PCa Patients Research Articles

LINC01518 predicts poor prognosis of prostate cancer and promotes its progression by regulating hsa-miR-320a/CNKSR2 axis

BackgroundProstate cancer (PCa) is the most common malignancy of the male genitourinary system. Understanding the molecular mechanism of PCa and its prognostic markers will assist in the selection of better treatment.AimTo explore the role of LINC01518 in the development of PCa and its prognostic value, and to understand its specific regulatory mechanism.MethodsThe levels of LINC01518, hsa-miR-320a, and CNKSR2 mRNA were detected by RT-qPCR. ROC curve was constructed to evaluate the prognostic value of LINC01518 in PCa. The effects of LINC01518 on the functions of PCa cells were demonstrated by CCK-8, Transwell test, and the detection of apoptotic markers, after LINC01518 knockdown. The interaction between hsa-miR-320a and LINC01518 or CNKSR2 mRNA was examined by constructing luciferase vectors.ResultsLINC01518 was abnormally expressed in PCa cells and tumor tissues, and knockdown of it inhibited the growth of PCa cells. LINC01518 predicted castration-resistant prostate cancer (CRPC) outcomes in PCa patients with AUC of 0.803, sensitivity and specificity of 75.4% and 74.6%, respectively. Hsa-miR-320a mimics reduced luciferase activity in PCa cells transfected with WT-LINC01518/CNKSR2 plasmids. Knocking down LINC01518 reduced CNKSR2 level, and this regulatory effect disappeared with the inhibition of hsa-miR-320a.ConclusionHigh levels of LINC01518 predicted poor prognosis in PCa patients and promoted CNKSR2 expression by competitively binding hsa-miR-320a, contributing to the progression of PCa.

Predictive diagnostic value of mean platelet volume to platelet count and neutrophil to lymphocyte ratios in the gray zone of prostate cancer with tPSA between 4 to 10 ng/mL.

Exploration of the Predictive Diagnostic Value of Mean Platelet Volume to Platelet Count Ratio (MPV/PLT,PVI) and Neutrophil-to-Lymphocyte Ratio (NLR) in the tPSA Gray Zone of Prostate Cancer. A retrospective study was conducted on 65 prostate cancer (Pca) patients and 52 benign prostatic hyperplasia (BPH) patients who underwent transperineal prostate biopsy at Xiangtan Central Hospital from December 2021 to December 2023. Descriptive statistics and logistic regression models were used to investigate the predictive diagnostic value of PVI and NLR in the tPSA gray zone of prostate cancer. Receiver operating characteristic (ROC) curves were constructed based on PVI and NLR values to determine the classification thresholds. A total of 117 patients were enrolled, including 65 cases of prostate cancer (PCa) and 52 cases of benign prostatic hyperplasia (BPH). There were no statistically significant differences in age, BMI, history of hypertension, history of diabetes, history of coronary heart disease, pre-biopsy white blood cell count, history of drinking, history of smoking, and tPSA between the PCa and BPH patients. The results of logistic regression analysis showed that PVI (OR=2.03, 95%CI: 1.34~3.07, P<0.00) and NLR (OR=0.32, 95%CI: 0.18~0.58, P<0.00) were independent predictors for diagnosing prostate cancer in the tPSA gray zone (VIF=1.04).The maximum area under the curve (AUC) for PVI was 0.70, with an optimal cut-off value of 0.05 (P ≤ 0.01). The maximum AUC for NLR was 0.76, with an optimal cut-off value of 2.86 (P ≤ 0.01).The calibration curve showed good consistency between the predicted and actual outcomes in both the PCa and BPH groups, indicating that the nomogram model had good predictive performance.When using PVI and NLR to plot the receiver operating characteristic (ROC) curves to predict the assessment of PCa in the tpsa gray zone, the area under the curve (AUC) for PVI was the largest at 0.70, with an optimal cutoff value of 0.05 (P ≤ 0.01). The AUC for NLR was the largest at 0.76, with an optimal cutoff value of 2.86 (P ≤ 0.01). PVI and NLR have certain predictive diagnostic value for Pca in the tPSA gray zone, and appropriate use of PVI and NLR can improve the positive rate of early screening for Pca in the gray zone.

Dynamic Soluble IL-6R/Soluble gp130 Ratio as a Potential Indicator for the Prostate Malignancy Phenotype-A Multicenter Case-Control Study.

Prostate tumors, if prostate cancer or adenoma, represent a major public health challenge. Progress in research on inflammation has revealed a connection between inflammation, immunity, and cancer. In this context, this study aimed to find IL-6 signaling systemic abnormalities in the inflammatory tumor microenvironment. This study was case-controlled, multicentered, and included 86 patients, 43 diagnosed with BPH and 43 diagnosed with PCa, between January 2019 and January 2020. The study group was homogenous and the studied parameters were IL-6 complex (IL-6, soluble receptor IL-6R, soluble glycoprotein gp130), acute phase proteins (C reactive protein-CRP, acid alpha1 glycoprotein-AGPA, ferritin, albumin, transferrin), and oxidative stress-associated variables (malondialdehyde-MDA, carbonylated protein-PCO, 8-hydroxy-deoxy guanosine-8-OHdG, total antioxidant status-bTAS). The inflammatory microenvironment determined IL-6 signaling alterations (over-regulation of sIL-6R and suppression of sgp130 in PCa versus BPH), changes in acute phase reaction markers (increased serum levels of CRP, AGPA, ferritin, and decreased serum levels of albumin, transferrin) that were much more evident in PCa compared to BPH, an imbalance between macromolecular oxidative damage (MDA, PCO, 8-OHdG) and endogenous antioxidants (TAS) that was more accentuated in PCa compared with BPH, and a representative association between the sIL-6R/sgp130 ratio and inflammatory/oxidative stress-related factors only in PCa patients. Our study reconfirms the anterior concept that IL-6 promotes prostatic tumorigenesis. In this study, we first demonstrated that a high sIL-6R/sgp130 ratio facilitates prostate malignancy.

Precision molecular insights for prostate cancer prognosis: tumor immune microenvironment and cell death analysis of senescence-related genes by machine learning and single-cell analysis

BackgroundProstate cancer (PCa) is a prevalent malignancy among men, primarily originating from the prostate epithelium. It ranks first in global cancer incidence and second in mortality rates, with a rising trend in China. PCa's subtle initial symptoms, such as urinary issues, necessitate diagnostic measures like digital rectal examination, prostate-specific antigen (PSA) testing, and tissue biopsy. Advanced PCa management typically involves a multifaceted approach encompassing surgery, radiation, chemotherapy, and hormonal therapy. The involvement of aging genes in PCa development and progression, particularly through the mTOR pathway, has garnered increasing attention.MethodsThis study aimed to explore the association between aging genes and biochemical PCa recurrence and construct predictive models. Utilizing public gene expression datasets (GSE70768, GSE116918, and TCGA), we conducted extensive analyses, including Cox regression, functional enrichment, immune cell infiltration estimation, and drug sensitivity assessments. The constructed risk score model, based on aging-related genes (ARGs), demonstrated superior predictive capability for PCa prognosis compared to conventional clinical features. High-risk genes positively correlated with risk, while low-risk genes displayed a negative correlation.ResultsAn ARGs-based risk score model was developed and validated for predicting prognosis in prostate adenocarcinoma (PRAD) patients. LASSO regression analysis and cross-validation plots were employed to select ARGs with prognostic significance. The risk score outperformed traditional clinicopathological features in predicting PRAD prognosis, as evidenced by its high AUC (0.787). The model demonstrated good sensitivity and specificity, with AUC values of 0.67, 0.675, 0.696, and 0.696 at 1, 3, 5, and 8 years, respectively, in the GEO cohort. Similar AUC values were observed in the TCGA cohort at 1, 3, and 5 years (0.67, 0.659, 0.667, and 0.743). The model included 12 genes, with high-risk genes positively correlated with risk and low-risk genes negatively correlated.ConclusionsThis study presents a robust ARGs-based risk score model for predicting biochemical recurrence in PCa patients, highlighting the potential significance of aging genes in PCa prognosis and offering enhanced predictive accuracy compared to traditional clinical parameters. These findings open new avenues for research on PCa recurrence prediction and therapeutic strategies.

MicroRNA-506 as a tumor suppressor in prostate cancer by regulation of Hippo signaling pathway

IntroductionProstate cancer (PCa) is considered as one of the leading causes of cancer related deaths among males globally. There is a high rate of tumor relapse and metastasis in PCa patients that is associated with chemo-resistance. Beside the elimination of tumor cells, chemotherapy has an adverse effect in normal cells. Therefore, it is required to clarify the molecular tumor biology to introduce novel markers to predict the drug response in PCa patients. Hippo signaling pathway is an important regulator of cell proliferation, migration, and drug response that can be modulated by microRNAs (miRNAs). Since, miR-506 deregulation has been reported in PCs, in the present study we assessed a probable role of miR-506 in regulation of Hippo signaling pathway during prostate tumor cell migration and drug resistance. Materials and methodsThe mRNA expression levels of Hippo signaling components were assessed in miR-506 ectopic expressed in comparison with control PC3 cells. Apoptosis assay, drug response, and cell migration were also assessed to find the role of miR-506 in prostate tumor cell aggressiveness. ResultsMiR-506 promoted the Hippo signaling pathway via TAZ and CTGF up regulations, while BIRC5, FAT, and C-MYC down regulations in PC3 cells. MiR-506 significantly reduced prostate tumor cell migration (p = 0.019) and Paclitaxel (TXL) resistance (p = 0.0006), while promoted apoptosis (p < 0.0001). ConclusionmiR-506 exerted a tumor suppressor role during PCa progression by activation of Hippo pathway. MiR-506 targeted the FAT to activate the YAP/TAZ and nuclear translocation where it can bind with TEAD to up regulate the CTGF that resulted in reduced PCa cell migration. The WNT inhibition and C-MYC down regulation by the CTGF resulted in BIRC5 down regulation that induced apoptosis in PCa cells. Therefore, miR-506 can be introduced as a therapeutic marker in PCa following the confirmation by the animal studies and further clinical trials.

Identifying Key Genes as Progression Indicators of Prostate Cancer with Castration Resistance Based on Dynamic Network Biomarker Algorithm and Weighted Gene Correlation Network Analysis.

Background: Androgen deprivation therapy (ADT) is the mainstay of treatment for prostate cancer, yet dynamic molecular changes from hormone-sensitive to castration-resistant states in patients treated with ADT remain unclear. Methods: In this study, we combined the dynamic network biomarker (DNB) method and the weighted gene co-expression network analysis (WGCNA) to identify key genes associated with the progression to a castration-resistant state in prostate cancer via the integration of single-cell and bulk RNA sequencing data. Based on the gene expression profiles of CRPC in the GEO dataset, the DNB method was used to clarify the condition of epithelial cells and find out the most significant transition signal DNB modules and genes included. Then, we calculated gene modules associated with the clinical phenotype stage based on the WGCNA. IHC was conducted to validate the expression of the key genes in CRPC and primary PCa patients Results:Nomograms, calibration plots, and ROC curves were applied to evaluate the good prognostic accuracy of the risk prediction model. Results: By combining single-cell RNA sequence data and bulk RNA sequence data, we identified a set of DNBs, whose roles involved in androgen-associated activities indicated the signals of a prostate cancer cell transition from an androgen-dependent state to a castration-resistant state. In addition, a risk prediction model including the risk score of four key genes (SCD, NARS2, ALDH1A1, and NFXL1) and other clinical-pathological characteristics was constructed and verified to be able to reasonably predict the prognosis of patients receiving ADT. Conclusions: In summary, four key genes from DNBs were identified as potential diagnostic markers for patients treated with ADT and a risk score-based nomogram will facilitate precise prognosis prediction and individualized therapeutic interventions of CRPC.

Machine learning-based cell death marker for predicting prognosis and identifying tumor immune microenvironment in prostate cancer

