Biomarkers for prostate cancer detection and risk stratification

Farha, Mark; Salami, Simpa S.

doi:10.1177/17562872221103988

Cited by 28 publications

(22 citation statements)

References 116 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Concerning confounding factors, we put forward some thoughts. There was a direct correlation between the malignant degree of PCa and PSA [ 67 ]. The higher the malignant degree of the tumor, the higher the serum level of prostaglandin-specific antigen.…”

Section: Discussionmentioning

confidence: 99%

Association of Statin Use with the Risk of Incident Prostate Cancer: A Meta-Analysis and Systematic Review

Liu

et al. 2022

Journal of Oncology

View full text Add to dashboard Cite

Background. With the growth and aging of population, the incidence of prostate cancer will increase year by year, which is bound to bring greater economic burden to the society. There has been greater interest in the anticancer effects of statin in recent years. It is controversial whether statin use is associated with the risk of prostate cancer (PCa). Thus, we conducted a meta-analysis and systematic review to explore the effects of statin use and their duration and cumulative dose on the overall incidence of PCa. Method. The study was conducted according to the latest guidelines for PRISMA 2020. We searched PubMed and other databases for studies about the association of statin use with the risk of incident prostate cancer between January 1, 1990, and April 11, 2022. Two independent researchers extracted data and evaluated the quality of the studies. R x64 4.1.2 and random-effects model were used for data statistics. Relative risk (RR) and odds ratio (OR) effective values with a 95% confidence interval (95% CI) were used to assess the main results. Results. The results of 6 RCT and 26 cohort studies showed that statins did not significantly associate with the incidence of PCa (RR = 0.94, 95% CI: 0.82–1.08). The similar results were obtained from 9 case-control studies (OR = 1.03, 95% CI: 0.99–1.07). However, statins were associated with a lower risk of Pca (RR = 0.44, 95% CI: 0.28–0.70) when the cumulative defined daily dose (cDDD) was high. Using statins for more than five years could be associated with a reduced incidence of Pca (RR = 0.47, 95% CI: 0.23–0.97). There was a significant heterogeneity in these studies (RCT and cohort study: I2 = 98%, P < 0.01; case-control study: I2 = 72%, P < 0.01). Conclusion. We concluded that statins had a neutral association with the overall risk of PCa. High cDDD and long duration were associated with a lower risk of PCa.

show abstract

Section: Discussionmentioning

confidence: 99%

Association of Statin Use with the Risk of Incident Prostate Cancer: A Meta-Analysis and Systematic Review

Liu

et al. 2022

Journal of Oncology

View full text Add to dashboard Cite

show abstract

“…For example, cluster 25 included STMN1 as a marker gene, a known over-expressed oncoprotein in prostate cancer that controls cell proliferation 25 (Figure 6H). Cluster 6 included PCA3, HPGD, and FABP5; the former produces a protein which is a key prostate cancer biomarker in urine tests 26 , while the latter two genes have been implicated as metabolic regulators of prostate cancer 27,28 . The third significant luminal epithelial subpopulation (cluster 0) had marker genes which included ACPP, the gene encoding the prostate cancer biomarker PAP 29 .…”

Section: Resultsmentioning

confidence: 99%

Cytocipherdetermines significantly different populations of cells in single cell RNA-seq data

