Genomic methylation variations predict the susceptibility of six chemotherapy related adverse effects and cancer development for Chinese colorectal cancer patients

Development of an epigenetic clock resistant to changes in immune cell composition

Epigenetic clocks are age predictors that use machine-learning models trained on DNA CpG methylation values to predict chronological or biological age. Increases in predicted epigenetic age relative to chronological age (epigenetic age acceleration) are connected to aging-associated pathologies, and changes in epigenetic age are linked to canonical aging hallmarks. However, epigenetic clocks rely on training data from bulk tissues whose cellular composition changes with age. Here, we found that human naive CD8+ T cells, which decrease in frequency during aging, exhibit an epigenetic age 15-20 years younger than effector memory CD8+ T cells from the same individual. Importantly, homogenous naive T cells isolated from individuals of different ages show a progressive increase in epigenetic age, indicating that current epigenetic clocks measure two independent variables, aging and immune cell composition. To isolate the age-associated cell intrinsic changes, we created an epigenetic clock, the IntrinClock, that did not change among 10 immune cell types tested. IntrinClock shows a robust predicted epigenetic age increase in a model of replicative senescence in vitro and age reversal during OSKM-mediated reprogramming.

Discovery of novel DNA methylation biomarker panels for the diagnosis and differentiation between common adenocarcinomas and their liver metastases

Differentiation between adenocarcinomas is sometimes challenging. The promising avenue for discovering new biomarkers lies in bioinformatics using DNA methylation analysis. Utilizing a 2853-sample identification dataset and a 782-sample independent verification dataset, we have identified diagnostic DNA methylation biomarkers that are hypermethylated in cancer and differentiate between breast invasive carcinoma, cholangiocarcinoma, colorectal cancer, hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma and stomach adenocarcinoma. The best panels for cancer type exhibit sensitivity of 77.8-95.9%, a specificity of 92.7-97.5% for tumors, a specificity of 91.5-97.7% for tumors and normal tissues and a diagnostic accuracy of 85.3-96.4%. We have shown that the results can be extended from the primary cancers to their liver metastases, as the best panels diagnose and differentiate between pancreatic adenocarcinoma liver metastases and breast invasive carcinoma liver metastases with a sensitivity and specificity of 83.3-100% and a diagnostic accuracy of 86.8-91.9%. Moreover, the panels could detect hypermethylation of selected regions in the cell-free DNA of patients with liver metastases. At the same time, these were unmethylated in the cell-free DNA of healthy donors, confirming their applicability for liquid biopsies.

Machine learning-based meta-analysis of colorectal cancer and inflammatory bowel disease

Colorectal cancer (CRC) is a major global health concern, resulting in numerous cancer-related deaths. CRC detection, treatment, and prevention can be improved by identifying genes and biomarkers. Despite extensive research, the underlying mechanisms of CRC remain elusive, and previously identified biomarkers have not yielded satisfactory insights. This shortfall may be attributed to the predominance of univariate analysis methods, which overlook potential combinations of variants and genes contributing to disease development. Here, we address this knowledge gap by presenting a novel multivariate machine-learning strategy to pinpoint genes associated with CRC. Additionally, we applied our analysis pipeline to Inflammatory Bowel Disease (IBD), as IBD patients face substantial CRC risk. The importance of the identified genes was substantiated by rigorous validation across numerous independent datasets. Several of the discovered genes have been previously linked to CRC, while others represent novel findings warranting further investigation. A Python implementation of our pipeline can be accessed publicly at https://github.com/AriaSar/CRCIBD-ML.

A Systematic Review of Clinical Validated and Potential miRNA Markers Related to the Efficacy of Fluoropyrimidine Drugs

Colorectal cancer (CRC) is becoming increasingly prevalent worldwide. Fluoropyrimidine drugs are the primary chemotherapy regimens in routine clinical practice of CRC. However, the survival rate of patients on fluoropyrimidine-based chemotherapy varies significantly among individuals. Biomarkers of fluoropyrimidine drugs'' efficacy are needed to implement personalized medicine. This review summarized fluoropyrimidine drug-related microRNA (miRNA) by affecting metabolic enzymes or showing the relevance of drug efficacy. We first outlined 42 miRNAs that may affect the metabolism of fluoropyrimidine drugs. Subsequently, we filtered another 41 miRNAs related to the efficacy of fluoropyrimidine drugs based on clinical trials. Bioinformatics analysis showed that most well-established miRNA biomarkers were significantly enriched in the cancer pathways instead of the fluoropyrimidine drug metabolism pathways. The result also suggests that the miRNAs screened from metastasis patients have a more critical role in cancer development than those from non-metastasis patients. There are five miRNAs shared between these two lists. The miR-21, miR-215, and miR-218 can suppress fluoropyrimidine drugs'' catabolism. The miR-326 and miR-328 can reduce the efflux of fluoropyrimidine drugs. These five miRNAs could jointly act by increasing intracellular levels of fluoropyrimidine drugs'' cytotoxic metabolites, leading to better chemotherapy responses. In conclusion, we demonstrated that the dynamic changes in the transcriptional regulation via miRNAs might play significant roles in the efficacy and toxicity of the fluoropyrimidine drug. The reported miRNA biomarkers would help evaluate the efficacy of fluoropyrimidine drug-based chemotherapy and improve the prognosis of colorectal cancer patients.

Milk and Egg Are Risk Factors for Adverse Effects of Capecitabine-Based Chemotherapy in Chinese Colorectal Cancer Patients

Background:

Chemotherapy-induced adverse effects (CIAEs) remain a challenging problem due to their high incidences and negative impacts on treatment in Chinese colorectal cancer (CRC) patients. We aimed to identify risk factors and predictive markers for CIAEs using food/nutrition data in CRC patients receiving post-operative capecitabine-based chemotherapy.

Methods:

Food/nutrition data from 130 Chinese CRC patients were analyzed. Univariate and multivariate analyses were used to identify CIAE-related food/nutrition factors. Prediction models were constructed based on the combination of these factors. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the discrimination ability of models.

Results:

A total of 20 food/nutrition factors associated with CIAEs were identified in the univariate analysis after adjustments for total energy and potential confounding factors. Based on multivariate analysis, we found that, among these factors, dessert, eggs, poultry, and milk were associated with several CIAEs. Most importantly, poultry was an overall protective factor; milk and egg were risk factors for hand-foot syndrome (HFS) and bone marrow suppression (BMS), respectively. Developed multivariate models in predicting grade 1 to 3 CIAEs and grade 2/3 CIAEs both had good discrimination (AUROC values from 0.671 to 0.778, 0.750 to 0.946 respectively), which had potential clinical application value in the early prediction of CIAEs, especially for more severe CIAEs.

Conclusions:

Our findings suggest that patients with high milk and egg intakes should be clinically instructed to control their corresponding dietary intake to reduce the likelihood of developing HFS and BMS during capecitabine-based chemotherapy, respectively.

Trial registration:

ClinicalTrials.gov Identifier: NCT03030508.

Spermine-Related DNA Hypermethylation and Elevated Expression of Genes for Collagen Formation are Susceptible Factors for Chemotherapy-Induced Hand-Foot Syndrome in Chinese Colorectal Cancer Patients

Hand-foot syndrome (HFS) is a common capecitabine-based chemotherapy-related adverse event (CRAE) in patients with colorectal cancer (CRC). It is of great significance to comprehensively identify susceptible factors for HFS, and further to elucidate the biomolecular mechanism of HFS susceptibility. We performed an untargeted multi-omics analysis integrating DNA methylation, transcriptome, and metabolome data of 63 Chinese CRC patients who had complete CRAE records during capecitabine-based chemotherapy. We found that the metabolome changes for each of matched plasma, urine, and normal colorectal tissue (CRT) in relation to HFS were characterized by chronic tissue damage, which was indicated by reduced nucleotide salvage, elevated spermine level, and increased production of endogenous cytotoxic metabolites. HFS-related transcriptome changes of CRT showed an overall suppressed inflammation profile but increased M2 macrophage polarization. HFS-related DNA methylation of CRT presented gene-specific hypermethylation on genes mainly for collagen formation. The hypermethylation was accumulated in the opensea and shore regions, which elicited a positive effect on gene expression. Additionally, we developed and validated models combining relevant biomarkers showing reasonably good discrimination performance with the area under the receiver operating characteristic curve values from 0.833 to 0.955. Our results demonstrated that the multi-omics variations associated with a profibrotic phenotype were closely related to HFS susceptibility. HFS-related biomolecular variations in CRT contributed more to the relevant biomolecular mechanism of HFS than in plasma and urine. Spermine-related DNA hypermethylation and elevated expression of genes for collagen formation were closely associated with HFS susceptibility. These findings provided new insights into the susceptible factors for chemotherapy-induced HFS, which can promote the implementation of individualized treatment against HFS.