Prostate cancer biomarkers detection using nanoparticles based electrochemical biosensors

Singh, Sima; Gill, Atal A.S.; Nlooto, Manimbulu; Karpoormath, Rajshekhar

doi:10.1016/j.bios.2019.03.065

Cited by 95 publications

(45 citation statements)

References 118 publications

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“…There are various potential biomarkers for prostate cancer that are being studied clinically. For a list of potential biomarkers for prostate cancer, the readers are referred to [79]. Modeling work aiming to incorporate multiple biomarkers can potentially result in increasing the accuracy of the model's predictive power.…”

Section: Dimonte (2010) Dimonte Et Al (2012)mentioning

confidence: 99%

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

et al. 2020

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We review and synthesize key findings and limitations of mathematical models for prostate cancer, both from theoretical work and data-validated approaches, especially concerning clinical applications. Our focus is on models of prostate cancer dynamics under treatment, particularly with a view toward optimizing hormone-based treatment schedules and estimating the onset of treatment resistance under various assumptions. Population models suggest that intermittent or adaptive therapy is more beneficial to delay cancer relapse as compared to the standard continuous therapy if treatment resistance comes at a competitive cost for cancer cells. Another consensus among existing work is that the standard biomarker for cancer growth, prostate-specific antigen, may not always correlate well with cancer progression. Instead, its doubling rate appears to be a better indicator of tumor growth. Much of the existing work utilizes simple ordinary differential equations due to difficulty in collecting spatial data and due to the early success of using prostate-specific antigen in mathematical modeling. However, a shift toward more complex and realistic models is taking place, which leaves many of the theoretical and mathematical questions unexplored. Furthermore, as adaptive therapy displays better potential than existing treatment protocols, an increasing number of studies incorporate this treatment into modeling efforts. Although existing modeling work has explored and yielded useful insights on the treatment of prostate cancer, the road to clinical application is still elusive. Among the pertinent issues needed to be addressed to bridge the gap from modeling work to clinical application are (1) real-time data validation and model identification, (2) sensitivity analysis and uncertainty quantification for model prediction, and (3) optimal treatment/schedule while considering drug properties, interactions, and toxicity. To address these issues, we suggest in-depth studies on various aspects of the parameters in dynamical models such as the evolution of parameters over time. We hope this review will assist future attempts at studying prostate cancer.Prostate cancer (PCa) is the leading cause of cancer in US men (behind non-melanoma skin cancers), as well as the second leading cause of cancer-related mortality in men. The American Cancer Society estimates that in 2020, 191,930 Americans will be diagnosed with prostate cancer, with approximately 21.5% of all new cancer cases in men. The average five-year survival rate for all cases of prostate cancer is about 99%, which is a 31% point higher than in the 1970s [1]. However, once cancer metastasizes, the five-year survival rate drops to 30%, an increase of 10 percentage-point compared to the 1970s [2]. While the incidence of prostate cancer in the US is decreasing (see Figure 1), one possible cause is doctors' discouragement of early PCa diagnosis due to the fear of overtreating cancer. Globally, the incidence of prostate cancer is increasing, especially among developing nations [3].

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Section: Dimonte (2010) Dimonte Et Al (2012)mentioning

confidence: 99%

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

et al. 2020

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“…Prostate cancer (PCa) is one of the most common diseases and its targeting needs greater concentration of active to the organ and tissues affected by malignancy. Both organic (nanoemulsions, liposomes, niosomes, and polymeric nanocapsule) and inorganic (carbon nanotubes, gold nanoparticles, magnetic nanoparticles, silica mesoporous nanoparticles, quantum dots, selenium nanoparticles) are types of nanocarrier that have shown greater efficacy as drug delivery systems for greater number of active pharmaceutical agent (API) for targeting prostate tumor as shown in Figure 1 [ 77 ]. The following nanomaterials are capable of increasing active (receptor mediated endocytosis and decorating of nanoparticles with different ligands helps to achieve active targeting) targeting and passive targeting (enhanced permeability and retention effect EPR) [ 78 ].…”

