Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Zhou, Min; Hong, Shasha; Li, Bingshu; Liu, Cheng; Hu, Ming; Min, Jie; Tang, Jianming; Li, Hong

doi:10.3389/fgene.2021.675197

Cited by 4 publications

(4 citation statements)

References 54 publications

(61 reference statements)

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“…IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4 [ 23 ]. Zhou et al identified a DNA methylation-driven genes signature, including PON3, MFAP4, AKAP12, and BHMT2 [ 24 ]. Moreover, Zheng et al developed a risk stratification system based on glycolysis-related lncRNAs, including AC133644.2, CTD-2396E7.11, CTD-3065, J16.9, LINC00240, and TMEM254-AS1 [ 25 ].…”

Section: Exploring Clinical Benefit Of Signaturementioning

confidence: 99%

Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer

et al. 2022

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Background Ferroptosis and iron-metabolism are regulated by Long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of ferroptosis and iron-metabolism related lncRNAs (FIRLs) in OC is crucial for proposing therapeutic strategies and survival prediction. Methods In multi-omics data from OC patients, FIRLs were identified by calculating Pearson correlation coefficients with ferroptosis and iron-metabolism related genes (FIRGs). Cox-Lasso regression analysis was performed on the FIRLs to screen further the lncRNAs participating in FIRLs signature. In addition, all patients were divided into two robust risk subtypes using the FIRLs signature. Receiver operator characteristic (ROC) curve, Kaplan–Meier analysis, decision curve analysis (DCA), Cox regression analysis and calibration curve were used to confirm the clinical benefits of FIRLs signature. Meanwhile, two nomograms were constructed to facilitate clinical application. Moreover, the potential biological functions of the signature were investigated by genes function annotation. Finally, immune microenvironment, chemotherapeutic sensitivity, and the response of PARP inhibitors were compared in different risk groups using diversiform bioinformatics algorithms. Results The raw data were randomized into a training set (n = 264) and a testing set (n = 110). According to Pearson coefficients between FIRGs and lncRNAs, 1075 FIRLs were screened for univariate Cox regression analysis, and then LASSO regression analysis was used to construct 8-FIRLs signature. It is worth mentioning that a variety of analytical methods indicated excellent predictive performance for overall survival (OS) of FIRLs signature (p < 0.05). The multivariate Cox regression analysis showed that FIRLs signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the abundance of immune cells, immune-related pathways, and drug response were excavated in different risk subtypes (p < 0.05). Conclusion The FIRLs signature can independently predict overall survival and therapeutic effect in OC patients.

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Section: Exploring Clinical Benefit Of Signaturementioning

confidence: 99%

Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer

et al. 2022

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“…( 35 ), and the DNA methylation-driven gene signature established by Zhou et al. ( 36 ), as shown in Figure S2 .…”

