Comprehensive analysis of miRNA sequencing profiles identifies novel deregulated and prognostic biomarkers in hepatocellular carcinoma

Abstract Background: Liver cancer is the fourth most common cause of cancer-related death and rank sixth in terms of incident cases. We aim to identify a set of miRNAs and a miRNA-based signature related to tumorigenesis and prognosis in patients with hepatocellular carcinoma (HCC). Methods: We analyzed the miRNA sequencing profiles of 373 HCC patients downloaded from The Cancer Genome Atlas LIHC program. The isoform quantification profiles were transformed into 5p and 3p mature miRNA names. Differentially expressed (DE) miRNAs between tumor and adjacent normal tissues were identified by Wald test based on the negative binomial distribution. Prognostic miRNAs associated with overall survival were confirmed by multivariate Cox proportional hazards models. The miRNA-based signatures were obtained from the linear predictors of cox regression, and the prognostic performance was compared by Harrel’s C-index and revealed by the restricted mean survival (RMS) curve. Results: The selected twelve DE miRNAs showed a good performance to classify tumor tissues from normal tissues. Meanwhile, a miRNA-based prognostic signature of eight mature miRNAs was constructed, which significantly stratified patients into high- vs low-risk groups in terms of overall survival (hazard ratio, 4.11; 95% CI, 2.71-6.24; P<0.001). When integrated with clinical information, the composite miRNA-clinical signature showed improved prognostic accuracy relative to the eight-miRNA signature alone. As we set the follow-up time at 5 years, the estimated RMST difference between low- and high-risk group stratified by miRNA index was 1.39 (95% CI: 0.95-1.83) months, which is lesser than the difference between miRNA-clinical risk groups (1.63, 95%CI: 1.20-2.06). Functional enrichment analysis indicated that the target mRNAs of selected miRNAs were mainly enriched in cancer-related pathways and vital cell biological processes. Conclusions: The proposed DE miRNAs and miRNA-clinical signature are promising biomarkers for discrimination and predicting overall survival respectively in HCC patients. These biomarkers may have significant relevance for development of new drug research and targeting therapies for HCC patients.

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Identification of novel cell glycolysis related gene signature predicting survival in patients with breast cancer

Scientific Reports ◽

10.1038/s41598-021-83628-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Feng Jiang ◽

Chuyan Wu ◽

Ming Wang ◽

Ke Wei ◽

Jimei Wang

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Cox Regression ◽

Single Gene ◽

Prediction Method ◽

Gene Signature ◽

The Cancer Genome Atlas ◽

Cox Models ◽

Normal Tissues ◽

Predictive Risk

AbstractOne of the most frequently identified tumors and a contributing cause of death in women is breast cancer (BC). Many biomarkers associated with survival and prognosis were identified in previous studies through database mining. Nevertheless, the predictive capabilities of single-gene biomarkers are not accurate enough. Genetic signatures can be an enhanced prediction method. This research analyzed data from The Cancer Genome Atlas (TCGA) for the detection of a new genetic signature to predict BC prognosis. Profiling of mRNA expression was carried out in samples of patients with TCGA BC (n = 1222). Gene set enrichment research has been undertaken to classify gene sets that vary greatly between BC tissues and normal tissues. Cox models for additive hazards regression were used to classify genes that were strongly linked to overall survival. A subsequent Cox regression multivariate analysis was used to construct a predictive risk parameter model. Kaplan–Meier survival predictions and log-rank validation have been used to verify the value of risk prediction parameters. Seven genes (PGK1, CACNA1H, IL13RA1, SDC1, AK3, NUP43, SDC3) correlated with glycolysis were shown to be strongly linked to overall survival. Depending on the 7-gene-signature, 1222 BC patients were classified into subgroups of high/low-risk. Certain variables have not impaired the prognostic potential of the seven-gene signature. A seven-gene signature correlated with cellular glycolysis was developed to predict the survival of BC patients. The results include insight into cellular glycolysis mechanisms and the detection of patients with poor BC prognosis.

