Biomarkers in Prostate Cancer Diagnosis: From Current Knowledge to the Role of Metabolomics and Exosomes

Salciccia, Stefano; Capriotti, Anna Laura; Laganà, Aldo; Fais, Stefano; Logozzi, Mariantonia; Berardinis, Ettore De; Busetto, Gian Maria; Pierro, Giovanni Battista Di; Ricciuti, Gian Piero; Giudice, Francesco Del; Sciarra, Alessandro; Carroll, Peter R.; Cooperberg, Matthew R.; Sciarra, Beatrice; Maggi, Martina

doi:10.3390/ijms22094367

Cited by 69 publications

(66 citation statements)

References 110 publications

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“…Thus, the effect of the biomarkers on treatment decisions and its correlation with survival outcomes need to be further confirmed in further studies. With development of the liquid biopsy technique, some hematochemical biomarkers, such as circulating tumor cells and circulating tumor DNA (ctDNA), have been applied widely in various urinary tumor management (24)(25)(26)(27)(28). CtDNA status and cfDNA fragment size are clinically used as biomarkers for prognosis and disease monitoring in RCC (29,30).…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Genomic Landscape in Chinese Clear Cell Renal Cell Carcinoma Patients

et al. 2021

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Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC). The genomic landscape in Chinese ccRCC needs to be elucidated. Herein, we investigated the molecular features of Chinese ccRCC patients. Genomic profiling of DNA was performed through next-generation sequencing (NGS) in Chinese patients with ccRCC between January 2017 and March 2020. Clinical information including age, gender, and tumor histology was collected. Immunohistochemistry (IHC) staining for PD-L1 expression was performed using PD-L1 IHC 22C3 pharmDx assay or Ventana PD-L1 SP263 assay. Data analyses were performed using R 3.6.1. A total of 880 Chinese ccRCC patients who have undergone NGS were included in this study. The most common somatic alterations were detected in VHL (59.7%), PBRM1 (18.0%), SETD2 (12.2%), BAP1 (10.2%), and TP53 (9.4%). Compared with The Cancer Genome Atlas (TCGA) database, a higher mutation frequency of VHL (59.7% vs. 50.0%, p < 0.001) and TP53 (9.4% vs. 3.5%, p < 0.001) and a lower mutation frequency of PBRM1 (18.0% vs. 31.0%, p < 0.001) were found in the Chinese cohort. Of the 460 patients who were evaluated for PD-L1 expression, 139 (30.2%) had positive PD-L1 expression. The median tumor mutational burden (TMB) value was 4.5 muts/Mb (range, 0–46.0). Five (0.7%) patients were identified as microsatellite instability-high (MSI-H). Furthermore, 52 (5.9%) patients were identified to carry pathogenic or likely pathogenic germline mutations in 22 cancer predisposition genes. This is the first large-scale comprehensive genomic analysis for Chinese ccRCC patients, and these results might provide a better understanding of molecular features in Chinese ccRCC patients, which can lead to an improvement in the personalized treatment for these patients.

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Section: Discussionmentioning

confidence: 99%

Comprehensive Genomic Landscape in Chinese Clear Cell Renal Cell Carcinoma Patients

et al. 2021

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“…Precision oncology aims at selecting the best therapy for the "right" patient by considering variables related to both specific features of each individual's unique disease, as well as the patient's health status [48] . One of the biological phenomena underlying precision oncology is the fact that, in cancer, cells reprogram their metabolism to support all phases of disease including tumor initiation, progression, and metastasis, as well as drug resistance [49] .…”

Section: Cancermentioning

confidence: 99%

Precision medicine approaches in metabolic disorders and target organ damage: where are we now, and where are we going?

Lonardo¹,

Byrne²,

Targher³

2021

MTOD

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In this review, we discuss selected topics which are relevant to implementing precision medicine in metabolic disorders. Personalization of diet and exercise may help in preventing obesity and type 2 diabetes (T2D). Weight loss should be personalized based on age, sex, ethnicity, and coexisting comorbidities. Advances in our understanding of the pathophysiology, genetics, and epigenetics of obesity promise to offer tailored management options. Careful risk assessment is necessary prior to intervention. Risk may be underestimated, e.g., in women, in different ethnic groups, and in people with T2D. More personalized approaches could be useful among persons who failed to respond to traditional risk factor management, such as pharmacological treatment for dyslipidemia and arterial hypertension. Nonalcoholic fatty liver disease/metabolic-associated fatty liver disease (NAFLD/MAFLD) is both a cause and an effect of altered glucose and lipid metabolism. Personalized medicine approaches could be key to identify more effective pharmacological strategies as well as to reverse this common and burdensome metabolic liver disease. Finally, metabolomics could be used to identify relevant biomarkers for cancer diagnosis, staging, and prognostication. Cancers of the colon and rectum, breast, prostate, thyroid, and ovaries illustrate the notion that cancer cell metabolic derangements may be utilized in clinical practice. A true personalization of pharmacotherapies should be pursued especially in obese patients with cancer.

