Bladder and prostate cancers are among the most prevalent malignancies in men, posing challenges in treatment and prognosis. This study aimed to evaluate the expression of the oncogenes TOP2A, UBE2C and CKS2 in the bladder (BLCA) and prostate (PRAD) cancers and assess the potential inhibitory effects of sitagliptin through molecular docking. Microarray datasets from the NCBI–GEO database were analyzed to identify differentially expressed genes (DEGs), visualized using volcano plots, UMAP and Venn diagrams. TOP2A, UBE2C and CKS2 expression levels were linked to cancer progression, with mutation and interaction analysis performed using cBioPortal, STRING and GeneMANIA. In silico docking evaluated the binding affinity of sitagliptin, PNU-74654 and Crizotinib. Results showed significant overexpression of TOP2A, UBE2C and CKS2 in advanced stages of BLCA and PRAD, correlating with poorer survival. Sitagliptin exhibited strong binding affinities (-178.98, -125, -112 kcal/mol) to these oncogenes, suggesting its inhibitory potential. PNU-74654 and Crizotinib showed lower binding affinities. This study highlights sitagliptin as a promising therapeutic agent targeting TOP2A, UBE2C and CKS2, with potential to reduce cancer cell proliferation and induce apoptosis. These findings support the development of personalized treatment approaches for bladder and prostate cancer.
Native fluorescence spectra are acquired from fresh normal and cancerous human prostate tissues. The fluorescence data are analyzed using an unsupervised machine learning algorithm such as non-negative matrix factorization. The nonnegative spectral components are retrieved and attributed to the native fluorophores such as collagen, reduced nicotinamide adenine dinucleotide (NADH), and flavin adenine dinucleotide (FAD) in tissue. The retrieved scores of the components are used to estimate the relative concentrations of the native fluorophores such as NADH and FAD and the redox ratio. A supervised machine learning algorithm such as support vector machine (SVM) is used to classify normal and cancerous tissue samples based on either the relative concentrations of NADH and FAD or the redox ratio alone. Various statistical measures such as sensitivity, specificity, and accuracy, along with the area under receiver operating characteristic (ROC) curve are used to show the classification performance. A cross validation method such as leave-one-out is used to further evaluate the predictive performance of the SVM classifier to avoid bias due to overfitting, and the accuracy was found to be 93.3%.
Human prostate cancer PC3 cells were treated in vitro with psychosomatic power emitted by a Buddhist-Zen Master. A significant decrease of growth rate was observed as determined by MTT assay after 48 hours. These cells also had two- to three-fold higher levels of prostatic acid phosphatase (PAcP) activity, a prostate tissue-specific differentiation antigen. In addition, the treated cells formed fewer and smaller colonies in soft agar as compared with control cells, which displayed anchorage-independent growth. These observations provide insight into the suppressive effects of healing power through the practice of Buddhist-Zen meditation on tumor progression. The emitted bioenergy may be suggested as an alternative and feasible approach for cancer research and patient treatment.
Prostate cancer is the most prevalent type of cancer in the United States. The most common site of prostate cancer metastasis is bone. CXCL12 is preferentially expressed in bone and is targeted by prostate cancer cells, which over-express the receptor for CXCL12, CXCR4. In response to CXCL12 stimulation, Rac1, a GTPase, along with its effectors, regulates actin polymerization to form lamellipodia, which is a critical event for cell migration. Cortactin, an actin-binding protein, is recruited to the lamellipodia and is phosphorylated at tyrosine residues. The phosphorylated cortactin is also involved in cell migration. The inhibition of Rac1 activity using a dominant negative Rac1 impairs lamellipodial protrusion as well as cortactin translocation and cortactin phosphorylation. Denbinobin, a substance extracted from Dendrobium nobile, has anticancer effects in many cancer cell lines. Whether denbinobin can inhibit prostate cancer cell migration is not clear. Here, we report that denbinobin inhibited Rac1 activity. The inhibition of Rac1 activity prevented lamellipodial formation. Cortactin phosphorylation and translocation to the lamellipodia were also impaired, and PC3 cells were unable to migrate. These results indicate that denbinobin prevents CXCL12-induced PC3 cell migration by inhibiting Rac1 activity.
Ursolic acid (UA), a pentacyclic triterpenoid, is known to exert antitumor activity in breast, lung, liver and colon cancers. Nonetheless, the underlying mechanism of ursolic acid in prostate cancer cells still remains unclear. In the present study, we report the chemotherapeutic effects of ursolic acid as assessed using in vitro and in vivo models. Treatment of human prostate cancer cells (LNCaP and PC-3) with UA inhibited the proliferation and induced apoptosis in both cell lines as characterized by the increased Annexin V-binding. The induction of apoptosis by UA was associated with a decrease in the levels of Bcl-2, Bcl-xl, survivin, and activated caspase-3. Treatment with UA also inhibited the expression of phosphatidylinositol-3-kinase (PI3K), phosphorylation of Akt and mTOR signaling proteins. Further, administration of UA significantly inhibited the growth of LNCaP prostate tumor xenografts in athymic nude mice, which was associated with inhibition of cell proliferation, induction of apoptosis of tumor cells and decreased expression of PI3K downstream factors, such as p-Akt and p-mTOR in tumor xenograft tissues. Our study demonstrates that UA not only inhibits cell growth but also induces apoptosis through modulation of the PI3K/Akt/mTOR pathway in human prostate cancer cells. We suggest that UA may be a new chemotherapeutic candidate against prostate cancer.