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Identification of Hypoxia-Related Differentially Expressed Genes and Construction of the Clinical Prognostic Predictor in Hepatocellular Carcinoma by Bioinformatic Analysis

BioMed Research International ◽

10.1155/2021/7928051 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Hao Guo ◽

Jing Zhou ◽

Yanjun Zhang ◽

Zhi Wang ◽

Likun Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Model ◽

Molecular Mechanisms ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Clinical Prognosis ◽

Prognostic Predictor ◽

Key Genes

Background. Hypoxia closely relates to malignant progression and appears to be prognostic for outcome in hepatocellular carcinoma (HCC). Our research is aimed at mining the hypoxic-related genes (HRGs) and constructing a prognostic predictor (PP) model on clinical prognosis in HCC patients. Methods. RNA-sequencing data about HRGs and clinical data of patients with HCC were obtained from The Cancer Genome Atlas (TCGA) database portal. Differentially expressed HRGs between HCC and para-carcinoma tissue samples were obtained by applying the Wilcox analysis in R statistical software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used for gene functional enrichment analyses. Then, the patients who were asked to follow up for at least one month were enrolled in the following study. Cox proportional risk regression model was applied to obtain key HRGs which related to overall survival (OS) in HCC. PP was constructed and defined, and the accuracy of PP was validated by constructing the signature in a training set and validation set. Connectivity map (CMap) was used to find potential drugs, and gene set cancer analysis (GSCA) was also performed to explore the underlying molecular mechanisms. Results. Thirty-seven differentially expressed HRGs were obtained. It contained 28 upregulated and 9 downregulated genes. After the univariate Cox regression model analysis, we obtained 27 prognosis-related HRGs. Of these, 25 genes were risk factors for cancer, and 2 genes were protective factors. The PP was composed by 12 key genes (HDLBP, SAP30, PFKP, DPYSL4, SLC2A1, HMOX1, PGK1, ERO1A, LDHA, ENO2, SLC6A6, and TPI1). GSCA results showed the overall activity of these 12 key genes in 10 cancer-related pathways. Besides, CMap identified deferoxamine, crotamiton, talampicillin, and lycorine might have effects with HCC. Conclusions. This study firstly reported 12 prognostic HRGs and constructed the model of the PP. This comprehensive research of multiple databases helps us gain insight into the biological properties of HCC and provides deferoxamine, crotamiton, talampicillin, and lycorine as potential drugs to fight against HCC.

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Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.657051 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shenglan Cai ◽

Xingwang Hu ◽

Ruochan Chen ◽

Yiya Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cells ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Immune Genes ◽

Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

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The Landscape of Glycosyltransferase Gene Signature for Overall Survival Prediction in Hepatocellular Carcinoma

10.21203/rs.3.rs-114747/v1 ◽

2020 ◽

Author(s):

Qiang Cai ◽

Shizhe Yu ◽

Jian Zhao ◽

Duo Ma ◽

Long Jiang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Risk Score ◽

Prognostic Value ◽

Cox Regression ◽

Risk Groups ◽

Gene Signature ◽

Low Risk ◽

Regression Analyses ◽

Risk Patients

Abstract Background: Hepatocellular carcinoma (HCC) is heterogeneous disease occurring in the background of chronic liver diseases. The role of glycosyltransferase (GT) genes have recently been the focus of research associating with the development of tumors. However, the prognostic value of GT genes in HCC remains not elucidated. This study aimed to demonstrate the GT genes related to the prognosis of HCC through bioinformatics analysis.Methods: The GT genes signatures were identified from the training set of The Cancer Genome Atlas (TCGA) dataset using univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then, we analyzed the prognostic value of GT genes signatures related to the overall survival (OS) of HCC patients. A prognostic model was constructed, and the risk score of each patient was calculated as formula, which divided HCC patients into high- and low-risk groups. Kaplan-Meier (K-M) and Receiver operating characteristic (ROC) curves were used to assess the OS of HCC patients. The prognostic value of GT genes signatures was further investigated in the validation set of TCGA database. Univariate and multivariate Cox regression analyses were performed to demonstrate the independent factors on OS. Finally, we utilized the gene set enrichment analysis (GSEA) to annotate the function of these genes between the two risk categories. Results: In this study, we identified and validated 4 GT genes as the prognostic signatures. The K-M analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 4 gene signatures could predict OS for 3-, 5-, and 7-year in patients with HCC, revealing the prognostic ability of these gene signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for HCC. Functional analysis further revealed that immune-related pathways were enriched, and immune status in HCC were different between the two risk groups.Conclusion: In conclusion, a novel GT genes signature can be used for prognostic prediction in HCC. Thus, targeting GT genes may be a therapeutic alternative for HCC.

