Prospective Study of CRMP4 Promoter Methylation in Prostate Biopsies as a Predictor For Lymph Node Metastases

A urine extracellular vesicle lncRNA classifier for high-grade prostate cancer and increased risk of progression: A multi-center study

To construct a urine extracellular vesicle long non-coding RNA (lncRNA) classifier that can detect high-grade prostate cancer (PCa) of grade group 2 or greater and estimate the risk of progression during active surveillance, we identify high-grade PCa-specific lncRNAs by combined analyses of cohorts from TAHSY, TCGA, and the GEO database. We develop and validate a 3-lncRNA diagnostic model (Clnc, being made of AC015987.1, CTD-2589M5.4, RP11-363E6.3) that can detect high-grade PCa. Clnc shows higher accuracy than prostate cancer antigen 3 (PCA3), multiparametric magnetic resonance imaging (mpMRI), and two risk calculators (Prostate Cancer Prevention Trial [PCPT]-RC 2.0 and European Randomized Study of Screening for Prostate Cancer [ERSPC]-RC) in the training cohort (n = 350), two independent cohorts (n = 232; n = 251), and TCGA cohort (n = 499). In the prospective active surveillance cohort (n = 182), Clnc at diagnosis remains a powerful independent predictor for overall active surveillance progression. Thus, Clnc is a potential biomarker for high-grade PCa and can also serve as a biomarker for improved selection of candidates for active surveillance.

Plasma extracellular vesicle circRNA signature and resistance to abiraterone in metastatic castration-resistant prostate cancer

BACKGROUND

We aimed to develop and validate a plasma extracellular vesicle circular RNA (circRNA)-based signature that can predict overall survival (OS) in first-line abiraterone therapy for metastatic castration-resistant prostate cancer (mCRPC) patients.

METHODS

In total, 582 mCRPC patients undergoing first-line abiraterone therapy from four institutions were sorted by three phases. In the discovery phase, 30 plasma samples from 30 case-matched patients with or without early progression were obtained to generate circRNA expression profiles using RNA sequencing. In the training phase, differentially expressed circRNAs were examined using digital droplet PCR in a training cohort (n = 203). The circRNA signature was constructed using a least absolute shrinkage and selection operator Cox regression to predict OS. In the validation phase, the prognostic ability of this signature was prospectively validated in two external cohorts (Cohort I, n = 183; Cohort II, n = 166).

RESULTS

We developed a five-circRNA signature, based on circCEP112, circFAM13A, circBRWD1, circVPS13C and circMACROD2, which successfully stratified patients into high-risk and low-risk groups. The prognostic ability of this signature was prospectively validated in two external cohorts (P < 0.0001, P < 0.0001). Patients with high-risk scores had shorter OS than patients with low-risk scores.

CONCLUSION

This five-circRNA signature is a reliable predictor of OS for mCRPC patients undergoing abiraterone.

CRMP4 CpG Hypermethylation Predicts Upgrading to Gleason Score ≥ 8 in Prostate Cancer

Background

This study determined the predictive value of CRMP4 promoter methylation in prostate tissues collected by core needle biopsies for a postoperative upgrade of Gleason Score (GS) to ≥8 in patients with low-risk PCa.

Method

A retrospective analysis of the clinical data was conducted from 631 patients diagnosed with low-risk PCa by core needle biopsy at multiple centers and then underwent Radical Prostatectomy (RP) from 2014-2019. Specimens were collected by core needle biopsy to detect CRMP4 promoter methylation. The pathologic factors correlated with the postoperative GS upgrade to ≥8 were analyzed by logistic regression. The cut-off value for CRMP4 promoter methylation in the prostate tissues collected by core needle biopsy was estimated from the ROC curve in patients with a postoperative GS upgrade to ≥8.

Result

Multivariate logistic regression showed that prostate volume, number of positive cores, and CRMP4 promoter methylation were predictive factors for a GS upgrade to ≥8 (OR: 0.94, 95% CI: 0.91-0.98, P=0.003; OR: 3.16, 95% CI: 1.81-5.53, P<0.001; and OR: 1.43, 95% CI: 1.32-1.55, P<0.001, respectively). The positive predictive rate was 85.2%, the negative predictive rate was 99.3%, and the overall predictive rate was 97.9%. When the CRMP4 promoter methylation rate was >18.00%, the low-risk PCa patients were more likely to escalate to high-risk patients. The predictive sensitivity and specificity were 86.9% and 98.8%, respectively. The area under the ROC curve (AUC) was 0.929 (95% CI: 0.883-0.976; P<0.001). The biochemical recurrence (BCR)-free survival, progression-free survival (PFS), and cancer-specific survival (CSS) were worse in patients with CRMP4 methylation >18.0% and postoperative GS upgrade to ≥8 than in patients without an upgrade (P ≤ 0.002).

