Tumor microenvironment heterogeneity in bladder cancer identifies biologically distinct subtypes predicting prognosis and anti-PD-L1 responses.

Bladder cancer (BCa) is heterogeneous in the tumour microenvironment (TME). However, the role of the TME in BCa in modulating the response to immunotherapy has not been fully explored. We therefore analysed fractions of immune cells using CIBERSORTx and clustered BCa into subtypes. We also analyzed weighted correlation networks to generate immunotherapy-related hub genes that we used to construct a prediction model using multivariate Cox and LASSO regression analyses. We found that BCa comprised three subtypes (C1‒C3). The prognosis of the patients was the most favourable and the response rate to anti-programmed death ligand 1 (PD-L1) was the highest in C1 among the three subtypes. Immune cells, including CD8+, CD4+ memory activated, and follicular helper T cells, activated NK cells, and M1 macrophages infiltrated the C1 subtype. The C2 subtype was enriched in M0 macrophages and activated mast cells, and the C3 subtype was enriched in B and resting immune cells. Mechanistically, the enhanced immunogenicity of subtypes C1 and C2 correlated positively with a higher response rate, whereas the dysregulated ECM-related pathways in the C2 subtype and glycolytic and fatty acid metabolic pathways in the C3 subtype impaired the responses of patients to anti-PD-L1 therapy. We also constructed a TME-related signature based on 18 genes that performed well in terms of overall survival. In conclusion, we determined prognoses and anti-PD-L1 responses by analysing TME heterogeneity in BCa.

Clinical characteristics and prostate-cancer-specific mortality of competitive risk nomogram in the second primary prostate cancer.

Background: With the development of early diagnosis and treatment, the second primary malignancy (SPM) attracts increasing attention. The second primary prostate cancer (spPCa) is an important class of SPM, but remains poorly understood. Methods: We retrospectively analyzed 3,322 patients with spPCa diagnosed between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. Chi-square test was applied to compare demographic and clinical variables and analyze causes of death. Multivariate competitive risk regression model was used to identify risk factors associated with prostate-cancer-specific mortality (PCSM), and these factors were enrolled to build a nomogram of competitive risk. The C-index, calibration curve, and decision curve analysis (DCA) were employed to evaluate the discrimination ability of our nomogram. Results: The median follow-up (interquartile range, IQR) time was 47 (24-75) months, and the median (IQR) diagnosis interval between the first primary cancer (FPC) and spPCa was 32 (16-57) months. We found that the three most common sites of SPM were the urinary system, digestive system, and skin. Through multivariate competitive risk analysis, we enrolled race (p < 0.05), tumor-node-metastasis (TNM) stage (p < 0.001), Gleason score (p < 0.05), surgery (p = 0.002), and radiotherapy (p = 0.032) to construct the model to predict the outcomes of spPCa. The C-index was 0.856 (95% CI, 0.813-0.899) and 0.905 (95% CI, 0.941-0.868) in the training and validation set, respectively. Moreover, both the calibration curve and DCA illustrated that our nomogram performed well in predicting PCSM. Conclusion: In conclusion, we identified four risk factors associated with the prognosis of spPCa and construct a competing risk nomogram, which performed well in predicting the 3-, 5-, and 10-year PCSM.

Clinical value of M1 macrophage-related genes identification in bladder urothelial carcinoma and in vitro validation.

Background: Tumor microenvironment (TME) takes a non-negligible role in the progression and metastasis of bladder urothelial carcinoma (BLCA) and tumor development could be inhibited by macrophage M1 in TME. The role of macrophage M1-related genes in BLCA adjuvant therapy has not been studied well. Methods: CIBERSOR algorithm was applied for identification tumor-infiltrating immune cells (TICs) subtypes of subjects from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data sets. We identified potential modules of M1 macrophages by weighted gene co-expression network analysis (WGCNA). Nomogram was determined by one-way Cox regression and lasso regression analysis for M1 macrophage genes. The data from GEO are taken to verify the models externally. Kaplan-Meier and receiver operating characteristic (ROC) curves validated prognostic value of M1 macrophage genes. Finally, we divided patients into the low-risk group (LRG) and the high-risk group (HRG) based on the median risk score (RS), and the predictive value of RS in patients with BLCA immunotherapy and chemotherapy was investigated. Bladder cancer (T24, 5637, and BIU-87) and bladder uroepithelial cell line (SV-HUC-1) were used for in vitro validation. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was employed to validate the associated genes mRNA level. Results: 111 macrophage M1-related genes were identified using WGCNA. RS model containing three prognostically significant M1 macrophage-associated genes (FBXO6, OAS1, and TMEM229B) was formed by multiple Cox analysis, and a polygenic risk model and a comprehensive prognostic line plot was developed. The calibration curve clarified RS was a good predictor of prognosis. Patients in the LRG were more suitable for programmed cell death protein 1 (PD1) and cytotoxic T lymphocyte associate protein-4 (CTLA4) combination immunotherapy. Finally, chemotherapeutic drug models showed patients in the LRG were more sensitive to gemcitabine and mitomycin. RT-qPCR result elucidated the upregulation of FBXO6, TMEM229B, and downregulation of OAS1 in BLCA cell lines. Conclusion: A predictive model based on M1 macrophage-related genes can help guide us in the treatment of BLCA.

