Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer

LRP1 induces anti-PD-1 resistance by modulating the DLL4-NOTCH2-CCL2 axis and redirecting M2-like macrophage polarisation in bladder cancer

The tumour microenvironment (TME) drives bladder cancer (BLCA) progression. Targeting the TME has emerged as a promising strategy for BLCA treatment in recent years. Furthermore, checkpoint blockade therapies are only beneficial for a minority of patients with BLCA, and drug resistance is a barrier to achieving significant clinical effects of anti-programmed cell death protein-1 (PD-1)/programmed death protein ligand-1 (PD-L1) therapy. In this study, higher low-density lipoprotein receptor-related protein 1 (LRP1) levels were related to a poorer prognosis for patients with various cancers, including those with higher grades and later stages of BLCA. Enrichment analysis demonstrated that LRP1 plays a role in the epithelial-mesenchymal transition (EMT), NOTCH signalling pathway, and ubiquitination. LRP1 knockdown in BLCA cells delayed BLCA progression both in vivo and in vitro. Furthermore, LRP1 knockdown suppressed EMT, reduced DLL4-NOTCH2 signalling activity, and downregulated M2-like macrophage polarisation. Patients with BLCA and higher LRP1 levels responded weakly to anti-PD-1 therapy in the IMvigor210 cohort. Moreover, LRP1 knockdown enhanced the therapeutic effects of anti-PD-1 in mice. Taken together, our findings suggest that LRP1 is a potential target for improving the efficacy of anti-PD-1/PD-L1 therapy by preventing EMT and M2-like macrophage polarisation by blocking the DLL4-NOTCH2 axis.

Identification and immune landscape analysis of fatty acid metabolism genes related subtypes of gastric cancer

Fatty acid metabolism (FAM) is associated with prognosis and immune microenvironment remodeling in many tumors. It is currently unknown how FAM affects the immunological microenvironment and prognosis of Gastric cancer (GC). Therefore, the current work aims to categorize GC samples based on the expression status of genes involved in FAM and to identify populations that might benefit from immunotherapy. In total, 50 FAM genes associated with overall survival (OS) were determined through univariate Cox proportional hazard regression analysis by mining the public TCGA and GEO databases. The GSE84437 and TCGA-STAD cohort samples were divided into two clusters using the "NMF" R package. According to the survival curve, patients in Cluster-1 showed considerably longer OS than those in Cluster-2. Patients in Cluster-1 exhibited earlier T stages, more intestinal GCs, and were older. MSI molecular subtypes were mainly distributed in Cluster-1, while GS molecular subtypes were distributed primarily in Cluster-2. There were 227 upregulated and 22 down-regulated genes (logFC > 1 or logFC < - 1, FDR < 0.05) in Cluster-2 compared with Cluster-1. One hub module (edges = 64, nodes = 12) was identified with a module score of 11.636 through Cytoscape plug-in MCODE. KEGG and GO analysis showed that the hub genes were associated with the cell cycle and cell division. Different immune cell infiltrates profile, and immune pathway enrichment existed between the subtypes. In conclusion, the current findings showed that practically all immunological checkpoint and immunoregulatory genes were elevated in patients with Cluster-2 GC, indicating that FAM subtypes may be crucial in GC immunotherapy.

Prognosis analysis and validation of lipid metabolism-associated lncRNAs and tumor immune microenvironment in bladder cancer

BACKGROUND

Numerous types of research revealed that long noncoding RNAs (lncRNAs) played a significant role in immune response and the tumor microenvironment of bladder cancer (BLCA). Dysregulated lipid metabolism is considered to be one of the major risk factors for BLCA, the study aimed to detect the lipid metabolism-related lncRNAs (LMRLs) along with their potential prognostic values and immune correlations in BLCA.

METHODS

We collected lipid metabolism-related genes, expression profiles, and clinical information on BLCA from the Molecular Signature Database (MSigDB) and the TCGA database, respectively. Differentially expressed lipid metabolism genes (DE-LMRGs) and differentially expressed long non-coding RNAs (DE-lncRNAs) were selected using the limma package. Spearman correlation analysis was employed to explore the correlations between DE-lncRNAs and DE-LMRGs and to further develop protein-protein interaction (PPI) networks and perform mutational analysis. The least absolute shrinkage and selection operator (LASSO) and univariate Cox analysis were then employed to construct a prognostic risk model. The performance of the model was evaluated using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and consistency indices. In addition, we downloaded the GSE31684 dataset for external validation of the prognostic signature. Moreover, we explored the association of the risk model with immune cell infiltration and chemotherapy response analysis to reveal the tumor immune microenvironment of BLCA. Finally, RT-qPCR was utilized to validate the expression of prognostic genes.

