A Novel Prognostic Model Based on Ferroptosis-Related Gene Signature for Bladder Cancer

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.

An endothelial-related prognostic index for bladder cancer patients

BACKGROUND

Within the tumor microenvironment, endothelial cells hold substantial sway over bladder cancer (BC) prognosis. Herein, we aim to elucidate the impact of endothelial cells on BC patient outcomes by employing an integration of single-cell and bulk RNA sequencing data.

METHODS

All data utilized in this study were procured from online databases. R version 3.6.3 and relevant packages were harnessed for the development and validation of an endothelial-associated prognostic index (EPI).

RESULTS

EPI was formulated, incorporating six genes (CYTL1, FAM43A, GSN, HSPG2, RBP7, and SLC2A3). EPI demonstrated significant prognostic value in both The Cancer Genome Atlas (TCGA) and externally validated dataset. Functional results revealed a profound association between EPI and endothelial cell functionality, as well as immune-related processes. Our findings suggest that patients with low-risk EPI scores are more likely to respond positively to immunotherapy, as indicated by immune checkpoint activity, immune infiltration, tumor mutational burden, stemness index, TIDE, and IMvigor210 analyses. Conversely, individuals with high-risk EPI scores exhibited heightened sensitivity to cisplatin, docetaxel, and gemcitabine treatment regimens.

CONCLUSION

We have effectively discerned pivotal genes from the endothelial cell perspective and constructed an EPI for BC patients, thereby offering promising prospects for precision medicine.

Cancer radioresistance is characterized by a differential lipid droplet content along the cell cycle

BACKGROUND

Cancer radiation treatments have seen substantial advancements, yet the biomolecular mechanisms underlying cancer cell radioresistance continue to elude full understanding. The effectiveness of radiation on cancer is hindered by various factors, such as oxygen concentrations within tumors, cells' ability to repair DNA damage and metabolic changes. Moreover, the initial and radiation-induced cell cycle profiles can significantly influence radiotherapy responses as radiation sensitivity fluctuates across different cell cycle stages. Given this evidence and our prior studies establishing a correlation between cancer radiation resistance and an increased number of cytoplasmic Lipid Droplets (LDs), we investigated if LD accumulation was modulated along the cell cycle and if this correlated with differential radioresistance in lung and bladder cell lines.

RESULTS

Our findings identified the S phase as the most radioresistant cell cycle phase being characterized by an increase in LDs. Analysis of the expression of perilipin genes (a family of proteins involved in the LD structure and functions) throughout the cell cycle also uncovered a unique gene cell cycle pattern.

CONCLUSIONS

In summary, although these results require further molecular studies about the mechanisms of radioresistance, the findings presented here are the first evidence that LD accumulation could participate in cancer cells' ability to better survive X-Ray radiation when cells are in the S phase. LDs can represent new players in the radioresistance processes associated with cancer metabolism. This could open new therapeutic avenues in which the use of LD-interfering drugs might enhance cancer sensitivity to radiation.

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.

Identification of clinical prognostic factors and analysis of ferroptosis-related gene signatures in the bladder cancer immune microenvironment

BACKGROUND

Bladder cancer (BLCA) is a prevalent malignancy affecting the urinary system and poses a significant burden in terms of both incidence and mortality rates on a global scale. Among all BLCA cases, non-muscle invasive bladder cancer constitutes approximately 75% of the total. In recent years, the concept of ferroptosis, an iron-dependent form of regulated cell death marked by the accumulation of lipid peroxides, has captured the attention of researchers worldwide. Nevertheless, the precise involvement of ferroptosis-related genes (FRGs) in the anti-BLCA response remains inadequately elucidated.

METHODS

The integration of BLCA samples from the TCGA and GEO datasets facilitated the quantitative evaluation of FRGs, offering potential insights into their predictive capabilities. Leveraging the wealth of information encompassing mRNAsi, gene mutations, CNV, TMB, and clinical features within these datasets further enriched the analysis, augmenting its robustness and reliability. Through the utilization of Lasso regression, a prediction model was developed, enabling accurate prognostic assessments within the context of BLCA. Additionally, co-expression analysis shed light on the complex relationship between gene expression patterns and FRGs, unraveling their functional relevance and potential implications in BLCA.

RESULTS

FRGs exhibited increased expression levels in the high-risk cohort of BLCA patients, even in the absence of other clinical indicators, suggesting their potential as prognostic markers. GSEA revealed enrichment of immunological and tumor-related pathways specifically in the high-risk group. Furthermore, notable differences were observed in immune function and m6a gene expression between the low- and high-risk groups. Several genes, including MYBPH, SOST, SPRR2A, and CRNN, were found to potentially participate in the oncogenic processes underlying BLCA. Additionally, CYP4F8, PDZD3, CRTAC1, and LRTM1 were identified as potential tumor suppressor genes. Significant discrepancies in immunological function and m6a gene expression were observed between the two risk groups, further highlighting the distinct molecular characteristics associated with different prognostic outcomes. Notably, strong correlations were observed among the prognostic model, CNVs, SNPs, and drug sensitivity profiles.

