Comprehensive analysis of macrophage-related genes in prostate cancer by integrated analysis of single-cell and bulk RNA sequencing.

Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the "mgeneSim" function.

Interface Engineering to Drive High-Performance MXene/PbS Quantum Dot NIR Photodiode.

The realization of a controllable transparent conducting system with selective light transparency is crucial for exploring many of the most intriguing effects in top-illuminated optoelectronic devices. However, the performance is limited by insufficient electrical conductivity, low work function, and vulnerable interface of traditional transparent conducting materials, such as tin-doped indium oxide. Here, it is reported that two-dimensional (2D) titanium carbide (Ti3 C2 Tx ) MXene film acts as an efficient transparent conducting electrode for the lead sulfide (PbS) colloidal quantum dots (CQDs) photodiode with controllable near infrared transmittance. The solution-processed interface engineering of MXene and PbS layers remarkably reduces the interface defects of MXene/PbS CQDs and the carrier concentration in the PbS layer. The stable Ti3 C2 Tx /PbS CQDs photodiodes give rise to a high specific detectivity of 5.51 × 1012  cm W-1  Hz1/2 , a large dynamic response range of 140 dB, and a large bandwidth of 0.76 MHz at 940 nm in the self-powered state, ranking among the most exceptional in terms of comprehensive performance among reported PbS CQDs photodiodes. In contrast with the traditional photodiode technologies, this efficient and stable approach opens a new horizon to construct widely used infrared photodiodes with CQDs and MXenes.

A prognostic cuproptosis-related lncRNA predictive signature for bladder cancer patients.

Cuproptosis is a novel form of cell death in tumours. However, the clinical impact and mechanism of cuproptosis in bladder cancer (BC) remain unclear. This study aimed to explore the functions of long noncoding RNAs (lncRNAs) related to cuproptosis in BC and develop a prognostic predictive model. RNA sequencing and clinicopathological data were derived from The Cancer Genome Atlas and randomly divided into training and validation groups. Cuproptosis-related lncRNAs were identified by Cox regression analysis and least absolute shrinkage and selection operator, and the patients were divided into high- and low-risk groups according to the median value of the signature-based risk score. We established a signature of 17 cuproptosis-associated lncRNAs in the training set. In both sets, patients with higher signature-based risk scores had a notably higher probability of death (P ≤ 0.001) and a shorter survival duration. Cox regression analyses confirmed the risk score as an independent predictor of BC prognosis in the entire set. The area under the curve (AUC) values for 1-, 3-, and 5-year survival were 0.767, 0.734, and 0.764, respectively, confirming that the signature could determine the prognosis of BC. A signature-based nomogram was developed, and its prediction accuracy was validated using calibration curves. Several drugs, including Gemcitabine, Oxaliplatin, Mitoxantrone, Camptothecin, Cytarabine and Irinotecan may benefit low-risk BC patients more. Finally, in vitro experiments confirmed that the cuproptosis-related lncRNAs are highly expressed in bladder cancer cells after cuproptosis induced by exogenous copper ions. In conclusion, a cuproptosis-related lncRNA signature independently predicted prognosis in BC, indicating a possible mechanism and clinical treatment approach.

Tactile Near-Sensor Analogue Computing for Ultrafast Responsive Artificial Skin.

Ultrafast artificial skin enables unprecedented tactile internet applications in prosthetics, robotics, and human-machine interactions. However, current artificial skin systems that rely on front-end interface electronics typically perform redundant data transfer and analogue-to-digital conversions for decision-making, causing long latency (milliseconds). Here, a near-sensor analogue computing system based on a flexible memristor array for artificial skin applications is reported. This system, which seamlessly integrates a tactile sensor array with a flexible hafnium oxide memristor array, can simultaneously sense and compute raw multiple analogue pressure signals without interface electronics. As a proof-of-concept, the system is used for real-time noise reduction and edge detection of tactile stimuli. One sensing-computing operation of this system takes about 400 ns and consumes on average 1000 times less power than a conventional interface electronic system. The results demonstrate that near-sensor analogue computing offers an ultrafast and energy-efficient route to large-scale artificial skin systems.

MRI-Based Nomogram of Prostate Maximum Sectional Area and Its Zone Area for Prediction of Prostate Cancer.

