Prognosis Of Prostate Cancer Research Articles

Association between low density lipoprotein cholesterol levels and prostate cancer risk in non-hypertensive middle-aged and older American men

Often linked with the risk of various diseases, blood low-density lipoprotein cholesterol (LDL-C) levels are typically deemed more favorable when lower. The objective of this investigation is to elucidate the link between blood LDL-C levels and the risk of prostate cancer (PCa) in middle-aged and older men without hypertension in the United States. Utilizing continuous data from the National Health and Nutrition Examination Survey (NHANES) database spanning 2003–2010, a selection of 1,223 non-hypertensive men aged ≥ 40 years was made from a pool of 41,156 participants, ensuring no missing information. Regression analyses were employed to investigate the correlation between blood LDL-C levels and the PCa risk, while identifying potential inflection points indicative of threshold effects. Additionally, we scrutinized the linkage between cholesterol-lowering prescription drug usage and PCa. In our study of 2,224 participants, we found no significant correlation between blood LDL-C levels and the PCa risk after adjusting for confounding variables (Odds Ratio = 0.99; P-value > 0.05). However, upon conducting a subgroup analysis, we discovered a meaningful correlation between lower blood LDL-C levels and an increased PCa risk in the non-hypertensive population (Odds Ratio = 0.99; P-value < 0.05). Meanwhile, we identified a threshold effect and a tipping point at an LDL-C levels of 67 mg/dl. Furthermore, a significant correlation was identified between cholesterol-lowering prescription drug usage and a heightened PCa risk in the non-hypertensive population (Odds Ratio = 18.87; P-value < 0.05; P for interaction < 0.05). Our results indicate that in non-hypertensive middle-aged and older men residing in the United States, lower blood LDL-C levels are not necessarily better and the PCa risk escalates when blood LDL-C levels drop below 67 mg/dl, which may guide early screening and prognosis of PCa in specific populations. This finding calls for further validation via larger sample sizes and a more in-depth analysis of PCa history.

Combining tissue biomarkers with mpMRI to diagnose clinically significant prostate cancer. Analysis of 21 biomarkers in the PICTURE study.

Serum PSA and digital rectal examination remain the key diagnostic tools for detecting prostate cancer. However, due to the limited specificity of serum PSA, the applicability of this marker continues to be controversial. Recent use of image-guided biopsy along with pathological assessment and the use of biomarkers has dramatically improved the diagnosis of clinically significant cancer. Despite the two modalities working together for diagnosis biomarker research often fails to correlate findings with imaging. We looked at 21 prostate cancer biomarkers correlating our results with mpMRI data to investigate the hypothesis that biomarkers along with mpMRI data make a powerful tool to detect clinically significant prostate cancer. Biomarkers were selected based on the existing literature. Using a tissue microarray comprised of samples from the PICTURE study, with biopsies at 5 mm intervals and mpMRI data we analysed which biomarkers could differentiate benign and malignant tissue. Biomarker data were also correlated with pathological grading, mpMRI, serum PSA, age and family history. AGR2, CD10 and EGR protein expression was significantly different in both matched malignant and benign tissues. AMACR, ANPEP, GDF15, MSMB, PSMA, PTEN, TBL1XR1, TP63, VPS13A and VPS28 showed significantly different expression between Gleason grades in malignant tissue. The majority of the biomarkers tested did not correlate with mpMRI data. However, CD10, KHDRBS3, PCLAF, PSMA, SIK2 and GDF15 were differentially expressed with prostate cancer progression. AMACR and PTEN were identified in both pathological and image data evaluation. There is a high demand to develop biomarkers that would help the diagnosis and prognosis of prostate cancer. Tissue biomarkers are of particular interest since immunohistochemistry remains a cheap, reliable method that is widely available in pathology departments. These results demonstrate that testing biomarkers in a cohort consistent with the current diagnostic pathway is crucial to identifying biomarker with potential clinical utility.