CONCLUSIONS

FRGs are associated with the onset and progression of BLCA. A FRGs signature offers a viable alternative to predict BLCA, and these FRGs show a prospective research area for BLCA targeted treatment in the future.

Identification of SLC2A3 as a prognostic indicator correlated with the NF-κB/EMT axis and immune response in head and neck squamous cell carcinoma

SLC2A3 is an important member of the glucose transporter superfamily. It has been recently suggested that upregulation of SLC2A3 is associated with poor survival and acts as a prognostic marker in a variety of tumors. Unfortunately, the prognostic role of SLC2A3 in head and neck squamous cell carcinoma (HNSC) is less known. In the present study, we analyzed SLC2A3 expression in HNSC and its correlation with prognosis using TCGA and GEO databases. The results showed that SLC2A3 mRNA expression was higher in HNSC compared with adjacent normal tissues, which was validated with our 9 pairs of HNSC specimens. Moreover, high SLC2A3 expression predicted poor prognosis in HNSC patients. Mechanistically, GSEA revealed that high expression of SLC2A3 was enriched in epithelial-mesenchymal transition (EMT) and NF-κB signaling. In HNSC cell lines, SLC2A3 knockdown inhibited cell proliferation and migration. In addition, NF-κB P65 and EMT-related gene expression was suppressed upon SLC2A3 knockdown, indicating that SLC2A3 may play a preeminent role in the progression of HNSC through the NF-κB/EMT axis. Meanwhile, the expression of SLC2A3 was negatively correlated with immune cells, suggesting that SLC2A3 may be involved in the immune response in HNSC. The correlation between SLC2A3 expression and drug sensitivity was further assessed. In conclusion, our study demonstrated that SLC2A3 could predict the prognosis of HNSC patients and mediate the progression of HNSC via the NF-κB/EMT axis and immune responses.

Identification of disulfidptosis-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognosis model in colorectal cancer

BACKGROUND

Colorectal cancer is the second leading cause of cancer-related deaths, which imposes a significant societal burden. Regular screening and emerging molecular tumor markers have important implications for detecting the progression and development of colorectal cancer. Disulfidptosis is a newly defined type of programmed cell death triggered by abnormal accumulation of disulfide compounds in cells that stimulate disulfide stress. Currently, there is no relevant discussion on this mechanism and colorectal cancer.

METHODS

We classified the disulfidptosis-related subtypes of colorectal cancer using bioinformatics methods. Through secondary clustering of differentially expressed genes between subtypes, we identified characteristic genes of the disulfidptosis subtype, constructed a prognostic model, and searched for potential biomarkers through clinical validation.

RESULTS

Using disulfidptosis-related genes collected from the literature, we classified colorectal cancer patients from public databases into three subtypes. The differentially expressed genes between subtypes were clustered into three gene subtypes, and eight characteristic genes were screened to construct a prognostic model.

CONCLUSION

The disulfidptosis mechanism has important value in the classification of colorectal cancer patients, and characteristic genes selected based on this mechanism can serve as a new potential biological marker for colorectal cancer.

A prognostic signature based on seven T-cell-related cell clustering genes in bladder urothelial carcinoma

Bladder urothelial carcinoma (BLCA) is one of the most common cancer-related deaths in the world, along with high mortality. Due to the difficult detection of early symptoms, the treatment for this disease is still dissatisfactory. Thus, the current research hotspot is beginning to focus on the immune microenvironment in this disease, aiming to provide guidance for diagnosis and treatment. In this study, the single-cell RNA sequencing data downloaded from the gene expression omnibus database was used to classify the immune cells of BLCA. And the final seven T-cell-related cell clustering genes associated with BLCA prognosis (HSPA2, A2M, JUN, PDGFRB, GBP2, LGALS1, and GAS6) were screened out, and then used for constructing the prognostic model against BLCA based on the Cox and LASSO regression analysis. Satisfactorily, the model could efficiently evaluate the overall survival of BLCA and had the potential to be applied for the clinic treatment. Moreover, we also revealed that the difference in immune infiltration levels and gene mutation might account for the diverse prognosis in BLCA patients. In a word, our findings provided a novel insight for designing efficient immunotherapies for BLCA.

