell carcinoma was divided in two subtypes, enriched in cell cycle-related pathways and cell adhesion-related pathways respectively. The other interesting result is that we identified 92 potential pan-cancer genes that were consistently upregulated across multiple cancer types. Knockdown of FAM64A or TROAP inhibited the growth of cancer cells, suggesting that these genes may promote tumor development and are worthy of further validations. Our results suggest that transcriptional response profiles of paired tumor-normal tissues can provide novel perspectives in pan-cancer analysis.

SUBMITTER: Hu S 

PROVIDER: S-EPMC5522216 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Transcriptional response profiles of paired tumor-normal samples offer novel perspectives in pan-cancer analysis.

Hu Shuofeng S   Yuan Hanyu H   Li Zongcheng Z   Zhang Jian J   Wu Jiaqi J   Chen Yaowen Y   Shi Qiang Q   Ren Wu W   Shao Ningsheng N   Ying Xiaomin X  

Oncotarget 20170601 25


Both tumor and adjacent normal tissues are valuable in cancer research. Transcriptional response profiles represent the changes of gene expression levels between paired tumor and adjacent normal tissues. In this study, we performed a pan-cancer analysis based on the transcriptional response profiles from 633 samples across 13 cancer types. We obtained two interesting results. Using consensus clustering method, we characterized ten clusters with distinct transcriptional response patterns and enri  ...[more]

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