Clinical M2 Macrophage-Related Genes Can Serve as a Reliable Predictor of Lung Adenocarcinoma.

Background: Numerous studies have found that infiltrating M2 macrophages play an important role in the tumor progression of lung adenocarcinoma (LUAD). However, the roles of M2 macrophage infiltration and M2 macrophage-related genes in immunotherapy and clinical outcomes remain obscure. Methods: Sample information was extracted from TCGA and GEO databases. The TIME landscape was revealed using the CIBERSORT algorithm. Weighted gene co-expression network analysis (WGCNA) was used to find M2 macrophage-related gene modules. Through univariate Cox regression, lasso regression analysis, and multivariate Cox regression, the genes strongly associated with the prognosis of LUAD were screened out. Risk score (RS) was calculated, and all samples were divided into high-risk group (HRG) and low-risk group (LRG) according to the median RS. External validation of RS was performed using GSE68571 data information. Prognostic nomogram based on risk signatures and other clinical information were constructed and validated with calibration curves. Potential associations of tumor mutational burden (TMB) and risk signatures were analyzed. Finally, the potential association of risk signatures with chemotherapy efficacy was investigated using the pRRophetic algorithm. Results: Based on 504 samples extracted from TCGA database, 183 core genes were identified using WGCNA. Through a series of screening, two M2 macrophage-related genes (GRIA1 and CLEC3B) strongly correlated with LUAD prognosis were finally selected. RS was calculated, and prognostic risk nomogram including gender, age, T, N, M stage, clinical stage, and RS were constructed. The calibration curve shows that our constructed model has good performance. HRG patients were suitable for new ICI immunotherapy, while LRG was more suitable for CTLA4-immunosuppressive therapy alone. The half-maximal inhibitory concentrations (IC50) of the four chemotherapeutic drugs (metformin, cisplatin, paclitaxel, and gemcitabine) showed significant differences in HRG/LRG. Conclusions: In conclusion, a comprehensive analysis of the role of M2 macrophages in tumor progression will help predict prognosis and facilitate the advancement of therapeutic techniques.

Clinical Eosinophil-Associated Genes can Serve as a Reliable Predictor of Bladder Urothelial Cancer.

Background: Numerous studies have shown that infiltrating eosinophils play a key role in the tumor progression of bladder urothelial carcinoma (BLCA). However, the roles of eosinophils and associated hub genes in clinical outcomes and immunotherapy are not well known. Methods: BLCA patient data were extracted from the TCGA database. The tumor immune microenvironment (TIME) was revealed by the CIBERSORT algorithm. Candidate modules and hub genes associated with eosinophils were identified by weighted gene co-expression network analysis (WGCNA). The external GEO database was applied to validate the above results. TIME-related genes with prognostic significance were screened by univariate Cox regression analysis, lasso regression, and multivariate Cox regression analysis. The patient's risk score (RS) was calculated and divided subjects into high-risk group (HRG) and low-risk group (LRG). The nomogram was developed based on the risk signature. Models were validated via receiver operating characteristic (ROC) curves and calibration curves. Differences between HRG and LRG in clinical features and tumor mutational burden (TMB) were compared. The Immune Phenomenon Score (IPS) was calculated to estimate the immunotherapeutic significance of RS. Half-maximal inhibitory concentrations (IC50s) of chemotherapeutic drugs were predicted by the pRRophetic algorithm. Results: 313 eosinophil-related genes were identified by WGCNA. Subsequently, a risk signature containing 9 eosinophil-related genes (AGXT, B3GALT2, CCDC62, CLEC1B, CLEC2D, CYP19A1, DNM3, SLC5A9, SLC26A8) was finally developed via multiplex analysis and screening. Age (p < 0.001), grade (p < 0.001), and RS (p < 0.001) were independent predictors of survival in BLCA patients. Based on the calibration curve, our risk signature nomogram was confirmed as a good predictor of BLCA patients' prognosis at 1, 3, and 5 years. The association analysis of RS and immunotherapy indicated that low-risk patients were more credible for novel immune checkpoint inhibitors (ICI) immunotherapy. The chemotherapeutic drug model suggests that RS has an effect on the drug sensitivity of patients. Conclusions: In conclusion, the eosinophil-based RS can be used as a reliable clinical predictor and provide insights into the precise treatment of BLCA.

