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Exploring useful prognostic markers and developing a robust prognostic model for patients with prostate cancer are crucial for clinical practice. We applied a deep learning algorithm to construct a prognostic model and proposed the deep learning-based ferroptosis score (DLFscore) for the prediction of prognosis and potential chemotherapy sensitivity in prostate cancer. Based on this prognostic model, there was a statistically significant difference in the disease-free survival probability between patients with high and low DLFscore in the The Cancer Genome Atlas (TCGA) cohort (P

Tao Wang,1,* Lei Dong,2,* Juanjuan Sun,3,* Jialiang Shao,1 Jian Zhang,1 Siteng Chen,1 Chaofu Wang,2 Gangfeng Wu,4 Xiang Wang1 1Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 3Department of Pathology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 4Department of Urology, Shaoxing People’s Hospital, Shaoxing, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Gangfeng WuDepartment of Urology, Shaoxing People’s Hospital, No. 568 Zhongxing North Road, Shaoxing, Zhejiang, 312000, People’s Republic of ChinaEmail gaffwu@sina.comXiang WangDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People’s Republic of ChinaEmail xiang.wang1@shgh.cnBackground: The Gleason grading system is a major tool used for prediction of prostate cancer (PCa) behavior. Because of heterogeneity and sampling errors, prognosis is variable even among patients with the same Gleason score (GS). Therefore, more accurate biomarkers that complement the Gleason system are needed to improve the clinical management of PCa.Methods: Formalin-fixed, paraffin embedded tissue samples were obtained from radical prostatectomy (RP) (patient set 1, n=53) and needle biopsy (patient set 2, n=107; patient set 3, n=119). Cancer tissues from pure regions of each Gleason pattern (GP) were separately collected using laser-captured microdissection, followed by Real-time-PCR to determine the relative expression of miRNAs, including miR-1-5p, miR-21-5p, miR-30d-5p, miR-100-5p, miR-145-5p, miR-224-5p, and miR-708-5p. miRNA’s association with Gleason upgrading (GU) was evaluated using receiver operator characteristics (ROC) curve and multivariate logistic regression analysis. The integrated miRNA targets prediction and enrichment analyses were performed to determine the potential functions of miRNA.Results: It was found that miR-145-5p in GP3 from radical prostatectomy (RP) were overexpressed in patients with GS6 PCa compared with GS7 patients, which was further confirmed in a larger biopsy cohort. ROC curve analysis revealed that miR-145-5p in biopsy was significantly associated with GU upon RP. In multivariate analyses, miR-145-5p was an independent predictor of GU.Conclusion: Our study indicated that differential expression of miRNAs existed in GP3 from pure GS6 and GS7 PCa, highlighting a path toward the clinical use of miRNAs in predicting GU and assisting in treatment modality selection.Keywords: prostate cancer, microRNA, biopsy, active surveillance, Gleason upgrading

Recent studies have reported that MLST8 is upregulated in many malignant tumors. Nevertheless, the underlying molecular mechanism is still unclear. The aim of this work was to investigate how MLST8 contributes to the development and progression of clear cell renal cell carcinoma (ccRCC). MLST8 is an oncogenic protein in the TCGA database and ccRCC clinical specimens. We also ascertain that MLST8 interacts with FBXW7, which was universally regarded as an E3 ubiquitin ligase. MLST8 can be degraded and ubiquitinated by tumor suppressor FBXW7. FBXW7 recognizes a consensus motif (T/S) PXX (S/T/D/E) of MLST8 and triggers MLST8 degradation via the ubiquitin‐proteasome pathway. Strikingly, the activated cyclin dependent kinase 1 (CDK1) kinase engages in the MLST8 phosphorylation required for FBXW7‐mediated degradation. In vitro, we further prove that MLST8 is an essential mediator of FBXW7 inactivation‐induced tumor growth, migration, and invasion. Furthermore, the MLST8 and FBXW7 proteins are negatively correlated in human renal cancer specimens. Our findings suggest that MLST8 is a putative oncogene that functions via interaction with FBXW7, and inhibition MLST8 could be a potential future target in ccRCC treatment., Schematic diagram of the CDK1‐FBXW7α signaling axis in the regulation of MLST8 protein degradation.

Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected 643 H&E‐stained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TCGA). We constructed and cross‐verified automatic diagnosis and prognosis models by performing a machine learning algorithm based on pathomics data. Our study indicated that high diagnostic efficiency of the machine learning‐based diagnosis model was observed in patients with BCa, with area under the curve (AUC) values of 96.3%, 89.2%, and 94.1% in the training cohort, test cohort, and external validation cohort, respectively. Our diagnosis model also performed well in distinguishing patients with BCa from patients with glandular cystitis, with an AUC value of 93.4% in the General cohort. Significant differences were found in overall survival in TCGA cohort (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.56‐2.81, P, We extracted quantitative features from H&E‐stained images and used the features to construct bladder cancer diagnostic and prognostic models based on computational recognition of digital pathology. A machine learning histopathological image signature derived from digital pathology demonstrated high accuracy in bladder cancer diagnosis and survival prediction. The findings highlighted the potential clinical utility of machine learning for histopathologic image analysis in bladder cancer.

Due to the complicated histopathological characteristics of renal neoplasms, traditional distinguishing of clear cell renal cell carcinoma (ccRCC) by naked eyes of experienced pathologist remains labor intensive and time consuming. Here, we extracted quantitative features of hematoxylin-eosin-stained images using CellProfiler and performed machine learning method to develop and verify a novel computational recognition of digital pathology for diagnosis and prognosis of ccRCC patients in the training, test and external validation cohort. The diagnostic model based on digital pathology could accurately distinguish ccRCC from normal renal tissues, with area under the curve (AUC) of 96.0%, 94.5% and 87.6% in the training, test and external validation cohorts, respectively. It could also accurately distinguish ccRCC from other pathological types of renal cancer, with AUC values of 97.0% and 81.4% in the Cancer Genome Atlas (TCGA) cohort and General cohort. We next developed and verified a computational recognition prognosis model with risk score. There was a significant difference in disease-free survival comparing patients with high vs low risk score in training cohort (hazard ratio = 2.72, P < .0001) and validation cohort (hazard ratio = 9.50, P = .0091). The integrated nomogram based on our computational recognition risk score and clinicopathologic factors demonstrated excellent survival prediction for ccRCC patients, with increased accuracy by 6.6% in patients from Shanghai General Hospital and by 2.5% in patients from TCGA cohort when compared to current tumor stages/grade systems. These results indicate the potential clinical use of our machine learning histopathological image signature in diagnosis and survival prediction of ccRCC.

Objective This study investigates sleep patterns of 4th- and 5th-grade students using actigraphy. Methods The study included 257 students enrolled in a Southwestern US school district who participated in a novel sleep science curriculum during the Spring 2016-17 and Fall 2017-18 semesters and met the study inclusion criteria. As part of this curriculum, participants underwent 5 to 7 days of continuous wrist actigraphy and completed an online sleep diary. Results Approximately two-thirds of the 9-11-year-old 4th- and 5th-grade students slept less than the minimum 9 hours per night recommended by both the American Academy of Sleep Medicine/Sleep Research Society and the National Sleep Foundation. The sleep midpoint time on weekends was about 1 hour later than on weekdays. There was a significant effect of age on sleep duration. Compared to 9-year old students, a larger proportion of 10-year old students had a sleep duration less than 8.5 hours. Boys had shorter sleep duration than girls, and a larger percentage of boys obtained less than 9 hours of sleep compared to girls. Conclusions Insufficient sleep is a highly prevalent condition among 9-11-year-old 4th- and 5th-grade elementary students. Importantly, there is a difference between sleep patterns on weekdays and weekends which may portend greater problems with sleep in adolescence and young adulthood.

