Assessing the causal relationship between immune cells and prostatitis: evidence from bidirectional mendelian randomization analysis.

Prostatitis represents a common disease of the male genitourinary system, significantly impacting the physical and mental health of male patients. While numerous studies have suggested a potential link between immune cell activity and prostatitis, the exact causal role of immune cells in prostatitis remains uncertain. This study aims to explore the causal relationship between immune cell characteristics and prostatitis using a bidirectional Mendelian randomization approach. This study utilizes data from the public GWAS database and employs bidirectional Mendelian randomization analysis to investigate the causal relationship between immune cells and prostatitis. The causal relationship between 731 immune cell features and prostatitis was primarily investigated through inverse variance weighting (IVW), complemented by MR-Egger regression, a simple model, the weighted median method, and a weighted model. Ultimately, the results underwent sensitivity analysis to assess the heterogeneity, horizontal pleiotropy, and stability of Single Nucleotide Polymorphisms (SNPs) in immune cells and prostatitis. MR analysis revealed 17 immune cells exhibiting significant causal effects on prostatitis. In contrast, findings from reverse MR indicated a significant causal relationship between prostatitis and 13 immune cells. Our study utilizes bidirectional Mendelian Randomization to establish causal relationships between specific immune cell phenotypes and prostatitis, highlighting the reciprocal influence between immune system behavior and the disease. Our findings suggest targeted therapeutic approaches and the importance of including diverse populations for broader validation and personalized treatment strategies.

A multicenter study on the application of artificial intelligence radiological characteristics to predict prognosis after percutaneous nephrolithotomy.

Background: A model to predict preoperative outcomes after percutaneous nephrolithotomy (PCNL) with renal staghorn stones is developed to be an essential preoperative consultation tool. Objective: In this study, we constructed a predictive model for one-time stone clearance after PCNL for renal staghorn calculi, so as to predict the stone clearance rate of patients in one operation, and provide a reference direction for patients and clinicians. Methods: According to the 175 patients with renal staghorn stones undergoing PCNL at two centers, preoperative/postoperative variables were collected. After identifying characteristic variables using PCA analysis to avoid overfitting. A predictive model was developed for preoperative outcomes after PCNL in patients with renal staghorn stones. In addition, we repeatedly cross-validated their model's predictive efficacy and clinical application using data from two different centers. Results: The study included 175 patients from two centers treated with PCNL. We used a training set and an external validation set. Radionics characteristics, deep migration learning, clinical characteristics, and DTL+Rad-signature were successfully constructed using machine learning based on patients' pre/postoperative imaging characteristics and clinical variables using minimum absolute shrinkage and selection operator algorithms. In this study, DTL-Rad signal was found to be the outstanding predictor of stone clearance in patients with renal deer antler-like stones treated by PCNL. The DTL+Rad signature showed good discriminatory ability in both the training and external validation groups with AUC values of 0.871 (95% CI, 0.800-0.942) and 0.744 (95% CI, 0.617-0.871). The decision curve demonstrated the radiographic model's clinical utility and illustrated specificities of 0.935 and 0.806, respectively. Conclusion: We found a prediction model combining imaging characteristics, neural networks, and clinical characteristics can be used as an effective preoperative prediction method.

E2F2 serves as an essential prognostic biomarker and therapeutic target for human renal cell carcinoma by presenting "E2F2/miR-16-5p/SPTLC1" schema.

BACKGROUND: Renal cell carcinoma (RCC) is a common malignant tumor of the urinary system with high mortality and morbidity. Although E2F2, a classical transcription factor implicated in cell cycle, has been shown to foster tumorigenesis in several human cancers, it could not draw a satisfy answer referring to precise downstream signaling axis in RCC development yet. METHODS: Based on the publicly available data from TCGA database, expression patterns of E2F2, SPTLC1 and miR-16-5p were identified, either with the ability to predict the prognosis of patients with RCC, which was further validated in 38 paired RCC tissues and matched adjacent tissues by RT-qPCR and Western blot, respectively. Their cellular biofunctions were evaluated using MTT, EdU, Colony formation and transwell assays. Chromatin immunoprecipitation (ChIP) and luciferase reporter assay were employed to certain the exquisite core transcription regulatory circuitry of E2F2/miR-16-5p/SPTLC1 in RCC progression, which was also determined in xenograft tumor model. RESULTS: Consistent with the public TCGA database, E2F2 was significantly increased in RCC tissues and cells, indicating shorter overall survival. Mechanistically, E2F2 served as a transcriptional activator of miR-16-5p, thus accounting for its negative regulation on SPTLC1 expression. E2F2 knockdown-mediated suppressive biofunctions on RCC cells were rescued by miR-16-5p mimics, while this effect was abolished again by SPTLC1 overexpression. Role of E2F2 on RCC tumorigenesis via the miR-16-5p/SPTLC1 axis was verified both in vitro and in vivo. CONCLUSION: E2F2 promoted RCC progression via the miR-16-5p/SPTLC1 axis, which may represent a novel prognostic and therapeutic biomarker for RCC.

