Literature-mining And Bioinformatics Analysis Of Androgen-independent Prostate Cancer-specific Genes And Drug Screening

Prostate cancer continues to be the most common lethal malignancy diagnosed in American men and the second leading cause of male cancer mortality.the incidence of prostate cancer in China although much lower than Western countries, but in recent years showed significant growth trend.In recent years, with the rapid development of molecular biology, people got a better understanding the occurrence and development of tumor, The occurrence and development of tumor is the result of multi-gene changes, in this process there are several proto-oncogenes are activated and the inactivation of tumor suppressor genes, tumor genesis and development is the result of proto-oncogenes, tumor suppressor genes and growth factors control disorders,the incidence of prostate cancer is no exception.However, the development and progression of prostate cancer is very complicated, showing multi-step, multi-gene complex characteristics.and,the molecular mechanism of prostate cancer is by far still ambiguous. The survival and growth of prostate cancer cells is initially dependent on the presence of androgens, and virtually all prostate cancer patients respond when first treated with androgen deprivation. However, resistance to hormone blockade ultimately results in the recurrence of highly aggressive and metastatic prostate cancer that is androgen independent. Androgen-independent prostate cancer (AIPC) is therefore clinically defined as the progression of the disease under hormonal ablation. With the biomedical literature explosive growth and high-throughput bio-technology development,as well as literature mining and bioinformatics,we have a new idea to study the molecular mechanisms of tumor, Bioinformatics is defined as a scientific discipline that encompasses all aspects of biological information acquisition, processing, storage, distribution, analysis and interpretation, that combines the tools and techniques of mathematics, computer science and biology with the aim of understanding the biological significance of a variety of data.Lists of genes highly-associated with prostate cancer were obtained from mining PubMed by FACTA tool, and analyzed by a set of bioinformatics tools. Investigation of genes specifically expressed in androgen-independent and androgen-dependent prostate cancer (ADPC) may further our understanding on the two major mechanistic states of prostate cancer, and provide novel means for clinical diagnosis and treatment.Mining the differential expressed genes and drug discovery in androgen-independent prostate cancer by Bioinformatics analysis, we found that such method is a rational approach for studying the molecular mechanisms of tumor and drug discovery. There are two parts in this study:Part1:Literature-mining and bioinformatics analysis of androgen-independent prostate cancer-specific genes. Lists of genes highly-associated with prostate cancer were obtained from mining PubMed by FACTA tool, and analyzed by a set of bioinformatics tools including GATHER, PANTHER,STRING, ToppGene and Connectivity Map. After careful and vigorous screening by domain-experts,128 genes specifically expressed in androgen-independent prostate cancer (AIPC) were identified as compared to 23 genes specific to ADPC. Bioinformatics analysis showed that AIPC-specific genes play essential roles in such important biological processes as cell signal transductio,cell adhesion apoptosis,oncogenesis,cell proliferation and cell differentiation.Lists of genes highly-associated with prostate cancer were analyzed by a set of bioinformatics tools including Connectivity Map,DrugBank and ToppGene. bioinformatics analysis revealed several putative candidates, including thioridazine and novobiocin. Part 2:Influence of novobiocin on the proliferation of prostate carcinoma cells PC-3. By our study, novobiocin was shown to suppress the PC-3 of AIPC by MTT. AND Mining the possible mechanisms of novobiocin by bioinformatics analysis.In summary, using bioinformatics can effectively analyze genes of PCa and gain the internal information.Mining the specifically expressed genes in androgen-independent prostate cancer by Bioinformatics analysis, we found that such genes as MMP9,EGFR,MMP2,ADM,MIF,IGFBP3,IL2,MET,BAD,RHOA,SPP1,EP300,SMAD3,RAE1,PTK2,TGFB2 may play an important role in ADPC transforming into AIPC. And found that novobiocin could suppress the proliferation of PC-3 cell line. Literature-mining and bioinformatics analysis of androgen-independent prostate cancer-specific genes and Drug Screening,may further our understanding on the two major mechanistic states of prostate cancer, and provide novel means for clinical diagnosis and treatment.