DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

Wu, Zeng-Hong; Tang, Yun; Zhou, Yan

doi:10.1177/1073274820988519

Cited by 11 publications

(8 citation statements)

References 26 publications

(28 reference statements)

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“…Previous evidence suggests that SLC5A8 alleviates disease progression by regulating the Wnt signaling pathway [16], which requires further exploration in patients with thyroid cancer. Moreover, DNA methylation biomarkers have been reported to accurately predict the prognosis of patients with malignant cancers such as colon [29], breast [30], and pancreatic cancers [31]. Our COX multivariate analysis showed that SLC5A8 methylation is one of the risk factors affecting the prognosis of thyroid cancer.…”

Section: Discussionmentioning

confidence: 66%

Role of hypermethylated SLC5A8 in follicular thyroid cancer diagnosis and prognosis prediction

Yang,

Liao,

Yang

et al. 2023

Preprint

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OBJECTIVE Thyroid cancer is one of the most frequently reported endocrine system malignancies. It is difficult to distinguish follicular thyroid cancer (FTC) from follicular thyroid adenoma (FTA) during pathological diagnosis in patients without lymph nodes or distant metastases. Therefore, we conducted a retrospective study to investigate the significance of SLC5A8 methylation and expression in the diagnosis and prognosis of FTC. METHODS We used 165 tissue samples, including FTC (n = 58), thyroid tumors of uncertain malignant potential (TT-UMP, n = 40), and FTA (n = 67), to explore the differences in SLC5A8 methylation and mRNA transcription in different pathological types. Survival analysis was conducted to evaluate the recurrence rate at a five-year follow-up. RESULTS The SLC5A8 methylation positive rate was higher in patients with thyroglobulin ≥ 40 µg/l and Chol ≥ 5.17 mmol/l, and it was higher in patients with FTC (n = 42, 72.4%) than those with FTA (n = 27, 40.3%) and TT-UMP (n = 23, 57.5%). The relative concentration of SLC5A8 mRNA was lower in patients with FTC than in those with FTA (p < 0.05). At five-year follow-ups, patients that were SLC5A8 methylation-positive had a higher recurrence rate than those who were methylation-negative. CONCLUSIONS Our current study indicates that SLC5A8 gene methylation occurs more commonly in patients with FTC than in those with FTA. The differences in SLC5A8 methylation and expression among FTA, FTC, and TT-UMP provide an important basis for further exploration of epigenetic changes in the occurrence, development, and prognosis of thyroid cancer. Our findings need to be further validated in larger populations with long-term follow-up in the future.

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Section: Discussionmentioning

confidence: 66%

Role of hypermethylated SLC5A8 in follicular thyroid cancer diagnosis and prognosis prediction

Yang,

Liao,

Yang

et al. 2023

Preprint

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“…Based on selected CpGs in the prior stage, eight studies performed additional feature selection, using methods including LASSO (38, 45, 55, 57, 69, 78, 80), stepwise selection (38, 39), and the Akaike information criterion (64, 77), to further refine the number of CpGs. In contrast, four studies (23, 33, 34)(47) directly trained classification models with clusters as labels using Bayesian networks or random forest, while 17 studies constructed prognostic models (37-39, 45, 46, 53-57, 64, 69, 70, 75, 77, 78, 80). Lastly, models developed in the previous stage were mostly internally validated by split-sample validation, albeit four studies validated the model externally in an independent dataset (33, 50, 57, 78).…”

Section: Resultsmentioning

confidence: 99%

“…Among the 39 studies that adopted clustering-based workflows (5, 18-23, 29, 32-35, 37-39, 43, 45-47, 50, 53-57, 61, 62, 64, 69, 70, 75, 77-80, 83, 84, 87, 88), most started with a preliminary screening of CpG sites (Figure 4.1). The most frequently used pre-selection approach was selecting CpG sites that were significantly associated with prognosis by first performing univariable Cox regression analysis, and then performing multivariable Cox analysis with adjustment for clinical variables.…”

Section: Resultsmentioning

confidence: 99%

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: a systematic review of epigenome-wide studies

Yuan

Edelmann

Fan

et al. 2022

Preprint

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Background: DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)- based analytic techniques might help overcome the challenges of analyzing high- dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis. Methods: We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 8 June 2022. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from relevant guidelines. Results: Seventy-six studies were included in this review. Three major types of ML- based workflows were identified: 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not 2 been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques. Conclusions: There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.

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“…DNA methylation plays an important role in the progression and prognosis of breast cancer patients. Also, it has an important regulatory role in gene expression in cancer cells (Wu et al, 2021; Zhang et al, 2022). Previously, the downregulation of SVIP in some cancers, such as head and neck tumors, was shown to be associated with hypermethylation of SVIP promoter CpG island (Llinàs-Arias et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Differential expression and function of SVIP in breast cancer cell lines andin silicoanalysis of its expression and prognostic potential in human breast cancer

Şahar

Ballar

2023

Preprint

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The heterogeneity of cancer strongly suggests the need to explore additional pathways to target. As cancer cells have increased proteotoxic stress, targeting proteotoxic stress-related pathways such as endoplasmic reticulum stress is attracting attention as new anticancer treatments. One of the downstream responses to endoplasmic reticulum stress is endoplasmic reticulum-associated degradation (ERAD), a major degradation pathway that facilitates proteasome-dependent degradation of unfolded or misfolded proteins. Recently, SVIP, an endogenous ERAD inhibitor, has been implicated in cancer progression, especially in glioma, prostate, and head and neck cancers. Here, the data of several RNA-seq and gene array studies were combined to evaluate the SVIP gene expression analysis on a variety of cancers, with a particular focus on breast cancer. SVIP was found to be overexpressed in primary breast tumors compared to normal tissues correlated well with its promoter methylation status and genetic alterations. Similarly, immunoblotting analysis showed that SVIP was expressed significantly higher in breast cancer cell lines compared to non-tumorigenic epithelial cell line. On the other hand, the expression of the key proteins of gp78-mediated ERAD did not exhibit such a pattern. Interestingly, silencing of SVIP enhanced the proliferation of p53 wt MCF7 cells but not p53 mutant T47D cells, however increased migration ability of both cell lines. Interestingly, SVIP expression is high in primary breast tumors but low in breast metastatic tumors. This correlates well with a lower probability of survival of breast cancer patients with lower SVIP expression compared to the patients with overexpressed SVIP. Overall, our data revealing the differential expression and function of SVIP on breast cancer cell lines together within silicodata analysis suggest that SVIP may have complex functions in breast cancer progression and has the potential to be a therapeutic target for breast cancer.

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DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

Cited by 11 publications

References 26 publications

Role of hypermethylated SLC5A8 in follicular thyroid cancer diagnosis and prognosis prediction

Role of hypermethylated SLC5A8 in follicular thyroid cancer diagnosis and prognosis prediction

Machine learning in the identification of prognostic DNA methylation biomarkers among patients with cancer: a systematic review of epigenome-wide studies

Differential expression and function of SVIP in breast cancer cell lines andin silicoanalysis of its expression and prognostic potential in human breast cancer

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