Data-driven translational prostate cancer research: from biomarker discovery to clinical decision

Molecular landscape for risk prediction and personalized therapeutics of castration-resistant prostate cancer: at a glance

Prostate cancer (PCa) is commonly occurred with high incidence in men worldwide, and many patients will be eventually suffered from the dilemma of castration-resistance with the time of disease progression. Castration-resistant PCa (CRPC) is an advanced subtype of PCa with heterogeneous carcinogenesis, resulting in poor prognosis and difficulties in therapy. Currently, disorders in androgen receptor (AR)-related signaling are widely acknowledged as the leading cause of CRPC development, and some non-AR-based strategies are also proposed for CRPC clinical analyses. The initiation of CRPC is a consequence of abnormal interaction and regulation among molecules and pathways at multi-biological levels. In this study, CRPC-associated genes, RNAs, proteins, and metabolites were manually collected and integrated by a comprehensive literature review, and they were functionally classified and compared based on the role during CRPC evolution, i.e., drivers, suppressors, and biomarkers, etc. Finally, translational perspectives for data-driven and artificial intelligence-powered CRPC systems biology analysis were discussed to highlight the significance of novel molecule-based approaches for CRPC precision medicine and holistic healthcare.

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Enhanced NIR-II Fluorescent Lateral Flow Biosensing Platform Based on Supramolecular Host-Guest Self-Assembly for Point-of-Care Testing of Tumor Biomarkers

Point-of-care detection of tumor biomarkers with high sensitivity remains an enormous challenge in the early diagnosis and mass screening of cancer. Fluorescent lateral flow immunoassay (LFA) is an attractive platform for point-of-care testing due to its inherent advantages. Particularly, a fluorescent probe is crucial to improving the analytical performance of the LFA platform. Herein, we developed an enhanced second near-infrared (NIR-II) LFA (ENIR-II LFA) platform based on supramolecular host-guest self-assembly for detection of the prostate-specific antigen (PSA) as a model analyte. In this platform, depending on the effective supramolecular surface modification strategy, cucurbit[7]uril (CB[7])-covered rare-earth nanoparticles (RENPs) emitting in the NIR-II (1000-1700 nm) window were prepared and employed as an efficient fluorescent probe (RENPs-CB[7]). Benefiting from its superior optical properties, such as low autofluorescence, excellent photostability, enhanced fluorescence intensity, and increased antibody-conjugation efficiency, the ENIR-II LFA platform displayed a wide linear detection range from 0.65 to 120 ng mL-1, and the limit of detection was down to 0.22 ng mL-1 for PSA, which was 18.2 times lower than the clinical cutoff value. Moreover, the testing time was also shortened to 6 min. Compared with the commercial visible fluorescence LFA kit (VIS LFA) and the previously reported NIR-II LFA based on a RENPs-PAA probe, this ENIR-II LFA demonstrated more competitive advantages in analytical sensitivity, detection range, testing time, and production cost. Overall, the ENIR-II LFA platform offers great potential for the highly sensitive, rapid, and convenient detection of tumor biomarkers and is expected to serve as a useful technique in the general population screening of the high-incidence cancer region.

RNA m6A modification in prostate cancer: A new weapon for its diagnosis and therapy

Prostate cancer (PCa) is the most common malignant tumor and the second leading cause of cancer-related mortality in men worldwide. Despite significant advances in PCa therapy, the underlying molecular mechanisms have yet to be fully elucidated. Recently, epigenetic modification has emerged as a key player in tumor progression, and RNA-based N6-methyladenosine (m6A) epigenetic modification was found to be crucial. This review summarizes comprehensive state-of-art mechanisms underlying m6A modification, its implication in the pathogenesis, and advancement of PCa in protein-coding and non-coding RNA contexts, its relevance to PCa immunotherapy, and the ongoing clinical trials for PCa treatment. This review presents potential m6A-based targets and paves a new avenue for diagnosing and treating PCa, providing new guidelines for future related research through a systematic review of previous results.

