Identification of a novel immune microenvironment signature predicting survival and therapeutic options for bladder cancer

A Multi-Gene Signature of Non-Muscle-Invasive Bladder Cancer Identifies Patients Who Respond to Immunotherapies Including Bacillus Calmette-Guérin and Immune Checkpoint Inhibitors

Approximately 75% of bladder cancer cases originate as non-muscle-invasive bladder cancer (NMIBC). Despite initial diagnosis, NMIBC commonly recurs, with up to 45% advancing to muscle-invasive bladder cancer (MIBC) and metastatic disease. Treatment for high-risk NMIBC typically includes procedures like transurethral resection and, depending on recurrence risk, intravesical chemotherapy or immunotherapy such as Bacillus Calmette-Guérin (BCG). However, persistent shortages of BCG necessitate alternative first-line treatments. We aim to use a multi-gene signature in high-risk NMIBC patients to determine whether patients may benefit from immune checkpoint inhibitors (ICIs) as an alternative to BCG and to evaluate their clinical utility. The multi-gene signature obtained from the three independent NMIBC cohorts was applied to stratify the UROMOL2016 cohort (n = 476) using consensus clustering. Each subtype was distinguished by biological pathway analysis. Validation analysis using a machine learning algorithm was performed in six independent cohorts including the BRS (n = 283) cohort treated with BCG and the IMvigor210 (n = 298) clinical trials treated with PD-L1 inhibitors. Based on consensus cluster analysis, NMIBC patients in the UROMOL2016 cohort were classified into three classes exhibiting distinguished characteristics, including DNA damage repair (DDR). Survival analysis showed that the NMIBC-DDR class had the highest rates of disease progression (progression-free survival, p = 0.002 by log-rank test) in the UROMOL cohort and benefited from BCG and ICIs (respectively, p = 0.02 and p = 0.03 by log-rank test). This study suggests that the multi-gene signature may have a role in identifying high-risk NMIBC patients and improving the responsiveness of ICIs. Additionally, we propose immunotherapy as a new first-line treatment for patients with high-risk NMIBC because of the shortage of BCG supply. It is important to help more patients prioritize cancer immunotherapy.

Prognostic impact of tumor-associated neutrophils in breast cancer

OBJECTIVES

Neutrophils are the most common type of leukocyte in mammals and play an essential role in the innate immune system and anti-cancer responses. However, recent studies identified the presence of tumor-associated neutrophils (TANs) as a poor prognostic factor. The present study investigated whether relationships exist between TANs and the clinicopathological factors and genetic status of breast cancer.

METHODS

A total of 196 breast cancer patients with sufficient biopsy, breast-conserving surgery, or mastectomy specimens between 2014 and 2021 in Hokuto Hospital were included.

RESULTS

TANs were individually counted in the tumor stroma (TS) and tumor nest (TN). A higher density of TANs in both TS and TN correlated with tumor size (TS P = 0.010; TN P = 0.001), a high histological grade (TS P < 0.001; TN P < 0.001), the histological type (TS P = 0.009; TN P = 0.034), a high ratio of lymph node metastasis (TS P < 0.001; TN P < 0.001), an advanced stage of cancer (TS P < 0.001; TN P = 0.002), intrinsic subtypes (TS P < 0.001; TN P < 0.001), ERBB2 (TS P < 0.001; TN P < 0.001), MAP3K1 (TS P = 0.002; TN P = 0.023), and TP53 (TS P < 0.001; TN P < 0.001). A higher density of TANs in TS and TN also correlated with shorter disease-free survival and overall survival (P < 0.001).

CONCLUSION

The present results suggest that a higher density of TANs correlates with unfavorable prognostic factors in breast cancer. Further research on clinicopathological and genetic factors associated with TANs in breast cancer is needed.