Background: Lactic acid was previously considered a waste product of glycolysis, and has now become a key metabolite for cancer development, maintenance and metastasis. So far, numerous studies have confirmed that tumor lactic acid levels are associated with increased metastasis, tumor recurrence and poor prognosis. However, the prognostic value of lactic acid metabolism and transporter related genes in patients with clear cell renal cell carcinoma has not been explored.Results: We selected lactic acid metabolism and transporter related twenty-one genes for LASSO cox regression analysis in the E-MTAB-1980 cohort, and finally screened three genes (PNKD, SLC16A8, SLC5A8) to construct a clinical prognostic model for patients with clear cell renal cell carcinoma. Based on the prognostic model we constructed, the over survival (hazard ratio = 4.117, 95% CI: 1.810 - 9.362, p < 0.0001) of patients in the high-risk group and the low-risk group in the training set E-MTAB-1980 cohort had significant differences, and similar results (hazard ratio = 1.909, 95% CI: 1.414 - 2.579 p < 0.0001) were also observed in the validation set TGCA cohort. Subsequently, survival prediction was carried out based on the survival prognosis model we constructed. In the E-MTAB-1980 cohort, the area under ROC curve (AUC) of 1-, 3- and 5- years was 0.71, 0.72, and 0.77, respectively. The AUC of 1-, 3- and 5- years in TCGA cohort was 0.65, 0.65, and 0.63, respectively. Using CIBERSORT algorithm to analyze the differences in immune cell infiltration in different risk groups, we found that dendritic cells and CD4+ memory cells in the high-risk group were significantly lower than those in the low-risk group, while Treg cells were higher than in the low-risk group. Finally, through gene enrichment analysis, we found that the signal pathway that is strongly related to the prognostic model is the cell cycle. Conclusions: We constructed a prognostic model using lactic acid metabolism and transporter related genes, which can accurately predict the prognosis of patients. The poor prognosis of the high-risk group may be related to the regulation of cell cycle by lactic acid metabolism.

Cuproptosis is a newly discovered type of cell death. The role and potential mechanism of Cuproptosis-related genes (CRGs) in the prognosis of cancer patients are not fully understood. In this study, we included two cohorts of clear cell renal cell carcinoma patients, TCGA and E-MTAB-1980. The TCGA cohort is used as a training set to construct a CRG signature using the LASSO-cox regression analysis, and E-MTAB-1980 is used as a cohort for verification. A total of eight genes (FDX1, LIAS, LIPT1, DLAT, PDHA1, MTF1, GLS, CDKN2A) were screened to construct a prognostic model in the TCGA cohort. There is a significant difference in OS (p < 0.0001) between the high and low cuproptosis score group, and a similar difference is also observed in the OS (p = 0.0054) of the E-MTAB-1980 cohort. The area under the ROC curves (AUC) were 0.87, 0.82, and 0.78 at 1, 3, and 5 years in the TCGA cohort, respectively. Finally, gene set enrichment analysis revealed that CRGs were associated with cell cycle and mitotic signaling pathways.

BACKGROUND: Traditional histopathology performed by pathologists through naked eyes is insufficient for accurate survival prediction of clear cell renal cell carcinoma (ccRCC). METHODS: A total of 483 whole slide images (WSIs) data from three patient cohorts were retrospectively analyzed. We performed machine learning algorithm to identify optimal digital pathological features and constructed machine learning-based pathomics signature (MLPS) for ccRCC patients. Prognostic performance of the prognostic model was also verified in two independent validation cohorts. RESULTS: MLPS could significantly distinguish ccRCC patients with high survival risk, with hazard ratio of 15.05, 4.49 and 1.65 in three independent cohorts, respectively. Cox regression analysis revealed that the MLPS could act as an independent prognostic factor for ccRCC patients. Integration nomogram based on MLPS, tumour stage system and tumour grade system improved the current survival prediction accuracy for ccRCC patients, with area under curve value of 89.5%, 90.0%, 88.5% and 85.9% for 1-, 3-, 5- and 10-year disease-free survival prediction. DISCUSSION: The machine learning-based pathomics signature could act as a novel prognostic marker for patients with ccRCC. Nevertheless, prospective studies with multicentric patient cohorts are still needed for further verifications.