Crosstalk between Mesenchymal Stem Cells and Cancer Stem Cells Reveals a Novel Stemness-Related Signature to Predict Prognosis and Immunotherapy Responses for Bladder Cancer Patients

Mesenchymal stem cells (MSCs) and cancer stem cells (CSCs) maintain bladder cancer (BCa) stemness and facilitate the progression, metastasis, drug resistance, and prognosis. Therefore, we aimed to decipher the communication networks, develop a stemness-related signature (Stem. Sig.), and identify a potential therapeutic target. BCa single-cell RNA-seq datasets (GSE130001 and GSE146137) were used to identify MSCs and CSCs. Pseudotime analysis was performed by Monocle. Stem. Sig. was developed by analyzing the communication network and gene regulatory network (GRN) that were decoded by NicheNet and SCENIC, respectively. The molecular features of the Stem. Sig. were evaluated in TCGA-BLCA and two PD-(L)1 treated datasets (IMvigor210 and Rose2021UC). A prognostic model was constructed based on a 101 machine-learning framework. Functional assays were performed to evaluate the stem traits of the hub gene. Three subpopulations of MSCs and CSCs were first identified. Based on the communication network, the activated regulons were found by GRN and regarded as the Stem. Sig. Following unsupervised clustering, two molecular subclusters were identified and demonstrated distinct cancer stemness, prognosis, immunological TME, and response to immunotherapy. Two PD-(L)1 treated cohorts further validated the performance of Stem. Sig. in prognosis and immunotherapeutic response prediction. A prognostic model was then developed, and a high-risk score indicated a poor prognosis. Finally, the hub gene SLC2A3 was found exclusively upregulated in extracellular matrix-related CSCs, predicting prognosis, and shaping an immunosuppressive tumor microenvironment. Functional assays uncovered the stem traits of SLC2A3 in BCa by tumorsphere formation and western blotting. The Stem. Sig. derived from MSCs and CSCs can predict prognosis and response to immunotherapy for BCa. Besides, SLC2A3 may serve as a promising stemness target facilitating cancer effective management.

Construction and validation of a bladder cancer risk model based on autophagy-related genes

Autophagy has an important association with tumorigenesis, progression, and prognosis. However, the mechanism of autophagy-regulated genes on the risk prognosis of bladder cancer (BC) patients has not been fully elucidated yet. In this study, we created a prognostic model of BC risk based on autophagy-related genes, which further illustrates the value of genes associated with autophagy in the treatment of BC. We first downloaded human autophagy-associated genes and BC datasets from Human Autophagy Database and The Cancer Genome Atlas (TCGA) database, and finally obtained differential prognosis-associated genes for autophagy by univariate regression analysis and differential analysis of cancer versus normal tissues. Subsequently, we downloaded two datasets from Gene Expression Omnibus (GEO), GSE31684 and GSE15307, to expand the total number of samples. Based on these genes, we distinguished the molecular subtypes (C1, C2) and gene classes (A, B) of BC by consistent clustering analysis. Using the genes merged from TCGA and the two GEO datasets, we conducted least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to obtain risk genes and construct autophagy-related risk prediction models. The accuracy of this risk prediction model was assessed by receiver operating characteristic (ROC) and calibration curves, and then nomograms were constructed to predict the survival of bladder cancer patients at 1, 3, and 5 years, respectively. According to the median value of the risk score, we divided BC samples into the high- and low-risk groups. Kaplan-Meier (K-M) survival analysis was performed to compare survival differences between subgroups. Then, we used single sample gene set enrichment analysis (ssGSEA) for immune cell infiltration abundance, immune checkpoint genes, immunotherapy response, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and tumor mutation burden (TMB) analysis for different subgroups. We also applied quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry (IHC) techniques to verify the expression of these six genes in the model. Finally, we chose the IMvigor210 dataset for external validation. Six risk genes associated with autophagy (SPOCD1, FKBP10, NAT8B, LDLR, STMN3, and ANXA2) were finally screened by LASSO regression algorithm and multivariate Cox regression analysis. ROC and calibration curves showed that the model established was accurate and reliable. Univariate and multivariate regression analyses were used to verify that the risk model was an independent predictor. K-M survival analysis indicated that patients in the high-risk group had significantly worse overall survival than those in the low-risk group. Analysis by algorithms such as correlation analysis, gene set variation analysis (GSVA), and ssGSEA showed that differences in immune microenvironment, enrichment of multiple biologically active pathways, TMB, immune checkpoint genes, and human leukocyte antigens (HLAs) were observed in the different risk groups. Then, we constructed nomograms that predicted the 1-, 3-, and 5-year survival rates of different BC patients. In addition, we screened nine sensitive chemotherapeutic drugs using the correlation between the obtained expression status of risk genes and drug sensitivity results. Finally, the external dataset IMvigor210 verified that the model is reliable and efficient. We established an autophagy-related risk prognostic model that is accurate and reliable, which lays the foundation for future personalized treatment of bladder cancer.

