A 14-Methylation-Driven Differentially Expressed RNA as a Signature for Overall Survival Prediction in Patients with Uterine Corpus Endometrial Carcinoma

Zeng, Zhi; Cheng, Juan; Ye, Qingjian; Zhang, Yuan; Shen, Xiaoting; Cai, Jiarong; Li, Manchao

doi:10.1089/dna.2019.5313

Cited by 7 publications

(6 citation statements)

References 55 publications

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“…In line with this hypothesis, we developed a signature panel of 11 methylated mRNAs (MMP7, SLCO3A1, KCNF1, RFXAP, NTRK3, NAV1, HOXA13, HAS2, CBX2, HIST1H2AJ, SNX4) and 3 methylated lncRNAs (MEG3, DSCR9, and HCP5). This signature was shown to independently predict the 5-year RFS of PCa patients, with AUC of 0.969 for the training TCGA dataset and AUC of 0.811 for the testing E-MTAB-6131, both of which were higher than the predictive accuracy of the combined model of 52-gene methylation signature and clinical features (AUC = 0.78) in the study of Geybels et al 21 Similar to the reports by Zeng et al, 23 our lncRNA-mRNA-based methylation signature was demonstrated to have a higher prognostic power than the lncRNA- (AUC = 0.959 vs 0.743; C-index = 0.885 vs 0.723) and mRNA-based signature (AUC =0.959 vs 0.93; C-index = 0.885 vs 0.864). The superiority of our methylation signature to clinical features for RFS prediction was also evidence: TNM and PSA were even not identified to be associated with RFS in the multivariate analysis; patients with the same Gleason score (especially those with Gleason score lower than 8 who were usually considered to have a favorable prognosis in clinic 33 ) could also be further divided into the high-risk group and the low-risk group.…”

Section: Discussionsupporting

confidence: 70%

“…Compared with the study of Geybels et al, 21 our methylation signature not only included protein-encoding messenger RNAs (mRNAs), but also long non-coding RNAs (lncRNAs). Previous studies suggested that the prognostic power of methylated lncRNAs- 22 or lncRNA-mRNA-based 23 prognostic signature was higher than that of mRNA alone. Accordingly, our signature may be more helpful for predicting prognosis and guiding the individualized treatment of PCa patients.…”

Section: Introductionmentioning

confidence: 96%

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Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

Cai

Yang

Chen

et al. 2021

PGPM

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Background: Radical prostatectomy is the main treatment for prostate cancer (PCa), a common cancer type among men. Recurrence frequently occurs in a proportion of patients. Therefore, there is a great need to early screen those patients to specifically schedule adjuvant therapy to improve the recurrence-free survival (RFS) rate. This study aims to develop a biomarker to predict RFS for patients with PCa based on the data of methylation, an important heritable contributor to carcinogenesis. Methods: Methylation expression data of PCa patients were downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus database (GSE26126), and the European Bioinformatics Institute (E-MTAB-6131). The stable co-methylation modules were identified by weighted gene co-expression network analysis. The genes in modules were overlapped with differentially methylated RNAs (DMRs) screened by MetaDE package in three datasets, which were used to screen the prognostic genes using least absolute shrinkage and selection operator analyses. The prognostic performance of the prognostic signature was assessed by survival curve analysis. Results: Five co-methylation modules were considered preserved in three datasets. A total of 192 genes in these 5 modules were overlapped with 985 DMRs, from which a signature panel of 11 methylated messenger RNAs and 3 methylated long non-coding RNAs was identified. This signature panel could independently predict the 5-year RFS of PCa patients, with an area under the receiver operating characteristic curve (AUC) of 0.969 for the training TCGA dataset and 0.811 for the testing E-MTAB-6131 dataset, both of which were higher than the predictive accuracy of Gleason score (AUC = 0.689). Also, the patients with the same Gleason score (6-7 or 8-10) could be further divided into the high-risk group and the low-risk group. Conclusion: These results suggest that our prognostic model may be a promising biomarker for clinical prediction of RFS in PCa patients.

