Background: The tumor microenvironment (TME) has emerged as a crucial factor in cancer development and progression. Recent findings have indicated that tumor-infiltrating immune cells (TICs) in the TME may predict cancer prognosis and response to treatment. Herein, we sought to identify critical modulators of the kidney renal clear cell carcinoma (KIRC) TME.Methods: KIRC datasets from The Cancer Genome Atlas (TCGA) were analyzed using the ESTIMATE algorithm to determine the ImmuneScore and StromalScore. By profiling the differentially expressed genes (DEGs) in the ImmuneScore and StromalScore, we finally identified the immune-and stromal-related DEGs of the cases, through which we then performed intersection analysis to determine the immunerelated genes (IRGs). Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify critical IRGs and construct a prognostic model. The CIBERSORT algorithm was used to calculate the relative content of 22 immune cell types. Finally, the datasets from the Gene Expression Omnibus (GEO) database were analyzed to validate results from the above analyses.Experimental validation was used on KIRC tissues by quantitative polymerase chain reaction (qPCR) and western blot.Results: We found that the ImmuneScore was negatively correlated with patients' prognosis. Intersection analysis of the ImmuneScore and StromalScore identified 118 IRGs that were enriched in immune-related functions. Following IRGs screening by Cox and LASSO regression analyses, six genes were identified and used to construct a KIRC prognostic model. Intersection analysis of these six genes and protein-protein interaction (PPI) were performed and obtained the most critical gene: Potassium Calcium-Activated Channel Subfamily N Member 4 (KCNN4). Further analysis showed that KCNN4 expression was higher in tumor samples relative to normal controls, and was negatively correlated with prognosis. CIBERSORT analysis revealed significant correlation between KCNN4 expression and multiple types of TICs, demonstrating that KCNN4 may affect KIRC prognosis by influencing the TME immune status. Ultimately, the GEO datasets and validation experiments confirmed that KCNN4 was highly expressed in tumor tissues compared to the corresponding normal tissues.

Point your SmartPhone at the code above. If you have a QR code reader the video abstract will appear. Or use: https://www.youtube.com/watch?v=vyHEYxL16_M Background: Treatment of castration-resistant prostate cancer (CRPC) is an enormous challenge. As E2F transcription factor 1 (E2F1) is an essential factor in CRPC, this study investigated the genes and pathways controlled by E2F1 and their effects on cellular behavior in CRPC. Methods: In vitro assays were used to evaluate cellular proliferation, apoptosis, and behavior. Cellular expression was quantified by RNA sequencing (RNA-seq). Gene coexpression was assessed using the GeneMANIA database, and correlations were analyzed with the GEPIA server. Altered pathways of differentially expressed genes (DEGs) were revealed by functional annotation. Module analysis was performed using the STRING database and hub genes were filtered with the Cytoscape software. Some DEGs were validated by real-time quantitative PCR (RT-qPCR). Results: Knockdown of E2F1 significantly inhibited proliferation and accelerated apoptosis in PC3 cells but not in DU145 cells. Invasion and migration were reduced for both cell lines. A total of 1811 DEGs were identified in PC3 cells and 27 DEGs in DU145 cells exhibiting E2F1 knockdown. Ten overlapping DEGs, including TMOD2 and AIF1L, were identified in both knockdown cell lines and were significantly enriched for association with actin filament organization pathways. TMOD2 and KREMEN2 were genes co-expressed with E2F1; six overlapping DEGs were positively correlated with transcription factor E2F1. DEGs of the PC3 and DU145 groups were associated with multiple pathways. Five DEGs that overlapped between the two cell lines and three hub DEGs from PC3 cells were validated by RT-qPCR. Conclusion: The results of this study suggest that E2F1 has a critical role in regulating actin filaments, as indicated by the change in expression level of several genes, including TMOD2 and AIF1L, in CRPC. This extends our understanding of the cellular responses affected by E2F1 in CRPC.

