Dataset Information

Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas.

ABSTRACT: Tumor metastasis is a major contributor to mortality of cancer patients, but the process remains poorly understood. Molecular comparisons between primary tumors and metastases can provide insights into the pathways and processes involved. Here, we systematically analyzed and cataloged molecular correlates of metastasis using The Cancer Genome Atlas (TCGA) datasets across 11 different cancer types, these data involving 4,473 primary tumor samples and 395 tumor metastasis samples (including 369 from melanoma). For each cancer type, widespread differences in gene transcription between primary and metastasis samples were observed. For several cancer types, metastasis-associated genes from TCGA comparisons were found to overlap extensively with external results from independent profiling datasets of metastatic tumors. Although some differential expression patterns associated with metastasis were found to be shared across multiple cancer types, by and large each cancer type showed a metastasis signature that was distinctive from those of the other cancer types. Functional categories of genes enriched in multiple cancer type-specific metastatic overexpression signatures included cellular response to stress, DNA repair, oxidation-reduction process, protein deubiquitination, and receptor activity. The TCGA-derived prostate cancer metastasis signature in particular could define a subset of aggressive primary prostate cancer. Transglutaminase 2 protein and mRNA were both elevated in metastases from breast and melanoma cancers. Alterations in miRNAs and in DNA methylation were also identified. IMPLICATIONS: Our findings suggest that there are different molecular pathways to metastasis involved in different cancers. Our catalog of alterations provides a resource for future studies investigating the role of specific genes in metastasis.

SUBMITTER: Chen F

PROVIDER: S-EPMC6359982 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas.

Chen Fengju F Zhang Yiqun Y Varambally Sooryanarayana S Creighton Chad J CJ

Molecular cancer research : MCR 20181106 2

Tumor metastasis is a major contributor to mortality of cancer patients, but the process remains poorly understood. Molecular comparisons between primary tumors and metastases can provide insights into the pathways and processes involved. Here, we systematically analyzed and cataloged molecular correlates of metastasis using The Cancer Genome Atlas (TCGA) datasets across 11 different cancer types, these data involving 4,473 primary tumor samples and 395 tumor metastasis samples (including 369 fr ...[more]

PMID: 30401717

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Project description:BackgroundThe Cancer Genome Atlas (TCGA) has generated comprehensive molecular profiles. We aim to identify a set of genes whose expression patterns can distinguish diverse tumor types. Those features may serve as biomarkers for tumor diagnosis and drug development.MethodsUsing RNA-seq expression data, we undertook a pan-cancer classification of 9,096 TCGA tumor samples representing 31 tumor types. We randomly assigned 75% of samples into training and 25% into testing, proportionally allocating samples from each tumor type.ResultsWe could correctly classify more than 90% of the test set samples. Accuracies were high for all but three of the 31 tumor types, in particular, for READ (rectum adenocarcinoma) which was largely indistinguishable from COAD (colon adenocarcinoma). We also carried out pan-cancer classification, separately for males and females, on 23 sex non-specific tumor types (those unrelated to reproductive organs). Results from these gender-specific analyses largely recapitulated results when gender was ignored. Remarkably, more than 80% of the 100 most discriminative genes selected from each gender separately overlapped. Genes that were differentially expressed between genders included BNC1, FAT2, FOXA1, and HOXA11. FOXA1 has been shown to play a role for sexual dimorphism in liver cancer. The differentially discriminative genes we identified might be important for the gender differences in tumor incidence and survival.ConclusionsWe were able to identify many sets of 20 genes that could correctly classify more than 90% of the samples from 31 different tumor types using TCGA RNA-seq data. This accuracy is remarkable given the number of the tumor types and the total number of samples involved. We achieved similar results when we analyzed 23 non-sex-specific tumor types separately for males and females. We regard the frequency with which a gene appeared in those sets as measuring its importance for tumor classification. One third of the 50 most frequently appearing genes were pseudogenes; the degree of enrichment may be indicative of their importance in tumor classification. Lastly, we identified a few genes that might play a role in sexual dimorphism in certain cancers.

Dataset Information

Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas.

Publications

Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across The Cancer Genome Atlas.

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