The Cancer Genome Atlas Pan-Cancer analysis project

Weinstein, John N.; Collisson, Eric A.; Mills, Gordon B.; Shaw, Kenna; Ozenberger, Brad; Ellrott, Kyle; Shmulevich, Ilya; Sander, Chris; Stuart, Joshua M.

doi:10.1038/ng.2764

Cited by 6,951 publications

(5,348 citation statements)

References 43 publications

Supporting

Mentioning

5,021

Contrasting

Unclassified

Order By: Relevance

“…Toward this goal, the clinical relevance of the DEGs identified by the different analyses was investigated using TCGA (Cancer Genome Atlas Research Network et al ., 2013), which contains data such as tumor gene expression and clinical outcomes from patients with cancer. Due to the small number of DEGs identified in the detectable siAPE1/undetectable siAPE1 analysis, it was excluded from this TCGA analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Pathway analysis identified numerous pathways influenced by APE1 knockdown, including eukaryotic initiation factor 2 (EIF2) signaling, protein kinase A signaling, and mechanistic target of Rapamycin (mTOR) signaling. Using data from The Cancer Genome Atlas (TCGA) (Cancer Genome Atlas Research Network et al ., 2013), the clinical relevance of the differentially expressed genes (DEGs) was assessed by fitting a Cox proportional hazards model. A number of the DEGs were validated in a heterogeneous APE1 population using siRNA knockdown and quantitative real‐time PCR (qRT‐PCR).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

APE1/Ref‐1 knockdown in pancreatic ductal adenocarcinoma – characterizing gene expression changes and identifying novel pathways using single‐cellRNAsequencing

et al. 2017

View full text Add to dashboard Cite

Apurinic/apyrimidinic endonuclease 1/redox factor‐1 (APE1/Ref‐1 or APE1) is a multifunctional protein that regulates numerous transcription factors associated with cancer‐related pathways. Because APE1 is essential for cell viability, generation of APE1‐knockout cell lines and determining a comprehensive list of genes regulated by APE1 has not been possible. To circumvent this challenge, we utilized single‐cell RNA sequencing to identify differentially expressed genes (DEGs) in relation to APE1 protein levels within the cell. Using a straightforward yet novel statistical design, we identified 2837 genes whose expression is significantly changed following APE1 knockdown. Using this gene expression profile, we identified multiple new pathways not previously linked to APE1, including the EIF2 signaling and mechanistic target of Rapamycin pathways and a number of mitochondrial‐related pathways. We demonstrate that APE1 has an effect on modifying gene expression up to a threshold of APE1 expression, demonstrating that it is not necessary to completely knockout APE1 in cells to accurately study APE1 function. We validated the findings using a selection of the DEGs along with siRNA knockdown and qRT‐PCR. Testing additional patient‐derived pancreatic cancer cells reveals particular genes (ITGA1,TNFAIP2,COMMD7,RAB3D) that respond to APE1 knockdown similarly across all the cell lines. Furthermore, we verified that the redox function of APE1 was responsible for driving gene expression of mitochondrial genes such as PRDX5 and genes that are important for proliferation such as SIPA1 and RAB3D by treating with APE1 redox‐specific inhibitor, APX3330. Our study identifies several novel genes and pathways affected by APE1, as well as tumor subtype specificity. These findings will allow for hypothesis‐driven approaches to generate combination therapies using, for example, APE1 inhibitor APX3330 with other approved FDA drugs in an innovative manner for pancreatic and other cancer treatments.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

APE1/Ref‐1 knockdown in pancreatic ductal adenocarcinoma – characterizing gene expression changes and identifying novel pathways using single‐cellRNAsequencing

et al. 2017

View full text Add to dashboard Cite

show abstract

“…42,43 Bioinformatics TCGA-Assembler was executed in R to download, assemble, and process public Head and Neck Squamous Cell Carcinoma (HNSCC) Illumina RNA-SeqV2 normalized gene expression data from TCGA for 218 HNSCC patients. 44,45 …”

