Gene Environment Interactions and Predictors of Colorectal Cancer in Family-Based, Multi-Ethnic Groups

Validating Healthy Eating Index, Glycemic Index, and Glycemic Load with Modern Diets for E-Health Era

Predictors of healthy eating parameters, including the Healthy Eating Index (HEI), Glycemic Index (GI), and Glycemic Load (GL), were examined using various modern diets (n = 131) in preparation for personalized nutrition in the e-health era. Using Nutrition Data Systems for Research computerized software and artificial intelligence machine-learning-based predictive validation analyses, we included domains of HEI, caloric source, and various diets as the potentially modifiable factors. HEI predictors included whole fruits and whole grains, and empty calories. Carbohydrates were the common predictor for both GI and GL, with total fruits and Mexican diets being additional predictors for GI. The median amount of carbohydrates to reach an acceptable GL < 20 was predicted as 33.95 g per meal (median: 3.59 meals daily) with a regression coefficient of 37.33 across all daily diets. Diets with greater carbohydrates and more meals needed to reach acceptable GL < 20 included smoothies, convenient diets, and liquids. Mexican diets were the common predictor for GI and carbohydrates per meal to reach acceptable GL < 20; with smoothies (12.04), high-school (5.75), fast-food (4.48), Korean (4.30), Chinese (3.93), and liquid diets (3.71) presenting a higher median number of meals. These findings could be used to manage diets for various populations in the precision-based e-health era.

CpG Site-Based Signature Predicts Survival of Colorectal Cancer

BACKGROUND

A critical unmet medical need in clinical management of colorectal cancer (CRC) pivots around lack of noninvasive and or minimally invasive techniques for early diagnosis and prognostic prediction of clinical outcomes. Because DNA methylation can capture the regulatory landscape of tumors and can be measured in body fluids, it provides unparalleled opportunities for the discovery of early diagnostic and prognostics markers predictive of clinical outcomes. Here we investigated use of DNA methylation for the discovery of potential clinically actionable diagnostic and prognostic markers for predicting survival in CRC.

METHODS

We analyzed DNA methylation patterns between tumor and control samples to discover signatures of CpG sites and genes associated with CRC and predictive of survival. We conducted functional analysis to identify molecular networks and signaling pathways driving clinical outcomes.

RESULTS

We discovered a signature of aberrantly methylated genes associated with CRC and a signature of thirteen (13) CpG sites predictive of survival. We discovered molecular networks and signaling pathways enriched for CpG sites likely to drive clinical outcomes.

CONCLUSIONS

The investigation revealed that CpG sites can predict survival in CRC and that DNA methylation can capture the regulatory state of tumors through aberrantly methylated molecular networks and signaling pathways.

Validating Accuracy of an Internet-Based Application against USDA Computerized Nutrition Data System for Research on Essential Nutrients among Social-Ethnic Diets for the E-Health Era

Internet-based applications (apps) are rapidly developing in the e-Health era to assess the dietary intake of essential macro-and micro-nutrients for precision nutrition. We, therefore, validated the accuracy of an internet-based app against the Nutrition Data System for Research (NDSR), assessing these essential nutrients among various social-ethnic diet types. The agreement between the two measures using intraclass correlation coefficients was good (0.85) for total calories, but moderate for caloric ranges outside of <1000 (0.75) and >2000 (0.57); and good (>0.75) for most macro- (average: 0.85) and micro-nutrients (average: 0.83) except cobalamin (0.73) and calcium (0.51). The app underestimated nutrients that are associated with protein and fat (protein: −5.82%, fat: −12.78%, vitamin B12: −13.59%, methionine: −8.76%, zinc: −12.49%), while overestimated nutrients that are associated with carbohydrate (fiber: 6.7%, B9: 9.06%). Using artificial intelligence analytics, we confirmed the factors that could contribute to the differences between the two measures for various essential nutrients, and they included caloric ranges; the differences between the two measures for carbohydrates, protein, and fat; and diet types. For total calories, as an example, the source factors that contributed to the differences between the two measures included caloric range (<1000 versus others), fat, and protein; for cobalamin: protein, American, and Japanese diets; and for folate: caloric range (<1000 versus others), carbohydrate, and Italian diet. In the e-Health era, the internet-based app has the capacity to enhance precision nutrition. By identifying and integrating the effects of potential contributing factors in the algorithm of output readings, the accuracy of new app measures could be improved.

