13-lncRNAs Signature to Improve Diagnostic and Prognostic Prediction of Hepatocellular Carcinoma

2020 ◽  
Vol 23 ◽  
Author(s):  
Xinxin Zhang ◽  
Jia Yu ◽  
Juan Hu ◽  
Fang Tan ◽  
Juan Zhou ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Diagnostic Model ◽  
Diagnosis And Prognosis ◽  
Potential Marker

Background: Hepatocellular carcinoma (HCC) is a common cancer with a high mortality rate and is usually detected at middle or late stage, missing the optimal treatment period. The current study aims to identify potential long noncoding RNAs (lncRNAs) biomarkers that contribute to diagnosis and prognosis of HCC. Method: The differentially expressed lncRNAs (DElncRNAs) in HCC patients were detected from the Cancer Genome Atlas (TCGA) dataset. LncRNAs signature was screened by LASSO regression, univariate and multivariate Cox regression. The models for predicting diagnosis and prognosis were established respectively. The prognostic model was evaluated by Kaplan-Meier survival curve receiver operating characteristic (ROC) curve and stratified analysis. The diagnostic model was validated by ROC. The lncRNAs signature was further demonstrated by functional enrichment analysis. Results: We found the 13-lncRNAs signature that had a good performance in predicting prognosis and could help to improve the value of diagnosis. In the training set, testing set and entire cohort, the low risk group had longer survival than the high risk group (median OS: 3124 vs 649 days, 2456 vs 770 days and 3124 vs 755 days ). It performed well in 1-, 3-, and 5- year survival prediction. 13-lncRNAs-based risk score, age and race were good predictors of prognosis. The AUC of diagnosis were 0.9487, 0.9265 and 0.9376 respectively. Meanwhile the 13-lncRNAs were involved in important pathways including the cell cycle and multiple metabolic pathways. Conclusion: In our study, the 13-lncRNAs signature may be a potential marker for prognosis of HCC and improve the diagnosis.

scholarly journals Screening the genome for HCC-specific CpG methylation signatures as biomarkers for diagnosis and prognosis evaluation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Rui-kun Zhang ◽  
Jia-lin Liu
Keyword(s):  
Cox Regression ◽  
Malignant Tumors ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Training Set ◽  
Cpg Sites ◽  
Diagnosis And Prognosis ◽  
Patient Prognosis ◽  
Prognosis Evaluation

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and invasive malignant tumors in the world. The change in DNA methylation is a key event in HCC. Methods Methylation datasets for HCC and 17 other types of cancer were downloaded from The Cancer Genome Atlas (TCGA). The CpG sites with large differences in methylation between tumor tissues and paracancerous tissues were identified. We used the HCC methylation dataset downloaded from the TCGA as the training set and removed the overlapping sites among all cancer datasets to ensure that only CpG sites specific to HCC remained. Logistic regression analysis was performed to select specific biomarkers that can be used to diagnose HCC, and two datasets—GSE157341 and GSE54503—downloaded from GEO as validation sets were used to validate our model. We also used a Cox regression model to select CpG sites related to patient prognosis. Results We identified 6 HCC-specific methylated CpG sites as biomarkers for HCC diagnosis. In the training set, the area under the receiver operating characteristic (ROC) curve (AUC) for the model containing all these sites was 0.971. The AUCs were 0.8802 and 0.9711 for the two validation sets from the GEO database. In addition, 3 other CpG sites were analyzed and used to create a risk scoring model for patient prognosis and survival prediction. Conclusions Through the analysis of HCC methylation datasets from the TCGA and Gene Expression Omnibus (GEO) databases, potential biomarkers for HCC diagnosis and prognosis evaluation were ascertained.

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

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.

scholarly journals Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

2020 ◽  
Author(s):  
Li Liu ◽  
She Tian ◽  
Zhu Li ◽  
Yongjun Gong ◽  
Hao Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Principal Component ◽  
High Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

scholarly journals A Novel Platelet-Related Gene Signature for Predicting the Prognosis of Triple-Negative Breast Cancer

2022 ◽  
Vol 9 ◽  
Author(s):  
Jindong Xie ◽  
Yutian Zou ◽  
Feng Ye ◽  
Wanzhen Zhao ◽  
Xinhua Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Clinical Prognosis