BackgroundTime-lapse microscopy has been widely used in biomedical experiments because it can visualize the molecular activities of living cells in real time. However, biomedical researchers are still conducting cell lineage analysis manually. Developing automatic lineage tracing algorithms is a challenging task. In the past two decades, deep neural networks (DNNs) became have shown outstanding performance on computer vision tasks. They can learn complex visual features, capture long-range temporal dependencies, and have the potential to be used for automatic cell lineage analysis.MethodsIn this study, we propose a multi-task spatio-temporal feature based deep neural network for cell lineages analysis (Cell-STN). The Cell-STN extracts spatio-temporal features from microscopy image sequences by leveraging our convolutional long short-term memory based core block. And the proposed Cell-STN utilized a task specific network to predict the cell location, the mitosis event, and the apoptosis event in a multi-task manner.ResultsWe evaluated the Cell-STN on three in-house datasets (MCF7, U2OS, and HCT116) and one public dataset (Fluo-N2DL-HeLa). For cell tracking, we used peak-wise precision, track-wise precision, end-peak precision, and spatial distance as metrics. The overall results showed the Cell-STN models outperform other state-of-the-art cell trackers. For mitosis and apoptosis tasks, we used accuracy, F1-score, temporal distance, and spatial distance as metrics. The Cell-STN models achieved the highest performance on all datasets.ConclusionThis study presented a novel DNNs approach for cell lineage analysis in microscopy images. The Cell-STN showed outstanding performance on the four datasets. Additionally, the Cell-STN required minimal training data and can be adapted to new biological event detection tasks by appending task-specific layers. This algorithm has the potential to be used in real-world biomedical research.

Background: The Gleason grading system is a major tool used for prediction of prostate cancer (PCa) behavior. Because of heterogeneity and sampling errors, prognosis is variable even among patients with the same Gleason score (GS). Therefore, more accurate biomarkers that complement the Gleason system are needed to improve the clinical management of PCa.Methods: Formalin-fixed, paraffin embedded tissue samples were obtained from radical prostatectomy (RP) (patient set 1, n=53) and needle biopsy (patient set 2, n=107; patient set 3, n=119). Cancer tissues from pure regions of each Gleason pattern (GP) were separately collected using laser-captured microdissection, followed by Real-time-PCR to determine the relative expression of miRNAs, including miR-1-5p, miR-21-5p, miR-30d-5p, miR-100-5p, miR-145-5p, miR-224-5p, and miR-708-5p. miRNA’s association with Gleason upgrading (GU) was evaluated using receiver operator characteristics (ROC) curve and multivariate logistic regression analysis. The integrated miRNA targets prediction and enrichment analyses were performed to determine the potential functions of miRNA. Results: It was found that miR-145-5p in GP3 from radical prostatectomy (RP) were overexpressed in patients with GS6 PCa compared with GS7 patients, which was further confirmed in a larger biopsy cohort. ROC curve analysis revealed that miR-145-5p in biopsy was significantly associated with GU upon RP. In multivariate analyses, miR-145-5p was an independent predictor of GU. Conclusions: Our study indicated that differential expression of miRNAs existed in GP3 from pure GS6 and GS7 PCa, highlighting a path toward the clinical use of miRNAs in predicting GU and assisting in treatment modality selection.

Objectives: We carried out an integrated analysis based on multiple-dimensional types of data from cohorts of bladder cancer patients to identify multi-omics perspective (genomics and transcriptomics) on the tumor microenvironment on the bases of the programmed cell death 1 ligand (PD-L1) and CD8 T-cell infiltration in urothelial carcinoma. Methods: Multiple-dimensional types of data, including clinical, genomic and transcriptomic data of 408 bladder cancer patients were retrieved from the Cancer Genome Atlas database. Based on the median values of PD-L1 and CD8A, the tumor samples were grouped into four tumor microenvironment immune types (TMIT). The RNA sequencing profiles, somatic mutation and PD-L1 amplification data of bladder cancer were analyzed by different TMITs. Results: Our research demonstrated that 36.8% of the evaluated bladder cancer belonged to TMIT I (high PD-L1/high CD8A). TIMT subtypes were not significantly associated with overall survival or disease free survival in urothelial cancer. TMIT I facilitates CD8+ T-cell infiltration and activates T-effector and interferon gamma (IFN-γ) associated gene signature. The number of somatic mutations, cytolytic activity, IFN-γ mRNA expression and TIGIT mRNA expression in TMIT I was remarkably higher than those in other TMIT groups. Our results showed a high rate of C>T transversion and a high rate of transition/transversion (Ti/Tv) in TMIT I bladder tumors. The RB1 mutation was significantly associated with TMIT I bladder cancer and be significantly co-occurring with the TP53 mutation. However, FGFR3 mutation and TP53 mutation were mutually exclusive in TMIT II bladder tumors. More importantly, different amino acid changes by FGFR3/RB1 mutations were also found between TMIT I and TMIT II bladder cancer, such as amino acid changes in “Immunoglobulin I-set domain (260-356)”and “Protein tyrosine kinase (472-748)”. We also detected 9 genes as significantly cancer-associated genes in TMIT I bladder cancer, of which, RAD51C has been reported to play an important role in DNA damage responses. Further analysis concentrated on the potential molecular mechanism found that TMIT I was significantly associated with anti-tumor immune-related signaling pathway, and kataegis was present on chromosome 21 in TMIT I bladder tumors. Conclusions: The classification of bladder cancer into four TMITs on the bases of the PD-L1 expression and the CD8+ CTLs statuses is an appropriate approach for bladder tumor immunotherapy. TMIT I (high PD-L1/high CD8A) is significantly correlated with more somatic mutation burden, and facilitates CD8+ T-cell infiltration and activates T-effector and IFN-γ associated gene signature. Alteration landscape for somatic variants was different between TMIT I and TMIT II (low PD-L1/low CD8A).

