A novel 6-gene signature derived from tumor-infiltrating T cells and neutrophils predicts survival of bladder urothelial carcinoma

EMP1 correlated with cancer progression and immune characteristics in pan-cancer and ovarian cancer

This study examines the prognostic role and immunological relevance of EMP1 (epithelial membrane protein-1) in a pan-cancer analysis, with a focus on ovarian cancer. Utilizing data from TCGA, CCLE, and GTEx databases, we assessed EMP1 mRNA expression and its correlation with tumor progression, prognosis, and immune microenvironment across various cancers. Our results indicate that EMP1 expression is significantly associated with poor prognosis in multiple cancer types, including ovarian, bladder, testicular, pancreatic, breast, brain, and uveal melanoma. Immune-related analyses reveal a positive correlation between EMP1 and immune cell infiltration, particularly neutrophils, macrophages, and dendritic cells, as well as high expression of immune checkpoint such as CD274, HAVCR2, IL10, PDCD1LG2, and TGFB1 in most tumors. In vivo experiments confirm that EMP1 promotes ovarian cancer cell proliferation, metastasis, and invasion. In conclusion, EMP1 emerges as a potential prognostic biomarker and therapeutic target in various cancers, particularly ovarian cancer, due to its influence on tumor progression and immune cell dynamics. Further research is warranted to elucidate the precise mechanisms of EMP1 in cancer biology and to translate these findings into clinical applications.

From mucosal infection to successful cancer immunotherapy

The clinical management of advanced malignancies of the upper and lower urinary tract has been revolutionized with the advent of immune checkpoint blockers (ICBs). ICBs reinstate or bolster pre-existing immune responses while creating new T cell specificities. Immunogenic cancers, which tend to benefit more from immunotherapy than cold tumours, harbour tumour-specific neoantigens, often associated with a high tumour mutational burden, as well as CD8+ T cell infiltrates and ectopic lymphoid structures. The identification of beneficial non-self tumour antigens and natural adjuvants is the focus of current investigation. Moreover, growing evidence suggests that urinary or intestinal commensals, BCG and uropathogenic Escherichia coli influence long-term responses in patients with kidney or bladder cancer treated with ICBs. Bacteria infecting urothelium could be a prominent target for T follicular helper cells and B cells, linking innate and cognate CD8+ memory responses. In the urinary tract, commensal flora differ between healthy and tumoural mucosae. Although antibiotics can affect the prognosis of urinary tract malignancies, bacteria can have a major influence on cancer immunosurveillance. Beyond their role as biomarkers, immune responses against uropathogenic commensals could be harnessed for the design of future immunoadjuvants that can be advantageously combined with ICBs.

Construction and experimental validation of a B cell-related gene signature to predict the prognosis and immunotherapeutic sensitivity in bladder cancer

BACKGROUND

B cells are essential components of tumor microenvironment and exert important functions in anti-tumor immune response. However, the prognosis value of B cell-related genes in bladder cancer (BLCA) remains obscure.

MATERIALS AND METHODS

The infiltrating levels of B cells were measured via the CD20 staining in the local samples and the computational biology analyses in the TCGA-BLCA cohort. The single-cell RNA sequencing analysis, gene-pair strategy, LASSO regression, random forest, and Cox regression were used for B cell-related signature construction. TCGA-BLCA cohort was chosen as the training cohort, and three independent cohorts from GEO and the local cohort were used for external validation. 326 B cells were adopted to explore the association between the model and B cells' biological processes. TIDE algorithm and two BLCA cohorts receiving anti-PD1/PDL1 treatment were utilized to detect its predictive ability to the immunotherapeutic response.

RESULTS

High infiltration levels of B cells heralded favorable prognosis, both in the TCGA-BLCA cohort and the local cohort (all P < 0.05). A 5-gene-pair model was established and served as a significant prognosis predictor across multiple cohorts (pooled hazard ratio = 2.79, 95% confidence interval = 2.22-3.49). The model could evaluate the prognosis effectively in 21 of 33 cancer types (P < 0.05). The signature was negatively associated with B cells' activation, proliferation, and infiltrating levels, and could serve as a potential predictor of immunotherapeutic outcomes.

CONCLUSIONS

A B cell-related gene signature was constructed to predict the prognosis and immunotherapeutic sensitivity in BLCA, helping to guide the personalized treatment.

Development and evaluation of an adenosine-to-inosine RNA editing-based prognostic model for survival prediction of bladder cancer patients

Adenosine-to-inosine RNA editing (ATIRE) is a common form of ribonucleic acid (RNA) editing, which has highlighted the importance of ATIRE in tumors. However, its role in bladder cancer (BLCA) remains poorly understood. To study ATIRE impact on BLCA patient prognosis, we obtained ATIRE, gene expression, and clinical data from the Cancer Genome Atlas (TCGA) database for 251 patients, randomly dividing them into training and testing groups. Univariate proportional hazards model (COX) regression identified prognosis-associated ATIRE loci, while the least absolute shrinkage and selection operator (LASSO) selected final loci to construct prognostic models and generate ATIRE scores. We developed a nomogram to predict BLCA patients' overall survival (OS) and analyzed the effect of ATIRE editing levels on host gene expression. We also compared immune cell infiltration and drug treatment between patients with high and low ATIRE scores. The ATIRE prognostic prediction model was constructed using ten ATIRE loci that are closely associated with BLCA survival. Patients with high ATIRE scores showed significantly worse OS than those with low ATIRE scores. Furthermore, the nomogram, which incorporates the ATIRE score, can better predict the prognosis of patients. Multiple functional and pathway changes associated with immune responses, as well as significant differences in immune cell infiltration levels and response to drug therapy were observed between patients with high and low ATIRE scores. This study represented the first comprehensive analysis of the role of ATIRE events in BLCA patient prognosis and provided new insights into potential prognostic markers for BLCA research.

