TY - JOUR
T1 - Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening
AU - Han, Guangchun
AU - Zhao, Wei
AU - Song, Xiaofeng
AU - Kwok-Shing Ng, Patrick
AU - Karam, Jose A.
AU - Jonasch, Eric
AU - Mills, Gordon B.
AU - Zhao, Zhongming
AU - Ding, Zhiyong
AU - Jia, Peilin
N1 - Funding Information:
We would like to express our gratitude to our colleagues in the University of Texas Health Science Center at Houston's Bioinformatics and Systems Medicine Laboratory. Finally, we are grateful to the TCGA Research Network, as our results are partially based upon their data. This work was partially supported by National Institutes of Health grant [R01LM011177]. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. In addition, the Dr. Doris L. Ross Professorship from the University of Texas Health Science Center at Houston supports Dr. Zhao. The startup funding from the School of Biomedical Informatics, the University of Texas Health Science Center at Houston supports Dr. Jia and was used to cover the publication costs.
Publisher Copyright:
© 2017 The Author(s).
PY - 2017/10/3
Y1 - 2017/10/3
N2 - Background: In 2016, it is estimated that there will be 62,700 new cases of kidney cancer in the United States, and 14,240 patients will die from the disease. Because the incidence of kidney renal clear cell carcinoma (KIRC), the most common type of kidney cancer, is expected to continue to increase in the US, there is an urgent need to find effective diagnostic biomarkers for KIRC that could help earlier detection of and customized treatment strategies for the disease. Accordingly, in this study we systematically investigated KIRC's prognostic biomarkers for survival using the reverse phase protein array (RPPA) data and the high throughput sequencing data from The Cancer Genome Atlas (TCGA). Results: With comprehensive data available in TCGA, we systematically screened protein expression based survival biomarkers in 10 major cancer types, among which KIRC presented many protein prognostic biomarkers of survival time. This is in agreement with a previous report that expression level changes (mRNAs, microRNA and protein) may have a better performance for prognosis of KIRC. In this study, we also identified 52 prognostic genes for KIRC, many of which are involved in cell-cycle and cancer signaling, as well as 15 tumor-stage-specific prognostic biomarkers. Notably, we found fewer prognostic biomarkers for early-stage than for late-stage KIRC. Four biomarkers (the RPPA protein IDs: FASN, ACC1, Cyclin_B1 and Rad51) were found to be prognostic for survival based on both protein and mRNA expression data. Conclusions: Through pan-cancer screening, we found that many protein biomarkers were prognostic for patients' survival in KIRC. Stage-specific survival biomarkers in KIRC were also identified. Our study indicated that these protein biomarkers might have potential clinical value in terms of predicting survival in KIRC patients and developing individualized treatment strategies. Importantly, we found many biomarkers in KIRC at both the mRNA expression level and the protein expression level. These biomarkers shared a significant overlap, indicating that they were technically replicable.
AB - Background: In 2016, it is estimated that there will be 62,700 new cases of kidney cancer in the United States, and 14,240 patients will die from the disease. Because the incidence of kidney renal clear cell carcinoma (KIRC), the most common type of kidney cancer, is expected to continue to increase in the US, there is an urgent need to find effective diagnostic biomarkers for KIRC that could help earlier detection of and customized treatment strategies for the disease. Accordingly, in this study we systematically investigated KIRC's prognostic biomarkers for survival using the reverse phase protein array (RPPA) data and the high throughput sequencing data from The Cancer Genome Atlas (TCGA). Results: With comprehensive data available in TCGA, we systematically screened protein expression based survival biomarkers in 10 major cancer types, among which KIRC presented many protein prognostic biomarkers of survival time. This is in agreement with a previous report that expression level changes (mRNAs, microRNA and protein) may have a better performance for prognosis of KIRC. In this study, we also identified 52 prognostic genes for KIRC, many of which are involved in cell-cycle and cancer signaling, as well as 15 tumor-stage-specific prognostic biomarkers. Notably, we found fewer prognostic biomarkers for early-stage than for late-stage KIRC. Four biomarkers (the RPPA protein IDs: FASN, ACC1, Cyclin_B1 and Rad51) were found to be prognostic for survival based on both protein and mRNA expression data. Conclusions: Through pan-cancer screening, we found that many protein biomarkers were prognostic for patients' survival in KIRC. Stage-specific survival biomarkers in KIRC were also identified. Our study indicated that these protein biomarkers might have potential clinical value in terms of predicting survival in KIRC patients and developing individualized treatment strategies. Importantly, we found many biomarkers in KIRC at both the mRNA expression level and the protein expression level. These biomarkers shared a significant overlap, indicating that they were technically replicable.
KW - Kidney renal clear cell carcinoma (KIRC)
KW - Pan-cancer screening
KW - Prognostic biomarker
KW - Protein expression
KW - Reverse phase protein Array (RPPA)
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U2 - 10.1186/s12864-017-4026-6
DO - 10.1186/s12864-017-4026-6
M3 - Article
C2 - 28984208
AN - SCOPUS:85030309528
SN - 1471-2164
VL - 18
JO - BMC Genomics
JF - BMC Genomics
M1 - 678
ER -