Section: Resultsmentioning

confidence: 99%

Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

et al. 2022

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BackgroundPyroptosis is regulated by long non-coding RNAs (lncRNAs) in ovarian cancer (OC). Therefore, a comprehensive analysis of pyroptosis-related lncRNAs (PRLs) in OC is crucial for developing therapeutic strategies and survival prediction.MethodsBased on public database raw data, mutations in the landscape of pyroptosis-related genes (PRGs) in patients with OC were investigated thoroughly. PRLs were identified by calculating Pearson correlation coefficients. Cox and LASSO regression analyses were performed on PRLs to screen for lncRNAs participating in the risk signature. Furthermore, receiver operating characteristic (ROC) curves, Kaplan–Meier survival analyses, decision curve analysis (DCA) curves, and calibration curves were used to confirm the clinical benefits. To assess the ability of the risk signature to independently predict prognosis, it was included in a Cox regression analysis with clinicopathological parameters. Two nomograms were constructed to facilitate clinical application. In addition, potential biological functions of the risk signature were investigated using gene function annotation. Subsequently, immune-related landscapes and BRCA1/2 mutations were compared in different risk groups using diverse bioinformatics algorithms. Finally, we conducted a meta-analysis and in-vitro assays on alternative lncRNAs.ResultsA total of 374 patients with OC were randomized into training and validation cohorts (7:3). A total of 250 PRLs were selected from all the lncRNAs. Subsequently, a risk signature (DICER1-AS1, MIR600HG, AC083880.1, AC109322.1, AC007991.4, IL6R-AS1, AL365361.1, and AC022098.2) was constructed to distinguish the risk of patient survival. The ROC curve, K-M analysis, DCA curve, and calibration curve indicated excellent predictive performance for determining overall survival (OS) based on the risk signature in each cohort (p < 0.05). The Cox regression analysis indicated that the risk signature was an independent prognostic factor for OS (p < 0.05). Moreover, significant differences in the immune response and BRCA1 mutations were identified in different groups distinguished by the risk signature (p < 0.05). Interestingly, in-vitro assays showed that an alternative lncRNA (DICER1-AS1) could promote OC cell proliferation.ConclusionThe PRL risk signature could independently predict overall survival and guide treatment in patients with OC.

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“…As an important type of epigenetic modification, DNA methylation has been reported to participate in the process of disease occurrence, diagnosis, prognosis evaluation and treatment [10,22,39,40]. In OC, DNA methylation also shows value in prognostic evaluation, but the prediction efficiency is not particularly accurate, and whether there are other values remains unclear [12,13].…”

Section: Discussionmentioning

confidence: 99%

“…Recently, it has been reported that DNA methylation status, as well as DNA methylationrelated genes, can be used in the diagnosis, treatment and prognosis of diseases [9][10][11]. Meanwhile, DNA methylation probes or methylation-related genes have also been reported for the prognostic assessment of OC [12,13]. However, the predictive efficacy of these methylation related models is not particularly accurate, and it is not clear whether there are other values in addition to the prognostic value.…”

Section: Introductionmentioning

confidence: 99%

A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients

Wang

Fang

2023

J Ovarian Res

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Background Ovarian cancer (OC) is one of the most life-threatening cancers affecting women worldwide. Recent studies have shown that the DNA methylation state can be used in the diagnosis, treatment and prognosis prediction of diseases. Meanwhile, it has been reported that the DNA methylation state can affect the function of immune cells. However, whether DNA methylation-related genes can be used for prognosis and immune response prediction in OC remains unclear. Methods In this study, DNA methylation-related genes in OC were identified by an integrated analysis of DNA methylation and transcriptome data. Prognostic values of the DNA methylation-related genes were investigated through least absolute shrinkage and selection operator (LASSO) and Cox progression analyses. Immune characteristics were investigated by CIBERSORT, correlation analysis and weighted gene co-expression network analysis (WGCNA). Results Twelve prognostic genes (CA2, CD3G, HABP2, KCTD14, PI3, SERPINB5, SLAMF7, SLC9A2, STC2, TBP, TREML2 and TRIM27) were identified and a risk score signature and a nomogram based on prognostic genes and clinicopathological features were constructed for the survival prediction of OC patients in the training and two validation cohorts. Subsequently, the differences in the immune landscape between the high- and low-risk score groups were systematically investigated. Conclusions Taken together, our study explored a novel efficient risk score signature and a nomogram for the survival prediction of OC patients. In addition, the differences of the immune characteristics between the two risk groups were clarified preliminarily, which will guide the further exploration of synergistic targets to improve the efficacy of immunotherapy in OC patients.

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Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

Cited by 4 publications

References 54 publications

Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer

Integrated clinical characteristics and omics analysis identifies a ferroptosis and iron-metabolism-related lncRNA signature for predicting prognosis and therapeutic responses in ovarian cancer

Derivation, Comprehensive Analysis, and Assay Validation of a Pyroptosis-Related lncRNA Prognostic Signature in Patients With Ovarian Cancer

A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients

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