Clinical Significance and Tumor Microenvironment Characterization of a Novel Immune-Related Gene Signature in Bladder Cancer

Cancer immunotherapy plays a crucial role in bladder cancer (BC) progression. Increasing evidence has elucidated the clinicopathologic significance of the tumor microenvironment (TME) in predicting outcomes and therapeutic efficacy. This study sought to establish a comprehensive analysis of the immune-gene signature combined with TME to assist in BC prognosis. We selected sixteen immune-related genes (IRGs) after a weighted gene co-expression network and survival analysis. Enrichment analysis revealed that these IRGs were actively involved in Mitophagy and Renin secretion pathways. After multivariable COX analysis, the IRGPI comprising NCAM1, CNTN1, PTGIS, ADRB3, and ANLN was established to predict the overall survival of BC, which was validated in both TCGA and GSE13507 cohorts. In addition, a TME gene signature was developed for molecular and prognosis subtyping with unsupervised clustering, followed by a panoramic landscape characterization of BC. In summary, the IRGPI model developed in our study provided a valuable tool with an improved prognosis for BC.

Comprehensive analysis of cuproptosis-related genes on bladder cancer prognosis, tumor microenvironment invasion, and drug sensitivity

Cuproptosis, a newly discovered form of programmed cell death, plays a vital role in the occurrence and development of tumors. However, the role of cuproptosis in the bladder cancer tumor microenvironment remains unclear. In this study, we developed a method for predicting the prognostic outcomes and guiding the treatment selection for patients with bladder cancer. We obtained 1001 samples and survival data points from The Cancer Genome Atlas database and Gene Expression Omnibus database. Using cuproptosis-related genes (CRGs) identified in previous studies, we analyzed CRG transcriptional changes and identified two molecular subtypes, namely high- and low-risk patients. The prognostic features of eight genes (PDGFRB, COMP, GREM1, FRRS1, SDHD, RARRES2, CRTAC1, and HMGCS2) were determined. The CRG molecular typing and risk scores were correlated with clinicopathological features, prognosis, tumor microenvironment cell infiltration characteristics, immune checkpoint activation, mutation burden, and chemotherapy drug sensitivity. Additionally, we constructed an accurate nomogram to improve the clinical applicability of the CRG_score. qRT-PCR was used to detect the expression levels of eight genes in bladder cancer tissues, and the results were consistent with the predicted results. These findings may help us to understand the role of cuproptosis in cancer and provide new directions for the design of personalized treatment and prediction of survival outcomes in patients with bladder cancer.

Distinct immune and inflammatory response patterns contribute to the identification of poor prognosis and advanced clinical characters in bladder cancer patients

Due to the molecular heterogeneity, most bladder cancer (BLCA) patients show no pathological responses to immunotherapy and chemotherapy yet suffer from their toxicity. This study identified and validated three distinct and stable molecular clusters of BLCA in cross-platform databases based on personalized immune and inflammatory characteristics. H&E-stained histopathology images confirmed the distinct infiltration of immune and inflammatory cells among clusters. Cluster-A was characterized by a favorable prognosis and low immune and inflammatory infiltration but showed the highest abundance of prognosis-related favorable immune cell and inflammatory activity. Cluster-B featured the worst prognosis and high immune infiltration, but numerous unfavorable immune cells exist. Cluster-C had a favorable prognosis and the highest immune and inflammatory infiltration. Based on machine learning, a highly precise predictive model (immune and inflammatory responses signature, IIRS), including FN1, IL10, MYC, CD247, and TLR2, was developed and validated to identify the high IIRS-score group that had a poor prognosis and advanced clinical characteristics. Compared to other published models, IIRS showed the highest AUC in 5 years of overall survival (OS) and a favorable predictive value in predicting 1- and 3- year OS. Moreover, IIRS showed an excellent performance in predicting immunotherapy and chemotherapy's response. According to immunohistochemistry and qRT-PCR, IIRS genes were differentially expressed between tumor tissues with corresponding normal or adjacent tissues. Finally, immunohistochemical and H&E-stained analyses were performed on the bladder tissues of 13 BLCA patients to further demonstrate that the IIRS score is a valid substitute for IIR patterns and can contribute to identifying patients with poor clinical and histopathology characteristics. In conclusion, we established a novel IIRS depicting an IIR pattern that could independently predict OS and acts as a highly precise predictive biomarker for advanced clinical characters and the responses to immunotherapy and chemotherapy.

