scholarly journals MOMC-3. Hypermethylation and overexpression of HOX genes are poor prognosticators in Lower-Grade Glioma

2021 ◽  
Vol 3 (Supplement_2) ◽  
pp. ii4-ii4
Author(s):  
Yasin Mamatjan ◽  
Mathew Voisin ◽  
Farshad Nassiri ◽  
Fabio Moraes ◽  
Severa Bunda ◽  
...  
Keyword(s):  
Hox Genes ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
P Value ◽  
Low Grade ◽  
Lower Grade ◽  
Cox Regression Analysis ◽  
Hox Gene ◽  
Lower Grade Glioma ◽  
Diffuse Gliomas

Abstract Diffuse gliomas represent over 80% of malignant brain tumors ranging from low-grade to aggressive high-grade lesions. Molecular characterization of these tumors led to the development of new classification system comprising specific glioma subtypes. While this provides novel molecular insight into gliomas it does not fully explain the variability in patient outcome. To identify and characterize a predictive signature of outcome in diffuse gliomas, we utilized an integrative molecular analysis (methylation, mRNA, copy number variation (CNV) and mutation data) using multiple molecular platforms, including a total of 310 IDH mutant glioma samples from University Health Network (UHN) and German Cancer Research Center (DKFZ) together with 419 samples from The Cancer Genome Atlas (TCGA). Cox regression analysis of methylation data from the UHN cohort identified CpG-based signatures that split the glioma cohort into two prognostic groups strongly predicting survival (p-value < 0.0001). The CpG-based signatures were reliably validated using two independent datasets from TCGA and DKFZ cohorts (both p-values < 0.0001). The results show that the methylation signatures that predict poor outcome also correlated with G-CIMP low status, elevated CNV instability and hypermethylation of a set of HOX gene probes. Further study in diffuse lower-grade glioma (LGG) using integrated mRNA and methylation (iRM) analyses showed that parallel HOX gene overexpression and hypermethylation in the same direction were significantly associated with increased mutational load, high aneuploidy and worse survival (p-value < 0.0001). Furthermore, this iRM high group was characterized by a 7-HOX gene signature showed significant survival differences not only in IDH mutant LGG but also in IDH wildtype LGG. These results demonstrate the importance of HOX genes in predicting the outcome of diffuse gliomas to identify relevant molecular subtyping independent of histology.

The Role of Surgery in IDH-Wild-Type Lower-Grade Gliomas: Threshold at a High Extent of Resection Should be Pursued

Neurosurgery ◽  
2021 ◽  
Author(s):  
Peng Wang ◽  
Chen Luo ◽  
Peng-jie Hong ◽  
Wen-ting Rui ◽  
Shuai Wu
Keyword(s):  
Cox Regression ◽  
Progression Free Survival ◽  
Extent Of Resection ◽  
Lower Grade ◽  
Wild Type ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Lower Grade Glioma ◽  
Clinical Benefits

Abstract BACKGROUND While maximizing extent of resection (EOR) is associated with longer survival in lower-grade glioma (LGG) patients, the number of cases remains insufficient in determining a EOR threshold to elucidate the clinical benefits, especially in IDH-wild-type LGG patients. OBJECTIVE To identify the effects of EOR on the survival outcomes of IDH-wild-type LGG patients. METHODS IDH-wild-type LGG patients were retrospectively reviewed. The effect of EOR and other predictor variables on overall survival (OS) and progression-free survival (PFS) was analyzed using Cox regression models and the Kaplan-Meier method. RESULTS A total of 94 patients (median OS: 48.9 mo; median follow-up: 30.6 mo) were included in this study. In the multivariable Cox regression analysis, postoperative residual volume was associated with prolonged OS (HR = 2.238; 95% confidence interval [CI], 1.130-4.435; P = .021) and PFS (HR = 2.075; 95% CI, 1.113-3.869; P = .022). Thresholds at a minimum EOR of 97.0% or a maximum residue of 3.0 cm3 were necessary to impact OS positively. For the telomerase reverse transcriptase (TERT)p-wild-type group, such an association was absent. Significant differences in survival existed between the TERTp-wild-type and mutant patients who underwent relatively incomplete resections (residual ≥2.0 cm3 + TERTp wild type: median OS of 62.6 mo [95% CI: 39.7-85.5 mo]; residual ≥2.0 cm3 + TERTp mutant: median OS of 20.0 mo [95% CI:14.6-25.4 mo]). CONCLUSION Our results support the core role of maximal safe resection in the treatment of IDH-wild-type LGGs, especially for IDH-wild-type + TERTp-mutant LGGs. Importantly, the survival benefits of surgery could only be elucidated at a high EOR cut-off point.