OBJECTIVE: To reduce unnecessary prostate biopsies, we designed a magnetic resonance imaging (MRI)-based nomogram prediction model of prostate maximum sectional area (PA) and investigated its zone area for diagnosing prostate cancer (PCa). METHODS: MRI was administered to 691 consecutive patients before prostate biopsies from January 2012 to January 2020. PA, central gland sectional area (CGA), and peripheral zone sectional area (PZA) were measured on axial T2-weighted prostate MRI. Multivariate logistic regression analysis and area under the receiver operating characteristic (ROC) curve were performed to evaluate and integrate the predictors of PCa. Based on multivariate logistic regression coefficients after excluding combinations of collinear variables, three models and nomograms were generated and intercompared by Delong test, calibration curve, and decision curve analysis (DCA). RESULTS: The positive rate of PCa was 46.74% (323/691). Multivariate analysis revealed that age, PSA, MRI, transCGA, coroPZA, transPA, and transPAI (transverse PZA-to-CGA ratio) were independent predictors of PCa. Compared with no PCa patients, transCGA (AUC = 0.801) was significantly lower and transPAI (AUC = 0.749) was significantly higher in PCa patients. Both of them have a significantly higher AUC than PSA (AUC = 0.714) and PV (AUC = 0.725). Our best predictive model included the factors age, PSA, MRI, transCGA, and coroPZA with the AUC of 0.918 for predicting PCa status. Based on this predictive model, a novel nomogram for predicting PCa was conducted and internally validated (C-index = 0.913). CONCLUSIONS: We found the potential clinical utility of transCGA and transPAI in predicting PCa. Then, we firstly built the nomogram based on PA and its zone area to evaluate its diagnostic efficacy for PCa, which could reduce unnecessary prostate biopsies.

Evaluation of immune status in testis and macrophage polarization associated with testicular damage in patients with nonobstructive azoospermia.

OBJECTIVE: Immune cells residing in the testicular interstitial space form the immunological microenvironment of the testis. They are assumed to play a role in maintaining testicular homeostasis and immune privilege. However, the immune status and related cell polarization in patients with nonobstructive azoospermia (NOA) remains poorly characterized. System evaluation of the testis immunological microenvironment in NOA patients may help to reveal the mechanisms of idiopathic azoospermia. STUDY DESIGN: The gene expression patterns of immune cells in normal human testes were systematically analyzed by single-cell RNA sequencing (scRNA-seq) and preliminarily verification by the human protein atlas (HPA) online database. The immune cell infiltration profiles and immune status of patients with NOA was analyzed by single-sample gene set enrichment analysis (ssGSEA) and gene set variation analysis (GSVA) based on four independent public microarray datasets (GSE45885, GSE45887, GSE9210, and GSE145467), obtained from Gene Expression Omnibus (GEO) online database. The relationship between immune cells and spermatogenesis score was further analyzed by Spearman correlation analysis. Finally, immunohistochemistry (IHC) staining was performed to identify the main immune cell types and their polarization status in patients with NOA. RESULTS: Both scRNA-seq and HPA analysis showed that testicular macrophages represent the largest pool of immune cells in the normal testis, and also exhibit an attenuated inflammatory response by expressing high levels of tolerance proteins (CD163, IL-10, TGF-ß, and VEGF) and reduced expression of TLR signaling pathway-related genes. Correlation analysis revealed that the testicular immune score and macrophages including M1 and M2 macrophages were significantly negatively correlated with spermatogenesis score in patients with NOA (GSE45885 and GSE45887). In addition, the number of M1 and M2 macrophages was significantly higher in patients with NOA (GSE9210 and GSE145467) than in normal testis. GSVA analysis indicated that the immunological microenvironment in NOA tissues was manifested by activated immune system and pro-inflammatory status. IHC staining results showed that the number of M1 and M2 macrophages was significantly higher in NOA tissues than in normal testis and negatively correlated with the Johnson score. CONCLUSION: Testicular macrophage polarization may play a vital role in NOA development and is a promising potential therapeutic target.

S100A14 inhibits cell growth and epithelial-mesenchymal transition (EMT) in prostate cancer through FAT1-mediated Hippo signaling pathway.

Prostate cancer (PCA) is an epithelial malignant tumor occurring in the prostate gland. It is the second most common male cancer in the world and one of the top five cancer deaths in men. To combat this disease, it is needed to identify important tumor suppressor genes and elucidate the molecular mechanisms. S100 calcium-binding protein A14 (S100A14), a member of the S100 family, is located on chromosome 1q21.3 and contains an EF-hand motif that binds calcium. S100A14 is involved in a variety of tumor biological processes in several types of cancers. Its expression level and related biological functions are tissue or tumor specific. However, its possible effects on prostate cancer are still unclear. Herein, we found the low expression of S100A14 in human prostate cancer tissues and cell lines. S100A14 suppressed the proliferation of prostate cancer cells and promoted cell apoptosis. Additionally, S100A14 suppressed the motility and EMT processes of prostate cancer cells. We further found S100A14 promoted the expression of FAT1 and activated the Hippo pathway, which, therefore, suppressed the prostate cancer progression. The in vivo assays confirmed that S100A14 suppressed tumor growth of prostate cancer cells through FAT1-mediated Hippo pathway in mice. In conclusion, we clarified the mechanism underlying S100A14 suppressing prostate cancer progression and, therefore, we thought S100A14 could serve as a tumor suppressor protein.