ARPC5 is transcriptionally activated by KLF4, and promotes cell migration and invasion in prostate cancer via up-regulating ADAM17 : ARPC5 serves as an oncogene in prostate cancer.

BACKGROUND: Prostate cancer (PCa) is one of the most common cancers in men worldwide. Actin-related protein 2/3 complex subunit 5 (ARPC5) has been validated as a critical regulator in several kinds of human tumors. However, whether ARPC5 is implicated in PCa progression remains largely unknown. METHODS: PCa specimens and PCa cell lines were obtained for detecting gene expressions using western blot and quantitative reverse transcriptase PCR (qRT-PCR). PCa cells transfected with ARPC5 shRNA or a disintegrin and metalloprotease 17 (ADAM17) overexpressed plasmids were harvested for assessing cell proliferation, migration and invasion by using cell counting kit-8 (CCK-8), colony formation and transwell assays, respectively. The interaction relationship between molecules was testified with chromatin immunoprecipitation and luciferase reporter assay. Xenograft mice model was conducted for confirming the role of ARPC5/ADAM17 axis in vivo. RESULTS: Upregulated ARPC5 was observed in PCa tissues and cells, as well as forecasted poor prognosis of PCa patients. Depletion of ARPC5 inhibited PCa cell proliferation, migration and invasion. Krüppel-like factor 4 (KLF4) was identified to be a transcriptional activator of ARPC5 via binding with its promoter region. Furthermore, ADAM17 served as a downstream effector of ARPC5. ADAM17 overexpression overturned ARPC5 knockdown-induced repressive impacts on PCa progression in vitro and in vivo. CONCLUSION: Collectively, ARPC5 was activated by KLF4 and upregulated ADAM17 to promote PCa progression, which might act as a promising therapeutic target and prognostic biomarker for PCa.

A risk stratification and prognostic prediction model for lung adenocarcinoma based on aging-related lncRNA.

To create a risk model of aging-related long non-coding RNAs (arlncRNAs) and determine whether they might be useful as markers for risk stratification, prognosis prediction, and targeted therapy guidance for patients with lung adenocarcinoma (LUAD). Data on aging genes and lncRNAs from LUAD patients were obtained from Human Aging Genomic Resources 3 and The Cancer Genome Atlas, and differential co-expression analysis of established differentially expressed arlncRNAs (DEarlncRNAs) was performed. They were then paired with a matrix of 0 or 1 by cyclic single pairing. The risk coefficient for each sample of LUAD individuals was obtained, and a risk model was constructed by performing univariate regression, least absolute shrinkage and selection operator regression analysis, and univariate and multivariate Cox regression analysis. Areas under the curve were calculated for the 1-, 3-, and 5-year receiver operating characteristic curves to determine Akaike information criterion-based cutoffs to identify high- and low-risk groups. The survival rate, correlation of clinical characteristics, malignant-infiltrating immune-cell expression, ICI-related gene expression, and chemotherapeutic drug sensitivity were contrasted with the high- and low-risk groups. We found that 99 DEarlncRNAs were upregulated and 12 were downregulated. Twenty pairs of DEarlncRNA pairs were used to create a prognostic model. The 1-, 3-, and 5-year survival curve areas of LUAD individuals were 0.805, 0.793, and 0.855, respectively. The cutoff value to classify patients into two groups was 0.992. The mortality rate was higher in the high-risk group. We affirmed that the LUAD outcome-related independent predictor was the risk score (p < 0.001). Validation of tumor-infiltrating immune cells and ICI-related gene expression differed substantially between the groups. The high-risk group was highly sensitive to docetaxel, erlotinib, gefitinib, and paclitaxel. Risk models constructed from arlncRNAs can be used for risk stratification in patients with LUAD and serve as prognostic markers to identify patients who might benefit from targeted and chemotherapeutic agents.

