Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

Unveiling potential: urinary exosomal mRNAs as non-invasive biomarkers for early prostate cancer diagnosis.

BACKGROUND: This study investigated the use of urinary exosomal mRNA as a potential biomarker for the early detection of prostate cancer (PCa). METHODS: Next-generation sequencing was utilized to analyze exosomal RNA from 10 individuals with confirmed PCa and 10 individuals without cancer. Subsequent validation through qRT-PCR in a larger sample of 43 PCa patients and 92 healthy controls revealed distinct mRNA signatures associated with PCa. RESULTS: Notably, mRNAs for RAB5B, WWP1, HIST2H2BF, ZFY, MARK2, PASK, RBM10, and NRSN2 showed promise as diagnostic markers, with AUC values between 0.799 and 0.906 and significance p values. Combining RAB5B and WWP1 in an exoRNA diagnostic model outperformed traditional PSA tests, achieving an AUC of 0.923, 81.4% sensitivity, and 89.1% specificity. CONCLUSIONS: These findings highlight the potential of urinary exosomal mRNA profiling, particularly focusing on RAB5B and WWP1, as a valuable strategy for improving the early detection of PCa.

Urinary exosomal prostate-specific antigen is a noninvasive biomarker to detect prostate cancer: Not only old wine in new bottles.

The study aimed at evaluating the performance of urinary exosomal prostate-specific antigen (UE-PSA) to predict the results of initial prostate biopsies and discriminate clinically significant prostate cancer (Gleason score ≥ 7, csPCa) from nonsignificant PCa (Gleason score < 7, nsPCa) plus benign patients. Two hundred seventy-two consecutive participants were admitted who underwent a prostate biopsy. The UE-PSA expression was detected by enzyme-linked immunosorbent assay (ELISA). The predictive power and clinical value of UE-PSA was assessed by receiver operating characteristic (ROC), decision curve analysis (DCA) and waterfall plots. UE-PSA was upregulated in PCa compared to benign patients (P < .001) and csPCa compared to nsPCa plus benign patients (P < .001). UE-PSA achieved an AUC of 0.953 (0.905-0.989) in distinguishing PCa from benign patients and an AUC of 0.879 (0.808-0.941) in predicting csPCa from nsPCa plus benign patients. These results were validated in an additional multicenter cohort. In addition, DCA showed that UE-PSA achieved the highest net benefit at almost any threshold probability compared to tPSA and %fPSA. As the waterfall plot showed, the UE-PSA assay could avoid 57.6% (155 cases) and 34.6% (93 cases) unnecessary biopsies while only missing 2.6% (7 cases) and 1.5% (4 cases) of the cases of csPCa at the cutoff value of 90% and 95% sensitivity, respectively. We validated that UE-PSA presented great diagnostic power and clinical utility to diagnose PCa and csPCa. UE-PSA could be a promising noninvasive biomarker to improve PCa detection.

Artificial intelligence for the diagnosis of clinically significant prostate cancer based on multimodal data: a multicenter study.

BACKGROUND: The introduction of multiparameter MRI and novel biomarkers has greatly improved the prediction of clinically significant prostate cancer (csPCa). However, decision-making regarding prostate biopsy and prebiopsy examinations is still difficult. We aimed to establish a quick and economic tool to improve the detection of csPCa based on routinely performed clinical examinations through an automated machine learning platform (AutoML). METHODS: This study included a multicenter retrospective cohort and two prospective cohorts with 4747 cases from 9 hospitals across China. The multimodal data, including demographics, clinical characteristics, laboratory tests, and ultrasound reports, of consecutive participants were retrieved using extract-transform-load tools. AutoML was applied to explore potential data processing patterns and the most suitable algorithm to build the Prostate Cancer Artificial Intelligence Diagnostic System (PCAIDS). The diagnostic performance was determined by the receiver operating characteristic curve (ROC) for discriminating csPCa from insignificant prostate cancer (PCa) and benign disease. The clinical utility was evaluated by decision curve analysis (DCA) and waterfall plots. RESULTS: The random forest algorithm was applied in the feature selection, and the AutoML algorithm was applied for model establishment. The area under the curve (AUC) value in identifying csPCa was 0.853 in the training cohort, 0.820 in the validation cohort, 0.807 in the Changhai prospective cohort, and 0.850 in the Zhongda prospective cohort. DCA showed that the PCAIDS was superior to PSA or fPSA/tPSA for diagnosing csPCa with a higher net benefit for all threshold probabilities in all cohorts. Setting a fixed sensitivity of 95%, a total of 32.2%, 17.6%, and 26.3% of unnecessary biopsies could be avoided with less than 5% of csPCa missed in the validation cohort, Changhai and Zhongda prospective cohorts, respectively. CONCLUSIONS: The PCAIDS was an effective tool to inform decision-making regarding the need for prostate biopsy and prebiopsy examinations such as mpMRI. Further prospective and international studies are warranted to validate the findings of this study. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2100048428. Registered on 06 July 2021.