Identification of ferroptosis, necroptosis, and pyroptosis-associated genes in periodontitis-affected human periodontal tissue using integrated bioinformatic analysis

Introduction: Periodontitis is a chronic inflammatory oral disease that destroys soft and hard periodontal support tissues. Multiple cell death modes including apoptosis, necroptosis, pyroptosis, and ferroptosis play a crucial role in the pathogenicity of inflammatory diseases. This study aimed to identify genes associated with ferroptosis, necroptosis, and pyroptosis in different cells present in the periodontium of periodontitis patients. Methods: Gingival tissues' mRNA sequencing dataset GSE173078 of 12 healthy control and 12 periodontitis patients' and the microarray dataset GSE10334 of 63 healthy controls and 64 periodontitis patients' were obtained from Gene Expression Omnibus (GEO) database. A total of 910 differentially expressed genes (DEGs) obtained in GSE173078 were intersected with necroptosis, pyroptosis, and ferroptosis-related genes to obtain the differential genes associated with cell death (DCDEGs), and the expression levels of 21 differential genes associated with cell death were verified with dataset GSE10334. Results: Bioinformatic analysis revealed 21 differential genes associated with cell death attributed to ferroptosis, pyroptosis, and necroptosis in periodontitis patients compared with healthy controls. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses revealed that 21 differential genes associated with cell death were related to various cellular and immunological pathways including inflammatory responses, necroptosis, and osteoclast differentiation. Additionally, the single-cell RNA (scRNA) sequencing data GSE171213 of 4 healthy controls and 5 periodontitis patients' periodontal tissue was analyzed to obtain cell clustering and cell types attributed to differential genes associated with cell death. We found that among 21 DCDEGs, SLC2A3, FPR2, TREM1, and IL1B were mainly upregulated in neutrophils present in the periodontium of periodontitis patients. Gene overlapping analysis revealed that IL-1B is related to necroptosis and pyroptosis, TREM1 and FPR2 are related to pyroptosis, and SLC2A3 is related to ferroptosis. Finally, we utilized the CIBERSORT algorithm to assess the association between DCDEGs and immune infiltration phenotypes, based on the gene expression profile of GSE10334. The results revealed that the upregulated SLC2A3, FPR2, TREM1, and IL1B were positively correlated with neutrophil infiltration in the periodontium. Discussion: The findings provide upregulated SLC2A3, FPR2, TREM1, and IL1B in neutrophils as a future research direction on the mode and mechanism of cell death in periodontitis and their role in disease pathogenicity.

Comprehensive characterization of endoplasmic reticulum stress in bladder cancer revealing the association with tumor immune microenvironment and prognosis

Background: This study constructs a molecular subtype and prognostic model of bladder cancer (BLCA) through endoplasmic reticulum stress (ERS) related genes, thus helping to clinically guide accurate treatment and prognostic assessment. Methods: The Bladder Cancer (BLCA) gene expression data was downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We clustered by ERS-related genes which obtained through GeneCards database, results in the establishment of a new molecular typing of bladder cancer. Further, we explored the characteristics of each typology in terms of immune microenvironment, mutations, and drug screening. By analyzing the ERS-related genes with univariate Cox, LASSO and multivariate Cox analyses, we also developed the four-gene signature, while validating the prognostic effect of the model in GSE32894 and GSE13507 cohorts. Finally, we evaluated the prognostic value of the clinical data in the high and low ERS score groups and constructed a prognostic score line graph by Nomogram. Results: We constructed four molecular subtypes (C1- C4) of bladder cancer, in which patients with C2 had a poor prognosis and those with C3 had a better prognosis. The C2 had a high degree of TP53 mutation, significant immune cell infiltration and high immune score. In contrast, C3 had a high degree of FGFR3 mutation, insignificant immune cell infiltration, and reduced immune checkpoint expression. After that, we built ERS-related risk signature to calculate ERS score, including ATP2A3, STIM2, VWF and P4HB. In the GSE32894 and GSE13507, the signature also had good predictive value for prognosis. In addition, ERS scores were shown to correlate well with various clinical features. Finally, we correlated the ERS clusters and ERS score. Patients with high ERS score were more likely to have the C2 phenotype, while patients with low ERS score were C3. Conclusion: In summary, we identified four novel molecular subtypes of BLCA by ERS-related genes which could provide some new insights into precision medicine. Prognostic models constructed from ERS-related genes can be used to predict clinical outcomes. Our study contributes to the study of personalized treatment and mechanisms of BLCA.

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.

Identification of novel immune ferroptosis-related genes associated with clinical and prognostic features in breast cancer

