Dataset Information

Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening.

ABSTRACT:

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.

SUBMITTER: Han G

PROVIDER: S-EPMC5629613 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature

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Publications

Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening.

Han Guangchun G Zhao Wei W Song Xiaofeng X Kwok-Shing Ng Patrick P Karam Jose A JA Jonasch Eric E Mills Gordon B GB Zhao Zhongming Z Ding Zhiyong Z Jia Peilin P

BMC genomics 20171003 Suppl 6

<h4>Background</h4>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 th ...[more]

PMID: 28984208

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Project description:Background: Kidney renal clear cell carcinoma (KIRC) is the malignancy originated from the renal epithelium, with a high rate of distant metastasis. Aberrant alternative splicing (AS) of pre-mRNA are widely reported to be involved in the tumorigenesis and metastasis of multiple cancers. The aim of this study is to explore the mechanism of alternative splicing events (ASEs) underlying tumorigenesis and metastasis of KIRC. Methods: RNA-seq of 537 KIRC samples downloaded from the TCGA database and ASEs data from the TCGASpliceSeq database were used to identify ASEs in patients with KIRC. The univariate and Lasso regression analysis were used to screen the most significant overall survival-related ASEs (OS-SEs). Based on those, the OS-SEs model was proposed. The interaction network of OS-SEs and splicing factors (SFs) with absolute value of correlation coefficient value >0.750 was constructed by Pearson correlation analysis. The OS-SEs significantly related to distant metastasis and clinical stage were identified by non-parametric test, and those were also integrated into co-expression analysis with prognosis-related Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways identified by Gene Set Variation Analysis (GSVA). ASEs with significance were selected for multiple online database validation. Results: A total of prognostic 6,081 overall survival-related ASEs (OS-SEs) were identified by univariate Cox regression analysis and a prediction model was constructed based on 5 OS-SEs screened by Lasso regression with the Area Under Curve of 0.788. Its risk score was also illustrated to be an independent predictor, which the good reliability of the model. Among 390 identified candidate SFs, DExD-Box Helicase 39B (DDX39B) was significantly correlated with OS and metastasis. After external database validation, Retained Intron of Ras Homolog Family Member T2 (RHOT2) and T-Cell Immune Regulator 1 (TCIRG1) were identified. In the co-expression analysis, overlapped co-expression signal pathways for RHOT2 and TCIRG1 were sphingolipid metabolism and N-glycan biosynthesis. Conclusions: Based on the results of comprehensive bioinformatic analysis, we proposed that aberrant DDX39B regulated RHOT2-32938-RI and TCIRG1-17288-RI might be associated with the tumorigenesis, metastasis, and poor prognosis of KIRC via sphingolipid metabolism or N-glycan biosynthesis pathway.

Project description:BackgroundImmunity exerts momentous functions in the progression and treatment of kidney renal clear cell carcinoma (KIRC). A better understanding of the relationship between KIRC and immunity may make a great contribution to evaluating the prognosis and immune-related therapeutic response of KIRC.MethodsA series of information such as RNA sequence, clinical data, and tumor mutation burden (TMB) of KIRC patients were downloaded through The Cancer Genome Atlas (TCGA). Next, combining the survival information and gene expression data of TCGA and Gene Expression Omnibus (GEO), we established an immune gene-related prognosis model (IGRPM) and analyzed it. Then we constructed a nomogram which was convenient for clinicians to judge the prognosis of KIRC. Last but not the least, the expressions of some genes used to construct IGRPM in early KIRC, and adjacent normal tissues were verified through real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). Perl (strawberry-perl-5.30.0.1-64bit), R software (4.0.3), and GraphPad Prism 7 were used to process the relevant data.ResultsThe single-sample gene set enrichment analysis (ssGSEA) showed that there were significant differences in StromalScore, ImmuneScore, ESTIMATEScore, TumorPurity, 22 kinds of human immune cells infiltration, and HLA genes expression between high immunity group (Immunity_H) and low immunity group (Immunity_L). The Immunity_H expressed more immune-related genes and enriched more immune-related functions than the Immunity_L. In addition, compared with the low-risk group, the high-risk group had worse survival outcome and higher TMB. Combining IGRPM-based risk characteristic and TMB, we found that low-TMB + low-risk was the most beneficial to the survival outcome of KIRC patients. The risk characteristic based on IGRPM could be used as an independent prognostic factor for KIRC, and the nomogram constructed for evaluating the prognosis of KIRC showed excellent predictive potential. The RT-qPCR results suggested that not all the genes used to construct IGRPM showed differential expression in early KIRC compared with adjacent normal tissues, but all these genes had significant influence on the prognosis of KIRC.ConclusionThese comprehensive immune assessments and survival predictions, integrating multiple aspects of data and clinical information, can provide additional value to the current Tumor Node Metastasis staging system for risk stratification of KIRC and may facilitate the development of KIRC immunotherapy.

Dataset Information

Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening.

Background

Results

Conclusions

Publications

Unique protein expression signatures of survival time in kidney renal clear cell carcinoma through a pan-cancer screening.

Similar Datasets

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