Abstract A007: Characterization of miR-141 and miR-145 expression in cancer stem cells of metastatic prostate cancer patients

Abstract Background: Prostate cancer (PCA) is the second most prevalent cancer globally and the fifth leading cause of cancer-related mortality. However, effective prognostic biomarkers for metastatic PCA remain lacking. Recent research highlights the role of microRNAs (miRNAs) in regulating cancer stem cells (CSCs) and influencing tumour progression across various cancers. Therefore, in this we investigated the effects of miR-141 and miR-145 expression on the metastatic PCA of CSCs and assessed their potential role as biomarkers for metastatic PCA prognosis. Methods: The expression levels of miR-141 and miR-145 were quantified using real-time qPCR. Flow cytometry was employed to assess the levels of prostate cancer stem cells (PCSCs) in tissue samples from patients with localized (non-metastatic) and metastatic PCA. Results: Our findings revealed a significant increase in miR-141 expression, accompanied by elevated levels of CD44^high/CD24^low and CD133^high, in metastatic PCA compared to localized PCA. Conversely, reduced miR-145 expression was associated with enhanced stemness characteristics, as indicated by increased CD44^high/CD24^low and CD133^high levels, in metastatic PCA compared to localized PCA. Additionally, prostate-specific antigen (PSA) and testosterone levels exhibited a significant rise (p&amp;lt;0.001), while apoptosis rates decreased in both localized and metastatic PCA compared to localized PCA. Conclusions: The collective findings of this study suggest that miR-141 and miR-145, in conjunction with CSC markers, hold promise as diagnostic and prognostic tools for PCA. They may also be valuable for detecting minimal residual disease and improving therapeutic strategies for managing metastatic PCA. Citation Format: Kamla Kant Shukla, Gautam Ram Choudhary. Characterization of miR-141 and miR-145 expression in cancer stem cells of metastatic prostate cancer patients [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A007.

Comparison of androgen receptor mutation detection between plasma extracellular vesicle DNA and cell-free DNA and its relationship to prostate cancer prognosis

Background In liquid biopsy, mutation detection is primarily performed using cell-free DNA (cfDNA). However, the numerous advantages of extracellular vesicle (EV) DNA for mutation detection have gradually garnered the attention of researchers in recent years. This study aimed to compare the differences between EV DNA and cfDNA in mutation detection and explore the role of plasma androgen receptor (AR) mutations in the prognosis of prostate cancer (PCa). Methods We compared the biological characteristics of plasma extracellular vesicle DNA (p-EV DNA) and cfDNA by capillary electrophoresis and concentration detection. Subsequently, we performed pan-oncogene-targeted sequencing in paired tissue and plasma samples from five patients with PCa to verify the feasibility of mutation detection using p-EV DNA and cfDNA. Further, we conducted AR mutation detection in expanded samples to compare the differences between EV DNA and cfDNA in mutation detection and to analyse their role in PCa. Results p-EV DNA fragments were larger than plasma cell-free DNA (p-cfDNA) fragments; however, there was no significant difference in their concentrations in the plasma of patients with PCa. Feasibility analysis revealed that major mutations associated with PCa detected in tissue samples could be identified in both p-EV DNA and p-cfDNA. Advantage comparison found that, although cfDNA could detect more mutations, AR mutations in EV DNA were more strongly associated with a poor prognosis of PCa than cfDNA. Conclusion Mutation detection using either EV DNA or cfDNA is both feasible in PCa liquid biopsies, and EV DNA AR mutations have an advantage in prognostic assessment for PCa. This study lays the foundation for future research on EV DNA-related biomarkers.

Investigating racial differences in mortality and survival outcomes following radical prostatectomy: a systematic review and meta-analysis

BackgroundNotable variations in cancer rates, survival rates, mortality exist among certain populations in the US. We aim to examine how prostate cancer prognosis differs among black and white men from various ethnic backgrounds undergoing radical prostatectomy.MethodsWe performed a comprehensive search across MEDLINE and Google Scholar databases from inception to March 2023 for observational studies including black and white men undergoing radical prostatectomy. Outcomes of interest included either overall survival, prostate cancer survival, biochemical recurrence, or all-cause mortality. Studies were pooled using hazard ratios (HR) with 95% confidence intervals (CIs) using Review Manager (RevMan) version 5.4.1.ResultsIn patients undergoing radical prostatectomy, the risk of all-cause mortality was significantly greater in blacks in comparison to whites ([HR] 1.16, 95% CI 1.12–1.21; I2 = 51%; p < 0.00001). Pooled analysis shows higher rate of prostate-specific mortality in blacks treated compared to whites ([HR]: 1.08, 95% CI 0.86–1.37; I2 = 81%; p = 0.50). The risk of biochemical recurrence was significantly higher in blacks undergoing radical prostatectomy than in whites (HR: 1.27; 95% CI 1.11–1.45; I2 = 58%; p = 0.0007). On regional subgroup analysis, the mid-west and multi-institutional centers revealed a higher rate of biochemical recurrence in blacks than in whites. No significant difference was noted between blacks and whites for overall survival.ConclusionsOur research demonstrated elevated rates of all-cause mortality, prostate-specific mortality, and biochemical recurrence, with no significant differences in overall survival between blacks and whites. Further research is warranted to assess ethnic and geographic differences at a large scale.

