Distinct telomere length and molecular signatures in seminoma and non-seminoma of testicular germ cell tumor
Year: 2018
Type: article
Abstract: Testicular germ cell tumors (TGCTs) are classified into two main subtypes, seminoma (SE) and non-seminoma (NSE), but their molecular distinctions remain largely unexplored. Here, we used expression data for mRNAs and microRNAs (miRNAs) from The Cancer Genome Atlas (TCGA) to perform a systematic investigation to explain the different telomere length (TL) features between NSE (n = 48) and SE (n = 55). We found that TL elongation was dominant in NSE, whereas TL shortening prevailed in SE. We further showed that both mRNA and miRNA expression profiles could clearly distinguish these two subtypes. Notably, four telomere-related genes (TelGenes) showed significantly higher expression and positively correlated with telomere elongation in NSE than SE: three telomerase activity-related genes (TERT, WRAP53 and MYC) and an independent telomerase activity gene (ZSCAN4). We also found that the expression of genes encoding Yamanaka factors was positively correlated with telomere lengthening in NSE. Among them, SOX2 and MYC were highly expressed in NSE versus SE, while POU5F1 and KLF4 had the opposite patterns. These results suggested that enhanced expression of both TelGenes (TERT, WRAP53, MYC and ZSCAN4) and Yamanaka factors might induce telomere elongation in NSE. Conversely, the relative lack of telomerase activation and low expression of independent telomerase activity pathway during cell division may be contributed to telomere shortening in SE. Taken together, our results revealed the potential molecular profiles and regulatory roles involving the TL difference between NSE and SE, and provided a better molecular understanding of this complex disease.
Source: Briefings in Bioinformatics
DOI: 10.1093/bib/bby020
Language: English
Subfield: Physiology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Awards: G1129153677, G119557380, G1299450604, Data Science and Informatics Core for Cancer Research, G3716394409, G3813486454, Integrative bioinformatics and functional characterization of oncogenic driver aberrations in cancer, TCPA: an Integrated Bioinformatics Resource for Functional Cancer Proteomic Data, G771694649, G9000154053, G9000336273, Predicting Phenotype by Deep Learning Heterogeneous Multi-Omics Data
OpenAlex ID: W2789608597