TP53 gene implications in prostate cancer evolution: potential role in tumor classification.

Background and aims: Prostate adenocarcinoma (PRAD) is a complex disease that can be driven by alterations in both coding and noncoding genes. Recent research has identified coding and non-coding genes that are considered to play important roles in prostate cancer evolution and which may be used as biomarkers for disease diagnosis, prognosis, and treatment. TP53 is a critical hub gene in prostate cancer. Advanced studies have demonstrated the crosstalk between coding and non-coding RNAs, particularly microRNAs (miRNAs). Methods: In this study, we investigated the roundabout of TP53 and their regulatory miRNAs (miR-15a-5p, miR-34a-5p, and miR-141-3p) based on the TCGA data set. We validated an additional patient cohort of 28 matched samples of patients with PRAD at tissue and plasma level. Results: Therefore, using the UALCAN online database, we evaluated the expression level in PRAD of these genes revealing overexpression of TP53. qRT-PCR validation step endorsed the expression level for these genes. Additionally, we evaluated the expression level of the four key miRNAs (miR-15a-5p, miR-34a-5p, and miR-141-3p) interconnected as a network at tissue and plasma levels. Conclusions: Through these results, we demonstrated the essential function of TP53 and its associated miRNAs that play a significant role in tumor control, highlighting miRNAs' potential as future therapeutic targets and biomarkers with important implications in managing prostate cancer.

Insights into the Human Microbiome and Its Connections with Prostate Cancer.

The human microbiome represents the diversity of microorganisms that live together at different organ sites, influencing various physiological processes and leading to pathological conditions, even carcinogenesis, in case of a chronic imbalance. Additionally, the link between organ-specific microbiota and cancer has attracted the interest of numerous studies and projects. In this review article, we address the important aspects regarding the role of gut, prostate, urinary and reproductive system, skin, and oral cavity colonizing microorganisms in prostate cancer development. Various bacteria, fungi, virus species, and other relevant agents with major implications in cancer occurrence and progression are also described. Some of them are assessed based on their values of prognostic or diagnostic biomarkers, while others are presented for their anti-cancer properties.

New Insights into the Multivariate Analysis of SER Spectra Collected on Blood Samples for Prostate Cancer Detection: Towards a Better Understanding of the Role Played by Different Biomolecules on Cancer Screening: A Preliminary Study.

It is possible to obtain diagnostically relevant data on the changes in biochemical elements brought on by cancer via the use of multivariate analysis of vibrational spectra recorded on biological fluids. Prostate cancer and control groups included in this research generated almost similar SERS spectra, which means that the values of peak intensities present in SERS spectra can only give unspecific and limited information for distinguishing between the two groups. Our diagnostic algorithm for prostate cancer (PCa) differentiation was built using principal component analysis and linear discriminant analysis (PCA-LDA) analysis of spectral data, which has been widely used in spectral data management in many studies and has shown promising results so far. In order to fully utilize the entire SERS spectrum and automatically determine the most meaningful spectral features that can be used to differentiate PCa from healthy patients, we perform a multivariate analysis on both the entire and specific spectral intervals. Using the PCA-LDA model, the prostate cancer and control groups are clearly distinguished in our investigation. The separability of the following two data sets is also evaluated using two alternative discrimination techniques: principal least squares discriminant analysis (PLS-DA) and principal component analysis-support vector machine (PCA-SVM).

MicroRNA Dysregulation in Prostate Cancer.

