Circulating metabolite biomarkers: a game changer in the human prostate cancer diagnosis

Krishnan, Sabareeswaran; Kanthaje, Shruthi; Punchappady, Devasya Rekha; Mujeeburahiman, M; Ratnacaram, Chandrahas Koumar

doi:10.1007/s00432-022-04113-y

Cited by 6 publications

(5 citation statements)

References 82 publications

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“…Metabolomics reveals functional information about the interactions of genes and the environment, unique from other omics approaches. Although progress has been made in urine metabolomics for PC biomarker research [ 38 , 39 , 46 ], a universally recognized biomarker/panel for predicting PC or sPC remains elusive. The urine metabolomics models presented in this study, especially the combined models, robustly predicted PC or sPC before biopsy in men at risk.…”

Section: Discussionmentioning

confidence: 99%

“…Our models may have a great translational impact for several reasons. First, they surpassed or equaled the performance of most other liquid biopsies [ 32 – 34 , 38 , 39 , 46 ], including the widely used PCA3 [ 48 ] and SelectMDx [ 47 ] tests, as well as the most recent ones [ 60 , 61 ], greatly improving the accuracy of predicting sPC before biopsy. Second, our data showed that unnecessary biopsies could be reduced, lessening the burden on patients and healthcare resources.…”

Section: Discussionmentioning

confidence: 99%

“…Third, our DCA demonstrated a significant clinical benefit in Models II and III at lower threshold probabilities. Additionally, our models are noninvasive, eliminating the need for prostate massage or RNA handling, and offer enhanced clinical relevance through the NCCN risk groupings, while also being tailored to patients of different age groups compared to other popular models [ 38 , 39 , 46 ] in PC and sPC diagnoses. In the future, to optimize the effectiveness of our tests and maximize their impact, we will utilize targeted GC‒MS to accurately quantify metabolite markers.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study

Huang,

Chen,

Chang

et al. 2023

J Transl Med

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Purpose Currently, there are no accurate markers for predicting potentially lethal prostate cancer (PC) before biopsy. This study aimed to develop urine tests to predict clinically significant PC (sPC) in men at risk. Methods Urine samples from 928 men, namely, 660 PC patients and 268 benign subjects, were analyzed by gas chromatography/quadrupole time-of-flight mass spectrophotometry (GC/Q-TOF MS) metabolomic profiling to construct four predictive models. Model I discriminated between PC and benign cases. Models II, III, and GS, respectively, predicted sPC in those classified as having favorable intermediate risk or higher, unfavorable intermediate risk or higher (according to the National Comprehensive Cancer Network risk groupings), and a Gleason sum (GS) of ≥ 7. Multivariable logistic regression was used to evaluate the area under the receiver operating characteristic curves (AUC). Results In Models I, II, III, and GS, the best AUCs (0.94, 0.85, 0.82, and 0.80, respectively; training cohort, N = 603) involved 26, 24, 26, and 22 metabolites, respectively. The addition of five clinical risk factors (serum prostate-specific antigen, patient age, previous negative biopsy, digital rectal examination, and family history) significantly improved the AUCs of the models (0.95, 0.92, 0.92, and 0.87, respectively). At 90% sensitivity, 48%, 47%, 50%, and 36% of unnecessary biopsies could be avoided. These models were successfully validated against an independent validation cohort (N = 325). Decision curve analysis showed a significant clinical net benefit with each combined model at low threshold probabilities. Models II and III were more robust and clinically relevant than Model GS. Conclusion This urine test, which combines urine metabolic markers and clinical factors, may be used to predict sPC and thereby inform the necessity of biopsy in men with an elevated PC risk.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study

Huang,

Chen,

Chang

et al. 2023

J Transl Med

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“…The present study of radiogenomics in localised PCa is both exploratory and hypothesis-driven. Multiple studies have demonstrated the diagnostic potential of metabolomics for prostate cancer and have demonstrated the diagnostic potential of metabolomics and exosomes for the differential diagnosis of prostate cancer [ 51 , 52 , 53 , 54 ]. However, this study sought a priority of a pure genomics approach and assessed the correlation between quantitative texture radiomic features of biparametric MRI and hypoxia-related gene expression in men with localised prostate cancer.…”