A novel T-cell proliferation-associated regulator signature pre-operatively predicted the prognostic of bladder cancer.

Background: Bladder cancer (BCa) is a remarkably malignant and heterogeneous neoplastic disease, and its prognosis prediction is still challenging. Even with the mounting researches on the mechanisms of tumor immunotherapy, the prognostic value of T-cell proliferation regulators in bladder cancer remains elusive. Methods: Herein, we collected mRNA expression profiles and relevant clinical information of bladder cancer sufferers from a publicly available data base. Then, the LASSO Cox regression model was utilized to establish a multi-gene signature for the TCGA cohort to predict the prognosis and staging of bladder cancer. Eventually, the predictive power of the model was validated by randomized grouping. Results: The outcomes revealed that most genes related to T-cell proliferation in the TCGA cohort exhibited different expressions between BCa cells and neighboring healthy tissues. Univariable Cox regressive analyses showed that four DEGs were related to OS in bladder cancer patients (p<0.05). We constructed a histogram containing four clinical characteristics and separated sufferers into high- and low-risk groups. High-risk sufferers had remarkably lower OS compared with low-risk sufferers (P<0.001). Eventually, the predictive power of the signature was verified by ROC curve analyses, and similar results were obtained in the validation cohort. Functional analyses were also completed, which showed the enrichment of immune-related pathways and different immune status in the two groups. Moreover, by single-cell sequencing, our team verified that CXCL12, a T-lymphocyte proliferation regulator, influenced bladder oncogenesis and progression by depleting T-lymphocyte proliferation in the tumor microenvironment, thus promoting tumor immune evasion. Conclusion: This study establishes a novel T cell proliferation-associated regulator signature which can be used for the prognostic prediction of bladder cancer. The outcomes herein facilitate the studies on T-cell proliferation and its immune micro-environment to ameliorate prognoses and immunotherapeutic responses.

Prognostic Signature Development on the Basis of Macrophage Phagocytosis-Mediated Oxidative Phosphorylation in Bladder Cancer.

Background: Macrophages are correlated with the occurrence and progression of bladder cancer (BCa). However, few research has focused on the predictive relevance of macrophage phagocytosis-mediated oxidative phosphorylation (MPOP) with BCa overall survival. Herein, we aimed to propose the targeted macrophage control based on MPOP as a treatment method for BCa immunotherapy. Methods: The mRNA expression data sets and clinical data of bladder cancer originated from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) data set. A systematic study of several GEO data sets found differentially expressed macrophage phagocytosis regulators (DE-MPR) between BCa and normal tissues. To discover overall survival-associated DE-MPR and develop prognostic gene signature with performance validated based on receiver operating curves and Kaplan-Meier curves, researchers used univariate and Lasso Cox regression analysis (ROC). External validation was done with GSE13057 and GSE69795. To clarify its molecular mechanism and immune relevance, GO/KEGG enrichment analysis and tumor immune analysis were used. To find independent bladder cancer prognostic variables, researchers employed multivariate Cox regression analysis. Finally, using TCGA data set, a predictive nomogram was built. Results: In BCa, a four-gene signature of oxidative phosphorylation composed of PTPN6, IKZF3, HDLBP, and EMC1 was found to predict overall survival. With the MPOP feature, the ROC curve showed that TCGA data set and the external validation data set performed better in predicting overall survival than the traditional AJCC stage. The four-gene signature can identify cancers from normal tissue and separate patients into the high-risk and low-risk groups with different overall survival rates. The four MPOP-gene signature was an independent predictive factor for BCa. In predicting overall survival, a nomogram integrating genetic and clinical prognostic variables outperformed AJCC staging. Multiple oncological features and invasion-associated pathways were identified in the high-risk group, which were also correlated with significantly lower levels of immune cell infiltration. Conclusion: This paper found the MPOP-feature gene and developed a predictive nomogram capable of accurately predicting bladder cancer overall survival. The above discoveries can contribute to the development of personalized treatments and medical decisions.

A novel marker based on necroptosis-related long non-coding RNA for forecasting prognostic in patients with clear cell renal cell carcinoma.