The important role of N6-methyladenosine (m6A) RNA methylation regulator in carcinogenesis and progression of clear-cell renal cell carcinoma (ccRCC) is poorly understood by now. In this study, we performed comprehensive analyses of m6A RNA methylation regulators in 975 ccRCC samples and 332 adjacent normal tissues and identified ccRCC-related m6A regulators. Moreover, the m6A diagnostic score based on ccRCC-related m6A regulators could accurately distinguish ccRCC from normal tissue in the Meta-cohort, which was further validated in the independent GSE-cohort and The Cancer Genome Atlas-cohort, with an area under the curve of 0.924, 0.867, and 0.795, respectively. Effective survival prediction of ccRCC by m6A risk score was also identified in the Cancer Genome Atlas training cohort and verified in the testing cohort and the independent GSE22541 cohort, with hazard ratio values of 3.474, 1.679, and 2.101 in the survival prognosis, respectively. The m6A risk score was identified as a risk factor of overall survival in ccRCC patients by the univariate Cox regression analysis, which was further verified in both the training cohort and the independent validation cohort. The integrated nomogram combining m6A risk score and predictable clinicopathologic factors could accurately predict the survival status of the ccRCC patients, with an area under the curve values of 85.2, 82.4, and 78.3% for the overall survival prediction in 1-, 3- and 5-year, respectively. Weighted gene co-expression network analysis with functional enrichment analysis indicated that m6A RNA methylation might affect clinical prognosis through regulating immune functions in patients with ccRCC.

Lactic acid was previously considered a waste product of glycolysis, and has now become a key metabolite for cancer development, maintenance and metastasis. So far, numerous studies have confirmed that tumor lactic acid levels are associated with increased metastasis, tumor recurrence and poor prognosis. However, the prognostic value of lactic acid metabolism and transporter related genes in patients with clear cell renal cell carcinoma has not been explored. We selected lactic acid metabolism and transporter related twenty-one genes for LASSO cox regression analysis in the E-MTAB-1980 cohort, and finally screened three genes (PNKD, SLC16A8, SLC5A8) to construct a clinical prognostic model for patients with clear cell renal cell carcinoma. Based on the prognostic model we constructed, the over survival (hazard ratio = 4.117, 95% CI: 1.810–9.362, p < 0.0001) of patients in the high-risk group and the low-risk group in the training set E-MTAB-1980 cohort had significant differences, and similar results (hazard ratio = 1.909, 95% CI: 1.414–2.579 p < 0.0001) were also observed in the validation set TGCA cohort. Using the CIBERSORT algorithm to analyze the differences in immune cell infiltration in different risk groups, we found that dendritic cells, M1 macrophages, and CD4+ memory cells in the high-risk group were significantly lower than those in the low-risk group, while Treg cells were higher than in the low-risk group. Finally, through gene enrichment analysis, we found that the signal pathway that is strongly related to the prognostic model is the cell cycle.