Identification of a Novel Tumor Microenvironment Prognostic Signature for Bladder Urothelial Carcinoma.

Background: The tumor microenvironment (TME) regulates the proliferation and metastasis of solid tumors and the effectiveness of immunotherapy against them. We investigated the prognostic role of TME-related genes based on transcriptomic data of bladder urothelial carcinoma (BLCA) and formulated a prediction model of TME-related signatures. Methods: Molecular subtypes were identified using the non-negative matrix factorization (NMF) algorithm based on TME-related genes from the TCGA database. TME-related genes with prognostic significance were screened with univariate Cox regression analysis and lasso regression. Nomogram was developed based on risk genes. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used for inner and outer validation of the model. Risk scores (RS) of patients were calculated and divided into high-risk group (HRG) and low-risk group (LRG) to compare the differences in clinical characteristics and PD-L1 treatment responsiveness between HRG and LRG. Results: We identified two molecular subtypes (C1 and C2) according to the NMF algorithm. There were significant differences in overall survival (OS) (p<0.05), progression-free survival (PFS) (p<0.05), and immune cell infiltration between the two subtypes. A total of eight TME-associated genes (CABP4, ZNF432, BLOC1S3, CXCL11, ANO9, OAS1, FBN2, CEMIP) with independent prognostic significance were screened to build prognostic risk models. Age (p<0.001), grade (p<0.001), and RS (p<0.001) were independent predictors of survival in BLCA patients. The developed RS nomogram was able to predict the prognosis of BLCA patients at 1, 3, and 5 years more potentially than the models of other investigators according to ROC and DCA. RS showed significantly higher values (p = 0.047) in patients with stable disease (SD)/progressive disease (PD) compared to patients with complete response (CR)/partial response (PR). Conclusions: We successfully clustered and constructed predictive models for TME-associated genes and helped guide immunotherapy strategies.

Application of m6A and TME in Predicting the Prognosis and Treatment of Clear Cell Renal Cell Carcinoma.

Background: Previous studies have shown that RNA N6-methyladenosine (m6A) plays an important role in the construction of the tumor microenvironment (TME). However, how m6A plays a role in the TME of clear cell renal cell carcinoma remains unclear. Methods: Based on 23 m6A modulators, we applied consensus cluster analysis to explore the different m6A modification profiles of ccRCC. The CIBERSORT method was employed to reveal the correlation between TME immune cell infiltration and different m6A modification patterns. A m6A score was constructed using a principal component analysis algorithm to assess and quantify the m6A modification patterns of individual tumors. Results: Three distinct m6A modification patterns of ccRCC were identified. The characteristics of TME cell infiltration in these three patterns were consistent with immune rejection phenotype, immune inflammation phenotype, and immune desert phenotype. In particular, when m6A scores were high, TME was characterized by immune cell infiltration and patient survival was higher (p < 0.05). When m6A scores were low, TME was characterized by immunosuppression and patient survival was lower (p < 0.05). The immunotherapy cohort confirmed that patients with higher m6A scores had significant therapeutic advantages and clinical benefits. Conclusions: The m6A modification plays an important role in the formation of TME. The m6A scoring system allows the identification of m6A modification patterns in individual tumors, discriminates the immune infiltrative features of TME, and provides more effective prognostic indicators and treatment strategies for immunotherapy.

CXCL12 and CD3E as Indicators for Tumor Microenvironment Modulation in Bladder Cancer and Their Correlations With Immune Infiltration and Molecular Subtypes.

