Clinical Perspective of Prostate Cancer

LINC01146/F11R facilitates growth and metastasis of prostate cancer under the regulation of TGF-β

The effect of long intergenic non-protein coding RNAs (lncRNAs) was verified in prostate cancer (PCa), but the mechanism of LINC01146 in PCa is unclear. Bioinformatics was applied to analyze LINC01146 expression in PCa and predict target genes of LINC01146, followed by the verification of qRT-PCR, RNA pull-down and co-immunoprecipitation (Co-IP). The correlation between LINC01146 expression and clinicopathological characteristics was investigated. The location of LINC01146 in PCa cells was detected by fluorescence in situ hybridization (FISH). After interference with LINC01146 or/and F11 receptor (F11R) or treated with transforming growth factor beta 1 (TGF-β1), the function of LINC01146 in PCa in vitro or in vivo was determined by CCK-8, colony formation, flow cytometry, scratch test, transwell assay, xenograft experiment and western blot. LINC01146 and F11R were over-expressed in PCa and positively correlated with poor prognosis. LINC01146 located in the cytoplasm and combined with F11R. LINC01146 overexpression impeded apoptosis, facilitated viability, proliferation, migration and invasion in PCa cells in vitro, promoted tumor growth in vivo, downregulated E-cadherin, Bax and Cleaved caspase-3, and upregulated N-cadherin, Vimentin and PCNA, but LINC01146 silencing did the opposite. F11R was positively regulated by LINC01146 and F11R depletion negated the effect of LINC01146 overexpression on malignant phenotypes of PCa cells. The expression of LINC01146 and F11R was regulated by TGF-β1. The promoting role of TGF-β1 in migration, invasion and F11R in PCa cells was reversed by LINC01146 silencing. LINC01146 upregulated F11R to facilitate malignant phenotypes of PCa cells, which was regulated by TGF-β.

Prognostic value of PNN in prostate cancer and its correlation with therapeutic significance

Prostate cancer (PCa) is the most common malignancy. New biomarkers are in demand to facilitate the management. The role of the pinin protein (encoded by PNN gene) in PCa has not been thoroughly explored yet. Using The Cancer Genome Atlas (TCGA-PCa) dataset validated with Gene Expression Omnibus (GEO) and protein expression data retrieved from the Human Protein Atlas, the prognostic and diagnostic values of PNN were studied. Highly co-expressed genes with PNN (HCEG) were constructed for pathway enrichment analysis and drug prediction. A prognostic signature based on methylation status using HCEG was constructed. Gene set enrichment analysis (GSEA) and the TISIDB database were utilised to analyse the associations between PNN and tumour-infiltrating immune cells. The upregulated PNN expression in PCa at both transcription and protein levels suggests its potential as an independent prognostic factor of PCa. Analyses of the PNN's co-expression network indicated that PNN plays a role in RNA splicing and spliceosomes. The prognostic methylation signature demonstrated good performance for progression-free survival. Finally, our results showed that the PNN gene was involved in splicing-related pathways in PCa and identified as a potential biomarker for PCa.

Machine learning techniques on homological persistence features for prostate cancer diagnosis

The rapid evolution of image processing equipment and techniques ensures the development of novel picture analysis methodologies. One of the most powerful yet computationally possible algebraic techniques for measuring the topological characteristics of functions is persistent homology. It's an algebraic invariant that can capture topological details at different spatial resolutions. Persistent homology investigates the topological features of a space using a set of sampled points, such as pixels. It can track the appearance and disappearance of topological features caused by changes in the nested space created by an operation known as filtration, in which a parameter scale, in our case the intensity of pixels, is increased to detect changes in the studied space over a range of varying scales. In addition, at the level of machine learning there were many studies and articles witnessing recently the combination between homological persistence and machine learning algorithms. On another level, prostate cancer is diagnosed referring to a scoring criterion describing the severity of the cancer called Gleason score. The classical Gleason system defines five histological growth patterns (grades). In our study we propose to study the Gleason score on some glands issued from a new optical microscopy technique called SLIM. This new optical microscopy technique that combines two classic ideas in light imaging: Zernike’s phase contrast microscopy and Gabor’s holography. Persistent homology features are computed on these images. We suggested machine learning methods to classify these images into the corresponding Gleason score. Machine learning techniques applied on homological persistence features was very effective in the detection of the right Gleason score of the prostate cancer in these kinds of images and showed an accuracy of above 95%.

