Signature In Bladder Cancer Research Articles

Prognostic Significance and Immune Landscape of an Efferocytosis-Related Gene Signature in Bladder Cancer.

Bladder cancer poses a significant global health challenge, underscoring the imperative for precise prognostic instruments to advance patient care. Against the backdrop of efferocytosis's increasingly recognized role in cancer, this research endeavors to develop and authenticate a prognostic signature intricately linked to efferocytosis in bladder cancer. LASSO-COX regression analysis crafted an efferocytosis-related genes risk prognostic model, followed by the construction of a column chart. External validation sets confirmed the predictive accuracy of both the model and chart. Clinical, tumor microenvironment, drug sensitivity, and immunotherapy analyses were employed to comprehensively assess efferocytosis-related scores. The expression of TGFB3 key genes was validated via RT-PCR and western blotting. Further validation included Transwell, Wound healing, Colony formation, and EDU assays. We formulated and validated an efferocytosis-related genes risk model in bladder cancer, comprising 13 core genes. The risk model demonstrated autonomous prognostic significance in both univariate and multivariate Cox analyses. Following the multivariate analysis, we devised a nomogram. Moreover, by utilizing individual risk scores derived from this risk model, we successfully stratified patients into two discernible risk cohorts, unveiling noteworthy variances in immune infiltration profiles and responsiveness to immunotherapy. Notably, the model's key gene TGFB3 was validated through comprehensive experimental investigations, including Transwell assays for migration and invasion and Wound healing assays for motility on the T24 and BIU cell lines. This study has furnished innovative perspectives on an efferocytosis-related prognostic signature, elucidating the prognosis and immune milieu intricacies in patients with bladder cancer.

Characterization of the stem cell landscape and identification of a stemness-associated prognostic signature in bladder cancer

It is accepted that cancer stem cells (CSCs) are key to the occurrence, progression, drug resistance, and recurrence of bladder cancer (BLCA). Here, we aimed to characterize the landscapes of CSCs and investigate the biological and clinical signatures based on a prognostic model constructed by genes associated with CSCs. The malignant epithelial cells were discovered and sorted into six clusters through single cell analysis. C2 was identified as the CSCs. The signaling involved in the interactions between C2, cancer-associated fibroblasts (CAFs), and immune cells mainly consisted of MK, THBS, ANGPTL, VISFATIN, JAM, and ncWNT pathways. The CSC-like prognostic index (CSCLPI) constructed by the random survival forest was a reliable risk factor for BLCA and had a stable and powerful effect on predicting the overall survival of patients with BLCA. The level of CAFs was higher among patients with higher CSCLPI scores, suggesting that CAFs play a significant role in regulating biological characteristics. The CSCLPI-developed survival prediction nomogram has the potential to be applied clinically to predict the 1-, 2-, 3-, and 5-year overall survival of patients with BLCA. The CSCLPI can be used for prognostic prediction and drug treatment evaluation in the clinic.

Comprehensive analysis of scRNA-seq and bulk RNA-seq reveal the characteristics of disulfidptosis and a prognostic signature in BLCA.

Disulfidptosis is a newly discovered mode of cell death. However, its biological mechanism in bladder cancer (BLCA) is still uncharacterized. In this investigation, we firstly examined the expression and mutation of disulfidptosis-related genes (DRGs) in BLCA. Two disulfidptosis phenotypes associated with DRGs expression patterns and immune cell infiltration were built. A disulfidptosis risk score signature was constructed based on ten differentially expressed genes (DEGs) between the disulfidptosis subtypes, which allowed patients to be stratified into high- and low-risk groups. We further confirmed that the disulfidptosis risk score signature has great power to predict prognosis, immune cell infiltration, and immunotherapy efficacy in BLCA. Additionally, we analyzed the differences in therapeutic sensitivities between high- and low-risk groups concerning targeted inhibitor therapy and immunotherapy. Analysis of single-cell RNA sequencing was conducted of the ten hub DRGs. Of the ten genes, we found that DUSP2 and SLCO1B3 were differentially expressed in BLCA tissues and adjacent normal tissues, and were markedly associated with patients' prognosis. Functional experiments revealed that overexpression of DUSP2 or knockdown of SLCO1B3 significantly inhibited cell proliferation, migration, and invasion in BLCA cells. In all, we present a fresh disulfidptosis-related prognostic signature, which has a remarkable capacity to characterize the immunological landscape and prognosis of BLCA patients.

Prognostic model development and molecular subtypes identification in bladder urothelial cancer by oxidative stress signatures.

Mounting studies indicate that oxidative stress (OS) significantly contributes to tumor progression. Our study focused on bladder urothelial cancer (BLCA), an escalating malignancy worldwide that is growing rapidly. Our objective was to verify the predictive precision of genes associated with overall survival (OS) by constructing a model that forecasts outcomes for bladder cancer and evaluates the prognostic importance of these genetic markers. Transcriptomic data were obtained from TCGA-BLCA and GSE31684, which are components of the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. To delineate distinct molecular subtypes, we employed the non-negative matrix factorization (NMF)method. The significance of OS-associated genes in predicting outcomes was assessed using lasso regression, multivariate Cox analysis, and univariate Cox regression analysis. For external validation, we employed the GSE31684 dataset. CIBERSORT was utilized to examine the tumor immune microenvironment (TIME). A nomogram was created and verified using calibration and receiver operating characteristic (ROC) curves, which are based on risk signatures. We examined variations in clinical characteristics and tumor mutational burden (TMB) among groups classified as high-risk and low-risk. To evaluate the potential of immunotherapy, the immune phenomenon score (IPS) was computed based on the risk score. In the end, the pRRophetic algorithm was employed to forecast the IC50 values of chemotherapy medications. In our research, we examined the expression of 275 genes associated with OS in 19 healthy and 414 cancerous tissues of the bladder obtained from the TCGA database. As a result, a new risk signature was created that includes 4 genes associated with OS (RBPMS, CRYAB, P4HB, and PDGFRA). We found two separate groups, C1 and C2, that showed notable variations in immune cells and stromal score. According to the Kaplan-Meier analysis, patients classified as high-risk experienced a considerably reduced overall survival in comparison to those categorized as low-risk (P<0.001). The predictive capability of the model was indicated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve surpassing 0.6. Our model showed consistent distribution of samples from both the GEO database and TCGA data. Both the univariate and multivariate Cox regression analyses validated the importance of the risk score in relation to overall survival (P < 0.001). According to our research, patients with a lower risk profile may experience greater advantages from using a CTLA4 inhibitor, whereas patients with a higher risk profile demonstrated a higher level of responsiveness to Paclitaxel and Cisplatin. In addition, methotrexate exhibited a more positive outcome in patients with low risk compared to those with high risk. Our research introduces a novel model associated with OS gene signature in bladder cancer, which uncovers unique survival results. This model can assist in tailoring personalized treatment approaches and enhancing patient therapeutic effect in the management of bladder cancer.

Molecular subtypes, tumor microenvironment infiltration characterization and prognosis model based on cuproptosis in bladder cancer.

Cuproptosis is a kind of cell death dependent on copper. We aimed to explore the functions of the cuproptosis in the tumor microenvironment (TME) and construct a cuproptosis-related prognosis signature in bladder cancer (BCa). Using BCa patients in the public cohort, the cuproptosis-related molecular subtypes and cuproptosis-related prognosis signature were developed. Three cuproptosis-related molecular subtypes, with different prognoses and TME characteristics, were identified in BCa. The cuproptosis-related prognosis signature can divide patients into high- and low-risk groups with different prognoses, TME characteristics, chemotherapeutic drug susceptibility and immunotherapeutic response. Low risk group patients had a favored prognosis and response to immunotherapy. The dysregulation of cuproptosis-related genes expression levels was validated in multiple BCa cells using in vitro experiments. Cuproptosis has an important role in the tumor progression and the characterization of TME in BCa. The cuproptosis-related prognosis signature is a useful biomarker that can reflect the prognosis, TME characteristics, immunotherapeutic response and chemotherapeutic drug susceptibility in BCa patients.

Clinical Significance and Tumor Microenvironment Characterization of a Novel Immune-Related Gene Signature in Bladder Cancer

Cancer immunotherapy plays a crucial role in bladder cancer (BC) progression. Increasing evidence has elucidated the clinicopathologic significance of the tumor microenvironment (TME) in predicting outcomes and therapeutic efficacy. This study sought to establish a comprehensive analysis of the immune-gene signature combined with TME to assist in BC prognosis. We selected sixteen immune-related genes (IRGs) after a weighted gene co-expression network and survival analysis. Enrichment analysis revealed that these IRGs were actively involved in Mitophagy and Renin secretion pathways. After multivariable COX analysis, the IRGPI comprising NCAM1, CNTN1, PTGIS, ADRB3, and ANLN was established to predict the overall survival of BC, which was validated in both TCGA and GSE13507 cohorts. In addition, a TME gene signature was developed for molecular and prognosis subtyping with unsupervised clustering, followed by a panoramic landscape characterization of BC. In summary, the IRGPI model developed in our study provided a valuable tool with an improved prognosis for BC.

An IFN-γ-related signature predicts prognosis and immunotherapy response in bladder cancer: Results from real-world cohorts.

Bladder cancer (BLCA) is featured with high incidence and mortality. Whether the IFN-γ signaling could be used as an immunotherapy determinant for BLCA has not been fully confirmed. In this study, the transcriptome data and clinical information of BLCA samples were collected from The Cancer Genome Atlas (TCGA). Besides, four immunotherapy cohorts including IMvigor210 cohort, Gide cohort, Van Allen cohort, and Lauss cohort were collected. The Xiangya real-world cohort was used for independent validation. An IFN-γ-related signature was developed and validated in BLCA for predicting prognosis, mutation, tumor microenvironment status, and immunotherapy response. This is the first study focusing on the comprehensive evaluation of predictive values on the IFN-γ-related signature in BLCA. The potential clinical application of the IFN-γ-related signature was expected to be further validated with more prospective clinical cohorts.

Construction of Pyroptosis-Related Prognostic and Immune Infiltration Signature in Bladder Cancer.

Pyroptosis is a kind of programmed cell death related to inflammation, which is closely related to cancer. The goal of this study is to establish and verify pyroptosis-related gene signature to predict the prognosis of patients with bladder cancer (BLCA) and explore its relationship with immunity. Somatic mutation, copy number variation, correlation, and expression of 33 pyroptosis-related genes were evaluated based on The Cancer Genome Atlas (TCGA) database. BLCA cases were divided into two clusters by consistent clustering and found that pyroptosis-related genes were related to the overall survival (OS) of BLCA. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to construct the signature (including 7 pyroptosis-realated genes). Survival analysis curve and receiver operating characteristic curve (ROC) showed that this signature could predict the prognosis of BLCA patients. Univariate and multivariate Cox regression analysis showed the independent prognostic value of this model. Immune infiltration analysis showed that the six types of immune cells have significantly different infiltrations. The effect of immunotherapy is better in the low-risk group. In summary, our effort indicated the potential role of pyroptosis-related genes in BLCA and provided new perspectives on the prognosis of BLCA and new ideas for immunotherapy.

Fibroblast Common Serum Response Signature-Related Classification Affects the Tumour Microenvironment and Predicts Prognosis in Bladder Cancer.

Abnormal oncogenic signatures provide important clues regarding cancer prognosis and treatment. We analysed the variations in 189 oncogenic signature gene sets between normal and tumourous tissues from The Cancer Genome Atlas (TCGA) and found that the “CSR_LATE_UP” signature was the most upregulated oncogenic signature gene set in bladder cancer. Next, we developed a common serum response (CSR) risk score (CRS) model based on fibroblast CSR genes and systematically analysed the correlations of these genes or the CRSs with survival, previously reported molecular subtypes, clinicopathological features, cancer signalling pathways, chemotherapeutic responses, and the tumour microenvironment using TCGA and validation cohorts. The CRS could predict the malignant phenotype, chemotherapeutic efficacy, immune invasion, and disease prognosis. Inflammatory signalling pathways (e.g., inflammatory response, TNFA signalling via NFƘB, IFNα response, and IL2-STAT5 signalling) were markedly upregulated in patients with high CRS. Notably, the CSR-related gene ANLN was positively correlated with CD8+ immune cell infiltration, PD-L1 expression, and sensitivity to PD-L1 inhibitors and could thus provide guidance for clinical immunotherapy. This study highlights the crucial role of the CSR signature in bladder cancer and provides a CRS model for accurate predictions of the disease prognosis and chemotherapy and immunotherapy responses.

A pyroptosis-related lncRNA signature in bladder cancer.

Pyroptosis, a type of programmed cell death, is implicated in the tumorigenesis, development and migration of cancer, which can be regulated by long non-coding RNAs (lncRNAs). Our research aimed to investigate the prognostic role of pyroptosis-related lncRNAs and the relationship to the tumor immune microenvironment through bioinformatics analysis. The clinical and RNA-sequencing data of bladder cancer patients were downloaded from The Cancer Genome Atlas (TCGA). And 412 bladder cancer subjects with clinical information were divided into training and testing cohort. And 52 reported pyroptosis-related genes were used to screen pyroptosis-related lncRNAs. A pyroptosis-related lncRNA signature was constructed based on Cox regression analyses. A 9-pyroptosis-related-lncRNA signature was identified to separate patients with bladder cancer into two groups. The prognosis of bladder cancer patients in the high-risk group was significantly inferior compared with those in the low-risk group. Risk scores were validated to develop an independent prognostic indicator based on multivariate Cox regression analysis. Receiver operating characteristic curve (ROC) analysis examined the signature on overall survival. The area under time-dependent ROC curve (AUC) at 1-, 3, and 5-years measured 0.747, 0.783, and 0.768, respectively. Analysis of the immune landscape and PD-L1 expression showed that PD-L1 is upregulated in the high-risk group. The immunocyte subtypes of the two groups were different. A novel pyroptosis-related lncRNA signature was identified with prognostic value for bladder cancer patients. Pyroptosis-related lncRNAs have a potential role in cancer immunology and may serve as prognostic or therapeutic targets.

Identification of a six-gene prognostic signature for bladder cancer associated macrophage.

As major components of the tumor microenvironment (TME), tumor-associated macrophages (TAMs) play an exceedingly complicated role in tumor progression and tumorigenesis. However, few studies have reported the specific TAM gene signature in bladder cancer. Herein, this study focused on developing a TAM-related prognostic model in bladder cancer patients based on The Cancer Genome Atlas (TCGA) data. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify key genes related to TAM (M2 macrophage). Gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis showed the functional categories of the key genes. Simultaneously, we used the Least Absolute Shrinkage and Selection Operator (LASSO) and univariate and multivariate Cox regressions to establish a TMA-related prognostic model containing six key genes: TBXAS1, GYPC, HPGDS, GAB3, ADORA3, and FOLR2. Subsequently, single-cell sequencing data downloaded from Gene Expression Omnibus (GEO) suggested that the six genes in the prognostic model were expressed in TAM specifically and may be involved in TAM polarization. In summary, our research uncovered six-TAM related genes that may have an effect on risk stratification in bladder cancer patients and could be regarded as potential TAM-related biomarkers.

Prognostic value and potential biological functions of ferroptosis-related gene signature in bladder cancer.

Bladder cancer (BC), as a genitourinary system tumor, is a highly prevalent tumor type. Ferroptosis is an iron-dependent oxidative cell death mechanism that is becoming increasingly recognized as a promising avenue for cancer therapy. However, further determination of the prospective prognostic value of ferroptosis for BC and investigation of the underlying mechanisms is required. The mRNA expression profiles and associated clinical data were downloaded from public databases such as The Cancer Genome Atlas, Gene Expression Omnibus and the IMvigor210 database. To construct a predictive formula, the least absolute shrinkage and selection operator Cox regression algorithm was used. In addition, a prognostic multigene signature was constructed using previously selected ferroptosis-related genes (FRGs). A total of 28 FRGs were differentially expressed between tumor and normal samples with |log2 fold change| >1 and adjusted P<0.05. A prognostic model was then established and it was validated in the GEO cohort using six genes: Glutamate-cysteine ligase modifier subunit, crystallin α-B, transferrin receptor, zinc finger E-box binding homeobox 1, squalene epoxidase and glucose-6-phosphate dehydrogenase (G6PD). Numerous important pathways involved in the development of the immune system and cancer were indicated to be significantly different between the two risk groups. In addition, it was discovered that G6PD expression subgroups that were associated with immunotherapy response in patients with BC had similar prognostic features to risk score subgroups. In the present study, a gene signature with a prognostic value for ferroptosis in BC was successfully developed and the potential value of G6PD was identified for future research.

In Silico Establishment and Validation of Novel Lipid Metabolism-Related Gene Signature in Bladder Cancer.

Background Aberrant lipid metabolism is an alteration common to many types of cancer. Dysregulation of lipid metabolism is considered a major risk factor for bladder cancer. Accordingly, we focused on genes related to lipid metabolism and screened novel markers for predicting the prognosis of bladder cancer. Methods RNA-seq data for bladder cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The nonnegative matrix factorization (NMF) algorithm was used to classify the molecular subtypes. Weighted correlation network analysis (WGCNA) was applied to identify coexpressed genes, and least absolute shrinkage and selection operator (LASSO) multivariate Cox analysis was used to construct a prognostic risk model. External validation data and in vitro experiments were used to verify the results from in silico analysis. Results Bladder cancer samples were grouped into two clusters based on the NMF algorithm. A total of 1467 genes involved in coexpression modules were identified in WGCNA. We finally established a 5-gene signature (TM4SF1, KCNK5, FASN, IMPDH1, and KCNJ15) that exhibited good stability across different datasets and was also an independent risk factor for prognosis. Furthermore, the predictive efficacy of our model was generally higher than the predictive efficacy of other published models. Distinct risk groups of patients also showed significantly different immune infiltration cell patterns and associations with clinical variables. Moreover, the 5 signature genes were verified in clinical samples by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry, which were in agreement with the in silico analysis. For in vitro experiments, knockdown of IMPDH1 markedly inhibited cell proliferation in bladder cancer. Conclusion We established a 5-gene prognosis signature based on lipid metabolism in bladder cancer, which could be an effective prognostic indicator.

Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Bladder Cancer.

Objective Bladder cancer (BC) is the most common malignancy in the urinary system and is prone to recurrence and metastasis. Pyroptosis is a kind of cell necrosis that is triggered by the gasdermin protein family. lncRNAs are noncoding RNAs that are more than 200 nucleotides long. Both pyroptosis and lncRNAs are associated with tumor development and progression. This study is aimed at exploring and establishing a prognostic signature of BC based on pyroptosis-related lncRNAs. Methods In this study, The Cancer Genome Atlas (TCGA) database provided us with the RNA sequencing transcriptome data of bladder cancer patients, and we identified differentially expressed pyroptosis-related lncRNAs in bladder cancer. Then, the prognostic significance of these lncRNAs was assessed using univariate Cox regression analysis and LASSO regression analysis. Subsequently, 4 pyroptosis-related lncRNAs, namely, AL121652.1, AL161729.4, AC007128.1, and AC124312.3, were identified by multivariate Cox regression analysis, thus constructing the prognostic risk model. Then, we compared the levels of immune infiltration, differences in cell function, immune checkpoints, and m6A-related gene expression levels between the high- and low-risk groups. Result Patients were divided into low-risk or high-risk groups based on the median risk score. Kaplan–Meier survival analysis indicated that the overall survival of bladder cancer patients in the low-risk group was substantially superior to that in the high-risk group (p < 0.001). The receiver operating characteristic (ROC) curve further confirmed the credibility of our model. Moreover, gene set enrichment analysis (GSEA) indicated that these were different signal pathways significantly enriched between the two groups. Immune infiltration, immune checkpoint, and N6-methyladenosine-related gene analysis also reflected that there were notable differences between the two groups. Conclusion Therefore, this prognostic risk model is based on the level of pyroptotic lncRNAs, which is conducive to individualized assessment of the risk of patients and provides a reference for clinical treatment. This will also help provide insights into the prognosis and treatment of bladder cancer.