Background: Recent studies have reported that MLST8 is upregulated in many malignant tumors. Nevertheless, the underlying molecular mechanisms is still unclear. The aim of this work was to investigate how MLST8 contribute to the development and progression of renal cancer (ccRCC).Methods: To identify molecular mediators of the oncogenic tumor function of MLST8, we analyzed a quantitative mass spectrometry by a previous study and checked the amino acid sequence in MLST8. Immunoprecipitation and Western Blotting were used to analyze the interaction between FBXW7 and MLST8. Transwell assays determined cell migration and invasion. In vivo experiments were performed to verify tumor growth. Immunohistochemistry was used to analyze protein levels in patients’ tumor samples. Results: MLST8 is an oncogenic protein in TCGA database and ccRCC clinical specimens. We also ascertain that MLST8 interacts with FBXW7, which was universally regarded as an E3 ubiquitin ligase. MLST8 can be degraded and ubiquitinated by tumor suppressor FBXW7. FBXW7 recognizes a consensus motif (T/S) PXX (S/T/D/E) of MLST8 and triggers MLST8 degradation via the ubiquitin-proteasome pathway. Strikingly, the activated CDK1 kinase engages in the MLST8 phosphorylation required for FBXW7-mediated degradation. In vitro and vivo assay, we further prove that MLST8 is an essential mediator of FBXW7 inactivation-induced tumor growth, migration, and invasion. Furthermore, MLST8 and FBXW7 protein are negatively correlated in human renal cancer specimens. Conclusions: Our findings suggest that MLST8 is a putative oncogene that functions via interaction with FBXW7, and inhibition MLST8 can be a potential future target in ccRCC treatment.

Study ObjectivesThe frequency of cortical arousals is an indicator of sleep quality. Additionally, cortical arousals are used to identify hypopneic events. However, it is inconvenient to record electroencephalogram (EEG) data during home sleep testing. Fortunately, most cortical arousal events are associated with autonomic nervous system activity that could be observed on an electrocardiography (ECG) signal. ECG data have lower noise and are easier to record at home than EEG. In this study, we developed a deep learning-based cortical arousal detection algorithm that uses a single-lead ECG to detect arousal during sleep.MethodsThis study included 1,547 polysomnography records that met study inclusion criteria and were selected from the Multi-Ethnic Study of Atherosclerosis database. We developed an end-to-end deep learning model consisting of convolutional neural networks and recurrent neural networks which: (1) accepted varying length physiological data; (2) directly extracted features from the raw ECG signal; (3) captured long-range dependencies in the physiological data; and (4) produced arousal probability in 1-s resolution.ResultsWe evaluated the model on a test set (n = 311). The model achieved a gross area under precision-recall curve score of 0.62 and a gross area under receiver operating characteristic curve score of 0.93.ConclusionThis study demonstrated the end-to-end deep learning approach with a single-lead ECG has the potential to be used to accurately detect arousals in home sleep tests.

Background: Chuanxiong Rhizoma is one of the traditional Chinese medicines which have been used for years in the treatment of diabetic nephropathy (DN). However, the mechanism of Chuanxiong Rhizoma in DN has not yet been fully understood. Methods: We performed network pharmacology to construct targeted proteins interaction network of Chuanxiong Rhizoma. Active ingredients were acquired from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. DRUGBANK database was used to predict targeted proteins of Chuanxiong Rhizoma. Gene ontology (GO) biological process analyses and KEGG pathway enrichment analyses were also performed for functional prediction of the targeted proteins. Molecular docking was applied for evaluating the drug interactions between hub targets and active ingredients. Results: Twenty-eight targets fished by 6 active ingredients of Chuanxiong Rhizoma were obtained in the study. The top 10 significant GO analyses, as well as 6 KEGG pathways, were enriched for genomic analysis. We also acquired 1366 differentially expressed genes associated with DN from GSE30528 dataset, including five targeted genes: KCNH2, NCOA1, KDR, NR3C2 and ADRB2. Molecular docking analysis successfully combined KCNH2, NCOA1, KDR and ADRB2 to Myricanone with docking scores from 4.61 to 6.28. NR3C2 also displayed good binding forces (8.13 and 8.34) with Wallichilide and Sitosterol, revealing good binding forces to active compounds of Chuanxiong Rhizoma. Conclusions: Chuanxiong Rhizoma might take part in the treatment of DN through pathways associated with steroid hormone, estrogen, thyroid hormone and IL-17. KCNH2, NCOA1, KDR, ADRB2 and NR3C2 were proved to be the hub targets closely related to corresponding active ingredients of Chuanxiong Rhizoma.

