scholarly journals OncoRank: A pan-cancer method of combining survival correlations and its application to mRNAs, miRNAs, and lncRNAs

2016 ◽  
Author(s):  
Jordan Anaya
Keyword(s):  
Mirna Target ◽  
Patient Survival ◽  
The Cancer Genome Atlas ◽  
P Values ◽  
Cancer Pathways ◽  
Large Numbers ◽  
Protective Genes ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

OncoRank adopts a method for finding recurrent miRNA-target interactions to find genes with consistent relationships to patient survival across cancers. Genes are first ranked in each cancer by their Cox coefficients, and these ranks are then combined by applying Fisher's method. Using ranks instead of the raw coefficients or p-values allows each cancer to be weighted equally and prevents bias from cancers with large numbers of patients. OncoLnc (http://www.oncolnc.org) is a newly available resource for Cox coefficients and utilizes data from 21 cancers in The Cancer Genome Atlas. Using this resource I applied OncoRank to mRNAs, miRNAs, and lncRNAs and in each case found consistently harmful or protective genes. These genes may be members of central cancer pathways and should be of interest to cancer researchers.

scholarly journals OncoRank: A pan-cancer method of combining survival correlations and its application to mRNAs, miRNAs, and lncRNAs

2016 ◽  
Author(s):  
Jordan Anaya
Keyword(s):  
Mirna Target ◽  
Patient Survival ◽  
The Cancer Genome Atlas ◽  
P Values ◽  
Cancer Pathways ◽  
Large Numbers ◽  
Protective Genes ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

OncoRank adopts a method for finding recurrent miRNA-target interactions to find genes with consistent relationships to patient survival across cancers. Genes are first ranked in each cancer by their Cox coefficients, and these ranks are then combined by applying Fisher's method. Using ranks instead of the raw coefficients or p-values allows each cancer to be weighted equally and prevents bias from cancers with large numbers of patients. OncoLnc (http://www.oncolnc.org) is a newly available resource for Cox coefficients and utilizes data from 21 cancers in The Cancer Genome Atlas. Using this resource I applied OncoRank to mRNAs, miRNAs, and lncRNAs and in each case found consistently harmful or protective genes. These genes may be members of central cancer pathways and should be of interest to cancer researchers.

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

2018 ◽  
Vol 17 (2) ◽  
pp. 476-487 ◽  
Author(s):  
Fengju Chen ◽  
Yiqun Zhang ◽  
Sooryanarayana Varambally ◽  
Chad J. Creighton
Keyword(s):  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

scholarly journals PR/SET Domain Family and Cancer: Novel Insights from the Cancer Genome Atlas

2018 ◽  
Vol 19 (10) ◽  
pp. 3250 ◽  
Author(s):  
Anna Sorrentino ◽  
Antonio Federico ◽  
Monica Rienzo ◽  
Patrizia Gazzerro ◽  
Maurizio Bifulco ◽  
...  
Keyword(s):  
Family Members ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Driver Gene ◽  
Rna Seq ◽  
Set Domain ◽  
Primary Tumors ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

The PR/SET domain gene family (PRDM) encodes 19 different transcription factors that share a subtype of the SET domain [Su(var)3-9, enhancer-of-zeste and trithorax] known as the PRDF1-RIZ (PR) homology domain. This domain, with its potential methyltransferase activity, is followed by a variable number of zinc-finger motifs, which likely mediate protein–protein, protein–RNA, or protein–DNA interactions. Intriguingly, almost all PRDM family members express different isoforms, which likely play opposite roles in oncogenesis. Remarkably, several studies have described alterations in most of the family members in malignancies. Here, to obtain a pan-cancer overview of the genomic and transcriptomic alterations of PRDM genes, we reanalyzed the Exome- and RNA-Seq public datasets available at The Cancer Genome Atlas portal. Overall, PRDM2, PRDM3/MECOM, PRDM9, PRDM16 and ZFPM2/FOG2 were the most mutated genes with pan-cancer frequencies of protein-affecting mutations higher than 1%. Moreover, we observed heterogeneity in the mutation frequencies of these genes across tumors, with cancer types also reaching a value of about 20% of mutated samples for a specific PRDM gene. Of note, ZFPM1/FOG1 mutations occurred in 50% of adrenocortical carcinoma patients and were localized in a hotspot region. These findings, together with OncodriveCLUST results, suggest it could be putatively considered a cancer driver gene in this malignancy. Finally, transcriptome analysis from RNA-Seq data of paired samples revealed that transcription of PRDMs was significantly altered in several tumors. Specifically, PRDM12 and PRDM13 were largely overexpressed in many cancers whereas PRDM16 and ZFPM2/FOG2 were often downregulated. Some of these findings were also confirmed by real-time-PCR on primary tumors.