A comprehensive review on recent approaches for cancer drug discovery associated with artificial intelligence

Through the revolutionization of artificial intelligence (AI) technologies in clinical research, significant improvement is observed in diagnosis of cancer. Utilization of these AI technologies, such as machine and deep learning, is imperative for the discovery of novel anticancer drugs and improves existing/ongoing cancer therapeutics. However, building a model for complicated cancers and their types remains a challenge due to lack of effective therapeutics that hinder the establishment of effective computational tools. In this review, we exploit recent approaches and state-of-the-art in implementing AI methods for anticancer drug discovery, and discussed how advances in these applications need to be considered in the current cancer therapeutics. Considering the immense potential of AI, we explore molecular docking and their interactions to recognize metabolic activities that support drug design. Finally, we highlight corresponding strategies in applying machine and deep learning methods to various types of cancer with their pros and cons.

A Tale of Two Cancers: A Current Concise Overview of Breast and Prostate Cancer

Cancer is a global issue, and it is expected to have a major impact on our continuing global health crisis. As populations age, we see an increased incidence in cancer rates, but considerable variation is observed in survival rates across different geographical regions and cancer types. Both breast and prostate cancer are leading causes of morbidity and mortality worldwide. Although cancer statistics indicate improvements in some areas of breast and prostate cancer prevention, diagnosis, and treatment, such statistics clearly convey the need for improvements in our understanding of the disease, risk factors, and interventions to improve life span and quality of life for all patients, and hopefully to effect a cure for people living in developed and developing countries. This concise review compiles the current information on statistics, pathophysiology, risk factors, and treatments associated with breast and prostate cancer.

Methods for Stratification and Validation Cohorts: A Scoping Review

Personalized medicine requires large cohorts for patient stratification and validation of patient clustering. However, standards and harmonized practices on the methods and tools to be used for the design and management of cohorts in personalized medicine remain to be defined. This study aims to describe the current state-of-the-art in this area. A scoping review was conducted searching in PubMed, EMBASE, Web of Science, Psycinfo and Cochrane Library for reviews about tools and methods related to cohorts used in personalized medicine. The search focused on cancer, stroke and Alzheimer’s disease and was limited to reports in English, French, German, Italian and Spanish published from 2005 to April 2020. The screening process was reported through a PRISMA flowchart. Fifty reviews were included, mostly including information about how data were generated (25/50) and about tools used for data management and analysis (24/50). No direct information was found about the quality of data and the requirements to monitor associated clinical data. A scarcity of information and standards was found in specific areas such as sample size calculation. With this information, comprehensive guidelines could be developed in the future to improve the reproducibility and robustness in the design and management of cohorts in personalized medicine studies.

Nano-Theranostics for the Sensing, Imaging and Therapy of Prostate Cancers

We highlight hereby recent developments in the emerging field of theranostics, which encompasses the combination of therapeutics and diagnostics in a single entity aimed for an early-stage diagnosis, image-guided therapy as well as evaluation of therapeutic outcomes of relevance to prostate cancer (PCa). Prostate cancer is one of the most common malignancies in men and a frequent cause of male cancer death. As such, this overview is concerned with recent developments in imaging and sensing of relevance to prostate cancer diagnosis and therapeutic monitoring. A major advantage for the effective treatment of PCa is an early diagnosis that would provide information for an appropriate treatment. Several imaging techniques are being developed to diagnose and monitor different stages of cancer in general, and patient stratification is particularly relevant for PCa. Hybrid imaging techniques applicable for diagnosis combine complementary structural and morphological information to enhance resolution and sensitivity of imaging. The focus of this review is to sum up some of the most recent advances in the nanotechnological approaches to the sensing and treatment of prostate cancer (PCa). Targeted imaging using nanoparticles, radiotracers and biomarkers could result to a more specialised and personalised diagnosis and treatment of PCa. A myriad of reports has been published literature proposing methods to detect and treat PCa using nanoparticles but the number of techniques approved for clinical use is relatively small. Another facet of this report is on reviewing aspects of the role of functional nanoparticles in multimodality imaging therapy considering recent developments in simultaneous PET-MRI (Positron Emission Tomography-Magnetic Resonance Imaging) coupled with optical imaging in vitro and in vivo, whilst highlighting feasible case studies that hold promise for the next generation of dual modality medical imaging of PCa. It is envisaged that progress in the field of imaging and sensing domains, taken together, could benefit from the biomedical implementation of new synthetic platforms such as metal complexes and functional materials supported on organic molecular species, which can be conjugated to targeting biomolecules and encompass adaptable and versatile molecular architectures. Furthermore, we include hereby an overview of aspects of biosensing methods aimed to tackle PCa: prostate biomarkers such as Prostate Specific Antigen (PSA) have been incorporated into synthetic platforms and explored in the context of sensing and imaging applications in preclinical investigations for the early detection of PCa. Finally, some of the societal concerns around nanotechnology being used for the detection of PCa are considered and addressed together with the concerns about the toxicity of nanoparticles–these were aspects of recent lively debates that currently hamper the clinical advancements of nano-theranostics. The publications survey conducted for this review includes, to the best of our knowledge, some of the most recent relevant literature examples from the state-of-the-art. Highlighting these advances would be of interest to the biomedical research community aiming to advance the application of theranostics particularly in PCa diagnosis and treatment, but also to those interested in the development of new probes and methodologies for the simultaneous imaging and therapy monitoring employed for PCa targeting.