RESULTS

A total of 48 DE-LncRNAs and 33 DE-LMRGs were found to be robustly correlated, and were used to construct a lncRNA-mRNA co-expression network, in which ACACB, ACOX2, and BCHE showed high mutation rates. Then, a risk model based on three LMRLs (RP11-465B22.8, MIR100HG, and LINC00865) was constructed. The risk model effectively distinguished between the clinical outcomes of BLCA patients, with high-risk scores indicating a worse prognosis and with substantial prognostic prediction accuracy. The model's results were consistent in the GSE31684 dataset. In addition, a nomogram was constructed based on the risk score, age, pathological T-stage, and pathological N-stage, which showed robust predictive power. Immune landscape analysis indicated that the risk model was significantly associated with T-cell CD4 memory activation, M1 macrophage, M2 macrophage, dendritic cell activation, and T-cell regulatory. We predicted that 49 drugs would perform satisfactorily in the high-risk group. Additionally, we found five m6A regulators associated with the high- and low-risk groups, suggesting that upstream regulation of LncRNA could be a novel target for BLCA treatment. Finally, RT-qPCR showed that RP11-465B22.8 was highly expressed in BLCA, while MIR100HG and LINC00865 were downregulated in BLCA.

CONCLUSION

Our findings suggest that the three LMRLs may serve as potential prognostic and immunotherapeutic biomarkers in BLCA. In addition, our study provides new ideas for understanding the pathogenic mechanisms and developing therapeutic strategies for BLCA patients.

LDL receptor related protein 1 is an adverse prognostic biomarker that correlates with stromal remodeling and macrophages infiltration in bladder cancer

Introduction

Bladder cancer (BLCA) is a highly heterogeneous disease influenced by the tumor microenvironment, which may affect patients' response to immune checkpoint blockade therapy. Therefore, identifying molecular markers and therapeutic targets to improve treatment is essential. In this study, we aimed to investigate the prognostic significance of LRP1 in BLCA.

Methods

We analyzed TCGA and IMvigor210 cohorts to investigate the relationship of LRP1 with BLCA prognosis. We utilized gene mutation analysis and enrichment to identify LRP1-associated mutated genes and biological processes. Deconvolution algorithms and single-cell analysis were used to understand the tumor-infiltrated cells and biological pathways associated with LRP1 expression. Immunohistochemistry was conducted to validate the bioinformatics analysis.

Results

Our study revealed that LRP1 was an independent risk factor for overall survival in BLCA patients and was associated with clinicopathological features and FGFR3 mutation frequency. Enrichment analysis demonstrated that LRP1 was involved in extracellular matrix remodeling and tumor metabolic processes. Furthermore, the ssGSEA algorithm revealed that LRP1 was positively correlated with the activities of tumor-associated pathways. Our study also found that high LRP1 expression impaired patients' responsiveness to ICB therapy in BLCA, which was predicted by TIDE prediction and validated by IMvigor210 cohort. Immunohistochemistry confirmed the expression of LRP1 in Cancer-Associated Fibroblasts (CAFs) and macrophages in the tumor microenvironment of BLCA.

Discussion

Our study suggests that LRP1 may be a potential prognostic biomarker and therapeutic target in BLCA. Further research on LRP1 may improve BLCA precision medicine and enhance the efficacy of immune checkpoint blockade therapy.

Construction of cancer- associated fibroblasts related risk signature based on single-cell RNA-seq and bulk RNA-seq data in bladder urothelial carcinoma

Background

The ability of cancer-associated fibroblasts (CAFs) to encourage angiogenesis, tumor cell spread, and increase treatment resistance makes them pro-tumorigenic. We aimed to investigate the CAF signature in Bladder urothelial carcinoma (BLCA) and, for clinical application, to build a CAF-based risk signature to decipher the immune landscape and screen for suitable treatment BLCA samples.

Methods

CAF-related genes were discovered by superimposing CAF marker genes discovered from single-cell RNA-seq (scRNA-seq) data taken from the GEO database with CAF module genes discovered by weighted gene co-expression network analysis (WGCNA) using bulk RNA-seq data from TCGA. After identifying prognostic genes related with CAF using univariate Cox regression, Lasso regression was used to build a risk signature. With microarray data from the GEO database, prognostic characteristics were externally verified. For high and low CAF-risk categories, immune cells and immunotherapy responses were analyzed. Finally, a nomogram model based on the risk signature and prospective chemotherapeutic drugs were examined.