Obesity-Associated Differentially Methylated Regions in Colon Cancer

Obesity with adiposity is a common disorder in modern days, influenced by environmental factors such as eating and lifestyle habits and affecting the epigenetics of adipose-based gene regulations and metabolic pathways in colorectal cancer (CRC). We compared epigenetic changes of differentially methylated regions (DMR) of genes in colon tissues of 225 colon cancer cases (154 non-obese and 71 obese) and 15 healthy non-obese controls by accessing The Cancer Genome Atlas (TCGA) data. We applied machine-learning-based analytics including generalized regression (GR) as a confirmatory validation model to identify the factors that could contribute to DMRs impacting colon cancer to enhance prediction accuracy. We found that age was a significant predictor in obese cancer patients, both alone (p = 0.003) and interacting with hypomethylated DMRs of ZBTB46, a tumor suppressor gene (p = 0.008). DMRs of three additional genes: HIST1H3I (p = 0.001), an oncogene with a hypomethylated DMR in the promoter region; SRGAP2C (p = 0.006), a tumor suppressor gene with a hypermethylated DMR in the promoter region; and NFATC4 (p = 0.006), an adipocyte differentiating oncogene with a hypermethylated DMR in an intron region, are also significant predictors of cancer in obese patients, independent of age. The genes affected by these DMR could be potential novel biomarkers of colon cancer in obese patients for cancer prevention and progression.

Validating Accuracy of a Mobile Application against Food Frequency Questionnaire on Key Nutrients with Modern Diets for mHealth Era

In preparation for personalized nutrition, an accurate assessment of dietary intakes on key essential nutrients using smartphones can help promote health and reduce health risks across vulnerable populations. We, therefore, validated the accuracy of a mobile application (app) against Food Frequency Questionnaire (FFQ) using artificial intelligence (AI) machine-learning-based analytics, assessing key macro- and micro-nutrients across various modern diets. We first used Bland and Altman analysis to identify and visualize the differences between the two measures. We then applied AI-based analytics to enhance prediction accuracy, including generalized regression to identify factors that contributed to the differences between the two measures. The mobile app underestimated most macro- and micro-nutrients compared to FFQ (ranges: -5% for total calories, -19% for cobalamin, -33% for vitamin E). The average correlations between the two measures were 0.87 for macro-nutrients and 0.84 for micro-nutrients. Factors that contributed to the differences between the two measures using total calories as an example, included caloric range (1000-2000 versus others), carbohydrate, and protein; for cobalamin, included caloric range, protein, and Chinese diet. Future studies are needed to validate actual intakes and reporting of various diets, and to examine the accuracy of mobile App. Thus, a mobile app can be used to support personalized nutrition in the mHealth era, considering adjustments with sources that could contribute to the inaccurate estimates of nutrients.

MTHFR C677T and A1298C Polymorphisms in Breast Cancer, Gliomas and Gastric Cancer: A Review

Folate (vitamin B9) is found in some water-soluble foods or as a synthetic form of folic acid and is involved in many essential biochemical processes. Dietary folate is converted into tetrahydrofolate, a vital methyl donor for most methylation reactions, including DNA methylation. 5,10-methylene tetrahydrofolate reductase (MTHFR) is a critical enzyme in the folate metabolism pathway that converts 5,10-methylenetetrahydrofolate into 5-methyltetrahydrofolate, which produces a methyl donor for the remethylation of homocysteine to methionine. MTHFR polymorphisms result in reduced enzyme activity and altered levels of DNA methylation and synthesis. MTHFR polymorphisms have been linked to increased risks of several pathologies, including cancer. Breast cancer, gliomas and gastric cancer are highly heterogeneous and aggressive diseases associated with high mortality rates. The impact of MTHFR polymorphisms on these tumors remains controversial in the literature. This review discusses the relationship between the MTHFR C677T and A1298C polymorphisms and the increased risk of breast cancer, gliomas, and gastric cancer. Additionally, we highlight the relevance of ethnic and dietary aspects of population-based studies and histological stratification of highly heterogeneous tumors. Finally, this review discusses these aspects as potential factors responsible for the controversial literature concerning MTHFR polymorphisms.