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“…These newly found biomarkers would manage PCa patients by guiding biopsy and improving on the false-positive rate of PSA (48). F u r t h e r m o r e , c o m b i n i n g b l o o d b i o m a r k e r s w i t h multiparametric magnetic resonance imaging (mpMRI) would be of particular value for PCa management in clinic (49). However, the study had some limitations.…”

Section: Discussionmentioning

confidence: 98%

Metabolomics Profiling Discriminates Prostate Cancer From Benign Prostatic Hyperplasia Within the Prostate-Specific Antigen Gray Zone

Chen

Chen³

et al. 2021

Front. Oncol.

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ObjectiveProstate cancer (PCa) is the second most common male malignancy globally. Prostate-specific antigen (PSA) is an important biomarker for PCa diagnosis. However, it is not accurate in the diagnostic gray zone of 4–10 ng/ml of PSA. In the current study, the performance of serum metabolomics profiling in discriminating PCa patients from benign prostatic hyperplasia (BPH) individuals with a PSA concentration in the range of 4–10 ng/ml was explored.MethodsA total of 220 individuals, including patients diagnosed with PCa and BPH within PSA levels in the range of 4–10 ng/ml and healthy controls, were enrolled in the study. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics method was utilized to characterize serum metabolic profiles of participants. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods were used for multivariate analysis. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic value of candidate metabolites in differentiating PCa from BPH. Correlation analysis was conducted to explore the relationship between serum metabolites and common clinically used fasting lipid profiles.ResultsSeveral differential metabolites were identified. The top enriched pathways in PCa subjects such as glycerophospholipid and glycerolipid metabolisms were associated with lipid metabolism. Lipids and lipid-like compounds were the predominant metabolites within the top 50 differential metabolites selected using fold-change threshold >1.5 or <2/3, variable importance in projection (VIP) > 1, and Student’s t-test threshold p < 0.05. Eighteen lipid or lipid-related metabolites were selected including 4-oxoretinol, anandamide, palmitic acid, glycerol 1-hexadecanoate, dl-dihydrosphingosine, 2-methoxy-6Z-hexadecenoic acid, 3-oxo-nonadecanoic acid, 2-hydroxy-nonadecanoic acid, N-palmitoyl glycine, 2-palmitoylglycerol, hexadecenal, d-erythro-sphingosine C-15, N-methyl arachidonoyl amine, 9-octadecenal, hexadecyl acetyl glycerol, 1-(9Z-pentadecenoyl)-2-eicosanoyl-glycero-3-phosphate, 3Z,6Z,9Z-octadecatriene, and glycidyl stearate. Selected metabolites effectively discriminated PCa from BPH when PSA levels were in the range of 4–10 ng/ml (area under the curve (AUC) > 0.80). Notably, the 18 identified metabolites were negatively corrected with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and Apo-B levels in PCa patients; and some were negatively correlated with high-density lipoprotein cholesterol (HDL-C) and Apo-A levels. However, the metabolites were not correlated with triglycerides (TG).ConclusionThe findings of the present study indicate that metabolic reprogramming, mainly lipid metabolism, is a key signature of PCa. The 18 lipid or lipid-associated metabolites identified in this study are potential diagnostic markers for differential diagnosis of PCa patients and BPH individuals within a PSA level in the gray zone of 4–10 ng/ml.

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Biomarkers in Prostate Cancer Diagnosis: From Current Knowledge to the Role of Metabolomics and Exosomes

Cited by 69 publications

References 110 publications

Comprehensive Genomic Landscape in Chinese Clear Cell Renal Cell Carcinoma Patients

Comprehensive Genomic Landscape in Chinese Clear Cell Renal Cell Carcinoma Patients

Precision medicine approaches in metabolic disorders and target organ damage: where are we now, and where are we going?

Metabolomics Profiling Discriminates Prostate Cancer From Benign Prostatic Hyperplasia Within the Prostate-Specific Antigen Gray Zone

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