TCPA v3.0: An Integrative Platform to Explore the Pan-Cancer Analysis of Functional Proteomic Data

Chen, Mei-Ju May; Li, Jun; Wang, Yumeng; Akbani, Rehan; Lu, Yiling; Mills, Gordon B.; Liang, Han

doi:10.1074/mcp.ra118.001260

Cited by 60 publications

(57 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first group of articles describes new methods and tools for studying associations between proteomics data and other types of omics data, including somatic mutations, somatic copy number alterations (CNA), DNA methylation, mRNA expression, protein phosphorylation, and morphological features. Chen et al (2) present an updated version of the cancer proteome atlas (TCPA), which includes reversephase protein arrays (RPPA)-based proteomic data for ϳ8000 patient samples across 32 cancer types through The Cancer Genome Atlas (TCGA) project. The updated version introduces a new module called "TCGA Pan-cancer Analysis," which provides comprehensive protein-centric analyses that integrate RPPA data with other TCGA data across cancer types.…”

mentioning

confidence: 99%

Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems

Zhang

Küster

2019

Molecular & Cellular Proteomics

View full text Add to dashboard Cite

mentioning

confidence: 99%

Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems

Zhang

Küster

2019

Molecular & Cellular Proteomics

View full text Add to dashboard Cite

“…Thus, we proposed that the methylation of STRIPAK genes might drive tumor progression via the downstream effectors as well as methylated YAP promotes tumorigenesis via increasing YAP/TEAD transcription (Fang et al, 2018). We also proposed that Src family tyrosine kinase (LCK) reversely regulated TRAF3IP3 methylation based on correlation analysis using cBioportal and TCPA (Gao et al, 2013;Chen M. M. et al, 2019) (Figures 7D,E). LCK, which belongs to Src-like non-receptor tyrosine kinase family, might lay upstream of TRAF3IP3, because knockout Traf3ip3 in CD4 + CD8 + double-positive thymocytes did not show difference in phosphorylation and expression of LCK compared with Traf3ip3 +/+ cells (Zou et al, 2015).…”

Section: Discussionmentioning

confidence: 94%

The Dysregulation and Prognostic Analysis of STRIPAK Complex Across Cancers

Xie

Wen

Qin

2020

Front. Cell Dev. Biol.

View full text Add to dashboard Cite

The striatin-interacting phosphatase and kinase (STRIPAK) is the highly conserved complex, which gains increased attention in physiology and pathology process recently. However, limited studies reported the details of STRIPAK complex in cancers while some results strongly suggested it plays a vital role in tumorigenesis. Hence, we systematically analyzed the molecular and survival profiles of 18 STRIPAK genes to assess the value of STRIPAK complex across cancers. Our findings revealed the low frequencies of DNA aberrances and incomparable expression difference of STRIPAK genes between normal and tumor tissues, but they showed strong prognostic value in cancers, especially the liver hepatocellular carcinoma (LIHC) and kidney renal clear cell carcinoma (KIRC). Interestingly, STRIPAK genes were observed the opposite pattern of survival and expression in the above two cancer types. PPP2R1A and TRAF3IP3 were proposed as the oncogenic genes in LIHC and KIRC, respectively. The STRIPAK genes serve as oncogenes may due to the methylation heterogeneity. Taken together, our comprehensive molecular analysis of STRIPAK complex provides resource to facilitate the understanding of mechanism and utilize the potential therapies to tumors.

show abstract

“…However, there are only 237 antibodies available so far. We found our 20 candidates are not included in the TCPA database (v3.0 [67]). Our strategy still has the strength to explore the more possible biomarkers from RNA-Seq datasets in cancer research.…”

Section: Discussionmentioning

confidence: 99%

A Global Genome-Wide Scan with Optimal Cutoff Mining for Emerging Biomarkers in Head and Neck Squamous Cell Carcinoma

Chi

Hsiao

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: The survival analysis of the Cancer Genome Atlas (TCGA) dataset is a well-known method to discover the gene expression-based prognostic biomarkers of head and neck squamous cell carcinoma (HNSCC). In order to utilize a continuous gene expression for survival analysis, it is necessary to determine a cutoff point by the dichotomization of the patients. There is some optimization software for cutoff determination. However, those predetermined cutoffs by software usually set at the median, 1/4 quantile, or 3/4 quantile of RNA sequencing (RNA-Seq) value to find a significant P-value of the Kaplan-Meier curve. There are few clinicopathological features available on their pre-processed data sets. Methods: We developed a comprehensive workflow by R script, running on the Rstudio platform. It includes data retrieving and pre-processing, feature selection, cutoff mining engine, Kaplan-Meier survival analysis, Cox proportional hazard modeling, and biomarker discovery. Results: Using this workflow on the TCGA HNSCC cohort, we scanned human protein-coding genes (20,500) programmatically. After adjustment with other confounders, we found that the clinical tumor stage and the surgical margin involvement are independent risk factors in patient survival. According to the resulting tables with Bonferroni adjusted P-value under optimal cutoff as well as hazard ratio (>=1.5), there were ten candidate biomarkers, named as DKK1, CAMK2N1, STC2, PGK1, SURF4, USP10, NDFIP1, FOXA2, STIP1, and DKC1, which are significantly associated with the poor prognosis of overall survival (OS). At the same time, the other ten genes were over-expressed in the better survival patients (with hazard ratio <=0.5), named as ZNF557, ZNF266, IL19, MYO1H, FCGBP, LOC148709, EVPLL, PNMA5, IQCN (previous name as KIAA1683), and NPB. Further validations are warranted.Conclusions: We suggested this analysis tool equipped with an optimal cutoff finder will help with biomarker discovery of protein-coding genes, in terms of tumor-agnostic therapy.

show abstract

TCPA v3.0: An Integrative Platform to Explore the Pan-Cancer Analysis of Functional Proteomic Data

Cited by 60 publications

References 23 publications

Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems

Proteomics Is Not an Island: Multi-omics Integration Is the Key to Understanding Biological Systems

The Dysregulation and Prognostic Analysis of STRIPAK Complex Across Cancers

A Global Genome-Wide Scan with Optimal Cutoff Mining for Emerging Biomarkers in Head and Neck Squamous Cell Carcinoma

Contact Info

Product

Resources

About