Conclusion

A CRMP4 promoter methylation rate >18.00% in prostate cancer tissues indicated that patients were more likely to escalate from low-to-high risk after undergoing an RP. We recommend determining CRMP4 promoter methylation before RP for low-risk PCa patients.

Clinical and Biological Significance of DNA Methylation-Driven Differentially Expressed Genes in Biochemical Recurrence After Radical Prostatectomy

Background: Biochemical recurrence (BCR) after radical prostatectomy indicates poor prognosis in patients with prostate cancer (PCA). DNA methylation (DNAm) is a critical factor in tumorigenesis and has attracted attention as a biomarker for the diagnosis, treatment, and prognosis of PCA. However, the predictive value of DNAm-derived differentially expressed genes (DMGs) in PCA with BCR remains elusive.

Methods: We filtered the methylated genes and the differentially expressed genes (DGEs) for more than 1,000 clinical samples from the TCGA cohort using the chAMP and DESeq2 packages of R language, respectively. Next, we integrated the DNAm beta value and gene expression data with the Mithymix package of R language to obtain the DMGs. Then, 1,000 times Cox LASSO regression with 10-fold cross validation was performed to screen signature DMGs and establish a predictive classifier. Univariate and multivariate cox regressive analyses were used to identify the prognostic factors to build a predictive model, and its performance was measured by receiver operating characteristic, calibration curves, and Harrell’s concordance index (C-index). Additionally, a GEO dataset was used to validate the prognostic classifier.

Results: One hundred DMGs were mined using the chAMP and Methymix packages of R language. Of these, seven DMGs (CCK, CD38, CYP27A1, EID3, HABP2, LRRC4, and LY6G6D) were identified to build the prognostic classifier (Classifier) through LASSO analysis. Moreover, univariate and multivariate Cox regression analysis determined that the Classifier and pathological T stage (pathological_T) were independent predictors of BCR (hazard ratio (HR 2.2), (95% CI 1.4–3.5), p < 0.0012, and (HR 1.8), (95% CI 1.0–3.2), p < 0.046). A nomogram based on the Classifier was constructed, with high prediction accuracy for BCR-free survival in TCGA and GEO datasets. GSEA enrichment analysis showed that the DMGs were mainly enriched in the metabolism pathways.

Conclusion: We identified and validated the nomogram of BCR-free survival for PCA patients, which has the potential to guide treatment decisions for patients at differing risks of BCR. Our study deepens the understanding of DMGs in the pathogenesis of PCA.

Artificial Intelligence Combined With Big Data to Predict Lymph Node Involvement in Prostate Cancer: A Population-Based Study

Background

A more accurate preoperative prediction of lymph node involvement (LNI) in prostate cancer (PCa) would improve clinical treatment and follow-up strategies of this disease. We developed a predictive model based on machine learning (ML) combined with big data to achieve this.

Methods

Clinicopathological characteristics of 2,884 PCa patients who underwent extended pelvic lymph node dissection (ePLND) were collected from the U.S. National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. Eight variables were included to establish an ML model. Model performance was evaluated by the receiver operating characteristic (ROC) curves and calibration plots for predictive accuracy. Decision curve analysis (DCA) and cutoff values were obtained to estimate its clinical utility.

Results

Three hundred and forty-four (11.9%) patients were identified with LNI. The five most important factors were the Gleason score, T stage of disease, percentage of positive cores, tumor size, and prostate-specific antigen levels with 158, 137, 128, 113, and 88 points, respectively. The XGBoost (XGB) model showed the best predictive performance and had the highest net benefit when compared with the other algorithms, achieving an area under the curve of 0.883. With a 5%~20% cutoff value, the XGB model performed best in reducing omissions and avoiding overtreatment of patients when dealing with LNI. This model also had a lower false-negative rate and a higher percentage of ePLND was avoided. In addition, DCA showed it has the highest net benefit across the whole range of threshold probabilities.

Conclusions

We established an ML model based on big data for predicting LNI in PCa, and it could lead to a reduction of approximately 50% of ePLND cases. In addition, only ≤3% of patients were misdiagnosed with a cutoff value ranging from 5% to 20%. This promising study warrants further validation by using a larger prospective dataset.

Combined analysis of CRMP4 methylation levels and CAPRA-S score predicts metastasis and outcomes in prostate cancer patients

The present study analyzed the predictive value of combined analysis of collapsin response mediator protein 4 (CRMP4) methylation levels and the Cancer of the Prostate Risk Assessment (CAPRA-S) Postsurgical score of patients who required adjuvant hormone therapy (AHT) after radical prostatectomy (RP). We retrospectively analyzed 305 patients with prostate cancer (PCa) who received RP and subsequent androgen deprivation therapy (ADT). Two hundred and thirty patients with clinically high-risk PCa underwent immediate ADT, and 75 patients with intermediate risk PCa underwent deferred ADT. CRMP4 methylation levels in biopsies were determined, and CAPRA-S scores were calculated. In the deferred ADT group, the values of the hazard ratios for tumor progression and cancer-specific mortality (CSM) in patients with ≥15% CRMP4 methylation were 6.81 (95% CI: 2.34–19.80) and 12.83 (95% CI: 2.16–26.10), respectively. Receiver-operating characteristic curve analysis indicated that CRMP4 methylation levels ≥15% served as a significant prognostic marker of tumor progression and CSM. In the immediate ADT group, CAPRA-S scores ≥6 and CRMP4 methylation levels ≥15% were independent predictors of these outcomes (uni- and multi-variable Cox regression analyses). The differences in the 5-year progression-free survival between each combination were statistically significant. Combining CAPRA-S score and CRMP4 methylation levels improved the area under the curve compared with the CRMP4 or CAPRA-S model. Therefore, CRMP4 methylation levels ≥15% were significantly associated with a poor prognosis and their combination with CAPRA-S score accurately predicted tumor progression and metastasis for patients requiring AHT after RP.

CRMP4a suppresses cell motility by sequestering RhoA activity in prostate cancer cells

Objectives: Distant metastasis is a critical factor for cancer-associated death. Our previous studies identified collapsin response mediator protein 4a (CRMP4a) as a metastasis suppressor in prostate cancers. Enhancing CRMP4 expression by promoter-targeted small activating RNAs reduced cell migration in vitro and abolished distal metastasis in mouse xenograft models. In this study, we investigated the mechanism for CRMP4a-mediated suppression of cell migration. Methods: PC-3 cells were stably infected with lentiviruses expressing CRMP4a cDNA or a shRNA sequence. Cytoskeletal organization was analyzed by measuring cellular focal adhesion area and number, percentage of cell area and lamellipodia numbers after phalloidin staining or anti-vinculin immunocytofluorescent staining. Cell migration was evaluated with Transwell^TM chambers coated with MatriGel. RhoA activation was determined with a Rhotekin RBD agarose bead-based assay kit. Lentiviruses harboring RhoA-Q63L or RhoA-T19N mutant constructs were used to overexpress mutant RhoA proteins. Results: CRMP4a overexpression largely reduced while CRMP4a knockdown remarkably increased cytoskeletal organization in PC-3 cells. CRMP4a immunoprecipitation pulled down RhoA but not cdc42 or Rac1 proteins. Manipulating CRMP4a expression levels reversely altered active RhoA levels. Overexpression of RhoA active (Q63L) but not inactive (T19N) mutants reversed CRMP4a-mediated reduction of cancer cell migration while RhoA inhibitor Rhosin diminished CRMP4a shRNA-induced increase of cancer cell migration. CRMP4a overexpression also largely reduced cell spreading that was abolished by overexpressing RhoA active mutant. Conclusion: Our data demonstrated that CRMP4a interacts with RhoA and sequesters its activity, resulting in suppression of cytoskeletal organization, cell migration and spreading.

Calpain-2 triggers prostate cancer metastasis via enhancing CRMP4 promoter methylation through NF-κB/DNMT1 signaling pathway

BACKGROUND

Metastasis is the major cause of cancer-specific death in patients with prostate cancer (PCa). We previously reported that collapsing response mediator protein-4 (CRMP4) is a PCa metastasis-suppressor gene and the hypermethylation in CRMP4 promoter is responsible for the transcription repression in metastatic PCa. However, the underlying mechanisms remain unknown. In this study, we aimed to investigate the role of calpain-2 in CRMP4 promoter hypermethylation and its functional modulation in PCa metastasis.

METHODS

Calpain-2 expression in PCa tissues (n = 87) and its specific mechanisms of functional modulation in CRMP4 expression via limited enzymatic cleavage was investigated. We then focused on the cooperative crosstalk of calpain-2 and NF-κB RelA/p65 in CRMP4 promoter methylation for the initiation of PCa metastasis. Statistical differences between groups were determined using a two-tailed Student's t-test. P < 0.05 indicated statistically significant.

RESULTS

Calpain-2 was differentially upregulated in metastatic PCa compared with localized PCa. Moreover, calpain-2 cleaved CRMP4 into the N-terminally fragment which promoted migration and invasion in PCa cells via nuclear translocation and activation of E2F1-mediated DNA methyltransferase 1 (DNMT1) expression. NF-κB RelA/p65 recruited DNMT1 to bind to and methylate CRMP4 promoter in which Serine276 phosphorylation of p65 was essential. Furthermore, CRMP4 exhibited anti-metastatic function via inhibiting the expression of VEGFC through Semaphorin3B-Neuropilin2 signaling.

CONCLUSION

Calpain-2 may contribute to the promoter methylation of CRMP4 to repress its transcription, leading to the metastasis of PCa via enhancing VEGFC expression.

Collapsin response mediator protein 4 promotor methylation level as a potential predictor for diagnosing primary malignant lymphoma of the prostate

Background

Primary malignant lymphoma of the prostate (PMLP) is prone to occur in the elderly, and it has no significant correlation with lactate dehydrogenase (LDH) and prostate specific antigen (PSA). Clinical symptoms and imaging data of PMLP remain unspecific, and its prognosis is poor. A previous result showed that collapsin response mediator protein 4 (CRMP4) promotor methylation can be used as a predictor for lymph node metastases in prostate biopsies. However, the relationship between CRMP4 promotor methylation and PMLP has not been studied.

Methods

We investigated the clinicopathological features of PMLP and the significance of CRMP4 methylation in PMLP. The clinical data and diagnosis information of 10 patients with PMLP were retrospectively analyzed. The CRMP4 promotor methylation level in paraffin-embedded tissues of the 10 patients with PMLP were determined and then compared to limited prostate cancer (LPCa) and its negative lymph node tissue [LPCa-LN (−) (10 cases)] and also to metastatic prostate adenocarcinoma (mPCa) and its positive lymph node tissue [mPCa-LN (+) (10 cases)]. Methylation of the CRMP4 promotor in each group was analyzed statistically. A receiver operating characteristic (ROC) curve was used to analyze the diagnostic value of CRMP4 methylation in PMLP.

Results

The average methylation value of CRMP4 in 10 PMLP patients, 20 cases of prostate adenocarcinoma tissue, 10 cases LPCa-LN (−) and 10 cases mPCa-LN (+) were 42.3, 30.6, 6.7 and 20.3%, respectively. A Kruskal–Wallis test showed that the difference of CRMP4 methylation was significant (X² = 38.0, P < 0.001). An ROC curve analysis found that CRMP4 methylation > 40.9% could diagnose PMLP. This method had 90% sensitivity and 95% specificity under conditions of CRMP4 methylation > 40.9%. The area under the curve (AUC) was 0.957.

Conclusions

Methylation of the CRMP4 gene was significantly increased in PMLP, and it is expected to become a new predictor for PMLP.

SEC-induced activation of ANXA7 GTPase suppresses prostate cancer metastasis

Annexin A7 (ANXA7) is a suppressor of tumorigenesis and metastasis in prostate cancer. Activated ANXA7 GTPase promotes prostate cancer cell apoptosis. However, the role and underlying mechanism of ANXA7 GTPase in prostate cancer metastasis have not been established. RKIP is a metastatic suppressor and downregulated in prostate cancer metastases. The binding of RKIP and its target proteins could inhibit the activation of its interactive partners. However, the effect of RKIP on ANXA7 GTPase activation is not clear. Here, we report that activation of ANXA7 GTPase by a small molecule SEC ((S)-ethyl 1-(3-(4-chlorophenoxy)-2-hydroxypropyl)-3- (4-methoxyphenyl)-1H-pyrazole-5-carboxylate) effectively inhibited prostate cancer metastasis. Mechanistically, activated ANXA7 promoted AMPK phosphorylation, leading to decreased mTORC1 activity, suppressed STAT3 nuclear translocation, and downregulation of pro-metastatic genes, including CCL2, APLN, and IL6ST. Conversely, RKIP interacted with ANXA7 and impaired activation of ANXA7 GTPase by SEC and its downstream signaling pathway. Notably, SEC treatment suppressed metastasis of prostate cancer cells in in vivo orthotopic analysis. Together, our findings provide a novel insight into how metastasis of prostate cancer with low RKIP expression is suppressed by SEC-induced activation of ANXA7 GTPase via the AMPK/mTORC1/STAT3 signaling pathway.