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American Society of Clinical Oncology... Jun 2024The management of renal cell carcinoma (RCC) has advanced significantly in the past two decades. Many promising functional imaging modalities such as radiolabeled tracer... (Review)
Review
The management of renal cell carcinoma (RCC) has advanced significantly in the past two decades. Many promising functional imaging modalities such as radiolabeled tracer targeting carbonic anhydrase IX and prostate-specific membrane antigen are under development to detect primary kidney tumors, stage systemic disease, and assess treatment response in RCC. Immune checkpoint inhibitors targeting PD-1 and cytotoxic T-cell lymphocyte-4 have changed the treatment paradigm in advanced RCC. Trials investigating novel mechanisms such as LAG-3 immune checkpoint inhibition, chimeric antigen receptor T-cell therapies, and T-cell engagers targeting RCC-associated antigens are currently ongoing. With the rapidly changing treatment landscape of RCC, the treatment sequence strategies will continue to evolve. Familiarity with the toxicities associated with the therapeutic agents and how to manage them are essential to achieve optimal patient outcomes. This review summarizes the recent developments of functional imaging and immunotherapy strategies in RCC, and the evidence supports treatment sequencing.
Topics: Humans; Carcinoma, Renal Cell; Immunotherapy; Kidney Neoplasms; Immune Checkpoint Inhibitors
PubMed: 38875505
DOI: 10.1200/EDBK_438658 -
Medicine Jun 2024This study aims to analyze the effective components of Polygonum capitatum (PC) inhibiting Escherichia coli based on network pharmacology methods and predict its... (Review)
Review
This study aims to analyze the effective components of Polygonum capitatum (PC) inhibiting Escherichia coli based on network pharmacology methods and predict its molecular mechanism of action. PC compounds and targets were collected from the TCMSP database, Swiss Target Prediction, and the literature. E coli targets were searched using the GeneCards database. The targets of E coli and the targets of the active ingredients of PC were taken as intersections to obtain the intersecting targets. The resulting overlapping targets were uploaded to the STRING database to construct the protein interaction network diagram of E coli target inhibition. The key targets for the inhibitory effect of PC on E coli were obtained. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed by uploading key targets into the DAVID database. The results showed that there were 50 targets for PC to inhibit E coli. Among them, there are 5 core targets, mainly including AKT1, TNF, EGFR, JUN, and ESR1. A total of 196 gene ontology functional analysis results and 126 Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis results were obtained. These include cellular response to cadmium-ion, cellular response to reactive oxygen species, pathways in cancer, prostate cancer, and PI3K-Akt signaling pathway. Molecular docking results indicate that Lutedin, Hirsutin, Flazin, and Ellagic acid in PC have high affinity for the target genes AKT1, TNF, MAPK3 and EGFR. PC exerts its inhibitory effect on E coli through multi-targets and multi-pathways, which provides a new basis for the new use of PC as an old medicine.
Topics: Polygonum; Molecular Docking Simulation; Escherichia coli; Network Pharmacology; Humans; Protein Interaction Maps; Drugs, Chinese Herbal
PubMed: 38875382
DOI: 10.1097/MD.0000000000038536 -
JAMA Network Open Jun 2024Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care...
IMPORTANCE
Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care is not well understood.
OBJECTIVE
To develop a linkage between a prostate genomic classifier (GC) and clinical data across payers and sites of care in the US.
DESIGN, SETTING, AND PARTICIPANTS
In this cohort study, clinical and transcriptomic data from clinical use of a prostate GC between 2016 and 2022 were linked with data aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Participants were anonymously linked between datasets by deterministic methods through a deidentification engine using encrypted tokens. Algorithms were developed and refined for identifying prostate cancer diagnoses, treatment timing, and clinical outcomes using diagnosis codes, Common Procedural Terminology codes, pharmacy codes, Systematized Medical Nomenclature for Medicine clinical terms, and unstructured text in the EHR. Data analysis was performed from January 2023 to January 2024.
EXPOSURE
Diagnosis of prostate cancer.