scholarly journals Clinical Value of lncRNA LUCAT1 Expression in Liver Cancer and its Potential Pathways

2019 ◽  
Vol 28 (4) ◽  
pp. 439-447 ◽  
Author(s):  
Yan Jiao ◽  
Yanqing Li ◽  
Bai Ji ◽  
Hongqiao Cai ◽  
Yahui Liu
Keyword(s):  
Liver Cancer ◽  
Roc Curve ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Rank Test ◽  
P Value ◽  
Rna Seq ◽  
Cox Regression Analysis

Background and Aims: Emerging studies indicate that long noncoding RNAs (lncRNAs) play a role as prognostic markers in many cancers, including liver cancer. Here, we focused on the lncRNA lung cancer-associated transcript 1 (LUCAT1) for liver cancer prognosis. Methods: RNA-seq and phenotype data were downloaded from the Cancer Genome Atlas (TCGA). Chisquare tests were used to evaluate the correlations between LUCAT1 expression and clinical features. Survival analysis and Cox regression analysis were used to compare different LUCAT1 expression groups (optimal cutoff value determined by ROC). The log-rank test was used to calculate the p-value of the Kaplan-Meier curves. A ROC curve was used to evaluate the diagnostic value. Gene Set Enrichment Analysis (GSEA) was performed, and competing endogenous RNA (ceRNA) networks were constructed to explore the potential mechanism. Results: Data mining of the TCGA -Liver Hepatocellular Carcinoma (LIHC) RNA-seq data of 371 patients showed the overexpression of LUCAT1 in cancerous tissue. High LUCAT1 expression was associated with age (p=0.007), histologic grade (p=0.009), T classification (p=0.022), and survival status (p=0.002). High LUCAT1 patients had a poorer overall survival and relapse-free survival than low LUCAT1 patients. Multivariate analysis identified LUCAT1 as an independent risk factor for poor survival. The ROC curve indicated modest diagnostic performance. GSEA revealed the related signaling pathways, and the ceRNA network uncovered the underlying mechanism. Conclusion: High LUCAT1 expression is an independent prognostic factor for liver cancer.

scholarly journals 46 Integration of multiple platforms to discover idh-mutant glioma subtypes

2018 ◽  
Vol 45 (S3) ◽  
pp. S14-S15
Author(s):  
Yasin Mamatjan ◽  
Farshad Nassiri ◽  
Severa Bunda ◽  
Fabio Moraes ◽  
Kenneth D. Aldape ◽  
...  
Keyword(s):  
Hox Genes ◽  
The Cancer Genome Atlas ◽  
Research Network ◽  
P Value ◽  
Biologically Relevant ◽  
Group 4 ◽  
Wide Range ◽  
Diffuse Gliomas ◽  
Cancer Genome Atlas ◽  
Promoter Mutations

Purpose: Diffuse gliomas can be divided on the basis of presence or absence of mutation in IDH genes. IDH-mutant diffuse gliomas represent a wide range of clinical outcome, which is not accounted for by current clinical and pathologic parameters. We aim to identify clinically and biologically relevant subgroups within IDH-mutant gliomas to gain a deeper insight into finer sub-classification. Methods: We used 412 IDH-mutant glioma samples that were profiled by The Cancer Genome Atlas (TCGA) Research Network, utilising methylation/mRNA datasets to identify subtypes with unique molecular signatures. We applied a Similarity Network Fusion (SNF) on individual platforms and their integrations. Results: SNF approach split glioma into four groups. The integrated RNA/methylation subtype produced a highly prognostic groups that predict survival (p-value=0.003) compared to mRNA and methylation alone. We observed a high degree of correlation between integrative subtypes and somatic mutations. Groups 1&4 had higher TERT promoter mutations (35% and 16%, respectively) compared to groups 2&3. Groups 1&4 showed increased TERT expression (34% and 14% respectively), and high percentage of TP53 and ATRX mutations. Multivariate analysis after adjusting for confounding factors including grade and age showed prognostic factors associated with survival (HR=3.2, p-value=0.001) in group 4 versus others. Conclusions: The results indicate that clinically relevant alterations exist within IDH-mutant gliomas that could stratify patients for treatment. Interestingly, group 4 showed high expression of HOX genes (18/18) (p-value=0.01) and higher methylation of Hox genes (21) (p-value=0.01) compared to others. Higher expression of specific Hox genes were associated with worse survival.

scholarly journals Identification and Validation of an Energy Metabolism-Related lncRNA-mRNA Signature for Lower-Grade Glioma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jingwei Zhao ◽  
Le Wang ◽  
Bo Wei
Keyword(s):  
Energy Metabolism ◽  
Risk Score ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Coexpression Network ◽  
Normal Brain ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Lower Grade Glioma ◽  
Genome Atlas

Energy metabolic processes play important roles for tumor malignancy, indicating that related protein-coding genes and regulatory upstream genes (such as long noncoding RNAs (lncRNAs)) may represent potential biomarkers for prognostic prediction. This study will develop a new energy metabolism-related lncRNA-mRNA prognostic signature for lower-grade glioma (LGG) patients. A GSE4290 dataset obtained from Gene Expression Omnibus was used for screening the differentially expressed genes (DEGs) and lncRNAs (DELs). The Cancer Genome Atlas (TCGA) dataset was used as the prognosis training set, while the Chinese Glioma Genome Atlas (CGGA) was for the validation set. Energy metabolism-related genes were collected from the Molecular Signatures Database (MsigDB), and a coexpression network was established between energy metabolism-related DEGs and DELs to identify energy metabolism-related DELs. Least absolute shrinkage and selection operator (LASSO) analysis was performed to filter the prognostic signature which underwent survival analysis and nomogram construction. A total of 1613 DEGs and 37 DELs were identified between LGG and normal brain tissues. One hundred and ten DEGs were overlapped with energy metabolism-related genes. Twenty-seven DELs could coexpress with 67 metabolism-related DEGs. LASSO regression analysis showed that 9 genes in the coexpression network were the optimal signature and used to construct the risk score. Kaplan-Meier curve analysis showed that patients with a high risk score had significantly worse OS than those with a low risk score (TCGA: HR=3.192, 95%CI=2.182‐4.670; CGGA: HR=1.922, 95%CI=1.431‐2.583). The predictive accuracy of the risk score was also high according to the AUC of the ROC curve (TCGA: 0.827; CGGA: 0.806). Multivariate Cox regression analyses revealed age, IDH1 mutation, and risk score as independent prognostic factors, and thus, a prognostic nomogram was established based on these three variables. The excellent prognostic performance of the nomogram was confirmed by calibration and discrimination analyses. In conclusion, our findings provided a new biomarker for the stratification of LGG patients with poor prognosis.

scholarly journals PANX2 and brain lower grade glioma genesis: A bioinformatic analysis

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110118
Author(s):  
XiaoXue Xu ◽  
YueHan Hao ◽  
Shuang Xiong ◽  
ZhiYi He
Keyword(s):  
Cox Regression ◽  
Atp Release ◽  
Underlying Mechanism ◽  
Bioinformatic Analysis ◽  
Receptor Interaction ◽  
Low Grade ◽  
Lower Grade ◽  
Cox Regression Analysis ◽  
Pathway Analyses ◽  
Vesicle Cycle

