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BackgroundTo reduce lung cancer burden in the US, a better understanding of biological mechanisms in early disease development could provide new opportunities for risk stratification.MethodsIn a nested case-control study, we measured blood leukocyte DNA methylation levels in pre-diagnostic samples collected from 430 men and women in the 1989 CLUE II cohort. Median time from blood drawn to diagnosis was 14 years for all participants. We compared DNA methylation levels by case/control status to identify novel genomic regions, both single CpG sites and differentially methylated regions (DMRs), while controlling for known DNA methylation changes associated with smoking using a previously described pack-years based smoking methylation score. Stratification analyses were conducted by time from blood draw to diagnosis, histology, and smoking status.ResultsWe identified sixteen single CpG sites and forty DMRs significantly associated with lung cancer risk (q < 0.05). The identified genomic regions were associated with genes including H19, HOXA4, RUNX3, BRICD5, PLXNB2, and RP13. For the single CpG sites, the strongest association was noted for cg09736286 in the DIABLO gene (OR [for 1 SD] = 2.99, 95% CI: 1.95-4.59, P-value = 4.81 × 10−7). For the DMRs, we found that CpG sites in the HOXA4 region were hypermethylated in cases compared to controls.ConclusionThe single CpG sites and DMRs that we identified represented significant measurable differences in lung cancer risk, providing new insights into the biological processes of early lung cancer development and potential biomarkers for lung cancer risk stratification.

Structured AbstractBackgroundPancreatic cancer is projected to become the second leading cause of cancer-related death by 2030 in the United States. DNA methylation (DNAm) age may reflect age-related variations in the biological changes and abnormalities related to cancer development.MethodWe conducted a pooled analysis using prediagnostic blood samples of pancreatic cancer cases and matched controls selected from the Nurses’ Health Study (NHS), the Physician’s Health Study (PHS), and the Health Professionals Follow-up Study (HPFS). We used three DNAm aging clocks (Hannum, Horvath, and PhenoAge) to estimate subjects’ DNAm age, epigenetic age acceleration (AA) and intrinsic epigenetic age acceleration (IEAA) metrics. We performed conditional logistic regression and multivariable Cox proportional hazard regression to examine associations between six AA and IEAA metrics and risk of pancreatic cancer and survival, respectively.ResultsA total of 393 incidence pancreatic cancer cases and 431 matched controls from the NHS, PHS, and HPFS cohorts were included in this analysis. The medians of all three epigenetic AA and three IEAA metrics were consistently above zero (indicating accelerated age) among cases, while they were below zero (indicating decelerated age) among the matched controls. Comparing participants in the highest quartile of age acceleration metrics, the pancreatic cancer risks were significantly increased by 67% to 83% for Hannum and PhenoAge AA or IEAA metrics with minimal of 7- to 9-years accelerated ages. Except for Hovarth AA and IEAA metrics, there were significant dose-response trends, such that higher age accelerations were associated with higher pancreatic cancer risk, but the relationships were nonlinear. Stratified analyses showed heterogeneous associations, varying by participants’ characteristics and by epigenetic AA or IEAA metrics. As time to diagnosis increased, the ORs of pancreatic cancer for the Hannum AA and Horvath AA or IEAA metrics trended upwards, while the ORs for the PhenoAge AA or IEAA and Hannum IEAA metrics trended downward. Overall, we observed no significant association between pancreatic cancer survival and any of the prediagnostic epigenetic AA or IEAA metrics.ConclusionOur results indicate DNAm age acceleration is associated with an increased risk of pancreatic cancer in a nonlinear, dose-response manner. Epigenetic IEAA metrics may be a useful addition to current methods for pancreatic cancer risk prediction.

Background: Oral microbiota is believed to play important roles in systemic diseases, including cancer. Methods: We collected oral samples (tongue, buccal, supragingival, and saliva) and pancreatic tissue or intestinal samples from 52 subjects, and characterized 16S rRNA genes using high-throughput DNA sequencing. Results: Bray–Curtis plot showed clear separations between bacterial communities in the oral cavity and those in intestinal and pancreatic tissue samples. PERMANOVA tests indicated that bacterial communities from buccal samples were similar to supragingival and saliva samples, and pancreatic duct samples were similar to pancreatic tumor samples, but all other samples were significantly different from each other. A total of 73 unique Amplicon Sequence Variants (ASVs) were shared between oral and pancreatic or intestinal samples. Only four ASVs showed significant concordance, and two specific bacterial species (Gemella morbillorum and Fusobacterium nucleatum subsp. vincentii) showed consistent presence or absence patterns between oral and intestinal or pancreatic samples, after adjusting for within-subject correlation and disease status. Lastly, microbial co-abundance analyses showed distinct strain-level cluster patterns among microbiome members in buccal, saliva, duodenum, jejunum, and pancreatic tumor samples. Conclusions: Our findings indicate that oral, intestinal, and pancreatic bacterial microbiomes overlap but exhibit distinct co-abundance patterns in patients with pancreatic cancer and other gastrointestinal diseases.