MAIN OUTCOMES AND MEASURES
The primary outcomes were biochemical recurrence and development of prostate cancer metastases after diagnosis or radical prostatectomy (RP). The sensitivity of the linkage and identification algorithms for clinical and administrative data were calculated relative to clinical and pathological information obtained during the GC testing process as the reference standard.
RESULTS
A total of 92 976 of 95 578 (97.2%) participants who underwent prostate GC testing were successfully linked to administrative and clinical data, including 53 871 who underwent biopsy testing and 39 105 who underwent RP testing. The median (IQR) age at GC testing was 66.4 (61.0-71.0) years. The sensitivity of the EHR linkage data for prostate cancer diagnoses was 85.0% (95% CI, 84.7%-85.2%), including 80.8% (95% CI, 80.4%-81.1%) for biopsy-tested participants and 90.8% (95% CI, 90.5%-91.0%) for RP-tested participants. Year of treatment was concordant in 97.9% (95% CI, 97.7%-98.1%) of those undergoing GC testing at RP, and 86.0% (95% CI, 85.6%-86.4%) among participants undergoing biopsy testing. The sensitivity of the linkage was 48.6% (95% CI, 48.1%-49.1%) for identifying RP and 50.1% (95% CI, 49.7%-50.5%) for identifying prostate biopsy.
CONCLUSIONS AND RELEVANCE
This study established a national-scale linkage of transcriptomic and longitudinal clinical data yielding high accuracy for identifying key clinical junctures, including diagnosis, treatment, and early cancer outcome. This resource can be leveraged to enhance understandings of disease biology, patterns of care, and treatment effectiveness.
Topics: Humans; Male; Prostatic Neoplasms; Middle Aged; Aged; Transcriptome; Electronic Health Records; Cohort Studies; Longitudinal Studies; Prostatectomy; Information Storage and Retrieval; Algorithms
PubMed: 38874922
DOI: 10.1001/jamanetworkopen.2024.17274 -
Aging Jun 2024Prostate cancer is one of the serious health problems of older male, about 13% of male was affected by prostate cancer. Prostate cancer is highly heterogeneity disease...
Prostate cancer is one of the serious health problems of older male, about 13% of male was affected by prostate cancer. Prostate cancer is highly heterogeneity disease with complex molecular and genetic alterations. So, targeting the gene candidates in prostate cancer in single-cell level can be a promising approach for treating prostate cancer. In the present study, we analyzed the single cell sequencing data obtained from 2 previous reports to determine the differential gene expression of prostate cancer in single-cell level. By using the network pharmacology analysis, we identified the therapeutic targets of formononetin in immune cells and tissue cells of prostate cancer. We then applied molecular docking to determine the possible direct binding of formononetin to its target proteins. Our result identified a cluster of differential gene expression in prostate cancer which can serve as novel biomarkers such as immunoglobulin kappa C for prostate cancer prognosis. The result of network pharmacology delineated the roles of formononetin's targets such CD74 and THBS1 in immune cells' function of prostate cancer. Also, formononetin targeted insulin receptor and zinc-alpha-2-glycoprotein which play important roles in metabolisms of tissue cells of prostate cancer. The result of molecular docking suggested the direct binding of formononetin to its target proteins including INSR, TNF, and CXCR4. Finally, we validated our findings by using formononetin-treated human prostate cancer cell DU145. For the first time, our result suggested the use of formononetin for treating prostate cancer through targeting different cell types in a single-cell level.
PubMed: 38874510
DOI: 10.18632/aging.205935 -
Aging Jun 2024Urological malignancies, including kidney, bladder, and prostate cancer, are major health concerns worldwide. Inflammation has been implicated in the pathogenesis of...
BACKGROUND
Urological malignancies, including kidney, bladder, and prostate cancer, are major health concerns worldwide. Inflammation has been implicated in the pathogenesis of these cancers, and circulating inflammatory proteins may play a role in their development. However, the causal relationship between specific plasma proteins and urological malignancies remains unclear.
METHODS
We performed a two-sample Mendelian randomization (MR) analysis using summary statistics from genome-wide association studies (GWAS). Instrumental variables representing genetic variants associated with circulating inflammatory proteins were used to infer causality on the risk of kidney, bladder, and prostate cancer. Four MR methods were utilized to provide robust effect estimates.