Section: Discussionmentioning

confidence: 99%

Radiogenomics Reveals Correlation between Quantitative Texture Radiomic Features of Biparametric MRI and Hypoxia-Related Gene Expression in Men with Localised Prostate Cancer

Ogbonnaya

Alsaedi

Alhussaini

et al. 2023

JCM

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Objectives: To perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes. Materials and Methods: Images from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the important hypoxia-related genes with 28 radiomic texture features. Then, cBioportal was accessed, and a gene-specific query was executed to extract the Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Based on each selected gene profile, correlation analysis using Pearson’s coefficients and survival analysis using Kaplan–Meier estimators were performed. Results: The quantitative bpMR imaging textural features, including the histogram and grey level co-occurrence matrix (GLCM), correlated with three hypoxia-related genes (ANGPTL4, VEGFA, and P4HA1) based on RNA sequencing using the TempO-Seq method. Further radiogenomic analysis, including data accessed from the cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcomes, with a median survival ratio of 81.11:133.00 months in those with and without alterations in genes, respectively. Conclusion: This study found that there is a correlation between the radiomic texture features extracted from bpMRI in localised prostate cancer and the hypoxia-related genes that are differentially expressed. The analysis of expression data based on cBioportal revealed that these hypoxia-related genes, which were the focus of the study, are linked to an unfavourable survival outcomes in prostate cancer patients.

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“…However, those markers are below the threshold for detection in localized PCa, even in high-risk patients [4]. Consequently, blood liquid-biopsy for early diagnosis of PCa has shown several drawbacks, limiting its clinical utility [5]. For instance, the first studies demonstrated that CTCs in localized PCa were detected in a lower number of patients (between 8% and 27%) compared to metastatic castration resistant PCa.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Semen Self-Sampling Yield Predictors and CTC Isolation by Multi-Color Flow Cytometry for Liquid Biopsy of Localized Prostate Cancer

Saitta

Simone

Fasulo

et al. 2023

Cancers

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Liquid biopsy (LB) for prostate cancer (PCa) detection could represent an alternative to biopsy. Seminal fluid (SF) is a source of PCa-specific biomarkers, as 40% of ejaculate derives from the prostate. We tested the feasibility of an SF-based LB by evaluating the yield of semen self-sampling in a cohort of >750 patients with clinically localized PCa. The overall SF collection yield was 18.2% (39% when considering only compliant patients), with about a half of the patients (53.15%) not consenting to SF donation. Independent favorable predictors for SF collection were younger age and lower prostate volume. We implemented a protocol to enrich prostate-derived cells by multi-color flow cytometry and applied it on SF and urine samples from 100 patients. The number of prostate-enriched cells (SYTO-16+ PSMA+ CD45−) was variable, with higher numbers of cells isolated from SF than urine (p value < 0.001). Putative cancer cells (EpCAMhigh) were 2% of isolated cells in both specimens. The fraction of EpCAMhigh cells over prostate-enriched cells (PSMA+) significantly correlated with patient age in both semen and urine, but not with other clinical parameters, such as Gleason Score, ISUP, or TNM stage. Hence, enumeration of prostate-derived cells is not sufficient to guide PCa diagnosis; additional molecular analyses to detect patient-specific cancer lesions will be needed.

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Circulating metabolite biomarkers: a game changer in the human prostate cancer diagnosis

Cited by 6 publications

References 82 publications

Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study

Prediction of clinically significant prostate cancer through urine metabolomic signatures: A large-scale validated study

Radiogenomics Reveals Correlation between Quantitative Texture Radiomic Features of Biparametric MRI and Hypoxia-Related Gene Expression in Men with Localised Prostate Cancer

Evaluation of Semen Self-Sampling Yield Predictors and CTC Isolation by Multi-Color Flow Cytometry for Liquid Biopsy of Localized Prostate Cancer

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