Personalized discovery of altered pathways in clear cell renal cell carcinoma using accumulated normal sample data.
OBJECTIVE
To identify altered pathways in an individual with clear cell renal cell carcinoma (ccRCC) using accumulated normal sample data.


METHODS
Gene expression data of E-GEOD-40435 was downloaded from the ArrayExpress database. Gene-level statistics of genes in tumor and normal samples were computed. Then, the Average Z method was applied to calculate the individual pathway aberrance score (iPAS). Subsequently, the significantly altered pathways in a ccRCC sample were identified using T-test based on the pathway statistics values of normal and ccRCC samples. Moreover, the identified altered pathways were verified through two methods: one was assessing classification capability for microarray data samples, and the other was computing the changed percentage of each pathway in ccRCC samples.


RESULTS
Based on the threshold, 886 altered pathways were identified in all samples. The most significant pathways were potassium transport channels, proton-coupled monocarboxylate transport, beta oxidation of octanoyl-CoA to hexanoyl-CoA, antigen presentation: folding, assembly and peptide loading of class I MHC, and so on. Additionally, iPAS separated ccRCC from normal controls with an accuracy of 0.980. Moreover, a total of 5 significant pathways with change in 100% ccRCC samples were extracted including proton-coupled monocarboxylate transport, antigen presentation: folding, assembly and peptide loading of class I MHC, and so on.


CONCLUSIONS
iPAS is useful to predict marker pathways for ccRCC with a high accuracy. Pathways of proton-coupled monocarboxylate transport, and antigen presentation: folding, assembly and peptide loading of class I MHC might play crucial roles in ccRCC progression.
Department of Nephrology, The First Affiliated Hospital of Huzhou Teachers College, Huzhou 313000, Zhejiang Province, China.
Xiaoyi
X
Wang
Min
M
Ding
Yong
Y
Yang
Yuehua
Y
Feng
Zhanqin
Z
Shi
Fengping
F
Qiu
Ming
M
Zhu
D014408:Biomarkers, Tumor / Q000235:genetics*; D002292:Carcinoma, Renal Cell / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D016022:Case-Control Studies; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D007680:Kidney Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D015398:Signal Transduction / Q000235:genetics
D014408:Biomarkers, Tumor