The interaction between DNA methylation and tumor immune microenvironment: from the laboratory to clinical applications

Zhu, Daoqi; Zeng, Siying; Su, Chao; Li, Jingjun; Xuan, Yiwen; Lin, Yongkai; Xu, Enwu; Fan, Qin

doi:10.1186/s13148-024-01633-x

Cited by 4 publications

(1 citation statement)

References 69 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Tumor immune microenvironment (TIME) consists of a variety of immune cells and stromal components, creating a complex landscape that is important in tumor progression ( 130 ). The immune system plays an important role in regulating tumor cell death, harnessing these immune cells to battle the tumor cells could serve as potential targets for cancer therapy ( 131 , 132 ).…”

Section: Emerging Roles Of Lncrna In Ovarian Cancer Biologymentioning

confidence: 99%

The roles of long non-coding RNAs in ovarian cancer: from functions to therapeutic implications

Hu,

Yuan,

Yang

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

Long non-coding RNAs (lncRNAs) are multifunctional and participate in a variety of biological processes and gene regulatory networks. The deregulation of lncRNAs has been extensively implicated in diverse human diseases, especially in cancers. Overwhelming evidence demonstrates that lncRNAs are essential to the pathophysiological processes of ovarian cancer (OC), acting as regulators involved in metastasis, cell death, chemoresistance, and tumor immunity. In this review, we illustrate the expanded functions of lncRNAs in the initiation and progression of OC and elaborate on the signaling pathways in which they pitch. Additionally, the potential clinical applications of lncRNAs as biomarkers in the diagnosis and treatment of OC were emphasized, cementing the bridge of communication between clinical practice and basic research.

show abstract

Section: Emerging Roles Of Lncrna In Ovarian Cancer Biologymentioning

confidence: 99%

The roles of long non-coding RNAs in ovarian cancer: from functions to therapeutic implications

Hu,

Yuan,

Yang

et al. 2024

Front. Oncol.

View full text Add to dashboard Cite

show abstract

A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin

De Velasco,

Sakai,

Mitani

et al. 2024

Int J Clin Oncol

View full text Add to dashboard Cite

Background Genome DNA methylation profiling is a promising yet costly method for cancer classification, involving substantial data. We developed an ensemble learning model to identify cancer types using methylation profiles from a limited number of CpG sites. Methods Analyzing methylation data from 890 samples across 10 cancer types from the TCGA database, we utilized ANOVA and Gain Ratio to select the most significant CpG sites, then employed Gradient Boosting to reduce these to just 100 sites. Results This approach maintained high accuracy across multiple machine learning models, with classification accuracy rates between 87.7% and 93.5% for methods including Extreme Gradient Boosting, CatBoost, and Random Forest. This method effectively minimizes the number of features needed without losing performance, helping to classify primary organs and uncover subgroups within specific cancers like breast and lung. Conclusions Using a gradient boosting feature selector shows potential for streamlining methylation-based cancer classification.

show abstract

Inflammatory breast cancer microenvironment repertoire based on DNA methylation data deconvolution reveals actionable targets to enhance the treatment efficacy

Calanca,

Faldoni,

Souza

et al. 2024

J Transl Med

View full text Add to dashboard Cite

Background Although the clinical signs of inflammatory breast cancer (IBC) resemble acute inflammation, the role played by infiltrating immune and stromal cells in this aggressive disease is uncharted. The tumor microenvironment (TME) presents molecular alterations, such as epimutations, prior to morphological abnormalities. These changes affect the distribution and the intricate communication between the TME components related to cancer prognosis and therapy response. Herein, we explored the global DNA methylation profile of IBC and surrounding tissues to estimate the microenvironment cellular composition and identify epigenetically dysregulated markers. Methods We used the HiTIMED algorithm to deconvolve the bulk DNA methylation data of 24 IBC and six surrounding non-tumoral tissues (SNT) (GSE238092) and determine their cellular composition. The prognostic relevance of cell types infiltrating IBC and their relationship with clinicopathological variables were investigated. CD34 (endothelial cell marker) and CD68 (macrophage marker) immunofluorescence staining was evaluated in an independent set of 17 IBC and 16 non-IBC samples. Results We found lower infiltration of endothelial, stromal, memory B, dendritic, and natural killer cells in IBC than in SNT samples. Higher endothelial cell (EC) and stromal cell content were related to better overall survival. EC proportions positively correlated with memory B and memory CD8+ T infiltration in IBC. Immune and EC markers exhibited distinct DNA methylation profiles between IBC and SNT samples, revealing hypermethylated regions mapped to six genes (CD40, CD34, EMCN, HLA-G, PDPN, and TEK). We identified significantly higher CD34 and CD68 protein expression in IBC compared to non-IBC. Conclusions Our findings underscored cell subsets that distinguished patients with better survival and dysregulated markers potentially actionable through combinations of immunotherapy and epigenetic drugs.

show abstract

The interaction between DNA methylation and tumor immune microenvironment: from the laboratory to clinical applications

Cited by 4 publications

References 69 publications

The roles of long non-coding RNAs in ovarian cancer: from functions to therapeutic implications

The roles of long non-coding RNAs in ovarian cancer: from functions to therapeutic implications

A machine learning-based method for feature reduction of methylation data for the classification of cancer tissue origin

Inflammatory breast cancer microenvironment repertoire based on DNA methylation data deconvolution reveals actionable targets to enhance the treatment efficacy

Contact Info

Product

Resources

About