Global expression of AMACR transcripts predicts risk for prostate cancer - a systematic comparison of AMACR protein and mRNA expression in cancerous and noncancerous prostate

Multiplex imaging of localized prostate tumors reveals altered spatial organization of AR-positive cells in the microenvironment

Mapping the spatial interactions of cancer, immune, and stromal cell states presents novel opportunities for patient stratification and for advancing immunotherapy. While single-cell studies revealed significant molecular heterogeneity in prostate cancer cells, the impact of spatial stromal cell heterogeneity remains poorly understood. Here, we used cyclic immunofluorescent imaging on whole-tissue sections to uncover novel spatial associations between cancer and stromal cells in low- and high-grade prostate tumors and tumor-adjacent normal tissues. Our results provide a spatial map of single cells and recurrent cellular neighborhoods in the prostate tumor microenvironment of treatment-naive patients. We report unique populations of mast cells that show distinct spatial associations with M2 macrophages and regulatory T cells. Our results show disease-specific neighborhoods that are primarily driven by androgen receptor-positive (AR+) stromal cells and identify inflammatory gene networks active in AR+ prostate stroma.

The implications of single-cell RNA-seq analysis in prostate cancer: unraveling tumor heterogeneity, therapeutic implications and pathways towards personalized therapy

In recent years, advancements in single-cell and spatial transcriptomics, which are highly regarded developments in the current era, particularly the emerging integration of single-cell and spatiotemporal transcriptomics, have enabled a detailed molecular comprehension of the complex regulation of cell fate. The insights obtained from these methodologies are anticipated to significantly contribute to the development of personalized medicine. Currently, single-cell technology is less frequently utilized for prostate cancer compared with other types of tumors. Starting from the perspective of RNA sequencing technology, this review outlined the significance of single-cell RNA sequencing (scRNA-seq) in prostate cancer research, encompassing preclinical medicine and clinical applications. We summarize the differences between mouse and human prostate cancer as revealed by scRNA-seq studies, as well as a combination of multi-omics methods involving scRNA-seq to highlight the key molecular targets for the diagnosis, treatment, and drug resistance characteristics of prostate cancer. These studies are expected to provide novel insights for the development of immunotherapy and other innovative treatment strategies for castration-resistant prostate cancer. Furthermore, we explore the potential clinical applications stemming from other single-cell technologies in this review, paving the way for future research in precision medicine.

Proteomic-based stratification of intermediate-risk prostate cancer patients

Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in the Gleason grade group (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or overtreatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign sample runs from 278 patients. Three proteins (F5, TMEM126B, and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomize prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.

Analysis of genetic biomarkers, polymorphisms in ADME-related genes and their impact on pharmacotherapy for prostate cancer

Prostate cancer (PCa) is a non-cutaneous malignancy in males with wide variation in incidence rates across the globe. It is the second most reported cause of cancer death. Its etiology may have been linked to genetic polymorphisms, which are not only dominating cause of malignancy casualties but also exerts significant effects on pharmacotherapy outcomes. Although many therapeutic options are available, but suitable candidates identified by useful biomarkers can exhibit maximum therapeutic efficacy. The single-nucleotide polymorphisms (SNPs) reported in androgen receptor signaling genes influence the effectiveness of androgen receptor pathway inhibitors and androgen deprivation therapy. Furthermore, SNPs located in genes involved in transport, drug metabolism, and efflux pumps also influence the efficacy of pharmacotherapy. Hence, SNPs biomarkers provide the basis for individualized pharmacotherapy. The pharmacotherapeutic options for PCa include hormonal therapy, chemotherapy (Docetaxel, Mitoxantrone, Cabazitaxel, and Estramustine, etc.), and radiotherapy. Here, we overview the impact of SNPs reported in various genes on the pharmacotherapy for PCa and evaluate current genetic biomarkers with an emphasis on early diagnosis and individualized treatment strategy in PCa.

Cancer of unknown primary eventually diagnosed as poorly differentiated prostate cancer: a case report

BACKGROUND

Prostate cancer has been well known to have a high prevalence among middle-aged and older men, with high incidence of metastases to the bone-the main metastatic site. However, prostate cancer among those less than 50 years of age is extremely rare, and neck swelling is seldom the initial symptom.

