Identifying prognostic features by bottom-up approach and correlating to drug repositioning.
BACKGROUND
Traditionally top-down method was used to identify prognostic features in cancer research. That is to say, differentially expressed genes usually in cancer versus normal were identified to see if they possess survival prediction power. The problem is that prognostic features identified from one set of patient samples can rarely be transferred to other datasets. We apply bottom-up approach in this study: survival correlated or clinical stage correlated genes were selected first and prioritized by their network topology additionally, then a small set of features can be used as a prognostic signature.


METHODS
Gene expression profiles of a cohort of 221 hepatocellular carcinoma (HCC) patients were used as a training set, 'bottom-up' approach was applied to discover gene-expression signatures associated with survival in both tumor and adjacent non-tumor tissues, and compared with 'top-down' approach. The results were validated in a second cohort of 82 patients which was used as a testing set.


RESULTS
Two sets of gene signatures separately identified in tumor and adjacent non-tumor tissues by bottom-up approach were developed in the training cohort. These two signatures were associated with overall survival times of HCC patients and the robustness of each was validated in the testing set, and each predictive performance was better than gene expression signatures reported previously. Moreover, genes in these two prognosis signature gave some indications for drug-repositioning on HCC. Some approved drugs targeting these markers have the alternative indications on hepatocellular carcinoma.


CONCLUSIONS
Using the bottom-up approach, we have developed two prognostic gene signatures with a limited number of genes that associated with overall survival times of patients with HCC. Furthermore, prognostic markers in these two signatures have the potential to be therapeutic targets.
Key Laboratory of Biomedical Photonics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China; Shanghai Center for Bioinformation Technology, Shanghai Institutes of Biomedicine, Shanghai Academy of Science and Technology, Shanghai, 201203, P. R. China; Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China.
Wei
W
Li
Shanghai Center for Bioinformation Technology, Shanghai Institutes of Biomedicine, Shanghai Academy of Science and Technology, Shanghai, 201203, P. R. China.
Jian
J
Yu
Shanghai Center for Bioinformation Technology, Shanghai Institutes of Biomedicine, Shanghai Academy of Science and Technology, Shanghai, 201203, P. R. China; Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai, 200240, P. R. China.
Baofeng
B
Lian
Shanghai Center for Bioinformation Technology, Shanghai Institutes of Biomedicine, Shanghai Academy of Science and Technology, Shanghai, 201203, P. R. China; Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai, 200031, P. R. China.
Han
H
Sun
Key Laboratory of Biomedical Photonics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China; Shanghai Center for Bioinformation Technology, Shanghai Institutes of Biomedicine, Shanghai Academy of Science and Technology, Shanghai, 201203, P. R. China; Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China.
Jing
J
Li
Shanghai Center for Bioinformation Technology, Shanghai Institutes of Biomedicine, Shanghai Academy of Science and Technology, Shanghai, 201203, P. R. China; Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai, 200240, P. R. China.
Menghuan
M
Zhang
Shanghai Center for Bioinformation Technology, Shanghai Institutes of Biomedicine, Shanghai Academy of Science and Technology, Shanghai, 201203, P. R. China.
Ling
L
Li
Shanghai Center for Bioinformation Technology, Shanghai Institutes of Biomedicine, Shanghai Academy of Science and Technology, Shanghai, 201203, P. R. China; Department of Bioinformatics and Biostatistics, Shanghai Jiaotong University, Shanghai, 200240, P. R. China; Key Laboratory of Systems Biology, Chinese Academy of Sciences, Shanghai, 200031, P. R. China.
Yixue
Y
Li
Key Laboratory of Biomedical Photonics of Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China; Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, 430074, P. R. China.
Qian
Q
Liu
Shanghai Center for Bioinformation Technology, Shanghai Institutes of Biomedicine, Shanghai Academy of Science and Technology, Shanghai, 201203, P. R. China.
Lu
L
Xie
D014408:Biomarkers, Tumor / Q000378:metabolism; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis* / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism; D016000:Cluster Analysis; D015331:Cohort Studies; D019295:Computational Biology / Q000379:methods*; D058492:Drug Repositioning / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000188:drug therapy* / Q000235:genetics / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D060066:Protein Interaction Maps; D016019:Survival Analysis
D014408:Biomarkers, Tumor