Precision health: A nursing perspective

Precision health refers to personalized healthcare based on a person’s unique genetic, genomic, or omic composition within the context of lifestyle, social, economic, cultural and environmental influences to help individuals achieve well-being and optimal health. Precision health utilizes big data sets that combine omics (i.e. genomic sequence, protein, metabolite, and microbiome information) with clinical information and health outcomes to optimize disease diagnosis, treatment and prevention specific to each patient. Successful implementation of precision health requires interprofessional collaboration, community outreach efforts, and coordination of care, a mission that nurses are well-positioned to lead. Despite the surge of interest and attention to precision health, most nurses are not well-versed in precision health or its implications for the nursing profession. Based on a critical analysis of literature and expert opinions, this paper provides an overview of precision health and the importance of engaging the nursing profession for its implementation. Other topics reviewed in this paper include big data and omics, information science, integration of family health history in precision health, and nursing omics research in symptom science. The paper concludes with recommendations for nurse leaders in research, education, clinical practice, nursing administration and policy settings for which to develop strategic plans to implement precision health.

Identification of novel pathogenic MSH2 mutation and new DNA repair genes variants: investigation of a Tunisian Lynch syndrome family with discordant twins

Background

Lynch syndrome (LS) is a highly penetrant inherited cancer predisposition syndrome, characterized by autosomal dominant inheritance and germline mutations in DNA mismatch repair genes. Despite several genetic variations that have been identified in various populations, the penetrance is highly variable and the reasons for this have not been fully elucidated. This study investigates whether, besides pathogenic mutations, environment and low penetrance genetic risk factors may result in phenotype modification in a Tunisian LS family.

Patients and methods

A Tunisian family with strong colorectal cancer (CRC) history that fulfill the Amsterdam I criteria for the diagnosis of Lynch syndrome was proposed for oncogenetic counseling. The index case was a man, diagnosed at the age of 33 years with CRC. He has a monozygotic twin diagnosed at the age of 35 years with crohn disease. Forty-seven years-old was the onset age of his paternal uncle withCRC. An immunohistochemical (IHC) labeling for the four proteins (MLH1, MSH2, MSH6 and PMS2) of the MisMatchRepair (MMR) system was performed for the index case. A targeted sequencing of MSH2, MLH1 and a panel of 85 DNA repair genes was performed for the index case and for his unaffected father.

Results

The IHC results showed a loss of MSH2 but not MLH1, MSH6 and PMS2 proteins expression. Genomic DNA screening, by targeted DNA repair genes sequencing, revealed an MSH2 pathogenic mutation (c.1552C>T; p.Q518X), confirmed by Sanger sequencing. This mutation was suspected to be a causal mutation associated to the loss of MSH2 expression and it was found in first and second degree relatives. The index case has smoking and alcohol consumption habits. Moreover, he harbors extensive genetic variations in other DNA-repair genes not shared with his unaffected father.

Conclusion

In our investigated Tunisian family, we confirmed the LS by IHC, molecular and in silico investigations. We identified a novel pathogenic mutation described for the first time in Tunisia. These results come enriching the previously reported pathogenic mutations in LS families. Our study brings new arguments to the interpretation of MMR expression pattern and highlights new risk modifiers genes eventually implicated in CRC. Twins discordance reported in this work underscore that disease penetrance could be influenced by both genetic background and environmental factors.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1961-9) contains supplementary material, which is available to authorized users.