Establishment and Validation of a Tumor Microenvironment Prognostic Model for Predicting Bladder Cancer Survival Status Based on Integrated Bioinformatics Analyses

This study was designed to analyze the characteristics of bladder cancer-related genes and establish a prognostic model of bladder cancer. The model passed an independent external validation set test. Differentially expressed genes (DEGs) related to bladder cancer were obtained from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) databases. WGCNA was used to fit the GSE188715, TCGA, and GTEx RNA-Seq data. Fusing the module genes with the high significance in tumor development extracted from WGCNA and DEGs screened from multiple databases. 709 common prognostic-related genes were obtained. The 709 genes were enriched in the Gene Ontology database. Univariate Cox and LASSO regression analyses were used to screen out 21 prognostic-related genes and further multivariate Cox regression established a bladder cancer prognostic model consisting of 8 genes. After the eight-gene prognostic model was established, the Human Protein Atlas (HPA) database, GEPIA 2, and quantitative real-time PCR (qRT-PCR) verified the differential expression of these genes. Gene Set Enrichment Analysis and immune infiltration analysis found biologically enrichment pathways and cellular immune infiltration related to this bladder cancer prognostic model. Then, we selected bladder cancer patients in the TCGA database to evaluate the predictive ability of the model on the training set and validation set. The overall survival status of the two TCGA patient groups in the training and the test sets was obtained by Kaplan–Meier survival analysis. Three-year survival rates in the training and test sets were 37.163% and 25.009% for the low-risk groups and 70.000% and 62.235% for the high-risk groups, respectively. Receiver operating characteristic curve (ROC) analysis showed that the areas under the curve (AUCs) for the training and test sets were above 0.7. In an external independent validation database GSE13507, Kaplan–Meier survival analysis showed that the three-year survival rates of the high-risk and the low-risk groups in this database were 56.719% and 76.734%, respectively. The AUCs of the ROC drawn in the external validation set were both above 0.65. Here, we constructed a prognostic model of bladder cancer based on data from the GEO, TCGA, and GTEx databases. This model has potential prognostic and clinical auxiliary diagnostic value.

Pyroptosis-Related Patterns Predict Tumor Immune Landscape and Immunotherapy Response in Bladder Cancer

Background: Bladder cancer (BC) is a leading cause of death from malignancy, with significant heterogeneity in the immunotherapeutic responsiveness of advanced status. Pyroptosis, a newly discovered inflammatory programmed cell death, is confirmed to play an indispensable role in tumorigenesis and anti-tumor activity. However, the effect of pyroptosis on the tumor-immune landscape remodeling and immunotherapy in BC remains elusive.

Methods: We comprehensively evaluated the mRNA expression and genomic alterations of 33 pyroptosis-related genes (PRGs) in BC and evaluated the patterns of pyroptosis in publicly available BC datasets. An unsupervised clustering method was used to classify patients into distinct patterns. Then, we established a pyroptosis-related signature score (PS-score) model to quantify the pyroptosis-related patterns of individual BC patients using principal component analysis. Furthermore, we correlated the patterns with the immune landscape and response efficacy of immunotherapy.

Results: Two pyroptosis-related patterns were identified in BC, and distinct patterns showed various immune characteristics. Patterns with a high expression level of PRGs exhibited a survival advantage and showed higher infiltration of cytotoxic lymphocytes. Tumors with a low PS-score were characterized by high tumor-infiltrating lymphocytes and considered “hot.” Further analysis revealed that the PS-score was an independent prognostic factor and could predict the response to immunotherapy for patients with advanced BC. We found a significant positive association between AHNAK2, AHNAK nucleoprotein 2, expression, and PS-score. Functional assays showed that AHNAK2 knockdown was correlated with attenuated invasive ability.

