Identification of a prognostic signature based on immune-related genes in bladder cancer

Identification and validation of molecular subtypes' characteristics in bladder urothelial carcinoma based on autophagy-dependent ferroptosis

Background

Nowadays, more evidences indicated that autophagy-dependent ferroptosis regulatory molecules (ADFRMs) may be closely related to various tumors. In current study, we intended to establish a prognostic ADFRMs signature and investigated its potential roles in bladder urothelial carcinoma (BLCA).

Methods

Two distinct clusters were determined by consensus clustering with expression of 119 identified ADFRMs in BLCA. The tumor microenvironment was investigated through "CIBERSORT" algorithm, and enrichment analyses were utilized to seek molecular characteristics of differentially expressed genes (DEGs) between clusters. Moreover, a 2-ADFRMs prognostic signature including TRIB3 and WIPI1 was identified in TCGA cohort and further evaluated in the GSE13507 cohort. The qRT-PCR was conducted to examine the expression of prognostic genes. Further, the risk score was gained through calculating the level of TRIB3 and WIPI1 expression through the coefficient. The correlations between risk score with clinicopathologica features, tumor microenvironment, and drug sensitivity were explored.

Results

Patients in TCGA-BLCA were grouped into two clusters with different expression patterns of ADFRMs. And the overall survival, tumor microenvironment and biological functions were significant different between two clusters. Moreover, a 2-ADFRMs model was constructed, and patients were separated into a low-risk and high-risk group. Survival analysis indicated patients with low risk promised a good prognosis, suggesting the risk score determined with ADFRMs signature exhibited an acceptable capacity for survival prediction in BLCA. Correlation analysis demonstrated risk score had close ties with age, stage, and tumor microenvironment. In vivo, the expression of prognostic genes was identified to be up-regulated in BLCA cell line T24.

Conclusion

The constructed 2-ADFRMs signature was a promising model to predict prognosis and correlated with tumor microenvironment, which had latent clinical value in the intervention for BLCA.

Correlation analysis between immune-related genes and cell infiltration revealed prostate cancer immunotherapy biomarkers linked to T cells gamma delta

Prostate cancer (PCa) is a urological malignancy with poor prognosis. Immune-related genes are associated with immune infiltration in prostate cancer, but their role in immunogenic PCa is less well understood. We assessed the infiltration patterns of 22 immune cells in PCa and the relationship of immune-related differentially expressed genes (IDEGs) with them. The 87 IDEGs are involved in the interaction between the extracellular matrix and the tumor microenvironment. The model, including seven IDEGs (SLPI, DES, IAPP, NPY, ISG15, PLA2G2A, and HLA-DMB), showed a good predictive power. The SLPI expression is positively correlated with the infiltration level of T cells gamma delta. In addition, PCa has high infiltration levels in Macrophages M1 (18.07%) and Dendritic cells activated (17.64%). The correlation analysis between IDEGs and immune cell infiltration suggested that PCa immunotherapy biomarkers may be closely related to T cells gamma delta.

Expression of ferroptosis-related gene correlates with immune microenvironment and predicts prognosis in gastric cancer

The study is to explore the role of ferroptosis-related genes (FRGs) in the occurrence and development of gastric cancer (GC), and to construct a new prognosis signature to predict the prognosis in GC. Clinical information and corresponding RNA data of GC patients were downloaded from TCGA and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, CIBERSORT, McpCounter and TIMER algorithm were used to analyze the infiltration of immune cells in two molecular subgroups. LASSO algorithm and multivariate Cox analysis were used to construct a prognostic risk signature. Functional analysis was conducted to elucidate the underlying mechanisms. Finally, the FRPGs were verified by Quantitative Real-Time PCR. We obtained 16 FRGs and divided GC patients into two subgroups by consistent clustering. Cluster C1 with a higher abundance of immune cell infiltration but lower probability in response to immunotherapy, it was reasonable to speculate that Cluster C1 was in accordance with the immune rejection type. Functional analysis showed that the biological process of DEGs in training cohort mainly included immune globulin, and human immune response mediated by circulating immune globulin. GSEA analysis showed that compared with Cluster C2, Cluster C1 showed lower expression in lipid metabolism. The nomogram combined with risk signature and clinical features can accurately predict the prognosis of GC patients. We identified two molecular subtypes, Clusters C1 and C2. In Cluster C1, patients with poor prognosis present with a hyperimmune status and low lipid metabolism, and we speculate that Cluster C1 was in accordance with the immune rejection type. The risk model based on FRPGs can accurately predict the prognosis of GC. These results indicated that ferroptosis is associated with TIME, and deserved considerable attention in determining immunotherapy treatment strategy for GC patients.

Tumor Expression Profile Analysis Developed and Validated a Prognostic Model Based on Immune-Related Genes in Bladder Cancer

Background: Bladder cancer (BLCA) ranks 10th in incidence among malignant tumors and 6th in incidence among malignant tumors in males. With the application of immune therapy, the overall survival (OS) rate of BLCA patients has greatly improved, but the 5-year survival rate of BLCA patients is still low. Furthermore, not every BLCA patient benefits from immunotherapy, and there are a limited number of biomarkers for predicting the immunotherapy response. Therefore, novel biomarkers for predicting the immunotherapy response and prognosis of BLCA are urgently needed.

Methods: The RNA sequencing (RNA-seq) data, clinical data and gene annotation files for The Cancer Genome Atlas (TCGA) BLCA cohort were extracted from the University of California, Santa Cruz (UCSC) Xena Browser. The BLCA datasets GSE31684 and GSE32894 from the Gene Expression Omnibus (GEO) database were extracted for external validation. Immune-related genes were extracted from InnateDB. Significant differentially expressed genes (DEGs) were identified using the R package “limma,” and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis for the DEGs were performed using R package “clusterProfiler.” Least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the signature model. The infiltration level of each immune cell type was estimated using the single-sample gene set enrichment analysis (ssGSEA) algorithm. The performance of the model was evaluated with receiver operating characteristic (ROC) curves and calibration curves.

Results: In total, 1,040 immune-related DEGs were identified, and eight signature genes were selected to construct a model using LASSO regression analysis. The risk score of BLCA patients based on the signature model was negatively correlated with OS and the immunotherapy response. The ROC curve for OS revealed that the model had good accuracy. The calibration curve showed good agreement between the predictions and actual observations.

Conclusions: Herein, we constructed an immune-related eight-gene signature that could be a potential biomarker to predict the immunotherapy response and prognosis of BLCA patients.

Development of a Ferroptosis-Related lncRNA Signature to Predict the Prognosis and Immune Landscape of Bladder Cancer

The tight relationship between ferroptotic cell death and immune response demonstrated by recent studies enlightened us to detect the underlying roles of ferroptosis-related long noncoding RNAs (frlncRNAs) in the tumor microenvironment of bladder cancer (BCa). We collected 121 ferroptosis regulators from previous studies. Based on their expression values, 408 cases with BCa were clustered. The patients in different clusters showed diverse immune infiltration, immunotherapy response, and chemotherapy effectiveness, revalidating the tight correlation with ferroptosis and tumor immunity. Through differential, coexpression, Kaplan-Meier, Lasso, and Cox analysis, we developed a 22-lncRNA-pair signature to predict the prognosis of BCa based on gene-pair strategy, where there is no need for definite expression values. The areas under the curves are all over 0.8. The risk model also helped to predict immune infiltration, immunotherapeutic outcomes, and chemotherapy sensitivity. Totally, the prognostic assessment model indicated a promising predictive value, also providing clues for the interaction between ferroptosis and BCa immunity.