Results

Combining scRNA-seq and bulk-seq data analysis yielded a total of 124 CAF-related genes. LRP1, ANXA5, SERPINE2, ECM1, RBP1, GJA1, and FKBP10 were the seven BLCA prognostic genes that remained after univariate Cox regression and LASSO regression analyses. Then, based on these genes, prognostic characteristics were created and validated to predict survival in BLCA patients. Additionally, risk signature had a strong correlation with known CAF scores, stromal scores, and certain immune cells. The CAF-risk signature was identified as an independent prognostic factor for BLCA using multifactorial analysis, and its usefulness in predicting immunotherapy response was confirmed. Based on risk classification, we projected six highly sensitive anticancer medicines for the high-risk group.

Conclusion

The prognosis of BLCA may be accurately predicted using CAF-based risk signature. With a thorough understanding of the BLCA CAF-signature, it might be able to explain the BLCA patients' response to immunotherapy and identify a potential target for BLCA treatment.

Identification of a metabolism-related gene signature predicting overall survival for bladder cancer

Reprogramming of metabolism is becoming a novel hallmark of cancer. This study aims to perform bioinformatics analysis of metabolism-related genes in bladder cancer, and to construct a signature of metabolism-related genes for predicting the prognosis. A total of 373 differentially expressed metabolism-related genes were identified from TCGA database. Taking survival time and clinical information into consideration, we constructed a risk score to predict clinical prognosis. Low-risk patients had a better prognosis than high-risk patients. Multivariate analysis showed that risk score was an independent prognostic indicator in bladder cancer. ROC curve also proved that risk score had better ability to predict prognosis than other individual indicators. Nomogram also showed a clinical net benefit to evaluate the prognosis of bladder cancer patients. GSEA revealed several metabolism-related pathways that were differentially enriched in the high-risk and low-risk groups, which might help to explain the underlying mechanisms. This signature was confirmed to be an effective prognostic biomarker in bladder cancer.

An HDAC9-associated immune-related signature predicts bladder cancer prognosis

Background

The close relationship between histone deacetylase 9 (HDAC9) and immunity has attracted attention. We constructed an immune signature for HDAC9, a vital epigenetic modification, to predict the survival status and treatment benefits in bladder cancer (BC).

Methods

An exhaustive analysis of HDAC9 and immunology via the tumor and immune system interaction database (TISIDB) was performed, and an immune prognostic risk signature was developed based on genes enriched in the top five immune-related pathways under high HDAC9 status. Comprehensive analysis of survival curves and Cox regression were used to estimate the effectiveness of the risk signature. The relationship between immunological characteristics and the risk score was evaluated, and the mechanisms were also explored.

Results

In the TISIDB, HDAC9 was closely related to various immunological characteristics. The risk signature was obtained based on genes related to prognosis enriched in the top five immune-related pathways under high HDAC9 status. The survival rate of the high-risk BC patients was poor. The risk score was closely related to multiple immunological characteristics, drug sensitivity, immunotherapy benefits and biofunctions.

Conclusion

An immune-related prognostic signature established for HDAC9 expression status could independently predict the prognosis of BC patients. The use of this signature could help clinicians make personalized treatment decisions.

Ogt Demonstrated Conspicuous Clinical Significance in Cancers, from Pan-Cancer to Small-Cell Lung Cancer

The clinical progression of small-cell lung cancer (SCLC) remains pessimistic. The aim of the present study was to promote the understanding of the clinical significance and mechanism of O-linked N-acetylglucosamine (GlcNAc) transferase (OGT) in SCLC. Wilcoxon tests, standardized mean difference (SMD), and Kruskal–Wallis tests were utilized to compare OGT level differences among the experimental and control groups. The univariate Cox regression analysis, Kaplan–Meier curves, and receiver operating characteristic curves were applied to determine OGT's clinical relevance in cancers. The Spearman correlation analysis and enrichment analysis were utilized to explore the underlying mechanisms of OGT in cancers. For the first time in the field, we provide an overview of OGT in 32 cancers using a large number of samples (n = 21,196), determining distinct OGT expression in 25 cancers and its prognosis effects in 12 cancers. Furthermore, using 950 samples from multiple sources, upregulated OGT was found in both mRNA and protein levels in SCLC (SMD = 0.93, 95% CI [0.24, 1.63]). Higher OGT levels represented a more unfavorable disease-free interval for SCLC patients (p < 0.001). The research also identified OGT expression as a potential marker for SCLC prediction (sensitivity = 0.79, specificity = 0.86, and AUC = 0.88). The high expression of OGT in SCLC may result from the positive regulation of two transcription factors—DEK and XRN2. We primarily investigated the underlying mechanisms of OGT in SCLC. Herein, based on the analyses from pan-cancer to SCLC, OGT demonstrated conspicuous clinical significance. OGT may be an underlying biomarker for the treatment and identification of some cancers, including SCLC.