Conclusion: This work comprehensively demonstrated the potential function of pyroptosis-related patterns in the bladder tumor-immune landscape and identified their therapeutic liability in immunotherapy. Our study enhanced our understanding of the immune landscape and provided a new approach toward more effective immunotherapy strategies.

A Novel Ferroptosis-Related Prognostic Signature Reveals Macrophage Infiltration and EMT Status in Bladder Cancer

Bladder cancer (BC) belongs to one of the most common and highly heterogeneous malignancies. Ferroptosis is a newly discovered regulated cell death (RCD), characterized by accumulation of toxic lipid peroxides, and plays a crucial role in tumor progression. Here, we conducted a comprehensive analysis on the transcriptomics data of ferroptosis-related genes in BC based on The Cancer Genome Atlas (TCGA) and three Gene Expression Omnibus (GEO) datasets. In our study, a 6-gene signature was identified based on the potential prognostic ferroptotic regulatory genes. Furthermore, our signature revealed a good independent prognostic ability in BC. Patients with low-risk score exhibited higher FGFR3 mutation rates while high risk score had a positive association with higher RB1 mutation rates. Meanwhile, higher proportions of macrophages were observed in high BC risk group simultaneously with four methods. Unexpectedly, the risk score showed a significant positive correlation with epithelial-mesenchymal transition (EMT) status. Functional assays indicated that CRYAB and SQLE knockdown was associated with attenuated invasion capacity. Our study revealed a ferroptosis-related risk model for predicting prognostic and BC progression. Our results indicate that targeting ferroptosis may be a therapeutic strategy for BC.

Construction and External Validation of a Ferroptosis-Related Gene Signature of Predictive Value for the Overall Survival in Bladder Cancer

Purpose: To identify whether ferroptosis-related genes play predictive roles in bladder cancer patients and to develop a ferroptosis-related gene signature to predict overall survival outcomes.

Materials and Methods: We downloaded the mRNA expression files and clinical data of 256 bladder samples (188 bladder tumour and 68 nontumour samples) from the GEO database and 430 bladder samples (411 bladder tumour and 19 nontumour samples) from the TCGA database. A multigene signature based on prognostic ferroptosis-related genes was constructed by least absolute shrinkage and selection operator Cox regression analysis in the GEO cohort. The TCGA cohort was used to validate the ferroptosis-related gene signature. Next, functional enrichment analysis, including both Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses, was performed to elucidate the mechanism underlying the signature. The ssGSEA scores of 16 immune cells and 13 immune-related pathway activities between the high-risk and low-risk groups were also analysed in our study.

Results: Thirty-three (67.3%) ferroptosis-related genes were differentially expressed between bladder tumour samples and nontumour samples in the GEO cohort. The intersection of prognostic ferroptosis-related genes and differentially expressed genes identified four prognostic targets, including ALOX5, FANCD2, HMGCR and FADS2. The least absolute shrinkage and selection operator Cox regression successfully built a 4-gene signature: risk score value = e^sum (each gene’s normalized expression * each gene’s coefficient). Univariate and multivariate Cox regression analyses were performed in both the GEO and TCGA cohorts to test the independent prognostic value of the 4-gene risk signature. Multivariate Cox regression analysis in the GEO cohort identified age (p < 0.001), grade (p = 0.129) and risk score (p = 0.016) as independent prognostic predictors for overall survival. Multivariate Cox regression analysis in the TCGA cohort also identified age (p = 0.002), stage (p < 0.001) and risk score (p = 0.006) as independent prognostic predictors for overall survival. The type II IFN response was determined to be significantly weakened in the high-risk group in both the GEO and TCGA cohorts.

Conclusion: We successfully built a ferroptosis-related gene signature of significant predictive value for bladder cancer. These results suggest a novel research direction for targeted therapy of bladder cancer in the future.