A Single-Cell Transcriptome Profiling of Triptolide-Induced Nephrotoxicity in Mice.

Triptolide (TP), an active component isolated from the traditional Chinese herb Tripterygium wilfordii Hook F (TWHF), shows great promise for treating inflammation-related diseases. However, its potential nephrotoxic effects remain concerning. The mechanism underlying TP-induced nephrotoxicity is inadequately elucidated, particularly at single-cell resolution. Hence, single-cell RNA sequencing (scRNA-seq) of kidney tissues from control and TP-treated mice is performed to generate a thorough description of the renal cell atlas upon TP treatment. Heterogeneous responses of nephron epithelial cells are observed after TP exposure, attributing differential susceptibility of cell subtypes to excessive reactive oxygen species and increased inflammatory responses. Moreover, TP disrupts vascular function by activating endothelial cell immunity and damaging fibroblasts. Severe immune cell damage and the activation of pro-inflammatory Macro_C1 cells are also observed with TP treatment. Additionally, ligand-receptor crosstalk analysis reveals that the SPP1 (osteopontin) signaling pathway targeting Macro_C1 cells is triggered by TP treatment, which may promote the infiltration of Macro_C1 cells to exacerbate renal toxicity. Overall, this study provides comprehensive information on the transcriptomic profiles and cellular composition of TP-associated nephrotoxicity at single-cell resolution, which can strengthen the understanding of the pathogenesis of TP-induced nephrotoxicity and provide valuable clues for the discovery of new therapeutic targets to ameliorate TP-associated nephrotoxicity.

The urinary microbiota composition and functionality of calcium oxalate stone formers.

Background: Accumulated evidences indicate that dysbiosis of the urinary microbiota is associated with kidney stone formation. In the present study, we aimed to investigate the urinary microbiota composition and functionality of patients with calcium oxalate stones and compare it with those of healthy individuals. Method: We collected bladder urine samples from 68 adult patients with calcium oxalate stones and 54 age-matched healthy controls by transurethral catheterization. 16S rRNA gene and shotgun sequencing were utilized to characterize the urinary microbiota and functionality associated with calcium oxalate stones. Results: After further exclusion, a total of 100 subjects was finally included and analyzed. The diversity of the urinary microbiota in calcium oxalate stone patients was not significantly different from that of healthy controls. However, the urinary microbiota structure of calcium oxalate stone formers significantly differed from that of healthy controls (PERMANOVA, r = 0.026, P = 0.019). Differential representation of bacteria (e.g., Bifidobacterium) and several enriched functional pathways (e.g., threonine biosynthesis) were identified in the urine of calcium oxalate stone patients. Conclusion: Our results showed significantly different urinary microbiota structure and several enriched functional pathways in calcium oxalate stone patients, which provide new insight into the pathogenesis of calcium oxalate stones.

Single-cell transcriptomics reveals the ameliorative effect of rosmarinic acid on diabetic nephropathy-induced kidney injury by modulating oxidative stress and inflammation.

Diabetic nephropathy (DN) is a severe complication of diabetes, characterized by changes in kidney structure and function. The natural product rosmarinic acid (RA) has demonstrated therapeutic effects, including anti-inflammation and anti-oxidative-stress, in renal damage or dysfunction. In this study, we characterized the heterogeneity of the cellular response in kidneys to DN-induced injury and RA treatment at single cell levels. Our results demonstrated that RA significantly alleviated renal tubular epithelial injury, particularly in the proximal tubular S1 segment and on glomerular epithelial cells known as podocytes, while attenuating the inflammatory response of macrophages, oxidative stress, and cytotoxicity of natural killer cells. These findings provide a comprehensive understanding of the mechanisms by which RA alleviates kidney damage, oxidative stress, and inflammation, offering valuable guidance for the clinical application of RA in the treatment of DN.

Case report: A collision tumor of clear cell renal cell carcinoma and clear cell papillary renal cell tumor.

We report the case of a 51-year-old woman who was initially hospitalized in the respiratory department with cough and fever. Urinary computed tomography (CT) showed two different incidental masses in the right kidney. The patient underwent a radical right nephrectomy without lymph node dissection and postoperative adjuvant treatment. The pathological examination of the surgical specimens showed a collision tumor composed of a clear cell renal cell carcinoma (CCRCC) and a clear cell papillary renal cell tumor (CCPRCT). To the best of our knowledge, this is the first such case reported to date. No recurrence of local or distant metastasis was found during routine follow-up 14 months after the operation.

