Pan-cancer analysis identifies telomerase-associated signatures and cancer subtypes

作者：Zhenhua Luo, Weixu Wang, Feng Li, Zhou Songyang, Xuyang Feng, Changchang Xin, Zhiming Dai, Yuanyan Xiong · 发表于：Molecular Cancer · 年份：2019 · DOI：10.1186/s12943-019-1035-x · 被引用次数：99 · 研究领域：Telomeres, Telomerase, and Senescence、Clusterin in disease pathology、Nuclear Structure and Function

Cancer cells become immortalized through telomere maintenance mechanisms, such as telomerase reverse transcriptase (TERT) activation. In addition to maintaining telomere length, TERT activates manifold cell survival signaling pathways. However, telomerase-associated gene signatures in cancer remain elusive. We performed a systematic analysis of TERT high (TERT high ) and low (TERT low ) cancers using multidimensional data from The Cancer Genome Atlas (TCGA). Multidimensional data were analyzed by propensity score matching weight algorithm. Coexpression networks were constructed by weight gene coexpression network analysis (WGCNA). Random forest classifiers were generated to identify cancer subtypes. The TERT high -specific mRNA expression signature is associated with cell cycle-related coexpression modules across cancer types. Experimental screening of hub genes in the cell cycle module suggested TPX2 and EXO1 as potential regulators of telomerase activity and cell survival. MiRNA analysis revealed that the TERT high -specific miR-17-92 cluster can target biological processes enriched in TERT low cancer and that its expression is negatively correlated with the tumor/normal telomere length ratio. Intriguingly, TERT high cancers tend to have mutations in extracellular matrix organization genes and amplify MAPK signaling. By mining the clinical actionable gene database, we uncovered a number of TERT high -specific somatic mutations, amplifications and high expression genes conta...