Long noncoding RNA as a potential diagnostic tool for prostate cancer: a systematic review and meta-analysis.

PURPOSE: To identify consistently expressed lncRNAs and suitable lncRNAs with high sensitivity and specificity from multiple independent studies as potential biomarkers for PCa diagnostics. METHODS: We searched multiple electronic databases including PubMed, Web of Science, EMBASE, Cochrane Library, CNKI, CQVIP, Wanfang, and CBMdisc for studies published up to July 2022. The quality of the included studies was assessed by two independent reviewers based on the QUADAS-2 tool using Review Manager 5.3. A vote-counting method was used based on the ranking of potential molecular biomarkers. The top-ranked lncRNAs were further assessed for diagnostic value using Meta-disc version 1.4 software. RESULTS: Among the 26 included studies, 2 circulating lncRNAs (PCA3 and MALAT-1) were reported 3 or more times in PCa patients versus non-PCa patients. In further analysis, the areas under the curve of the summary receiver operating characteristic curves for PCA3 and MALAT-1 distinguishing PCa patients were 0.775 and 0.771, respectively. CONCLUSIONS: Based on the current evidence, PCA3 and MALAT-1 are reliable lncRNAs for the diagnosis of PCa.

Structural Change and Radical Generation of a Cadmium-Based Metal-Organic Framework Induced by an Electric Field.

Herein we report the structural change and radical generation of a cadmium-based metal-organic framework (Cd-MOF) induced by external electric fields. Under a weaker single electric field, different coordination modes of Cd-L lead to 3D → 2D structural change. Under stronger superposed electric fields, Cd-MOF was excited to produce a stable free radical. This study will provide a new avenue for the controlled assembly of MOFs.

Circulating exosomal mRNA signatures for the early diagnosis of clear cell renal cell carcinoma.

BACKGROUND: There are no proven tumor biomarkers for the early diagnosis of clear cell renal cell carcinoma (ccRCC) thus far. This study aimed to identify novel biomarkers of ccRCC based on exosomal mRNA (emRNA) profiling and develop emRNA-based signatures for the early detection of ccRCC. METHODS: Four hundred eighty-eight participants, including 226 localized ccRCCs, 73 patients with benign renal masses, and 189 healthy controls, were recruited. Circulating emRNA sequencing was performed in 12 ccRCCs and 22 healthy controls in the discovery phase. The candidate emRNAs were evaluated with 108 ccRCCs and 70 healthy controls in the test and training phases. The emRNA-based signatures were developed by logistic regression analysis and validated with additional cohorts of 106 ccRCCs, 97 healthy controls, and 73 benign individuals. RESULTS: Five emRNAs, CUL9, KMT2D, PBRM1, PREX2, and SETD2, were identified as novel potential biomarkers of ccRCC. We further developed an early diagnostic signature that comprised KMT2D and PREX2 and a differential diagnostic signature that comprised CUL9, KMT2D, and PREX2 for RCC detection. The early diagnostic signature displayed high accuracy in distinguishing ccRCCs from healthy controls, with areas under the receiver operating characteristic curve (AUCs) of 0.836 and 0.830 in the training and validation cohorts, respectively. The differential diagnostic signature also showed great performance in distinguishing ccRCCs from benign renal masses (AUC = 0.816), including solid masses (AUC = 0.810) and cystic masses (AUC = 0.832). CONCLUSIONS: We established and validated novel emRNA-based signatures for the early detection of ccRCC and differential diagnosis of uncertain renal masses. These signatures could be promising and noninvasive biomarkers for ccRCC detection and thus improve the prognosis of ccRCC patients.