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Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5970085 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Honglan Guo ◽

Qinqiao Fan

Keyword(s):

Hepatocellular Carcinoma ◽

Roc Curve ◽

Cox Regression ◽

Enrichment Analysis ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

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A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

10.21203/rs.3.rs-141169/v2 ◽

2021 ◽

Author(s):

Renjie Liu ◽

Guifu Wang ◽

Chi Zhang ◽

Dousheng Bai

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Molecular Mechanisms ◽

Risk Model ◽

Cox Regression ◽

Risk Groups ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Risk Scores

Abstract Background: Dysregulation of the balance between proliferation and apoptosis is the basis for human hepatocarcinogenesis. In many malignant tumors, such as hepatocellular carcinoma (HCC), there is a correlation between apoptotic dysregulation and poor prognosis. However, the prognostic values of apoptosis-related genes (ARGs) in HCC have not been elucidated. Methods: To screen for differentially expressed ARGs, the expression levels of 161 ARGs from The Cancer Genome Atlas (TCGA) database(https://cancergenome.nih.gov/) were analyzed. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to evaluate the underlying molecular mechanisms of differentially expressed ARGs in HCC. The prognostic values of ARGs were established using Cox regression, and subsequently, a prognostic risk model for scoring patients was developed. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value of the model. Results: Compared to normal tissues, 43 highly up-regulated and 8 down-regulated ARGs in HCC tissues were screened. GO analysis results revealed that these 51 genes are indeed related to the apoptosis function. KEGG analysis revealed that these 51 genes were correlated with MAPK, P53, TNF, and PI3K-AKT signaling pathways, while Cox regression revealed that 5 ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were associated with prognosis and were, therefore, obtained to develop the prognostic model. Based on the median risk scores, patients were categorized into high-risk and low-risk groups. Patients in the low-risk groups exhibited significantly elevated two-year or five-year survival probabilities (p < 0.0001). The risk model had a better clinical potency than the other clinical characteristics, with the area under the ROC curve (AUC = 0.741). The prognosis of HCC patients was established from a plotted nomogram. Conclusion: Based on the differential expression of ARGs, we established a novel risk model for predicting HCC prognosis. This model can also be used to inform the individualized treatment of HCC patients.

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Identification of prognostic immune-related lncRNA signature predicting the overall survival for colorectal cancer

10.21203/rs.3.rs-620796/v1 ◽

2021 ◽

Author(s):

Jianxin Li ◽

Ting Han ◽

Xin Wang ◽

Yinchun Wang ◽

Qingqiang Yang

Keyword(s):

Colorectal Cancer ◽

Overall Survival ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Enrichment Analysis ◽

High Risk Group ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Regression Analyses

Abstract Background Long non-coding RNA (lncRNA) is an important regulator of gene expression and serves fundamental role in immune regulation. The present study aimed to develop a novel immune-related lncRNA signature to accurately assess the prognosis of patients with colorectal cancer (CRC). Methods Transcriptome data and clinical information of patients with CRC were downloaded from The Cancer Genome Atlas (TCGA), and the immune-related mRNAs were extracted from immunomodulatory gene datasets IMMUNE RESPONSE and IMMUNE SYSTEM PROCESS based on the Molecular Signatures Database (MSigDB). Then, the immune-related lncRNAs were identified by a correlation analysis between immune-related mRNAs and lncRNAs. Subsequently, univariate, lasso and multivariate Cox regression were used to identify an immune-related lncRNA signature in training cohort, and the predict ability of the signature was further confirmed in the testing cohort and the entire TCGA cohort. Finally, the lncRNA-mRNA co-expression network was established to explore the biological role of the immune-related lncRNA signature. Results In total, 272 Immune-related lncRNAs were identified, five of which were applied to construct an immune-related lncRNA signature based on univariate, lasso and multivariate Cox regression analyses. The signature divided patients with CRC into low- and high-risk groups, and patients with CRC in high-risk group had poorer overall survival than those in low-risk group. Univariate and multivariate Cox regression analyses confirmed that the signature could be an independent prognostic factor in human CRC. Furthermore, functional enrichment analysis revealed that the immune-related lncRNA signature was significantly enriched in immune process and tumor classical pathways. Conclusions The present study revealed that the novel immune-related lncRNA signature could be exploited as underlying molecular biomarkers and therapeutic targets for the patients with CRC.