Introduction: Breast cancer is the most common form of cancer among women, it is critical to identify potential targets and prognostic biomarkers. Ferroptosis combined with immunity shows a pivotal role in a variety of tumors, which provides new opportunities to detect and treat breast cancer. Methods: Our first step was to combine multiple datasets to search for immune ferroptosis-related mRNAs. In the next step, risk signatures were created using Least Absolute Shrinkage and Selection Operator (LASSO). After that, based on the results of the multivariate Cox analysis, we created a prognostic nomogram and validated the model's accuracy. Finally, functional enrichment analysis, single sample gene set enrichment analysis (ssGSEA), immunity and drug sensitivity correlation analysis were performed to explore the possible mechanisms by which these immune ferroptosis associated mRNAs affect BRCA survival. Results: An immune ferroptosis signature (IFRSig) consisting of 5 mRNAs was constructed and showed excellent predictability in the training and validation cohorts. A correlation analysis revealed that clinical characteristics were closely related to risk characteristics. Our nomogram model, which we created by combining risk characteristics and clinical parameters, was proven to be accurate at predicting BRCA prognosis. Further, we divided patients into lowrisk and high-risk groups based on the expression of the model-related genes. Compared with low-risk group, high-risk group showed lower levels of immune cell infiltration, immune-related functions, and immune checkpoints molecules, which may associate with the poor prognosis. Discussion: The IFRSig could be used to predict overall survival (OS) and treatment response in BRCA patients and could be viewed as an independent prognostic factor. The findings in this study shed light on the role of immune ferroptosis in the progression of BRCA.

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.

A novel prognostic model based on cellular senescence-related gene signature for bladder cancer

Background

Cellular senescence plays crucial role in the progression of tumors. However, the expression patterns and clinical significance of cellular senescence-related genes in bladder cancer (BCa) are still not clearly clarified. This study aimed to establish a prognosis model based on senescence-related genes in BCa.

Methods

The transcriptional profile data and clinical information of BCa were downloaded from TCGA and GEO databases. The least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression analyses were performed to develop a prognostic model in the TCGA cohort. The GSE13507 cohort were used for validation. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were performed to investigate underlying mechanisms.

Results

A six-gene signature (CBX7, EPHA3, STK40, TGFB1I1, SREBF1, MYC) was constructed in the TCGA databases. Patients were classified into high risk and low risk group in terms of the median risk score. Survival analysis revealed that patients in the higher risk group presented significantly worse prognosis. Receiver operating characteristic (ROC) curve analysis verified the moderate predictive power of the risk model based on the six senescence-related genes signature. Further analysis indicated that the clinicopathological features analysis were significantly different between the two risk groups. As expected, the signature presented prognostic significance in the GSE13507 cohort. Functional analysis indicated that immune-related pathways activity, immune cell infiltration and immune-related function were different between two risk groups. In addition, risk score were positively correlated with multiple immunotherapy biomarkers.

Conclusion

Our study revealed that a novel model based on senescence-related genes could serve as a reliable predictor of survival for patients with BCa.

Identification and validation of a novel prognostic model based on platinum Resistance-related genes in bladder cancer

BACKGROUND

The depth of response to platinum in urothelial neoplasm tissues varies greatly. Biomarkers that have practical value in prognosis stratification are increasingly needed. Our study aimed to select a set of BC (bladder cancer)-related genes involved in both platinum resistance and survival, then use these genes to establish the prognostic model.

MATERIALS AND METHODS

Platinum resistance-related DEGs (differentially expressed genes) and tumorigenesis-related DEGs were identified. Ten most predictive co-DEGs were acquired followed by building a risk score model. Survival analysis and ROC (receiver operating characteristic) plot were used to evaluate the predictive accuracy. Combined with age and tumor stages, a nomogram was generated to create a graphical representation of survival rates at 1-, 3-, 5-, and 8-year in BC patients. The prognostic performance was validated in three independent BC datasets with platinum-based chemotherapy. The potential mechanism was explored by enrichment analysis.

RESULTS

PPP2R2B, TSPAN7, ATAD3C, SYT15, SAPCD1, AKR1B1, TCHH, AKAP12, AGLN3, and IGF2 were selected for our prognostic model. Patients in high- and low-risk groups exhibited a significant survival difference with HR (hazard ratio) = 2.7 (p < 0.0001). The prognostic nomogram of predicting 3-year OS (overall survival) for BC patients could yield an AUC (area under the curve) of 0.819. In the external validation dataset, the risk score also has a robust predictive ability.

CONCLUSION

A prognostic model derived from platinum resistance-related genes was constructed, we confirmed its value in predicting platinum-based chemotherapy benefits and overall survival for BC patients. The model might assist in therapeutic decisions for bladder malignancy.

Identification of susceptibility modules and hub genes of osteoarthritis by WGCNA analysis