Multi-task Bayesian model combining FDG-PET/CT imaging and clinical data for interpretable high-grade prostate cancer prognosis.

We propose a fully automatic multi-task Bayesian model, named Bayesian Sequential Network(BSN), for predicting high-grade(Gleason 8) prostate cancer(PCa) prognosis using pre-prostatectomy FDG-PET/CT images and clinical data. BSN performs one classification task and five survival tasks: predicting lymph node invasion (LNI), biochemical recurrence-free survival (BCR-FS), metastasis-free survival, definitive androgen deprivation therapy-free survival, castration-resistant PCa-free survival, and PCa-specific survival(PCSS). Experiments are conducted using a dataset of 295patients. BSN outperforms widely used nomograms on all tasks except PCSS, leveraging multi-task learning and imaging data. BSN also provides automated prostate segmentation, uncertainty quantification, personalized feature-based explanations, and introduces dynamic predictions, a novel approach that relies on short-term outcomes to refine long-term prognosis. Overall, BSN shows great promise in its ability to exploit imaging and clinicopathological data to predict poor outcome patients that need treatment intensification with loco-regional or systemic adjuvant therapy for high-risk PCa.

Integrated single-cell transcriptomic analyses identify a novel lineage plasticity-related cancer cell type involved in prostate cancer progression

Cancer cell plasticity is the ability of neoplastic cells to alter their identity and acquire new biological properties under microenvironmental pressures. In prostate cancer (PCa), lineage plasticity often results in therapy resistance and trans-differentiation to neuroendocrine (NE) lineage. However, identifying the cancer cells harboring lineage plasticity-related status remains challenging. Based on 13 multi-center human PCa bulk transcriptomic cohorts (samples=3314) and 9 bulk transcriptomic datasets derived from PCa experimental models, we established an integrated lineage plasticity-related gene signature, termed LPSig. Leveraging this gene signature, AUCell enrichment analysis was applied to identify the cell population with high lineage plasticity from a comprehensive single-cell RNA-sequencing (scRNA-seq) meta-atlas assembled by us, which consisted of 10 public human PCa scRNA-seq datasets (samples=93, cells=222,529). Moreover, additional scRNA-seq dataset of human PCa, multiplex immunohistochemistry staining for human PCa tissues, invitro and invivo functional experiments, as well as qPCR and Western blot analyses were employed to validate our findings. We found that LPSig could finely capture the dynamics of tumor lineage plasticity throughout the progression of PCa, accurately estimating the status of lineage plasticity. Based on LPSig, we identified a previously undefined minority population of lineage plasticity-related PCa cells (LPCs) from the human PCa scRNA-seq meta-atlas assembled by this study. Furthermore, in-depth dissection revealed pivotal roles of LPCs in trans-differentiation, tumor recurrence, and poor patient survival during PCa progression. Furthermore, we identified HMMR as a representative cell surface marker for LPCs, which was validated using additional scRNA-seq datasets and multiplexed immunohistochemistry. Moreover, HMMR was transcriptionally inhibited by androgen receptor (AR), and was required for the aggressive adenocarcinoma features and NE phenotype. Our study uncovers a novel population of lineage plasticity-related cells with low AR activity, stemness-like traits, and elevated HMMR expression, that may facilitate poor prognosis in PCa. This work was supported by National Key R&D Program of China (2022YFA0807000), National Natural Science Foundation of China (82160584), Advanced Prostate Cancer Diagnosis and Treatment Technology Innovation Team of Kunming Medical University (CXTD202216), and Reserve Talents of Young and Middle-aged Academic Leaders in Yunnan Province (202105AC160013).

Necrosis-Related lncRNAs: Biomarker Screening and Prognostic Prediction for Hot and Cold Tumours of Prostate Cancer.