Section: Nanomaterials: Applications In Treatment Of Prostate Cancmentioning

confidence: 99%

Nanotreatment and Nanodiagnosis of Prostate Cancer: Recent Updates

et al. 2020

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The fabrication and development of nanomaterials for the treatment of prostate cancer have gained significant appraisal in recent years. Advancements in synthesis of organic and inorganic nanomaterials with charge, particle size, specified geometry, ligand attachment etc have resulted in greater biocompatibility and active targeting at cancer site. Despite all of the advances made over the years in discovering drugs, methods, and new biomarkers for cancer of the prostate (PCa), PCa remains one of the most troubling cancers among people. Early on, effective diagnosis is an essential part of treating prostate cancer. Prostate-specific antigen (PSA) or serum prostate-specific antigen is the best serum marker widely accessible for diagnosis of PCa. Numerous efforts have been made over the past decade to design new biosensor-based strategies for biomolecules detection and PSA miniaturization biomarkers. The growing nanotechnology is expected to have a significant effect in the immediate future on scientific research and healthcare. Nanotechnology is thus predicted to find a way to solve one of the most and long-standing problem, “early cancer detection”. For early diagnosis of PCa biomarkers, different nanoparticles with different approaches have been used. In this review, we provide a brief description of the latest achievements and advances in the use of nanoparticles for PCa biomarker diagnosis.

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“…Prostate cancer (PCa) is the most prevalent malignant tumors in male genitourinary system, accompanied with extremely high morbidity and lethality 1 . PCa commonly starts in the outermost layer of the prostate caused by several epigenetic changes and then deteriorates to uncontrolled cell proliferation, differentiation and invasion to adjacent tissues 2 . Effective treatments for PCa are focused on surgery and radiotherapy 3,4 .…”

Section: Introductionmentioning

confidence: 99%

DLX6‐AS1 accelerates cell proliferation through regulating miR‐497‐5p/SNCG pathway in prostate cancer

Zhu

Zhao

et al. 2020

Environmental Toxicology

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Prostate cancer (PCa) has become the second leading cause of cancer‐related mortality in males worldwide. Although the long noncoding RNA DLX6‐AS1 has been recognized to be an oncogene in multiple cancers, the biological function and regulatory mechanism of DLX6‐AS1 in prostate cancer are still obscure. In the present study, we observed that DLX6‐AS1 was significantly upregulated in PCa tissues and cells. Knockdown of DLX6‐AS1 inhibited PCa progression by suppressing cell proliferation and accelerating cell apoptosis. Molecular mechanism exploration indicated that DLX6‐AS1 acted as a sponge for miR‐497‐5p and synuclein gamma (SNCG) was a downstream target gene of miR‐497‐5p. In addition, there was a negative correlation between DLX6‐AS1 and miR‐497‐5p in PCa tissues. Rescue assays showed that SNCG overexpression could partially recover DLX6‐AS1 knockdown‐mediated inhibition of progression in PCa. Furthermore, xenograft tumor model was established to determine the role of DLX6‐AS1 in PCa tumor growth and the results suggested that DLX6‐AS1 could facilitate tumor growth by regulating SNCG in vivo. In conclusion, our study investigated the biological function and underlying mechanism of DLX6‐AS1 in PCa and validated that DLX6‐AS1 functioned as an oncogene through miR‐497‐5p/SNCG axis.

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Prostate cancer biomarkers detection using nanoparticles based electrochemical biosensors

Cited by 95 publications

References 118 publications

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

Review: Mathematical Modeling of Prostate Cancer and Clinical Application

Nanotreatment and Nanodiagnosis of Prostate Cancer: Recent Updates

DLX6‐AS1 accelerates cell proliferation through regulating miR‐497‐5p/SNCG pathway in prostate cancer

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