PANX2 forms large-pore channels mediating ATP release in response to physiological and pathological stimuli. Although PANX2 shows involvements in glioma genesis, the underlying mechanism remains unclear. PANX2 mRNA expression was analyzed via Oncomine and was confirmed via Gene Expression Profiling Interactive Analysis (GEPIA). The influence of PANX2 on overall survival (OS) of glioma was evaluated using LinkedOmics and further assessed through Cox regression analysis. The correlated genes with PANX2 acquired from LinkedOmics were validated through GEPIA and cBioPortal. Protein-protein interaction (PPI) of these genes was then obtained using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with MCODE plug-in. All the PANX2-related genes underwent Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The correlation between PANX2 and cancer immune infiltrates was evaluated via Tumor Immune Estimation Resource (TIMER). A higher expression of PANX2 only revealed a better OS in brain low grade glioma (LGG). PANX2-related genes in LGG functionally enriched in neuroactive ligand-receptor interaction, synaptic vesicle cycle, and calcium signaling. The hub genes from highest module of PPI were mainly linked to chemical synaptic transmission, plasma membrane, neuropeptide, and the pathway of neuroactive ligand-receptor interaction. Besides, PANX2 expression was negatively associated with infiltrating levels of macrophage, dendritic cells, and CD4+ T cells. This study demonstrated that PANX2 likely participated in LGG pathogenesis by affecting multiple molecular pathways and immune-related processes. PANX2 was associated with LGG prognosis and might become a promising therapeutic target of LGG.

scholarly journals TMOD-01. A PROGNOSTIC NOMOGRAM MODEL AND ONLINE SOFTWARE FOR PATIENTS WITH NEWLY DIAGNOSED LOWER-GRADE GLIOMAS

2021 ◽  
Vol 23 (Supplement_1) ◽  
pp. i35-i35
Author(s):  
Mingzhi Han
Keyword(s):  
Cox Regression ◽  
Molecular Subtype ◽  
Age At Diagnosis ◽  
Calibration Plot ◽  
Low Grade ◽  
Lower Grade ◽  
Newly Diagnosed ◽  
Who Grade ◽  
Cox Regression Analysis ◽  
Survival Probabilities

Abstract The nomogram represents a statistical model that incorporates multiple risk factors to estimate individualized survival probabilities. In this study, we developed a nomogram which provides an important tool for individulized survival predicition for newly diagnosed low-grade gliomas (LGG). A total number of 582 newly diagnosed LGG patients were included; the median age was 39.93 years and 42% were female. Cox regression analysis showed that younger age at diagnosis, WHO grade II vs. III, the IDHmut-codel vs. the IDHwt, and the IDHmut-non-codel vs. the IDHwt were significantly associated with better prognosis. The adjuvant treatment following surgery showed a trend towards improved survival. Subsequently, the nomogram to estimate 60-, 90-, and 120-month survival probabilities was established. Our data showed that the age at diagnosis was the largest contributor to patient survival, followed by molecular subtype, WHO grade, treatment and gender. The calibration plot showed that the observed and the nomogram predicted OS curves were well-aligned. In addition, we also validated our nomogram for LGG patients who received postsurgical adjuvant therapy through cross-validation and the calibration plot. Finally, we developed a free online tool for this nomogram (softwarewebsite:https://rrlnnomogram.shinyapps.io/LGG_Nom_Asian/). Overall, this model should be a useful tool for counseling patients in clinical practice including treatment decisions, follow-up, and prognosis.

scholarly journals Serine Incorporator 2 (SERINC2) Expression Predicts an Unfavorable Prognosis of Low-Grade Glioma (LGG): Evidence from Bioinformatics Analysis

2020 ◽  
Vol 70 (10) ◽  
pp. 1521-1532
Author(s):  
Chunxiao Qi ◽  
Lei Lei ◽  
Jinqu Hu ◽  
Gang Wang ◽  
Jiyuan Liu ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Membrane Lipids ◽  
Transmembrane Protein ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Normal Brain ◽  
Hub Genes ◽  
Cox Regression Analysis