[Fe2S2-Agx]-Hydrogenase Active-Site-Containing Coordination Polymers and Their Photocatalytic H2 Evolution Reaction Properties.

Three [Fe2S2-Agx]-hydrogenase active-site-containing coordination polymers (CPs), {[Fe2S2-Ag1](4-cpmt)2(CO)6(ClO4-)}n (1), {[Fe2S2-Ag2](4-cpmt)2(CO)6(OTf-)2(benzene)}n (2), and {[Fe2S2-Ag2](3-cpmt)2(CO)6(ClO4-)2}n (3), were obtained by a direct synthesis method from ligands [FeFe](4-cpmt)2(CO)6 [L1; 4-cpmt = (4-cyanophenyl)methanethiolate] and [FeFe](3-cpmt)2(CO)6 [L2; 3-cpmt = (3-cyanophenyl)methanethiolate] with silver salts. 1-3 represent the first examples of [FeFe]-hydrogenase-based CPs. It was worth noting that the Ag-S bonding between the Ag centers and S atoms of a [Fe2S2] cluster produced a novel [Fe2S2-Agx] (x = 1 or 2) catalytic site in all three polymers. The results of photochemical H2 generation experiments indicated that 2 and 3 containing [Fe2S2-Ag2] active sites showed obviously improved catalytic performances compared with ligands L1 and L2 and [Fe2S2-Ag1]-containing 1. This work provides a pioneering strategy for the direct synthesis of [Fe2S2]-based CPs or metal-organic frameworks.

Circulating exosomal mRNA profiling identifies novel signatures for the detection of prostate cancer.

The landscape and characteristics of circulating exosomal messenger RNAs (emRNAs) are poorly understood, which hampered the accurate detection of circulating emRNAs. Through comparing RNA sequencing data of circulating exosomes with the corresponding data in tissues, we illustrated the different characteristics of emRNAs compared to tissue mRNAs. We then developed an improved strategy for emRNA detection based on the features of circulating emRNAs. Using the optimized detection strategy, we further validated prostate cancer (PCa) associated emRNAs discovered by emRNA-seq in a large cohort of patients and identified emRNA signatures for PCa screening and diagnosis using logistic regression analysis. The receiver operating characteristic curve (ROC) analysis showed that the circulating emRNA-based screening signature yielded an area under the ROC curve (AUC) of 0.948 in distinguishing PCa patients from healthy controls. The circulating emRNA-based diagnostic signature also showed a great performance in predicting prostate biopsy results (AUC: 0.851). In conclusion, our study developed an optimized emRNA detection strategy and identified novel emRNA signatures for the detection of PCa.

The previously uncharacterized lncRNA APP promotes prostate cancer progression by acting as a competing endogenous RNA.

Long noncoding RNAs (lncRNAs) promote cell proliferation, migration, invasion and castration resistance in prostate cancer (PCa). Understanding the inherited molecular mechanisms by which lncRNAs contribute to the progression of PCa to a lethal disease could have an important impact on cancer detection, diagnosis and prognosis. In our study, PCa-associated lncRNA transcripts from RNA-seq data were identified and screened via bioinformatics analysis, NCBI annotations and literature review. We identified a novel lncRNA, lncAPP (lncRNA activated in PCa progression), which activates in PCa progression and is expressed in primary tumor tissues and urine samples of patients with localized or advanced PCa. Urinary-based lncAPP is a promising biomarker for predicting PCa progression. In vitro and in vivo studies demonstrated that lncAPP enhanced cell proliferation and promoted migration and invasion. The underlying mechanism of lncRNA was investigated by RNA immunoprecipitation, dual-luciferase reporter system assay, etc. Upregulation of lncAPP promoted cell migration and invasion via competitively binding miR218 to facilitate ZEB2/CDH2 expression. In addition, in vivo subcutaneous tumor xenograft models and tail intravenously injection metastatic models were constructed to evaluate lncRNA function. Targeting lncAPP/miR218 axis in cell lines and tumor xenografts restrained tumor progression properties both in vitro and in vivo. These results establish that lncAPP/miR218 axis plays a critical role in PCa progression, and they also suggest new strategies to prevent tumor progression for therapeutic purposes.

Robotic enucleation of adrenal masses: technique and outcomes.