The Emerging Roles of LINC00665 in Human Cancers

Long non-coding RNAs (lncRNAs) are non-coding RNAs that have more than 200 nucleotides and can participate in the regulation of gene expression in various ways. An increasing number of studies have shown that the dysregulated expression of lncRNAs is related to the occurrence and progression of human cancers. LINC00665 is a novel lncRNA, which is abnormally expressed in various human cancers, such as lung cancer, breast cancer, prostate cancer, and glioma. LINC00665 functions in many biological processes of tumor cells, such as cell proliferation, migration, invasion, angiogenesis, and metabolism, and is related to the clinicopathological characteristics of cancer patients. LINC00665 can play biological functions as a ceRNA, directly binding and interacting with proteins, and as an upstream molecule regulating multiple signaling pathways. In this review, we comprehensively summarize the expression level, function, and molecular mechanisms of LINC00665 in different human cancers and emphasize that LINC00665 is a promising new diagnostic, prognostic biomarker, and therapeutic target.

HFBD: a biomarker knowledge database for heart failure heterogeneity and personalized applications

MOTIVATION

Heart failure (HF) is a cardiovascular disease with a high incidence around the world. Accumulating studies have focused on the identification of biomarkers for HF precision medicine. To understand the HF heterogeneity and provide biomarker information for the personalized diagnosis and treatment of HF, a knowledge database collecting the distributed and multiple-level biomarker information is necessary.

RESULTS

In this study, the HF biomarker knowledge database (HFBD) was established by manually collecting the data and knowledge from literature in PubMed. HFBD contains 2618 records and 868 HF biomarkers (731 single and 137 combined) extracted from 1237 original articles. The biomarkers were classified into proteins, RNAs, DNAs, and the others at molecular, image, cellular and physiological levels. The biomarkers were annotated with biological, clinical and article information as well as the experimental methods used for the biomarker discovery. With its user-friendly interface, this knowledge database provides a unique resource for the systematic understanding of HF heterogeneity and personalized diagnosis and treatment of HF in the era of precision medicine.

AVAILABILITY

The platform is openly available at http://sysbio.org.cn/HFBD/.

Elevated Expression of Glycerol-3-Phosphate Phosphatase as a Biomarker of Poor Prognosis and Aggressive Prostate Cancer

The limitations of the biomarker prostate-specific antigen (PSA) necessitate the pursuit of biomarkers capable of better identifying high-risk prostate cancer (PC) patients in order to improve their therapeutic management and outcomes. Aggressive prostate tumors characteristically exhibit high rates of glycolysis and lipogenesis. Glycerol 3-phosphate phosphatase (G3PP), also known as phosphoglycolate phosphatase (PGP), is a recently identified mammalian enzyme, shown to play a role in the regulation of glucose metabolism, lipogenesis, lipolysis, and cellular nutrient-excess detoxification. We hypothesized that G3PP may relieve metabolic stress in cancer cells and assessed the association of its expression with PC patient prognosis. Using immunohistochemical staining, we assessed the epithelial expression of G3PP in two different radical prostatectomy (RP) cohorts with a total of 1797 patients, for whom information on biochemical recurrence (BCR), metastasis, and mortality was available. The association between biomarker expression, biochemical recurrence (BCR), bone metastasis, and prostate cancer-specific survival was established using log-rank and multivariable Cox regression analyses. High expression of G3PP in PC epithelial cells is associated with an increased risk of BCR, bone metastasis, and PC-specific mortality. Multivariate analysis revealed high G3PP expression in tumors as an independent predictor of BCR and bone metastasis development. High G3PP expression in tumors from patients eligible for prostatectomies is a new and independent prognostic biomarker of poor prognosis and aggressive PC for recurrence, bone metastasis, and mortality.

