scholarly journals Computational Detection of Breast Cancer Invasiveness with DNA Methylation Biomarkers

Cells ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 326 ◽  
Author(s):  
Chunyu Wang ◽  
Ning Zhao ◽  
Linlin Yuan ◽  
Xiaoyan Liu
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Clinical Decision Making ◽  
Metastatic Cancer ◽  
The Cancer Genome Atlas ◽  
Cancer Data ◽  
Potential Biomarker ◽  
Cancer Aggressiveness ◽  
Cancer Invasiveness ◽  
Breast Cancer Invasiveness

Breast cancer is the most common female malignancy. It has high mortality, primarily due to metastasis and recurrence. Patients with invasive and noninvasive breast cancer require different treatments, so there is an urgent need for predictive tools to guide clinical decision making and avoid overtreatment of noninvasive breast cancer and undertreatment of invasive cases. Here, we divided the sample set based on the genome-wide methylation distance to make full use of metastatic cancer data. Specifically, we implemented two differential methylation analysis methods to identify specific CpG sites. After effective dimensionality reduction, we constructed a methylation-based classifier using the Random Forest algorithm to categorize the primary breast cancer. We took advantage of breast cancer (BRCA) HM450 DNA methylation data and accompanying clinical data from The Cancer Genome Atlas (TCGA) database to validate the performance of the classifier. Overall, this study demonstrates DNA methylation as a potential biomarker to predict breast tumor invasiveness and as a possible parameter that could be included in the studies aiming to predict breast cancer aggressiveness. However, more comparative studies are needed to assess its usability in the clinic. Towards this, we developed a website based on these algorithms to facilitate its use in studies and predictions of breast cancer invasiveness.

scholarly journals DNA Methylation Based Molecular Subtypes Predict Prognosis in Breast Cancer Patients

2021 ◽  
Vol 28 ◽  
pp. 107327482098851
Author(s):  
Zeng-Hong Wu ◽  
Yun Tang ◽  
Yan Zhou
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Consensus Clustering ◽  
Breast Cancer Patients ◽  
Cancer Subtypes ◽  
Novel Biomarkers

Background: Epigenetic changes are tightly linked to tumorigenesis development and malignant transformation’ However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells. Methods: In this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA). Results: Seven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups. Conclusions: The model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.

The glycobiological landscape of breast cancer: insights into galectins and O-GlcNAc homeostasis-related genes

2021 ◽  
Author(s):  
Rada Tazhitdinova ◽  
Alexander V Timoshenko
Keyword(s):  
Breast Cancer ◽  
Cancer Biology ◽  
Breast Cancer Subtype ◽  
The Cancer Genome Atlas ◽  
Correlation Relationship ◽  
Cancer Data ◽  
Normal Tissues ◽  
Genes Encoding ◽  
Genetic Landscape ◽  
Cancer Tissues

Abstract Purpose This study aimed to assess the functional associations between genes of the glycobiological landscape encoding galectins and O-GlcNAc cycle enzymes in the context of breast cancer biology and clinical applications. Methods An in silico analysis of the breast cancer data from The Cancer Genome Atlas was conducted comparing expression, pairwise correlations, and prognostic value for 17 genes encoding galectins, O-GlcNAc cycle enzymes, and cell stemness-related transcription factors. Results Multiple general and breast cancer subtype-specific differences in galectin/O-GlcNAc genetic landscape markers were observed and classified. Specifically, LGALS12 was found to be significantly downregulated in breast cancer tissues across all subtypes while LGALS2 and GFPT1 showed potential as prognostic markers. Remarkably, there was an overall loss of both correlation strength and correlation relationship between expression of galectin/O-GlcNAc landscape genes in the breast cancer samples versus normal tissues. Six gene pairs (GFPT1/LGALS1, GFPT1/LGALS3, GFPT1/LGALS12, GFPT1/KLF4, OGT/LGALS12, and OGT/KLF4) were found to be potential diagnostic markers for breast cancer. Conclusions These findings indicate that the glycobiological landscape of breast cancer underwent significant remodeling, which might be associated with switching galectin gene regulation within a framework of O-GlcNAc homeostasis.

scholarly journals Epigenome-wide DNA methylation and risk of breast cancer: a systematic review

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Kaoutar Ennour-Idrissi ◽  
Dzevka Dragic ◽  
Francine Durocher ◽  
Caroline Diorio
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Risk Of Bias ◽  
Global Dna Methylation ◽  
Dna Hypomethylation ◽  
Epigenetic Age ◽  
Potential Biomarker

