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What Signatures Dominantly Associate with Gene Age?

ABSTRACT: As genes originate at different evolutionary times, they harbor distinctive genomic signatures of evolutionary ages. Although previous studies have investigated different gene age-related signatures, what signatures dominantly associate with gene age remains unresolved. Here we address this question via a combined approach of comprehensive assignment of gene ages, gene family identification, and multivariate analyses. We first provide a comprehensive and improved gene age assignment by combining homolog clustering with phylogeny inference and categorize human genes into 26 age classes spanning the whole tree of life. We then explore the dominant age-related signatures based on a collection of 10 potential signatures (including gene composition, gene length, selection pressure, expression level, connectivity in protein-protein interaction network and DNA methylation). Our results show that GC content and connectivity in protein-protein interaction network (PPIN) associate dominantly with gene age. Furthermore, we investigate the heterogeneity of dominant signatures in duplicates and singletons. We find that GC content is a consistent primary factor of gene age in duplicates and singletons, whereas PPIN is more strongly associated with gene age in singletons than in duplicates. Taken together, GC content and PPIN are two dominant signatures in close association with gene age, exhibiting heterogeneity in duplicates and singletons and presumably reflecting complex differential interplays between natural selection and mutation.

SUBMITTER: Yin H

PROVIDER: S-EPMC5174733 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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What Signatures Dominantly Associate with Gene Age?

Yin Hongyan H Wang Guangyu G Ma Lina L Yi Soojin V SV Zhang Zhang Z

Genome biology and evolution 20161013 10

As genes originate at different evolutionary times, they harbor distinctive genomic signatures of evolutionary ages. Although previous studies have investigated different gene age-related signatures, what signatures dominantly associate with gene age remains unresolved. Here we address this question via a combined approach of comprehensive assignment of gene ages, gene family identification, and multivariate analyses. We first provide a comprehensive and improved gene age assignment by combining ...[more]

PMID: 27609935

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Project description:BACKGROUND:Clinical variables may correlate with lack of response to treatment (primary resistance) or clinical benefit in patients with clear cell renal cell carcinoma (ccRCC) treated with anti-programmed death 1/ligand one antibodies. METHODS:In this multi-institutional collaboration, clinical characteristics of patients with primary resistance (defined as progression on initial computed tomography scan) were compared to patients with clinical benefit using Two sample t-test and Chi-square test (or Fisher's Exact test). The Kaplan-Meier method was used to estimate the distribution of progression-free survival (PFS) and overall survival (OS) in all patients and the subsets of patients with clinical benefit or primary resistance. Cox's regression model was used to evaluate the correlation between survival endpoints and variables of interest. To explore clinical factors in a larger, independent patient sample, The Cancer Genome Atlas (TCGA) was analyzed. RNAseq gene expression data as well as demographic and clinical information were downloaded for primary tumors of 517 patients included within TCGA-ccRCC. RESULTS:Of 90 patients, 38 (42.2%) had primary resistance and 52 (57.8%) had clinical benefit. Compared with the cohort of patients with initial benefit, primary resistance was more likely to occur in patients with worse ECOG performance status (p?=?0.03), earlier stage at diagnosis (p?=?0.04), had no prior nephrectomy (p?=?0.04) and no immune-related adverse events (irAE) (p?=?0.02). In patients with primary resistance, improved OS was significantly correlated with lower International Metastatic RCC Database Consortium risk score (p?=?0.02) and lower neutrophil:lymphocyte ratio (p?=?0.04). In patients with clinical benefit, improved PFS was significantly associated with increased BMI (p?=?0.007) and irAE occurrence (p?=?0.02) while improved OS was significantly correlated with overweight BMI (BMI 25-30; p?=?0.03) and no brain metastasis (p?=?0.005). The cohort TCGA-ccRCC was examined for the correlations between gene expression patterns, clinical factors, and survival outcomes observing associations of T-cell inflammation and angiogenesis signatures with histologic grade, pathologic stage and OS. CONCLUSIONS:Clinical characteristics including performance status, BMI and occurrence of an irAE associate with outcomes in patients with ccRCC treated with immunotherapy. The inverse association of angiogenesis gene signature with ccRCC histologic grade highlight opportunities for adjuvant combination VEGFR2 tyrosine kinase inhibitor and immune-checkpoint inhibition.

Project description:BackgroundAberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes.MethodsWe performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts.ResultsWe identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment.ConclusionsOur findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

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What Signatures Dominantly Associate with Gene Age?

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What Signatures Dominantly Associate with Gene Age?

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