Background: The incidence of clear cell renal cell carcinoma (ccRCC) is high and has increased gradually in recent years. At present, due to the lack of effective prognostic indicators, the prognosis of ccRCC patients is greatly affected.Necroptosis is a type of cell death, and along with cell necrosis is considered a new cancer treatment strategy. The aim of this study was to construct a new marker for predicting the prognosis of ccRCC patients based on long non-coding RNA (nrlncRNAs) associated with necroptosis. Methods: RNA sequence data and clinical information of ccRCC patients from the Cancer Genome Atlas database (TCGA) were downloaded. NrlncRNA was identified by Pearson correlation study. The differentially expressed nrlncRNA and nrlncRNA pairs were identified by univariate Cox regression and Lasso-Cox regression. Finally, a Kaplan-Meier survival study, Cox regression, clinicopathological features correlation study, and receiver operating characteristic (ROC) spectrum were used to evaluate the prediction ability of 25-nrlncrnas for markers. In addition, correlations between the risk values and sensitivity to tumor-infiltrating immune cells, immune checkpoint inhibitors, and targeted drugs were also investigated. Results: In the current research, a novel marker of 25-nrlncRNAs pairs was developed to improve prognostic prediction in patients with ccRCC. Compared with clinicopathological features, nrlncRNAs had a higher diagnostic validity for markers, with the 1-year, 3-years, and 5-years operating characteristic regions being 0.902, 0.835, and 0.856, respectively, and compared with the stage of 0.868, an increase of 0.034. Cox regression and stratified survival studies showed that this marker could be an independent predictor of ccRCC patients. In addition, patients with different risk scores had significant differences in tumor-infiltrating immune cells, immune checkpoint, and semi-inhibitory concentration of targeted drugs. The feature could be used to evaluate the clinical efficacy of immunotherapy and targeted drug therapy. Conclusion: 25-nrlncRNAs pair markers may help to evaluate the prognosis and molecular characteristics of ccRCC patients, which improve treatment methods and can be more used in clinical practice.

A Necroptosis-Related lncRNA to Develop a Signature to Predict the Outcome, Immune Landscape, and Chemotherapeutic Responses in Bladder Urothelial Carcinoma.

Objective: Many studies have drawn their attention to the immunotherapy of bladder urothelial carcinoma in terms of immunologic mechanisms of human body. These include immunogenicity of the tumor cells and involvement of long non-coding RNA (lncRNA). We constructed a necroptosis-related long noncoding RNA (nrlncRNA) risk factor model to predict BLCA outcomes and calculate correlations with chemosensitivity and immune infiltration. Methods: Transcriptomic data from BLCA specimens were accessed from The Cancer Genome Atlas, and nrlncRNAs were identified by performing co-expression analysis. Univariate analysis was performed to identify differentially expressed nrlncRNA pairs. We constructed least absolute contraction and selector operation regression models and drew receiver operating characteristic curves for 1-, 3-, and 5-year survival rates. Akaike information criterion (AIC) values for survival over 1 year were determined as cutoff values in high- and low-risk subgroups. We reassessed the differences between subgroups in terms of survival, clinicopathological characteristics, chemotherapy efficacy, tumor-infiltrating immune cells, and markers of immunosuppression. Results: We identified a total of 260 necroptosis-related lncRNA pairs, of which we incorporated 13 into the prognostic model. Areas under the curve of 1-, 3-, and 5- year survival time were 0.763, 0.836, and 0.842, respectively. We confirmed the excellent predictive performance of the risk model. Based on AIC values, we confirmed that the high-risk group was susceptible to unfavorable outcomes. The risk scores correlated with survival were age, clinical stage, grade, and tumor node metastases. The risk model was an independent predictor and demonstrated higher predictive power. The risk model can also be utilized to determine immune cell infiltration status, expression levels of immune checkpoint genes, and the sensitivity to cisplatin, doxorubicin, and methotrexate. Conclusion: We constructed a novel necroptosis-related signature that predicts BLCA outcomes and performs satisfactorily in the immune landscape and chemotherapeutic responses.

Bioinformatics Study Identified EGF as a Crucial Gene in Papillary Renal Cell Cancer.

