Colon cancer-specific diagnostic and prognostic biomarkers based on genome-wide abnormal DNA methylation

Novel DNA methylation-based epigenetic signatures in colorectal cancer from peripheral blood leukocytes

Colorectal cancer (CRC) is a multifactorial disease characterized by accumulation of multiple genetic and epigenetic alterations, transforming colonic epithelial cells into adenocarcinomas. Alteration of DNA methylation (DNAm) is a promising biomarker for predicting cancer risk and prognosis, but its role in CRC tumorigenesis is inconclusive. Notably, few DNAm studies have used pre-diagnostic peripheral blood (PB) DNA, causing difficulty in postulating the underlying biologic mechanism of CRC initiation. We conducted epigenome-wide association (EWA) scans in postmenopausal women from Women's Health Initiative (WHI) with their pre-diagnostic DNAm in PB leukocytes (PBLs) to prospectively evaluate CRC development. Our site-specific DNAm analyses across the genome adjusted for DNAm-age, leukocyte heterogeneities, as well as body mass index, diabetes, and insulin resistance. We validated 20 top EWA-CpGs in 2 independent CRC tissue datasets. Also, we detected differentially methylated regions (DMRs) associated with CRC, further mapped to transcriptomic profile, and finally conducted a Gene Set Enrichment Analysis. We detected multiple novel CpGs validated across WHI and tissue datasets. In particular, 2 CpGs (B4GALNT4cg10321339, SV2Bcg18144285) had the strongest effect on CRC risk. Results from our DMR scans contained MIR663cg06007966, which was also validated in EWA analyses. Also, we detected 1 methylome region in PEG10 of Chr7 shared across datasets. Our findings reflect both novel and well-established epigenomic and transcriptomic sites in CRC, warranting further functional validations. Our study contributes to better understanding of the complex interrelated mechanisms on the methylome underlying CRC tumorigenesis and suggests novel preventive DNAm-targets in PBLs for detecting at-risk individuals for CRC development.

Conserved methylation signatures associate with the tumor immune microenvironment and immunotherapy response

BACKGROUND

Aberrant DNA methylation is a major characteristic of cancer genomes. It remains unclear which biological processes determine epigenetic reprogramming and how these processes influence the variants in the cancer methylome, which can further impact cancer phenotypes.

METHODS

We performed pairwise permutations of 381,900 loci in 569 paired DNA methylation profiles of cancer tissue and matched normal tissue from The Cancer Genome Atlas (TCGA) and defined conserved differentially methylated positions (DMPs) based on the resulting null distribution. Then, we derived independent methylation signatures from 2,465 cancer-only methylation profiles from the TCGA and 241 cell line-based methylation profiles from the Genomics of Drug Sensitivity in Cancer (GDSC) cohort using nonnegative matrix factorization (NMF). We correlated DNA methylation signatures with various clinical and biological features, including age, survival, cancer stage, tumor immune microenvironment factors, and immunotherapy response. We inferred the determinant genes of these methylation signatures by integrating genomic and transcriptomic data and evaluated the impact of these signatures on cancer phenotypes in independent bulk and single-cell RNA/methylome cohorts.

RESULTS

We identified 7,364 differentially methylated positions (2,969 Hyper-DMPs and 4,395 Hypo-DMPs) in nine cancer types from the TCGA. We subsequently retrieved three highly conserved, independent methylation signatures (Hyper-MS1, Hypo-MS1, and Hypo-MS4) from cancer tissues and cell lines based on these Hyper and Hypo-DMPs. Our data suggested that Hypo-MS4 activity predicts poor survival and is associated with immunotherapy response and distant tumor metastasis, and Hypo-MS4 activity is related to TP53 mutation and FOXA1 binding specificity. In addition, we demonstrated a correlation between the activities of Hypo-MS4 in cancer cells and the fractions of regulatory CD4 + T cells with the expression levels of immunological genes in the tumor immune microenvironment.

CONCLUSIONS

Our findings demonstrated that the methylation signatures of distinct biological processes are associated with immune activity in the cancer microenvironment and predict immunotherapy response.

Expression mode and prognostic value of FXYD family members in colon cancer

The FXYD gene family comprises seven members that encode a class of small-membrane proteins characterized by an FXYD motif and interact with Na⁺/K⁺-ATPase. Until now, the expression patterns and prognostic roles of the FXYD family in colon cancer (CC) have not been systematically reported. Gene expression, methylation, clinicopathological features and the prognoses of CC patients were obtained from The Cancer Genome Atlas (TCGA) database. The expression feature and prognostic values of FXYD members were identified. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism underlying the function of the FXYD family in CC. Tumor Immune Estimation Resource (TIMER) and CIBERSORT analysis were used to assess the correlations between FXYD family members and tumor immune infiltrating cells (TIICs). FXYD family members were differentially expressed in CC except for FXYD2. FXYD2, FXYD3 and FXYD4 were revealed as independent prognostic factors for recurrence, while FXYD3 and FXYD7 were identified as prognostic factors for survival according to univariate and multivariate analyses with Cox regression. GSEA revealed that FXYD family members were involved in complicated biological functions underlying cancer progression. TIMER and CIBERSORT analyses showed significant associations between FXYD family genes and TIICs. The present study comprehensively revealed the expression mode and prognostic value of FXYD members in CC, providing insights for further study of the FXYD family as potential clinical biomarkers in CC.

Prognostic Score Model Based on Ten Differentially Methylated Genes for Predicting Clinical Outcomes in Patients with Adenocarcinoma of the Colon

Purpose

We aimed to screen novel genetic biomarkers for use in a prognostic score (PS) model for the accurate prediction of survival outcomes for patients with colon adenocarcinoma (COAD).

Methods

Gene expression and methylation data were downloaded from The Cancer Genome Atlas database, and the samples were randomly divided into training and validation sets for the screening of differentially methylated genes (DMGs) and differentially expressed genes (DEGs). Co-methylated genes were screened using weighted gene co-expression network analysis. Functional enrichment analysis was performed using the Database for Annotation, Visualization, and Integrated Discovery. Univariate and multivariate Cox regression analyses were performed to identify prognosis-related genes and clinical factors. Receiver operating characteristic curve analysis was carried out to evaluate the predictive performance of the PS model.

Results

In total, 1434 DEGs and 1038 DMGs were screened in the training set, among which 284 were found to be overlapping genes. For 127 of these overlapping genes, the methylation and expression levels were significantly negatively correlated. An optimal signature from 10 DMGs was identified to construct the PS model. Patients with a high PS seemed to have worse outcomes than those with a low PS. Moreover, cancer recurrence and the PS model status were independent prognostic factors.

Conclusion

This PS model based on an optimal 10-gene signature would help in the stratification of patients with COAD and improve the assessment of their clinical outcomes.