RESULTS
Our analysis identified several plasma proteins associated with a lower risk of kidney and bladder cancer, including Eukaryotic translation initiation factor 4E-binding protein 1, Caspase 8, Natural killer cell receptor 2B4, and Tumor necrosis factor ligand superfamily member 12. However, after adjusting for multiple testing, these associations did not remain statistically significant. For prostate cancer, CUB domain-containing protein 1 and Interleukin-10 receptor subunit beta were found to be protective, while Glial cell line-derived neurotrophic factor and SIR2-like protein 2 were identified as risk factors. After FDR adjustment, none of the inflammatory proteins were found to be significantly associated with a lower risk of prostate cancer.
CONCLUSION
Our findings suggest that certain plasma proteins may be involved in the development of urological malignancies. Mendelian randomization provides a useful framework for investigating causal relationships between inflammatory proteins and urological cancers, offering potential insights into their underlying biology and therapeutic targets.
PubMed: 38874503
DOI: 10.18632/aging.205934 -
PeerJ 2024Hepatocellular carcinoma (HCC) is an aggressive malignancy with limited effective treatment options. Phenethyl isothiocyanate (PEITC) is a bioactive substance present...
BACKGROUND
Hepatocellular carcinoma (HCC) is an aggressive malignancy with limited effective treatment options. Phenethyl isothiocyanate (PEITC) is a bioactive substance present primarily in the cruciferous vegetables. PEITC has exhibited anti-cancer properties in various cancers, including lung, bile duct, and prostate cancers. It has been demonstrated that PEITC can inhibit the proliferation, invasion, and metastasis of SK-Hep1 cells, while effectively inducing apoptosis and cell cycle arrest in HepG2 cells. However, knowledge of its anti-carcinogenic effects on Huh7.5.1 cells and its underlying mechanism remains elusive. In the present study, we aim to evaluate the anti-carcinogenic effects of PEITC on human HCC Huh7.5.1 cells.
METHODS
MTT assay and colony formation assay was performed to investigate the anti-proliferative effects of PEITC against Huh7.5.1 cells. The pro-apoptosis effects of PEITC were determined by Annexin V-FITC/PI double staining assay by flow cytometry (FCM), mitochondrial transmembrane potential (MMP) measurement, and Caspase-3 activity detection. A DAPI staining and terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) assay was conducted to estimate the DNA damage in Huh7.5.1 cells induced by PEITC. Cell cycle progression was determined by FCM. Transwell invasion assay and wound healing migration assay were performed to investigate the impact of PEITC on the migration and invasion of Huh7.5.1 cells. In addition, transcriptome sequencing and gene set enrichment analysis (GSEA) were used to explore the potential molecular mechanisms of the inhibitory effects of PEITC on HCC. Quantitative real-time PCR (qRT-PCR) analysis was performed to verify the transcriptome data.
RESULTS
MTT assay showed that treatment of Huh7.5.1 cells with PEITC resulted in a dose-dependent decrease in viability, and colony formation assay further confirmed its anti-proliferative effect. Furthermore, we found that PEITC could induce mitochondrial-related apoptotic responses, including a decrease of mitochondrial transmembrane potential, activation of Caspase-3 activity, and generation of intracellular reactive oxygen species. It was also observed that PEITC caused DNA damage and cell cycle arrest in the S-phase in Huh7.5.1 cells. In addition, the inhibitory effect of PEITC on the migration and invasion ability of Huh7.5.1 cells was assessed. Transcriptome sequencing analysis further suggested that PEITC could activate the typical MAPK, PI3K-Akt, and p53 signaling pathways, revealing the potential mechanism of PEITC in inhibiting the carcinogenic properties of Huh7.5.1 cells.
CONCLUSION
PEITC exhibits anti-carcinogenic activities against human HCC Huh7.5.1 cells by activating MAPK/PI3K-Akt/p53 signaling pathways. Our results suggest that PEITC may be useful for the anti-HCC treatment.
