Dataset Information

Lymphatic Dissemination in Prostate Cancer: Features of the Transcriptomic Profile and Prognostic Models.

ABSTRACT: Radical prostatectomy is the gold standard treatment for prostate cancer (PCa); however, it does not always completely cure PCa, and patients often experience a recurrence of the disease. In addition, the clinical and pathological parameters used to assess the prognosis and choose further tactics for treating a patient are insufficiently informative and need to be supplemented with new markers. In this study, we performed RNA-Seq of PCa tissue samples, aimed at identifying potential prognostic markers at the level of gene expression and miRNAs associated with one of the key signs of cancer aggressiveness-lymphatic dissemination. The relative expression of candidate markers was validated by quantitative PCR, including an independent sample of patients based on archival material. Statistically significant results, derived from an independent set of samples, were confirmed for miR-148a-3p and miR-615-3p, as well as for the CST2, OCLN, and PCAT4 genes. Considering the obtained validation data, we also analyzed the predictive value of models based on various combinations of identified markers using algorithms based on machine learning. The highest predictive potential was shown for the "CST2 + OCLN + pT" model (AUC = 0.863) based on the CatBoost Classifier algorithm.

SUBMITTER: Pudova EA

PROVIDER: S-EPMC9916851 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Lymphatic Dissemination in Prostate Cancer: Features of the Transcriptomic Profile and Prognostic Models.

Pudova Elena A EA Kobelyatskaya Anastasiya A AA Katunina Irina V IV Snezhkina Anastasiya V AV Fedorova Maria S MS Pavlov Vladislav S VS Bakhtogarimov Ildar R IR Lantsova Margarita S MS Kokin Sergey P SP Nyushko Kirill M KM Alekseev Boris Ya BY Kalinin Dmitry V DV Melnikova Nataliya V NV Dmitriev Alexey A AA Krasnov George S GS Kudryavtseva Anna V AV

International journal of molecular sciences 20230126 3

Radical prostatectomy is the gold standard treatment for prostate cancer (PCa); however, it does not always completely cure PCa, and patients often experience a recurrence of the disease. In addition, the clinical and pathological parameters used to assess the prognosis and choose further tactics for treating a patient are insufficiently informative and need to be supplemented with new markers. In this study, we performed RNA-Seq of PCa tissue samples, aimed at identifying potential prognostic m ...[more]

PMID: 36768739

Similar Datasets

Project description:Purpose: Small-cell prostate carcinoma (SCPC) morphology predicts for a distinct clinical behavior, resistance to androgen ablation, and frequent but short responses to chemotherapy. The model systems we report reflect the biology of the human disease and can be used to improve our understanding of SCPC and to develop new therapeutic strategies for it. Experimental Design: We developed a set of CRPC xenografts and examined their fidelity to their human tumors of origin. We compared the expression and genomic profiles of SCPC and large cell neuroendocrine carcinoma (LCNEC) xenografts to those of typical prostate adenocarcinoma xenografts and used a panel of 60 human tumors to validate our findings using immunohistochemistry. Results: We show that SCPC and LCNEC xenograft models retain high fidelity to their human tumors of origin and are characterized by a marked upregulation of UBE2C and other M-phase cell cycle genes in the absence of AR, retinoblastoma (RB1) and cyclin D1 (CCND1) expression and confirm these findings in a panel of CRPC patients’ samples. In addition, array comparative genomic hybridization of the xenografts showed that the SCPC/LCNEC tumors display more copy number variations than the adenocarcinoma counterparts and that there is amplification of the UBE2C locus and microdeletions of RB1 in a subset of these, but no AR nor CCND1 deletions. Moreover, the AR, RB1, and CCND1 promoters showed no CpG methylation in the SCPC xenografts. Conclusion: Modeling human prostate cancer with xenografts allows in-depth and detailed studies of its underlying biology. The detailed clinical annotation of the donor tumors enables associations of anticipated relevance to be made. Futures studies in the xenografts will address the functional significance of the findings. 22 samples were analysed, that included MDA PCa 79 (n = 3), 117-9 (n = 3), 130 (n = 2), 144-4 (n = 4), 144-13 (n = 5), 146-10 (n = 3), 155-2 (n = 1), and 155-12 (n = 1). MDA PCA 79, 117-9 and 130 samples had the pathologic characteristics of prostate adenocarcinoma and were compared against MDA PCA 144-4, 144-13, 146-10 and 155-12 that have the pathologic features of prostate small cell/ large cell neuroendocrine carcinoma

Project description:Background informationProstate cancer (PCa) is a common disease but only a small subset of patients are at risk of developing metastasis and lethal disease, and identifying which patients will progress is challenging because of the heterogeneity underlying tumour progression. Understanding this heterogeneity at the molecular level and the resulting clinical impact is a critical step necessary for risk stratification. Defining genomic fingerprint elucidates molecular variation and may improve PCa risk stratification, providing more accurate prognostic information of tumour aggressiveness (or lethality) for prognostic biomarker development. Therefore, we explored transcriptomic differences between patients with indolent disease outcome and patients who developed metastasis post-radical prostatectomy using genome-wide expression data in the post radical prostatectomy clinical space before metastatic spread.ResultsBased on differential expression analysis, patients with adverse pathological findings who are at higher risk of developing metastasis have a distinct transcriptomic fingerprint that can be detected on surgically removed prostate specimens several years before metastasis detection. Nearly half of the transcriptomic fingerprint features were non-coding RNA highlighting their pivotal role in PCa progression. Protein-coding RNA features in the fingerprint are involved in multiple pathways including cell cycle, chromosome structure maintenance and cytoskeleton organisation. The metastatic transcriptomic fingerprint was determined in independent cohorts verifying the association between the fingerprint and metastatic patients. Further, the fingerprint was confirmed in metastasis lesions demonstrating that the fingerprint represents early metastatic transcriptomic changes, suggesting its utility as a prognostic tool to predict metastasis and provide clinical value in the early radical prostatectomy setting.ConclusionsHere, we show that transcriptomic patterns of metastatic PCa exist that can be detected early after radical prostatectomy. This metastatic fingerprint has potential prognostic ability that can impact PCa treatment management potentially circumventing the requirements for unnecessary therapies.

Dataset Information

Lymphatic Dissemination in Prostate Cancer: Features of the Transcriptomic Profile and Prognostic Models.

Publications

Lymphatic Dissemination in Prostate Cancer: Features of the Transcriptomic Profile and Prognostic Models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets