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

Prediction of bladder cancer prognosis and immune microenvironment assessment using machine learning and deep learning models

Bladder cancer (BCa) is a heterogeneous malignancy characterized by distinct immune subtypes, primarily due to differences in tumor-infiltrating immune cells and their functional characteristics. Therefore, understanding the tumor immune microenvironment (TIME) landscape in BCa is crucial for prognostic prediction and guiding precision therapy. In this study, we integrated 10 machine learning algorithms to develop an immune-related machine learning signature (IRMLS) and subsequently created a deep learning model to detect the IRMLS subtype based on pathological images. The IRMLS proved to be an independent prognostic factor for overall survival (OS) and demonstrated robust and stable performance (p < 0.01). The high-risk group exhibited an immune-inflamed phenotype, associated with poorer prognosis and higher levels of immune cell infiltration. We further investigated the cancer immune cycle and mutation landscape within the IRMLS model, confirming that the high-risk group is more sensitive to immune checkpoint immunotherapy (ICI) and adjuvant chemotherapy with cisplatin (p = 2.8e-10), docetaxel (p = 8.8e-13), etoposide (p = 1.8e-07), and paclitaxel (p = 6.2e-13). In conclusion, we identified and validated a machine learning-based molecular characteristic, IRMLS, which reflects various aspects of the BCa biological process and offers new insights into personalized precision therapy for BCa patients.

Construction and multiple validations of a robust ferroptosis-related prognostic model in bladder cancer: A comprehensive study

Ferroptosis is iron-dependent programmed cell death that inhibits tumor growth, particularly in traditional treatment-resistant tumors. Prognostic models constructed from ferroptosis-related genes are lacking; prognostic biomarkers remain insufficient. We acquired gene expression data and corresponding clinical information for bladder cancer (BC) samples from public databases. Ferroptosis-related genes from the ferroptosis database were screened for clinical predictive value. We validated gene expression differences between tumors and normal tissues through polymerase chain reaction and western blotting. Gene ontology and Kyoto encyclopedia of genes and genomes enrichment analyses were conducted to explore signaling pathways affecting the overall survival of patients with BC. CIBERSORT was used to quantify the infiltration of 22 immune cell types. We identified 6 genes (EGFR, FADS1, ISCU, PGRMC1, PTPN6, and TRIM26) to construct the prognostic risk model. The high-risk group had a poorer overall survival than the low-risk group. Receiver operating characteristic curves demonstrated excellent predictive accuracy. The validation cohort and 3 independent datasets confirmed the models' general applicability and stability. BC tissues had elevated FADS1, PTPN6, and TRIM26 mRNA and protein levels and decreased ISCU levels. Enrichment analysis indicated that neurosecretory activity might be the main pathway affecting the overall survival. High- and low-risk groups had significantly different immune cell infiltration. Specific ferroptosis-related gene expression was associated with immune cell infiltration levels. The risk score was significantly correlated with patients' clinical characteristics. A novel, widely applicable risk model with independent predictive value for the prognosis of patients with BC was established; candidate molecules for future BC research were identified.

Ferroptosis-related gene signature predicts prognosis and immune microenvironment in prostate cancer

Background

Ferroptosis, an iron-dependent form of programmed cell death, significantly impacts cancer, yet its link to prostate cancer (PCa) prognosis remains underexplored. This study aims to develop and validate a ferroptosis-related gene signature to predict PCa prognosis and immune microenvironment differences, potentially identifying therapeutic targets.

Methods

RNA-sequencing data of 478 PCa patients and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. We investigated the disease-free survival (DFS) rates of the high- and low-risk groups using the Kaplan-Meier method. Functional differences between the high- and low-risk groups were investigated by a gene set enrichment analysis (GSEA), and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The link between ferroptosis risk score and immune status was examined using CIBERSORT. The expression levels of core prognostic genes in benign prostatic hyperplasia (BPH) and PCa were verified using quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, and immunohistochemistry (IHC).

Results

A novel ferroptosis-related prognostic gene signature was established and tested in the Gene Expression Omnibus (GEO) database based on univariate and multivariate Cox regression analyses. Patients with PCa were classified into high- and low-risk groups based on this ferroptosis signature. Patients in the high-risk group had worse outcomes than those in the low-risk group. The predictive accuracy of the model was demonstrated by a receiver operating characteristic (ROC) analysis. An additional enrichment analysis of TCGA cohort revealed the immune-related pathways were significantly upregulated in the high-risk group, with areas under the curve (AUCs) of 0.85 at 1 year, 0.82 at 3 years, and 0.76 at 5 years. In the GEO cohort, the AUCs reached 0.69 at 1 year, 0.74 at 3 years, and 0.75 at 5 years. An additional enrichment analysis indicated a significant upregulation of cytokine-related pathways, immune receptor activity, and other immune-related pathways in the high-risk group. Furthermore, the analysis revealed that the proportions of mast cells and plasma cells were significantly lower in the high-risk group compared to the low-risk group of PCa patients. Conversely, the proportion of regulatory T cells (Tregs) was significantly higher in the high-risk group than in the low-risk group. According to the qRT-PCR, Western blot, and IHC results, DRD4, SRC, AKR1C2, and AIFM2 expression was significantly higher in PCa than BPH. We also showed that the ferrostatin 1-treated LNCaP cells had higher expression levels of DRD4, SRC, and AKR1C2.

Conclusions

A prognostic signature of eight ferroptosis-related genes (FRGs) that may accurately predict PCa patient outcomes was constructed and validated. FRGs may contribute to anti-tumor immunity and serve as therapeutic targets in PCa.

Development of prognostic model incorporating a ferroptosis/cuproptosis-related signature and mutational landscape analysis in muscle-invasive bladder cancer

BACKGROUND

Muscle-invasive bladder cancer (MIBC) is a prevalent and aggressive malignancy. Ferroptosis and cuproptosis are recently discovered forms of programmed cell death (PCD) that have attracted much attention. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes remain unclear.

