The oncogenic role of the cerebral endothelial cell adhesion molecule (CERCAM) in bladder cancer cells in vitro and in vivo

Multi-cohort validation of Ascore: an anoikis-based prognostic signature for predicting disease progression and immunotherapy response in bladder cancer

Bladder cancer ranks as the 10th most common cancer worldwide, with deteriorating prognosis as the disease advances. While immune checkpoint inhibitors (ICIs) have shown promise in clinical therapy in both operable and advanced bladder cancer, identifying patients who will respond is challenging. Anoikis, a specialized form of cell death that occurs when cells detach from the extracellular matrix, is closely linked to tumor progression. Here, we aimed to explore the anoikis-based biomarkers for bladder cancer prognosis and immunotherapeutic decisions. Through consensus clustering, we categorized patients from the TCGA-BLCA cohort into two clusters based on anoikis-related genes (ARGs). Significant differences in survival outcome, clinical features, tumor immune environment (TIME), and potential ICIs response were observed between clusters. We then formulated a four-gene signature, termed "Ascore", to encapsulate this gene expression pattern. The Ascore was found to be closely associated with survival outcome and served as an independent prognosticator in both the TCGA-BLCA cohort and the IMvigor210 cohort. It also demonstrated superior predictive capacity (AUC = 0.717) for bladder cancer immunotherapy response compared to biomarkers like TMB and PD-L1. Finally, we evaluated Ascore's independent prognostic performance as a non-invasive biomarker in our clinical cohort (Gulou-Cohort1) using circulating tumor cells detection, achieving an AUC of 0.803. Another clinical cohort (Gulou-Cohort2) consisted of 40 patients undergoing neoadjuvant anti-PD-1 treatment was also examined. Immunohistochemistry of Ascore in these patients revealed its correlation with the pathological response to bladder cancer immunotherapy (P = 0.004). Impressively, Ascore (AUC = 0.913) surpassed PD-L1 (AUC = 0.662) in forecasting immunotherapy response and indicated better net benefit. In conclusion, our study introduces Ascore as a novel, robust prognostic biomarker for bladder cancer, offering a new tool for enhancing immunotherapy decisions and contributing to the tailored treatment approaches in this field.

CERCAM is a prognostic biomarker associated with immune infiltration of macrophage M2 polarization in head and neck squamous carcinoma

PURPOSE

This study aimed to investigate the relevance of cerebral endothelial cell adhesion molecule (CERCAM) expression to head and neck squamous cell carcinoma (HNSCC) prognosis and immune infiltration by macrophage M2 polarization.

METHODS

Timer, UALCAN and HPA databases was used to analyze the differences in mRNA and protein levels of CERCAM expression in HNSCC. The Timer database was also applied to analyze the correlation between CERCAM in HNSCC and immune infiltration. TCGA-HNSCC database was applied to analyze the correlation between CERCAM expression levels and clinicopathological features, and its diagnostic and prognostic value in HNSCC was also assessed. The cBioPortal and MethSurv databases were then applied to analyze the genetic variation and methylation status of CERCAM. In vitro cellular assays were performed to provide evidence that CERCAM promotes malignant biological behavior of tumors and promotes macrophage M2 polarization in tumors. Finally, underlying pathophysiological mechanisms of CERCAM involvement in the development of HNSCC were predicted using a bioinformatics approach.

RESULTS

CERCAM is significantly overexpressed in HNSCC and correlates with poor prognostic levels and has good performance in predicting survival status in HNSCC patients. Cox regression analysis indicates that CERCAM expression levels are independent risk factors for predicting OS, DSS, and PFI. CERCAM promotes tumor malignant biological behavior and promotes macrophage M2 polarization immune infiltration in HNSCC. In addition, CERCAM promotes tumor cell adhesion in head and neck squamous carcinoma and promotes tumor progression through several oncogenic signaling pathways.

CONCLUSION

CERCAM may serve as a new diagnostic and prognostic biomarker in HNSCC and is a promising therapeutic target for HNSCC.

Anoikis-associated signatures predict prognosis and immune response in bladder cancer

Objective: Anoikis is a type of programmed cell death that occurs in normal epithelial and endothelial cells. However, the specific role of anoikis regulators (ANRs) in bladder cancer (BLCA) remains unknown. Therefore, the objective of this study was to find subgroups that could identify different levels of anoikis resistance in BLCA and construct an anoikis scoring system to assess prognosis. Method: By obtaining ANRs from public datasets, subgroups of BLCA with varying degrees of anoikis resistance were identified, and risk was determined. Result: ANRs affects the occurrence and prognosis of BLCA and can be predicted by establishing risk models. Conclusion: The anoikis scoring system and anoikis-associated risk profiles may help develop more effective and personalized treatment strategies for BLCA patients.

