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Identification of a novel ubiquitination related gene signature for patients with breast cancer.

ABSTRACT: Ubiquitination related genes (URGs) are important biomarkers and therapeutic targets in cancer. However, URG prognostic prediction models have not been established in breast cancer (BC) before. Our study aimed to identify URGs to serve as potential prognostic indicators in patients with BC.The URGs were downloaded from the ubiquitin and ubiquitin-like conjugation database. GSE42568 and The Cancer Genome Atlas were exploited to screen differentially expressed URGs in BC. The univariate Cox proportional hazards regression analysis, least absolute shrinkage and selection operator analysis, and multivariate Cox proportional hazards regression analysis were employed to construct multi-URG signature in the training set (GSE42568). Kaplan-Meier curve and log-rank method analysis, and ROC curve were applied to validate the predictive ability of the multi-URG signature in BC. Next, we validated the signature in test set (GSE20685). Finally, we performed GSEA analysis to explore the mechanism.We developed a 4-URG (CDC20, PCGF2, UBE2S, and SOCS2) signature with good performance for patients with BC. According to this signature, BC patients can be classified into a high-risk and a low-risk group with significantly different overall survival. The predictive ability of this signature was favorable in the test set. Univariate and multivariate Cox regression analysis showed that the 4-URG signature was independent risk factor for BC patients. GSEA analysis showed that the 4-URG signature may related to the function of DNA replication, DNA repair, and cell cycle.Our study developed a novel 4-URG signature as a potential indicator for BC.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC9478291 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

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Identification of a novel ubiquitination related gene signature for patients with breast cancer.

Zheng Yuan Y Lu Wenliang W Chen Bo B Zhao Kankan K Zhao Kankan K

Medicine 20220901 37

Ubiquitination related genes (URGs) are important biomarkers and therapeutic targets in cancer. However, URG prognostic prediction models have not been established in breast cancer (BC) before. Our study aimed to identify URGs to serve as potential prognostic indicators in patients with BC.The URGs were downloaded from the ubiquitin and ubiquitin-like conjugation database. GSE42568 and The Cancer Genome Atlas were exploited to screen differentially expressed URGs in BC. The univariate Cox propor ...[more]

PMID: 36123926

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Project description:BackgroundBreast cancer (BRCA) is a malignant tumor with high morbidity and mortality, which is a threat to women's health worldwide. Ferroptosis is closely related to the occurrence and development of breast cancer. Here, we aimed to establish a ferroptosis-related prognostic gene signature for predicting patients' survival.MethodsGene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. The Least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis model was utilized to construct a multigene signature. The Kaplan-Meier (K-M) and Receiver Operating Characteristic (ROC) curves were plotted to validate the predictive effect of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway and single-sample gene set enrichment analysis (ssGSEA) were performed for patients between the high-risk and low-risk groups divided by the median value of risk score.ResultsWe constructed a prognostic signature consisted of nine ferroptosis-related genes (ALOX15, CISD1, CS, GCLC, GPX4, SLC7A11, EMC2, G6PD and ACSF2). The Kaplan-Meier curves validated the fine predictive accuracy of the prognostic signature (p < 0.001). The area under the curve (AUC) of the ROC curves manifested that the ferroptosis-related signature had moderate predictive power. GO and KEGG functional analysis revealed that immune-related responses were largely enriched, and immune cells, including activated dendritic cells (aDCs), dendritic cells (DCs), T-helper 1 (Th1), were higher in high-risk groups (p < 0.001). Oppositely, type I IFN response and type II IFN response were lower in high-risk groups (p < 0.001).ConclusionOur study indicated that the ferroptosis-related prognostic signature gene could serve as a novel biomarker for predicting breast cancer patients' prognosis. Furthermore, we found that immunotherapy might play a vital role in therapeutic schedule based on the level and difference of immune-related cells and pathways in different risk groups for breast cancer patients.

Project description:BackgroundAlthough breast cancer outcome has improved significantly with the recent use of molecularly targeted agents, reliable prognostic signatures are still unavailable because of tumor heterogeneity. Immune processes play an important role in tumor progression. Therefore, the aim of this study was to construct a prognostic signature based on immune-related genes (IRGs).MethodsClinical information and gene expression of 3,496 patients were extracted from eight public data sets. A total of 2,498 IRGs associated to 17 immune processes were downloaded from the ImmPort database. RNA sequencing (RNAseq) datasets [The Cancer Genome Atlas (TCGA) and GSE96058] were used as the training set (n=2,736) and all microarray datasets were used as validation set (n=760). IRGs related to prognosis were screened out from the training set and used to construct gene pairs. The Cox regression model was used, based on the immune-related gene pairs (IRGPs). The risk score of each patient was calculated and patients were stratified into high- and low-risk groups according to the optimal threshold of the risk score. Immune cell infiltration was evaluated between both groups.ResultsAmong the 129 prognostic-related immune genes, 8,256 IRGPs were constructed. After screening, 89 IRGPs, including 86 unique IRGs, were used in the prognostic prediction model. Patients in the high-risk group exhibited a significantly poorer overall survival (OS) both in the training set [hazard ratio (HR): 5.9, 95% confidence interval (CI): 4.61-7.54] and validation set (HR: 1.52, 95% CI: 1.16-1.98) compared to the low-risk group. In addition, patients in the high-risk group showed a significantly lower infiltration of CD8+ T cells than patients in the low-risk group.ConclusionsAn independent IRGP signature was constructed. Through pairwise comparison of a set of genes, the OS of patients could be predicted. This method avoids the impact of the batch effect caused by different sequencing platforms and has a promising application prospect.

Project description:The extensive and intricate relationships between circadian rhythm and cancer have been reported in numerous studies. However, in breast cancer (BC), the potential role of circadian clock-related genes (CCRGs) in prognosis prediction has not been fully clarified. The transcriptome data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. A CCRGs-based risk signature was established by differential expression analysis, univariate, Lasso and multivariate Cox regression analyses. we conducted a gene set enrichment analysis (GSEA) between groups. A nomogram integrating independent clinical factors and risk score was generated and evaluated by calibration curves and decision curve analysis (DCA). Differentially expression analysis revealed 80 differentially expressed CCRGs, and 27 of them were significantly associated with the overall survival (OS) of BC. BC can be classified into 4 molecular subtypes with significant differences in prognosis based on the 27 CCRGs. Three prognostic CCRGs, including desmocollin 1 (DSC1), LEF1, and protocadherin 9 (PCDH9), were identified to be independent risk factors of BC prognosis and were used to construct a risk score model. BC patients were divided into high- and low-risk groups, and there were significant differences in prognosis between the 2 groups both in the training and validation cohorts. It was found that patients in different groups of race, status, or T stage had significant levels of risk score. Furthermore, patients of different risk levels exhibit varying degrees of sensitivity to vinorelbine, lapatinib, metformin, and vinblastine. GSEA showed that in the high-risk group, immune response-related activities were dramatically repressed whereas cilium-related processes were significantly stimulated. Cox regression analysis demonstrated that age, N stage, radiotherapy and the risk score were independent prognostic risk factors of BC, and a nomogram was established based on these variables. The nomogram exerted a favorable concordance index (0.798) as well as calibration performance, which strongly supports the clinical application of the nomogram. Our study indicated the disruption of the expression of CCRGs in BC and built a favorable prognostic risk model based on 3 independent prognostic CCRGs. These genes may be applied as candidate molecular targets for the diagnosis and therapy of BC.

Project description:IntroductionBreast cancer is the most common malignant tumor in women worldwide. However, advanced multidisciplinary therapy cannot rescue the mortality of high-risk breast cancer metastasis. Ferroptosis is a newly discovered form of regulating cell death that related to cancer treatment, especially in eradicating aggressive malignancies that are resistant to traditional therapies. However, the prognostic value of ferroptosis-related gene in breast cancer remains unknown.Materials and methodsIn this study, a total of 1,057 breast cancer RNA expression data with clinical and follow-up information were downloaded from the TCGA cohort, multivariate Cox regression was used to construct the 11-gene prognostic ferroptosis-related gene signature. The breast cancer patients from the GEO cohort were used for validation. The expression levels of core prognostic genes were also verified in erastin-treated breast cancer cell lines by real-time polymerase chain action (PCR).Results and discussionOur results showed that 78% ferroptosis-related genes were differentially expressed between breast cancer tumor tissue and adjacent non-tumorous tissues, including 29 of them which were significantly correlated with OS in the univariate Cox regression analysis. Patients were divided into high-risk group and low-risk group by the 11-gene signature. Patients with high-risk scores had a higher probability of death earlier than the low-risk group both in the TCGA construction cohort and in the GEO validation cohort (all P < 0.001). Meanwhile, the risk score was proved to be an independent predictor for OS in both univariate and multivariate Cox regression analyses (HR > 1, P < 0.01). The predictive efficacy of the prognostic signature for OS was further verified by the time-dependent ROC curves. Moreover, we also enriched many immune-related biological processes in later functional analysis; the immune status showed a statistical difference between the two risk groups. In addition, the differences in expression levels of 11 core prognostic genes were examined in ferroptosis inducer-treated breast cancer cell lines.ConclusionIn conclusion, a novel ferroptosis-related gene model can be used for prognostic prediction in breast cancer. New ferroptosis-related genes may be used for breast cancer targeting therapy in the future.

Dataset Information

Identification of a novel ubiquitination related gene signature for patients with breast cancer.

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Identification of a novel ubiquitination related gene signature for patients with breast cancer.

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