Gene-Metabolite Interaction in the One Carbon Metabolism Pathway: Predictors of Colorectal Cancer in Multi-Ethnic Families

For personalized healthcare, the purpose of this study was to examine the key genes and metabolites in the one-carbon metabolism (OCM) pathway and their interactions as predictors of colorectal cancer (CRC) in multi-ethnic families. In this proof-of-concept study, we included a total of 30 participants, 15 CRC cases and 15 matched family/friends representing major ethnic groups in southern California. Analytics based on supervised machine learning were applied, with the target variable being specified as cancer, including the ensemble method and generalized regression (GR) prediction. Elastic Net with Akaike's Information Criterion with correction (AICc) and Leave-One-Out cross validation GR methods were used to validate the results for enhanced optimality, prediction, and reproducibility. The results revealed that despite some family members sharing genetic heritage, the CRC group had greater combined gene polymorphism-mutations than the family controls (p < 0.1) for five genes including MTHFR C677T, MTHFR A1298C, MTR A2756G, MTRR A66G, and DHFR 19bp. Blood metabolites including homocysteine (7 µmol/L), methyl-folate (40 nmol/L) with total gene mutations (≥4); age (51 years) and vegetable intake (2 cups), and interactions of gene mutations and methylmalonic acid (MMA) (400 nmol/L) were significant predictors (all p < 0.0001) using the AICc. The results were validated by a 3% misclassification rate, AICc of 26, and >99% area under the receiver operating characteristic curve. These results point to the important roles of blood metabolites as potential markers in the prevention of CRC. Future intervention studies can be designed to target the ways to mitigate the enzyme-metabolite deficiencies in the OCM pathway to prevent cancer.

Lung cancer susceptibility from GSTM1 deletion and air pollution with smoking status: a meta-prediction of worldwide populations

Glutathione S transferase mu 1 (GSTM1) gene has been associated with lung cancer (LC) risk, for GSTM1 enzyme playing a vital role in detoxification pathway and protective against toxic insults. The major objective of this study was to investigate GSTM1 deletion pattern and its association with LC in the world’s population by using meta-prediction techniques. The secondary objective was to examine the effects of air pollution, smoking status, and other factors for gene-environment interactions with GSTM1 deletion and LC risk. We completed a comprehensive search to yield a total of 170 studies (40,296 cases and 48,346 controls) published from 1999 to 2017 for meta-analyses. The results revealed that GSTM1 deletion type was associated with increased risk of LC, while GSTM1 present type provided protective effect for all populations combined worldwide. Subgroup analysis on the rank order of risks from highest to lowest, among racial–ethnic groups, were Chinese, South East Asian, other North Asian, European, and finally American. Additional predictive analyses presented that air pollution played a significant role with increased risks of GSTM1 deletion and LC susceptibility, and the risks increased for smokers with higher levels of air pollution. Based on the findings of meta-predictive analysis, increased air pollution levels and smoking status presented additive effects to the LC risk susceptibilities and GSTM1 gene polymorphisms, for gene-environment interactions. Future studies are needed to examine gene-environment interactions for GSTM1 interacting with environmental factors and dietary interventions to mitigate the toxic effects, for LC prevention.

Personalized Nutrition-Genes, Diet, and Related Interactive Parameters as Predictors of Cancer in Multiethnic Colorectal Cancer Families

