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ObjectiveMutations in cell-free circulating DNA (cfDNA) have been studied for tracking disease relapse in colorectal cancer (CRC). This approach requires personalised assay design due to the lack of universally mutated genes. In contrast, early methylation alterations are restricted to defined genomic loci allowing comprehensive assay design for population studies. Our objective was to identify cancer-specific methylated biomarkers which could be measured longitudinally in cfDNA (liquid biopsy) to monitor therapeutic outcome in patients with metastatic CRC (mCRC).DesignGenome-wide methylation microarrays of CRC cell lines (n=149) identified five cancer-specific methylated loci (EYA4, GRIA4, ITGA4, MAP3K14-AS1, MSC). Digital PCR assays were employed to measure methylation of these genes in tumour tissue DNA (n=82) and cfDNA from patients with mCRC (n=182). Plasma longitudinal assessment was performed in a patient subset treated with chemotherapy or targeted therapy.ResultsMethylation in at least one marker was detected in all tumour tissue samples and in 156 mCRC patient cfDNA samples (85.7%). Plasma marker prevalence was 71.4% for EYA4, 68.5% for GRIA4, 69.7% for ITGA4, 69.1% for MAP3K14-AS1% and 65.1% for MSC. Dynamics of methylation markers was not affected by treatment type and correlated with objective tumour response and progression-free survival.ConclusionThis five-gene methylation panel can be used to circumvent the absence of patient-specific mutations for monitoring tumour burden dynamics in liquid biopsy under different therapeutic regimens. This method might be proposed for assessing pharmacodynamics in clinical trials or when conventional imaging has limitations.

Breast cancer (BC) is the leading cause of cancer death in women. Adipokines, and other inflammation molecules linked to adiposity, are suspected to be involved in breast carcinogenesis, however prospective findings are inconclusive. In a prospective nested case-control study within the EPIC-Varese cohort, we used conditional logistic regression to estimate rate ratios (RRs) for BC, with 95% confidence intervals (CI), in relation to plasma levels of C-reactive protein (CRP), tumor necrosis factor-alpha (TNF-α), interleukin-6, leptin, and adiponectin, controlling for BC risk factors. After a median 14.9 years, 351 BC cases were identified and matched to 351 controls. No marker was significantly associated with BC risk overall. Significant interactions between menopausal status and CRP, leptin, and adiponectin were found. Among postmenopausal women, high CRP was significantly associated with increased BC risk, and high adiponectin with significantly reduced risk. Among premenopausal women, high TNF-α was associated with significantly increased risk, and high leptin with reduced risk; interleukin-6 was associated with increased risk only in a continuous model. These findings constitute further evidence that inflammation plays a role in breast cancer. Interventions to lower CRP, TNF-α, and interleukin-6 and increase adiponectin levels may contribute to preventing BC.

Background Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

Coronary artery diseases (CADs) continue to be the leading global contributors to multi-morbidity and mortality. Given the significant burden of CADs, there is a critical need to identify novel and effective biomarkers for risk assessment. This study sought to evaluate the potential of serum extracellular vesicle-derived small non-coding RNAs (sncRNAs) as predictive biomarkers for CAD risk. Using next-generation sequencing approach, the levels of extracellular vesicles (EVs)-associated sncRNAs were analysed in serum samples from 91 pre-clinical CAD cases and their matched healthy controls, sourced from the prospective EPICOR cohort. We evaluated the predictive ability of sncRNAs alone and in combination with polygenic risk score (PRS) PGS000329. We identified 44 differentially expressed microRNAs (miRNAs) and PIWI-interacting RNAs (piRNAs) (FDR < 0.05), which were then narrowed down to ten significant signals (|log2FC|>0.6) for technical validation. RT-qPCR analysis confirmed the trend of expression for two miRNAs (miR-194-5p and miR-451a) and six piRNAs (piR-20266, piR-23533, piR-27282, piR-28212, piR-1043, piR-619). The ROC curve from a Random Forest model showed a higher discrimination ability of piR-619 and piR-23,533 (AUC = 0.72) compared to the use of traditional risk factors alone (AUC = 0.68). To enhance CAD risk assessment, we integrated genetic data by stratifying the cohort into two groups based on the 80th percentile of the PGS000329. We observed an odds ratio (OR) of 2.8 (95% CI: 1.3–6.4, p  = 0.01) using PGS000329 alone. When the model was adjusted to include two piRNAs and smoking status, the OR increased to 3.26 (95% CI: 1.2–9.5, p  = 0.02). Even though this study is limited by the absence of an independent replication cohort, these findings suggest that the two piRNAs pattern could contribute to predict the risk of CAD and may provide valuable insights into the underlying pathogenesis of the disease, in particular integrating individual CAD-PRS.

The genomic variation of the Italian peninsula populations is currently under characterised: the only Italian whole-genome reference is represented by the Tuscans from the 1000 Genome Project. To address this issue, we sequenced a total of 947 Italian samples from three different geographical areas. First, we defined a new Italian Genome Reference Panel (IGRP1.0) for imputation, which improved imputation accuracy, especially for rare variants, and we tested it by GWAS analysis on red blood traits. Furthermore, we extended the catalogue of genetic variation investigating the level of population structure, the pattern of natural selection, the distribution of deleterious variants and occurrence of human knockouts (HKOs). Overall the results demonstrate a high level of genomic differentiation between cohorts, different signatures of natural selection and a distinctive distribution of deleterious variants and HKOs, confirming the necessity of distinct genome references for the Italian population.