Identification of Key MicroRNAs and Mechanisms in Prostate Cancer Evolution Based on Biomarker Prioritization Model and Carcinogenic Survey

Background: Prostate cancer (PCa) is occurred with increasing incidence and heterogeneous pathogenesis. Although clinical strategies are accumulated for PCa prevention, there is still a lack of sensitive biomarkers for the holistic management in PCa occurrence and progression. Based on systems biology and artificial intelligence, translational informatics provides new perspectives for PCa biomarker prioritization and carcinogenic survey.

Methods: In this study, gene expression and miRNA-mRNA association data were integrated to construct conditional networks specific to PCa occurrence and progression, respectively. Based on network modeling, hub miRNAs with significantly strong single-line regulatory power were topologically identified and those shared by the condition-specific network systems were chosen as candidate biomarkers for computational validation and functional enrichment analysis.

Results: Nine miRNAs, i.e., hsa-miR-1-3p, hsa-miR-125b-5p, hsa-miR-145-5p, hsa-miR-182-5p, hsa-miR-198, hsa-miR-22-3p, hsa-miR-24-3p, hsa-miR-34a-5p, and hsa-miR-499a-5p, were prioritized as key players for PCa management. Most of these miRNAs achieved high AUC values (AUC > 0.70) in differentiating different prostate samples. Among them, seven of the miRNAs have been previously reported as PCa biomarkers, which indicated the performance of the proposed model. The remaining hsa-miR-22-3p and hsa-miR-499a-5p could serve as novel candidates for PCa predicting and monitoring. In particular, key miRNA-mRNA regulations were extracted for pathogenetic understanding. Here hsa-miR-145-5p was selected as the case and hsa-miR-145-5p/NDRG2/AR and hsa-miR-145-5p/KLF5/AR axis were found to be putative mechanisms during PCa evolution. In addition, Wnt signaling, prostate cancer, microRNAs in cancer etc. were significantly enriched by the identified miRNAs-mRNAs, demonstrating the functional role of the identified miRNAs in PCa genesis.

Conclusion: Biomarker miRNAs together with the associated miRNA-mRNA relations were computationally identified and analyzed for PCa management and carcinogenic deciphering. Further experimental and clinical validations using low-throughput techniques and human samples are expected for future translational studies.

Alzheimer's Disease, and Breast and Prostate Cancer Research: Translational Failures and the Importance to Monitor Outputs and Impact of Funded Research

Dementia and cancer are becoming increasingly prevalent in Western countries. In the last two decades, research focused on Alzheimer's disease (AD) and cancer, in particular, breast cancer (BC) and prostate cancer (PC), has been substantially funded both in Europe and worldwide. While scientific research outcomes have contributed to increase our understanding of the disease etiopathology, still the prevalence of these chronic degenerative conditions remains very high across the globe. By definition, no model is perfect. In particular, animal models of AD, BC, and PC have been and still are traditionally used in basic/fundamental, translational, and preclinical research to study human disease mechanisms, identify new therapeutic targets, and develop new drugs. However, animals do not adequately model some essential features of human disease; therefore, they are often unable to pave the way to the development of drugs effective in human patients. The rise of new technological tools and models in life science, and the increasing need for multidisciplinary approaches have encouraged many interdisciplinary research initiatives. With considerable funds being invested in biomedical research, it is becoming pivotal to define and apply indicators to monitor the contribution to innovation and impact of funded research. Here, we discuss some of the issues underlying translational failure in AD, BC, and PC research, and describe how indicators could be applied to retrospectively measure outputs and impact of funded biomedical research.