The prognostic value of hedgehog signaling in bladder cancer by integrated bioinformatics

Bladder cancer is the second most prevalent urological malignancy. It's a big contributor to cancer-related deaths throughout the globe. Researchers discovered that the hedgehog signaling (HhS) pathway contributed to the onset and spread of many different kinds of cancer. Nevertheless, the present understanding of the function of HhS in the bladder cancer molecular landscape is incomplete. Raw data were gotten from the IMvigor210, the Gene Expression Omnibus, and The Cancer Genome Atlas databases. Bioinformatics was used to examine the HhS score of each sample, and the enrichment of differentially expressed genes (DEGs), differentiation characteristics, immunological infiltration, and metabolic activity. The HhS prognostic signature was developed with significant assistance from the least absolute shrinkage and selection operator regression and Cox regression. An HhS-related nomogram was developed to assist in the prediction of patients' survival probability. We found that HhS was linked to poor prognosis in bladder cancer, and its activation was linked to the Basal subtype of bladder cancer. Bladder cancer with high HhS activity has higher glycolysis, nucleotide metabolism, amino acid metabolism, and other cancer-promoting metabolic activities. Furthermore, HhS mediates an immunosuppressive microenvironment in bladder cancer on the basis that HhS negatively correlates with the CD8 + T cells and correlates positively with immune checkpoints and T cell exhaustion scores. Finally, an HhS-related signature was developed for predicting the prognosis of patients with bladder cancer. Targeting HhS may be a potential therapy choice for bladder cancer.

Prognostic Gene Expression-Based Signature in Clear-Cell Renal Cell Carcinoma

The inaccuracy of the current prognostic algorithms and the potential changes in the therapeutic management of localized ccRCC demands the development of an improved prognostic model for these patients. To this end, we analyzed whole-transcriptome profiling of 26 tissue samples from progressive and non-progressive ccRCCs using Illumina Hi-seq 4000. Differentially expressed genes (DEG) were intersected with the RNA-sequencing data from the TCGA. The overlapping genes were used for further analysis. A total of 132 genes were found to be prognosis-related genes. LASSO regression enabled the development of the best prognostic six-gene panel. Cox regression analyses were performed to identify independent clinical prognostic parameters to construct a combined nomogram which includes the expression of CERCAM, MIA2, HS6ST2, ONECUT2, SOX12, TMEM132A, pT stage, tumor size and ISUP grade. A risk score generated using this model effectively stratified patients at higher risk of disease progression (HR 10.79; p < 0.001) and cancer-specific death (HR 19.27; p < 0.001). It correlated with the clinicopathological variables, enabling us to discriminate a subset of patients at higher risk of progression within the Stage, Size, Grade and Necrosis score (SSIGN) risk groups, pT and ISUP grade. In summary, a gene expression-based prognostic signature was successfully developed providing a more precise assessment of the individual risk of progression.

Comprehensive FGFR3 alteration-related transcriptomic characterization is involved in immune infiltration and correlated with prognosis and immunotherapy response of bladder cancer

Background

Bladder cancer (BC) threatens the health of human beings worldwide because of its high recurrence rate and mortality. As an actionable biomarker, fibroblast growth factor receptor 3 (FGFR3) alterations have been revealed as a vital biomarker and associated with favorable outcomes in BC. However, the comprehensive relationship between the FGFR3 alteration associated gene expression profile and the prognosis of BC remains ambiguous.

Materials and Methods

Genomic alteration profile, gene expression data, and related clinical information of BC patients were downloaded from The Cancer Genomics database (TCGA), as a training cohort. Subsequently, the Weighted Gene Co-expression Network Analysis (WGCNA) was conducted to identify the hub modules correlated with FGFR3 alteration. The univariate, multivariate, and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to obtain an FGFR3 alteration-related gene (FARG) prognostic signature and FARG-based nomogram. The receiver operating characteristic (ROC) curve analysis was used for evaluation of the ability of prognosis prediction. The FARG signature was validated in four independent datasets, namely, GSE13507, GSE31684, GSE32548, and GSE48075, from Gene Expression Omnibus (GEO). Then, clinical feature association analysis, functional enrichment, genomic alteration enrichment, and tumor environment analysis were conducted to reveal differential clinical and molecular characterizations in different risk groups. Lastly, the treatment response was evaluated in the immunotherapy-related dataset of the IMvigor210 cohort and the frontline chemotherapy dataset of GSE48276, and the chemo-drug sensitivity was estimated via Genomics of Drug Sensitivity in Cancer (GDSC).

