scholarly journals The Cancer Genome Atlas expression profiles of low-grade gliomas

2014 ◽  
Vol 36 (4) ◽  
pp. E23 ◽  
Author(s):  
David D. Gonda ◽  
Vincent J. Cheung ◽  
Karra A. Muller ◽  
Amit Goyal ◽  
Bob S. Carter ◽  
...  
Keyword(s):  
Cpg Island ◽  
Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Molecular Characteristics ◽  
Low Grade ◽  
Cpg Island Methylator Phenotype ◽  
Low Grade Gliomas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Differentiating between low-grade gliomas (LGGs) of astrocytic and oligodendroglial origin remains a major challenge in neurooncology. Here the authors analyzed The Cancer Genome Atlas (TCGA) profiles of LGGs with the goal of identifying distinct molecular characteristics that would afford accurate and reliable discrimination of astrocytic and oligodendroglial tumors. They found that 1) oligodendrogliomas are more likely to exhibit the glioma-CpG island methylator phenotype (G-CIMP), relative to low-grade astrocytomas; 2) relative to oligodendrogliomas, low-grade astrocytomas exhibit a higher expression of genes related to mitosis, replication, and inflammation; and 3) low-grade astrocytic tumors harbor microRNA profiles similar to those previously described for glioblastoma tumors. Orthogonal intersection of these molecular characteristics with existing molecular markers, such as IDH1 mutation, TP53 mutation, and 1p19q status, should facilitate accurate and reliable pathological diagnosis of LGGs.

Identification of T-cell-inflamed gastric adenocarcinoma in The Cancer Genome Atlas (TCGA).

2017 ◽  
Vol 35 (7_suppl) ◽  
pp. 16-16 ◽  
Author(s):  
Steven Brad Maron ◽  
Jason John Luke ◽  
Raymond Hovey ◽  
Riyue Bao ◽  
Thomas Gajewski ◽  
...  
Keyword(s):  
T Cell ◽  
Objective Response ◽  
Copy Number Variations ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Molecular Characteristics ◽  
Chi Square ◽  
Primary Tumors ◽  
Cancer Genome Atlas ◽  
Genome Atlas

16 Background: Gastroesophageal adenocarcinoma (GEC) is a significant global health problem. KEYNOTE-012 demonstrated a 22% objective response rate in patients with PD-L1 expressing GEC that received pembrolizumab. A subset of patients (pts) tumors express a T cell “inflamed” (TCI) phenotype, which can be measured using an IFN-γ-based immune signature and may prove more predictive of clinical benefit. Using a 160 gene RNA-Seq immune expression profile, we sought to characterize the molecular environments of TCI versus non-TCI GEC patients in The Cancer Genome Atlas (TCGA). Methods: 395 GEC pts with primary tumors in TCGA were clustered into TCI, non-TCI, and intermediate subtypes using both unsupervised hierarchical and K-means clustering (k = 3). Molecular characteristics were categorized using data acquired via CbioPortal and the UCSC Xena repository. Only non-silent somatic mutations and copy number variations (CNVs) reaching GISTIC2 -2 or +2 thresholds were considered. Statistical comparisons were performed using chi-square, ANOVA, and t-test. Results: The TCI subtype contained patients from all TCGA-defined subtypes - EBV-associated (56%), MSI-high (16%), chromosomal unstable (6%), and genomically stable (27%). No significant differences were seen between TCI and non-TCI for tumor site or stage. Mutations in PTEN, PIK3CA, CDH1, and RHOA were more frequent in TCI patients. ERBB2, CCNE1, and KRAS CNVs were infrequent in TCI patients as were PDE4D deletions ( p< 0.05 ). TCI tumors had higher expression of both co-inhibitory (PD-1, PD-L1/L2, CD28/80, BTLA, LAG3) and co-stimulatory (CD137/40/27, OX40, GITR, ICOS) checkpoint molecules ( p< 10-7). Total mutation burden was no different between TCI and non-TCI pts when excluding MSI-high pts nor when assessing MSI-high alone. Conclusions: The IFN immune phenotype encompassed GEC patients from all TCGA subsets. Correlation of clinical outcome with checkpoint blockade is necessary to confirm these molecular associations and the independent predictive utility of this immune-profile stratification.

scholarly journals Screening and Identifying m6A Regulators as an Independent Prognostic Biomarker in Pancreatic Cancer Based on The Cancer Genome Atlas Database