Allyl isothiocyanate (AITC), one of the most widely studied phytochemicals, inhibits the survival of human prostate cancer cells while minimally affecting normal prostate epithelial cells. Our study demonstrates the mechanism of AITC-induced cell death in prostate cancer cells. AITC induces autophagy in RV1 and PC3 cells, judging from the increased level of LC3-II protein in a dose- and time-dependent manner, but not in the normal prostate epithelial cell (PrEC). Inhibition of autophagy in AITC-treated cells decreased cell viability and enhanced apoptosis, suggesting that the autophagy played a protective role. There are several pathways activated in ATIC-treated cells. We detected the phosphorylation forms of mTOR, ERK, AMPK, JNK and p38, and ERK AMPK and JNK activation were also detected. However, inhibition of AITC-activated ERK, AMPK and JNK by pre-treatment of specific inhibitors did not alter autophagy induction. Finally, increased beclin-1 expression was detected in AITC-treated cells, and inhibition of AITC-induced beclin-1 attanuated autophagy induction, indicating that AITC-induced autophagy occurs through upregulating beclin-1. Overall, our data show for the first time that AITC induces protective autophagy in Rv1 and PC3 cells through upregulation of beclin-1. Our results could potentially contribute to a therapeutic application of AITC in prostate cancer patients.
Prostate cancer (PCa) is the second most prevalent cancer in men worldwide. The majority of PCa incidences eventually progress to castration-resistant PCa (CRPC), thereby establishing an urgent need for new effective therapeutic strategies. This study aims to examine the effects of morusin, a prenylated flavonoid isolated from Morus alba L., on PCa progression and identify the regulatory mechanism of morusin. Cell growth, cell migration and invasion, and the expression of EMT markers were examined. Cycle progression and cell apoptosis were examined using flow cytometry and a TUNEL assay, while transcriptome analysis was performed using RNA-seq with results being further validated using real-time PCR and western blot. A xenograft PCa model was used to examine tumor growth. Our experimental results indicated that morusin significantly attenuated the growth of PC-3 and 22Rv1 human PCa cells; moreover, morusin significantly suppressed TGF-ββ-induced cell migration and invasion and inhibited EMT in PC-3 and 22Rv1 cells. Significantly, morusin treatment caused cell cycle arrest at the G2/M phase and induced cell apoptosis in PC-3 and 22Rv1 cells. Morusin also attenuated tumor growth in a xenograft murine model. The results of RNA-seq indicated that morusin regulated PCa cells through the Akt/mTOR signaling pathway, while our western blot results confirmed that morusin suppressed phosphorylation of AKT, mTOR, p70S6K, and downregulation of the expression of Raptor and Rictor in vitro and in vivo. These results suggest that morusin has antitumor activities on regulating PCa progression, including migration, invasion, and formation of metastasis, and might be a potential drug for CRPC treatment.
Stereology and stochastic geometry can be used as auxiliary tools for diagnostic purposes in tumour pathology. The role of first-order parameters and stochastic–geometric functions for the classification of the texture of biological tissues has been investigated recently. The volume fraction and surface area per unit volume, the pair correlation function and the centred quadratic contact density function of epithelium were estimated in three case series of benign and malignant lesions of glandular tissues. This approach was further extended by applying the Laslett test, i.e. a point process statistic computed after transformation of the convex tangent points of sectioned random sets from planar images. This method has not yet been applied to histological images so far. Also the nonlinear deterministic approach to tissue texture was applied by estimating the correlation dimension as a function of embedding dimension. We used the stochastic–geometric functions, the first-order parameters and the correlation dimensions for the classification of cases using various algorithms. Learning vector quantization was applied as neural paradigm. Applications included distinction between mastopathy and mammary cancer, between benign prostatic hyperplasia and prostatic cancer, and between chronic pancreatitis and pancreatic cancer. The same data sets were also classified with discriminant analysis and support vector machines. The stereological estimates provided high accuracy in the classification of individual cases. The question: which category of estimator is the most informative, cannot be answered globally, but must be explored empirically for each specific data set. The results obtained by the three algorithms were similar.