Results

There were a total of eleven genes (CERCAM, TPST1, OSBPL10, EMP1, CYTH3, NCRNA00201, PCDH10, GAP43, COLQ, DGKB, and SETBP1) identified in the FARG signature, which divided BC patients from the TCGA cohort into high- and low-risk groups. The Kaplan–Meier curve analysis demonstrated that BC patients in the low-risk group have superior overall survival (OS) than those in the high-risk group (median OS: 27.06 months vs. 104.65 months, p < 0.0001). Moreover, the FARG signature not only showed a good performance in prognosis prediction, but also could distinguish patients with different neoplasm disease stages, notably whether patients presented with muscle invasive phenotype. Compared to clinicopathological features, the FARG signature was found to be the only independent prognostic factor, and subsequently, a FARG-based prognostic nomogram was constructed with better ability of prognosis prediction, indicated by area under ROC curve (AUC) values for 1-, 3-, and 5-year OS of 0.69, 0.71, and 0.79, respectively. Underlying the FARG signature, multiple kinds of metabolism- and immune-related signaling pathways were enriched. Genomic alteration enrichment further identified that FGFR3 alterations, especially c.746C>G (p.Ser249Cys), were more prevalent in the low-risk group. Additionally, FARG score was positively correlated with ESTIMATE and TIDE scores, and the low-risk group had abundant enrichment of plasma B cells, CD8+ T cells, CD4+ naive T cells, and helper follicular T cells, implying that patients in the low-risk group were likely to make significant responses to immunotherapy, which was further supported by the analysis in the IMvigor210 cohort as there was a significantly higher response rate among patients with lower FARG scores. The analysis of the GDSC database finally demonstrated that low-risk samples were more sensitive to methotrexate and tipifarnib, whereas those in the high-risk group had higher sensitivities in cisplatin, docetaxel, and paclitaxel, instead.

Conclusion

The novel established FARG signature based on a comprehensive FGFR3 alteration-related transcriptomic profile performed well in prognosis prediction and was also correlated with immunotherapy and chemotherapy treatment responses, which had great potential in future clinical applications.

Prediction of Response to Radiotherapy by Characterizing the Transcriptomic Features in Clinical Tumor Samples across 15 Cancer Types

Purpose

Radiotherapy (RT) is one of the major cancer treatments. However, the responses to RT vary among individual patients, partly due to the differences of the status of gene expression and mutation in tumors of patients. Identification of patients who will benefit from RT will improve the efficacy of RT. However, only a few clinical biomarkers were currently used to predict RT response. Our aim is to obtain gene signatures that can be used to predict RT response by analyzing the transcriptome differences between RT responder and nonresponder groups.

Materials and Methods

We obtained transcriptome data of 1664 patients treated with RT from the TCGA database across 15 cancer types. First, the genes with a significant difference between RT responder (R group) and nonresponder groups (PD group) were identified, and the top 100 genes were used to build the gene signatures. Then, we developed the predictive model based on binary logistic regression to predict patient response to RT.

Results

We identified a series of differentially expressed genes between the two groups, which are involved in cell proliferation, migration, invasion, EMT, and DNA damage repair pathway. Among them, MDC1, UCP2, and RBM45 have been demonstrated to be involved in DNA damage repair and radiosensitivity. Our analysis revealed that the predictive model was highly specific for distinguishing the R and PD patients in different cancer types with an area under the curve (AUC) ranging from 0.772 to 0.972. It also provided a more accurate prediction than that from a single-gene signature for the overall survival (OS) of patients.

Conclusion

The predictive model has a potential clinical application as a biomarker to help physicians create optimal treatment plans. Furthermore, some of the genes identified here may be directly involved in radioresistance, providing clues for further studies on the mechanism of radioresistance.

TEAD4 functions as a prognostic biomarker and triggers EMT via PI3K/AKT pathway in bladder cancer

Background

The distant metastasis is the primary cause of cancer morbidity and mortality for bladder cancer (BLCA) paitents. All the recommended therapy for it largely depends on how far the cancer has invaded. It has been confirmed that epithelial to mesenchymal transition (EMT) is the leading reason for the BLCA metastasis which makes BLCA difficult to cure. The aim of the present study is to identify the BLCA-related genes that can be used as the new prognostic biomarker and treatment target, and to investigate the functional mechanisms of TEAD4 in EMT dysregulation.