Background: Due to a lack of knowledge of the disease process, papillary renal cell carcinoma (PRCC) has a dismal outlook. This research was aimed at uncovering the possible biomarkers and the underlying principles in PRCC using a bioinformatics method. Methods: We searched the Gene Expression Omnibus (GEO) datasets to obtain the GSE11151 and GSE15641 gene expression profiles of PRCC. We used the R package limma to identify the differentially expressed genes (DEGs). The online tool DAVID and ClusterProfiler package in R software were used to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway dominance, respectively. The STRING database was utilized to construct the PPI network of DEGs. Using the Cytoscape technology, a protein-protein interaction (PPI) network that associated with DEGs was created, and the hub genes were identified using the Cytoscape plug-in CytoHubba. The hub genes were subjected to a Kaplan-Meier analysis to identify their correlations with survival rates. Results: From the selected datasets, a total of 240 common DEGs were identified in the PRCC, including 50 upregulated genes and 190 downregulated regulated genes. Renal growth, external exosome, binding of heparin, and metabolic processes were all substantially associated with DEGs. The CytoHubba plug-in-based analysis identified the 10 hub genes (ALB, KNG1, C3, CXCL12, EGF, TIMP1, VCAN, PLG, LAMC1, and CASR) from the original PPI network. The higher expression group of EGF was associated with poor outcome in patients with PRCC. Conclusions: We revealed important genes and proposed biological pathways that may be implicated in the formation of PRCC. EGF might be a predictive biomarker for PRCC and therefore should be investigated as a novel treatment strategy.

Risk coefficient model of necroptosis-related lncRNA in predicting the prognosis of patients with lung adenocarcinoma.

Model algorithms were used in constructing the risk coefficient model of necroptosis-related long non-coding RNA in identifying novel potential biomarkers in the prediction of the sensitivity to chemotherapeutic agents and prognosis of patients with lung adenocarcinoma (LUAD). Clinic and transcriptomic data of LUAD were obtained from The Cancer Genome Atlas. Differently expressed necroptosis-related long non-coding RNAs got identified by performing both the univariate and co-expression Cox regression analyses. Subsequently, the least absolute shrinkage and selection operator technique was adopted in constructing the nrlncRNA model. We made a comparison of the areas under the curve, did the count of the values of Akaike information criterion of 1-year, 2-year, as well as 3-year receiver operating characteristic curves, after which the cut-off value was determined for the construction of an optimal model to be used in identifying high risk and low risk patients. Genes, tumor-infiltrating immune cells, clinical correlation analysis, and chemotherapeutic agents data of both the high-risk and low-risk subgroups were also performed. We identified 26 DEnrlncRNA pairs, which were involved in the Cox regression model constructed. The curve areas under survival periods of 1 year, 2 years, and 3 years of patients with LUAD were 0.834, 0.790, and 0.821, respectively. The cut-off value set was 2.031, which was used in the identification of either the high-risk or low-risk patients. Poor outcomes were observed in patients belonging to the high-risk group. The risk score was the independent predictor of the LUAD outcome (p < 0.001). The expression levels of immune checkpoint and infiltration of specific immune cells were anticipated by the gene risk model. The high-risk group was found to be highly sensitive to docetaxel, erlotinib, cisplatin, and paclitaxel. The model established through nrlncRNA pairs irrespective of the levels of expression could give a prediction on the LUAD patients' prognosis and assist in identifying the patients who might gain more benefit from chemotherapeutic agents.

Ferroptosis-related long non-coding RNA signature predicts the prognosis of bladder cancer.

BACKGROUND: Ferroptosis is an iron-dependent programmed cell death modality that may have a tumor-suppressive function. Therefore, regulating ferroptosis in tumor cells could serve as a novel therapeutic approach. This article focuses on ferroptosis-associated long non-coding RNAs (lncRNAs) and their potential application as a prognostic predictor for bladder cancer (BCa). METHODS: We retrieved BCa-related transcriptome information and clinical information from the TCGA database and ferroptosis-related gene sets from the FerrDb database. Least absolute shrinkage and selection operator regression (LASSO) and Cox regression models were used to identify and develop predictive models and validate the model accuracy. Finally, we explored the inter-regulatory relationships between ferroptosis-related genes and immune cell infiltration, immune checkpoints, and m6A methylation genes. RESULTS: Kaplan-Meier analyses screened 11 differentially expressed lncRNAs associated with poor BCa prognosis. The signature (AUC = 0.720) could be utilized to predict BCa prognosis. Additionally, GSEA revealed immune and tumor-related pathways in the low-risk group. TCGA showed that the p53 signaling pathway, ferroptosis, Kaposi sarcoma - associated herpesvirus infection, IL - 17 signaling pathway, MicroRNAs in cancer, TNF signaling pathway, PI3K - Akt signaling pathway and HIF - 1 signaling pathway were significantly different from those in the high-risk group. Immune checkpoints, such as PDCD-1 (PD-1), CTLA4, and LAG3, were differentially expressed between the two risk groups. m6A methylation-related genes were significantly differentially expressed between the two risk groups. CONCLUSION: A new ferroptosis-associated lncRNAs signature developed for predicting the prognosis of BCa patients will improve the treatment and management of BCa patients.