METHODS

Data from the TCGA-BLCA project (as the training set), cBioPortal database, and GEO datasets (GSE13507 and GSE32894, as the test sets) were utilized to identify hub ferroptosis/cuproptosis-related genes (FRGs and CRGs) and develop a prognostic signature. Differential expression analysis (DEA) was conducted, followed by univariate and multivariate Cox's regression analyses and multiple machine learning (ML) techniques to select genetic features. The performance of the ferroptosis/cuproptosis-related signature was evaluated using Kaplan-Meier (K-M) survival analysis and receiver-operating characteristics (ROC) curves. Mutational and tumour immune microenvironment landscapes were also explored. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) experiments confirmed the expression patterns of the hub genes, and functional assays assessed the effects of SCD knockdown on cell viability, proliferation, and migration.

RESULTS

DEA revealed dysregulated FRGs and CRGs in the TCGA MIBC cohort. SCD, DDR2, and MT1A were identified as hub genes. A prognostic signature based on the sum of the weighted expression of these genes demonstrated strong predictive efficacy in the training and test sets. Nomogram incorporating this signature accurately predicted 1-, 3-, and 5-year survival probabilities in the TCGA cohort and GSE13507 dataset. Copy number variation (CNV) and tumour immune microenvironment analysis revealed that high risk score level groups were associated with immunosuppression and lower tumour purity. The associations of risk scores with immunotherapy and chemical drugs were also explored, indicating their potential for guiding treatment for MIBC patients. The dysregulated expression patterns of three hub genes were validated by RT-qPCR experiments.

CONCLUSIONS

Targeting hub FRGs and CRGs could be a promising therapeutic approach for MIBC. Our prognostic model offers a new framework for MIBC subtyping and can inform personalized therapeutic strategies.

Identification and validation of a novel risk model based on cuproptosis‑associated m6A for head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is a prevalent cancer with a poor survival rate due to anatomical limitations of the head and a lack of reliable biomarkers. Cuproptosis represents a novel cellular regulated death pathway, and N6-methyladenosine (m6A) is the most common internal RNA modification in mRNA. They are intricately connected to tumor formation, progression, and prognosis. This study aimed to construct a risk model for HNSCC using a set of mRNAs associated with m6A regulators and cuproptosis genes (mcrmRNA).

METHODS

RNA-seq and clinical data of HNSCC patients from The Cancer Genome Atlas (TCGA) database were analyzed to develop a risk model through the least absolute shrinkage and selection operator (LASSO) analysis. Survival analysis and receiver operating characteristic (ROC) analysis were performed for the high- and low-risk groups. Additionally, the model was validated using the GSE41613 dataset from the Gene Expression Omnibus (GEO) database. GSEA and CIBERSORT were applied to investigate the immune microenvironment of HNSCC.

RESULTS

A risk model consisting of 32 mcrmRNA was developed using the LASSO analysis. The risk score of patients was confirmed to be an independent prognostic indicator by multivariate Cox analysis. The high-risk group exhibited a higher tumor mutation burden. Additionally, CIBERSORT analysis indicated varying levels of immune cell infiltration between the two groups. Significant disparities in drug sensitivity to common medications were also observed. Enrichment analysis further unveiled significant differences in metabolic pathways and RNA processing between the two groups.

CONCLUSIONS

Our risk model can predict outcomes for HNSCC patients and offers valuable insights for personalized therapeutic approaches.

TFRC, associated with hypoxia and immune, is a prognostic factor and potential therapeutic target for bladder cancer

BACKGROUND

Bladder cancer is a common malignancy of the urinary system, and the survival rate and recurrence rate of patients with muscular aggressive (MIBC) bladder cancer are not ideal. Hypoxia is a pathological process in which cells acquire special characteristics to adapt to anoxic environment, which can directly affect the proliferation, invasion and immune response of bladder cancer cells. Understanding the exact effects of hypoxia and immune-related genes in BLCA is helpful for early assessment of the prognosis of BLCA. However, the prognostic model of BLCA based on hypoxia and immune-related genes has not been reported.

PURPOSE

Hypoxia and immune cell have important role in the prognosis of bladder cancer (BLCA). The aim of this study was to investigate whether hypoxia and immune related genes could be a novel tools to predict the overall survival and immunotherapy of BLCA patients.

METHODS

First, we downloaded transcriptomic data and clinical information of BLCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A combined hypoxia and immune signature was then constructed on the basis of the training cohort via least absolute shrinkage and selection operator (LASSO) analysis and validated in test cohort. Afterwards, Kaplan-Meier curves, univariate and multivariate Cox and subgroup analysis were employed to assess the accuracy of our signature. Immune cell infiltration, checkpoint and the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were used to investigate the immune environment and immunotherapy of BLCA patients. Furthermore, we confirmed the role of TFRC in bladder cancer cell lines T24 and UMUC-3 through cell experiments.

RESULTS

A combined hypoxia and immune signature containing 8 genes were successfully established. High-risk group in both training and test cohorts had significantly poorer OS than low-risk group. Univariate and multivariate Cox analysis indicated our signature could be regarded as an independent prognostic factor. Different checkpoint was differently expressed between two groups, including CTLA4, HAVCR2, LAG3, PD-L1 and PDCD1. TIDE analysis indicated high-risk patients had poor response to immunotherapy and easier to have immune escape. The drug sensitivity analysis showed that high-risk group patients were more potentially sensitive to many drugs. Meanwhile, TFRC could inhibit the proliferation and invasion ability of T24 and UMUC-3 cells.

CONCLUSION

A combined hypoxia and immune-related gene could be a novel predictive model for OS and immunotherapy estimation of BLCA patients and TFRC could be used as a potential therapeutic target in the future.

Integration of multi-omics and clinical treatment data reveals bladder cancer therapeutic vulnerability gene combinations and prognostic risks

Background

Bladder cancer (BCa) is a common malignancy of the urinary tract. Due to the high heterogeneity of BCa, patients have poor prognosis and treatment outcomes. Immunotherapy has changed the clinical treatment landscape for many advanced malignancies, opening new avenues for the precise treatment of malignancies. However, effective predictors and models to guide clinical treatment and predict immunotherapeutic outcomes are still lacking.

Methods

We downloaded BCa sample data from The Cancer Genome Atlas to identify anti-PD-L1 immunotherapy-related genes through an immunotherapy dataset and used machine learning algorithms to build a new PD-L1 multidimensional regulatory index (PMRI) based on these genes. PMRI-related column-line graphs were constructed to provide quantitative tools for clinical practice. We analyzed the clinical characteristics, tumor immune microenvironment, chemotherapy response, and immunotherapy response of patients based on PMRI system. Further, we performed function validation of classical PMRI genes and their correlation with PD-L1 in BCa cells and screening of potential small-molecule drugs targeting PMRI core target proteins through molecular docking.

Results

PMRI, which consists of four anti-PD-L1 immunotherapy-associated genes (IGF2BP3, P4HB, RAC3, and CLK2), is a reliable predictor of survival in patients with BCa and has been validated using multiple external datasets. We found higher levels of immune cell infiltration and better responses to immunotherapy and cisplatin chemotherapy in the high PMRI group than in the low PMRI group, which can also be used to predict immune efficacy in a variety of solid tumors other than BCa. Knockdown of IGF2BP3 inhibited BCa cell proliferation and migration, and IGF2BP3 was positively correlated with PD-L1 expression. We performed molecular docking prediction for each of the core proteins comprising PMRI and identified 16 small-molecule drugs with the highest affinity to the target proteins.

Conclusions

Our PD-L1 multidimensional expression regulation model based on anti-PD-L1 immunotherapy-related genes can accurately assess the prognosis of patients with BCa and identify patient populations that will benefit from immunotherapy, providing a new tool for the clinical management of intermediate and advanced BCa.

BLCA prognostic model creation and validation based on immune gene-metabolic gene combination

BACKGROUND

Bladder cancer (BLCA) is a prevalent urinary system malignancy. Understanding the interplay of immunological and metabolic genes in BLCA is crucial for prognosis and treatment.

METHODS

Immune/metabolism genes were extracted, their expression profiles analyzed. NMF clustering found prognostic genes. Immunocyte infiltration and tumor microenvironment were examined. Risk prognostic signature using Cox/LASSO methods was developed. Immunological Microenvironment and functional enrichment analysis explored. Immunotherapy response and somatic mutations evaluated. RT-qPCR validated gene expression.

RESULTS

We investigated these genes in 614 BLCA samples, identifying relevant prognostic genes. We developed a predictive feature and signature comprising 7 genes (POLE2, AHNAK, SHMT2, NR2F1, TFRC, OAS1, CHKB). This immune and metabolism-related gene (IMRG) signature showed superior predictive performance across multiple datasets and was independent of clinical indicators. Immunotherapy response and immune cell infiltration correlated with the risk score. Functional enrichment analysis revealed distinct biological pathways between low- and high-risk groups. The signature demonstrated higher prediction accuracy than other signatures. qRT-PCR confirmed differential gene expression and immunotherapy response.

CONCLUSIONS

The model in our work is a novel assessment tool to measure immunotherapy's effectiveness and anticipate BLCA patients' prognosis, offering new avenues for immunological biomarkers and targeted treatments.

Identification and Validation of Ferroptosis-Related Genes in Patients with Acute Spinal Cord Injury

Ferroptosis plays crucial roles in the pathology of spinal cord injury (SCI). The purpose of this study was to identify differentially expressed ferroptosis-related genes (DE-FRGs) in human acute SCI by bioinformatics analysis and validate the hub DE-FRGs in non-SCI and SCI patients. The GSE151371 dataset was downloaded from the Gene Expression Omnibus and difference analysis was performed. The differentially expressed genes (DEGs) in GSE151371 overlapped with the ferroptosis-related genes (FRGs) obtained from the Ferroptosis Database. A total of 41 DE-FRGs were detected in 38 SCI samples and 10 healthy samples in GSE151371. Then, enrichment analyses of these DE-FRGs were performed for functional annotation. The GO enrichment results showed that upregulated DE-FRGs were mainly associated with reactive oxygen species and redox reactions, and the KEGG enrichment analysis indicated involvement in some diseases and ferroptosis pathways. Protein-protein interaction (PPI) analysis and lncRNA-miRNA-mRNA regulatory network were performed to explore the correlations between genes and regulatory mechanisms. The relationship between DE-FRGs and differentially expressed mitochondria-related genes (DE-MRGs) was also analyzed. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the hub DE-FRGs in clinical blood samples from acute SCI patients and healthy controls. Consistent with the bioinformatics results, qRT-PCR of the clinical samples indicated similar expression levels of TLR4, STAT3, and HMOX1. This study identified DE-FRGs in blood samples from SCI patients, and the results could improve our understanding of the molecular mechanisms of ferroptosis in SCI. These candidate genes and pathways could be therapeutic targets for SCI.

Homeobox Gene Expression Dysregulation as Potential Diagnostic and Prognostic Biomarkers in Bladder Cancer

Homeobox genes serve as master regulatory transcription factors that regulate gene expression during embryogenesis. A homeobox gene may have either tumor-promoting or tumor-suppressive properties depending on the specific organ or cell lineage where it is expressed. The dysregulation of homeobox genes has been reported in various human cancers, including bladder cancer. The dysregulated expression of homeobox genes has been associated with bladder cancer clinical outcomes. Although bladder cancer has high risk of tumor recurrence and progression, it is highly challenging for clinicians to accurately predict the risk of tumor recurrence and progression at the initial point of diagnosis. Cystoscopy is the routine surveillance method used to detect tumor recurrence. However, the procedure causes significant discomfort and pain that results in poor surveillance follow-up amongst patients. Therefore, the development of reliable non-invasive biomarkers for the early detection and monitoring of bladder cancer is crucial. This review provides a comprehensive overview of the diagnostic and prognostic potential of homeobox gene expression dysregulation in bladder cancer.

Identification and Validation of Molecular Subtype and Prognostic Signature for Bladder Cancer Based on Neutrophil Extracellular Traps

Neutrophil extracellular traps (NETs) could promote tumor growth and distant metastases. Molecular subtypes of bladder cancer were identified with consensus cluster analysis. A NETs-related prognostic signature was constructed with LASSO cox regression analysis. As a result, we identified three subtypes of bladder cancer, which had a distinct difference in prognosis, immune microenvironment, TIDE score, mRNAsi score and IC50 score. We also developed a prognostic signature based on 5 NETs-related genes, which had a good performance in clinical outcome prediction of bladder cancer. These results may provide more data about the vital role of NETs in bladder cancer.

Glutathione S-transferase zeta 1 alters the HMGB1/GPX4 axis to drive ferroptosis in bladder cancer cells

OBJECTIVE

The ability of glutathione S-transferase zeta 1 (GSTZ1) to modulate homeostasis of cellular redox and induce ferroptosis was explored in bladder cancer cells, and the involvement of the high mobility group protein 1/glutathione peroxidase 4 (HMGB1/GPX4) in these effects was studied.

METHODS

BIU-87 cells stably overexpressing GSTZ1 were transfected with appropriate plasmids to deplete HMGB1 or overexpress GPX4, then treated with deferoxamine and ferrostatin-1. Antiproliferative effects were assessed by quantifying levels of ferroptosis markers, such as iron, glutathione (GSH), malondialdehyde (MDA), reactive oxygen species (ROS), GPX4, transferrin, and ferritin.

RESULTS

GSTZ1 was significantly downregulated in bladder cancer cells. GSTZ1 overexpression downregulated GPX4 and GSH, while greatly increasing levels of iron, MDA, ROS, and transferrin. GSTZ1 overexpression also decreased proliferation of BIU-87 cells and activated HMGB1/GPX4 signaling. The effects of GSTZ1 on ferroptosis and proliferation were antagonized by HMGB1 knockdown or GPX4 overexpression.

CONCLUSION

GSTZ1 induces ferroptotic cell death and alters cellular redox homeostasis in bladder cancer cells, and these effects involve activation of the HMGB1/GPX4 axis.

Identification of natural killer cell associated subtyping and gene signature to predict prognosis and drug sensitivity of lung adenocarcinoma

Introduction: This research explored the immune characteristics of natural killer (NK) cells in lung adenocarcinoma (LUAD) and their predictive role on patient survival and immunotherapy response. Material and methods: Molecular subtyping of LUAD samples was performed by evaluating NK cell-associated pathways and genes in The Cancer Genome Atlas (TCGA) dataset using consistent clustering. 12 programmed cell death (PCD) patterns were acquired from previous study. Riskscore prognostic models were constructed using Least absolute shrinkage and selection operator (Lasso) and Cox regression. The model stability was validated in Gene Expression Omnibus database (GEO). Results: We classified LUAD into three different molecular subgroups based on NK cell-related genes, with the worst prognosis in C1 patients and the optimal in C3. Homologous Recombination Defects, purity and ploidy, TMB, LOH, Aneuploidy Score, were the most high-expressed in C1 and the least expressed in C3. ImmuneScore was the highest in C3 type, suggesting greater immune infiltration in C3 subtype. C1 subtypes had higher TIDE scores, indicating that C1 subtypes may benefit less from immunotherapy. Generally, C3 subtype presented highest PCD patterns scores. With four genes, ANLN, FAM83A, RHOV and PARP15, we constructed a LUAD risk prediction model with significant differences in immune cell composition, cell cycle related pathways between the two risk groups. Samples in C1 and high group were more sensitive to chemotherapy drug. The score of PCD were differences in high- and low-groups. Finally, we combined Riskscore and clinical features to improve the performance of the prediction model, and the calibration curve and decision curve verified that the great robustness of the model. Conclusion: We identified three stable molecular subtypes of LUAD and constructed a prognostic model based on NK cell-related genes, maybe have a greater potential for application in predicting immunotherapy response and patient prognosis.

A novel signature of cuproptosis-related lncRNAs predicts prognosis in glioma: Evidence from bioinformatic analysis and experiments

Background: Glioma patients often experience unfavorable outcomes and elevated mortality rates. Our study established a prognostic signature utilizing cuproptosis-associated long non-coding RNAs (CRLs) and identified novel prognostic biomarkers and therapeutic targets for glioma. Methods: The expression profiles and related data of glioma patients were obtained from The Cancer Genome Atlas, an accessible online database. We then constructed a prognostic signature using CRLs and evaluated the prognosis of glioma patients by means of Kaplan-Meier survival curves and receiver operating characteristic curves. A nomogram based on clinical features was employed to predict the individual survival probability of glioma patients. Functional enrichment analysis was conducted to identify crucial CRL-related enriched biological pathways. The role of LEF1-AS1 in glioma was validated in two glioma cell lines (T98 and U251). Results: We developed and validated a prognostic model for glioma with 9 CRLs. Patients with low-risk had a considerably longer overall survival (OS). The prognostic CRL signature may serve independently as an indicator of prognosis for glioma patients. In addition, functional enrichment analysis revealed significant enrichment of multiple immunological pathways. Notable differences were observed between the two risk groups in terms of immune cell infiltration, function, and immune checkpoints. We further identified four drugs based on their different IC50 values from the two risk groups. Subsequently, we discovered two molecular subtypes of glioma (cluster one and cluster two), with the cluster one subtype exhibiting a remarkably longer OS compared to the cluster two subtype. Finally, we observed that inhibition of LEF1-AS1 curbed the proliferation, migration, and invasion of glioma cells. Conclusion: The CRL signatures were confirmed as a reliable prognostic and therapy response indicator for glioma patients. Inhibition of LEF1-AS1 effectively suppressed the growth, migration, and invasion of gliomas; therefore, LEF1-AS1 presents itself as a promising prognostic biomarker and potential therapeutic target for glioma.

Bladder cancer tissue-derived exosomes suppress ferroptosis of T24 bladder cancer cells by transporting miR-217

It has been reported that miR-217 can inhibit the oncogenic activity and progression of bladder cancer (BCa) cells, but it has not been explored whether miR-217 is involved in the regulation of ferroptosis. In the present study, RNA transfection, real-time PCR, flow cytometry, Western blotting assays, immunofluorescence and ELISA were performed to explore the effects and mechanisms of miR-217 in BCa tissue-derived exosomes. We found that extracellular fluid from bladder cancer tissue promoted the growth and miR-217 expression of T24 cells and inhibited ferroptosis. MiR-217 was confirmed to inhibit ferroptosis in bladder cancer cells by RNA interference and functional assays. By cell membrane fluorescence probe (CM-Dil) labeling, inhibiting exosome secretion by GW4689 and exosome extraction, we determined that BCa tissue-derived exosomes transport miR-217 into T24 cells. Culture of T24 cells with extracellular fluid after RNA interference showed that exosomes carrying miR-217 derived from BCa tissues inhibited ferroptosis of T24 cells. We conclude that bladder cancer tissue-derived exosomes inhibit ferroptosis of T24 bladder cancer cells by transporting miR-217. The results of our study provide a new insight into the progression 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.

Construction of cuproptosis-related gene signature to predict the prognosis and immunotherapy efficacy of patients with bladder cancer through bioinformatics analysis and experimental validation

Background: A new form of cell death, copper-dependent cell death (termed cuproptosis), was illustrated in a recent scientific study. However, the biological function or prognostic value of cuproptosis regulators in bladder cancer (BLCA) remains unknown. Materials and Methods: Sequencing data obtained from BLCA samples in TCGA and GEO databases were preprocessed for analysis. Biological function and immune cell infiltration levels evaluated by gene set variation analysis (GSVA) were employed to calculate enrichment scores. Iteration least absolute shrinkage and selection operator (LASSO) and COX regression model were employed to select feature genes and construct a novel cuproptosis-related (CR) score signature. The genomics of drug sensitivity in cancer (GDSC) and tumor immune dysfunction and exclusion (TIDE) analysis were used to predict the chemotherapy and immunotherapy efficacy for BLCA patients. The relative expression of the genes involved in the signature was also verified by real-time quantitative PCR (qRT-PCR) in cell lines and tissues. Results: Expression abundance and the prognostic value of cuproptosis regulators proved that cuproptosis might play a vital part in the carcinogenesis of BLCA. GSVA revealed that cuproptosis regulators might be associated with metabolism and metastasis-related pathways such as TGF-β, protein secretion, oxidative Phosphorylation, MYC targets, MTORC1, and adipogenesis pathways. CR scores could predict the prognosis and evaluate the chemotherapy and immunotherapy efficacies of BLCA. CR scores were positively correlated with EMT, MYC, MTORC1, HEDGEHOG, and E2F signaling pathways; meanwhile, they were negatively correlated with several immune cell infiltration levels such as CD8+ T cells, γδT cells, and activated dendritic cells. Several GEO datasets were used to validate the power of prognostic prediction, and a nomogram was also established for clinical use. The expressions of DDX10, RBM34, and RPL17 were significantly higher in BLCA cell lines and tissues in comparison with those in the corresponding normal controls. Conclusion: Cuproptosis might play an essential role in the progression of BLCA. CR scores could be helpful in the investigation of prognostic prediction and therapeutic efficacy and could make contributions to further studies in BLCA.

Integrated Analysis of a Competing Endogenous RNA Network Reveals a Ferroptosis-related 6-LncRNA Prognostic Signature in Clear Cell Renal Cell Carcinoma.. [DOI: 10.21203/rs.3.rs-2294111/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background: This study set out to elucidate the biological functions and prognostic role of ferroptosis-related lncRNAs based on a synthetic analysis of competing endogenous RNA (ceRNA) networks in clear cell renal cell carcinoma (ccRCC). Methods: Ferroptosis-related genes were obtained from the FerrDb database. The expression data and matched clinical information of lncRNAs, miRNAs, and mRNAs from The Cancer Genome Atlas (TCGA) database were obtained to identify differentially expressed RNAs (DERNAs). The lncRNA-miRNA-mRNA ceRNA network was established utilizing the common miRNAs that were predicted in the RNAHybrid, StarBase, and TargetScan databases. Then, by progressive univariate Cox regression, LASSO,and multivariate Cox regression analysis of gene expression data and clinical information, a ferroptosis-related lncRNA prognosis signature was constructed based on the lncRNAs in ceRNA. Finally, the influence of independent lncRNAs on ccRCC was explored through a series of functional and unsupervised cluster analysis. Results: A total of 35 ferroptosis-related DEmRNAs, 356 DElncRNAs, and 132 DEmiRNAs were sorted out from the KIRC cohort of TCGA database. Overlapping DElncRNA-DEmiRNA and DEmiRNA-DEmRNA interactions among the RNAHybrid, StarBase, and TargetScan databases were constructed and identified, then a ceRNA network with 77 axes related to ferroptosis was established utilizing mutual DEmiRNAs in two interaction networks as nodes. Through synthetic analysis of the expression data and clinical information of 27 lncRNAs in the ceRNA network, a 6-ferroptosis-lncRNA signature including PVT1, CYTOR, MIAT, SNHG17, LINC00265, and LINC00894 was identified in the training set. Kaplan-Meier, PCA, t-SNE analysis, risk score curve, and ROC curve were performed to confirm the validity of the signature in the training set and secondly verified in the validation set. Finally, ssGSEA and ESTIMATE analysis showed that the signature was related with immune cell infiltration and could predict immune-related phenotypes. Conclusions: Our research underlines the role of the 6-ferroptosis-lncRNA signature as a predictor of prognosis and a therapeutic alternative for KIRC.

Development and validation of a novel hypoxia-related signature for prognostic and immunogenic evaluation in head and neck squamous cell carcinoma

Hypoxia plays a critical role in head and neck squamous cell carcinoma (HNSCC) prognosis. However, till now, robust and reliable hypoxia-related prognostic signatures have not been established for an accurate prognostic evaluation in HNSCC patients. This article focused on establishing a risk score model to evaluate the prognosis and guide treatment for HNSCC patients. RNA-seq data and clinical information of 502 HNSCC patients and 44 normal samples were downloaded from The Cancer Genome Atlas (TCGA) database. 433 samples from three Gene Expression Omnibus (GEO) datasets were incorporated as an external validation cohort. In the training cohort, prognostic-related genes were screened and LASSO regression analyses were performed for signature establishment. A scoring system based on SRPX, PGK1, STG1, HS3ST1, CDKN1B, and HK1 showed an excellent prediction capacity for an overall prognosis for HNSCC patients. Patients were divided into high- and low-risk groups, and the survival status of the two groups exhibited a statistically significant difference. Subsequently, gene set enrichment analysis (GSEA) was carried out to explore the underlying mechanisms for the prognosis differences between the high- and low-risk groups. The tumor immune microenvironment was evaluated by CIBERSORT, ESTIMATE, TIDE, and xCell algorithm, etc. Then, we explored the relationships between this prognostic model and the levels of immune checkpoint-related genes. Cox regression analysis and nomogram plot indicated the scoring system was an independent predictor for HNSCC. Moreover, a comparison of predictive capability has been made between the present signature and existing prognostic signatures for HNSCC patients. Finally, we detected the expression levels of proteins encoded by six-HRGs via immunohistochemical analysis in tissue microarray. Collectively, a novel integrated signature considering both HRGs and clinicopathological parameters will serve as a prospective candidate for the prognostic evaluation of HNSCC patients.

Integrated assessment of the clinical and biological value of ferroptosis-related genes in multiple myeloma

Background

Ferroptosis is an iron-dependent mode of cell death that could be induced by erastin and exert antitumor effects. However, the clinical and biological roles of ferroptosis-related gene (FRG) signature and the therapeutic value of erastin in multiple myeloma (MM) remained unknown.

Methods

Clinical and gene expression data of MM subjects were extracted from the Gene Expression Omnibus (GEO) public database. Univariable cox analysis was applied to determine FRGs related to survival and the least absolute shrinkage and selection operator (LASSO) regression analysis was used to develop a prognostic model. Prediction accuracy of the model was estimated by receiver operating characteristic (ROC) curves. Functional pathway enrichments and infiltrating immune status were also analyzed. We conducted in vitro experiments to investigate the combination therapy of erastin and doxorubicin.

Results

17 FRGs were strongly associated with patient survival and 11 genes were identified to construct the prognostic model. ROC curves indicated great predictive sensitivity and specificity of the model in all cohorts. Patients were divided into low- and high-risk groups by median risk score in each cohort and the survival of the low-risk group was significantly superior than that of the high-risk group. We also observed a close relevance between functional pathways and immune infiltration with risk scores. Moreover, we combined erastin and doxorubicin in our in vitro experiments and found synergetic antitumor effects of the two agents, and the underlying mechanism is the overgeneration of intracellular Reactive Oxygen Species (ROS).

Conclusions

We demonstrated the important value of ferroptosis in patient prognosis and as a potential antitumor target for MM.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02742-4.

A multifaceted and feasible prognostic model of amino acid metabolism-related genes in the immune response and tumor microenvironment of head and neck squamous cell carcinomas

We investigated the role of amino acid metabolism (AAM) in head and neck squamous cell carcinoma (HNSCC) tissues to explore its prognostic value and potential therapeutic strategies. A risk score based on four AAM-related genes (AMG) was constructed that could predict the prognosis of HNSCC. These four genes were up-regulated in HNSCC tissues and might act as oncogenes. Internal validation in The Cancer Genome Atlas (TCGA) by bootstrapping showed that patients with high-risk scores had a poorer prognosis than patients with low-risk scores, and this was confirmed in the Gene Expression Omnibus (GEO) cohort. There were also differences between the high-risk and low-risk groups in clinical information and different anatomical sites such as age, sex, TNM stage, grade stage, surgery or no surgery, chemotherapy, radiotherapy, no radiotherapy, neck lymph node dissection or not, and neck lymphovascular invasion, larynx, overlapping lesion of lip, and oral cavity and pharynx tonsil of overall survival (OS). Immune-related characteristics, tumor microenvironment (TME) characteristics, and immunotherapy response were significantly different between high- and low-risk groups. The four AMGs were also found to be associated with the expression of markers of various immune cell subpopulations. Therefore, our comprehensive approach revealed the characterization of AAM in HNSCC to predict prognosis and guide clinical therapy.

Novel Pyroptosis-Related Gene Signatures Identified as the Prognostic Biomarkers for Bladder Carcinoma

Background

Bladder carcinoma (BLCA) is a common malignant tumor with high morbidity and mortality in the urinary system. Pyroptosis is a pattern of programmed cell death that is closely associated with progression of tumors. Therefore, it is significant to probe the expression of pyroptosis-related genes (PRGs) in BLCA.

Methods

The differentially expressed genes in normal and BLCA tissues were first obtained from the Cancer Genome Atlas (TCGA) database analysis, as well as PRGs from the National Center for Biotechnology Information (NCBI) database, intersecting to obtain differentially expressed pyroptosis-related genes (DEPRGs) in BLCA. With the construction of a prognostic model of pyroptosis by regression analysis, we derived and validated key genes, which were ascertained as a separate prognostic marker by individual prognostic and clinical relevance analysis. In addition, we gained six immune cells from the Tumor Immune Evaluation Resource (TIMER) website and analyzed the relationship between pyroptosis prognostic genes and immune infiltration.

Result

Our results revealed that 31 DEPRGs were available by comparing normal and BLCA tissues with |log2 (fold change, FC)| &gt; 0.5 and FDR &lt;0.05. Four key genes (CRTAC1, GSDMB, AIM2, and FOXO3) derived from the pyroptosis prognostic model were experimentally validated for consistent expression in BLCA patients. Following risk scoring, the low-risk group of BLCA patients had noticeably higher overall survival (OS) than the high-risk group (p &lt; 0.001). Risk score was still an independent prognostic factor (HR = 1.728, 95% CI =1.289–2.315, p &lt; 0.001). In addition, we found remarkable correlations among the expression of pyroptosis-related prognostic genes and the immune infiltration of CD4+ T cells, CD8+ T cells, B cells, dendritic cells, macrophages, and neutrophils.