BackgroundProstate cancer (PCa) incidence and mortality rates are rising, necessitating precise prognostic tools to guide personalized treatment. Dysregulation of programmed cell death pathways in tumor suppression and cancer development has garnered increasing attention, providing a new research direction for identifying biomarkers and potential therapeutic targets. MethodsIntegrating multiple database resources, we constructed and optimized a prognostic signature based on the expression of programmed cell death-related genes (PCDRG) using ten machine learning algorithms. Model performance and prognostic effects were further evaluated. We analyzed the relationships between signature and clinicopathological features, somatic mutations, drug sensitivity, and the tumor immune microenvironment, and constructed a nomogram. The expression level of PCDRGs were evaluated and compared. ResultsOf 1560 PCDRGs, 149 were differentially expressed in PCa, with 34 associated with biochemical recurrence. The PCDRG-derived index (PCDI), constructed using the random forest algorithm, exhibited optimal prognostic performance, successfully stratifying PCa patients into two groups with significant prognostic differences. Patients with high PCDI scores exhibited poorer survival and lower immunotherapy benefit. PCDI was closely associated with the infiltration of specific immune cells, particularly positive correlations with macrophages and T helper cells, and negative correlations with neutrophils, suggesting that PCDI may influence the tumor immune microenvironment, thereby affecting patient prognosis and treatment response. PCDI was associated with age, pathological stage, somatic mutations, and drug sensitivity. The PCDI-based nomogram demonstrated excellent performance in predicting biochemical recurrence in PCa patients. Finally, the differential expression of these PCDRGs was verified based on cell lines and PCa patient expression profile data. ConclusionThis study developed an effective prognostic indicator for prostate cancer, PCDI, using machine learning approaches. PCDI reflects the link between aberrant programmed cell death pathways and disease progression and treatment response.

1721: Process mining for care path analysis in PCa patients undergoing salvage radiotherapy: an AIRO study

1721: Process mining for care path analysis in PCa patients undergoing salvage radiotherapy: an AIRO study

2437: Clinical predictors of late lymphopenia in PCa patients undergoing radical or post-operative WPRT

2437: Clinical predictors of late lymphopenia in PCa patients undergoing radical or post-operative WPRT

Prognostic and immunological implications of heterogeneous cell death patterns in prostate cancer

BackgroundProstate cancer is one of the most common cancers in men with a significant proportion of patients developing biochemical recurrence (BCR) after treatment. Programmed cell death (PCD) mechanisms are known to play critical roles in tumor progression and can potentially serve as prognostic and therapeutic biomarkers in PCa. This study aimed to develop a prognostic signature for BCR in PCa using PCD-related genes.Materials and methodsWe conducted an analysis of 19 different modes of PCD to develop a comprehensive model. Bulk transcriptomic, single-cell transcriptomic, genomic, and clinical data were collected from multiple cohorts, including TCGA-PRAD, GSE58812, METABRIC, GSE21653, and GSE193337. We analyzed the expression and mutations of the 19 PCD modes and constructed, evaluated, and validated the model.ResultsTen PCD modes were found to be associated with BCR in PCa, with specific PCD patterns exhibited by various cell components within the tumor microenvironment. Through Lasso Cox regression analysis, we established a Programmed Cell Death Index (PCDI) utilizing an 11-gene signature. High PCDI values were validated in five independent datasets and were found to be associated with an increased risk of BCR in PCa patients. Notably, older age and advanced T and N staging were associated with higher PCDI values. By combining PCDI with T staging, we constructed a nomogram with enhanced predictive performance. Additionally, high PCDI values were significantly correlated with decreased drug sensitivity, including drugs such as Docetaxel and Methotrexate. Patients with lower PCDI values demonstrated higher immunophenoscores (IPS), suggesting a potentially higher response rate to immune therapy. Furthermore, PCDI was associated with immune checkpoint genes and key components of the tumor microenvironment, including macrophages, T cells, and NK cells. Finally, clinical specimens validated the differential expression of PCDI-related PCDRGs at both the gene and protein levels.ConclusionIn conclusion, we developed a novel PCD-based prognostic feature that successfully predicted BCR in PCa patients and provided insights into drug sensitivity and potential response to immune therapy. These findings have significant clinical implications for the treatment of PCa.

Advancement in Mechanistic Alteration of IGFs Family of Receptors to Target Prostate and Cervical Cancer: An Updated Review

Insulin-like Growth Factors (IGFs) significantly impact mammalian physiology, including growth, development, ageing, and disease progression. The IGF system is composed of various growth factor receptors, including IGF-1R and IGF-2R. Serum IGF-1 levels and IGF-1R activation, along with downstream signaling components, are increasingly recognized as key factors in the expansion of prostate and cervical cancer. The study on IGF-1/IGF-1R activity and regulation is crucial in PCa research and cervical cancer studies. This signaling pathway significantly influences various cancer cell processes, such as survival, migration, and resistance to treatment. The inhibitors targeting IGF-1/IGF-1R have been developed to prevent the progression of cancer. The use of nanotechnology, including trap decoys, magnetic iron oxide nanoparticles, and protein nanotubes, has significantly improved the treatment of cervical cancer. These agents have shown promise in preclinical models for cervical cancer research, but their efficacy in PCa patients requires clinical trial validation. Combining androgen deprivation therapy or chemotherapy with IGF-1R antagonists, using consistent predictive markers and evolving novel agents, may improve the results. This review highlights the importance of IGF-1 signaling in PCa and cervical cancer development and underscores its significance in potential cancer therapy strategies. The study has also explored prospective approaches to the next generation of IGF axis-targeting drugs.