Bladder cancer (BLCA) represents the ninth most common malignant tumor in the world and is characterized by high recurrence risk. Tumor microenvironment (TME) plays an important role in regulating the progression of BLCA. Immunotherapy, including Bacillus Calmette-Guerin (BCG) and programmed cell death protein 1 (PD-1)/programmed death ligand 1 (PD-L1), is closely associated with TME and is widely used for treating BLCA. But parts of BLCA patients have no response to these treatment ways, thus a better understanding of the complex TME of BLCA is still needed. We downloaded the gene expression profile and corresponding clinical information of 414 BLCA patients from the TCGA database. Via the ESTIMATE and CIBERSORT algorithm, we identified the two hub genes (CXCL12 and CD3E) and explored their correlations with immune infiltration. We found that BLCA patients with higher expression of CXCL12 and lower expression of CD3E had prolonged survival. Gene set enrichment analysis (GSEA) revealed that both CXCL12 and CD3E were enriched in immune-related pathways. We also discovered that stromal score and the level of CXCL12 were higher in luminal subtype, and immune score and the level of CD3E were higher in the basal subtype. Furtherly, we found that CXCL12 was associated with naive B cells, resting mast cell, M2 macrophages, follicular helper T cells, and dendritic cells. CD8+ T cells, CD4+ T cells, regulatory T cells (Tregs), and macrophages were correlated with CD3E. In conclusions, we found that CXCL12 and CD3E might serve as indicators of TME modulation in BLCA. Therapy targeting CXCL12 and CD3E had the potential as novel therapeutic strategy.

Clinical characteristics and overall survival nomogram of second primary malignancies after prostate cancer, a SEER population-based study.

Prostate cancer (PCa) is the most prevalent cancer among males and the survival period of PCa has been significantly extended. However, the probability of suffering from second primary malignancies (SPMs) has also increased. Therefore, we downloaded SPM samples from the SEER database and then retrospectively analyzed the general characteristics of 34,891 PCa patients diagnosed between 2000 and 2016. After excluding cases with unknown clinical information, 2203 patients were used to construct and validate the overall survival (OS) nomogram of SPM patients after PCa. We found that approximately 3.69% of PCa patients were subsequently diagnosed with SPMs. In addition, the three most prevalent sites of SPM were respiratory and intrathoracic organs, skin, and hematopoietic system. The top three histological types of SPMs were squamous cell carcinoma, adenoma and adenocarcinoma, nevi and melanoma. Through univariate and multivariate Cox regression analysis, we found that the site of SPM, age, TNM stage, SPM surgery history, and PCa stage were associated with the OS of SPM. By virtue of these factors, we constructed a nomogram to predict the OS of SPM. The C-index in the training set and validation set were 0.824 (95CI, 0.806-0.842) and 0.862 (95CI, 0.840-0.884), respectively. Furthermore, we plotted the receiver operating characteristic curve (ROC) and the area under curve (AUC) which showed that our model performed well in assessing the 3-year (0.861 and 0.887) and 5-year (0.837 and 0.842) OS of SPMs in the training and validation set. In summary, we investigated the general characteristics of SPMs and constructed a nomogram to predict the prognosis of SPM following PCa.

Identification of the Six-RNA-Binding Protein Signature for Prognosis Prediction in Bladder Cancer.

RNA-binding proteins (RBPs) are a kind of gene regulatory factor that presents a significant biological effect in the initiation and development of various tumors, including bladder cancer (BLCA). However, the RBP-based prognosis signature for BLCA has not been investigated. In this study, we attempted to develop an RBP-based classifier to predict overall survival (OS) for BLCA based on transcriptome analysis. We extracted data of BLCA patients from The Cancer Genome Atlas database (TCGA) and UCSC Xena. Finally, a total of 398 cases without missing clinical data were enrolled and six RBPs (FLNA, HSPG2, AHNAK, FASTKD3, POU5F1, and PCSK9) associated with OS of BLCA were identified through univariate and multivariate Cox regression analysis. Online analyses and immunohistochemistry validated the prognostic value and expression of six RBPs. Risk scores were calculated to divide patients into high-risk and low-risk level, and patients in the high-risk group tended to have a poor prognosis. In addition, the receiver operating characteristic (ROC) curve analysis was performed to assess the prognostic value of RBPs, and the area under the curve (AUC) values were 0.711 and 0.706, respectively, in the training set and validating set. The findings were further validated in an external validation set. Subsequently, the 6-RBP-based signature and pathological stage were used to construct the nomogram to predict the 3- and 5-years OS of BLCA patients. Also, this 6-RBP-based signature was highly related to recurrence-free survival of BLCA. Weighted co-expression network analysis (WGCNA) combined with functional enrichment analysis contributed to study the potential pathways of six RBPs, including keratinocyte differentiation, RHO GTPases activate PNKs, epithelial tube morphogenesis, establishment or maintenance of cell polarity, and so on. In summary, the 6-RBP-based signature holds the potentiality to serve as a novel prognostic predictor of OS for BLCA.