Multi-omics analysis of tumor angiogenesis characteristics and potential epigenetic regulation mechanisms in renal clear cell carcinoma.

BACKGROUND: Tumor angiogenesis, an essential process for cancer proliferation and metastasis, has a critical role in prognostic of kidney renal clear cell carcinoma (KIRC), as well as a target in guiding treatment with antiangiogenic agents. However, tumor angiogenesis subtypes and potential epigenetic regulation mechanisms in KIRC patient remains poorly characterized. System evaluation of angiogenesis subtypes in KIRC patient might help to reveal the mechanisms of KIRC and develop more target treatments for patients. METHOD: Ten independent tumor angiogenesis signatures were obtained from molecular signatures database (MSigDB) and gene set variation analysis was performed to calculate the angiogenesis score in silico using the Cancer Genome Atlas (TCGA) KIRC dataset. Tumor angiogenesis subtypes in 539 TCGA-KIRC patients were identified using consensus clustering analysis. The potential regulation mechanisms was studied using gene mutation, copy number variation, and differential methylation analysis (DMA). The master transcription factors (MTF) that cause the difference in tumor angiogenesis signals were completed by transcription factor enrichment analysis. RESULTS: The angiogenesis score of a prognosis related angiogenesis signature including 189 genes was significantly correlated with immune score, stroma score, hypoxia score, and vascular endothelial growth factor (VEGF) signal score in 539 TCGA KIRC patients. MMRN2, CLEC14A, ACVRL1, EFNB2, and TEK in candidate gene set showed highest correlation coefficient with angiogenesis score in TCGA-KIRC patients. In addition, all of them were associated with overall survival in both TCGA-KIRC and E-MTAB-1980 KIRC data. Clustering analysis based on 183 genes in angiogenesis signature identified two prognosis related angiogenesis subtypes in TCGA KIRC patients. Two clusters also showed different angiogenesis score, immune score, stroma score, hypoxia score, VEGF signal score, and microenvironment score. DMA identified 59,654 differential methylation sites between two clusters and part of these sites were correlated with tumor angiogenesis genes including CDH13, COL4A3, and RHOB. In addition, RFX2, SOX13, and THRA were identified as top three MTF in regulating angiogenesis signature in KIRC patients. CONCLUSION: Our study indicate that evaluation the angiogenesis subtypes of KIRC based on angiogenesis signature with 183 genes and potential epigenetic mechanisms may help to develop more target treatments for KIRC patients. Video Abstract.

The roles of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen concentrations in predicting prostate cancer and high-risk prostate cancer.

Prostate biopsies are frequently performed to screen for prostate cancer (PCa) with complications such as infections and bleeding. To reduce unnecessary biopsies, here we designed an improved predictive model of MRI-based prostate volume and associated zone-adjusted prostate-specific antigen (PSA) concentrations for diagnosing PCa and risk stratification. Multiparametric MRI administered to 422 consecutive patients before initial transrectal ultrasonography-guided 13-core prostate biopsies from January 2012 to March 2018 at Fujian Medical University Union Hospital. Univariate and multivariate logistic regression analyses and determination of the area under the curve (AUC) of the receiver operating characteristic (ROC) curve was performed to evaluate and integrate the predictors of PCa and high-risk prostate cancer (HR-PCa). The detection rates of PCa was 43.84% (185/422). And the detection rates of HR-PCa was 71.35% (132/185) in PCa patients. Multivariate analysis revealed that prostate volume(PV), PSA density(PSAD), transitional zone volume(TZV), PSA density of the transitional zone(PSADTZ), and MR were independent predictors of PCa and HR-PCa. PSA, peripheral zone volume(PZV) and PSA density of the peripheral zone(PSADPZ) were independent predictors of PCa but not HR-PCa. The AUC of our best predictive model including PSA + PV + PSAD + MR + TZV or PSA + PV + PSAD + MR + PZV was 0.906 for PCa. The AUC of the best predictive model of PV + PSAD + MR + TZV was 0.893 for HR-PCa. In conclusion, our results will likely improve the detection rate of prostate cancer, avoiding unnecessary prostate biopsies, and for evaluating risk stratification.