Identification of prognostic DNA methylation biomarkers in patients with gastrointestinal adenocarcinomas: A systematic review of epigenome-wide studies

Tumor DNA Methylation Profiles Enable Diagnosis, Prognosis Prediction, and Screening for Cervical Cancer

Background

DNA-methylation-based machine learning algorithms have demonstrated powerful diagnostic capabilities, and these tools are currently emerging in many fields of tumor diagnosis and patient prognosis prediction. This work aimed to identify novel DNA methylation diagnostic biomarkers for differentiating cervical cancer (CC) from normal tissues, as well as a prognostic prediction model to predict survival of CC patients.

Methods

The methylation profiles with the available clinical characteristics were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) program. We first screened out the differential methylation sites in CC and normal tissues and performed multiple statistical analyses to discover DNA methylation diagnostic markers that are used to distinguish CC and normal control. Then, we developed a methylation-based survival model to improve risk stratification.

Results

A diagnostic prediction panel consists of five CpG markers that could predict cervical cancer versus normal tissue with highly correct rate of 100%, and cg16428251, cg22341310, and cg23316360 which in diagnostic prediction panel all could yield high sensitivity and specificity for detection of CC and normal in six cohorts (area under curve [AUC] > 0.8), in addition to excellent performance in discriminating between CC and normal sample. The diagnostic marker panel also effectively predicted the CIN3 versus normal tissue with high accuracy in two datasets (AUC = 0.80, 0.789, respectively). Furthermore, a prognostic prediction model aggregated two CpG markers that effectively stratified the prognosis of high-risk and low-risk groups (training cohort: hazard ratio [HR] 4, 95% CI: 1.7–9.6, P = 0.0021; testing cohort: hazard ratio [HR] 1.9, 95% CI: 1.2–3.1, P = 0.0072).

Conclusion

The findings of our study showed that DNA methylation markers are of great value in the diagnosis and prognosis of CC.

When left does not seem right: epigenetic and bioelectric differences between left- and right-sided breast cancer

Background

During embryogenesis lateral symmetry is broken, giving rise to Left/Right (L/R) breast tissues with distinct identity. L/R-sided breast tumors exhibit consistently-biased incidence, gene expression, and DNA methylation. We postulate that a differential L/R tumor-microenvironment crosstalk generates different tumorigenesis mechanisms.

Methods

We performed in-silico analyses on breast tumors of public datasets, developed xenografted tumors, and conditioned MDA-MB-231 cells with L/R mammary extracts.

Results

We found L/R differential DNA methylation involved in embryogenic and neuron-like functions. Focusing on ion-channels, we discovered significant L/R epigenetic and bioelectric differences. Specifically, L-sided cells presented increased methylation of hyperpolarizing ion channel genes and increased Ca²⁺ concentration and depolarized membrane potential, compared to R-ones. Functional consequences were associated with increased proliferation in left tumors, assessed by KI67 expression and mitotic count.

Conclusions

Our findings reveal considerable L/R asymmetry in cancer processes, and suggest specific L/R epigenetic and bioelectric differences as future targets for cancer therapeutic approaches in the breast and many other paired organs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10020-022-00440-5.

ZNF677 downregulation by promoter hypermethylation as a driver event through gastric tumorigenesis

Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide, due to poor prognosis and treatment failure; demanding new diagnostic and therapeutic targets. Therefore, in the present study, the methylation and expression status of ZNF677, as a promising tumor suppressor, were investigated in GC. Gene Expression Omnibus (GEO) datasets were used to initially evaluate ZNF677 expression and methylation in GC samples. Confirmation was performed on fifty internal samples, including gastric tumors and adjacent normal specimens, using q-MSP and q-PCR methods. Further validations were done using The Cancer Genome Atlas (TCGA) data on human cancers. The obtained results in silico and experimentally illustrated that ZNF677 is significantly hypermethylated and downregulated through gastric tumorigenesis. ZNF677 methylation levels were also correlated with perineural invasion (p = 0.0382) in internal samples. Furthermore, Spearman's correlation analysis showed that ZNF677 methylation is negatively (r = -0.4614, p < 0.0001) correlated with its mRNA expression levels. ROC curve analysis also illustrated the high diagnostic value of ZNF677 methylation for early detection of GC (AUC = 0.8592). Gene set enrichment analysis further revealed that ZNF677 participates in the regulation of cellular processes such as cell proliferation in GC. Moreover, in addition to hypermethylation in other malignancies, including breast, lung, and colorectal cancers, ZNF677 was hypermethylated in precancerous gastric tissues with intestinal metaplasia, indicating its methylation as a driver event through tumorigenesis. Taken together, our results suggest ZNF677 as a potential tumor suppressor gene, which could be considered as a diagnostic and therapeutic target for GC.

Genome-wide methylation and expression profiling identify a novel epigenetic signature in gastrointestinal pan-adenocarcinomas

Aim: To identify methylation-driven genes and establish a novel epigenetic signature for gastrointestinal (GI) pan-adenocarcinomas. Materials & methods: Methylation and RNA-seq data for GI adenocarcinomas were downloaded from the Cancer Genome Atlas database. A methylation-driven gene signature was established by multivariate Cox regression analysis. We developed a prognostic nomogram using a combination of methylation-driven gene risk score and clinicopathological variables. A joint survival analysis based on gene expression and methylation was conducted to further investigate the prognostic role of methylation-driven genes. Results: An epigenetic signature was established based on five methylation-driven genes. We also established a prognostic nomogram based on methylation-driven gene risk score and clinicopathologic factors, with a favorable predictive ability. Joint survival analysis revealed that 28 methylation-driven genes could be independent prognostic factors for overall survival for GI adenocarcinomas. Conclusion: An epigenetic signature was established that effectively predicts the overall survival for GI adenocarcinomas across anatomic boundaries.