Exploring the clinical and biological significance of the cell cycle-related gene CHMP4C in prostate cancer

BackgroundProstate cancer (PCa) stands as the second most prevalent malignancy impacting male health, and the disease’s evolutionary course presents formidable challenges in the context of patient treatment and prognostic management. Charged multivesicular body protein 4 C (CHMP4C) participates in the development of several cancers by regulating cell cycle functions. However, the role of CHMP4C in prostate cancer remains unclear.MethodsIn terms of bioinformatics, multiple PCa datasets were employed to scrutinize the expression of CHMP4C. Survival analysis coupled with a nomogram approach was employed to probe into the prognostic significance of CHMP4C. Gene set enrichment analysis (GSEA) was conducted to interrogate the functional implications of CHMP4C. In terms of cellular experimentation, the verification of RNA and protein expression levels was executed through the utilization of qRT-PCR and Western blotting. Upon the establishment of a cell line featuring stable CHMP4C knockdown, a battery of assays, including Cell Counting Kit-8 (CCK-8), wound healing, Transwell, and flow cytometry, were employed to discern the impact of CHMP4C on the proliferation, migration, invasion, and cell cycle function of PCa cells.ResultsThe expression of CHMP4C exhibited upregulation in both PCa cells and tissues, and patients demonstrating elevated CHMP4C expression levels experienced a notably inferior prognosis. The nomogram, constructed using CHMP4C along with clinicopathological features, demonstrated a commendable capacity for prognostic prediction. CHMP4C knockdown significantly inhibited the proliferation, migration, and invasion of PCa cells (LNcaP and PC3). CHMP4C could impact the advancement of the PCa cell cycle, and its expression might be regulated by berberine. Divergent CHMP4C expression among PCa patients could induce alterations in immune cell infiltration and gene mutation frequency.ConclusionsOur findings suggest that CHMP4C might be a prognostic biomarker in PCa, potentially offering novel perspectives for the advancement of precision therapy for PCa.

Quantitative Investigation of MicroRNA-32 in the Urine of Prostate Cancer Patients and Its Relationship With Clinicopathological Characteristics

IntroductionProstate cancer (PCa) is one of the most common cancers worldwide. PCa diagnosis is mostly based on solid biopsy and prostate-specific antigen (PSA), which have the disadvantages of being invasive and insensitive, respectively. Recently, the detection of microRNAs (miRNAs) in expressed prostatic secretions (EPS) has been a promising approach for PCa diagnosis. The aim of this study is to quantify transcriptional levels of miRNA-32 in the urine of prostate cancer patients. Materials and methodsIn this study, we evaluated the expression of miRNA-32 in the urine of 27 PCa patients, 48 benign prostatic hyperplasia (BPH) and 20 healthy controls, using quantitative real-time PCR (qPCR). The expression levels were then compared with the clinicopathological characteristics of patients. ResultsThe expression level of miRNA-32 in PCa patients was significantly higher than the control group (P < .01) and BPH cases (P < .01), and was associated with advanced tumor stage (P < .05). In addition, the expression of miRNA-32 had significant correlation with patients’ age (r = 0.39, P = .043). Area under ROC curve (AUC) for the discrimination of PCa samples from control and BPH samples were 0.93 (P < .0001) and 0.78 (P < .0001), respectively. We also used logistic regression analysis to integrate the results of PSA, prostate volume and miRNA-32, and presented a predictive model for distinguishing PCa from BPH, highlighting the clinical utility of miRNA-32 in cancer diagnosis and risk assessment. ConclusionsMeasurement of miRNA-32 expression in urine may have significance for the detection of PCa. Inclusion of miRNA-32 in logistic regression along with PSA and prostate volume increases the accuracy of cancer diagnosis.