OBJECTIVES: To evaluate the feasibility and safety of the application of robotic enucleation of adrenal masses (REAM). METHODS: Thirteen patients at Shanghai Changhai Hospital who underwent robotic enucleation of adrenal mass from February 2017 to March 2018 were reviewed. After mobilizing the adrenal gland and clamping the feeding blood vessels, the tumor was enucleated and reconstruction was performed. Relevant clinical data were recorded including baseline patient and tumor characteristics, and perioperative outcomes (operating time, ischemic time, estimated blood loss, complications, and so on). RESULTS: All cases were successfully completed without conversion to total adrenalectomy or open surgery. The mean operative time was 75 min (range 60-95), with a mean warm ischemia time of 12 min (range 8-17). The estimated blood loss was 20 mL (range 10-50). No intraoperative complications were observed, and no steroid replacement was given post-operatively. After a median follow-up period of 12 months (range 9-15), no evidence of disease recurrence was detected. CONCLUSIONS: Robotic enucleation of adrenal masses is a safe and feasible procedure with excellent short-term functional and oncologic outcomes. Steroid supplementation is not necessary and recurrence is not usual with limited follow-up. Long-term follow-up and larger studies should be conducted to further evaluate outcomes of this robotic adrenal-sparing approach.

Replicate systematic review and meta-analyses on robotic surgery: a quality appraisal and overlap investigation.

BACKGROUND: The number of publications of systematic reviews and meta-analyses (MAs) on robotic surgery have been increasing, including many investigating the same topic. Their quality and extent of overlap remains unclear. We assessed the quality of the MAs in this area and investigated the extent of their overlap. METHODS: Relevant studies were identified by searching the MEDLINE, EMBASE, and Cochrane Library databases up to August 1, 2017. Reporting and methodological quality levels were assessed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Assessment of Multiple Systematic Reviews (AMSTAR) checklists. A thorough investigation of the extent of overlap was performed. RESULTS: In total, 90 MAs in 5 surgical subspecialties were included after full-text review. The mean reporting and methodological quality scores were 22.5 (83.2%) and 7.6 (69.2%), respectively. Authors from university-affiliated institutions and the presence of statistician or epidemiologist coauthors were associated with better-reporting quality scores. The topics with the most overlapping MAs (all ≥ 6) were robot-assisted thyroidectomy, prostatectomy, gastrectomy, colectomy, and fundoplication. 36 (40%) of the included MAs cited previous MAs on the same topic. Among the 7 MAs comparing robot-assisted radical prostatectomy to the open procedure, most (6/7) drew the same conclusion. 50 to 86% of MAs on this topic included the same trials as primary studies. CONCLUSION: Conducting multiple overlapping MAs with identical conclusions on the same topic that are of suboptimal quality may be a waste of resource and effort. Authors from university-affiliated institutes and experts in epidemiology and statistics are more likely to conduct MAs that have better quality. More guidelines and registries are needed to avoid overlapping MAs.

Exosomes derived from hypoxia-preconditioned mesenchymal stromal cells ameliorate cognitive decline by rescuing synaptic dysfunction and regulating inflammatory responses in APP/PS1 mice.