A non-invasive 25-Gene PLNM-Score urine test for detection of prostate cancer pelvic lymph node metastasis.

BACKGROUND: Prostate cancer patients with pelvic lymph node metastasis (PLNM) have poor prognosis. Based on EAU guidelines, patients with >5% risk of PLNM by nomograms often receive pelvic lymph node dissection (PLND) during prostatectomy. However, nomograms have limited accuracy, so large numbers of false positive patients receive unnecessary surgery with potentially serious side effects. It is important to accurately identify PLNM, yet current tests, including imaging tools are inaccurate. Therefore, we intended to develop a gene expression-based algorithm for detecting PLNM. METHODS: An advanced random forest machine learning algorithm screening was conducted to develop a classifier for identifying PLNM using urine samples collected from a multi-center retrospective cohort (n = 413) as training set and validated in an independent multi-center prospective cohort (n = 243). Univariate and multivariate discriminant analyses were performed to measure the ability of the algorithm classifier to detect PLNM and compare it with the Memorial Sloan Kettering Cancer Center (MSKCC) nomogram score. RESULTS: An algorithm named 25 G PLNM-Score was developed and found to accurately distinguish PLNM and non-PLNM with AUC of 0.93 (95% CI: 0.85-1.01) and 0.93 (95% CI: 0.87-0.99) in the retrospective and prospective urine cohorts respectively. Kaplan-Meier plots showed large and significant difference in biochemical recurrence-free survival and distant metastasis-free survival in the patients stratified by the 25 G PLNM-Score (log rank P < 0.001 and P < 0.0001, respectively). It spared 96% and 80% of unnecessary PLND with only 0.51% and 1% of PLNM missing in the retrospective and prospective cohorts respectively. In contrast, the MSKCC score only spared 15% of PLND with 0% of PLNM missing. CONCLUSIONS: The novel 25 G PLNM-Score is the first highly accurate and non-invasive machine learning algorithm-based urine test to identify PLNM before PLND, with potential clinical benefits of avoiding unnecessary PLND and improving treatment decision-making.

Identification of a 24-gene panel and a novel marker of PODXL2 essential for the pathological diagnosis of early prostate cancer.

Precise diagnosis of early prostate cancer (PCa) is critical for preventing tumor progression. However, the diagnostic outcomes of currently used markers are far from satisfactory due to the low sensitivity or specificity. Here, we identified a diagnostic subpopulation in PCa tissue with the integrating analysis of single-cell and bulk RNA-seq. The representative markers of this subpopulation were extracted to perform intersection analysis with early-PCa-related gene module generated from weighted correlation network analysis (WGCNA). A total of 24 overlapping genes were obtained, the diagnostic roles of which were validated by distinguishing normal and tumorous prostate samples from the public dataset. A least absolute shrinkage and selection operator (LASSO) model was constructed based on these genes and the obtained 24-gene panel showed high sensitivity and specificity for PCa diagnosis, with better identifying capability of PCa than the commercially used gene panel of Oncotype DX. The top two risk factors, TRPM4 and PODXL2, were verified to be highly expressed in early PCa tissues by multiplex immunostaining, and PODXL2 was more sensitive and specific compared to TRPM4 and the pathologically used marker AMACR for early PCa diagnosis, suggesting a novel and promising pathology marker.

Alterations of plasma exosomal proteins and motabolies are associated with the progression of castration-resistant prostate cancer.