Methods

The "limma" R package was used to identify the differentially expressed genes (DEGs) between the normal and the tumor samples from TCGA BLCA and GTEx databases. Kaplan–Meier and UniCox analysis were used to filter DEGs with prognostic value in BLCA. Step muti-Cox analysis was used to construct a prognostic risk score model based on clinical phenotype characters. Gene set enrichment analysis (GSEA) was performed to explore the possible molecular mechanisms affecting the prognosis in BLCA. Unsupervised hierarchical clustering analysis was performed to evaluate the effects of EMT process on the prognosis. Single-sample GSEA (ssGSEA) was used to calculate the correlation betweeen the expression of DEGs and EMT enrichment scores. TEAD4 expression and its association with pathological grading and survival were appraised in samples from TCGA dataset and BLCA tissue microarray. Colony formation assays and CCK8 assays were performed to study the changes in BLCA cell proliferation when the TEAD4 levels was down- or up-regulated in BLCA cells. Transwell and wound healing assays were utilized to analyze the impact of TEAD4 on the invasion and metastasis of the BLCA cells. Western Blot was carried out to detect the changes of EMT-related markers and the active molecules involved in PI3K/AKT signaling in BLCA cells. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was conducted on the genes related to TEAD4 expression. 740Y-P (activator of PI3K/AKT pathway) and LY294002 (inhibitor of PI3K/AKT pathway) were applied to evaluate the contribution of PI3K/AKT signaling pathway in the EMT of BLCA cells. To examine the in vivo effect of TEAD4 on tumor metastasis, we designed a metastatic nude-mouse model by tail vein injection of TEAD4-knockdown BLCA cells. And PET/CT imaging was used to assess the extent of lung metastases.

Results

A total of 1592 DEGs were recognized, among which 4 DEGs have been identified as independent prognostic factors for BLCA, such as FASN, IGFL2, PLOD1 and TEAD4. TCGA BLCA samples were divided into significantly different low- and high-risk groups according to the median risk score; GSEA analysis showed that HALLMARK EMT pathway was the top enriched gene signature when compared high-risk and low-risk groups, which was also verified by unsupervised cluster analysis. EMT signature-derived ssGSEA scores demonstrated that TEAD4 had the most positive correlation with EMT process. In addition, TEAD4 expression was upregulated in TCGA BLCA samples and correlated with pT stage, tumor stage and tumor grade. Functional studies showed that TEAD4 knockdown via lentiviral TEAD4 shRNA inhibited cell migration and invasion in vitro and in vivo, with the reduced expression of EMT related markers in BLCA cell lines; the migration and invasion of TEAD4 knockdown cells could be restored by ectopic expression of TEAD4. Meanwhile, KEGG enrichment analysis of genes related to TEAD4 expression showed that enrichment was significantly related to PI3K/AKT pathway. The pathway inhibitor LY294002 blocked the TEAD4-induced enhancement of migration and invasion as well as the expression EMT-related markers, whereas the agonist 740Y-P rescued the decreased migration, invasion and EMT induced by TEAD4 knockdown.

Conclusions

TEAD4 is closely correlated with poor prognosis in BLCA and mediates its metastasis through regulating EMT via PI3K/AKT pathway, proving that TEAD4 is not only an effective biomarker for predicting the prognosis but also a great potential target for treatment of metastatic BLCA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13046-022-02377-3.

Identification of Bladder Cancer Subtypes Based on Necroptosis-Related Genes, Construction of a Prognostic Model

Background

Necroptosis is associated with the development of many tumors but in bladder cancer the tumor microenvironment (TME) and prognosis associated with necroptosis is unclear.

Methods

We classified patients into different necroptosis subtypes by the expression level of NRGS (necroptosis-related genes) and analyzed the relationship between necroptosis subtypes of bladder cancer and TME, then extracted differentially expressed genes (DEGS) of necroptosis subtypes, classified patients into different gene subtypes according to DEGS, and performed univariate COX analysis on DEGS to obtain prognosis-related DEGS. All patients included in the analysis were randomized into the Train and Test groups in a 1:1 ratio, and the prognostic model was obtained using the LASSO algorithm and multivariate COX analysis with the Train group as the sample, and external validation of the model was conducted using the GSE32894.

Results

Two necroptosis subtypes and three gene subtypes were obtained by clustering analysis and the prognosis-related DEGS was subjected to the LASSO algorithm and multivariate COX analysis to determine six predictors to construct the prognostic model using the formula: riskScore = CERCAM × 0.0035 + POLR1H × −0.0294 + KCNJ15 × −0.0172 + GSDMB × −0.0109 + EHBP1 × 0.0295 + TRIM38 × −0.0300. The results of the survival curve, roc curve, and risk curve proved the reliability of the prognostic model by validating the model with the test group and the results of the calibration chart of the Nomogram applicable to the clinic also showed its good accuracy. Necroptosis subtype A with high immune infiltration had a higher risk score than necroptosis subtype B, gene subtype B with low immune infiltration had a lower risk score than gene subtypes A and C, CSC index was negatively correlated with the risk score and drug sensitivity prediction showed that commonly used chemotherapeutic agents were highly sensitive to the high-risk group.

Conclusion

Our analysis of NRGS in bladder cancer reveals their potential role in TME, immunity, and prognosis. These findings may improve our understanding of necroptosis in bladder cancer and provide some reference for predicting prognosis and developing immunotherapies.