The Impact of Radical Prostatectomy Versus Radiation Therapy on Cancer-Specific Mortality for Nonmetastatic Prostate Cancer: Analysis of an Other-Cause Mortality Matched Cohort

IntroductionStudies comparing radical prostatectomy (RP) to radiation therapy (RT) have consistently shown that patients undergoing RT have a higher risk of other-cause mortality (OCM) compared to RP, signifying poor health status of the former patients. We aimed to evaluate the impact of RP versus RT on cancer-specific mortality (CSM) over a cohort with equivalent OCM risk. Patients and MethodsThe SEER database was queried to identify patients with nonmetastatic PCa between 2004 and 2009. Patients were matched based on their calculated 10-year OCM risk and further stratified for D'Amico Risk Score and Gleason Grade. A Cox-regression model was used to calculate the 10-year OCM risk. Propensity-score based on the calculated OCM risk were used to match RP and RT patients. Cumulative incidence curves and Competing-risk regression analyses were used to examine the impact of treatment on CSM in the matched cohort. ResultsWe identified 55,106 PCa patients treated with RP and 36,674 treated with RT. After match, 6,506 patients were equally distributed for RT versus RP, with no difference in OCM rates (P = .2). The 10-year CSM rates were 8.8% versus 0.6% (P = .01) for RT versus RP in patients with unfavorable-intermediate-risk (Gleason Score 4 + 3) and 7.9% versus 3.9% (P = .003) for high-risk disease. There was no difference in CSM among RT and RP patients for favorable-intermediate-risk (Gleason Score 3 + 4) and low-risk disease. ConclusionsIn a matched cohort of PCa patients with comparable OCM between the 2 arms, RP yielded a more favorable CSM rate compared to RT only for unfavorable-intermediate- and high-risk groups.

The effects of crocin and crocetin on immune cells of prostate cancer patients in co-culture with adipose-derived mesenchymal stem cells

Similarly to crocin and crocetin, Adipose-derived mesenchymal stem cells (AD-MSCs) exhibit potent anti-apoptotic and antioxidant properties. This study investigates the effects of crocin and crocetin on PBMCs of PCa patients co-cultured with AD-MSCs. We found that crocin and crocetin at concentrations of 3, 6, 12 μM increased the cell viability of PBMCs co-cultured with AD- MSCs and at concentrations of 3, 6, 12, 25, 50, 100 μM increased the cell viability of PBMCs without co-culture with AD-MSCs. At concentrations of 6, 12, 25 μM, decreased the percentage of apoptosis and increased the percentage of (CD3+CD4+) and (CD3+CD8+) cells among PBMCs co-cultured with or without AD-MSCs. At a concentration of 6 μM, also increased percentage of (CD45+CD19+) cells among PBMCs in co-culture. In addition, they at concentrations of 6, 12, 25 μM decreased the level of MDA and increased SOD in PBMCs. Overall further research should evaluate the clinical effects of combining crocin and crocetin with AD-MSCs for treating PCa and other cancers.

Primary tumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET/CT for the prediction of biochemical recurrence in prostate cancer

PurposeThe aim of this study is to research the value of the texture analysis of primary tumors in pre-treatment [68Ga]Ga-PSMA PET in the prediction of the development of biochemical recurrence (BCR) in prostate cancer patients who underwent definitive therapies. Methods51 patients with prostate adenocarcinoma who had a pre-treatment [68Ga]Ga-PSMA-11 PET/CT and underwent definitive radiotherapy (RT) or radical prostatectomy (RP) were included in the study. Demographics, clinicopathologic features, the presence of BCR, and the last follow-up date of patients were recorded. Textural and conventional PET parameters (maximum standardized uptake value (SUVmax), total lesion-PSMA (TL-PSMA), and PSMA-tumor volume (PSMA-TV)) were obtained from PET/CT images using LifeX program. Parameters were grouped using the Youden index in ROC analysis. Factors predicting the BCR were determined using Cox regression analyses. Results29 (56.9%) patients have received primary curative RT, while the remaining 22 (43.1%) patients have undergone RP. 5 (22.7%) patients with RP and 3 (10.3%) patients with curative RT have developed BCR during the follow-up. INTENSITY-BASED-minimum grey level (P=.050), GLCM-sum variance (P=.019), and GLCM-cluster prominence (P=.050) were associated with BCR in univariate analysis. INTENSITY-BASED-minimum grey level (P=.009) and GLCM-sum variance (P=.004) were found as independent predictors of BCR in the multivariate analysis. ConclusionTumor heterogeneity on pre-treatment [68Ga]Ga-PSMA PET is associated with a high risk of BCR in PCa patients who underwent definitive therapies.