Abstract Background DNA methylation is a potential biomarker for early detection of breast cancer. However, robust evidence of a prospective relationship between DNA methylation patterns and breast cancer risk is still lacking. The objective of this study is to provide a systematic analysis of the findings of epigenome-wide DNA methylation studies on breast cancer risk, in light of their methodological strengths and weaknesses. Methods We searched major databases (MEDLINE, EMBASE, Web of Science, CENTRAL) from inception up to 30th June 2019, for observational or intervention studies investigating the association between epigenome-wide DNA methylation (using the HM450k or EPIC BeadChip), measured in any type of human sample, and breast cancer risk. A pre-established protocol was drawn up following the Cochrane Reviews rigorous methodology. Study selection, data abstraction, and risk of bias assessment were performed by at least two investigators. A qualitative synthesis and systematic comparison of the strengths and weaknesses of studies was performed. Results Overall, 20 studies using the HM450k BeadChip were included, 17 of which had measured blood-derived DNA methylation. There was a consistent trend toward an association of global blood-derived DNA hypomethylation and higher epigenetic age with higher risk of breast cancer. The strength of associations was modest for global hypomethylation and relatively weak for most of epigenetic age algorithms. Differences in length of follow-up periods may have influenced the ability to detect associations, as studies reporting follow-up periods shorter than 10 years were more likely to observe an association with global DNA methylation. Probe-wise differential methylation analyses identified between one and 806 differentially methylated CpGs positions in 10 studies. None of the identified differentially methylated sites overlapped between studies. Three studies used breast tissue DNA and suffered major methodological issues that precludes any conclusion. Overall risk of bias was critical mainly because of incomplete control of confounding. Important issues relative to data preprocessing could have limited the consistency of results. Conclusions Global DNA methylation may be a short-term predictor of breast cancer risk. Further studies with rigorous methodology are needed to determine spatial distribution of DNA hypomethylation and identify differentially methylated sites associated with risk of breast cancer. Prospero registration number CRD42020147244

scholarly journals TGFβ and EGF signaling orchestrates the AP-1- and p63 transcriptional regulation of breast cancer invasiveness

Oncogene ◽  
2020 ◽  
Vol 39 (22) ◽  
pp. 4436-4449 ◽  
Author(s):  
Anders Sundqvist ◽  
Eleftheria Vasilaki ◽  
Oleksandr Voytyuk ◽  
Yu Bai ◽  
Masato Morikawa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Transcriptional Regulation ◽  
Cancer Invasiveness ◽  
Breast Cancer Invasiveness ◽  
Egf Signaling

Diffusion-weighted imaging using ADC mapping as an imaging biomarker for breast cancer invasiveness.

2013 ◽  
Vol 31 (15_suppl) ◽  
pp. 11093-11093
Author(s):  
Hubert Bickel ◽  
Wolfgang Bogner ◽  
Peter Christian Dubsky ◽  
Rupert Bartsch ◽  
Margaretha Rudas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Diffusion Weighted Imaging ◽  
Malignant Tumors ◽  
Imaging Biomarker ◽  
Benign Tumors ◽  
Non Invasive ◽  
Diffusion Weighted ◽  
Cancer Invasiveness ◽  
Adc Values ◽  
Breast Cancer Invasiveness

11093 Background: Recently, functional imaging techniques such as diffusion weighted imaging (DWI) have been added to routine MR and have shown great potential for improving breast cancer diagnosis. DWI depicts cellular diffusivity on a molecular level and can be quantified using the apparent diffusion coefficient (ADC). In malignant tumors diffusivity is restricted, leading to lower ADC values than benign tumors. The aim of this study was to proof, that DWI can be used to differentiate benign from malignant tumors and to elucidate if ADC can serve as an imaging biomarker for breast cancer invasiveness. Methods: In this IRB-approved study 250 patients with 267 suspicious breast lesions (BI-RADS IV-V) were included. All patients underwent routine MR at 3T. A DWI-sequence was added to a standard imaging protocol, increasing measurement time by 2:30 min. The lesions were identified in routine MR and DWI sequences and ADC values of the lesions were calculated. Histopathology was used as the standard of reference for all lesions. Appropriate statistical tests were used to compare the ADC values of benign and malignant tumors (cut-off value 1.25×10-3mm²/s), of invasive and non-invasive disease and between different invasive tumor subtypes. Results: There were 91 benign (mean ADC 1.58×10-3mm²/s) and 176 malignant (.94×10-3mm²/s) lesions, sensitivity and specificity were 94.3% (PPV 95.4%, CI 0.91-0.98) and 91.2% (NPV 89.2%, CI 0.81-0.94). 155 lesions were invasive cancers (median ADC .90×10-3mm²/s), while 21 were non-invasive ductal carcinoma in situ (1.22×10-3mm²/s). The invasive cancers were 130 invasive ductal (median ADC .91×10-3mm²/s) and 25 invasive lobular cancers (.83×10-3mm²/s). ADC was significantly different between benign and malignant lesions (p<.001) and between invasive and non-invasive cancers (p<.001), while no significant difference could be found between the invasive cancer subtypes (p=.163). Conclusions: Diffusion-weighted imaging reliably allows differentiation of benign and malignant breast tumors. The data suggest that ADC can be used as a non-invasive imaging biomarker for breast cancer invasiveness and may be of importance to treatment planning and outcome in breast cancer patients.