The objectives of this work were the search for novel prognostic biomarkers for the diagnosis of prostate cancer (PCa) and the improvement of therapy outcomes in cases with a poor prognosis and the failure of immunotherapy. The GTEx (Genotypic Tissue Expression Project) and TCGA (The Cancer Genome Atlas) databases were used to find out the co-expressed long non-coding RNAs (lncRNAs) associated with necrosis status based on statistics and univariate Cox regression tests. IncRNA associated with necrosis was screened by least absolute shrinkage and selection operator (Lasso) analysis, and the predictive model was further verified by Kaplan-Meier analysis, Receiver Operating Characteristic (ROC) curve analysis, Cox regression, nomogram and calibration curve. Also, immune analysis, principle component analysis, gene set enrichment analysis and prediction of semi-maximum inhibitory concentration in risk groups were conducted. The model successfully identified 16 necrosis-related lncRNA models, demonstrating good consistency among the calibration map and prognosis expectation. The ROC curve's area under the curve (AUC) for 1-year overall survival was 0.726, 0.763 and 0.770. The risk groups identified by the model could guide systematic treatment due to significant differences in semi-inhibitory concentrations. The study also demonstrated that the model could differentiate amongst hot and cold tumours and provide accurate mediation, with cluster 2 recognised as a hot tumour and likely to benefit from immunotherapy drugs. In conclusion, the given study supports the potential of necrosis-related lncRNAs as a biomarker for predicting the prognosis and personalised treatment for PCa.

Development of a Novel Risk Signature for Predicting the Prognosis and Immunotherapeutic Response of Prostate Cancer by Integrating Ferroptosis and Immune-Related Genes.

Ferroptosis and immune response correlation studies have not been reported in prostate cancer (PCa), and the main goal of this paper is to identify biomarkers that can be used for early diagnosis of prostate cancer. Data on PCa were retrieved from the TCGA and MSKCC2010 databases. Thereafter, the differentially expressed ferroptosis-related genes (DE-FRGs: ACSF2) and immune-related genes (DE-IRGs: ANGPT1, NPPC, and PTGDS) were identified using the "limma" package. Additionally, we used univariate and multivariate Cox regression analyses to obtain biochemical relapse (BCR)-free survival-related genes and construct a risk signature. Patients with high-risk scores were characterized by poor BCR-free survival, relatively low immune cell abundance, and comparably weak expression of immune checkpoint molecules. Moreover, gene set variation analysis (GSVA) was performed to explore the biological pathways related to the risk signature. Single sample gene set enrichment analysis (ssGESA) was applied to evaluate the status of immune cells in patients with PCa, which demonstrated that the risk score was intimately affiliated with immune response and cancer pathways. Ultimately, the connection between the risk score and response of PCa patients to immunotherapy was appraised using the TIDE algorithm. The TIDE algorithm implied that the high-risk score PCa population might benefit more from immunotherapy regimens. Finally, qRT-PCR were used to evaluate the expression of DE-FRGs and DE-IRGs in PCa cell and normal prostate epithelial cells. The result of qRT-PCR showed that the mRNA expression levels of ACSF2, ANGPT1, NPPC, and PTGDS in normal prostate epithelial cell were higher than that in PCa cells. Therefore, a risk score model was generated based on one DE-FRG and three DE-IRGs, which could predict the BCR-free survival and response of immunotherapy for patients with PCa.

Characterizing adipocytokine-related signatures for prognosis prediction in prostate cancer.

Prostate cancer (PCa) is a prevalent malignant tumor in males, with a significant incidence of biochemical recurrence (BCR) despite advancements in treatment. Adipose tissue surrounding the prostate, known as periprostatic adipose tissue (PPAT), contributes to PCa invasion through adipocytokine production. However, the relationship between adipocytokine-related genes and PCa prognosis remains understudied. This study was conducted to provide a theoretical basis and serve as a reference for the use of adipocytokine-related genes as prognostic markers in PCa. Transcriptome and survival data of PCa patients from The Cancer Genome Atlas (TCGA) database were analyzed. Differential gene expression analysis was conducted using the DESeq2 and limma packages. Prognostic genes were identified through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression. A prognostic model was developed and validated utilizing receiver operating characteristic (ROC) and Kaplan-Meier (K-M) curves. Assessments of immune cell infiltration and drug sensitivity were also carried out. Subsequently, the function of BNIP3L gene in PCa was verified. A total of 47 adipocytokine-related differentially expressed genes (DEGs) were identified. Five genes (PPARGC1A, APOE, BNIP3L, STEAP4, and C1QTNF3) were selected as prognostic markers. The prognostic model demonstrated significant predictive accuracy in both training and validation cohorts. Patients with higher risk scores exhibited poorer survival outcomes. Immune cell infiltration analysis revealed that the high-risk group had increased immune and ESTIMATE scores, while the low-risk group had higher tumor purity. In vitro experiments confirmed the suppressive effects of BNIP3L on PCa cell proliferation, migration, and invasion. The prognostic model independently predicts the survival of patients with PCa, aiding in prognostic prediction and therapeutic efficacy. It expands the study of adipocytokine-related genes in PCa, presenting novel targets for treatment.