Immunotherapeutic Significance of a Prognostic Alternative Splicing Signature in Bladder Cancer.

Objectives: Bladder cancer is the fourth most common malignancy in men in the United States. Aberrant alternative splicing (AS) events are involved in the carcinogenesis, but the association between AS and bladder cancer remains unclear. This study aimed to construct an AS-based prognostic signature and elucidate the role of the tumor immune microenvironment (TIME) and the response to immunotherapy and chemotherapy in bladder cancer. Methods: Univariate Cox regression analysis was performed to detect prognosis-related AS events. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were employed to build prognostic signatures. Kaplan–Meier survival analysis, multivariate Cox regression analysis, and receiver operating characteristic (ROC) curves were conducted to validate the prognostic signatures. Then, the Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and tumor immune estimation resource (TIMER) databases were searched and the single-sample gene set enrichment analysis (ssGSEA) algorithm and CIBERSORT method were performed to uncover the context of TIME in bladder cancer. The Tumor Immune Dysfunction and Exclusion (TIDE) web tool and pRRophetic algorithm were used to predict the response to immunotherapy and chemotherapy. Finally, we constructed a correlation network between splicing factors (SFs) and survival-related AS events. Results: A total of 4684 AS events were significantly associated with overall survival in patients with bladder cancer. Eight prognostic signatures of bladder cancer were established, and a clinical survival prediction model was built. In addition, the consolidated prognostic signature was closely related to immune infiltration and the response to immunotherapy and chemotherapy. Furthermore, the correlation identified EIF3A, DDX21, SDE2, TNPO1, and RNF40 as hub SFs, and function analysis found ubiquitin-mediated proteolysis is correlated most significantly with survival-associated AS events. Conclusion: Our findings highlight the prognostic value of AS for patients with bladder cancer and reveal pivotal players of AS events in the context of TIME and the response to immunotherapy and chemotherapy, which may be important for patient management and treatment.

An Unfolded Protein Response Related Signature Could Robustly Predict Survival Outcomes and Closely Correlate With Response to Immunotherapy and Chemotherapy in Bladder Cancer.

Background: The unfolded protein response (UPR) plays a significant role in maintaining protein hemostasis in tumor cells, which are crucial for tumor growth, invasion, and resistance to therapy. This study aimed to develop a UPR-related signature and explore its correlation with immunotherapy and chemotherapy in bladder cancer. Methods: The differentially expressed UPR-related genes were put into Lasso regression to screen out prognostic genes, which constituted the UPR signature, and were incorporated into multivariate Cox regression to generate risk scores. Subsequently, the predictive performance of this signature was estimated by receiver operating characteristic (ROC) curves. The CIBERSORTx, the maftool, and Gene set enrichment analysis (GSEA) were applied to explore infiltrated immune cells, tumor mutational burden (TMB), and enriched signaling pathways in both risk groups, respectively. Moreover, The Cancer Immunome Atlas (TCIA) and Genomics of Drug Sensitivity in Cancer (GDSC) databases were used to predict responses to chemotherapy and immunotherapy. Results: Twelve genes constituted the UPR-related signature. Patients with higher risk scores had worse overall survival (OS) in training and three validation sets. The UPR-related signature was closely correlated with clinicopathologic parameters and could serve as an independent prognostic factor. M0 macrophages showed a significantly infiltrated difference in both risk groups. TMB analysis showed that the risk score in the wild type and mutation type of FGFR3 was significantly different. GSEA indicated that the immune-, extracellular matrix-, replication and repair associated pathways belonged to the high risk group and metabolism-related signal pathways were enriched in the low risk group. Prediction of immunotherapy and chemotherapy revealed that patients in the high risk group might benefit from chemotherapy, but had a worse response to immunotherapy. Finally, we constructed a predictive model with age, stage, and UPR-related risk score, which had a robustly predictive performance and was validated in GEO datasets. Conclusion: We successfully constructed and validated a novel UPR-related signature in bladder cancer, which could robustly predict survival outcomes and closely correlate with the response to immunotherapy and chemotherapy in bladder cancer.