Case Report: A Calculus-Free Ureteral Stent Forgotten for 29 Years.

Ureteral stents are widely used. If ureteral stents remain in place for extended periods, the probability of migration and stone formation increases substantially. However, a 29-year-old ureteral stent that was placed and did not develop calculus is rare. We reported a 45-year-old man admitted with pain in the left side of his waist and abdomen for more than 10 years. He underwent a ureterotomy 29 years prior to admission for left ureteral calculi, and a ureteral stent was placed postoperatively to prevent ureteral strictures. The ureteral stent was not removed in the hospital due to poor compliance on the part of the patient. This left ureteral stent was not visible on computed tomography (CT) and plain films. On ureteroscopy or flexible ureteroscopy, no new calculus was found in the left ureter and kidney. No calculus was found in the stent that is usually easily removed with calculus-removing forceps. This phenomenon is rare, and it highlights the importance of follow-up.

Increased NUSAP1 expression is associated with lymph node metastasis and survival prognosis in bladder urothelial carcinoma.

The main route of metastasis of bladder urothelial carcinoma is through lymph nodes; however, its exact mechanism remains unclear. In this study, we found an association of nucleolar and spindle associated protein 1 (NUSAP1) expression with BUC tissues along with lymph node metastasis and the survival prognosis. A total of 178 pathological specimens following radical bladder cancer resection were obtained. NUSAP1 expression was analyzed by immunohistochemistry. We evaluated the correlation between clinicopathological characteristics and NUSAP1 expression. Logistic regression was used to determine the independent variables that influenced lymph node metastasis. Uni- and multi-factorial Cox regression methods were used to determine the prognostic value of NUSAP1 expression in urothelial carcinoma of the bladder. High expression of NUSAP1 in BUC was not significantly related to the patient's gender, age, or tumor number (p > 0.05), however was significantly associated with pathological grade, tumor diameter, pathological stage, and lymph node metastasis (p < 0.05). Lymph node metastasis was significantly correlated with pathological stage, pathological grade, tumor number, tumor diameter, and NUSAP1 expression (p < 0.05); only NUSAP1 expression was an independent predictor of lymph node metastasis in BUC (OR:1.786, 95% CI 1.229-2.596, p = 0.002). In addition, high NUSAP1 expression was an independent prognostic predictor for BUC. In BUC, NUSAP1 showed high expression and was significantly associated with lymph node metastasis, pathological stage, pathological grade, and tumor diameter. NUSAP1 was an independent predictor of lymph node metastasis and prognosis in BUC; higher expression indicated poorer prognosis of BUC patients.

Identification of CXCL10 as a Prognostic Biomarker for Clear Cell Renal Cell Carcinoma.

Background: One of the widespread forms of kidney tumor is clear cell renal cell carcinoma (ccRCC), with poor prognosis and insensitivity to radio chemotherapy as there is limited capacity to understand the disease mechanism. This study aims at identifying potential biomarkers and the underlying processes of ccRCC using bioinformatics analysis. Methods: Transcriptome data of relevant samples were downloaded from The Cancer Genome Atlas (TCGA) database. R software was used to screen differentially expressed genes (DEGs) using the "edgeR" package. Two types of analysis-Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment-were accomplished by applying Database for Annotation, Visualization, and Integrated Discovery (DAVID) and Search Tool for the Retrieval of Interacting Genes database (STRING) online bioinformatics tools. A protein-protein interaction (PPI) network of the identified DEGs was constructed using Cytoscape software, and hub genes were subsequently selected via the Cytohubba plug-in. The selected genes were input into Oncomine for verification. Finally, selected hub genes were analyzed by doing survival analysis to notice the relationship between survival (OS) rate and the selected genes' level of expression. Results: There were 1,855 DEGs found connected to ccRCC, with 1,207 upregulated genes and 648 downregulated genes. G-protein-coupled receptor signaling pathway, integral component of membrane, calcium ion binding, and cytokine-cytokine receptor interaction were among the DEGs discovered. Oncomine confirmed the top six hub genes from the PPI network (C3, CXCR3, CXCL10, CCR5, CCL4, and CCL5). A high level of expression of CXCL10, one of these hub genes, was linked to a poor prognosis in individuals with ccRCC. The results of survival analysis showed that the expression level of CXCL10 was significantly correlated with the prognosis of ccRCC patients (p < 0.05). Conclusions: From the analysis, the following results were drawn: CXCL10 might be a potential prognostic biomarker and novel therapeutic target for ccRCC.