AMPK's double-faced role in advanced stages of prostate cancer

Prostate cancer (PCa) is the second leading cause of cancer deaths in men. Unfortunately, a very limited number of drugs are available for the relapsed and advanced stages of PCa, adding only a few months to survival; therefore, it is vital to develop new drugs. 5´ AMP-activated protein kinase (AMPK) is a master regulator of cell metabolism. It plays a significant role in the metabolism of PCa; hence, it can serve well as a treatment option for the advanced stages of PCa. However, whether this pathway contributes to cancer cell survival or death remains unknown. The present study reviews the possible pathways by which AMPK plays role in the advanced stages of PCa, drug resistance, and metastasis: (1) AMPK has a contradictory role in promoting glycolysis and the Warburg effect which are correlated with cancer stem cells (CSCs) survival and advanced PCa. It exerts its effect by interacting with hypoxia-induced factor 1 (HIF1) α, pyruvate kinase 2 (PKM2), glucose transporter (GLUT) 1 and pyruvate dehydrogenase complex (PDHC), which are key regulators of glycolysis; however, whether it promotes or discourage glycolysis is not conclusive. It can also exert an anti-CSC effect by negative regulation of NANOG and epithelial-mesenchymal transition (EMT) transcription factors, which are the major drivers of CSC maintenance; (2) the regulatory effect of AMPK on autophagy is also noticeable. Androgen receptors' expression increases AMPK activation through Calcium/calmodulin-dependent protein kinase 2 (CaMKK2) and induces autophagy. In addition, AMPK itself increases autophagy by downregulating the mammalian target of rapamycin complex (mTORC). However, whether increased autophagy inhibits or promotes cell death and drug resistance is contradictory. This study reveals that there are numerous pathways other than cell metabolism by which AMPK exerts its effects in the advanced stages of PCa, making it a priceless treatment target. Finally, we mention some drugs developed to treat the advanced stages of PCa by acting on AMPK.

RP1-59D14.5 triggers autophagy and represses tumorigenesis and progression of prostate cancer via activation of the Hippo signaling pathway

Prostate cancer (PCa) is one of the major malignant tumors among men worldwide. Long noncoding RNAs (lncRNAs) have been documented as important modulators in human cancers, including PCa. In our study, we investigated the role and potential mechanism of RP1-59D14.5 in PCa. RP1-59D14.5 expressed at a low level in PCa cells. Gain-of-function assays including colony formation and transwell assays displayed that RP1-59D14.5 overexpression repressed PCa cell proliferation, migration, and invasion. Besides, RP1-59D14.5 up-regulation induced autophagy in PCa cells. Mechanically, luciferase reporter assays and western blot verified that RP1-59D14.5 activated the Hippo pathway in PCa cells. Through RNA-binding protein immunoprecipitation (RIP) and RNA pull-down assays, we validated that RP1-59D14.5 functioned as a competing endogenous RNA (ceRNA) to regulate large tumor suppressor kinase 1/2 (LATS1/2) via targeting miR-147a. Moreover, RP1-59D14.5 recruited HUR to promote casein kinase 1 (CK1) expression. Collectively, RP1-59D14.5 promoted yes-associated protein (YAP) degradation to activate the Hippo pathway in PCa progression via targeting the miR-147a/LATS1/2 axis and recruiting HUR to promote the interaction of CK1 and β-transducin repeat-containing protein (βTrCP). These results implied that RP1-59D14.5 acted as a tumor suppressor in PCa, which might be a target for PCa treatment.