Construction and validation of a prognosis signature based on the immune microenvironment in gastric cancer

Background

Gastric cancer (GC) is an aggressive malignant tumor with a high degree of heterogeneity, and its immune microenvironment is closely associated with tumor growth, development and drug resistance. Therefore, a classification system of gastric cancer based explicitly on the immune microenvironment context might enrich the strategy for gastric cancer prognosis and therapy.

Methods

A total of 668 GC patients were collected from TCGA-STAD (n = 350), GSE15459 (n = 192), GSE57303 (n = 70) and GSE34942 (n = 56) datasets. Three immune-related subtypes (immunity-H, -M, and -L) were identified by hierarchical cluster analysis based on the ssGSEA score of 29 immune microenvironment-related gene sets. The immune microenvironment-related prognosis signature (IMPS) was constructed via univariate Cox regression, Lasso-Cox regression and multivariate Cox regression, and nomogram model combining IMPS and clinical variables was further constructed by the “rms” package. RT-PCR was applied to validate the expression of 7 IMPS genes between two human GC cell lines (AGS and MKN45) and one normal gastric epithelial cell line (GES-1).

Results

The patients classified as immunity-H subtype exhibited highly expressed immune checkpoint and HLA-related genes, with enriched naïve B cells, M1 macrophages and CD8 T cells. We further constructed and validated a 7-gene (CTLA4, CLDN6, EMB, GPR15, ENTPD2, VWF and AKR1B1) prognosis signature, termed as IMPS. The patients with higher IMPS expression were more likely to be associated with higher pathology grade, more advanced TNM stages, higher T and N stage, and higher ratio of death. In addition, the prediction values of the combined nomogram in predicting 1-year (AUC = 0.750), 3-year (AUC = 0.764) and 5-year (AUC = 0.802) OS was higher than IMPS and individual clinical characteristics.

Conclusions

The IMPS is a novel prognosis signature associated with the immune microenvironment and clinical characteristics. The IMPS and the combined nomogram model provide a relatively reliable predictive index for predicting the survival outcomes of gastric cancer.

Identification of immune-related and autophagy-related genes for the prediction of survival in bladder cancer

Background

Bladder cancer has the characteristics of high morbidity and mortality, and the prevalence of bladder cancer has been increasing in recent years. Immune and autophagy related genes play important roles in cancer, but there are few studies on their effects on the prognosis of bladder cancer patients.

Methods

Using gene expression data from the TCGA-BLCA database, we clustered bladder cancer samples into 6 immune-related and autophagy-related molecular subtypes with different prognostic outcomes based on 2208 immune-related and autophagy-related genes. Six subtypes were divided into two groups which had significantly different prognosis. Differential expression analysis was used to explore genes closely related to the progression of bladder cancer. Then we used Cox stepwise regression to define a combination of gene expression levels and immune infiltration indexes to construct the risk model. Finally, we built a Nomogram which consist of risk score and several other prognosis-related clinical indicators.

Results

The risk model suggested that high expression of C5AR2, CSF3R, FBXW10, FCAR, GHR, OLR1, PGLYRP3, RASGRP4, S100A12 was associated with poor prognosis, while high expression level of CD96, IL10, MEFV pointed to a better prognosis. Validation by internal and external dataset suggested that our risk model had a high ability to discriminate between the outcomes of patients with bladder cancer. The immunohistochemical results basically confirmed our results. The C-Index value and Calibration curves verified the robustness of Nomogram.

Conclusions

Our study constructed a model that included a risk score for patients with bladder cancer, which provided a lot of helps to predict the prognosis of patients with bladder cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12863-022-01073-7.

Multiomics analysis of ferroptosis-related molecular subtypes in muscle-invasive bladder cancer immunotherapy

BACKGROUND

The purpose of this study was to identify the ferroptosis-related molecular subtypes in muscle invasive bladder cancer (MIBC) associated with the tumor microenvironment (TME) and immunotherapy.

METHODS

Expression profiles and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) dataset. Nonnegative matrix factorization (NMF) analysis was performed to identify two molecular subtypes based on 41 ferroptosis-related prognostic genes. The differences between the two subtypes were compared in terms of prognosis, somatic mutations, gene ontology (GO), cytokines, pathways, immune cell infiltrations, stromal/immune scores, tumor purity and response to immunotherapy. We also constructed a risk prediction model using multivariate Cox regression analysis to analyze survival data based on differentially expressed genes (DEGs) between subtypes. In combination with clinicopathological features, a nomogram was constructed to provide a more accurate prediction for overall survival (OS).

RESULTS

Two molecular subtypes (C1 and C2) of MIBC were identified according to the expression of ferroptosis-related genes. The C2 subtype manifested poor prognosis, high enrichment in the cytokine-cytokine receptor interaction pathway, high abundance of immune cell infiltration, immune/stromal scores and low tumor purity. Additionally, C2 is less sensitive to immunotherapy. The risk prediction model based on five pivotal genes (SLC1A6, UPK3A, SLC19A3, CCL17 and UGT2B4) effectively predicted the prognosis of MIBC patients.

CONCLUSIONS

A novel MIBC classification approach based on ferroptosis-related gene expression profiles was established to provide guidance for patients who are more sensitive to immunotherapy. A nomogram with a five-gene signature was built to predict the prognosis of MIBC patients, which would be more accurate when combined with clinical factors.