Disulfidptosis-related subtype and prognostic signature in prostate cancer

BackgroundDisulfidptosis refers to cell death caused by the accumulation and bonding of disulfide in the cytoskeleton protein of SLC7A11-high level cells under glucose deprivation. However, the role of disulfidptosis-related genes (DRGs) in prostate cancer (PCa) classification and regulation of the tumor microenvironment remains unclear.MethodsFirstly, we analyzed the expression and mutation landscape of DRGs in PCa. We observed the expression levels of SLC7A11 in PCa cells through in vitro experiments and assessed the inhibitory effect of the glucose transporter inhibitor BAY-876 on SLC7A11-high cells using CCK-8 assay. Subsequently, we performed unsupervised clustering of the PCa population and analyzed the differentially expressed genes (DEGs) between clusters. Using machine learning techniques to select a minimal gene set and developed disulfidoptosis-related risk signatures for PCa. We analyzed the tumor immune microenvironment and the sensitivity to immunotherapy in different risk groups. Finally, we validated the accuracy of the prognostic signatures genes using single-cell sequencing, qPCR, and western blot.ResultsAlthough SLC7A11 can increase the migration ability of tumor cells, BAY-876 effectively suppressed the viability of prostate cancer cells, particularly those with high SLC7A11 expression. Based on the DRGs, PCa patients were categorized into two clusters (A and B). The risk label, consisting of a minimal gene set derived from DEGs, included four genes. The expression levels of these genes in PCa were initially validated through in vitro experiments, and the accuracy of the risk label was confirmed in an external dataset. Cluster-B exhibited higher expression levels of DRG, representing lower risk, better prognosis, higher immune cell infiltration, and greater sensitivity to immune checkpoint blockade, whereas Cluster A showed the opposite results. These findings suggest that DRGs may serve as targets for PCa classification and treatment. Additionally, we constructed a nomogram that incorporates DRGs and clinical pathological features, providing clinicians with a quantitative method to assess the prognosis of PCa patients.ConclusionThis study analyzed the potential connection between disulfidptosis and PCa, and established a prognostic model related to disulfidptosis, which holds promise as a valuable tool for the management and treatment of PCa patients.

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.

Ultralow Prostate-Specific Antigen (PSA) Levels and Improved Oncological Outcomes in Metastatic Hormone-Sensitive Prostate Cancer (mHSPC) Patients Treated with Apalutamide: A Real-World Multicentre Study.

Background/Objectives: Androgen receptor-targeted agents have significantly improved the prognosis of metastatic hormone-sensitive prostate cancer (mHSPC). Prostate-specific antigen (PSA) levels are key prognostic markers, with rapid and deep reductions associated with better outcomes. This study aims to assess the association between the new PSA cut-offs and survival in mHSPC patients treated with Apalutamide. Methods: We conducted a multicentre, retrospective analysis of mHSPC patients treated with Apalutamide between March 2021 and January 2023. Overall survival (OS) and radiographic progression-free survival (rFPS) were analyzed and stratified by the following PSA ranges: <0.02 ng/mL (ultralow), 0.02-0.2 ng/mL, and >0.2 ng/mL. Cox regression was applied to identify variables associated with OS and rPFS. Results: Among 193 patients, 34.2% had de novo mHSPC, with the majority classified as M1b. A total of 58.2% (110) of our cohort achieved ultralow PSA levels, with 20.6% between 0.02 and 0.2 ng/mL, and 21.2% of PSA levels > 0.2 ng/mL. Most patients reached ultralow PSA within six months. Low-volume, metachronous, and M1a subgroups displayed a higher prevalence of patients reaching ultralow PSA levels. At 18 months, OS was 100% in the ultralow PSA group, 94.4% for the 0.02-0.2 ng/mL group, and 67.7% in the >0.2 ng/mL group. Similarly, rPFS at 18 months was 100%, 93.5%, and 50.7%, respectively. Cox regression revealed that both ultralow PSA levels and ISUP grade had a significant impact on OS (HR of 8.256 and 0.164, respectively). For rPFS, only ultralow PSA levels had a significant impact (HR = 0.085). Conclusions: This real-world study of mHSPC patients treated with Apalutamide plus ADT revealed that achieving ultralow PSA levels is strongly associated with better oncological outcomes.