Nomograms to predict overall and cancer-specific survival in patients with penile cancer.

BACKGROUND: To develop and validate prognostic nomograms for predicting overall survival (OS) and cancer-specific survival (CSS) in patients with penile cancer (PC). METHODS: Based on the Surveillance, Epidemiology, and End Result (SEER) database, patients diagnosed with PC from 2010 to 2015 were enrolled in this study. For each patient, clinical characteristics and survival results were respectively collected. With the method of random-number generation, included patients were divided into the training cohort and the validation group. Subsequently, nomograms were constructed to predict 3- and 5-year OS and CSS based on the results of multivariate analyses. Kaplan-Meier (KM) method and the log-rank test were used to estimate survival curves of each variables. Finally, the calibration plots, concordance index (C-index), area under the receiver operating characteristic (ROC) curves were used to evaluate nomograms performance. RESULTS: Totally, 1,418 patients were eventually enrolled in the study, including 994 patients in the training cohort and 424 patients in the validation cohort. No significant difference was detected in the baseline characteristics between two cohorts. According to results of the uni- and multivariate analysis in the training cohort, 7 factors (including age, race, T stage, N stage, M stage, histology codes, and the use of surgery) for OS and 7 factors (including race, T stage, N stage, M stage, histology codes, the use of surgery and lymph node removal) for CSS were selected for constructing the nomograms. The C-indices for OS and CSS were 0.755 and 0.805 in the training cohort and 0.711, 0.737 in the validation cohort. In addition, the 3- and 5-year area under the ROC curve (AUC)s for OS were 0.792 and 0.771 in the training cohort, and 0.687 and 0.695 in the validation group. When it came to CSS, it was 0.83 and 0.826 in the training cohort and 0.758 and 0.746 in the validation cohort. Lastly, the calibration curves indicated a good consistency between the actual survival and the predictive survival. CONCLUSIONS: We firstly established survival models to predict OS and CSS in PC patients with good predictive ability. Further studies are needed to validate our results before clinical application in the future.

Research progress on bladder cancer molecular genetics.

Bladder cancer is a common malignant urinary tumor with a high rate of recurrence and quick progression, which threats human health. With the research on bladder cancer molecular genetics, the knowledge of gene modification and the development of molecular detection methods, more tumor markers have been discovered, which may have potential for early diagnosis, clinical examination and prognosis. This article reviews the research progress on bladder cancer molecular genetics.

Quantitative assessment of the association between miR-196a2 rs11614913 polymorphism and cancer risk: evidence based on 45,816 subjects.

MicroRNAs (miRNAs) are small non-coding RNA molecules, which participate in diverse biological processes and may regulate tumor suppressor genes or oncogenes. Single nucleotide polymorphisms (SNPs) in miRNA may contribute to diverse functional consequences, including cancer development, by altering miRNA expression. Numerous studies have shown the association between miR-196a2 rs11614913 SNPs and cancer risk; however, the results are generally debatable and inconclusive, mainly due to limited statistical power. We carried out a meta-analysis of 46 studies including 20,673 cases and 25,143 controls to assess the association between the miR-196a2 rs11614913 and cancer risk by pooled odds ratios (ORs) and 95 % confidence intervals (CIs). Overall, we found a significant association between the rs11614913 (C > T) polymorphism and cancer susceptibility (recessive model, OR = 0.89, 95 % CI = 0.81-0.98). In the stratified analysis by cancer type, significant association of cancer risk was observed in lung cancer (allelic contrast, OR = 0.89, 95 % CI = 0.82-0.97; homozygote comparison, OR = 0.79, 95 % CI = 0.67-0.94; recessive model, OR = 0.84, 95 % CI = 0.74-0.96) and liver cancer (allelic contrast, OR = 0.88, 95 % CI = 0.79-0.99; homozygote comparison, OR = 0.77, 95 % CI = 0.61-0.98; heterozygote comparison, OR = 0.84, 95 % CI = 0.74-0.95; dominant model, OR = 0.82, 95 % CI = 0.73-0.92). During further stratified analysis by ethnicity, the rs11614913 polymorphism showed statistically significant association with increased risks of cancer in Asians (heterozygote model, OR = 1.15, 95 % CI = 1.01-1.30) but not in Caucasians. This meta-analysis suggests that the miR-196a2 rs11614913 polymorphism may contribute to decreased susceptibility to cancer, especially including liver cancer and lung cancer. However, it may be a risk factor for cancer development in Asians. Larger, better studies of homogeneous cancer patients are needed to further assess the correlation between this polymorphism and cancer risk.