Abstract Serine Incorporator 2 (SERINC2) is a transmembrane protein that incorporates serine into membrane lipids. The function of SERINC2 in tumors has been reported, but the role of SERINC2 in gliomas is not fully understood. RNA-sequencing data from The Cancer Genome Atlas (TCGA) (530 cases of low-grade glioma (LGG) and 173 cases of glioblastoma multiforme (GBM)) and microarray data from Gene Expression Omnibus (GEO) (Accession No. GSE16011, 284 cases gliomas were included) were acquired. Bioinformatics analysis was performed as the primary method to examine the function of SERINC2 and its correlated genes in glioma. SERINC2 was highly expressed in GBM compared with LGG and normal brain tissues. Elevated SERINC2 expression predicted shorter 5-, 10-, and 15-year overall survival (OS) of LGG patients and isocitrate dehydrogenase-1 (IDH-1) mutation-type LGG patients but had no effect on the OS of GBM patients. Cox regression analysis showed that SERINC2 was an independent factor in LGG OS. Methylation analysis found that 13 CpG methylation sites (methylation450k) correlated with SERINC2 expression in LGG. The mRNA expression level of SERINC2 was significant lower in the DNA deletion group than in the intact and amplification groups. A total of 390 copositive and 244 conegative correlation genes with SERINC2 were obtained from LGG in TCGA-LGG and GSE16011. Gene ontology (GO) category and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that the copositive correlation genes were primarily enriched in the mitotic process and cell cycle. Combining the results from the protein-protein interaction (PPI) network of SERINC2 correlation genes and CytoHubba led to the selection of 10 hub genes (CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE). OncoLnc analysis confirmed that high expression levels of these hub genes were associated with poor OS in LGG. Our results suggested that aberrant SERINC2 expression existed in glioma and that its expression might be a potential prognostic marker in LGG patients. CDC20, FN1, AURKB, AURKA, KIF2C, BIRC5, CCNB2, UBE2C, CCNA2, and CENPE may be potential biomarkers and therapeutic targets for LGG.

scholarly journals A Novel 10-Gene Signature Predicts Poor Prognosis in Low Grade Glioma

2021 ◽  
Vol 20 ◽  
pp. 153303382199208
Author(s):  
Wentao Liu ◽  
Jiaxuan Zou ◽  
Rijun Ren ◽  
Jingping Liu ◽  
Gentang Zhang ◽  
...  
Keyword(s):  
Risk Score ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Gene Signature ◽  
Low Grade Glioma ◽  
The Cancer Genome Atlas ◽  
Low Grade ◽  
Normal Brain ◽  
Cox Regression Analysis ◽  
Genome Atlas

Aim: Low grade glioma (LGG) is a lethal brain cancer with relatively poor prognosis in young adults. Thus, this study was performed to develop novel molecular biomarkers to effectively predict the prognosis of LGG patients and finally guide treatment decisions. Methods: survival-related genes were determined by Kaplan-Meier survival analysis and multivariate Cox regression analysis using the expression and clinical data of 506 LGG patients from The Cancer Genome Atlas (TCGA) database and independently validated in a Chinese Glioma Genome Atlas (CGGA) dataset. A prognostic risk score was established based on a linear combination of 10 gene expression levels using the regression coefficients of the multivariate Cox regression models. GSEA was performed to analyze the altered signaling pathways between the high and low risk groups stratified by median risk score. Results: We identified a total of 1489 genes significantly correlated with patients’ prognosis in LGG. The top 5 protective genes were DISP2, CKMT1B, AQP7, GPR162 and CHGB, the top 5 risk genes were SP1, EYA3, ZSCAN20, ITPRIPL1 and ZNF217 in LGG. The risk score was predictive of poor overall survival and relapse-free survival in LGG patients. Pathways of small cell lung cancer, pathways in cancer, chronic myeloid leukemia, colorectal cancer were the top 4 most enriched pathways in the high risk group. SP1, EYA3, ZSCAN20, ITPRIPL1, ZNF217 and GPR162 were significantly up-regulated, while DISP2, CKMT1B, AQP7 were down-regulated in 523 LGG tissues as compared to 1141 normal brain controls. Conclusions: The 10-gene signature may become novel prognostic and diagnostic biomarkers to considerably improve the prognostic prediction in LGG.