CASE PRESENTATION

We herein report case of a 47-year-old Japanese male with poorly differentiated prostate cancer that had been initially diagnosed as a cancer of unknown primary with multiple lymph node and bone metastases before reaching a definitive diagnosis. The patient has been started on endocrine therapy and is currently alive without progression.

DISCUSSION AND CONCLUSION

When locating the primary lesion in men with cancer of unknown primary, it is important to consider the possibility of prostate cancer, confirm serum prostate-specific antigen levels, and perform local prostate evaluation.

Comparative Proteomic and Transcriptomic Analysis of the Impact of Androgen Stimulation and Darolutamide Inhibition

Several inhibitors of androgen receptor (AR) function are approved for prostate cancer treatment, and their impact on gene transcription has been described. However, the ensuing effects at the protein level are far less well understood. We focused on the AR signaling inhibitor darolutamide and confirmed its strong AR binding and antagonistic activity using the high throughput cellular thermal shift assay (CETSA HT). Then, we generated comprehensive, quantitative proteomic data from the androgen-sensitive prostate cancer cell line VCaP and compared them to transcriptomic data. Following treatment with the synthetic androgen R1881 and darolutamide, global mass spectrometry-based proteomics and label-free quantification were performed. We found a generally good agreement between proteomic and transcriptomic data upon androgen stimulation and darolutamide inhibition. Similar effects were found both for the detected expressed genes and their protein products as well as for the corresponding biological programs. However, in a few instances there was a discrepancy in the magnitude of changes induced on gene expression levels compared to the corresponding protein levels, indicating post-transcriptional regulation of protein abundance. Chromatin immunoprecipitation DNA sequencing (ChIP-seq) and Hi-C chromatin immunoprecipitation (HiChIP) revealed the presence of androgen-activated AR-binding regions and long-distance AR-mediated loops at these genes.

Molecular mechanisms, immune cell infiltration, and potential drugs for prostate cancer

BACKGROUND

The molecular mechanisms involved in the prostate cancer and their relationship with immune cell infiltration are not fully understood. The prostate cancer patients undergoing standard androgen deprivation therapy eventually develop castration resistant prostate cancer (CRPC) for which there is no effective treatment currently available, and the hub genes involved in this process remain unclear.

OBJECTIVE

To study prostate cancer systematically and comprehensively.

METHODS

Differentially expressed genes (DEGs) of prostate cancer were screened in The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Connectivity Map (Cmap) software was applied to discover potential treatment drugs. A protein-protein interaction (PPI) analysis was performed to obtained the hub genes, and the relationship between hub genes and immune cell infiltration was investigated. Next, RNAseq data of hormone-sensitive prostate cancer samples and CRPC samples obtained from TCGA database was further analyzed to identify DEGs. Finally, a PPI analysis was performed to obtain the hub genes.

RESULTS

A total of 319 DEGs were identified between prostate cancer samples and normal adjacent samples from TCGA database using comparative analysis. The KEGG pathway analysis showed significant correlations with drug metabolism, metabolism of xenobiotics by cytochrome P450, and chemical carcinogenesis. AMACR, FOLH1 and NPY, three hub genes, were found to be upregulated. FOLH1 was positively correlated with CD8+ T cell infiltration. FOLH1, AMACR, and NPY were negatively correlated with CD4+ T cell infiltration. A total of 426 DEGs were identified from RNAseq data of hormone-sensitive prostate cancer samples and CRPC samples using further comparative analysis. KEGG pathway enrichment analysis showed significant correlations with arachidonic acid metabolism, PPAR signaling pathway, AMPK signaling pathway, and metabolic pathways. The top 10 hub genes in PPI network were screened out, including PPARG, SREBF1, SCD, HMGCR, FASN, PTGS2, HMGCS2, SREBF2, FDFT1, and INSIG1. Among them, SCD and FASN are expected to be the potential therapeutic targets for CRPC.

CONCLUSIONS

AMACR, FOLH1 and NPY may be effective therapeutic targets and specific diagnostic markers for prostate cancer. AMACR, FOLH1, and NPY are also closely associated with immune cell infiltration in prostate cancer. Moreover, aminoglutethimide and resveratrol were found to be the promising drugs for treating prostate cancer. The progression of hormone-sensitive prostate cancer to CRPC may be related to arachidonic acid metabolism, PPAR signaling pathway, AMPK signaling pathway, and other metabolic pathways. SCD and FASN are expected to be the potential therapeutic targets for CRPC.

Identification of a distinct luminal subgroup diagnosing and stratifying early stage prostate cancer by tissue-based single-cell RNA sequencing

BACKGROUND

The highly intra-tumoral heterogeneity and complex cell origination of prostate cancer greatly limits the utility of traditional bulk RNA sequencing in finding better biomarker for disease diagnosis and stratification. Tissue specimens based single-cell RNA sequencing holds great promise for identification of novel biomarkers. However, this technique has yet been used in the study of prostate cancer heterogeneity.

METHODS

Cell types and the corresponding marker genes were identified by single-cell RNA sequencing. Malignant states of different clusters were evaluated by copy number variation analysis and differentially expressed genes of pseudo-bulks sequencing. Diagnosis and stratification of prostate cancer was estimated by receiver operating characteristic curves of marker genes. Expression characteristics of marker genes were verified by immunostaining.

RESULTS

Fifteen cell groups including three luminal clusters with different expression profiles were identified in prostate cancer tissues. The luminal cluster with the highest copy number variation level and marker genes enriched in prostate cancer-related metabolic processes was considered the malignant cluster. This cluster contained a distinct subgroup with high expression level of prostate cancer biomarkers and a strong distinguishing ability of normal and cancerous prostates across different pathology grading. In addition, we identified another marker gene, Hepsin (HPN), with a 0.930 area under the curve score distinguishing normal tissue from prostate cancer lesion. This finding was further validated by immunostaining of HPN in prostate cancer tissue array.

CONCLUSION

Our findings provide a valuable resource for interpreting tumor heterogeneity in prostate cancer, and a novel candidate marker for prostate cancer management.

The Role of AMACR, CD10, TMPRSS2-ERG, and p27 Protein Expression Among Different Gleason Grades of Prostatic Adenocarcinoma on Needle Biopsy

OBJECTIVE

We examined the immunohistochemical expression of α-methyl acyl coenzyme A racemase (AMACR), CD10, TMPRSS2-ERG, and p27 in prostate adenocarcinoma tumors with different Gleason growth patterns and nonneoplastic prostate tissues to elucidate their roles in prostate carcinogenesis and histological aggressiveness.

MATERIAL AND METHODS

In total, 80 archival core biopsy tissues diagnosed as prostate carcinoma, benign prostate hyperplasia, and atrophy cases were included. Immunoreactivity was evaluated by calculating the percentage of positively stained cells and the staining intensity. The mean values and test of significance were obtained using the Kruskal-Wallis test.

RESULTS

We obtained mostly intense immunoreactivity for AMACR, CD10, and ERG in adenocarcinomas. Although no significant differences were noted regarding AMACR and ERG expression, samples with Gleason growth patterns 3 and 5 tended to be strongly positive for ERG. Pattern 3 tumors exhibited the weakest positivity for CD10. The p27 expression was strong and diffuse in nonneoplastic prostate tissues. The loss of p27 expression was more frequent for pattern 5 tumors.

CONCLUSION

ERG and AMACR were powerful markers to detect cancer. Especially, ERG is evident in early tumors may reflect its interaction with functional androgen receptors in cancer initiation. Pattern 5 tumors associated with stroma may have been exposed to more stromal substrates and upregulate their CD10 content as a protein degrader. We suggest that CD10 expression is associated with an increasing tumor grade. Decreased concentrations of p27 protein might be implicated in prostate carcinogenesis and may be a useful immunohistochemical adjunct in predicting histological aggressiveness.

Current Knowledge on the Function of α-Methyl Acyl-CoA Racemase in Human Diseases

Branched chain fatty acids perform very important functions in human diet and drug metabolism. they cannot be metabolized in mitochondria and are instead processed and degraded in peroxisomes due to the presence of methyl groups on the carbon chains. Oxidative degradation pathways for lipids include α- and β-oxidation and several pathways. In all metabolic pathways, α-methyl acyl-CoA racemase (AMACR) plays an essential role by regulating the metabolism of lipids and drugs. AMACR regulates β-oxidation of branched chain lipids in peroxisomes and mitochondria and promotes chiral reversal of 2-methyl acids. AMACR defects cause sensory-motor neuronal and liver abnormalities in humans. These phenotypes are inherited and are caused by mutations in AMACR. In addition, AMACR has been found to be overexpressed in prostate cancer. In addition, the protein levels of AMACR have increased significantly in many types of cancer. Therefore, AMACR may be an important marker in tumors. In this review, a comprehensive overview of AMACR studies in human disease will be described.

