A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer

Single-cell RNA-binding protein pattern-mediated molecular subtypes depict the hallmarks of the tumor microenvironment in bladder urothelial carcinoma

Objectives

Bladder carcinoma (BC) is a common malignancy of the urinary tract. As a new hallmark of cancer for drug therapy, RNA-binding proteins (RBPs) are key regulatory factors in alternative splicing events. This work is to uncover the relationship between BC and RBP in order to find drug targets in BC.

Methods

In this work, data from single-cell RNA-seq GSE1355337, PRJNA662018, and the TCGA-Bladder urothelial carcinoma (BLCA) cohorts are integrated to identify their relationships. A scoring system is constructed according to RBPs gene expression and patients’ survival. A network is constructed to analyze the alternative splicing events and RBP genes.

Results

A scoring system identified 321 RBPs significantly associated with the prognosis of patients. Subsequent typing of these RBP genes in two single-cell datasets demonstrated that most of the RBP genes had variable copy numbers. Three RBP clusters were identified. Using RBP genes as a signature in BC epithelial cells allows for differentiation between different grades of BC samples. The novel RBP genes-based subtype system reflects BC clinical staging. Notably, CellChat analysis revealed that the RBP genes-associated cell subtypes of T cells had extensive interactions with epithelial cells. Further analysis showed that the ligand-receptor pair MIF-CXCR4 mediated the communication between RBP-associated subtypes of BC epithelial cells and T cells.

Conclusions

Taken together, RBP genes are associated with BC progress and offer new indicators for precision medicine in BC.

Construction of endothelial cell signatures for predicting the diagnosis, prognosis and immunotherapy response of bladder cancer via machine learning

We subtyped bladder cancer (BC) patients based on the expression patterns of endothelial cell (EC) -related genes and constructed a diagnostic signature and an endothelial cell prognostic index (ECPI), which are useful for diagnosing BC patients, predicting the prognosis of BC and evaluating drug sensitivity. Differentially expressed genes in ECs were obtained from the Tumour Immune Single-Cell Hub database. Subsequently, a diagnostic signature, a tumour subtyping system and an ECPI were constructed using data from The Cancer Genome Atlas and Gene Expression Omnibus. Associations between the ECPI and the tumour microenvironment, drug sensitivity and biofunctions were assessed. The hub genes in the ECPI were identified as drug candidates by molecular docking. Subtype identification indicated that high EC levels were associated with a worse prognosis and immunosuppressive effect. The diagnostic signature and ECPI were used to effectively diagnose BC and accurately assess the prognosis of BC and drug sensitivity among patients. Three hub genes in the ECPI were extracted, and the three genes had the closest affinity for doxorubicin and curcumin. There was a close relationship between EC and BC. EC-related genes can help clinicians diagnose BC, predict the prognosis of BC and select effective drugs.

Activated Mast Cells Combined with NRF2 Predict Prognosis for Esophageal Cancer

Background

Esophageal cancer (EC) had the sixth-highest mortality rate of all cancers due to its poor prognosis. Immune cells and mutation genes influenced the prognosis of EC, but their combined effect on predicting EC prognosis was unknown. In this study, we comprehensively analyzed the immune cell infiltration (ICI) and mutation genes and their combined effects for predicting prognosis in EC.

Methods

The CIBERSORT and ESTIMATE algorithms were used to analyse the ICI scape based on the TCGA and GEO databases. EC tissues and pathologic sections from Huai'an, China, were used to verify the key immune cells and mutation genes and their interactions.

Results

Stromal/immune score patterns and ICI/gene had no statistical significance in overall survival (OS) (p > 0.05). The combination of ICI and tumor mutation burden (TMB) showed that the high TMB and high ICI score group had the shortest OS (p = 0.004). We recognized that the key mutation gene NRF2 was significantly different in the high/low ICI score subgroups (p = 0.002) and positivity with mast cells (MCs) (p < 0.05). Through experimental validation, we found that the MCs and activated mast cells (AC-MCs) were more infiltration in stage II/III (p = 0.032; p = 0.013) of EC patients and that NRF2 expression was upregulated in EC (p = 0.045). AC-MCs combined with NRF2 had a poor prognosis, according to survival analysis (p = 0.056) and interactive analysis (p = 0.032).

