TRGAted: A web tool for survival analysis using protein data in the Cancer Genome Atlas.
Ontology highlight
ABSTRACT:
SUBMITTER: Borcherding N
PROVIDER: S-EPMC6173115 | biostudies-literature | 2018
REPOSITORIES: biostudies-literature
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Project description:Somatic amplifications of the LYL1 gene are relatively common occurrences in patients who develop uterine corpus endometrial carcinoma (UCEC) as opposed to other cancers. This study was undertaken to determine whether such genetic alterations affect survival outcomes of UCEC.In 370 patients with UCEC, we analysed clinicopathologic characteristics and corresponding genomic data from The Cancer Genome Atlas database. Patients were stratified according to LYL1 gene status, grouped as amplification or non-amplification. Heightened levels of cancer-related genes expressed in concert with LYL1 amplification were similarly investigated through differentially expressed gene and gene set enrichment analyses. Factors associated with survival outcomes were also identified.Somatic LYL1 gene amplification was observed in 22 patients (5.9%) with UCEC. Patients displaying amplification (vs. non-amplification) were significantly older at the time of diagnosis and more often were marked by non-endometrioid, high-grade, or advanced disease. In survival analysis, the amplification subset showed poorer progression-free survival (PFS) and overall survival (OS) rates (3-year PFS: 34.4% vs. 79.9%, P?=?0.031; 5-year OS: 25.1% vs. 84.9%, P?=?0.014). However, multivariate analyses adjusted for tumor histologic type, grade, and stage did not confirm LYL1 gene amplification as an independent prognostic factor for either PFS or OS. Nevertheless, MAPK, WNT, and cell cycle pathways were significantly enriched by LYL1 gene amplification (P?<?0.001, P?=?0.002, and P?=?0.004, respectively).Despite not being identified as an independent prognostic factor in UCEC, LYL1 gene amplification is associated with other poor prognostic factors and correlated with upregulation of cancer-related pathways.
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Project description:Human hemochromatosis protein (HFE) is involved in iron metabolism. Two major HFE polymorphisms, H63D and C282Y, have been associated with an increased risk of cancers. Previously, we reported decreased gender effects in overall survival based on H63D or C282Y HFE polymorphisms patients with glioblastoma multiforme (GBM). However, the effect of other single nucleotide variation (SNV) in the HFE gene on the cancer development and progression has not been systematically studied. To expand our finding in a larger sample, and to identify other HFE SNV, we analyzed the frequency of somatic SNV in HFE gene and its relationship to survival in GBM patients using The Cancer Genome Atlas (TCGA) GBM (Caucasian only) database. We found 9 SNVs with increased frequency in blood normal of TCGA GBM patients compared to the 1000Genome. Among 9 SNVs, 7 SNVs were located in the intron and 2 SNVs (i.e., H63D, C282Y) in the exon of HFE gene. The statistical analysis demonstrated that blood normal samples of TCGA GBM have more H63D (p = 0.0002, 95% Confidence interval (CI): 0.2119-0.3223) or C282Y (p = 0.0129, 95% CI: 0.0474-0.1159) HFE polymorphisms than 1000Genome. The Kaplan-Meier survival curve for the 264 GBM samples revealed no difference between wild type (WT) HFE and H63D, and WT HFE and C282Y GBM patients. In addition, there was no difference in the survival of male/female GBM patients based on HFE genotype. There was no correlation between HFE expression and survival. In conclusion, the current results suggest that somatic HFE polymorphisms do not impact GBM patients' survival in the TCGA data set of GBM.
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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.
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Project description:Head and neck squamous cell carcinoma (HNSCC) is the major histological type of head and neck cancer and no curative treatments are currently available. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) has produced large?scale sequencing data, which provide unprecedented opportunities to reveal molecular mechanisms of cancer. The present study analyzed the mRNA and micro (mi)RNA expression data of HNSCC and normal control tissues released by the TCGA database using a bioinformatics approach to explore underlying molecular mechanisms. The mRNA and miRNA expression data were downloaded from the TCGA database and differentially expressed genes (DEGs) and miRNAs (DEMs) between HNSCC and normal head and neck tissues were identified using TwoClassDif. Subsequently, the gene functions and pathways which are significantly altered in HNSCC were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Regulatory networks among DEGs and DEMs were then constructed, and transcription factors (TFs) potentially regulating the DEGs and DEMs were determined and a TF ? miRNA ? gene network was established. A total of 2,594 significant DEGs (1,087 upregulated and 1,507 downregulated), and 25 DEMs (8 upregulated and 17 downregulated) were identified in HNSCC compared with normal control samples. These DEGs were significantly enriched in GOs and KEGG pathways such as mitosis, cell cycle, Wnt, JAK/STAT and TLR signaling pathway. CPBP, NF?AT1 and miR?1 were situated in the central hub of the TF ? miRNA ? gene network, underlining their central roles in regulatory processes specific for HNSCC. The present study enhanced the current understanding of the molecular mechanisms underlying HNSCC and may offer novel strategies for its prevention, diagnosis and treatment.
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