The association of quantitative PSMA PET parameters with pathologic ISUP grade: an international multicenter analysis.

To assess if PSMA PET quantitative parameters are associated with pathologic ISUP grade group (GG) and upgrading/downgrading. PCa patients undergoing radical prostatectomy with or without pelvic lymph node dissection staged with preoperative PSMA PET at seven referral centres worldwide were evaluated. PSMA PET parameters which included SUVmax, PSMAvolume, and total PSMA accumulation (PSMAtotal) were collected. Multivariable logistic regression evaluated the association between PSMA PET quantified parameters and surgical ISUP GG. Decision-tree analysis was performed to identify discriminative thresholds for all three parameters related to the five ISUP GGs The ROC-derived AUC was used to determine whether the inclusion of PSMA quantified parameters improved the ability of multivariable models to predict ISUP GG ≥ 4. A total of 605 patients were included. Overall, 2%, 37%, 37%, 10% and 13% patients had pathologic ISUP GG1, 2, 3, 4, and 5, respectively. At multivariable analyses, all three parameters SUVmax, PSMAvolume and PSMAtotal were associated with GG ≥ 4 at surgical pathology after accounting for PSA and clinical T stage based on DRE, hospital and radioligand (all p < 0.05). Addition of all three parameters significantly improved the discrimination of clinical models in predicting GG ≥ 4 from 68% (95%CI 63 - 74) to 74% (95%CI 69 - 79) for SUVmax, 72% (95%CI 67 - 76) for PSMAvolume, 74% (70 - 79) for PSMAtotal and 75% (95%CI 71 - 80) when all parameters were included (all p < 0.05). Decision-tree analysis resulted in thresholds that discriminate between GG (SUVmax 0-6.5, 6.5-15, 15-28, > 28, PSMAvol 0-2, 2-9, 9-20 and > 20 and PSMAtotal 0-12, 12-98 and > 98). PSMAvolume was significantly associated with GG upgrading (OR 1.03 95%CI 1.01 - 1.05). In patients with biopsy GG1-3, PSMAvolume ≥ 2 was significantly associated with higher odds for upgrading to ISUP GG ≥ 4, compared to PSMAvolume < 2 (OR 6.36, 95%CI 1.47 - 27.6). Quantitative PSMA PET parameters are associated with surgical ISUP GG and upgrading. We propose clinically relevant thresholds of these parameters which can improve in PCa risk stratification in daily clinical practice.

Machine learning-driven mast cell gene signatures for prognostic and therapeutic prediction in prostate cancer

BackgroundThe role of Mast cells has not been thoroughly explored in the context of prostate cancer's (PCA) unpredictable prognosis and mixed immunotherapy outcomes. Our research aims to employs a comprehensive computational methodology to evaluate Mast cell marker gene signatures (MCMGS) derived from a global cohort of 1091 PCA patients. This approach is designed to identify a robust biomarker to assist in prognosis and predicting responses to immunotherapy. MethodsThis study initially identified mast cell-associated biomarkers from prostate adenocarcinoma (PRAD) patients across six international cohorts. We employed a variety of machine learning techniques, including Random Forest, Support Vector Machine (SVM), Lasso regression, and the Cox Proportional Hazards Model, to develop an effective MCMGS from candidate genes. Subsequently, an immunological assessment of MCMGS was conducted to provide new insights into the evaluation of immunotherapy responses and prognostic assessments. Additionally, we utilized Gene Set Enrichment Analysis (GSEA) and pathway analysis to explore the biological pathways and mechanisms associated with MCMGS. ResultsMCMGS incorporated 13 marker genes and was successful in segregating patients into distinct high- and low-risk categories. Prognostic efficacy was confirmed by survival analysis incorporating MCMGS scores, alongside clinical parameters such as age, T stage, and Gleason scores. High MCMGS scores were correlated with upregulated pathways in fatty acid metabolism and β-alanine metabolism, while low scores correlated with DNA repair mechanisms, homologous recombination, and cell cycle progression. Patients classified as low-risk displayed increased sensitivity to drugs, indicating the utility of MCMGS in forecasting responses to immune checkpoint inhibitors. ConclusionThe combination of MCMGS with a robust machine learning methodology demonstrates considerable promise in guiding personalized risk stratification and informing therapeutic decisions for patients with PCA.

