ROTS: reproducible RNA-seq biomarker detector-prognostic markers for clear cell renal cell cancer.
Recent comprehensive assessments of RNA-seq technology support its utility in quantifying gene expression in various samples. The next step of rigorously quantifying differences between sample groups, however, still lacks well-defined best practices. Although a number of advanced statistical methods have been developed, several studies demonstrate that their performance depends strongly on the data under analysis, which compromises practical utility in real biomedical studies. As a solution, we propose to use a data-adaptive procedure that selects an optimal statistic capable of maximizing reproducibility of detections. After demonstrating its improved sensitivity and specificity in a controlled spike-in study, the utility of the procedure is confirmed in a real biomedical study by identifying prognostic markers for clear cell renal cell carcinoma (ccRCC). In addition to identifying several genes previously associated with ccRCC prognosis, several potential new biomarkers among genes regulating cell growth, metabolism and solute transport were detected.
Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland Department of Mathematics and Statistics, University of Turku, Turku, FI-20014, Finland fatsey@utu.fi.
Fatemeh
F
Seyednasrollah
Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland Department of Medical Biochemistry, University of Turku, Turku, FI-20014, Finland.
Krista
K
Rantanen
Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland Department of Medical Biochemistry, University of Turku, Turku, FI-20014, Finland Department of Oncology and Radiotherapy, Turku University Hospital, FI-20520 Turku, Finland.
Panu
P
Jaakkola
Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, Turku, FI-20520, Finland Department of Mathematics and Statistics, University of Turku, Turku, FI-20014, Finland laliel@utu.fi.
Laura L
LL
Elo
D000465:Algorithms; D014408:Biomarkers, Tumor; D002292:Carcinoma, Renal Cell / Q000235:genetics* / Q000401:mortality*; D019295:Computational Biology / Q000379:methods*; D066264:Datasets as Topic; D015972:Gene Expression Regulation, Neoplastic; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods / Q000592:standards; D006801:Humans; D020407:Internet; D053208:Kaplan-Meier Estimate; D007680:Kidney Neoplasms / Q000235:genetics* / Q000401:mortality*; D015233:Models, Statistical; D011379:Prognosis; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software
D014408:Biomarkers, Tumor