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Prostatic cancer is a complex cancer that many factors contribute to. In clinical practice, statins and oral anti-diabetic drugs, is the main adjuvant treatment for prostatic cancer patients. In this paper, a kind of typical complex network – the sample specific network (SSN) was used to screen out differentially expressed genes (DEGs) between prostatic cancer and normal tissue. And by the degree of SSN, driving mutation genes (DMGs) were determined. Pearson correlation coefficient was used to construct SSN, in order to describe the special characteristics of the sample. In this way, people can realise personalised medical service. After the prediction of the degree top 30, top 20, top 10, and top 5 genes, the accuracy of prediction of driving genes with the top 5 genes is the highest. Different single samples of SSN from prostate cancer have specificity, but there is a common edge or network with the specific subnetwork of driving gene. The driving genes of prostate cancer can be predicted by our method.

Identification of potential driving genes in prostatic cancer using complex network analysis