Section: Migration Assaymentioning

confidence: 99%

MIEN1 promotes oral cancer progression and implicates poor overall survival

Rajendiran

Kpetemey

Maji

et al. 2015

Cancer Biology & Therapy

View full text Add to dashboard Cite

Abbreviations: CRS, current reformed smoker; CS, current smoker; GFP, green fluorescent protein; HNSCC, head and neck squamous cell carcinoma; MIEN1, migration and invasion enhancer 1; MMP-9, matrix metallopeptidase 9; NF-kB, nuclear factor kappalight-chain-enhancer of activated B cells; OSCC, oral squamous cell carcinoma; siRNA, small interfering RNA; TCGA, the cancer genome atlas; uPA, urokinase plasminogen activator; VEGF, vascular endothelial growth factorOral squamous cell carcinoma is a highly malignant tumor with the potential to invade local and distant sites and promote lymph node metastasis. Major players underlying the molecular mechanisms behind tumor progression are yet to be fully explored. Migration and invasion enhancer 1 (MIEN1), a novel protein overexpressed in various cancers, facilitates cell migration and invasion. In the present study we investigated the expression and role of MIEN1 in oral cancer progression using an in vitro model, patient derived oral tissues and existing TCGA data. Expression analysis using immortalized normal and cancer cells demonstrated increased expression of MIEN1 in cancer. Assays performed after MIEN1 knockdown in OSC-2 cells showed decreased migration, invasion and filopodia formation; while MIEN1 overexpression in DOK cells increased these characteristics and also up-regulated some Akt/NF-kB effectors, thereby suggesting an important role for MIEN1 in oral cancer progression. Immunohistochemical staining and analyses of oral tissue specimens, collected from patients over multiple visits, revealed significantly more staining in severe dysplasia and squamous cell carcinoma compared to mildly dysplastic or hyperplastic tissues. Finally, this was corroborated with the TCGA dataset, where MIEN1 expression was not only higher in intermediate and high grade cancer with significantly lower survival but also correlated with smoking. In summary, we demonstrate that MIEN1 expression not only positively correlates with oral cancer progression but also seems to be a critical molecular determinant in migration and invasion of oral cancer cells, thereby, playing a possible role in their metastatic dissemination.

show abstract

“…Large‐scale mRNA and miRNA expression profiles (Illumina HiSeq level 3), clinical follow‐up survival time, and clinical drug treatment records of cancer patients were obtained from TCGA data portal (Cancer Genome Atlas Research et al ., 2013). lncRNA expression profiles were retrieved from TANRIC (Li et al ., 2015), which is an open‐access web resource containing the expression data of lncRNA in large patient cohorts of 20 cancer types, including those in TCGA.…”

Section: Methodsmentioning

confidence: 99%

Inferences of individual drug responses across diverse cancer types using a novel competing endogenous RNA network

Zhang

Zhou

et al. 2018

Molecular Oncology

View full text Add to dashboard Cite

Differences in individual drug responses are an obstacle to progression in cancer treatment, and predicting responses would help to plan treatment. The accumulation of cancer molecular profiling and drug response data provides opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs. This study evaluated drug responses with a competing endogenous RNA (ceRNA) system that depended on competition between diverse RNA species. We identified drug response‐related ceRNA (DRCEs) by combining the sequence and expression data of long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA), and the survival data of cancer patients treated with drugs. We constructed a patient–drug two‐layer integrated network and used a linear weighting method to predict individual drug responses. DRCEs were found to be significantly enriched in known cancer and drug‐associated data resources, involved in biological processes known to mediate drug responses, and correlated to drug activity in cancer cell lines. The dysregulation of DRCE expression influenced drug response‐associated functions and pathways, suggesting DRCEs as potential therapeutic targets affecting drug responses. A further case study in breast invasive carcinoma (BRCA) found that DRCE expression was consistent with the drug response pattern and the aberrant expression of the two NEAT1‐related DRCEs may lead to poor response to tamoxifen therapy for patients with TP53 mutations. In summary, this study provides a framework for ceRNA‐based evaluation of clinical drug responses across multiple cancer types. Understanding the underlying molecular mechanisms of drug responses will allow improved response to chemotherapy and outcomes of cancer treatment.

show abstract

The Cancer Genome Atlas Pan-Cancer analysis project

Cited by 6,951 publications

References 43 publications

APE1/Ref‐1 knockdown in pancreatic ductal adenocarcinoma – characterizing gene expression changes and identifying novel pathways using single‐cellRNAsequencing

APE1/Ref‐1 knockdown in pancreatic ductal adenocarcinoma – characterizing gene expression changes and identifying novel pathways using single‐cellRNAsequencing

MIEN1 promotes oral cancer progression and implicates poor overall survival

Inferences of individual drug responses across diverse cancer types using a novel competing endogenous RNA network

Contact Info

Product

Resources

About