BACKGROUND: Current diagnosis tools for prostate cancer (PCa) such as serum PSA detection and prostate biopsy cannot distinguish dormant tumors from invasive malignancies, either be used as prognosis marker for castration resistant prostate cancer (CRPC), the lethal stage of PCa patients. Exosomes have been widely investigated as promising biomarkers for various diseases. We aim to characterize the proteomic and metabolomic profile of exosomes and to evaluate their potential value for the diagnosis of PCa, especially CRPC. We also investigate the functions of some specific exosome biomarkers in the progression of CRPC. METHODS: Integrated proteomics and metabolomics analysis were performed for plasma-derived exosomes collected from tumor-free controls (TFC), PCa and CRPC patients. Expression of specific exosomal proteins were further validated by targeted 4D-parallel reaction monitoring (PRM) mass spectrometry among the three cohorts. Tissue distribution and functional role of exosomal protein LRG1 was studied in clinical PCa tissue samples and cell line models. RESULTS: Three potential exosomal protein markers were identified. The apolipoprotein E level in PCa samples was 1.7-fold higher than that in TFC (receiver operating characteristic value, 0.74). Similarly, the levels of exosome-derived leucine-rich alpha2-glycoprotein 1 (LRG1) and inter-alpha-trypsin inhibitor heavy chain H3 (ITIH3) in the CRPC group were 1.7 and 2.04 times, respectively, higher than those in the PCa group (ROC values, 0.84 and 0.85, respectively), indicating that LRG1 and ITIH3 could serve as predictive markers for CRPC. For metabolomic evaluation of exosomes, a series of differentially expressed metabolites were identified, and a combined metabolite panel showed ROC value of 0.94 for distinguishing PCa from TFC and 0.97 for distinguishing CRPC from PCa. Immunohistochemistry of tissue microarray showed that LRG1 protein was significantly upregulated in advanced prostate cancer and functional assay revealed that ectopic expression of LRG1 can significantly enhance the malignant phenotype of prostate cancer cells. More importantly, PCa cell derived LRG1-overexpressed exosomes remarkably promoted angiogenesis. CONCLUSION: Integration of proteomics and metabolomics data generated proteomic and metabolic signatures of plasma exosomes that may facilitate discrimination of CRPC from PCa and TFC patients, suggesting the potential of exosomal proteins and metabolites as CRPC markers. The study also confirmed the important role of exosomal protein LRG1 in PCa malignant progression.

Docetaxel suppressed cell proliferation through Smad3/HIF-1α-mediated glycolysis in prostate cancer cells.

BACKGROUND: Tumor glycolysis is a critical event for tumor progression. Docetaxel is widely used as a first-line drug for chemotherapy and shown to have a survival advantage. However, the role of docetaxel in tumor glycolysis remained poorly understood. METHODS: The effect of Docetaxel in tumor glycolysis and proliferation were performed by CCK-8, Western blotting, real-time PCR, glucose, and lactate detection and IHC. ChIP and luciferase assay were used to analyze the mechanism of Docetaxel on Smad3-mediated HIF-1α transactivity. RESULTS: In this study, we showed that docetaxel treatment led to a significant inhibition of cell proliferation in prostate cancer cells through PFKP-mediated glycolysis. Addition of lactate, a production of glycolysis, could reverse the inhibitory effect of docetaxel on cell proliferation. Further analysis has demonstrated that phosphorylation of Smad3 (Ser213) was drastically decreased in response to docetaxel stimulation, leading to reduce Smad3 nuclear translocation. Luciferase and Chromatin immunoprecipitation (ChIP) analysis revealed that docetaxel treatment inhibited the binding of Smad3 to the promoter of the HIF-1α gene, suppressing transcriptional activation of HIF-1α. Moreover, ectopic expression of Smad3 in prostate cancer cells could overcome the decreased HIF-1α expression and its target gene PFKP caused by docetaxel treatment. Most importantly, endogenous Smad3 regulated and interacted with HIF-1α, and this interaction was destroyed in response to docetaxel treatment. What's more, both HIF-1α and PFKP expression were significantly reduced in prostate cancer received docetaxel treatment in vivo. CONCLUSION: These findings extended the essential role of docetaxel and revealed that docetaxel inhibited cell proliferation by targeting Smad3/HIF-1α signaling-mediated tumor Warburg in prostate cancer cells. Video Abstract.

A novel 8-gene panel for prediction of early biochemical recurrence in patients with prostate cancer after radical prostatectomy.