Osteoarthritis (OA) is a major cause of pain, disability, and social burden in the elderly throughout the world. Although many studies focused on the molecular mechanism of OA, its etiology remains unclear. Therefore, more biomarkers need to be explored to help early diagnosis, clinical outcome measurement, and new therapeutic target development. Our study aimed to retrieve the potential hub genes of osteoarthritis (OA) by weighted gene co-expression network analysis (WGCNA) and assess their clinical utility for predicting OA. Here, we integrated WGCNA to identify novel OA susceptibility modules and hub genes. In this study, we first selected 477 and 834 DEGs in the GSE1919 and the GSE55235 databases, respectively, from the Gene Expression Omnibus (GEO) website. Genes with p-value&lt;0.05 and | log2FC | &gt; 1 were included in our analysis. Then, WGCNA was conducted to build a gene co-expression network, which filtered out the most relevant modules and screened out 23 overlapping WGCNA-derived hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses elucidated that these hub genes were associated with cell adhesion molecules pathway, leukocyte activation, and inflammatory response. In addition, we conducted the protein–protein interaction (PPI) network in 23 hub genes, and the top four upregulated hub genes were sorted out (CD4, SELL, ITGB2, and CD52). Moreover, our nomogram model showed good performance in predicting the risk of OA (C-index = 0.76), and this model proved to be efficient in diagnosis by ROC curves (AUC = 0.789). After that, a single-sample gene set enrichment (ssGSEA) analysis was performed to discover immune cell infiltration in OA. Finally, human primary synoviocytes and immunohistochemistry study of synovial tissues confirmed that those candidate genes were significantly upregulated in the OA groups compared with normal groups. We successfully constructed a co-expression network based on WGCNA and found out that OA-associated susceptibility modules and hub genes, which may provide further insight into the development of pre-symptomatic diagnosis, may contribute to understanding the molecular mechanism study of OA risk genes.

Establishment and Validation of a Tumor Microenvironment Prognostic Model for Predicting Bladder Cancer Survival Status Based on Integrated Bioinformatics Analyses

This study was designed to analyze the characteristics of bladder cancer-related genes and establish a prognostic model of bladder cancer. The model passed an independent external validation set test. Differentially expressed genes (DEGs) related to bladder cancer were obtained from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) databases. WGCNA was used to fit the GSE188715, TCGA, and GTEx RNA-Seq data. Fusing the module genes with the high significance in tumor development extracted from WGCNA and DEGs screened from multiple databases. 709 common prognostic-related genes were obtained. The 709 genes were enriched in the Gene Ontology database. Univariate Cox and LASSO regression analyses were used to screen out 21 prognostic-related genes and further multivariate Cox regression established a bladder cancer prognostic model consisting of 8 genes. After the eight-gene prognostic model was established, the Human Protein Atlas (HPA) database, GEPIA 2, and quantitative real-time PCR (qRT-PCR) verified the differential expression of these genes. Gene Set Enrichment Analysis and immune infiltration analysis found biologically enrichment pathways and cellular immune infiltration related to this bladder cancer prognostic model. Then, we selected bladder cancer patients in the TCGA database to evaluate the predictive ability of the model on the training set and validation set. The overall survival status of the two TCGA patient groups in the training and the test sets was obtained by Kaplan–Meier survival analysis. Three-year survival rates in the training and test sets were 37.163% and 25.009% for the low-risk groups and 70.000% and 62.235% for the high-risk groups, respectively. Receiver operating characteristic curve (ROC) analysis showed that the areas under the curve (AUCs) for the training and test sets were above 0.7. In an external independent validation database GSE13507, Kaplan–Meier survival analysis showed that the three-year survival rates of the high-risk and the low-risk groups in this database were 56.719% and 76.734%, respectively. The AUCs of the ROC drawn in the external validation set were both above 0.65. Here, we constructed a prognostic model of bladder cancer based on data from the GEO, TCGA, and GTEx databases. This model has potential prognostic and clinical auxiliary diagnostic value.

A Novel Prognosis Signature Based on Ferroptosis-Related Gene DNA Methylation Data for Lung Squamous Cell Carcinoma

Ferroptosis-related genes regulating an iron- and lipid reactive oxygen species (ROS)-dependent form of programmed cell death suggest critical roles for ferroptosis in cancers. However, the prognostic value of ferroptosis-related epigenetic features such as DNA methylation in lung squamous cell carcinoma (LUSC) needs to be studied. Ferroptosis-related genes are collected from the FerrDb database, and the methylation data of these related genes in LUSC methylation data downloaded from the TCGA are retrieved. The DNA methylation data (362 LUSC samples) were analyzed to screen prognostic ferroptosis-related methylation sites. After patients with complete overall survival (OS) information were randomly separated into training cohort (n = 200) and validation cohort (n = 162), the least absolute shrinkage and selection operator (LASSO) and the Cox regression were used to establish and validate the prognostic signature. The time-dependent receiver operating characteristic (ROC) and Kaplan–Meier survival curve analyses, Harrell's concordance index (C-index), calibration analysis, and decision curve analysis (DCA) were performed to evaluate the risk signature and related nomogram. A series of other bioinformatics approaches such as mexpress, cbioportal, maftools, string, metascape, TIMER, and Kaplan–Meier survival curve analysis were also used to determine the methylation, mutation status, protein interaction network or functional enrichment, effects on immune cell infiltration, or expression level prognosis of those signature-related genes. A total of 137 DNA methylation sites were identified as prognostic predictors corresponding to 109 ferroptosis-related genes (FRGs). The methylation signature containing 31 methylation sites proved to be superior predictive efficiency in predicting the 1-, 3-, 5-, and 10-year OS. 8 out of 28 signature-related genes were significantly related to OS time or OS state in patients with LUSC. In addition, DUSP1, ZFN36, and ALOX5 methylation status also correlated with pathological M and ALOX5 methylation correlated with pathological N. The prognostic prediction efficiency of T, N, M, and the stage was inferior to that of the DNA methylation signature. LUSC patients in the high-risk group own a significantly larger number of variants of FRGs than those in the low-risk group. In addition, negative or positive correlation patterns were presented among the different infiltrating immune cells with risk scores or signature-related genes in patients with LUSC. The expression level of 15 signature-related genes showed a significant relationship with OS of LUSC patients. A novel prognostic nomogram survival model containing 4 factors including age, pathologic T, stage, and risk group was constructed and validated, AndC-index, decision curve analysis (DCA), and calibration analysis demonstrated its excellent predictive performance. The FRG DNA methylation data-based prognostic model acts as a powerful prognostic prediction indicator in LUSC patients and is advantageous over the traditional model based on T, N, M, and stage.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Comprehensive Characterization of Necroptosis-Related lncRNAs in Bladder Cancer Identifies a Novel Signature for Prognosis Prediction