Identification of a Novel Ferroptosis-Related Gene Prognostic Signature in Bladder Cancer.

BackgroundFerroptosis is a newly found non-apoptotic forms of cell death that plays an important role in tumors. However, the prognostic value of ferroptosis-related genes (FRG) in bladder cancer (BLCA) have not been well examined.MethodsFRG data and clinical information were collected from The Cancer Genome Atlas (TCGA). Then, significantly different FRGs were investigated by functional enrichment analyses. The prognostic FRG signature was identified by univariate cox regression and least absolute shrinkage and selection operator (LASSO) analysis, which was validated in TCGA cohort and Gene Expression Omnibus (GEO) cohort. Subsequently, the nomogram integrating risk scores and clinical parameters were established and evaluated. Additionally, Gene Set Enrichment Analyses (GSEA) was performed to explore the potential molecular mechanisms underlying our prognostic FRG signature. Finally, the expression of three key FRGs was verified in clinical specimens.ResultsThirty-two significantly different FRGs were identified from TCGA–BLCA cohort. Enrichment analyses showed that these genes were mainly related to the ferroptosis. Seven genes (TFRC, G6PD, SLC38A1, ZEB1, SCD, SRC, and PRDX6) were then identified to develop a prognostic signature. The Kaplan–Meier analysis confirmed the predictive value of the signature for overall survival (OS) in both TCGA and GEO cohort. A nomogram integrating age and risk scores was established and demonstrated high predictive accuracy, which was validated through calibration curves and receiver operating characteristic (ROC) curve [area under the curve (AUC) = 0.690]. GSEA showed that molecular alteration in the high- or low-risk group was closely associated with ferroptosis. Finally, experimental results confirmed the expression of SCD, SRC, and PRDX6 in BLCA.ConclusionHerein, we identified a novel FRG prognostic signature that maybe involved in BLCA. It showed high values in predicting OS, and targeting these FRGs may be an alternative for BLCA treatment. Further experimental studies are warranted to uncover the mechanisms that these FRGs mediate BLCA progression.

Integrated Analysis of a Competing Endogenous RNA Network Reveals a Prognostic lncRNA Signature in Bladder Cancer.

Long non-coding RNAs (lncRNAs) act as competing endogenous RNAs (ceRNAs) to regulate mRNA expression through sponging microRNA in tumorigenesis and progression. However, following the discovery of new RNA interaction, the differentially expressed RNAs and ceRNA regulatory network are required to update. Our study comprehensively analyzed the differentially expressed RNA and corresponding ceRNA network and thus constructed a potentially predictive tool for prognosis. “DESeq2” was used to perform differential expression analysis. Two hundred and six differentially expressed (DE) lncRNAs, 222 DE miRNAs, and 2,463 DE mRNAs were found in this study. The lncRNA-mRNA interactions in the miRcode database and the miRNA-mRNA interactions in the starBase, miRcode, and mirTarBase databases were searched, and a competing endogenous RNA (ceRNA) network with 186 nodes and 836 interactions was subsequently constructed. Aberrant expression patterns of lncRNA NR2F1-AS1 and lncRNA AC010168.2 were evaluated in two datasets (GSE89006, GSE31684), and real-time polymerase chain reaction was also performed to validate the expression pattern. Furthermore, we found that these two lncRNAs were independent prognostic biomarkers to generate a prognostic lncRNA signature by univariate and multivariate Cox analyses. According to the lncRNA signature, patients in the high-risk group were associated with a poor prognosis and validated by an external dataset. A novel genomic-clinicopathologic nomogram to improve prognosis prediction of bladder cancer was further plotted and calibrated. Our study deepens the understanding of the regulatory ceRNA network and provides an easy-to-do genomic-clinicopathological nomogram to predict the prognosis in patients with bladder cancer.