INTRODUCTION AND OBJECTIVE:The identification of predictive biomarkers that could discern advanced urothelial cancer patients who could benefit from checkpoint immunotherapy remains an ongoing chal...

The mechanisms underlying the resistance to immune checkpoint inhibitors (ICIs) therapy in metastatic urothelial carcinoma (mUC) patients are not clear. It is of great significance to discern mUC patients who could benefit from ICI therapy in clinical practice. In this study, we performed machine learning method and selected 10 prognostic genes for constructing the immunotherapy response nomogram for mUC patients. The calibration plot suggested that the nomogram had an optimal agreement with actual observations when predicting the 1- and 1.5-year survival probabilities. The prognostic nomogram had a favorable discrimination of overall survival of mUC patients, with area under the curve values of 0.815, 0.752, and 0.805 for ICI response (ICIR) prediction in the training cohort, testing cohort, and combined cohort, respectively. A further decision curve analysis showed that the prognostic nomogram was superior to either mutation burden or neoantigen burden for overall survival prediction when the threshold probability was >0.35. The immune infiltrate analysis indicated that the low ICIR-Score values in mUC patients were significantly related to CD8+ T cell infiltration and immune checkpoint-associated signatures. We also identified differentially mutated genes, which could act as driver genes and regulate the response to ICI therapy. In conclusion, we developed and validated an immunotherapy-responsive nomogram for mUC patients, which could be conveniently used for the estimate of ICI response and the prediction of overall survival probability for mUC patients.

Objectives: Copy number alteration (CNA) is one of the important genetic variations. Although there are many studies on renal cancer CNA, few studies are based on the Chinese population. In our study, our objective is to acquire the whole-genome CNA landscape in Chinese population and explore the tumor risk-associated functional genes in the CNA regions, by detecting whole-genome in the clear cell renal cancer (ccRCC) tissues. Methods: We enrolled 35 formalin fixed paraffin embedded samples, which were processed by Oncoscan assay, and then acquired the data of whole-genome CNA. Then genes annotation and enrichment analyzing were processed. Furthermore, the gene burden and the affected bp (base pair) per Mbp (million bp) regions in whole-genome were analyzed by comparison of different T stage affected by CNA. Results: We acquired the whole-genome CNA landscape by Oncoscan detection, and found out the high-frequency CNA regions which were not reported in previous studies, for example, 11P11, 22q11.23, 20q11.3 (PDRG1), and Xp22.33 so on. During the analyzing of genes annotation and enrichment, we found out some ccRCC functional genes in the CNA regions which might play a role in the biological process, for example, the copy number loss of DNA repair genes (TTC5、PARP2, etc.) and tumor suppressor genes (TADA3, VHL, BAP1, ERC2-IT1, etc.), the copy number gain of oncogenes (ABL2, MET, HUWE1, etc.) and Notch signal pathway genes (MDK, etc.). Besides, gene fusion (GSTTP and GSTTP2) was noticed at 22q11.23 which copy number loss occurred, and the frequency is 46%. And between the different T stage patients affected by CNA, the T2+T3 group carried more high-frequency CNA regions (P-value was 0.012). Conclusions: In this study, the whole-genome ccRCC CNA landscape in Chinese population was acquired, a few functional genes and fusion genes were found out. However, a larger scale of samples is still needed to validate our results.

Here we conducted an evidence-based study in developing and validating a urinary biomarker combination of gene methylation assays in patients with hematuria. A number of 99 urine samples were obtained and detected from Chinese patients with hematuria. The Cancer Genome Atlas cohort with methylation (HM450) beta-values and clinical data of 412 bladder cancer and 21 matching normal tissue was included as a validation series. A risk score formula was then developed and calculated by the targeted genes, weighted by their estimated regression coefficients from the multivariable binary logistic regression analyses, and evaluated by receiver operating characteristic (ROC) curves analysis. The combination assay of HOXA9, ONECUT2, PCDH17, PENK, TWIST1, VIM and ZNF154 was singled out according to the results of multivariate logistic regression analysis. The higher probability of DNA methylation of all the selected 7 genes was found in bladder cancer group than the control group. Remarkable higher DNA methylation beta-values of all the selected 7 genes were also displayed in bladder cancer tissues compared with their matching normal bladder tissues. And the AUC value of our risk score model were 0.894 and 0.851 in respective cohort, revealing highlighted predictive value of our risk score model on bladder cancer diagnosis. In conclusions, a urinary combined methylation assay of HOXA9, ONECUT2, PCDH17, PENK, TWIST1, VIM and ZNF154 displayed accurate prediction of bladder cancer in hematuria patients, which provided the guidance for the patients at early stage tumor and during the follow-up after operation. Of course, prospective study based on a hematuria cohort with a large sample size should be conducted to validate these findings in the future.