Approximately 25% of prostate cancer (PCa) cases experience biochemical recurrence (BCR) following radical prostatectomy (RP). The patients with BCR, especially with BCR ≤2 year after RP (early BCR), are more likely to develop clinical metastasis and castration resistance. Now decision-making regarding BCR after RP relies solely on clinical parameters. We thus attempted to establish an early BCR-risk prediction model by combining a molecular signature with clinicopathological features for guiding clinical decision-making. In this study, an 8-gene signature was derived, and these eight genes were SPTBN2, LGI3, TGM3, LENG9, HAS3, SLC25A27, PCDHGA1, and ADPRHL1. The Kaplan-Meier analysis revealed a significantly prolonged BCR-free survival in the patients with low-risk scores compared to those with high-risk scores in both training and validation datasets. Harrell's concordance index and time-dependent receiver operating characteristic analysis demonstrated that this gene signature tended to outperform three commercial panels at early BCR prediction. Moreover, this signature was also proven as an independent predictor of BCR-free survival. A nomogram, incorporating the gene signature and clinicopathologic features, was constructed and excellently predicted 1-, 2- and 3-year BCR-free survival of localized PCa patients after RP. Gene set enrichment analysis, tumor immunity, and mRNA expression profiling analysis showed that the high-risk group was more prone to the immunosuppressive microenvironment and impaired DNA damage response than the low-risk group. Collectively, we successfully developed a novel 8-gene signature as a powerful predictor for early BCR after RP and created a prognostic nomogram, which may help inform the clinical management of PCa.

Feasibility of contrast-enhanced ultrasound and flank position during percutaneous nephrolithotomy in patients with no apparent hydronephrosis: a randomized controlled trial.

PURPOSE: To investigate the puncture accuracy and feasibility of contrast-enhanced ultrasound (CEUS) guided percutaneous nephrolithotomy (PCNL) in flank position for patients with no apparent hydronephrosis. METHODS: Between May 2018 and June 2020, 72 kidney stone patients with no or mild hydronephrosis were randomized into two groups: a CEUS-guided PCNL group and a conventional ultrasound (US)-guided group. Patients' demographics and perioperative outcomes were compared, including the success rate of puncture via calyceal fornix, the success rate of a single-needle puncture, puncture time, operative time, postoperative hemoglobin loss, stone-free rate, incidence of complications and postoperative stay. RESULTS: The success rate of puncture via calyceal fornix for CEUS-guided group was significantly higher than that for conventional US-guided group (86.1 vs. 47.2%, p = 0.002). Patients performed with CEUS-guided PCNL required shorter renal puncture time than those guided with conventional US (36.5 s vs. 61.0 s, p < 0.001). The median postoperative hemoglobin loss in the CEUS-guided group was significantly lower than that in conventional US-guided group (2.5 vs. 14.5 g/L, p < 0.01). There was no statistically significant difference in the success rate of a single-needle puncture, operative time, stone-free rate, incidence of complications and postoperative stay between the two groups. CONCLUSION: CEUS guidance facilitates identification of the renal calyx fornix, and benefits more precise renal puncture and less hemoglobin loss in PCNL. CEUS-guided PCNL in flank position is a feasible approach to the treatment of kidney stone patients with no apparent hydronephrosis. TRIAL REGISTRATION NUMBER: ChiCTR1800015417.

Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients.

Objective: To avoid over-treatment of low-risk prostate cancer patients, it is important to identify clinically significant and insignificant cancer for treatment decision-making. However, no accurate test is currently available. Methods: To address this unmet medical need, we developed a novel gene classifier to distinguish clinically significant and insignificant cancer, which were classified based on the National Comprehensive Cancer Network risk stratification guidelines. A non-invasive urine test was developed using quantitative mRNA expression data of 24 genes in the classifier with an algorithm to stratify the clinical significance of the cancer. Two independent, multicenter, retrospective and prospective studies were conducted to assess the diagnostic performance of the 24-Gene Classifier and the current clinicopathological measures by univariate and multivariate logistic regression and discriminant analysis. In addition, assessments were performed in various Gleason grades/ISUP Grade Groups. Results: The results showed high diagnostic accuracy of the 24-Gene Classifier with an AUC of 0.917 (95% CI 0.892-0.942) in the retrospective cohort (n = 520), AUC of 0.959 (95% CI 0.935-0.983) in the prospective cohort (n = 207), and AUC of 0.930 (95% 0.912-CI 0.947) in the combination cohort (n = 727). Univariate and multivariate analysis showed that the 24-Gene Classifier was more accurate than cancer stage, Gleason score, and PSA, especially in the low/intermediate-grade/ISUP Grade Group 1-3 cancer subgroups. Conclusions: The 24-Gene Classifier urine test is an accurate and non-invasive liquid biopsy method for identifying clinically significant prostate cancer in newly diagnosed cancer patients. It has the potential to improve prostate cancer treatment decisions and active surveillance.