Background

Bladder cancer (BC) is one of the most serious genitourinary malignant diseases with a poor prognosis. Necroptosis is a regulated form of cell death, and targeting necroptosis is emerging as a potential tumor therapy strategy. Nevertheless, the roles of necroptosis-related long noncoding RNAs (nrlncRNAs) in BC remains to be illustrated. This work is aimed at studying the clinical implications of nrlncRNAs in BC.

Methods

The RNA-seq data and corresponding clinical data, downloaded from The Cancer Genome Atlas (TCGA) database, were utilized to obtain prognostic nrlncRNAs and construct a prediction nomogram for BC. The comprehensive profiling of the functional pathways, immune status, mutational landscape, and drug sensitivity related to the necroptosis-related lncRNA signature (NerRLsig) was performed.

Results

Herein, a signature consisting of 12 necroptosis-related lncRNAs (AC015802.4, AL391807.1, AL078644.1, AC023825.2, AL132655.2, AP003352.1, STAG3L5P-PVRIG2P-PILRB, AC024451.4, MAP3K14-AS1, AL731567.1, AC010542.5, and AC009299.2) was constructed. The established signature can independently predict the poor overall survival of BC patients. Additionally, the NerRLsig had higher diagnostic validity compared to other clinicopathological variables, with a greater area under the receptor operating characteristic and concordance index curves. Finally, we found the differences in the functional signaling pathway, immune status, mutational profile, and drug sensitivity between the two subgroups.

Conclusion

This research revealed that the prognostic NerRLsig and nomogram could accurately predict the prognosis of BC.

Upregulation of Ferroptosis-Related Fanconi Anemia Group D2 is a Poor Prognostic Factor and an Indicator of Tumor Immune Cell Infiltration in Lung Adenocarcinoma

Fanconi anemia (FA) group D2 (FANCD2) is a ferroptosis-related gene crucial for DNA damage repair and negative ferroptosis regulation. Our study aimed to evaluate its prognostic value as well as its association with ferroptosis and immune infiltration in lung adenocarcinoma (LUAD). Transcriptome sequencing data, clinical information, and immunohistochemistry data were collected from the TCGA, GEO, and HPA databases, respectively, for three independent cohorts. Univariate and multivariate analyses were used to assess the correlations between FANCD2 expression and overall survival or clinicopathological parameters. cBioPortal was utilized to investigate the FANCD2 alteration status. Gene and protein networks based on FANCD2 interactions were generated using GeneMANIA and STRING, respectively. Based on the CancerSEA database, the function of FANCD2 was explored at the single-cell level. The relationships between FANCD2 expression levels and tumor-infiltrating immune cells and their equivalent gene signatures were analyzed using TIMER, GEPIA, TISIDB, and ssGSEA databases. CIBERSORT was used to analyze the relevance of the infiltration of 24 types of immune cells. The results revealed that FANCD2 expression was significantly upregulated in LUAD and lung squamous cell carcinoma (LUSC) tissues than that in normal tissues. Further, the overexpression of FANCD2 was closely associated with poor survival for Patients with LUAD but not for patients with LUSC. FANCD2 expression levels were related to tumor-infiltrating immune cells and their matching gene signatures, including CD8⁺ T cells, natural killer (NK) cells, dendritic cells (DC), and Th2 cells in cases of LUAD. Therefore, FANCD2 was identified as a crucial molecule underlying the synergistic effects of ferroptosis and immunotherapy for Patients with LUAD.

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.