Conclusions

We presume that NRF2 combined with AC-MCs could be a marker to predict prognosis and could influence immunotherapy through regulating PD-L1 in the EC.

Three Prognostic Biomarkers Correlate with Immune Checkpoint Blockade Response in Bladder Urothelial Carcinoma

Aim

We aim to develop a signature that could accurately predict prognosis and evaluate the response to immune checkpoint blockade (ICB) in bladder urothelial carcinoma (BLCA).

Methods

Based on comprehensive analysis of public database, we identified prognosis-related hub genes and investigated their predictive values for the ICB response in BLCA.

Results

Among 69 common DEGs, three genes (AURKA, BIRC5, and CKS1B) were associated with poor prognosis, and which were related to histological subtypes, TP53 mutation status, and the C2 (IFN-gamma dominant) subtype. Three genes and their related risk model can effectively predict the response of immunotherapy. Their related drugs were identified through analysis of drug bank database.

Conclusions

Three genes could predict prognosis and evaluate the response to ICB in BLCA.

An Individualized Prognostic Signature for Clinically Predicting the Survival of Patients With Bladder Cancer

Background: The tumor immune microenvironment (TIME) plays an important role in the development and prognosis of bladder cancer. It is essential to conduct a risk model to explore the prognostic value of the immunologic genes and establish an individualized prognostic signature for predicting the survival of patients with bladder cancer.

Method: The differentially expressed immunologic genes (DEGs) are identified in The Cancer Genome Atlas (TCGA). The nonnegative matrix factorization (NMF) was used to stratify the DEGs in TCGA. We used the least absolute shrinkage and selection operator (LASSO) Cox regression and univariate Cox analysis to establish a prognostic risk model. A nomogram was used to establish an individualized prognostic signature for predicting survival. The potential pathways underlying the model were explored.

Results: A total of 1,018 DEGs were screened. All samples were divided into two clusters (C1 and C2) by NMF with different immune cell infiltration, and the C2 subtype had poor prognosis. We constructed a 15-gene prognostic risk model from TCGA cohort. The patients from the high-risk group had a poor overall survival rate compared with the low-risk group. Time-dependent ROC curves demonstrated good predictive ability of the signature (0.827, 0.802, and 0.812 for 1-, 3-, and 5-year survival, respectively). Univariate and multivariate Cox regression analyses showed that the immunologic prognostic risk model was an independent factor. The decision curve demonstrated a relatively good performance of the risk model and individualized prognostic signature, showing the best net benefit for 1-, 3-, and 5-year OS. Gene aggregation analysis showed that the high-risk group was mainly concentrated in tumorigenesis and migration and immune signaling pathways.

Conclusion: We established a risk model and an individualized prognostic signature, and these may be useful biomarkers for prognostic prediction of patients with bladder cancer.

Identification and validation of an immune-related gene pairs signature for three urologic cancers

Reliable biomarkers are needed to recognize urologic cancer patients at high risk for recurrence. In this study, we built a novel immune-related gene pairs signature to simultaneously predict recurrence for three urologic cancers. We gathered 14 publicly available gene expression profiles including bladder, prostate and kidney cancer. A total of 2,700 samples were classified into the training set (n = 1,622) and validation set (n = 1,078). The 25 immune-related gene pairs signature consisting of 41 unique genes was developed by the least absolute shrinkage and selection operator regression analysis and Cox regression model. The signature stratified patients into high- and low-risk groups with significantly different relapse-free survival in the meta-training set and its subpopulations, and was an independent prognostic factor of urologic cancers. This signature showed a robust ability in the meta-validation and multiple independent validation cohorts. Immune and inflammatory response, chemotaxis and cytokine activity were enriched with genes relevant to the signature. A significantly higher infiltration level of M1 macrophages was found in the high-risk group versus the low-risk group. In conclusion, our signature is a promising prognostic biomarker for predicting relapse-free survival in patients with urologic cancer.