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bi Lin ◽  
Yangyang Pan ◽  
Dinglai Yu ◽  
Shengjie Dai ◽  
Hongwei Sun ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Tumor Therapy ◽  
Disease Free Survival ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Background. Pancreatic cancer is one of the most malignant tumors of the digestive system, and its treatment has rarely progressed for the last two decades. Studies on m6A regulators for the past few years have seemingly provided a novel approach for malignant tumor therapy. m6A-related factors may be potential biomarkers and therapeutic targets. This research is focused on the gene characteristics and clinical values of m6A regulators in predicting prognosis in pancreatic cancer. Methods. In our study, we obtained gene expression profiles with copy number variation (CNV) data and clinical characteristic data of 186 patients with pancreatic cancer from The Cancer Genome Atlas (TCGA) portal. Then, we determined the alteration of m6a regulators and their correlation with clinicopathological features using the log-rank tests, Cox regression model, and chi-square test. Additionally, we validated the prognostic value of m6A regulators in the International Cancer Genome Consortium (ICGC). Results. The results suggested that pancreatic cancer patients with ALKBH5 CNV were associated with worse overall survival and disease-free survival than those with diploid genes. Additionally, upregulation of the writer gene ALKBH5 had a positive correlation with the activation of AKT pathways in the TCGA database. Conclusion. Our study not only demonstrated genetic characteristic changes of m6A-related genes in pancreatic cancer and found a strong relationship between the changes of ALKBH5 and poor prognosis but also provided a novel therapeutic target for pancreatic cancer therapy.

scholarly journals The Biological Function Delineated Across Pan-Cancer Levels Through lncRNA-Based Prognostic Risk Assessment Factors for Pancreatic Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xudong Tang ◽  
Mengyan Zhang ◽  
Liang Sun ◽  
Fengyan Xu ◽  
Xin Peng ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Prognostic Marker ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Research Network ◽  
Molecular Characteristics ◽  
Cancer Data ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

Long non-coding RNAs (lncRNAs) play key roles in tumors and function not only as important molecular markers for cancer prognosis, but also as molecular characteristics at the pan-cancer level. Because of the poor prognosis of pancreatic cancer, accurate assessment of prognosis is a key issue in the development of treatment plans for pancreatic cancer. Here we analyzed pancreatic cancer data from The Cancer Genome Atlas and The Genotype Tissue Expression database using Cox regression and lasso regression in analyses using a combination of the two databases as well as only The Cancer Genome Atlas database (Cancer Genome Atlas Research Network et al., 2013). A prognostic risk score model with significant correlation with pancreatic cancer survival was constructed, and two lncRNAs were investigated. Additional analysis of 33 cancers using the two lncRNAs showed that lncRNA TsPOAP1-AS1 was a prognostic marker of seven cancers, among which pancreatic cancer was the most significant, and lncRNA mi600hg was a prognostic marker of ovarian cancer and pancreatic cancer. LncRNA TsPOAP1-AS1 is associated with clinical stage and tumor mutation burden of some cancers as well as a strong degree of immune infiltration in many cancers, while a strong correlation between lncRNA mi600hg and microsatellite instability was observed in several cancers. The results of this study help further our understanding of the different functions of lncRNAs in cancer and may aid in the clinical application of lncRNAs as prognostic factors for cancer.

Identification of prognostic and immune-related lncRNAs in hepatocellular carcinoma based on the cancer genome atlas data mining

2020 ◽  
Vol 10 (8) ◽  
pp. 1189-1196
Author(s):  
Kaikai Ren ◽  
Jiakang Ma ◽  
Bo Zhou ◽  
Xiaoyan Lin ◽  
Mingyu Hou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Prognostic Indicators ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Hepatocellular carcinoma (HCC) is a malignancy originating from hepatocytes with a high rate of distant metastasis and recurrence. HCC prognosis remains poorly understood, although its diagnosis and treatment have improved globally. Therefore, it is necessary to identify reliable predictive and prognostic indicators of HCC. HCC gene expression profiles and corresponding clinical data were downloaded from The Cancer Genome Atlas. Seven lncRNAs (C10orf91, AC011352.3, AC015722.2, AC006372.1, PICSAR, AC110285.3, and AP001972.4) associated with immune and clinicopathological features were identified as biomarker candidates for HCC prognosis based on single-sample gene set enrichment analysis, the ESTIMATE algorithm, and Cox PHR analyses. Altogether, the findings revealed that the seven immune-related lncRNAs may provide a reference for improving HCC prognosis.

scholarly journals Molecular Characteristics of Uveal Melanoma: Insights from the Cancer Genome Atlas (TCGA) Project

Cancers ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1061 ◽  
Author(s):  
Mathieu F. Bakhoum ◽  
Bita Esmaeli
Keyword(s):  
Uveal Melanoma ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Molecular Characteristics ◽  
Tissue Samples ◽  
Molecular Investigation ◽  
Cancer Genome Atlas ◽  
First Time ◽  
Primary Tissue ◽  
Genome Atlas

The Cancer Genome Atlas (TCGA) uveal melanoma project was a comprehensive multi-platform deep molecular investigation of 80 uveal melanoma primary tissue samples supported by the National Cancer Institute. In addition to identification of important mutations for the first time, it identified four different clusters (subgroups) of patients paralleling prognosis. The findings of the TCGA marker paper are summarized in this review manuscript and other investigations that have stemmed from the findings of the TCGA project are reviewed.

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