scholarly journals Comprehensive analysis based on DNA methylation and RNA-seq reveals hypermethylation of the up-regulated WT1 gene with potential mechanisms in PAM50 subtypes of breast cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11377
Author(s):  
Chongyang Ren ◽  
Xiaojiang Tang ◽  
Haitao Lan
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Mechanisms ◽  
Molecular Subtypes ◽  
Wilms Tumor ◽  
Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Body Region ◽  
Rna Seq

Background Breast cancer (BC), one of the most widespread cancers worldwide, caused the deaths of more than 600,000 women in 2018, accounting for about 15% of all cancer-associated deaths in women that year. In this study, we aimed to discover potential prognostic biomarkers and explore their molecular mechanisms in different BC subtypes using DNA methylation and RNA-seq. Methods We downloaded the DNA methylation datasets and the RNA expression profiles of primary tissues of the four BC molecular subtypes (luminal A, luminal B, basal-like, and HER2-enriched), as well as the survival information from The Cancer Genome Atlas (TCGA). The highly expressed and hypermethylated genes across all the four subtypes were screened. We examined the methylation sites and the downstream co-expressed genes of the selected genes and validated their prognostic value using a different dataset (GSE20685). For selected transcription factors, the downstream genes were predicted based on the Gene Transcription Regulation Database (GTRD). The tumor microenvironment was also evaluated based on the TCGA dataset. Results We found that Wilms tumor gene 1 (WT1), a transcription factor, was highly expressed and hypermethylated in all the four BC subtypes. All the WT1 methylation sites exhibited hypermethylation. The methylation levels of the TSS200 and 1stExon regions were negatively correlated with WT1 expression in two BC subtypes, while that of the gene body region was positively associated with WT1 expression in three BC subtypes. Patients with low WT1 expression had better overall survival (OS). Five genes including COL11A1, GFAP, FGF5, CD300LG, and IGFL2 were predicted as the downstream genes of WT1. Those five genes were dysregulated in the four BC subtypes. Patients with a favorable 6-gene signature (low expression of WT1 and its five predicted downstream genes) exhibited better OS than that with an unfavorable 6-gene signature. We also found a correlation between WT1 and tamoxifen using STITCH. Higher infiltration rates of CD8 T cells, plasma cells, and monocytes were found in the lower quartile WT1 group and the favorable 6-gene signature group. In conclusion, we demonstrated that WT1 is hypermethylated and up-regulated in the four BC molecular subtypes and a 6-gene signature may predict BC prognosis.

scholarly journals Identification of prostate cancer specific methylation biomarkers from a multi-cancer analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yiyi Pu ◽  
Chao Li ◽  
Haining Yuan ◽  
Xiaoju Wang
Keyword(s):  
Prostate Cancer ◽  
Dna Methylation ◽  
In Silico ◽  
Prostate Cancer Screening ◽  
Methylation Status ◽  
The Cancer Genome Atlas ◽  
Translation Rate ◽  
Normal Prostate ◽  
Cancer Data ◽  
Significant Difference

Abstract Background Detecting prostate cancer at a non-aggressive stage is the main goal of prostate cancer screening. DNA methylation has been widely used as biomarkers for cancer diagnosis and prognosis, however, with low clinical translation rate. By taking advantage of multi-cancer data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we aimed to identify prostate cancer specific biomarkers which can separate between non-aggressive and aggressive prostate cancer based on DNA methylation patterns. Results We performed a comparison analysis of DNA methylation status between normal prostate tissues and prostate adenocarcinoma (PRAD) samples at different Gleason stages. The candidate biomarkers were selected by excluding the biomarkers existing in multiple cancers (pan-cancer) and requiring significant difference between PRAD and other urinary samples. By least absolute shrinkage and selection operator (LASSO) selection, 8 biomarkers (cg04633600, cg05219445, cg05796128, cg10834205, cg16736826, cg23523811, cg23881697, cg24755931) were identified and in-silico validated by model constructions. First, all 8 biomarkers could separate PRAD at different stages (Gleason 6 vs. Gleason 3 + 4: AUC = 0.63; Gleason 6 vs. Gleason 4 + 3 and 8–10: AUC = 0.87). Second, 5 biomarkers (cg04633600, cg05796128, cg23523811, cg23881697, cg24755931) effectively detected PRAD from normal prostate tissues (AUC ranged from 0.88 to 0.92). Last, 6 biomarkers (cg04633600, cg05219445, cg05796128, cg23523811, cg23881697, cg24755931) completely distinguished PRAD with other urinary samples (AUC = 1). Conclusions Our study identified and in-silico validated a panel of prostate cancer specific DNA methylation biomarkers with diagnosis value.

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