Topics: Humans; Isothiocyanates; Carcinoma, Hepatocellular; Liver Neoplasms; Proto-Oncogene Proteins c-akt; Cell Line, Tumor; Signal Transduction; Apoptosis; Cell Proliferation; Phosphatidylinositol 3-Kinases; Tumor Suppressor Protein p53
PubMed: 38873643
DOI: 10.7717/peerj.17532 -
Frontiers in Pharmacology 2024Metastatic castrate resistant prostate cancer (mCRPC) continues to have poor survival rates due to limited treatment options. Bi-specific T cell engagers (BiTEs) are a... (Review)
Review
Metastatic castrate resistant prostate cancer (mCRPC) continues to have poor survival rates due to limited treatment options. Bi-specific T cell engagers (BiTEs) are a promising class of novel immunotherapies with demonstrated success in haematological malignancies and melanoma. BiTEs developed for tumour associated antigens in prostate cancer have entered clinical testing. These trials have been hampered by high rates of treatment related adverse events, minimal or transient anti-tumour efficacy and generation of high titres of anti-drug antibodies. This paper aims to analyse the challenges faced by the different BiTE therapy constructs and the mCRPC tumour microenvironment that result in therapeutic resistance and identify possible strategies to overcome these issues.
PubMed: 38873417
DOI: 10.3389/fphar.2024.1399802 -
Frontiers in Oncology 2024Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer....
INTRODUCTION
Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer. Radiomics plays a key role and hold potential for PCa detection, reducing the need for unnecessary biopsies, characterizing tumor aggression, and overseeing PCa recurrence post-treatment.
METHODS
Furthermore, the integration of radiomics data with clinical and histopathological data can further enhance the understanding and management of PCa and decrease unnecessary transfers to specialized care for expensive and intrusive biopsies. Therefore, the aim of this study is to develop a risk model score to automatically detect PCa patients by integrating non-invasive diagnostic parameters (radiomics and Prostate-Specific Antigen levels) along with patient's age.
RESULTS
The proposed approach was evaluated using a dataset of 189 PCa patients who underwent bi-parametric MRI from two centers. Elastic-Net Regularized Generalized Linear Model achieved 91% AUC to automatically detect PCa patients. The model risk score was also used to assess doubt cases of PCa at biopsy and then compared to bi-parametric PI-RADS v2.
DISCUSSION
This study explored the relative utility of a well-developed risk model by combining radiomics, Prostate-Specific Antigen levels and age for objective and accurate PCa risk stratification and supporting the process of making clinical decisions during follow up.
PubMed: 38873254
DOI: 10.3389/fonc.2024.1323247 -
Frontiers in Genetics 2024Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote... (Review)
Review
Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.
PubMed: 38873108
DOI: 10.3389/fgene.2024.1383676 -
Chemical Science Jun 2024The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using...
The Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge series is focused on identifying small molecule inhibitors of protein targets using computational methods. Each challenge contains two phases, hit-finding and follow-up optimization, each of which is followed by experimental validation of the computational predictions. For the CACHE Challenge #1, the Leucine-Rich Repeat Kinase 2 (LRRK2) WD40 Repeat (WDR) domain was selected as the target for hit-finding and optimization. Mutations in LRRK2 are the most common genetic cause of the familial form of Parkinson's disease. The LRRK2 WDR domain is an understudied drug target with no known molecular inhibitors. Herein we detail the first phase of our winning submission to the CACHE Challenge #1. We developed a framework for the high-throughput structure-based virtual screening of a chemically diverse small molecule space. Hit identification was performed using the large-scale Deep Docking (DD) protocol followed by absolute binding free energy (ABFE) simulations. ABFEs were computed using an automated molecular dynamics (MD)-based thermodynamic integration (TI) approach. 4.1 billion ligands from Enamine REAL were screened with DD followed by ABFEs computed by MD TI for 793 ligands. 76 ligands were prioritized for experimental validation, with 59 compounds successfully synthesized and 5 compounds identified as hits, yielding a 8.5% hit rate. Our results demonstrate the efficacy of the combined DD and ABFE approaches for hit identification for a target with no previously known hits. This approach is widely applicable for the efficient screening of ultra-large chemical libraries as well as rigorous protein-ligand binding affinity estimation leveraging modern computational resources.
PubMed: 38873063
DOI: 10.1039/d3sc06880c