Transcriptome subtyping of metastatic Castration Resistance Prostate Cancer (mCRPC) for the precision therapeutics: an in silico analysis

Liang, Yingchun; Rong, Enlin; Qian, Jin; Ma, Chenkai; Hu, Jimeng

doi:10.1038/s41391-022-00495-9

Cited by 6 publications

(6 citation statements)

References 22 publications

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“…However, with a polymer concentration greater than 100 mg/2 mL, a considerable increase in the particle size was observed. It has been reported that, with an increase in polymer concentration, the viscosity of the organic phase is increased which decreases the efficiency of the sonication process, leading to bigger droplets being formed, resulting in increased particle size [ 3 ].…”

Section: Resultsmentioning

confidence: 99%

“…Currently, it is estimated that about 12.9% of men will be diagnosed with the disease during their lifetime [ 2 ]. Treatment is largely dependent on the stage and grade of cancer, and some men eventually develop metastatic prostate cancer with androgen deprivation therapy (ADT) as the standard of care [ 3 ]. Disease progression and resistance to ADT ultimately leads to the development of metastatic castration-resistant prostate cancer (mCRPC).…”

Section: Introductionmentioning

confidence: 99%

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Preparation, Optimization, and In-Vitro Evaluation of Brusatol- and Docetaxel-Loaded Nanoparticles for the Treatment of Prostate Cancer

Adekiya,

Moore,

Thomas

et al. 2024

Pharmaceutics

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Challenges to docetaxel use in prostate cancer treatment include several resistance mechanisms as well as toxicity. To overcome these challenges and to improve the therapeutic efficacy in heterogeneous prostate cancer, the use of multiple agents that can destroy different subpopulations of the tumor is required. Brusatol, a multitarget inhibitor, has been shown to exhibit potent anticancer activity and play an important role in drug response and chemoresistance. Thus, the combination of brusatol and docetaxel in a nanoparticle platform for the treatment of prostate cancer is expected to produce synergistic effects. In this study, we reported the development of polymeric nanoparticles for the delivery of brusatol and docetaxel in the treatment of prostate cancer. The one-factor-at-a-time method was used to screen for formulation and process variables that impacted particle size. Subsequently, factors that had modifiable effects on particle size were evaluated using a 24 full factorial statistical experimental design followed by the optimization of drug loading. The optimization of blank nanoparticles gave a formulation with a mean size of 169.1 nm ± 4.8 nm, in agreement with the predicted size of 168.333 nm. Transmission electron microscopy showed smooth spherical nanoparticles. The drug release profile showed that the encapsulated drugs were released over 24 h. Combination index data showed a synergistic interaction between the drugs. Cell cycle analysis and the evaluation of caspase activity showed differences in PC-3 and LNCaP prostate cancer cell responses to the agents. Additionally, immunoblots showed differences in survivin expression in LNCaP cells after treatment with the different agents and formulations for 24 h and 72 h. Therefore, the nanoparticles are potentially suitable for the treatment of advanced prostate cancer.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Preparation, Optimization, and In-Vitro Evaluation of Brusatol- and Docetaxel-Loaded Nanoparticles for the Treatment of Prostate Cancer

Adekiya,

Moore,

Thomas

et al. 2024

Pharmaceutics

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“…Therefore, through applying consensus clustering of gene expression, we can predict the cancer metastasis more accurately than through conventional histopathology [ 51 ]. Combined with other parameters such as clinical information and MRI, our score could help establish a strategy for PCa to inform clinicians on whether to escalate or deescalate the current treatment, though it requires validation in a multi-center prospective clinical trial [ 8 , 10 ]. Our m 6 A score can differentiate metastatic PCa from non-metastatic PCa, though highlighting its clinical utility though a large-scale clinical validation cohort is still required to compare with other biomarkers such as AR-V7 [ 52 ].…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have uncovered a few potential mechanisms of resistance in mCRPC, independent of driver mutation status [ 10 ]. ADT impairs androgen receptor (AR) signaling-dependent cell growth in prostate cancer by the reduction of secretory androgen [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

m6A Methylation Regulators Are Predictive Biomarkers for Tumour Metastasis in Prostate Cancer