Evaluation of Patient Factors That Influence Predictive Formulas for Determining Ureteral Stent Length When Compared to Direct Measurement

Objectives: To compare direct ureteral length measurements with predictive formulas used for the preoperative assessment of ureteral length, and to evaluate patient factors that impact the accuracy of these predictive formulas. Methods: Patients undergoing ureteral stenting for benign and malignant indications were included. Clinical factors analyzed were gender, race, height, weight, prior abdominal or pelvic surgery, radiation therapy, pelvic organ prolapse (POP), laterality, hydronephrosis, and pre-existing ureteral stent. Three predictive formulas and a common height-based formula were used. Direct ureteral measurements were obtained with a ruled 5F ureteral catheter. Predictive formulas were compared with direct ureteral measurements using scatterplot and Spearman's correlation coefficient. Univariate and multivariate logistic regressions were used to evaluate patient factors associated with ≥2 cm deviation from direct ureteral measurements. Results: A total of 108 patients (134 ureters) were analyzed. All predictive formulas correlated poorly with direct ureteral measurements, although as much as 60% of ureteral stent lengths were accurately predicted. Several patient factors significantly impacted accuracy of formulas: male gender (p = 0.04), POP (p = 0.05), body mass index (BMI) ≥25 (p = 0.03), and pre-existing ureteral stent (p = 0.05). Conclusion: Our study suggests that predictive formulas for ureteral stent length have poor accuracy when compared to direct measurement, especially for patients with elevated BMI and POP. Our institution considers direct ureteral measurement the gold standard for determining ureteral stent length-a method that is universally applicable and independent of patient factors.

Prostate Cancer Risk Stratification in Men With a Clinical Suspicion of Prostate Cancer Using a Unique Biparametric MRI and Expression of 11 Genes in Apparently Benign Tissue: Evaluation Using Machine-Learning Techniques

BACKGROUND

Accurate risk stratification of men with a clinical suspicion of prostate cancer (cSPCa) remains challenging despite the increasing use of MRI.

PURPOSE

To evaluate the diagnostic accuracy of a unique biparametric MRI protocol (IMPROD bpMRI) combined with clinical and molecular markers in men with cSPCa.

STUDY TYPE

Prospective single-institutional clinical trial (NCT01864135).

SUBJECTS

Eighty men with cSPCa.

FIELD STRENGTH/SEQUENCE

3T, surface array coils. Two T₂ -weighted and three diffusion-weighted imaging (DWI) acquisitions: 1) b-values 0, 100, 200, 300, 500 s/mm² ; 2) b-values 0,1500 s/mm² ; 3) b-values 0, 2000 s/mm² .

ASSESSMENT

IMPROD bpMRI examinations were qualitatively (IMPROD bpMRI Likert score) and quantitatively (DWI-based Gleason grade score) prospectively reported. Men with IMPROD bpMRI Likert 3-5 had two targeted biopsies followed by 12-core systematic biopsies (SB); those with IMPROD bpMRI Likert 1-2 had only SB. Additionally, 2-core from normal-appearing prostate areas were obtained for the mRNA expression of ACSM1, AMACR, CACNA1D, DLX1, PCA3, PLA2G7, RHOU, SPINK1, SPON2, TMPRSS2-ERG, and TDRD1 measured by quantitative reverse-transcription polymerase chain reaction.

STATISTICAL TESTS

Univariate and multivariate analysis using regularized least-squares, feature selection and tournament leave-pair-out cross-validation (TLPOCV), as well as 10 random splits of the data in training-testing sets, were used to evaluate the mRNA, clinical and IMPROD bpMRI parameters in detecting clinically significant prostate cancer (SPCa) defined as Gleason score ≥ 3 + 4. The evaluation metric was the area under the curve (AUC).

RESULTS

IMPROD bpMRI Likert demonstrated the highest TLPOCV AUC of 0.92. The tested clinical variables had AUC 0.56-0.73, while the mRNA and additional IMPROD bpMRI parameters had AUC 0.50-0.67 and 0.65-0.89 respectively. The combination of clinical and mRNA biomarkers produced TLPOCV AUC of 0.87, the highest TLPOCV performance without including IMPROD bpMRI Likert.

DATA CONCLUSION

The qualitative IMPROD bpMRI Likert score demonstrated the highest accuracy for SPCa detection compared with the tested clinical variables and mRNA biomarkers.