Administration of exosomes derived from mesenchymal stromal cells (MSCs) could improve some neurologic conditions by transferring functional biomolecules to recipient cells. Furthermore, exosomes from hypoxic progenitor cells exerted better therapeutic effects in organ injury through specific cargoes. However, there are no related reports about whether exosomes derived from MSCs or hypoxia-preconditioned MSCs (PC-MSCs) could prevent memory deficits in Alzheimer disease (AD). In this study, the exosomes derived from MSCs or PC-MSCs were systemically administered to transgenic APP/PS1 mice. The expression of miR-21 in MSCs was significantly increased after hypoxic treatment. Injection of exosomes from normoxic MSCs could rescue cognition and memory impairment according to results of the Morris water maze test, reduced plaque deposition, and Aß levels in the brain; could decrease the activation of astrocytes and microglia; could down-regulate proinflammatory cytokines (TNF-α and IL-1ß); and could up-regulate anti-inflammatory cytokines (IL-4 and -10) in AD mice, as well as reduce the activation of signal transducer and activator of transcription 3 (STAT3) and NF-κB. Compared to the group administered exosomes from normoxic MSCs, in the group administered exosomes from PC-MSCs, learning and memory capabilities were significantly improved; the plaque deposition and Aß levels were lower, and expression of growth-associated protein 43, synapsin 1, and IL-10 was increased; and the levels of glial fibrillary acidic protein, ionized calcium-binding adaptor molecule 1, TNF-α, IL-1ß, and activation of STAT3 and NF-κB were sharply decreased. More importantly, exosomes from PC-MSCs effectively increased the level of miR-21 in the brain of AD mice. Additionally, replenishment of miR-21 restored the cognitive deficits in APP/PS1 mice and prevented pathologic features. Taken together, these findings suggest that exosomes from PC-MSCs could improve the learning and memory capabilities of APP/PS1 mice, and that the underlying mechanism may lie in the restoration of synaptic dysfunction and regulation of inflammatory responses through regulation of miR-21.-Cui, G.-H., Wu, J., Mou, F.-F., Xie, W.-H., Wang, F.-B., Wang, Q.-L., Fang, J., Xu, Y.-W., Dong, Y.-R., Liu, J.-R., Guo, H.-D. Exosomes derived from hypoxia-preconditioned mesenchymal stromal cells ameliorate cognitive decline by rescuing synaptic dysfunction and regulating inflammatory responses in APP/PS1 mice.

Prostate Cancer Diagnosis Using Urine Sediment Analysis-Based α-Methylacyl-CoA Racemase Score: A Single-Center Experience.

To evaluate the diagnostic value of α-methylacyl-CoA racemase (AMACR) score in Han Chinese patients with prostate cancer (PCa) through urine sediment analysis. We collected 292 urine sediment samples after digital rectal examination. Levels of AMACR and prostate-specific antigen (PSA) messenger RNA (mRNAs) were evaluated by quantitative real time-polymerase chain reaction. The diagnostic value of AMACR score was assessed by receiver-operating characteristic analysis (ROC), Mann-Whitney test, logistic regression analysis and decision curve analysis. In all patients (n = 292), the area under the curve (AUC) for serum PSA, AMACR score, and a combinative model of these 2 parameters were 0.745 (95% confidence interval [CI]: 0.691-0.794), 0.753 (95% CI: 0.700-0.802), and 0.784 (95% CI: 0.732-0.830). No statistical difference was found between AMACR score and serum PSA (P = .826), while the combinative model was better than AMACR score (Z = 5.222, P < .001). Among patients with serum PSA level of 4 to 10 ng/mL (n = 121), the AMACR score was significantly higher in patients with PCa (P = 0.0002), while serum PSA showed no difference (P = 0.3023). Alpha-methylacyl-CoA racemase score (AUC = 0.712, 95% CI: 0.623-0.790) and a combinative model (AUC = 0.714, 95% CI: 0.626-0.793) showed a better diagnostic value than serum PSA (AUC = 0.559, 95% CI: 0.466-0.649), (P = .048, P = .042). Decision curve analysis showed a biopsy prediction model including AMACR score have a better net benefit when the threshold probability greater than 20%. The diagnostic model combing serum PSA and AMACR score has a better diagnostic value in patients with abnormal PSA level (including PSA level ranging from 4-10 ng/mL), and could reduce unnecessary prostate biopsy in clinical use.

Metabolomics and transcriptomics profiles reveal the dysregulation of the tricarboxylic acid cycle and related mechanisms in prostate cancer.

Genetic alterations drive metabolic reprograming to meet increased biosynthetic precursor and energy demands for cancer cell proliferation and survival in unfavorable environments. A systematic study of gene-metabolite regulatory networks and metabolic dysregulation should reveal the molecular mechanisms underlying prostate cancer (PCa) pathogenesis. Herein, we performed gas chromatography-mass spectrometry (GC-MS)-based metabolomics and RNA-seq analyses in prostate tumors and matched adjacent normal tissues (ANTs) to elucidate the molecular alterations and potential underlying regulatory mechanisms in PCa. Significant accumulation of metabolic intermediates and enrichment of genes in the tricarboxylic acid (TCA) cycle were observed in tumor tissues, indicating TCA cycle hyperactivation in PCa tissues. In addition, the levels of fumarate and malate were highly correlated with the Gleason score, tumor stage and expression of genes encoding related enzymes and were significantly related to the expression of genes involved in branched chain amino acid degradation. Using an integrated omics approach, we further revealed the potential anaplerotic routes from pyruvate, glutamine catabolism and branched chain amino acid (BCAA) degradation contributing to replenishing metabolites for TCA cycle. Integrated omics techniques enable the performance of network-based analyses to gain a comprehensive and in-depth understanding of PCa pathophysiology and may facilitate the development of new and effective therapeutic strategies.