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Section: Discussionsupporting

confidence: 70%

Section: Introductionmentioning

confidence: 96%

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

Cai

Yang

Chen

et al. 2021

PGPM

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“…Previous studies reported that six of the 10 genes included in our risk model play roles in breast tumor promotion or suppression: CALML5, BAMBI, QPRT, CLDN7, TARS , and NDUFB1 ( 44 - 49 ). Three of the 10 genes ( NDUFB10 , SERPINA3 , and PHLDB2I ) were not previously identified as related to breast cancer but were reportedly related to other cancers ( 50 - 52 ). Furthermore, one of the 10 genes ( MAL2 ) was reported to mediate the endocytosis and degradation of MHC-I complexes, which could lead to immune evasion of breast cancer cells ( 53 ).…”

Section: Discussionmentioning

confidence: 96%

Development and validation of a prognostic model and gene co-expression networks for breast carcinoma based on scRNA-seq and bulk-seq data

Ruan¹,

Chi²,

Wang³

et al. 2022

Ann Transl Med

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Background: Breast carcinoma is the most common malignancy among women worldwide. It is characterized by a complex tumor microenvironment (TME), in which there is an intricate combination of different types of cells, which can cause confusion when screening tumor-cell-related signatures or constructing a gene co-expression network. The recent emergence of single-cell RNA sequencing (scRNAseq) is an effective method for studying the changing omics of cells in complex TMEs. Methods:The Dysregulated genes of malignant epithelial cells was screened by performing a comprehensive analysis of the public scRNA-seq data of 58 samples. Co-expression and Gene Set Enrichment Analysis (GSEA) analysis were performed based on scRNA-seq data of malignant cells to illustrate the potential function of these dysregulated genes. Iterative LASSO-Cox was used to perform a second-round screening among these dysregulated genes for constructing risk group. Finally, a breast cancer prognosis prediction model was constructed based on risk grouping and other clinical characteristics.Results: Our results indicated a transcriptional signature of 1,262 genes for malignant breast cancer epithelial cells. To estimate the function of these genes in breast cancer, we also constructed a co-expression network of these dysregulated genes at single-cell resolution, and further validated the results using more than 300 published transcriptomics datasets and 31 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening datasets. Moreover, we developed a reliable predictive model based on the scRNA-seq and bulk-seq datasets.Conclusions: Our findings provide insights into the transcriptomics and gene co-expression networks during breast carcinoma progression and suggest potential candidate biomarkers and therapeutic targets for the treatment of breast carcinoma. Our results are available via a web app (https://prognosticpredictor. shinyapps.io/GCNBC/).

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“…Although new biomarkers are discovered every day, the use of gene signature can highlight the most important in practical application. Compared to other established signatures to assess OS in EnCa [ 21 , 22 ], our model was constructed and validated from more comprehensive cohorts and it seemed to be more convenient to be applied in clinical practice with fewer numbers of genes.…”

Section: Discussionmentioning

confidence: 99%

Establishment of a novel cell cycle-related prognostic signature predicting prognosis in patients with endometrial cancer

Liu

Mei

et al. 2020

Cancer Cell Int

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Background Endometrial cancer (EnCa) ranks fourth in menace within women’s malignant tumors. Large numbers of studies have proven that functional genes can change the process of tumors by regulating the cell cycle, thereby achieving the goal of targeted therapy. Methods The transcriptional data of EnCa samples obtained from the TCGA database was analyzed. A battery of bioinformatics strategies, which included GSEA, Cox and LASSO regression analysis, establishment of a prognostic signature and a nomogram for overall survival (OS) assessment. The GEPIA and CPTAC analysis were applied to validate the dysregulation of hub genes. For mutation analysis, the “maftools” package was used. Results GSEA identified that cell cycle was the most associated pathway to EnCa. Five cell cycle-related genes including HMGB3, EZH2, NOTCH2, UCK2 and ODF2 were identified as prognosis-related genes to build a prognostic signature. Based on this model, the EnCa patients could be divided into low- and high-risk groups, and patients with high-risk score exhibited poorer OS. Time-dependent ROC and Cox regression analyses revealed that the 5-gene signature could predict EnCa prognosis exactly and independently. GEPIA and CPTAC validation exhibited that these genes were notably dysregulated between EnCa and normal tissues. Lower mutation rates of PTEN, TTN, ARID1A, and etc. were found in samples with high-risk score compared with that with low-risk score. GSEA analysis suggested that the samples of the low- and high-risk groups were concentrated on various pathways, which accounted for the different oncogenic mechanisms in patients in two groups. Conclusion The current research construct a 5-gene signature to evaluate prognosis of EnCa patients, which may innovative clinical application of prognostic assessment.

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A 14-Methylation-Driven Differentially Expressed RNA as a Signature for Overall Survival Prediction in Patients with Uterine Corpus Endometrial Carcinoma

Cited by 7 publications

References 55 publications

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

Signature Panel of 11 Methylated mRNAs and 3 Methylated lncRNAs for Prediction of Recurrence-Free Survival in Prostate Cancer Patients

Development and validation of a prognostic model and gene co-expression networks for breast carcinoma based on scRNA-seq and bulk-seq data

Establishment of a novel cell cycle-related prognostic signature predicting prognosis in patients with endometrial cancer

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