It is not clear whether maintenance Bacillus Calmette-Guerin (BCG) is necessary for intermediate- or high-risk non-muscle-invasive bladder cancer (NMIBC). This systematic review and meta-analysis aimed to illustrate the effects of maintenance BCG for intermediate- or high-risk NMIBC.A comprehensive literature search of PubMed, EMBASE, Cochrane Library, ClinicalTrials.gov databases and International Clinical Trials Register (ICTRP) Search was conducted to identify relevant randomized controlled trials (RCTs) that have assessed the efficacy of maintenance or non-maintenance BCG therapy for patients with NMIBC. The maintenance group first received induction BCG instillations, and subsequently received BCG intravesical instillations regularly for at least 1 year, while the control group only received induction BCG instillations. Systematic review and meta-analysis were performed according to Preferred Reporting Items for Systematic Reviews and Meta-analysis Criteria.Ten RCTs were eligible in this systematic review. The meta-analysis showed that induction BCG followed by maintenance BCG instillation after transurethral resection (TUR) could reduce the risk ratios of tumor recurrence by 21% (RR = 0.79; 95% CI 0.70-0.89; P 0.0001) and prolong recurrence-free survival (RFS) by 33% (HR: 0.67; 95% CI, 0.54-0.82; P 0.001), compared with non-maintenance BCG. It could also reduce the risk ratios of tumor progression (RR = 0.81; 95% CI 0.68-0.97; P = 0.02). However, these pooled results should be considered with caution since the quality of evidences for outcomes ranged low. Subgroup analysis implied that different durations of maintenance BCG instillations might be one of the sources of potential clinical heterogeneity of included studies. Begg's funnel plot and Egger's test did not reveal any evidence of publication bias in this meta-analysis.Induction BCG followed by maintenance BCG instillation after TUR, compared with induction BCG along, can reduce the risk ratios of tumor recurrence and tumor progression, and prolong RFS. However, these results with a lower level of evidence should be treated with caution. The optimal maintenance schedule has yet to be determined and a large multi-institutional study in intermediate- and high-risk patients is also needed to determine the optimal maintenance schedule.

To compare the characteristics of urinary tract infection (UTI) between kidney transplant recipients and non-recipient patients.Forty-nine kidney transplant recipients with UTI (69 episodes) and 401 non-recipient patients with UTI (443 episodes) admitted in Nanfang Hospital from January 2003 to August 2014 were enrolled in the study. The characteristics of UTI were compared between two groups.In both groups of UTI, female patients comprised a greater proportion (63.3% and 58.6%) and Escherichia coli was the most common pathogen isolated (37.7% and 34.1%). However, the infection rate of Klebsiella pneumonia in recipients was higher than that in non-recipients (11.6% vs 3.2%, P= 0.001), while the infection rate of Candida albicans was lower (1.5% vs 11.3%, P=0.008) than that in non-recipients. Recipients were likely to develop antibiotic resistance and with a higher recurrence rate than non-recipient patients (38.8% vs 16.7%, P0.001). Compared to non-recipient UTI patients, the symptoms of urinary irritation in recipient UTI patients were more common. There was higher percentage of neutrophil granulocyte (72.65% ± 1.90% vs 68.59% ± 0.73%, P=0.048), lower proportion of lymphocytes (17.73% ± 1.27% vs 21.28% ± 0.61%, P=0.037), and less platelets [(187.64 ± 10.84) × 10(9)/L vs (240.76 ± 5.26) × 10(9)/L, P0.01] in recipients than in non-recipient UTI patients.These results indicate that the characteristics of UTI in kidney transplantation recipients and non-recipients patients are different.