Predictive role of ferroptosis-related long non-coding RNAs in bladder cancer and their association with immune microenvironment and immunotherapy response

BACKGROUND

We have previously reported that ferroptosis has an important role in bladder cancer development. In this study, we aimed to further explore the possible predictive ability of ferroptosis-related long non-coding RNAs (lncRNAs) in bladder cancer and their relation with immune microenvironment and immunotherapy response.

MATERIALS AND METHODS

The ferroptosis-related lncRNAs were identified by Pearson's correlation analysis. The predictive lncRNA signature was developed by univariate and multivariate regression analyses. Only the main effects of independent variables in multivariate analysis were included in this signature. The TCGA dataset was defined as the training cohort and GEO was the validation cohort in this study. All samples were grouped into a high- or low-risk group depending on risk signature. The prognostic role of lncRNA signature was explored through survival analysis and receiver operating characteristic curve (ROC) analysis in both TCGA and GEO cohorts. Additionally, the independent prognostic ability of the lncRNA signature was confirmed by multivariate independent analysis. Furthermore, the relationship between lncRNAs and immune microenvironment as well as immunotherapy response in bladder cancers was studied.

RESULTS

The Kaplan-Meier curves identified significantly poorer overall survival outcomes for high-risk groups in both TCGA (p < 0.001) and GEO (p < 0.001) cohorts. The area under the curve (AUC) during ROC analysis of 1, 3, and 5 years was 0.781 ± 0.046, 0.784 ± 0.027, and 0.817 ± 0.025, respectively, in the TCGA cohort and 0.665 ± 0.177, 0.719 ± 0.068, and 0.791 ± 0.055, respectively, in the GEO cohort. The multivariate independent analysis in TCGA cohort identified age (p = 0.003), stage (p < 0.001), and signature risk score (p < 0.001) as independent risk factors for overall survival. Furthermore, this study demonstrated a significant difference in infiltration levels of various immune cells between high- and low-risk groups. The high risk group tended to have a lower expression of proteins including PD1 (p < 0.01), PD-L1 (p < 0.01), CTLA-4 (p < 0.05), etc. corresponding to various immune checkpoints. Additionally, the immunotherapy trial confirmed that the high-risk group tended to have a poorer treatment response than the low-risk group (p < 0.001).

CONCLUSIONS

The ferroptosis-related lncRNAs exhibited a good predictive capacity for overall survival in bladder cancer. Additionally, they could be utilized to reveal tumour-immune microenvironment and immunotherapy responses.

Roles of ferroptosis in urologic malignancies

Ferroptosis, an iron-dependent form of non-apoptotic cell death, is believed to strongly contribute to the pathogenesis of multiple cancers. Recently, the positive association between ferroptosis and urologic malignancies has drawn considerable attention, while a comprehensive review focused on this issue is absent. Based on this review, ferroptosis has been implicated in the development and therapeutic responses of prostate cancer, kidney cancer, and bladder cancer. Mechanistically, a large number of biomolecules and tumor-associated signaling pathways, including DECR1, PANX2, HSPB1, ACOT8, SUV39H1, NCOA4, PI3K-AKT-mTOR signaling, VHL/HIF-2α pathway, and Hippo/TAZ signaling pathway, have been reported to regulate ferroptosis in urologic cancers. Ferroptosis inducers, such as erastin, ART, CPNPs, and quinazolinyl-arylurea derivatives, exert potential therapeutic effects per se and/or enhance the anticancer response of other anticancer drugs in urologic oncology. A better understanding of ferroptosis may provide a promising way to treat therapy-resistant urologic cancers.

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.

LncRNA RP11-89 facilitates tumorigenesis and ferroptosis resistance through PROM2-activated iron export by sponging miR-129-5p in bladder cancer

Long non-coding RNAs (lncRNAs) act as important regulators of tumorigenesis and development in bladder cancer. However, the underlying molecular mechanisms remain elusive. We previously identified a novel lncRNA signature related to immunity and progression in bladder cancer. Here we further explored the function of RP11-89, a lncRNA discovered in the previous signature. Loss- and gain-of function experiments were performed using CCK-8 assay, flow cytometry, Transwell assays, scratch tests and subcutaneous nude mouse models. High-throughput RNA sequencing was conducted to identify dysregulated genes in bladder cancer cells with RP11-89 knockdown or overexpression. Regulation of RP11-89 on miR-129-5p and PROM2 was explored through luciferase reporter assay, RIP assay and RNA pull-down assay. RP11-89 promoted cell proliferation, migration and tumorigenesis and inhibited cell cycle arrest via the miR-129-5p/PROM2 axis. We found that RP11-89 "sponges" miR-129-5p and upregulates PROM2. Elevated PROM2 in cells was associated with attenuated ferroptosis through iron export, formation of multivesicular bodies and less mitochondrial abnormalities. We demonstrated that RP11-89 is a novel tumorigenic regulator that inhibits ferroptosis via PROM2-activated iron export. RP11-89 may serve as a potential biomarker for targeted therapy in bladder cancer.