Regarded as the most invasive subtype, triple-negative breast cancer (TNBC) lacks the expression of estrogen receptors (ERs), progesterone receptors (PRs), and human epidermal growth factor receptor 2 (HER2) proteins. Platelets have recently been shown to be associated with metastasis of malignant tumors. Nevertheless, the status of platelet-related genes in TNBC and their correlation with patient prognosis remain unknown. In this study, the expression and variation levels of platelet-related genes were identified and patients with TNBC were divided into three subtypes. We collected cohorts from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, we constructed a seven-gene signature which classified the two cohorts of patients with TNBC into low- or high-risk groups. Patients in the high-risk group were more likely to have lower survival rates than those in the low-risk group. The risk score, incorporated with the clinical features, was confirmed as an independent factor for predicting the overall survival (OS) time. Functional enrichment analyses revealed the involvement of a variety of vital biological processes and classical cancer-related pathways that could be important to the ultimate prognosis of TNBC. We then built a nomogram that performed well. Moreover, we tested the model in other cohorts and obtained positive outcomes. In conclusion, platelet-related genes were closely related to TNBC, and this novel signature could serve as a tool for the assessment of clinical prognosis.

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

2020 ◽  
Author(s):  
Hui Zhang ◽  
Senmiao Ni ◽  
Changxian Li ◽  
Haoming Zhou ◽  
Jianling Bai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Negative Binomial ◽  
Cox Regression ◽  
Risk Groups ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Normal Tissues ◽  
Mirna Sequencing ◽  
Incident Cases

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.

scholarly journals Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoyu Deng ◽  
Qinghua Bi ◽  
Shihan Chen ◽  
Xianhua Chen ◽  
Shuhui Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

scholarly journals A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Shiqiang Hou ◽  
Jinyi Wang ◽  
Zhengjun Chai ◽  
Xuan Hong ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Gene Expression Signature ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Survival Prediction ◽  
Pathway Enrichment Analysis ◽  
Prognostic Information ◽  
Specificity And Sensitivity

Background. Lung adenocarcinoma (LUAD), a major and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to assess key genes and to develop a prognostic signature for the patient therapy with LUAD. Method. RNA expression profile and clinical data from 522 LUAD patients were accessed and downloaded from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were extracted and analyzed between normal tissues and LUAD samples. Then, a 14-DEG signature was developed and identified for the survival prediction in LUAD patients by means of univariate and multivariate Cox regression analyses. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to predict the potential biological functions and pathways of these DEGs. Results. Twenty-two out of 5924 DEGs in the TCGA dataset were screened and associated with the overall survival (OS) of LUAD patients. 14CID="C008" value=" "DEGs were finally selected and included in our development and validation model by risk score analysis. The ROC analysis indicated that the specificity and sensitivity of this profile signature were high. Further functional enrichment analyses indicated that these DEGs might regulate genes that affect the function of release of sequestered calcium ion into cytosol and pathways that associated with vibrio cholerae infection. Conclusion. Our study developed a novel 14-DEG signature providing more efficient and persuasive prognostic information beyond conventional clinicopathological factors for survival prediction of LUAD patients.

scholarly journals Development of Prognostic Indicator Based on Autophagy-Related lncRNA Analysis in Colon Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Weige Zhou ◽  
Shijing Zhang ◽  
Hui-biao Li ◽  
Zheyou Cai ◽  
Shuting Tang ◽  
...  
Keyword(s):  
Risk Score ◽  
Noncoding Rna ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Lasso Regression

There were no systematic researches about autophagy-related long noncoding RNA (lncRNA) signatures to predict the survival of patients with colon adenocarcinoma. It was necessary to set up corresponding autophagy-related lncRNA signatures. The expression profiles of lncRNAs which contained 480 colon adenocarcinoma samples were obtained from The Cancer Genome Atlas (TCGA) database. The coexpression network of lncRNAs and autophagy-related genes was utilized to select autophagy-related lncRNAs. The lncRNAs were further screened using univariate Cox regression. In addition, Lasso regression and multivariate Cox regression were used to develop an autophagy-related lncRNA signature. A risk score based on the signature was established, and Cox regression was used to test whether it was an independent prognostic factor. The functional enrichment of autophagy-related lncRNAs was visualized using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Ten prognostic autophagy-related lncRNAs (AC027307.2, AC068580.3, AL138756.1, CD27-AS1, EIF3J-DT, LINC01011, LINC01063, LINC02381, AC073896.3, and SNHG16) were identified to be significantly different, which made up an autophagy-related lncRNA signature. The signature divided patients with colon adenocarcinoma into the low-risk group and the high-risk group. A risk score based on the signature was a significantly independent factor for the patients with colon adenocarcinoma (HR=1.088, 95%CI=1.057−1.120; P<0.001). Additionally, the ten lncRNAs were significantly enriched in autophagy process, metabolism, and tumor classical pathways. In conclusion, the ten autophagy-related lncRNAs and their signature might be molecular biomarkers and therapeutic targets for the patients with colon adenocarcinoma.