Prostate cancer biology is complex, and needs to be deciphered. The latest evidence reveals the significant role of non-coding RNAs, particularly microRNAs (miRNAs), as key regulatory factors in cancer. Therefore, the identification of altered miRNA patterns involved in prostate cancer will allow them to be used for development of novel diagnostic and prognostic biomarkers. PATIENTS AND METHODS: We performed a miRNAs transcriptomic analysis, using microarray (10 matched pairs tumor tissue versus normal adjacent tissue, selected based on inclusion criteria), followed by overlapping with TCGA data. A total of 292 miRNAs were differentially expressed, with 125 upregulated and 167 downregulated in TCGA patients' cohort with PRAD (prostate adenocarcinoma), respectively for the microarray experiments; 16 upregulated and 44 downregulated miRNAs were found in our cohort. To confirm our results obtained for tumor tissue, we performed validation with qRT-PCR at the tissue and plasma level of two selected transcripts, and finally, we focused on the identification of altered miRNAs involved in key biological processes. RESULTS: A common signature identified a panel of 12 upregulated and 1 downregulated miRNA, targeting and interconnected in a network with the TP53, AGO2, BIRC5 gene and EGFR as a core element. Among this signature, the overexpressed transcripts (miR-20b-5p, miR-96-5p, miR-183-5p) and the downregulated miR-542-5p were validated by qRT-PCR in an additional patients' cohort of 34 matched tumor and normal adjacent paired samples. Further, we performed the validation of the expression level for miR-20b-5p, miR-96-5p, miR-183-5p plasma, on the same patients' cohort versus a healthy control group, confirming the overexpression of these transcripts in the PRAD group, demonstrating the liquid biopsy as a potential investigational tool in prostate cancer. CONCLUSION: In this pilot study, we provide evidence on miRNA dysregulation and its association with key functional components of the PRAD landscape, where an important role is acted by miR-20b-5p, miR-542-5p, or the oncogenic cluster miR-183-96-182.

PSA Based Biomarkers, Imagistic Techniques and Combined Tests for a Better Diagnostic of Localized Prostate Cancer.

Prostate cancer represents the most encountered urinary malignancy in males over 50 years old, and the second most diagnosed after lung cancer globally. Digital rectal examination and prostatic specific antigen were the long-time standard tools for diagnosis but with a significant risk of overdiagnosis and overtreatment. Magnetic resonance imaging recently entered the diagnosis process, but to this date, there is no specific biomarker that accurately indicates whether to proceed with the prostate biopsy. Research in this area has gone towards this direction, and recently, serum, urine, imagistic, tissue biomarkers, and Risk Calculators promise to help better diagnose and stratify prostate cancer. In order to eliminate the comorbidities that appear along with the diagnosis and treatment of this disease, there is a constant need to implement new diagnostic strategies. Important uro-oncology associations recommend the use of novel biomarkers in the grey area of prostate cancer, to better distinguish the next step in the diagnostic process. Although it is not that simple, they should be integrated according to the clinical policies, and it should be considered that statistical significance does not always equal clinical significance. In this review, we analyzed the contribution of prostate-specific antigen (PSA)-based biomarkers (PHI, PHID, 4Kscore, STHLM3), imagistic techniques (mp-MRI and mp-US), and combined tests in the early diagnosis process of localized prostate cancer.

Spontaneous and Induced Animal Models for Cancer Research.

Considering the complexity of the current framework in oncology, the relevance of animal models in biomedical research is critical in light of the capacity to produce valuable data with clinical translation. The laboratory mouse is the most common animal model used in cancer research due to its high adaptation to different environments, genetic variability, and physiological similarities with humans. Beginning with spontaneous mutations arising in mice colonies that allow for pursuing studies of specific pathological conditions, this area of in vivo research has significantly evolved, now capable of generating humanized mice models encompassing the human immune system in biological correlation with human tumor xenografts. Moreover, the era of genetic engineering, especially of the hijacking CRISPR/Cas9 technique, offers powerful tools in designing and developing various mouse strains. Within this article, we will cover the principal mouse models used in oncology research, beginning with behavioral science of animals vs. humans, and continuing on with genetically engineered mice, microsurgical-induced cancer models, and avatar mouse models for personalized cancer therapy. Moreover, the area of spontaneous large animal models for cancer research will be briefly presented.

MiRNA-Based Inspired Approach in Diagnosis of Prostate Cancer.

Prostate cancer is one of the most encountered cancer diseases in men worldwide and in consequence it requires the improvement of therapeutic strategies. For the clinical diagnosis, the standard approach is represented by solid biopsy. From a surgical point of view, this technique represents an invasive procedure that may imply several postoperative complications. To overcome these impediments, many trends are focusing on developing liquid biopsy assays and on implementing them in clinical practice. Liquid samples (blood, urine) are rich in analytes, especially in transcriptomic information provided by genetic markers. Additionally, molecular characterization regarding microRNAs content reveals outstanding prospects in understanding cancer progression mechanisms. Moreover, these analytes have great potential for prostate cancer early detection, more accurate prostate cancer staging and also for decision making respecting therapy schemes. However, there are still questionable topics and more research is needed to standardize liquid biopsy-based techniques.