Elevated LAMTOR4 Expression Is Associated with Lethal Prostate Cancer and Its Knockdown Decreases Cell Proliferation, Invasion, and Migration In Vitro.

Late endosomal/lysosomal adaptor, MAPK and mTOR, or LAMTOR, is a scaffold protein complex that senses nutrients and integrates growth factor signaling. The role of LAMTOR4 in tumorigenesis is still unknown. However, there is a considerable possibility that LAMTOR4 is directly involved in tumor cell proliferation and metastasis. In the current study, we investigated the protein expression of LAMTOR4 in a cohort of 314 men who were undergoing transurethral resection of prostate (TURP) consisting of incidental, advanced and castration-resistant cases. We also correlated the data with ERG and PTEN genomic status and clinicopathological features including Gleason score and patients' outcome. Additionally, we performed in vitro experiments utilizing knockdown of LAMTOR4 in prostate cell lines, and we performed mRNA expression assessment using TCGA prostate adenocarcinoma (TCGA-PRAD) to explore the potential differentially expressed genes and pathways associated with LAMTOR4 overexpression in PCa patients. Our data indicate that high LAMTOR4 protein expression was significantly associated with poor overall survival (OS) (HR: 1.44, CI: 1.01-2.05, p = 0.047) and unfavorable cause-specific survival (CSS) (HR: 1.71, CI: 1.06-2.77, p = 0.028). Additionally, when high LAMTOR4 expression was combined with PTEN-negative cases (score 0), we found significantly poorer OS (HR: 2.22, CI: 1.37-3.59, p = 0.001) and CSS (HR: 3.46, CI: 1.86-6.46, p < 0.0001). Furthermore, ERG-positive cases with high LAMTOR4 exhibited lower OS (HR: 1.98, CI: 1.18-3.31, p = 0.01) and CSS (HR: 2.54, CI: 1.32-4.87, p = 0.005). In vitro assessment showed that knockdown of LAMTOR4 decreases PCa cell proliferation, migration, and invasion. Our data further showed that knockdown of LAMTOR4 in the LNCaP cell line significantly dysregulated the β catenin/mTOR pathway and tumorigenesis associated pathways. Inhibiting components of the mTOR pathway, including LAMTOR4, might offer a strategy to inhibit tumor progression and metastasis in prostate cancer.

Influence of Tumor Characteristics and Time to Metastatic Disease on Oncological Outcomes in Metachronous Metastatic Prostate Cancer Patients

IntroductionMetachronous metastatic prostate cancer (mmPCa) patients harbor different characteristics and outcomes, relative to DeNovo metastatic PCa patients. Onset of metastatic disease might be influenced by primary PCa characteristics such as Gleason score (GS) or cancer stage, as well as overall survival (OS) by timing of metastatic onset. Patients and MethodsWe relied on an institutional tertiary-care database to identify mmPCa patients. Kaplan Meier and Cox Regression models tested for onset of metastases and OS, stratified according to GS, pathological stage and time to mmPCa. ResultsOf 341 mmPCa patients, 8% harbored GS6 versus 41% versus 51% GS7 and GS8-10. Median time to onset of metastatic disease was 79 versus 54 versus 41 months for GS6 versus GS7 versus GS8-10 (P = .01). Moreover, median time to onset of metastases was 64 versus 44 months for pT1-2 versus pT3-4 mmPCa patients undergoing radical prostatectomy (P = .027). In multivariable Cox regression models, higher GS and pT-stage was associated with earlier onset of metastases. Additionally, significant OS differences could be observed for time interval of < 24 versus 24-60 versus 60-120 versus ≥ 120 months between primary PCa diagnosis and onset of mmPCa. Specifically, median OS was 56 versus 69 versus 97 months versus not reached (P < .01) for these categories. In multivariable Cox regression, shorter time to metastatic onset was associated with shorter OS. ConclusionTiming of mmPCa is strongly influenced by grading and pT-stage in real-life setting. OS benefits can be observed with longer time interval between primary PCa diagnosis and onset of mmPCa.