Since most prostate tumors are initially hormone-sensitive, hormonal therapy with androgen suppression is a major treatment for them. In this hormonal therapy, however, a tumor relapse is a crucial problem. Androgen-independent tumor cells are considered to be responsible for such a relapse. These cells are not sensitive to androgen suppression but rather apt to proliferate even in an androgen-poor environment. Bruchovsky et al. proposed intermittent androgen suppression (IAS), which may prolong the relapse time when compared with continuous androgen suppression (CAS). IAS therapy is based on switching of medication through monitoring of the serum prostate-specific antigen (PSA). Namely, the medication is suspended when the PSA concentration falls below the lower threshold during on-treatment periods and it is reinstituted when the concentration exceeds the upper threshold during off-treatment periods. In this paper, we propose a model of partial differential equations (PDE) for IAS therapy, on the basis of our previous model of ordinary differential equations, under the assumption that the prostate tumor is a mixed assembly of androgen-dependent (AD) and androgen-independent (AI) cells. Numerical analysis compares the effect of the IAS therapy with that of the CAS therapy for different growth rates of the AI cells, which suggests an optimal protocol of the IAS therapy.
The relapse of tumor is a crucial problem in hormonal therapy of prostate cancer. The so-called androgen-independent cells are considered to be responsible for such a recurrence. These cells are not sensitive to androgen suppression but rather apt to proliferate even in an androgen-poor environment. Bruchovsky et al. in their experimental and clinical studies suggested that intermittent androgen suppression may delay or prevent the relapse when compared with continuous androgen suppression. This paper proposes a model at the macroscopic scale of prostate tumor growth under intermittent androgen suppression. Qualitative analysis shows that the tumor relapse cannot be avoided under continuous androgen suppression for typical parameter values. Numerical simulation supports the above-mentioned experimental and clinical suggestion, and implies an optimal medication scheme of intermittent androgen suppression therapy.
Chemotherapy is a common treatment for advanced prostate cancer. The standard approach relies on cytotoxic drugs, which aim at inhibiting proliferation and promoting cell death. Advanced prostatic tumors are known to rely on angiogenesis, i.e. the growth of local microvasculature via chemical signaling produced by the tumor. Thus, several clinical studies have been investigating antiangiogenic therapy for advanced prostate cancer, either as monotherapy or in combination with standard cytotoxic protocols. However, the complex genetic alterations that originate and sustain prostate cancer growth complicate the selection of the best chemotherapeutic approach for each patient’s tumor. Here, we present a mathematical model of prostate cancer growth and chemotherapy that may enable physicians to test and design personalized chemotherapeutic protocols in silico. We use the phase-field method to describe tumor growth, which we assume to be driven by a generic nutrient following reaction–diffusion dynamics. Tumor proliferation and apoptosis (i.e. programmed cell death) can be parameterized with experimentally-determined values. Cytotoxic chemotherapy is included as a term downregulating tumor net proliferation, while antiangiogenic therapy is modeled as a reduction in intratumoral nutrient supply. An additional equation couples the tumor phase field with the production of prostate-specific antigen, which is a prostate cancer biomarker that is extensively used in the clinical management of the disease. We prove the well posedness of our model and we run a series of representative simulations leveraging an isogeometric method to explore untreated tumor growth as well as the effects of cytotoxic chemotherapy and antiangiogenic therapy, both alone and combined. Our simulations show that our model captures the growth morphologies of prostate cancer as well as common outcomes of cytotoxic and antiangiogenic mono therapy and combined therapy. Additionally, our model also reproduces the usual temporal trends in tumor volume and prostate-specific antigen evolution observed in experimental and clinical studies.
Prostate cancer can be lethal in advanced stages, for which chemotherapy may become the only viable therapeutic option. While there is no clear clinical management strategy fitting all patients, cytotoxic chemotherapy with docetaxel is currently regarded as the gold standard. However, tumors may regain activity after treatment conclusion and become resistant to docetaxel. This situation calls for new delivery strategies and drug compounds enabling an improved therapeutic outcome. Combination of docetaxel with antiangiogenic therapy has been considered a promising strategy. Bevacizumab is the most common antiangiogenic drug, but clinical studies have not revealed a clear benefit from its combination with docetaxel. Here, we capitalize on our prior work on mathematical modeling of prostate cancer growth subjected to combined cytotoxic and antiangiogenic therapies, and propose an optimal control framework to robustly compute the drug-independent cytotoxic and antiangiogenic effects enabling an optimal therapeutic control of tumor dynamics. We describe the formulation of the optimal control problem, for which we prove the existence of at least a solution and determine the necessary first-order optimality conditions. We then present numerical algorithms based on isogeometric analysis to run a preliminary simulation study over a single cycle of combined therapy. Our results suggest that only cytotoxic chemotherapy is required to optimize therapeutic performance and we show that our framework can produce superior solutions to combined therapy with docetaxel and bevacizumab. We also illustrate how the optimal drug-naïve cytotoxic effects computed in these simulations may be successfully leveraged to guide drug production and delivery strategies by running a nonlinear least-square fit of protocols involving docetaxel and a new design drug. In the future, we believe that our optimal control framework may contribute to accelerate experimental research on chemotherapeutic drugs for advanced prostate cancer and ultimately provide a means to design and monitor its optimal delivery to patients.
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