Distance-based analytical device integrated with carbon nanomaterials for sarcosine quantification in human samples.

Astraightforward distance-based paper analytical device (dPAD) was developedfor monitoring sarcosine levels in human samples for the rapid diagnosis and prognosis of prostate cancer and related symptoms. This assay eliminates the need for the expensive horseradish peroxidase (HRP) enzyme by utilizing carbon nanodots (CDs) as a peroxidase-like nanozyme. The proposed dPAD sensor consists of a sample zone pre-deposited with sarcosine oxidase (SOx) and CDs, and a detection zone containing 3,3',5,5'-tetramethylbenzidine (TMB). When a solution containing sarcosine is added to the sample zone, hydroxyl radicals (•OH) are produced through SOx oxidation and subsequent peroxidase catalysis by the CDs. The formed •OH radicals immediately flow to the detection zone via capillary force, where they oxidize TMB, resulting in a visible colour change from colourless to blue. Sarcosine quantification is effortlessly accomplished by measuring the distance of the blue colour in the detection zone. The developed dPAD offers a linear working range between 12.5 and 35.0nmol L-1 (R2 = 0.9959) and a detection limit (LOD) of 10.0nmol L-1. This covers the clinical range for urinary sarcosine determination, thereby suggesting no additional sample preparation or dilution is needed. The sensor shows high precision with the highest relative standard deviation (RSD) of 4.58% and demonstrates excellent selectivity with no observed interferences. Furthermore, recovery studies in human control urine samples ranged from 98.67 to 101.50%, with the highest RSD of 2.03%. Correspondingly, our dPAD method showed a great match with the performance of a commercial ELISA method for detecting sarcosine in human control serum. The sensor is more cost-effective, user-friendly, and accessible than previous methods. Overall, the proposed method represents a promising analytical tool for sarcosine quantification. The concept is also applicable for broader analytical applications in detecting other biomolecules.

Integrated analysis of single-cell RNA-seq and bulk RNA-seq revealed key genes for bone metastasis and chemoresistance in prostate cancer.

Prostate cancer (PCa) is a serious malignancy. The main causes of PCa aggravation and death are unexplained resistance to chemotherapy and bone metastases. This study aimed to investigate the molecular mechanisms associated with the dynamic processes of progression, bone metastasis, and chemoresistance in PCa. Through comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data, Gene Expression Omnibus (GEO) tumor progression and metastasis-related genes were identified. These genes were subjected to lasso regression modeling using the Cancer Genome Atlas (TCGA) database. Tartrate-resistant acid phosphatase (TRAP) staining and real-time quantitative PCR (RT-qPCR) were used to evaluate osteoclast differentiation. CellMiner was used to confirm the effect of LDHA on chemoresistance. Finally, the relationship between LDHA and chemoresistance was verified using doxorubicin-resistant PCa cell lines. 7928 genes were identified as genes related to tumor progression and metastasis. Of these, 7 genes were found to be associated with PCa prognosis. The scRNA-seq and TCGA data showed that the expression of LDHA was higher in tumors and associated with poor prognosis of PCa. In addition, upregulation of LDHA in PCa cells induces osteoclast differentiation. Additionally, high LDHA expression was associated with resistance to Epirubicin, Elliptinium acetate, and doxorubicin. Cellular experiments demonstrated that LDHA knockdown inhibited doxorubicin resistance in PCa cells. LDHA may play a potential contributory role in PCa initiation and development, bone metastasis, and chemoresistance. LDHA is a key target for the treatment of PCa.

Serum ACSL4 levels in prostate cancer patients and its relationship between patient prognosis: A prospective observational study.

In this prospective observational study, our objective was to investigate the serum levels of Acyl-CoA synthetase long-chain family member 4 (ACSL4) in prostate cancer (PCa) patients and examine its association with other serum biomarkers, and the clinical outcomes of PCa patients. This prospective observational study was conducted from January 2019 to October 2021, including 103 cases of PCa patients and 101 cases of benign prostate hyperplasia (BPH) patients who received treatment at our hospital. All patients had their serum ACSL4 levels measured using enzyme-linked immunosorbent assay before treatment. The clinical outcomes included age, body mass index, gender, systolic blood pressure, diastolic blood pressure, tumor node metastasis stage, Gleason scores, and prostate volume and serum biomarkers were collected. All patients were followed up for 36 months, the overall survival and disease-free survival were recorded for all patients. All data used SPSS 26.0 for analysis. The phosphorus (P) and serum low-density lipoprotein cholesterol levels were significantly higher in PCa patients compared to BPH patients. Furthermore, compared to the BPH patients, the serum ACSL4 and free prostate-specific antigen levels were significantly decreased while serum total prostate-specific antigen (tPSA) levels were significantly elevated in PCa patients. Pearson correlation analysis showed a positive correlation between ACSL4 levels and free prostate-specific antigen levels, while a negative correlation was observed with P and tPSA levels. ACSL4 might serve as a biomarker for diagnosing PCa with the AUC was 0.747, cutoff value of 33.68 ng/mL, sensitivity of 70.3%, and specificity of 60.2%. Finally, we found that ACSL4, tPSA, and P were identified as risk factors associated with PCa patients. Our findings indicated that the serum levels of ACSL4 were significantly decreased in PCa patients compared to BPH patients. Serum ACSL4 could be used as a potential biomarker for early PCa diagnosis and prognosis.