Conclusion

Genes (CRTAC1, GSDMB, AIM2, and FOXO3) associated with pyroptosis are potential BLCA prognostic biomarkers that act as an essential part in the predictive prognosis of survival and immunotherapy of BLCA.

miR-373-3p Regulates the Proliferative and Migratory Properties of Human HTR8 Cells via SLC38A1 Modulation

The genetic pathogenesis of selective intrauterine growth restriction (sIUGR) remains elusive, with evidence suggesting an important role of epigenetic factors such as microRNAs. In this study, we explored the relevance of miR-373-3p to the occurrence of sIUGR. Hypoxia enhanced the levels of miR-373-3p and hypoxia-inducible factor (HIF)-1α, while HIF-1α knockdown not only boosted the migration and proliferation of HTR8 cells but also suppressed the hypoxia-induced upregulation of miR-373-3p and SLC38A1. By contrast, HIF-1α overexpression induced miR-373-3p downregulation and SLC38A1 upregulation, reducing cell growth and migration, which could be reversed by a miR-373-3p inhibitor. Importantly, the miR-373-3p inhibitor and mimic reproduced phenomena similar to those induced by HIF-1α downregulation and overexpression, respectively (including altered SLC38A1 expression, mTOR activation, cell growth, and migration). Mechanistically, the miRNA regulated cell behaviors and related mTOR signaling by targeting SLC38A1 expression through an interaction with the 3′-untranslated region of SLC38A1. The placental tissues of smaller sIUGR fetuses exhibited miR-373-3p and HIF-1α upregulation, SLC38A1 downregulation, and activated mTOR. Overall, miR-373-3p appears to restrict the growth and migration of HTR8 trophoblast cells by targeting SLC38A1, as observed in the placental tissues associated with smaller sIUGR fetuses, and it could have utility in the diagnosis and treatment of this disorder.

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.

Fatty Acid Synthase Is the Key Regulator of Fatty Acid Metabolism and Is Related to Immunotherapy in Bladder Cancer

Fatty acid metabolism (FAM) genes are potentially useful for predicting prognosis and immunotherapy response in bladder cancer (BC). To examine this, we constructed a prognostic model and identified key FAM genes in BC. Using transcriptional expression profiles and clinical data of BC patients from public datasets and Changhai (CH) hospital, we built and validated a risk-score model based on 13 prognostic FAM genes. Differential gene expression identified fatty acid synthase (FASN) as central to fatty acid metabolism in BC. FASN was differentially expressed between normal and tumor tissue, and was related to survival. In the CH dataset, FASN independently predicted muscle-invasive BC. FASN differential expression was significantly related to immune-cell infiltration and patients with low FASN expression responded better to immune checkpoint inhibitor (ICI) treatment. SREBF1 was predicted as the most significant transcription factor for FASN. Competing endogenous RNA network analysis suggested that lncRNA AC107027.3 may upregulate FASN by competitively binding miR-27A-3p, thereby regulating the immunotherapy response in BC. Dasatinib and temsirolimus are potential FASN-targeting drugs. Our model efficiently predicted prognosis in BC. FASN is central to fatty acid metabolism, and a potential indicator and regulator of ICI treatment.

Ferroptosis Patterns and Tumor Microenvironment Infiltration Characterization in Bladder Cancer

Background: Ferroptosis is a unique iron-dependent form of cell death and bladder cancer (BCa) is one of the top ten most common cancer types in the world. However, the role of ferroptosis in shaping the tumor microenvironment and influencing tumor clinicopathological features remains unknown.

Methods: Using the data downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we comprehensively evaluated the ferroptosis patterns of 570 BCa samples based on 234 validated ferroptosis genes reported in the FerrDb database and systematically correlated these ferroptosis patterns with tumor microenvironment (TME) cell-infiltrating characteristics. The ferroptosis score was constructed to quantify ferroptosis patterns of individuals using principal component analysis (PCA) algorithms.

Results: Four distinct ferroptosis patterns and two gene clusters were finally determined. Significant differences in clinical characteristics and the prognosis of patients were found among different ferroptosis patterns and gene clusters, so were in the mRNA transcriptome and the landscape of TME immune cell infiltration. We also established a set of scoring system to quantify the ferroptosis pattern of individual patients with BCa named the ferroptosis score, which was discovered to tightly interact with clinical signatures such as the TNM category and tumor grade and could predict the prognosis of patients with BCa. Moreover, tumor mutation burden (TMB) was positively correlated to the ferroptosis score, and the low ferroptosis score was related to a better response to immunotherapy using PD-1 blockade. Finally, we also found there existed a positive correlation between the sensitivity to cisplatin chemotherapy and ferroptosis score.

Conclusions: Our work demonstrated and interpreted the complicated regulation mechanisms of ferroptosis on the tumor microenvironment and that better understanding and evaluating ferroptosis patterns could be helpful in guiding the clinical therapeutic strategy and improving the prognosis of patients with BCa.

Construction of a Ferroptosis-Related Gene Signature for Head and Neck Squamous Cell Carcinoma Prognosis Prediction

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignant cancers, and few studies have demonstrated the value of ferroptosis-related genes in prognosis.

Methods

The original counts of RNA sequencing data and clinicopathological data were obtained from TCGA and GSE65858 datasets. Common ferroptosis-related genes related to prognosis were identified from the training set and were included in LASSO to determine the best prognosis. To evaluate the efficacy, time-dependent ROC and Kaplan–Meier (KM) survival analyses were applied. Moreover, univariate and multivariate Cox regression analyses were used to screen independent parameters of prognosis and build a nomogram. Eventually, possible biological pathways were proposed based on GSEA.

Results

Among 242 ferroptosis-related genes, we identified that the FLT3, IL6, Keap1, NQO1, SOCS1 and TRIB3 genes were significantly connected with HNSCC patient prognosis as a six-gene signature. After, the patients were divided into high- and low-risk groups based on the six-gene signature. The KM survival curves demonstrated that the high-risk group had worse OS (p < 0.0001) and higher AUC values (0.654, 0.735, and 0.679 for 1-, 3-, and 5-year survival, respectively) for the prognostic signature of the six genes compared with other genes, which were also validated in the GSE65858 dataset. Moreover, GSEA suggested that the epithelial mesenchymal transition pathway was abundant and that the mesenchymal status in the high-risk group was substantially higher than that in the low-risk group. Finally, the immune microenvironment and differences in the content of immune cell types were demonstrated.

Conclusion

We established a six-ferroptosis-related-gene model crossing clinical prognostic parameters that can predict HNSCC patient prognosis and provide a reliable prognostic evaluation tool to assist clinical treatment decisions.