scholarly journals The prognostic value of lncRNA SNHG4 and its potential mechanism in liver cancer

2020 ◽  
Vol 40 (1) ◽  
Author(s):  
Yan Jiao ◽  
Yanqing Li ◽  
Baoxing Jia ◽  
Qingmin Chen ◽  
Guoqiang Pan ◽  
...  
Keyword(s):  
Liver Cancer ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Rank Test ◽  
P Value ◽  
Expression Data ◽  
Potential Mechanism ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Log Rank Test

Abstract Background and object: Emerging evidence shows that non-coding RNA functions as new gene regulators and prognostic markers in several cancers, including liver cancer. Here, we focused on the small nucleolar RNA host gene 4 (SNHG4) in liver cancer prognosis based on The Cancer Genome Atlas (TCGA) data. Methods: The expression data and clinical information were downloaded from TCGA. Chi-square tests evaluated the correlation between SNHG4 expression and clinical parameters. Differences in survival between high and low expression groups (optic cutoff value determined by ROC) from Cox regression analysis were compared, and P-value was calculated by a log-rank test. Kaplan–Meier curves were compared with the log-rank test. GSEA and ceRNA network were conducted to explore the potential mechanism. Results: Data mining of lncRNA expression data for 371 patients with primary tumor revealed overexpression of SNHG4 in liver cancer. High SNHG4 expression was correlated with histological type (P = 0.01), histologic grade (P = 0.001), stage (P = 0.01), T classification (P = 0.004) and survival status (P = 0.013). Patients with high SNHG4 expression had poor overall survival and relapse-free survival compared with those with low SNHG4 expression. Multivariate analysis identified SNHG4 as an independent prognostic factor of poor survival in liver cancer. GSEA revealed related signaling pathway and ceRNA network explored the further mechanism. Conclusion: High SNHG4 expression is an independent predictor of poor prognosis in liver cancer.

scholarly journals TEAD4 is a novel independent predictor of prognosis in LGG patients with IDH mutation

2021 ◽  
Vol 16 (1) ◽  
pp. 323-335
Author(s):  
Hai-Yan Yuan ◽  
Ya-Juan Lv ◽  
Yi Chen ◽  
Dan Li ◽  
Xi Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Disease Free Survival ◽  
Predictive Biomarkers ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Lower Grade ◽  
Cox Regression Analysis ◽  
Study Results ◽  
Number Variation ◽  
Genome Atlas

Abstract TEA domain family members (TEADs) play important roles in tumor progression. Till now, the genomic status of TEADs in patients with glioma has not been well investigated. To confirm whether the genomic status of TEADs could affect the prognosis of patients with glioma, the copy number variation (CNV), mutation and expression data of glioma cohorts in The Cancer Genome Atlas, Gene Expression Omnibus and Chinese Glioma Genome Atlas were comprehensively analyzed. Results showed that TEAD CNV frequency in lower grade gliomas (LGGs) was higher than in glioblastoma multiforme (GBM). Multivariate cox regression analysis showed that TEAD4 CNV increase was significantly associated with overall survival (OS) and disease-free survival (DFS) in LGGs (OS p = 0.022, HR = 1.444, 95% CI: 1.054–1.978; DFS p = 0.005, HR = 1.485, 95% CI: 1.124–1.962), while not in GBM. Patients with TEAD4 CNV increase showed higher expression level of TEAD4 gene. In LGG patients with IDH mutation, those with higher TEAD4 expression levels had shorter OS and DFS. Integrating TEAD4 CNV increase, IDH mutations, TP53 mutation, ATRX mutation and 1p19q co-deletion would separate patients with LGG into four groups with significant differences in prognosis. These study results suggested that TEAD4 variations were independent predictive biomarkers for the prognosis in patients with LGG with IDH mutation.

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