A novel signature based on m6A related genes as the potential prognostic factor in hepatocellular carcinoma.

2020 ◽  
Author(s):  
Zhuomao Mo ◽  
Shaoju Luo ◽  
Hao Hu ◽  
Ling Yu ◽  
Zhirui Cao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Immune Therapy ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Potential Biomarker ◽  
Significant Difference

Abstract Background Many different signatures and models have been established for patients with hepatocellular carcinoma (HCC), but no signature based on m6A related genes was developed. The objective of this research was to establish the signature with m6A related genes in HCC. Methods Data from 377 HCC patients from The Cancer Genome Atlas (TCGA) database was downloaded. The included m6A related genes were selected by Cox regression analysis and the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, the nomogram was constructed and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the four m6A related genes (YTHDF2, YTHDF1, METTL3 and KIAA1429). Under the grouping from signature, patients in high risk group of showed the poor prognosis than those in low risk group. And significant difference was found in two kinds of immune cells (T cell gamma delta and NK cells activated) between two groups. The univariate and multivariate Cox regression analysis indicated that m6A related signature can be the potential independent prognosis factor in HCC. Finally, we developed a clinical risk model predicting the HCC prognosis and successfully verified it in C-index, calibration and ROC curve. Conclusion Our study identified the m6A related signature for predicting prognosis of HCC and provided the potential biomarker between m6A and immune therapy.

Development and validation of a robust immune-related risk signature for hepatocellular carcinoma

2020 ◽  
Author(s):  
Zaoqu Liu ◽  
Dechao Jiao ◽  
Xueliang Zhou ◽  
Yuan Yao ◽  
Zhaonan Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Risk Genes ◽  
Cox Regression Analysis ◽  
Related Risk

Abstract Background: A growing amount of evidence has suggested immune-related genes (IRGs) play a key role in the development of hepatocellular carcinoma (HCC). However, there have been no investigations proposing a reliable prognostic signature in terms of tumor immunology. This study aimed to develop a robust signature based on IRGs in HCC.Methods: A total of 597 HCC patients were enrolled. The TCGA database was utilized for discovery, and the ICGC database was utilized for validation. Multiple algorithms (including univariate Cox, LASSO, and multivariate Cox regression) were performed to identify key prognostic IRGs and establish an immune-related risk signature. Bioinformatics analysis and R soft tools were utilized to annotate underlying biological functions. Results: A total of 1416 differentially expressed mRNAs (DEMs) were screened in the TCGA cohort, of which 90 were differentially expressed IRGs (DEIRGs). Using univariate Cox regression analysis, we identified 33 prognostically relevant DEIRGs. Using LASSO regression and multivariate Cox regression analysis, we extracted 8 optimal DEIRGs (APLN, CDK4, CXCL2, ESR1, IL1RN, PSMD2, SEMA3F, and SPP1) to construct a risk signature with the ability to distinguish cases as having a high or low risk of unfavorable prognosis in the TCGA cohort, and the signature was verified in the ICGC cohort. The signature was prognostically significant in all stratified cohorts and was deemed an independent prognostic factor for HCC. We also built a nomogram with good performance by combining the signature with clinicopathological factors to increase the accuracy of predicting HCC prognosis. By investigating the relationship of the risk score and 8 risk genes from our signature with clinical traits, we found that the aberrant expression of the immune-related risk genes is correlated with the development of HCC. Moreover, the high-risk group was higher than the low-risk group in terms of tumor mutation burden (TMB), immune cell infiltration, and the expression of immune checkpoints (PD-1, PD-L1, and CTLA-4), and functional enrichment analysis indicated the signature enriched an intensive immune phenotype.Conclusions: This study developed a robust immune-related risk signature and built a predictive nomogram that reliably predict overall survival in HCC, which may be helpful for clinical management and personalized immunotherapy decisions.

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