Recent advances in proteome-wide label-free target deconvolution for bioactive small molecules.

Small-molecule drugs modulate biological processes and disease states through engagement of target proteins in cells. Assessing drug-target engagement on a proteome-wide scale is of utmost importance in better understanding the molecular mechanisms of action of observed beneficial and adverse effects, as well as in developing next generation tool compounds and drugs with better efficacies and specificities. However, systematic assessment of drug-target engagement has been an arduous task. With the continuous development of mass spectrometry-based proteomics instruments and techniques, various chemical proteomics approaches for drug target deconvolution (i.e., the identification of molecular target for drugs) have emerged. Among these, the label-free target deconvolution approaches that do not involve the chemical modification of compounds of interest, have gained increased attention in the community. Here we provide an overview of the basic principles and recent biological applications of the most important label-free methods including the cellular thermal shift assay, pulse proteolysis, chemical denaturant and protein precipitation, stability of proteins from rates of oxidation, drug affinity responsive target stability, limited proteolysis, and solvent-induced protein precipitation. The state-of-the-art technical implications and future outlook for the label-free approaches are also discussed.

Comprehensive assessment of PD-L1 and PD-L2 dysregulation in gastrointestinal cancers.

Background:PD-L1 and PD-L2 are ligands of PD-1. Their overexpression has been reported in different cancers. However, the underlying mechanism of PD-L1 and PD-L2 dysregulation and their related signaling pathways are still unclear in gastrointestinal cancers. Materials & methods: The expression of PD-L1 and PD-L2 were studied in The Cancer Genome Atlas and Genotype-Tissue Expression databases. The gene and protein alteration of PD-L1 and PD-L2 were analyzed in cBioportal. The direct transcription factor regulating PD-L1/PD-L2 was determined with ChIP-seq data. The association of PD-L1/PD-L2 expression with clinicopathological parameters, survival, immune infiltration and tumor mutation burden were investigated with data from The Cancer Genome Atlas. Potential targets and pathways of PD-L1 and PD-L2 were determined by protein enrichment, WebGestalt and gene ontology. Results: Comprehensive analysis revealed that PD-L1 and PD-L2 were significantly upregulated in most types of gastrointestinal cancers and their expressions were positively correlated. SP1 was a key transcription factor regulating the expression of PD-L1. Conclusion: Higher PD-L1 or PD-L2 expression was significantly associated with poor overall survival, higher tumor mutation burden and more immune and stromal cell populations. Finally, HIF-1, ERBB and mTOR signaling pathways were most significantly affected by PD-L1 and PD-L2 dysregulation. Altogether, this study provided comprehensive analysis of the dysregulation of PD-L1 and PD-L2, its underlying mechanism and downstream pathways, which add to the knowledge of manipulating PD-L1/PD-L2 for cancer immunotherapy.

Establishing a Urine-Based Biomarker Assay for Prostate Cancer Risk Stratification.