LEVEL OF EVIDENCE

1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:1540-1553.

Circulating mRNA signature as a marker for high-risk prostate cancer

Prostate cancer (PCa) is the second most common cancer in men. The indolent course of the disease makes the treatment choice a challenge for physicians and patients. In this study, a minimally invasive method was used to evaluate the potential of molecular markers in identifying patients with aggressive disease. Cell-free plasma samples from 60 PCa patients collected before radical prostatectomy were used to evaluate the levels of expression of eight genes (AMACR, BCL2, NKX3-1, GOLM1, OR51E2, PCA3, SIM2 and TRPM8) by quantitative real-time PCR. Overexpression of AMACR, GOLM1, TRPM8 and NKX3-1 genes was significantly associated with aggressive disease characteristics, including extracapsular extension, tumor stage and vesicular seminal invasion. A trio of genes (GOLM1, NKX3-1 and TRPM8) was able to identify high-risk PCa cases (85% of sensitivity and 58% of specificity), yielding a better overall performance compared with the biopsy Gleason score and prostate-specific antigen, routinely used in the clinical practice. Although more studies are required, these circulating markers have the potential to be used as an additional test to improve the diagnosis and treatment decision of high-risk PCa patients.

Metabolomics Contributions to the Discovery of Prostate Cancer Biomarkers

Prostate cancer (PCa) is one of the most frequently diagnosed cancers and a leading cause of death among men worldwide. Despite extensive efforts in biomarker discovery during the last years, currently used clinical biomarkers are still lacking enough specificity and sensitivity for PCa early detection, patient prognosis, and monitoring. Therefore, more precise biomarkers are required to improve the clinical management of PCa patients. In this context, metabolomics has shown to be a promising and powerful tool to identify novel PCa biomarkers in biofluids. Thus, changes in polyamines, tricarboxylic acid (TCA) cycle, amino acids, and fatty acids metabolism have been reported in different studies analyzing PCa patients' biofluids. The review provides an up-to-date summary of the main metabolic alterations that have been described in biofluid-based studies of PCa patients, as well as a discussion regarding their potential to improve clinical PCa diagnosis and prognosis. Furthermore, a summary of the most significant findings reported in these studies and the connections and interactions between the different metabolic changes described has also been included, aiming to better describe the specific metabolic signature associated to PCa.

RNA-based markers in biopsy cores with atypical small acinar proliferation: Predictive effect of T2E fusion positivity and MMP-2 upregulation for a subsequent prostate cancer diagnosis

BACKGROUND

Atypical small acinar proliferation (ASAP) is a precursor lesion of prostate cancer (PC), and PC develops from this suspicious focus or an unsampled malignant gland nearby. However, PC-related molecular alterations that could guide the timing of repeat biopsies and help monitor PC risk in ASAP-diagnosed patients have not been investigated. The purpose of this study was to first investigate the expression of seven different PC-related RNAs that included serine 2 (TMPRSS2): erythroblastosis virus E26 oncogene homolog (ERG) gene (TMPRSS2-ERG, T2E) fusion, alpha-methylacyl-CoA racemase (AMACR), kallikrein related peptidase 3 (KLK3), androgen receptor (AR), prostate cancer specific antigen 3 (PCA3), and matrix metalloproteinases (MMP)-2 and 9.

METHODS

PC-related RNAs were evaluated using a real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) system in pathologically ASAP-diagnosed prostate biopsy cores from 55 patients presenting with a normal digital rectal examination and a PSA level of 4-10 ng/mL.

RESULTS

We detected that positive T2E fusion status (P = 0.013) and the expression of AMACR (P = 0.016), AR (P = 0.016) and MMP-2 (P = 0.013) were independently and significantly associated with PC risk in ASAP patients. There were also several statistically significant correlations between expression levels. Additionally, we demonstrated that T2E fusion positive ASAP patients with higher MMP-2 expression were more likely to be diagnosed with PC at a subsequent biopsy during the follow-up period (P = 0.003).

CONCLUSIONS

Although, more clinical validations are needed for the stratification of PC risk in ASAP-diagnosed biopsy cores, our current results indicate that the coexistence of T2E fusion positivity with MMP-2 upregulation may help clinicians adjust their biopsy timetable and/or assessment of PC risk in ASAP-diagnosed patients with a PSA level of 4-10 ng/mL.