To personalize nutrition, the purpose of this study was to examine five key genes in the folate metabolism pathway, and dietary parameters and related interactive parameters as predictors of colorectal cancer (CRC) by measuring the healthy eating index (HEI) in multiethnic families. The five genes included methylenetetrahydrofolate reductase (MTHFR) 677 and 1298, methionine synthase (MTR) 2756, methionine synthase reductase (MTRR 66), and dihydrofolate reductase (DHFR) 19bp, and they were used to compute a total gene mutation score. We included 53 families, 53 CRC patients and 53 paired family friend members of diverse population groups in Southern California. We measured multidimensional data using the ensemble bootstrap forest method to identify variables of importance within domains of genetic, demographic, and dietary parameters to achieve dimension reduction. We then constructed predictive generalized regression (GR) modeling with a supervised machine learning validation procedure with the target variable (cancer status) being specified to validate the results to allow enhanced prediction and reproducibility. The results showed that the CRC group had increased total gene mutation scores compared to the family members (p < 0.05). Using the Akaike’s information criterion and Leave-One-Out cross validation GR methods, the HEI was interactive with thiamine (vitamin B1), which is a new finding for the literature. The natural food sources for thiamine include whole grains, legumes, and some meats and fish which HEI scoring included as part of healthy portions (versus limiting portions on salt, saturated fat and empty calories). Additional predictors included age, as well as gender and the interaction of MTHFR 677 with overweight status (measured by body mass index) in predicting CRC, with the cancer group having more men and overweight cases. The HEI score was significant when split at the median score of 77 into greater or less scores, confirmed through the machine-learning recursive tree method and predictive modeling, although an HEI score of greater than 80 is the US national standard set value for a good diet. The HEI and healthy eating are modifiable factors for healthy living in relation to dietary parameters and cancer prevention, and they can be used for personalized nutrition in the precision-based healthcare era.

The relationship between body mass index changes during chemotherapy and prognosis of patients with advanced colorectal cancer: A retrospective cohort study

We investigated the relationships between body mass index change (ΔBMI) and prognoses and clinical effects of patients with advanced colorectal cancer (CRC).From January 2008 to December 2012, 224patients with stage IV CRC were diagnosed in our hospital, and their clinical and pathological data were collected for this retrospective study. These patients were divided into lowΔ BMI group (ΔBMI ≤-0.45 kg/m) and high ΔBMI (ΔBMI >-0.45 kg/m) group.After 2 cycles of chemotherapy, there were no significant differences between prediagnosis BMI, ΔBMI, and clinical effects (P = .196; P = .59).There was also no significant difference in median progression-free survival of the high ΔBMI and low ΔBMI groups (P = .530). The overall survival (OS) time of the high ΔBMI group was significantly longer than that of the low ΔBMI group (P = .002). Family history (P = .041), eastern cooperative oncology group performance status (ECOG PS) score (P = .001), ΔBMI (P = .023), and carcinoembryonic antigen, (P = 0.02) were independent predictive factors of OS rates in patients with CRC. The relative risk was 0.72-fold for patients with CRC patients with high ΔBMI levels, relative to those with lower ΔBMI levels.Our results demonstrate that ΔBMI decreases predict poor prognoses for patients with advanced CRC, and elevated ΔBMI was a predictive factor for high survival rate. Thus, ΔBMI appears to be an independent predictive factor of CRC survival rates.

Predictors of the Healthy Eating Index and Glycemic Index in Multi-Ethnic Colorectal Cancer Families

For personalized nutrition in preparation for precision healthcare, we examined the predictors of healthy eating, using the healthy eating index (HEI) and glycemic index (GI), in family-based multi-ethnic colorectal cancer (CRC) families. A total of 106 participants, 53 CRC cases and 53 family members from multi-ethnic families participated in the study. Machine learning validation procedures, including the ensemble method and generalized regression prediction, Elastic Net with Akaike’s Information Criterion with correction and Leave-One-Out cross validation methods, were applied to validate the results for enhanced prediction and reproducibility. Models were compared based on HEI scales for the scores of 77 versus 80 as the status of healthy eating, predicted from individual dietary parameters and health outcomes. Gender and CRC status were interactive as additional predictors of HEI based on the HEI score of 77. Predictors of HEI 80 as the criterion score of a good diet included five significant dietary parameters (with intake amount): whole fruit (1 cup), milk or milk alternative such as soy drinks (6 oz), whole grain (1 oz), saturated fat (15 g), and oil and nuts (1 oz). Compared to the GI models, HEI models presented more accurate and fitted models. Milk or a milk alternative such as soy drink (6 oz) is the common significant parameter across HEI and GI predictive models. These results point to the importance of healthy eating, with the appropriate amount of healthy foods, as modifiable factors for cancer prevention.