A Novel Prognostic Signature Based on Ferroptosis-Related Genes Predicts the Prognosis of Patients With Advanced Bladder Urothelial Carcinoma

Bladder Urothelial Carcinoma (BLCA) is the major subtype of bladder cancer, and the prognosis prediction of BLCA is difficult. Ferroptosis is a newly discovered iron-dependent cell death pathway. However, the clinical value of ferroptosis-related genes (FRGs) on the prediction of BLCA prognosis is still uncertain. In this study, we aimed to construct a novel prognostic signature to improve the prognosis prediction of advanced BLCA based on FRGs. In the TCGA cohort, we identified 23 differentially expressed genes (DEGs) associated with overall survival (OS) via univariate Cox analysis (all P < 0.05). 8 optimal DEGs were finally screened to generate the prognostic risk signature through LASSO regression analysis. Patients were divided into two risk groups based on the median risk score. Survival analyses revealed that the OS rate in the high-risk group was significantly lower than that in the low-risk group. Moreover, the risk score was determined as an independent predictor of OS by the multivariate Cox regression analysis (Hazard ratio > 1, 95% CI = 1.724-2.943, P < 0.05). Many potential ferroptosis-related pathways were identified in the enrichment analysis in BLCA. With the aid of an external FAHWMU cohort (n = 180), the clinical predication value of the signature was further verified. In conclusion, the prognosis of advanced BLCA could be accurately predicted by this novel FRG-signature.

Multi-Omics Analysis of Novel Signature for Immunotherapy Response and Tumor Microenvironment Regulation Patterns in Urothelial Cancer

The tumor microenvironment (TME) is mainly composed of tumor cells, tumor-infiltrating immune cells, and stromal components. It plays an essential role in the prognosis and therapeutic response of patients. Nonetheless, the TME landscape of urothelial cancer (UC) has not been fully elucidated. In this study, we systematically analyzed several UC cohorts, and three types of TME patterns (stromal-activation subtype, immune-enriched subtype and immune-suppressive subtype) were defined. The tumor microenvironment signature (TMSig) was constructed by modified Lasso penalized regression. Patients were stratified into high- and low-TMSig score groups. The low-score group had a better prognosis (p < 0.0001), higher M1 macrophage infiltration (p < 0.01), better response to immunotherapy (p < 0.05), and more similar molecular characteristics to the luminal (differentiated) subtype. The accuracy of the TMSig for predicting the immunotherapy response was also verified in three independent cohorts. We highlighted that the TMSig is an effective predictor of patient prognosis and immunotherapy response. Quantitative evaluation of a single sample is valuable for us to combine histopathological and molecular characteristics to comprehensively evaluate the status of the patient. Targeted macrophage treatment has great potential for the individualized precision therapy of UC patients.

Novel Ferroptosis-Related Multigene Prognostic Models for Patients with Bladder Cancer

Objective

Bladder cancer contributes to a serious disease burden in clinical settings. The characteristics and prognosis of patients with muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC) are distinctly different. The study aims to figure out the respective role of ferroptosis in MIBC and NMIBC and to construct ferroptosis-related gene signatures that could predict patients' prognoses.

Methods

A total of 608 MIBC and 414 NMIBC RNA-seq transcriptome data with intact clinical and follow-up information were downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and Gene expression omnibus (GEO) database. Ferroptosis-related multigene prognostic models were constructed and externally validated, respectively, in MIBC and NMIBC. Further functional enrichment analyses were also performed to explicate the underlying mechanisms and the differences between the two bladder cancer subtypes.

Results

In MIBC, a 7-gene signature for prognostic prediction was constructed. Patients were then divided into high-risk and low-risk groups according to the risk scores calculated by the 7-gene prognostic model. Patients in the high-risk group presented an impaired OS when compared with patients in the low-risk group both in the training cohort and validation cohort. Further functional analyses revealed distinctly different immune statuses between the two risk-stratification groups, speculating that exhausted immune cell function was a cause of the worst OS in the high-risk group. In NMIBC, 6 ferroptosis-related genes were identified that were significantly correlated with recurrence-free survival (RFS). Similarly, a 6-gene prognostic model was constructed and verified as an independent prognostic predictor for RFS. Functional analyses revealed significant differences in the expressions of nuclear division genes between the high-risk group and low-risk group.

Conclusion

Two novel ferroptosis-related multigene prognostic models for, respectively, predicting OS in MIBC and RFS in NMIBC were identified in this study, which indicated ferroptosis played vital roles in the oncogenesis and development of MIBC and NMIBC.

Identification of Survival and Therapeutic Response-Related Ferroptosis Regulators in Bladder Cancer through Data Mining and Experimental Validation

Simple Summary

Based on machine learning methods, we constructed a prognostic signature to calculate the survival probability of BLCA and to further investigate the underlying mechanism of the ferroptosis-related signature. We found that the signature was not only correlated with the prognostic value but was also associated with the tumor microenvironment (TME), tumor mutation burden (TMB) and the curative outcomes of both immunotherapy and chemotherapy. Furthermore, we proved the reliability of the signature in some external datasets and built a risk score evaluation nomogram for clinical use.

Abstract

Ferroptosis has been reported to regulate tumorigenesis, metastasis, drug resistance and the immune response. However, the potential roles of ferroptosis regulators in the advancement of bladder cancer remain to be explored. We systematically evaluated the multidimensional alteration landscape of ferroptosis regulators in bladder cancer and checked if their expression correlated with the ferroptosis index. We used least absolute shrinkage and selection operator regression to form a signature consisting of seven ferroptosis regulator. We confirmed the signature’s prognostic and predictive accuracy with five independent datasets. A nomogram was built to predict the overall survival and risk of death of patients. The relative expression of the genes involved in the signature was also clarified by real-time quantitative PCR. We found the risk score was related to tumor progression and antitumor immunity-related pathways. Moreover, there existed negative association between the relative antitumor immune cell infiltration level and the risk score, and higher tumor mutation burden was found in the group of lower risk score. We used The Tumor Immune Dysfunction and Exclusion database and IMvigor210 cohort having immunotherapy efficacy results to confirm the prediction function of the risk score. Furthermore, the ferroptosis regulator signature could also reflect the chemotherapy sensitivity of bladder cancer.