Preclinical evaluation of a clinical prototype transrectal diffuse optical tomography system for monitoring photothermal therapy of focal prostate cancer

SIGNIFICANCE

Our work demonstrates in preclinical models that continuous-wave transrectal diffuse optical tomography (TRDOT) can be used to accurately monitor photothermal therapy (PTT) and, in particular, the progression of the photocoagulation boundary toward the rectum. When used in patients, this should prevent rectal damage during PTT, thereby achieving maximum treatment efficacy while ensuring safety, using a technology platform suitable for wide dissemination.

AIM

We aim to validate that TRDOT measurements analyzed using a shape-based image-reconstruction algorithm (SBDOT) allow localization of the photocoagulation boundary during PTT within ±1  mm toward the rectum in the transverse plane.

APPROACH

TRDOT measurements were performed in tissue-simulating phantoms, ex vivo tissues, and an in vivo canine prostate model. The accuracy and sensitivity of reconstructing the size and location of the coagulation zone were determined, based on changes in the tissue absorption and reduced scattering coefficients upon photocoagulation. The reconstruction also yields the native and coagulated tissue optical properties.

RESULTS

The TRDOT measurements and SBDOT reconstruction algorithm were confirmed to perform sufficiently well for clinical translation in PTT monitoring, recovering the location of the coagulation boundary within ±1  mm compared to the true value as determined by direct visualization postexcision and/or MRI.

CONCLUSIONS

Implementing previously described TRDOT instrumentation and SBDOT image reconstruction in different tissue models confirms the potential for clinincal translation, including required refinements of the system and reconstruction algorithm.

Application of network pharmacology and molecular docking to elucidate the potential mechanism of Astragalus-Scorpion against prostate cancer

The present study aimed to investigate the molecular mechanism of the Astragalus-Scorpion drug pair in the treatment of prostate cancer (PCa). We employed network pharmacology and molecular docking technology to retrieving the active ingredients and corresponding targets of Astragalus-Scorpion by using TCMSP, BATMAN-TCM, TCMID and Swiss Target Prediction Databases. The targets related to PCa were retrieved through GeneCards. Cytoscape software was used to construct the 'active ingredient-target disease' network, and GO and KEGG enrichment analyses were performed on the common targets. Autodock software was used for molecular docking verification. In total, 26 active ingredients, 340 potential targets related to active ingredients and 122 common targets were screened from Astragalus-Scorpion drug pair. The core targets of the protein-protein interaction (PPI) network were JUN, AKT1, IL6, MAPK1 and RELA, whereas the core active ingredients were quercetin, kaempferol, formononetin, 7-o-methylisomucronulatol and calycosin. Nearly 762 GO entries and 154 pathways were obtained by using the pathway enrichment analysis. Molecular docking results revealed that quercetin and kaempferol bind to AKT1 and formononetin binds to RELA, all of which were found to be stable bounds.

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.

In silico identification of key genes and signaling pathways targeted by a panel of signature microRNAs in prostate cancer

Accumulating evidence have suggested that some microRNAs are aberrantly expressed in prostate cancer. In our previous work, we had identified a panel of four differentially expressed microRNAs in prostate cancer. In the present study, we have investigated common molecular targets of this panel of miRNAs (DEMs) and key hub genes that can serve as potential candidate biomarkers in the pathogenesis and progression of prostate cancer. A joint bioinformatics approach was employed to identify differentially expressed genes (DEGs) in prostate cancer. Gene enrichment analysis followed by the protein-protein interaction (PPI) network construction and selection of hub genes was further performed using String and Cytoscape, respectively. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the identified hub genes was conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. In total, 496 genes were identified to be common targets of DEMs in prostate cancer and 13 key hub genes were identified from three modules of the PPI network of the DEGs. Further top five genes viz Rhoa, PI3KCA, CDC42, MAPK3, TP53 were used for Enrichment analysis which revealed their association with vital cellular and functional pathways in prostate cancer indicating their potential as candidate biomarkers in prostate cancer.