One of the major features of prostate cancer (PCa) is its heterogeneity, which often leads to uncertainty in cancer diagnostics and unnecessary biopsies as well as overtreatment of the disease. Novel non-invasive tests using multiple biomarkers that can identify clinically high-risk cancer patients for immediate treatment and monitor patients with low-risk cancer for active surveillance are urgently needed to improve treatment decision and cancer management. In this study, we identified 14 promising biomarkers associated with PCa and tested the performance of these biomarkers on tissue specimens and pre-biopsy urinary sediments. These biomarkers showed differential gene expression in higher- and lower-risk PCa. The 14-Gene Panel urine test (PMP22, GOLM1, LMTK2, EZH2, GSTP1, PCA3, VEGFA, CST3, PTEN, PIP5K1A, CDK1, TMPRSS2, ANXA3, and CCND1) was assessed in two independent prospective and retrospective urine study cohorts and showed high diagnostic accuracy to identify higher-risk PCa patients with the need for treatment and lower-risk patients for surveillance. The AUC was 0.897 (95% CI 0.939-0.855) in the prospective cohort (n = 202), and AUC was 0.899 (95% CI 0.964-0.834) in the retrospective cohort (n = 97). In contrast, serum PSA and Gleason score had much lower accuracy in the same 202 patient cohorts [AUC was 0.821 (95% CI 0.879-0.763) for PSA and 0.860 (95% CI 0.910-0.810) for Gleason score]. In addition, the 14-Gene Panel was more accurate at risk stratification in a subgroup of patients with Gleason scores 6 and 7 in the prospective cohort (n = 132) with AUC of 0.923 (95% CI 0.968-0.878) than PSA [AUC of 0.773 (95% CI 0.852-0.794)] and Gleason score [AUC of 0.776 (95% CI 0.854-0.698)]. Furthermore, the 14-Gene Panel was found to be able to accurately distinguish PCa from benign prostate with AUC of 0.854 (95% CI 0.892-0.816) in a prospective urine study cohort (n = 393), while PSA had lower accuracy with AUC of 0.652 (95% CI 0.706-0.598). Taken together, the 14-Gene Panel urine test represents a promising non-invasive tool for detection of higher-risk PCa to aid treatment decision and lower-risk PCa for active surveillance.

Development and validation of a 25-Gene Panel urine test for prostate cancer diagnosis and potential treatment follow-up.

BACKGROUND: Heterogeneity of prostate cancer (PCa) contributes to inaccurate cancer screening and diagnosis, unnecessary biopsies, and overtreatment. We intended to develop non-invasive urine tests for accurate PCa diagnosis to avoid unnecessary biopsies. METHODS: Using a machine learning program, we identified a 25-Gene Panel classifier for distinguishing PCa and benign prostate. A non-invasive test using pre-biopsy urine samples collected without digital rectal examination (DRE) was used to measure gene expression of the panel using cDNA preamplification followed by real-time qRT-PCR. The 25-Gene Panel urine test was validated in independent multi-center retrospective and prospective studies. The diagnostic performance of the test was assessed against the pathological diagnosis from biopsy by discriminant analysis. Uni- and multivariate logistic regression analysis was performed to assess its diagnostic improvement over PSA and risk factors. In addition, the 25-Gene Panel urine test was used to identify clinically significant PCa. Furthermore, the 25-Gene Panel urine test was assessed in a subset of patients to examine if cancer was detected after prostatectomy. RESULTS: The 25-Gene Panel urine test accurately detected cancer and benign prostate with AUC of 0.946 (95% CI 0.963-0.929) in the retrospective cohort (n = 614), AUC of 0.901 (0.929-0.873) in the prospective cohort (n = 396), and AUC of 0.936 (0.956-0.916) in the large combination cohort (n = 1010). It greatly improved diagnostic accuracy over PSA and risk factors (p < 0.0001). When it was combined with PSA, the AUC increased to 0.961 (0.980-0.942). Importantly, the 25-Gene Panel urine test was able to accurately identify clinically significant and insignificant PCa with AUC of 0.928 (95% CI 0.947-0.909) in the combination cohort (n = 727). In addition, it was able to show the absence of cancer after prostatectomy with high accuracy. CONCLUSIONS: The 25-Gene Panel urine test is the first highly accurate and non-invasive liquid biopsy method without DRE for PCa diagnosis. In clinical practice, it may be used for identifying patients in need of biopsy for cancer diagnosis and patients with clinically significant cancer for immediate treatment, and potentially assisting cancer treatment follow-up.