scholarly journals Erratum: Corrigendum: A pan-cancer proteomic perspective on The Cancer Genome Atlas

2015 ◽  
Vol 6 (1) ◽  
Author(s):  
Rehan Akbani ◽  
Patrick Kwok Shing Ng ◽  
Henrica M.J. Werner ◽  
Maria Shahmoradgoli ◽  
Fan Zhang ◽  
...  
Keyword(s):  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

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.

scholarly journals A pan-cancer analysis of prognostic genes

PeerJ ◽  
2016 ◽  
Vol 3 ◽  
pp. e1499 ◽  
Author(s):  
Jordan Anaya ◽  
Brian Reon ◽  
Wei-Min Chen ◽  
Stefan Bekiranov ◽  
Anindya Dutta
Keyword(s):  
Microarray Data ◽  
The Cancer Genome Atlas ◽  
Rna Seq ◽  
Cancer Pathogenesis ◽  
Gene Sets ◽  
Protective Genes ◽  
Cancer Genome Atlas ◽  
Cancer Types ◽  
Measure Of Association ◽  
Pan Cancer

Numerous studies have identified prognostic genes in individual cancers, but a thorough pan-cancer analysis has not been performed. In addition, previous studies have mostly used microarray data instead of RNA-SEQ, and have not published comprehensive lists of associations with survival. Using recently available RNA-SEQ and clinical data from The Cancer Genome Atlas for 6,495 patients, we have investigated every annotated and expressed gene’s association with survival across 16 cancer types. The most statistically significant harmful and protective genes were not shared across cancers, but were enriched in distinct gene sets which were shared across certain groups of cancers. These groups of cancers were independently recapitulated by both unsupervised clustering of Cox coefficients (a measure of association with survival) for individual genes, and for gene programs. This analysis has revealed unappreciated commonalities among cancers which may provide insights into cancer pathogenesis and rationales for co-opting treatments between cancers.

scholarly journals The Cancer Genome Atlas Pan-Cancer analysis project

2013 ◽  
Vol 45 (10) ◽  
pp. 1113-1120 ◽  
Author(s):  
John N Weinstein ◽  
◽  
Eric A Collisson ◽  
Gordon B Mills ◽  
Kenna R Mills Shaw ◽  
...  
Keyword(s):  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

scholarly journals Pan-cancer pharmacogenetics: targeted sequencing panels or exome sequencing?

2020 ◽  
Vol 21 (15) ◽  
pp. 1073-1084
Author(s):  
Laurentijn Tilleman ◽  
Björn Heindryckx ◽  
Dieter Deforce ◽  
Filip Van Nieuwerburgh
Keyword(s):  
Drug Interactions ◽  
Exome Sequencing ◽  
Informed Choice ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Targeted Sequencing ◽  
Driver Mutations ◽  
Cancer Genome Atlas ◽  
Pan Cancer ◽  
Genome Atlas

Aim: This study provides clinicians and researchers with an informed choice between current commercially available targeted sequencing panels and exome sequencing panels in the context of pan-cancer pharmacogenetics. Materials & methods: Nine contemporary commercially available targeted pan-cancer panels and the xGen Exome Research Panel v2 were investigated to determine to what extent they cover the pharmacogenetic variant–drug interactions in five available cancer knowledgebases, and the driver mutations and fusion genes in the Cancer Genome Atlas. Results: xGen Exome Research Panel v2 and TrueSight Oncology 500 target 71.0 and 68.9% of the pharmacogenetic interactions in the available knowledgebases; and 93.7 and 86.0% of the driver mutations in the Cancer Genome Atlas, respectively. All other studied panels target lower percentages. Conclusion: Exome sequencing outperforms pan-cancer targeted sequencing panels in terms of covered cancer pharmacogenetic variant–drug interactions and pharmacogenetic cancer variants.

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