BackgroundKidney renal clear cell carcinoma (KIRC) is the most frequently diagnosed subtype of renal cell carcinoma (RCC); however, the pathogenesis and diagnostic approaches for KIRC remain elusive. Using single-cell transcriptomic information of KIRC, we constructed a diagnostic model depicting the landscape of programmed cell death (PCD)-associated genes, namely cell death-related genes (CDRGs).MethodsIn this study, six CDRG categories, including apoptosis, necroptosis, autophagy, pyroptosis, ferroptosis, and cuproptosis, were collected. RNA sequencing (RNA-seq) data of blood-derived exosomes from the exoRBase database, RNA-seq data of tissues from The Cancer Genome Atlas (TCGA) combined with control samples from the GTEx databases, and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database were downloaded. Next, we intersected the differentially expressed genes (DEGs) of the KIRC cohort from exoRBase and the TCGA databases with CDRGs and DEGs obtained from single-cell datasets, further screening out the candidate biomarker genes using clinical indicators and machine learning methods and thus constructing a diagnostic model for KIRC. Finally, we investigated the underlying mechanisms of key genes and their roles in the tumor microenvironment using scRNA-seq, single-cell assays for transposase-accessible chromatin sequencing (scATAC-seq), and the spatial transcriptomics sequencing (stRNA-seq) data of KIRC provided by the GEO database.ResultWe obtained 1,428 samples and 216,155 single cells. After the rational screening, we constructed a 13-gene diagnostic model for KIRC, which had high diagnostic efficacy in the exoRBase KIRC cohort (training set: AUC = 1; testing set: AUC = 0.965) and TCGA KIRC cohort (training set: AUC = 1; testing set: AUC = 0.982), with an additional validation cohort from GEO databases presenting an AUC value of 0.914. The results of a subsequent analysis revealed a specific tumor epithelial cell of TRIB3high subset. Moreover, the results of a mechanical analysis showed the relatively elevated chromatin accessibility of TRIB3 in tumor epithelial cells in the scATAC data, while stRNA-seq verified that TRIB3 was predominantly expressed in cancer tissues.ConclusionsThe 13-gene diagnostic model yielded high accuracy in KIRC screening, and TRIB3high tumor epithelial cells could be a promising therapeutic target for KIRC.

Tremendous progress has been made in development of immunotherapeutic approaches for treatment of bladder urothelial carcinoma (BLCA). However, efficacy and safety of these approaches remain unsatisfactory, necessitating further investigations for identification of indicators for predicting prognosis and efficacy. In this study, we downloaded transcriptomic and clinical data of BLCA patients from The Cancer Genome Atlas (TCGA) database, and identified differentially expressed genes (DEGs) between tumor and normal tissues. We incorporated these DEGs in an intersection analysis with immune-related genes (IRGs) obtained from the Immunology Database and Analysis Portal (ImmPort) database, and identified immune-related DEGs. These genes were subjected to Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, then a prognostic model containing AHNAK, OAS1, NGF, PPY and SCG2 genes was constructed, for prediction of prognosis of BLCA and efficacy of immunotherapy. Finally, we explored the relationship between the prognostic model and tumor mutational burden (TMB), abundance of tumor-infiltrating immune cells (TICs) and immunotherapeutic targets, and found that patients with higher risk score (RS) had poorer prognosis and significantly lower levels of TMB. Patients in the low-RS group exhibited higher numbers of lymphoid cells, whereas those in the high-RS group exhibited higher proportions of myeloid cells. However, patients with high-RS tended to respond better to immunotherapy relative to those in the low-RS group. The constructed prognostic model provides a new tool for predicting prognosis of BLCA patients and efficacy of immunotherapy, offering a feasible option for management of the disease.

Tumor microenvironment (TME) plays an essential role in lung adenocarcinoma (LUAD) development and metastasis. With the development of TME research, it has been proved that differences in tumor-infiltrating immune cells (TICs) and gene expression profile are related to the prognosis of cancer. Our study aimed to identify key genes affecting immune state in TME of LUAD. We downloaded the RNA-seq data of LUAD cases from the TCGA database. ImmuneScore, StromalScore and ESTIMATEScore of each LUAD sample were calculated using ESTIMATE algorithm. Based on the median of different scores, LUAD samples were divided into high and low score groups. Differentially expressed genes (DEGs) between groups were obtained, and univariate Cox regression analysis and protein-protein interaction (PPI) network were used to screen shared DEGs generating in the intersection analysis. CIBORSORT algorithm was performed to calculate the relative contents of TICs for each LUAD sample, and correlation analysis between TICs and key genes was used to determine the influence of key genes to the TME. Finally, CCR2 and PTPRC, affecting the immune status of TME and the prognosis of LUAD, were acquired. Analysis based on the CIBERSORT algorithm suggested that CCR2 and PTPRC were correlated with a variety of TICs, and closely related to the clinical characteristics of the LUAD patients. Our research showed that CCR2 and PTPRC may be potential prognostic markers in LUAD, which may affect function of γδT cells and other immune cells by participating in the regulation of TME immune state.

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.