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A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer

BMC Cancer ◽

10.1186/s12885-021-08687-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yingchun Liang ◽

Fangdie Ye ◽

Chenyang Xu ◽

Lujia Zou ◽

Yun Hu ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognosis Prediction

Abstract Background The effective treatment and prognosis prediction of bladder cancer (BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis is closely related to tumour occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified an FRG signature with potential prognostic value for patients with BLCA. Methods The corresponding clinical data and mRNA expression profiles of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, and a Cox regression model was used to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. Results Clinical traits were combined with FRGs, and 15 prognosis-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD was related to poor survival in BLCA patients. Multivariate Cox regression was used to construct a prognostic model with 7 FRGs that divided patients into two risk groups. Compared with that in the low-risk group, the overall survival (OS) of patients in the high-risk group was significantly lower (P < 0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR = 1.772, P < 0.01). Receiver operating characteristic (ROC) curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in ssGSEA and different distributed patterns in PCA. Conclusion Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA.

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A Novel Five-gene Signature Predicts Overall Survival in Hepatocellular Carcinoma

10.21203/rs.3.rs-32144/v1 ◽

2020 ◽

Author(s):

Zhigang Wang ◽

Leyu Pan ◽

Deliang Guo ◽

Xiaofeng Luo ◽

Jie Tang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Regression Analysis ◽

Cox Regression ◽

Gene Signature ◽

Tnm Stage ◽

Time Dependent ◽

Quantitative Real Time Pcr ◽

Cox Regression Analysis ◽

Normal Tissues

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common challenges for public health worldwide. Due to its complex molecular and great heterogeneity, the effectiveness of existing HCC risk prediction models is unsatisfactory. Hence, more accurate prognostic models are pressingly needed. Materials and methods: Differentially expressed mRNAs (DEMs) between HCC and normal tissues were identified after downloading GSE1450 from gene omnibus (GEO) database. We randomly divided all patients into training and testing sets. Univariate Cox regression, lasso Cox regression and multivariable Cox regression analysis were used to constructed the prognostic gene signature in training set. Our study utilized Kaplan-Meier plot, time-dependent receiver operating characteristic (ROC), multivariable Cox regression analysis with clinical information, nomogram and decision curve analysis (DCA) to evaluate the predictive ability for overall survival of the novel gene signature in training, testing and whole sets. We also validated the prognostic capacity of the five-gene signature in an external validation set. The information of mutation of each gene was explored on cBioPortal online website. We performed gene set enrichment analysis (GSEA) to explore underlying mechanisms in the high and low risk group. Finally, quantitative real-time PCR was conducted to validate the expression tendency between 12 paired HCC and adjacent normal tissues. Results: Our study constructed a novel five-gene signature (CNIH4, SOX4, SPP1, SORBS2 and CCL19) for predicting overall survival of HCC. Time-dependent ROC curve indicated admirable ability in survival prediction in two datasets. Multivariable Cox regression analysis indicated that both this five-gene signature and TNM stage were two independent prognostic factors for overall survival of HCC patients. Combined with TNM stage clinical pathological parameters, the predictive capacity of nomogram had a decent improvement. The mutation of the five genes had no obvious variation. Plenty pathways were enriched by GSEA, including cell cycle and various metabolism. Furthermore, the mRNA levels of these five genes had signiﬁcantly diﬀerent expressions between HCC tissues and adjacent normal tissues by quantitative real-time PCR. Conclusions: A five-gene prognostic model and nomogram were constructed and validated for predicting prognostic of HCC patients. And the five-gene risk score with TNM stage models might help various HCC patients to customize individual therapies.

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A Novel Survival Model Based on Ferroptosis-related Gene Signature for Overall Survival Prediction in Bladder Cancer

10.21203/rs.3.rs-136951/v1 ◽

2021 ◽

Author(s):

Yingchun Liang ◽

Fangdie Ye ◽

Chenyang Xu ◽

Lujia Zou ◽

Yun Hu ◽

...

Keyword(s):

Overall Survival ◽

Bladder Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Prognosis Prediction

Abstract Background: The effective treatment and prognosis prediction of bladder cancer(BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis are closely related to tumor occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified a FRGs signature with potential prognostic value for patients with BLCA. Methods: The corresponding clinical data and the mRNA expression profile of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, Cox regression model was applied to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. Results: Clinical traits were combined with FRGs, so that 15 prognostic-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD are related to poor survival rates of BLCA patients. Multivariate Cox regression constructed a prognostic model with 7 FRGs and divided patients into two risk groups. Compared with the low-risk group, the overall survival(OS) of patients in the high-risk group was significantly lower (P <0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR> 1, P <0.01). ROC curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in the ssGSEA and different distributed patterns in PCA. Conclusion: Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA.

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