Effect of deep testosterone reduction on the prognosis of metastatic prostate cancer with high-volume disease

ObjectiveThis study aims to investigate the correlation between serum testosterone levels after one month of treatment and prognosis in patients with high-volume disease metastatic prostate cancer (mPCa) who are undergoing combined androgen blockade therapy (CAB).MethodsThe clinical data of 199 patients with high-volume disease mPCa, diagnosed through biopsy pathology and imaging, were retrospectively analyzed from January 2010 to October 2022 in the Department of Urology at the First Affiliated Hospital of Xinjiang Medical University. Among these patients, 111 cases had a deep reduction in serum testosterone (< 0.7 nmol/l) after one month of treatment, while 88 cases did not achieve a deep reduction (≥ 0.7 nmol/l). The study utilized the Kaplan–Meier method to plot survival curves and employed the multifactor COX regression model to analyze independent risk factors. The risk factors with a significance level of P < 0.05 in the multivariate analysis were included in the nomogram prediction model. The accuracy of the model was assessed using the ROC curve and the calibration curve, while the net benefit for patients was evaluated through the decision curve analysis (DCA).ResultsThe group that achieved deep testosterone reduction(DTR) had a higher proportion of PSA < 0.2 ng/ml and a greater PSA decline rate after six months of treatment (P < 0.05). The group that achieved DTR and the group that did not achieve DTR had a progression to castration resistant prostate cancer(CRPC) time of 17.93 ± 6.68 months and 13.43 ± 6.12 months, respectively (P < 0.001). The median progression-free survival time for the 2 groups were 18 months and 12 months, respectively (P < 0.001). The median overall survival times were 57 months and 32 months, respectively (P < 0.001). The median progression-free survival times were 18, 15, and 10 months for the group that achieved DTR within 1 month, the group that achieved DTR beyond 1 month but within 1 year, and the group that did not achieve DTR within 1 year, respectively (P < 0.001), and the median survival times were 57, 45, and 26 months, respectively (P < 0.001). COX multivariate analysis revealed that a testosterone level of ≥ 0.7 nmol/l at 1 month of treatment is an independent risk factor for the progression to CRPC and prognosis in patients with high-volume disease mPCa (P < 0.05). The risk of death in patients with a testosterone level of ≥ 0.7 nmol/l at 1 month of treatment was 2.087 times higher than that of patients with a level of < 0.7 nmol/l (P < 0.05). A nomogram prediction model was developed using independent risk factors, with the area under the ROC curve (AUC) for progression-free survival (PFS) at 12, 15, 18, and 21 months being 0.788, 0.772, 0.760, and 0.739, respectively. For 3 and 5 years, the AUCs for overall survival (OS) were 0.691 and 0.624. The calibration curve demonstrated good consistency between the model’s predicted values and the actual outcomes.ConclusionPatients with high-volume disease mPCa who receive CAB treatment may experience extended progression-free survival and overall survival if their serum testosterone levels are below 0.7 nmol/l after one month of treatment. The longer it takes to achieve DTR, the worse the patient’s prognosis may be. The nomogram prediction model developed in this study demonstrates good predictive ability in assessing the progression and prognosis of high-volume disease mPCa.