Integration of Metabolomics and Transcriptomics Reveals Major Metabolic Pathways and Potential Biomarker Involved in Prostate Cancer.

Prostate cancer is a highly prevalent tumor affecting millions of men worldwide, but poor understanding of its pathogenesis has limited effective clinical management of patients. In addition to transcriptional profiling or transcriptomics, metabolomics is being increasingly utilized to discover key molecular changes underlying tumorigenesis. In this study, we integrated transcriptomics and metabolomics to analyze 25 paired human prostate cancer tissues and adjacent noncancerous tissues, followed by further validation of our findings in an additional cohort of 51 prostate cancer patients and 16 benign prostatic hyperplasia patients. We found several altered pathways aberrantly expressed at both metabolic and transcriptional levels, including cysteine and methionine metabolism, nicotinamide adenine dinucleotide metabolism, and hexosamine biosynthesis. Additionally, the metabolite sphingosine demonstrated high specificity and sensitivity for distinguishing prostate cancer from benign prostatic hyperplasia, particularly for patients with low prostate specific antigen level (0-10 ng/ml). We also found impaired sphingosine-1-phosphate receptor 2 signaling, downstream of sphingosine, representing a loss of tumor suppressor gene and a potential key oncogenic pathway for therapeutic targeting. By integrating metabolomics and transcriptomics, we have provided both a broad picture of the molecular perturbations underlying prostate cancer and a preliminary study of a novel metabolic signature, which may help to discriminate prostate cancer from normal tissue and benign prostatic hyperplasia.

A novel urinary long non-coding RNA transcript improves diagnostic accuracy in patients undergoing prostate biopsy.

BACKGROUND: Long non-coding RNA (LncRNA) PCA3 has been a well-established urine biomarker for the detection of prostate cancer (PCa). Our previous study showed a novel LncRNA FR0348383 is up-regulated in over 70% of PCa compared with matched benign tissues. The aim of this study was to evaluate the diagnostic value of urinary FR0348383 for men undergoing prostate biopsy due to elevated PSA (PSA > 4.0 ng/ml) and/or abnormal digital rectal examination (DRE). METHODS: Post-DRE first-catch urine specimens prior to prostate biopsies were prospectively collected. After the whole transcriptome amplification, quantitative real time polymerase chain reaction was applied to quantify urine FR0348383 and PSA levels. The FR0348383 score was calculated as the ratio of PSA and FR0348383 mRNA (PSA mRNA/FR0348383 mRNA × 1000). The diagnostic value of FR0348383 score was evaluated by logistic regression and decision curve analysis. RESULTS: 213 cases with urine samples containing sufficient mRNA were included, 94 cases had serum PSA level 4.0-10.0 ng/ml. PCa was identified in 72 cases. An increasing FR0348383 score was correlated with an increasing probability of a positive biopsy (P < 0.001). Multivariable logistic analysis indicated FR0348383 score (P < 0.001), PSA (P = 0.004), age (P = 0.007), prostate volume (P < 0.001) were independent predictors of PCa. ROC analysis demonstrated FR0348383 score outperformed PSA, %free PSA, and PSA Density in the prediction of PCa in the subgroup of patients with grey area PSA (AUC: 0.815 vs. 0.562 vs. 0.599 vs. 0.645). When using a probability threshold of 30% in the grey zone cohort, The FR0348383 score would save 52.0% of avoidable biopsies without missing any high grade cancers. CONCLUSIONS: FR0348383 transcript in post-DRE urine may be a novel biomarker for detection of PCa with great diagnostic value, especially in the grey zone cohort. The application of FR0348383 score in clinical practice might avoid unnecessary prostate biopsies and increase the specificity of PCa diagnosis.

Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.