Balderson

Thor

Bodén

2022

Preprint

View full text Add to dashboard Cite

Identification of novel and known cell types with single cell RNA-seq (scRNA-seq) is revolutionising the study of multicellular organisms. However, typical scRNA-seq analysis involves many preprocessing steps and represents an abstraction of the original measurements, often resulting in clusters of single cells that may not display distinct gene expression. To mitigate this, cell clusters are typically validated as cell types by re-examination of the original expression measurements, often resulting in post-hoc manual alteration of clusters to ensure distinct gene expression. However, distinct cell populations may exist that are not clearly demarcated by a single marker gene, but instead co-express a unique combination of genes; a phenomenon which is difficult to detect by manual examination. Furthermore, manual examination of genes and post-hoc cluster editing is time-consuming, error-prone, and irreproducible. Here, we present Cytocipher, an scverse compatible bioinformatics method and software that scores cells for unique combinatorial gene co-expression and statistically tests whether clusters are significantly different. Application to both simulated and real data demonstrates that the combinatorial gene expression scoring outperforms existing per-cell gene enrichment methods, such as Giotto Parametric Analysis of Gene Set Enrichment and Scanpy-score. Furthermore, Cytocipher cluster-merging identified distinct CD8+ T cell subtypes in human peripheral blood mononuclear cells that were not identified in the original annotations. Cytocipher cluster-merging was also able to identify distinct intermediate states corresponding to cell lineage decisions and branch points in mouse pancreas development, not previously identified in the original annotations. Identification of significantly different clusters of cells is an important new methodological improvement to the existing analysis pipeline of scRNA-seq data. Utilisation of Cytocipher will thus ensure that single cell Atlas mapping efforts provide distinctly different and programmatically reproducible cell clusters. Cytocipher is available at https://github.com/BradBalderson/Cytocipher.

show abstract

“…However, growing evidence supports the use of risk stratification tools that combine clinical parameters, genomic biomarkers, and morphological and functional features able to either optimize health care or predict BCR in PCa patients [31][32][33][34]. Nevertheless, the lack of validation of these predictive tools in prospective randomized clinical trials represents the main limitation of their introduction in clinical practice.…”

Section: Discussionmentioning

confidence: 99%

Role of radiomic analysis of [18F]fluoromethylcholine PET/CT in predicting biochemical recurrence in a cohort of intermediate and high risk prostate cancer patients at initial staging

et al. 2023

View full text Add to dashboard Cite

Aim To study the feasibility of radiomic analysis of baseline [18F]fluoromethylcholine positron emission tomography/computed tomography (PET/CT) for the prediction of biochemical recurrence (BCR) in a cohort of intermediate and high-risk prostate cancer (PCa) patients. Material and methods Seventy-four patients were prospectively collected. We analyzed three prostate gland (PG) segmentations (i.e., PGwhole: whole PG; PG41%: prostate having standardized uptake value – SUV > 0.41*SUVmax; PG2.5: prostate having SUV > 2.5) together with three SUV discretization steps (i.e., 0.2, 0.4, and 0.6). For each segmentation/discretization step, we trained a logistic regression model to predict BCR using radiomic and/or clinical features. Results The median baseline prostate-specific antigen was 11 ng/mL, the Gleason score was > 7 for 54% of patients, and the clinical stage was T1/T2 for 89% and T3 for 9% of patients. The baseline clinical model achieved an area under the receiver operating characteristic curve (AUC) of 0.73. Performances improved when clinical data were combined with radiomic features, in particular for PG2.5 and 0.4 discretization, for which the median test AUC was 0.78. Conclusion Radiomics reinforces clinical parameters in predicting BCR in intermediate and high-risk PCa patients. These first data strongly encourage further investigations on the use of radiomic analysis to identify patients at risk of BCR. Clinical relevance statement The application of AI combined with radiomic analysis of [18F]fluoromethylcholine PET/CT images has proven to be a promising tool to stratify patients with intermediate or high-risk PCa in order to predict biochemical recurrence and tailor the best treatment options. Key Points • Stratification of patients with intermediate and high-risk prostate cancer at risk of biochemical recurrence before initial treatment would help determine the optimal curative strategy. • Artificial intelligence combined with radiomic analysis of [18F]fluorocholine PET/CT images allows prediction of biochemical recurrence, especially when radiomic features are complemented with patients’ clinical information (highest median AUC of 0.78). • Radiomics reinforces the information of conventional clinical parameters (i.e., Gleason score and initial prostate-specific antigen level) in predicting biochemical recurrence.

show abstract

Biomarkers for prostate cancer detection and risk stratification

Cited by 28 publications

References 116 publications

Association of Statin Use with the Risk of Incident Prostate Cancer: A Meta-Analysis and Systematic Review

Association of Statin Use with the Risk of Incident Prostate Cancer: A Meta-Analysis and Systematic Review

Cytocipherdetermines significantly different populations of cells in single cell RNA-seq data

Role of radiomic analysis of [18F]fluoromethylcholine PET/CT in predicting biochemical recurrence in a cohort of intermediate and high risk prostate cancer patients at initial staging

Contact Info

Product

Resources

About