An immune-related lncRNA model for predicting prognosis, immune landscape and chemotherapeutic response in bladder cancer.

Long noncoding RNAs (lncRNAs) participate in cancer immunity. We characterized the clinical significance of an immune-related lncRNA model and evaluated its association with immune infiltrations and chemosensitivity in bladder cancer. Transcriptome data of bladder cancer specimens were employed from The Cancer Genome Atlas. Dysregulated immune-related lncRNAs were screened via Pearson correlation and differential expression analyses, followed by recognition of lncRNA pairs. Then, a LASSO regression model was constructed, and receiver operator characteristic curves of one-, three- and five-year survival were established. Akaike information criterion (AIC) value of one-year survival was determined as the cutoff of high- and low-risk subgroups. The differences in survival, clinical features, immune cell infiltrations and chemosensitivity were compared between subgroups. Totally, 90 immune-related lncRNA pairs were identified, 15 of which were screened for constructing the prognostic model. The area under the curves of one-, three- and five-year survival were 0.806, 0.825 and 0.828, confirming the favorable predictive performance of this model. According to the AIC value, we clustered patients into high- and low-risk subgroups. High-risk score indicated unfavorable outcomes. The risk model was related to survival status, age, stage and TNM. Compared with conventional clinicopathological characteristics, the risk model displayed higher predictive efficacy and served as an independent predictor. Also, it could well characterize immune cell infiltration landscape and predict immune checkpoint expression and sensitivity to cisplatin and methotrexate. Collectively, the model conducted by paring immune-related lncRNAs regardless of expressions exhibits a favorable efficacy in predicting prognosis, immune landscape and chemotherapeutic response in bladder cancer.

Prognostic prediction of clear cell renal cell carcinoma based on lipid metabolism-related lncRNA risk coefficient model.

Objective: In order to predict the prognosis in patients with clear cell renal cell carcinoma (ccRCC) so as to understand cancer lipid metabolism and sensitivity to immune-targeting drugs, model algorithms were used to establish a risk coefficient model of long non-coding RNAs (lncRNAs) associated with lipid metabolism. Methods: The transcriptome data were retrieved from TCGA, and lncRNAs associated with lipid metabolism were obtained through Pearson correlation and differential expression analyses. Differentially expressed lipid metabolism-related lncRNAs and lipid metabolism-related lncRNA pairs were obtained using the R language software. The minimum absolute shrinkage method and the selector operation regression method were used to construct the model and draw the receiver operator characteristic curve. High-risk patients were differentiated from low-risk patients through the cut-off value, and the correlation analyses of the high-risk subgroup and low-risk subgroup were performed. Results: This research discovered that 25 pairs of lncRNAs were associated with the lipid metabolism of ccRCC, and 12 of these pairs were utilized to build the model. In combination with clinical data, the areas under the 1-, 3- and 5-year survival curves of ccRCC patients were 0.809, 0.764 and 0.792, separately. The cut-off value was used to perform subgroup analysis. The results showed that high-risk patients had poor prognosis. The results of Cox multivariate regressive analyses revealed that age and risk score were independent prediction factors of ccRCC prognosis. In addition, immune cell infiltration, the levels of gene expression at immune checkpoints, and high-risk patients more susceptible to sunitinib-targeted treatment were assessed by the risk model. Conclusion: Our team identified new prognostic markers of ccRCC and established risk models that could assess the prognosis of ccRCC patients and help determine which type of patients were more susceptible to sunitinib. These discoveries are vital for the optimization of risk stratification and personalized management.

An immune-related lncRNA risk coefficient model to predict the outcomes in clear cell renal cell carcinoma.

