Abstract
Biomarker-driven cancer therapy has met with significant clinical success. Identification of a biomarker implicated in a malignant phenotype and linked to poor clinical outcome is required if we are to develop these types of therapies. A subset of prostate adenocarcinoma (PACa) cases are treatment-resistant, making them an attractive target for such an approach. To identify target molecules implicated in shorter survival of patients with PACa, we established a bioinformatics-to-clinic sequential analysis approach, beginning with 2-step in silico analysis of a TCGA dataset for localized PACa. The effect of candidate genes identified by in silico analysis on survival was then assessed using biopsy specimens taken at the time of initial diagnosis of localized and metastatic PACa. We identified PEG10 as a candidate biomarker. Data from clinical samples suggested that increased expression of PEG10 at the time of initial diagnosis was linked to shorter survival time. Interestingly, PEG10 overexpression also correlated with expression of chromogranin A and synaptophysin, markers for neuroendocrine prostate cancer, a type of treatment-resistant prostate cancer. These results indicate that PEG10 is a novel biomarker for shorter survival of patients with PACa. Also, PEG10 expression at the time of initial diagnosis may predict focal neuroendocrine differentiation of PACa. Thus, PEG10 may be an attractive target for biomarker-driven cancer therapy. Thus, bioinformatics-to-clinic sequential analysis is a valid tool for identifying targets for precision oncology.
Highlights
Precision oncology, called biomarker-driven therapy, has greatly improved clinical outcomes in recent years
To identify target molecules implicated in shorter survival of patients with prostate adenocarcinoma (PACa), we established a bioinformatics-to-clinic sequential analysis approach, beginning with 2-step in silico analysis of a TCGA dataset for localized PACa
Biomarker-driven therapies such as trastuzumab for HER2-positive breast cancer and imatinib for chronic myeloid leukemia [1, 2] highlight the efficacy of targeting biomarkers associated with a poor prognosis and illustrate the importance of identifying those biomarkers involved in poor clinical outcomes for malignancies
Summary
Called biomarker-driven therapy, has greatly improved clinical outcomes in recent years. PACa patient, physicians measure levels of prostatespecific antigen (PSA), and use clinical staging and the Gleason score, which is a grading system based on the architectural pattern of tissue from a PACa biopsy [4,5,6]. In addition to these factors, castration-resistant prostate cancer (CRPC), a transformed prostate cancer mainly caused by continuous androgen deprivation, is linked to a poor prognosis due to limited therapeutic options [7]. The best molecular targets for neuroendocrine prostate cancer therapies have not yet been identified [8]
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