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

BACKGROUND: The highly intra-tumoral heterogeneity and complex cell origination of prostate cancer greatly limits the utility of traditional bulk RNA sequencing in finding better biomarker for disease diagnosis and stratification. Tissue specimens based single-cell RNA sequencing holds great promise for identification of novel biomarkers. However, this technique has yet been used in the study of prostate cancer heterogeneity. METHODS: Cell types and the corresponding marker genes were identified by single-cell RNA sequencing. Malignant states of different clusters were evaluated by copy number variation analysis and differentially expressed genes of pseudo-bulks sequencing. Diagnosis and stratification of prostate cancer was estimated by receiver operating characteristic curves of marker genes. Expression characteristics of marker genes were verified by immunostaining. RESULTS: Fifteen cell groups including three luminal clusters with different expression profiles were identified in prostate cancer tissues. The luminal cluster with the highest copy number variation level and marker genes enriched in prostate cancer-related metabolic processes was considered the malignant cluster. This cluster contained a distinct subgroup with high expression level of prostate cancer biomarkers and a strong distinguishing ability of normal and cancerous prostates across different pathology grading. In addition, we identified another marker gene, Hepsin (HPN), with a 0.930 area under the curve score distinguishing normal tissue from prostate cancer lesion. This finding was further validated by immunostaining of HPN in prostate cancer tissue array. CONCLUSION: Our findings provide a valuable resource for interpreting tumor heterogeneity in prostate cancer, and a novel candidate marker for prostate cancer management.

Target identification and validation of natural products with label-free methodology: A critical review from 2005 to 2020.

Natural products (NPs) have been an important source of therapeutic drugs in clinic use and contributed many chemical probes for research. The usefulness of NPs is however often marred by the incomplete understanding of their direct cellular targets. A number of experimental methods for drug target identification have been developed over the years. One class of methods, termed "label-free" methodology, exploits the energetic and biophysical features accompanying the association of macromolecules with drugs and other compounds in their native forms. Herein we review the working principles, assay implementations, and key applications of the most important approaches, and also give examples where they have been applied to NPs. We also assess the key advantages and limitations of each method. Furthermore, we address when and how the label-free methodology can be particularly useful considering some of the unique features of NP chemistry and bioactivation.

Analysis of Key Genes Regulating the Warburg Effect in Patients with Gastrointestinal Cancers and Selective Inhibition of This Metabolic Pathway in Liver Cancer Cells.

OBJECTIVE: The Warburg effect, also known as aerobic glycolysis, plays a dominant role in the development of gastrointestinal (GI) cancers. In this study, we analyzed the expression of key genes involved in the Warburg effect in GI cancers and investigated the effect of suppressing the Warburg effect in vitro in liver cancer cell lines. METHODS: The Cancer Genome Atlas (TCGA) RNA-Seq data were used to determine gene expression levels, which were analyzed with GraphPad Prism 7.00. Genetic alterations were queried with cBioPortal. The influence of the Warburg effect on liver cancer cell viability, migration and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) activity was determined by means of MTT, transwell and GAPDH activity assays. RESULTS: The levels of expression of genes associated with the Warburg effect were increased in tumors. To our knowledge, this is the first report of upregulated expression of CUEDC2, HMGB2, PFKFB4, PFKP and SIX1 in liver cancer. Clinically, overexpression of these genes was associated with significantly worse overall survival of liver cancer patients. In vitro, selective inhibition of GADPH suppressed the growth and metastasis of Huh-7, Bel7404 and Hep3B hepatocellular carcinoma cell lines. CONCLUSION: The Warburg effect may play an important role in GI cancers, especially in liver cancer.

Profiling the urinary microbiome in men with calcium-based kidney stones.

BACKGROUND: The dogma that urine is sterile in healthy individuals has been overturned by recent studies applying molecular-based methods. Mounting evidences indicate that dysbiosis of the urinary microbiota is associated with several urological diseases. In this study, we aimed to investigate the urinary microbiome of male patients with calcium-based kidney stones and compare it with those of healthy individuals. RESULTS: The diversity of the urinary microbiota in kidney stone patients was significantly lower than that of healthy controls based on the Shannon and Simpson index (P < 0.001 for both indices). The urinary microbiota structure also significantly differed between kidney stone patients and healthy controls (ANOSIM, R = 0.11, P < 0.001). Differential representation of inflammation associated bacteria (e.g., Acinetobacter) and several enriched functional pathways were identified in the urine of kidney stones patients. Meanwhile, we found the species diversity, overall composition of microbiota and predicted functional pathways were similar between bladder urine and renal pelvis urine in kidney stone patients. CONCLUSIONS: A marked dysbiosis of urinary microbiota in male patients with calcium-based kidney stones was observed, which may be helpful to interpret the association between bacteria and calcium-based kidney stones.