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Identification of Novel Diagnosis Biomarkers for Therapy-Related Neuroendocrine Prostate Cancer.

ABSTRACT: Background: Therapy-related neuroendocrine prostate cancer (NEPC) is a lethal castration-resistant prostate cancer (CRPC) subtype that, at present, lacks well-characterized molecular biomarkers. The clinical diagnosis of this disease is dependent on biopsy and histological assessment: methods that are experience-based and easily misdiagnosed due to tumor heterogeneity. The development of robust diagnostic tools for NEPC may assist clinicians in making medical decisions on the choice of continuing anti-androgen receptor therapy or switching to platinum-based chemotherapy. Methods: Gene expression profiles and clinical characteristics data of 208 samples of metastatic CRPC, including castration-resistant prostate adenocarcinoma (CRPC-adeno) and castration-resistant neuroendocrine prostate adenocarcinoma (CRPC-NE), were obtained from the prad_su2c_2019 dataset. Weighted Gene Co-expression Network Analysis (WGCNA) was subsequently used to construct a free-scale gene co-expression network to study the interrelationship between the potential modules and clinical features of metastatic prostate adenocarcinoma and to identify hub genes in the modules. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression analysis was used to build a model to predict the clinical characteristics of CRPC-NE. The findings were then verified in the nepc_wcm_2016 dataset. Results: A total of 51 co-expression modules were successfully constructed using WGCNA, of which three co-expression modules were found to be significantly associated with the neuroendocrine features and the NEPC score. In total, four novel genes, including NPTX1, PCSK1, ASXL3, and TRIM9, were all significantly upregulated in NEPC compared with the adenocarcinoma samples, and these genes were all associated with the neuroactive ligand receptor interaction pathway. Next, the expression levels of these four genes were used to construct an NEPC diagnosis model, which was successfully able to distinguish CRPC-NE from CRPC-adeno samples in both the training and the validation cohorts. Moreover, the values of the area under the receiver operating characteristic (AUC) were 0.995 and 0.833 for the training and validation cohorts, respectively. Conclusion: The present study identified four specific novel biomarkers for therapy-related NEPC, and these biomarkers may serve as an effective tool for the diagnosis of NEPC, thereby meriting further study.

SUBMITTER: Zhang C

PROVIDER: S-EPMC8503838 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundtRNA-derived RNA fragments (tRFs) are a novel class of small ncRNA that are derived from precursor or mature tRNAs. Recently, the general relevance of their roles and clinical values in tumorigenesis, metastasis, and recurrence have been increasingly highlighted. However, there has been no specific systematic study to elucidate any potential clinical significance for these tRFs in prostate adenocarcinoma (PRAD), one of the most common and malignant cancers that threatens male health worldwide. Here, we investigate the clinical value of 5'-tRFs in PRAD.MethodsSmall RNA sequencing data were analyzed to discover new 5'-tRFs biomarkers for PRAD. Machine learning algorithms were used to identify 5'-tRF classifiers to distinguish PRAD tumors from normal tissues. LASSO and Cox regression analyses were used to construct 5'-tRF prognostic predictive models. NMF and consensus clustering analyses were performed on 5'-tRF profiles to identify molecular subtypes of PRAD.ResultsThe overall levels of 5'-tRFs were significantly upregulated in the PRAD tumor samples compared to their adjacent normal samples. tRF classifiers composed of 13 5'-tRFs achieved AUC values as high as 0.963, showing high sensitivity and specificity in distinguishing PRAD tumors from normal samples. Multiple 5'-tRFs were identified as being associated with the PRAD prognosis. The tRF score, defined by a set of eight 5'-tRFs, was highly predictive of survival in PRAD patients. The combination of tRF and Gleason scores showed a significantly better performance than the Gleason score alone, suggesting that 5'-tRFs can offer PRAD patients additional and improved prognostic information. Four molecular subtypes of the PRAD tumor were identified based on their 5'-tRF expression profiles. Genetically, these 5'-tRFs PRAD tumor subtypes exhibited distinct genomic landscapes in tumor cells. Clinically, they showed marked differences in survival and clinicopathological features.Conclusions5'-tRFs are potential clinical biomarkers for the diagnosis, prognosis, and classification of tumor subtypes on a molecular level. These can help clinicians formulate personalized treatment plans for PRAD patients and may have similar potential applications for other disease types.

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Identification of Novel Diagnosis Biomarkers for Therapy-Related Neuroendocrine Prostate Cancer.

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