Development and Validation of a Hypoxia-Related Signature for Predicting Survival Outcomes in Patients With Bladder Cancer

ObjectivesThis study aimed to develop and validate a hypoxia signature for predicting survival outcomes in patients with bladder cancer.MethodsWe downloaded the RNA sequence and the clinicopathologic data of the patients with bladder cancer from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/repository?facetTab=files) and the Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) databases. Hypoxia genes were retrieved from the Molecular Signatures Database (https://www.gsea-msigdb.org/gsea/msigdb/index.jsp). Differentially expressed hypoxia-related genes were screened by univariate Cox regression analysis and Lasso regression analysis. Then, the selected genes constituted the hypoxia signature and were included in multivariate Cox regression to generate the risk scores. After that, we evaluate the predictive performance of this signature by multiple receiver operating characteristic (ROC) curves. The CIBERSORT tool was applied to investigate the relationship between the hypoxia signature and the immune cell infiltration, and the maftool was used to summarize and analyze the mutational data. Gene-set enrichment analysis (GSEA) was used to investigate the related signaling pathways of differentially expressed genes in both risk groups. Furthermore, we developed a model and presented it with a nomogram to predict survival outcomes in patients with bladder cancer.ResultsEight genes (AKAP12, ALDOB, CASP6, DTNA, HS3ST1, JUN, KDELR3, and STC1) were included in the hypoxia signature. The patients with higher risk scores showed worse overall survival time than the ones with lower risk scores in the training set (TCGA) and two external validation sets (GSE13507 and GSE32548). Immune infiltration analysis showed that two types of immune cells (M0 and M1 macrophages) had a significant infiltration in the high-risk group. Tumor mutation burden (TMB) analysis showed that the risk scores between the wild types and the mutation types of TP53, MUC16, RB1, and FGFR3 were significantly different. Gene-Set Enrichment Analysis (GSEA) showed that immune or cancer-associated pathways belonged to the high-risk groups and metabolism-related signal pathways were enriched into the low-risk group. Finally, we constructed a predictive model with risk score, age, and stage and validated its performance in GEO datasets.ConclusionWe successfully constructed and validated a novel hypoxia signature in bladder cancer, which could accurately predict patients’ prognosis.

Identification and validation of a hypoxia-related prognostic and immune microenvironment signature in bladder cancer

BackgroundBladder cancer is the leading causes of cancer-associated mortality and seriously affects population health. Hypoxia plays a key role in tumor development and immune escape, which contributes to malignant behaviors.MethodsIn this study, we analyzed the RNA-seq and clinical information of bladder cancer patients from The Cancer Genome Atlas (TCGA) database. To investigate the hypoxia-related prognostic and immune microenvironment in bladder cancer, we constructed a hypoxia-related risk model for overall survival (OS). The RNA-seq and clinical data of bladder cancer patients from the Gene Expression Omnibus (GEO) database were used as validation sets.ResultsThe hypoxia-related risk signature was significantly correlated with clinical outcomes and could independently predict OS outcomes. Furthermore, the hypoxia-related risk signature could effectively reflected the levels of immune cell type fractions and the expression of critical immune checkpoint genes were higher in the high-risk group compared to the low-risk group. We also validated the expression levels of the prognostic genes in bladder cancer and paracancerous tissue samples through qRT-PCR analysis.ConclusionWe established a 7 hypoxia-related gene (HRG) signature that can be used as an independent clinical predictor and provided a potential mechanism in bladder cancer immunotherapy.