OBJECTIVE: Using model algorithms, we constructed an immune-related long non-coding RNAs (lncRNAs) risk coefficient model to predict outcomes for patients with clear cell renal cell carcinoma (ccRCC) to understand the infiltration of tumor immune cells and the sensitivity to immune-targeted drugs. METHODS: Open genes data were downloaded from The Cancer Genome Atlas and The Immunology Database and Analysis Portal, and immune-related lncRNAs were obtained through Pearson correlation analysis. R language software was used to obtain differentially expressed immune-related lncRNAs and immune-related lncRNA pairs. The model was constructed using least absolute shrinkage and selector operation regression analysis, and receiver operator characteristic curves were drawn. The Akaike information criterion was used to distinguish the high-risk from the low-risk group. We also conducted correlation analysis for the high- and low-risk subgroups. RESULTS: We identified 27 immune-related lncRNAs pairs, 16 of which were included in the model construction. After merging clinical data, the areas under the curve of 1 -year, 3-year, and 5-year survival times of ccRCC patients were 0.867, 0.832, and 0.838, respectively. Subgroup analyses were conducted according to the cut-off value. We found that the high-risk group was associated with poor outcomes. The risk score and tumor stage were independent predictors of the outcome of ccRCC. The risk model predicted specific immune cell infiltration, immune checkpoint gene expression levels, and high-risk groups more sensitive to sunitinib targeted therapy. CONCLUSION: We obtained prognostic-related novel ccRCC markers and risk model that predicts the outcome of patients with ccRCC and helps identify those who can benefit from sunitinib.

Development of a prognostic index and screening of prognosis related genes based on an immunogenomic landscape analysis of bladder cancer.

BACKGROUND: Bladder cancer (BLCA) is one of the most common urinary tract malignant tumors. It is associated with poor outcomes, and its etiology and pathogenesis are not fully understood. There is great hope for immunotherapy in treating many malignant tumors; therefore, it is worthwhile to explore the use of immunotherapy for BLCA. METHODS: Gene expression profiles and clinical information were obtained from The Cancer Genome Atlas (TCGA), and immune-related genes (IRGs) were downloaded from the Immunology Database and Analysis Portal. Differentially-expressed and survival-associated IRGs in patients with BLCA were identified using computational algorithms and Cox regression analysis. We also performed functional enrichment analysis. Based on IRGs, we employed multivariate Cox analysis to develop a new prognostic index. RESULTS: We identified 261 IRGs that were differentially expressed between BLCA tissue and adjacent tissue, 30 of which were significantly associated with the overall survival (all P<0.01). According to multivariate Cox analysis, nine survival-related IRGs (MMP9, PDGFRA, AHNAK, OAS1, OLR1, RAC3, IGF1, PGF, and SH3BP2) were high-risk genes. We developed a prognostic index based on these IRGs and found it accurately predicted BLCA outcomes associated with the TNM stage. Intriguingly, the IRG-based prognostic index reflected infiltration of macrophages. CONCLUSIONS: An independent IRG-based prognostic index provides a practical approach for assessing patients' immune status and prognosis with BLCA. This index independently predicted outcomes of BLCA.

Use of endoscopic tissue morcellator in removing retroperitoneal fat in retroperitoneoscopic radical nephrectomy.

BACKGROUND: Evaluate the safety and effectiveness of using an endoscopic tissue morcellator (ETM) to remove the retroperitoneal fat during retroperitoneoscopic radical nephrectomy (RRN). METHODS: The use of ETM in the removal of retroperitoneal fat was retrospectively analyzed in patients who underwent RRN for localized renal cancer in our hospital from January 2010 to January 2018. We accrued the appropriate patients and divided them into two groups. The first group included patients of RRN where ETM was used to remove the retroperitoneal fat, while the second group was comprised of patients of RRN where ETM was not performed, which served as the control group. Each group was further divided into two subgroups, including obese patients (BMI ≥ 28) and patients suffering from high-volume renal cancer (Stage T2a). The differences between the two groups as well as their subgroups were analyzed and statistically compared. RESULTS: All 222 nephrectomies were completed under retroperitoneoscopy, ETM was used in 105 of these 222 patients. Among them, 31 cases were of obese patients, and 26 cases were of high-volume renal cancer patients. The other 117 patients had undergone RRN without the use of ETM. Among them, 36 cases were of obese patients, and 28 cases were of high-volume renal cancer patients. The differences in age, BMI, tumor position, and tumor size between the two groups were not statistically significant, P > 0.05. Both the surgical time and the blood loss for the ETM group were significantly lower than the control group, p < 0.05. In the subgroup analysis, the obese patients and patients with high tumor volume also showed a significantly lower surgical time and less blood loss, p < 0.05. The postoperative hospitalization time, the total survival rate, and the disease-free survival rate were not statistically significant, p > 0.05. CONCLUSIONS: The use of ETM in removing the retroperitoneal fat during the RRN can potentially reduce the surgical time and lessen the blood loss. This technique is especially advantageous for obese and large-volume tumor patients.