Rheumatoid arthritis (RA) is an autoimmune disease causing progressive joint damage. Early diagnosis and treatment is critical, but remains challenging due to RA complexity and heterogeneity. Machine learning (ML) techniques may enhance RA management by identifying patterns within multidimensional biomedical data to improve classification, diagnosis, and treatment predictions. In this review, we summarize the applications of ML for RA management. Emerging studies or applications have developed diagnostic and predictive models for RA that utilize a variety of data modalities, including electronic health records, imaging, and multi-omics data. High-performance supervised learning models have demonstrated an Area Under the Curve (AUC) exceeding 0.85, which is used for identifying RA patients and predicting treatment responses. Unsupervised learning has revealed potential RA subtypes. Ongoing research is integrating multimodal data with deep learning to further improve performance. However, key challenges remain regarding model overfitting, generalizability, validation in clinical settings, and interpretability. Small sample sizes and lack of diverse population testing risks overestimating model performance. Prospective studies evaluating real-world clinical utility are lacking. Enhancing model interpretability is critical for clinician acceptance. In summary, while ML shows promise for transforming RA management through earlier diagnosis and optimized treatment, larger scale multisite data, prospective clinical validation of interpretable models, and testing across diverse populations is still needed. As these gaps are addressed, ML may pave the way towards precision medicine in RA.

Spontaneous Fusion with Transformed Mesenchymal Stromal Cells Results in Complete Heterogeneity in Prostate Cancer Cells.

Tumor cells gain advantages in growth and survival by acquiring genotypic and phenotypic heterogeneity. Interactions with bystander cells in the tumor microenvironment contribute to the progression of heterogeneity. We have shown that fusion between tumor and bystander cells is one form of interaction, and that tumor-bystander cell fusion has contrasting effects. By trapping fusion hybrids in the heterokaryon or synkaryon state, tumor-bystander cell fusion prevents the progression of heterogeneity. However, if trapping fails, fusion hybrids will resume replication to form derivative clones with diverse genomic makeups and behavioral phenotypes. To determine the characteristics of bystander cells that influence the fate of fusion hybrids, we co-cultured prostate mesenchymal stromal cell lines and their spontaneously transformed sublines with LNCaP as well as HPE-15 prostate cancer cells. Subclones derived from cancer-stromal fusion hybrids were examined for genotypic and phenotypic diversifications. Both stromal cell lines were capable of fusing with cancer cells, but only fusion hybrids with the transformed stromal subline generated large numbers of derivative subclones. Each subclone had distinct cell morphologies and growth behaviors and was detected with complete genomic hybridization. The health conditions of the bystander cell compartment play a crucial role in the progression of tumor cell heterogeneity.

Disulfidptosis-related genes serve as potential prognostic biomarkers and indicate tumor microenvironment characteristics and immunotherapy response in prostate cancer.

Disulfidptosis, a newly identified programmed cell death pathway in prostate cancer (PCa), is closely associated with intracellular disulfide stress and glycolysis. This study aims to elucidate the roles of disulfidptosis-related genes (DRGs) in the pathogenesis and progression of PCa, with the goal of improving diagnostic and therapeutic approaches. We analyzed PCa datasets and normal tissue transcriptome data from TCGA, GEO, and MSKCC. Using consensus clustering analysis and LASSO regression, we developed a risk scoring model, which was validated in an independent cohort. The model's predictive accuracy was confirmed through Kaplan-Meier curves, receiver operating characteristic (ROC) curves, and nomograms. Additionally, we explored the relationship between the risk score and immune cell infiltration, and examined the tumor microenvironment and somatic mutations across different risk groups. We also investigated responses to immunotherapy and drug sensitivity. Our analysis identified two disulfidosis subtypes with significant differences in survival, immune environments, and treatment responses. According to our risk score, the high-risk group exhibited poorer progression-free survival (PFS) and higher tumor mutational burden (TMB), associated with increased immune suppression. Functional enrichment analysis linked high-risk features to key cancer pathways, including the IL-17 signaling pathway. Moreover, drug sensitivity analysis revealed varied responses to chemotherapy, suggesting the potential for disulfidosis-based personalized treatment strategies. Notably, we identified PROK1 as a crucial prognostic marker in PCa, with its reduced expression correlating with disease progression. In summary, our study comprehensively assessed the clinical implications of DRGs in PCa progression and prognosis, offering vital insights for tailored precision medicine approaches.