Identification of key genes participating in copper-diethyldithiocarbamate-related cell death process and predicting the development of prostate cancer

Copper (Cu) is used as a cofactor in all organisms, and yet it can be toxic at high intracellular concentrations, causing cell death. Diethyldithiocarbamate (DDC) is a Cu ionophore that can transport Cu effectively into the cell. Copper-diethyldithiocarbamate (Cu-DDC) can treat prostate cancer (PCa) and may correlate with the cell death process. However, the specific Cu-DDC-related cell death genes in PCa are still unknown. Information about the Cu-DDC-related cell death genes was obtained from a previous study. Concurrently, the RNA expression profiles and clinical data were downloaded from public databases such as GEO, TCGA, and CPGEA. Using data from TCGA database, the logistic and lasso regression models were generated using R software. The influence of these genes in affecting PCa progression and prognosis was analyzed. Finally, the expression of these genes was verified in clinical samples. We found five Cu-DDC-related cell death genes associated with the occurrence of PCa from GSE35988, a gene dataset, namely, CDKN2A, PRC1, CDK1, SOX2, and ZNF365. CDKN2A, PRC1, and CDK1 are known to influence PCa patients’ disease-free survival (DFS) status and were overexpressed, whereas SOX2 and ZNF365 were under-expressed in PCa in the different databases. Some of these genes can affect PCa progression. Consistent with the database results, the mRNA and protein expression of CDKN2A, PRC1, and CDK1 was also higher in clinical samples. In conclusion, we identified five hub genes which are important for Cu-DDC-related cell death process that can predict the development of PCa.

Periprostatic adipose tissue inhibits tumor progression by secreting apoptotic factors: A natural barrier induced by the immune response during the early stages of prostate cancer.

Prostate cancer (PCa) is the second most prevalent malignancy in men worldwide. The risk factors for PCa include obesity, age and family history. Increased visceral fat has been associated with high PCa risk, which has prompted previous researchers to investigate the influence of body composition and fat distribution on PCa prognosis. However, there is a lack of studies focusing on the mechanisms and interactions between periprostatic adipose tissue (PPAT) and PCa cells. The present study investigated the association between the composition of pelvic adipose tissue and PCa aggressiveness to understand the role played by this tissue in PCa progression. Moreover, PPAT-conditioned medium (CM) was prepared to assess the influence of the PPAT secretome on the pathophysiology of PCa. The present study included 50 patients with localized PCa who received robot-assisted radical prostatectomy. Medical records were collected, magnetic resonance imaging scans were analyzed and body compositions were calculated to identify the associations between adipose tissue volume and clinical PCa aggressiveness. In addition, CM was prepared from PPAT and perivesical adipose tissue (PVAT) collected from 25 patients during surgery, and its effects on the PCa cell lines C4-2 and LNCaP, and the prostate epithelial cell line PZ-HPV-7, were investigated using a cell proliferation assay and RNA sequencing (RNA-seq). The results revealed that the initial prostate-specific antigen level was significantly correlated with pelvic and periprostatic adipose tissue volumes. In addition, PPAT volume was significantly higher in patients with extracapsular tumor extension. PCa cell proliferation was significantly reduced when the cells were cultured in PPAT-CM compared with when they were cultured in control- and PVAT-CM. RNA-seq revealed that immune responses, and the cell death and apoptosis pathways were enriched in PPAT-CM-cultured cells indicating that the cytokines or other factors secreted from PPAT-CM induced PCa cell apoptosis. These findings revealed that the PPAT secretome may inhibit PCa cell proliferation by activating immune responses and promoting cancer cell apoptosis. This mechanism may act as a first-line defense during the early stages of PCa.

PDIA2 is associated with the prognosis of prostate cancer, and downregulation of PDIA2 delays the progression of prostate cancer cells.

Protein Disulfide-Isomerase A2 (PDIA2) is a gene that encodes proteins, responsible for protein folding and modification within cells. The development and course of many disorders are intimately linked to the aberrant expression of PDIA2. Nevertheless, more research is necessary to fully understand PDIA2's biological significance in pan-cancer, notably in prostate cancer (PCa). PDIA2 expression is elevated in various tumors and closely related to patient prognosis. Patients with prostate cancer who express PDIA2 high in particular have a bad prognosis in terms of progression-free survival. In addition, the upregulation of PDIA2 expression in prostate cancer patients is accompanied by higher Gleason scores, advanced tumor staging, lymph node metastasis, and elevated PSA levels. Detailed experiments further demonstrate that PDIA2 is a carcinogenic gene affecting prostate cancer cells' response to dasatinib therapy. For patients with prostate cancer, there is a clear positive connection between the expression level of PDIA2 and a bad prognosis. The prostate cancer treatment efficacy of dasatinib is hampered by PDIA2, which is intimately linked to the growth, invasion, and metastasis of PCa cells. In summary, our research highlights the potential of PDIA2 as a biomarker for the diagnosis and management of PCa.