Liang

Zhang

et al. 2022

Cancers

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Prostate cancer (PCa) is one of the most common cancers in men. Usually, most PCas at initial diagnosis are localized and hormone-dependent, and grow slowly. Patients with localized PCas have a nearly 100% 5-year survival rate; however, the 5-year survival rate of metastatic or progressive PCa is still dismal. N6-methyladenosine (m6A) is the most common post-transcriptional mRNA modification and is dynamically regulated by m6A regulators. A few studies have shown that the abnormal expression of m6A regulators is significantly associated with cancer progression and immune cell infiltration, but the roles of these regulators in PCa remain unclear. Here, we examined the expression profiles and methylation levels of 21 m6A regulators across the Cancer Genome Atlas (TCGA), 495 PCas by consensus clustering, and correlated the expression of m6A regulators with PCa progression and immune cell infiltration. Consensus clustering was applied for subtyping Pca samples into clusters based on the expression profiles of m6A regulators. Each subtype’s signature genes were obtained by a pairwise differential expression analysis. Featured pathways of m6A subtypes were predicted by Gene Ontology. The m6A score was developed to predict m6A activation. The association of the m6A score with patients’ survival, metastasis and immune cell infiltration was also investigated. We identified three distinct clusters in PCa based on the expression profiles of 21 m6A regulators by consensus clustering. The differential expression and pathway analyses on the three clusters uncovered the m6A regulators involved in metabolic processes and immune responses in PCa. Moreover, we developed an m6A score to evaluate the m6A regulator activation for PCa. The m6A score is significantly associated with Gleason scores and metastasis in PCa. The predictive capacity of the m6A score on PCa metastasis was also validated in another independent cohort with an area under the curve of 89.5%. Hence, our study revealed the critical role of m6A regulators in PCa progression and the m6A score is a promising predictive biomarker for PCa metastasis.

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“…Four R language packets “caret” [ 35 ], “dalex”, “randomForest” [ 36 ] and “xgboost” [ 37 ] are combined to build four machine learning models: Generalized Linear Models (GLM), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB). The “kernlab” [ 38 ] package has a built-in cluster of algorithms that can perform many tasks in machine learning.…”

Section: Methodsmentioning

confidence: 99%

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

Jia

Zhang

et al. 2023

BMC Med Inform Decis Mak

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Background Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine learning, the latest tool for the analysis of biological samples, is still relatively rarely used for in-depth analysis and prediction of diseases. Methods and results First, the differential expression of cuproptosis-related genes (CRGs) in the GSE108754 dataset was extracted and the heat map showed that the expression of NFE2L2 gene was significantly higher in the control group whereas the expression of GLS gene was significantly higher in the treatment group. Chromosome location analysis showed that both the genes were positively correlated and associated with chromosome 2. The results of immune infiltration and immune cell differential analysis showed differences in the four immune cells, significantly in Monocytes cells. Five new pathways were analyzed through two subgroups based on consistent clustering of CRG expression. Weighted correlation network analysis (WGCNA) set the screening condition to the top 25% to obtain the disease signature genes. Four machine learning algorithms: Generalized Linear Models (GLM), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) were used to screen the disease signature genes, and the final five marker genes for disease prediction. The models constructed by GLM method were proved to be more accurate in the validation of two datasets, GSE190215 and GSE188944. Conclusion We eventually identified two copper death-associated genes, NFE2L2 and GLS. A machine learning model-GLM was constructed to predict the prevalence of BPD disease, and five disease signature genes NFATC3, ERMN, PLA2G4A, MTMR9LP and LOC440700 were identified. These genes that were bioinformatics analyzed could be potential targets for identifying BPD disease and treatment.

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Transcriptome subtyping of metastatic Castration Resistance Prostate Cancer (mCRPC) for the precision therapeutics: an in silico analysis

Cited by 6 publications

References 22 publications

Preparation, Optimization, and In-Vitro Evaluation of Brusatol- and Docetaxel-Loaded Nanoparticles for the Treatment of Prostate Cancer

Preparation, Optimization, and In-Vitro Evaluation of Brusatol- and Docetaxel-Loaded Nanoparticles for the Treatment of Prostate Cancer

m6A Methylation Regulators Are Predictive Biomarkers for Tumour Metastasis in Prostate Cancer

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

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