Background MicroRNA-146a-5p (miR-146a-5p) is a key regulator of inflammatory processes. Expression of miR-146a-5p is altered in target organs of diabetic complications and deficiency of miR-146a-5p has been implicated in their pathogenesis. We investigated if serum miR-146a-5p levels were independently associated with micro/macrovascular complications of type 1 diabetes (DM1). Methods A nested case–control study from the EURODIAB PCS of 447 DM1 patients was performed. Cases (n = 294) had one or more complications of diabetes, whereas controls (n = 153) did not have any complication. Total RNA was isolated from all subjects and miR-146a-5p levels measured by qPCR. Both the endogenous controls U6 snRNA and the spike (Cel-miR-39) were used to normalize the results. Logistic regression analysis was carried out to investigate the association of miR-146a-5p with diabetes complications. Results MiR-146a-5p levels were significantly lower in cases [1.15 (0.32–3.34)] compared to controls [1.74 (0.44–6.74) P = 0.039]. Logistic regression analysis showed that levels of miR-146a-5p in the upper quartile were inversely associated with reduced odds ratio (OR) of all complications (OR 0.34 [95% CI 0.14–0.76]) and particularly with cardiovascular diseases (CVD) (OR 0.31 [95% CI 0.11–0.84]) and diabetic retinopathy (OR 0.40 [95% CI 0.16–0.99]), independently of age, sex, diabetes duration, A1c, hypertension, AER, eGFR, NT-proBNP, and TNF-α. Conclusions In this large cohort of DM1 patients, we reported an inverse and independent association of miR-146a-5p with diabetes chronic complications and in particular with CVD and retinopathy, suggesting that miR-146a-5p may be a novel candidate biomarker of DM1 complications.

Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology, which provides opportunities to advance personalized healthcare. However, the common occurrence of incomplete views makes extrapolation tasks difficult, and there is a lack of tailored methods for this critical issue. Here, we introduce LEOPARD, an innovative approach specifically designed to complete missing views in multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers the temporal knowledge to the omics-specific content, thereby completing missing views. The effectiveness of LEOPARD is validated on four real-world omics datasets constructed with data from the MGH COVID study and the KORA cohort, spanning periods from 3 days to 14 years. Compared to conventional imputation methods, such as missForest, PMM, GLMM, and cGAN, LEOPARD yields the most robust results across the benchmark datasets. LEOPARD-imputed data also achieve the highest agreement with observed data in our analyses for age-associated metabolites detection, estimated glomerular filtration rate-associated proteins identification, and chronic kidney disease prediction. Our work takes the first step toward a generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal dynamics and providing valuable insights into personalized healthcare. Missing modality challenges longitudinal omics studies. Here, the authors introduce LEOPARD, a pioneering neural network that uses style transfer to re-entangle omics data, enabling robust imputation and unlocking fresh insights into predictive healthcare and biological temporal dynamics.

Exposure to cigarette smoking affects the epigenome and could increase the risk of developing diseases such as cancer and cardiovascular disorders. Changes in DNA methylation associated with smoking may help to identify molecular pathways that contribute to disease etiology. Previous studies are not completely concordant in the identification of differentially methylated regions in the DNA of smokers. We performed an epigenome-wide DNA methylation study in a group of monozygotic (MZ) twins discordant for smoking habits to determine the effect of smoking on DNA methylation. As MZ twins are considered genetically identical, this model allowed us to identify smoking-related DNA methylation changes independent from genetic components. We investigated the whole blood genome-wide DNA methylation profiles in 20 MZ twin pairs discordant for smoking habits by using the Illumina HumanMethylation450 BeadChip. We identified 22 CpG sites that were differentially methylated between smoker and non-smoker MZ twins by intra-pair analysis. We confirmed eight loci already described by other groups, located in AHRR, F2RL3, MYOG1 genes, at 2q37.1 and 6p21.33 regions, and also identified several new loci. Moreover, pathway analysis showed an enrichment of genes involved in GTPase regulatory activity. Our study confirmed the evidence of smoking-related DNA methylation changes, emphasizing that well-designed MZ twin models can aid the discovery of novel DNA methylation signals, even in a limited sample population.

Mammographic breast cancer screening is effective in reducing breast cancer mortality. Nevertheless, several limitations are known. Therefore, developing an alternative or complementary non-invasive tool capable of increasing the accuracy of the screening process is highly desirable. The objective of this study was to identify circulating microRNA (miRs) ratios associated with BC in women attending mammography screening. A nested case–control study was conducted within the ANDROMEDA cohort (women of age 46–67 attending BC screening). Pre-diagnostic plasma samples, information on life-styles and common BC risk factors were collected. Small-RNA sequencing was carried out on plasma samples from 65 cases and 66 controls. miR ratios associated with BC were selected by two-sample Wilcoxon test and lasso logistic regression. Subsequent assessment by RT-qPCR of the miRs contained in the selected miR ratios was carried out as a platform validation. To identify the most promising biomarkers, penalised logistic regression was further applied to candidate miR ratios alone, or in combination with non-molecular factors. Small-RNA sequencing yielded 20 candidate miR ratios associated with BC, which were further assessed by RT-qPCR. In the resulting model, penalised logistic regression selected seven miR ratios (miR-199a-3p_let-7a-5p, miR-26b-5p_miR-142-5p, let-7b-5p_miR-19b-3p, miR-101-3p_miR-19b-3p, miR-93-5p_miR-19b-3p, let-7a-5p_miR-22-3p and miR-21-5p_miR-23a-3p), together with body mass index (BMI), menopausal status (MS), the interaction term BMI * MS, life-style score and breast density. The ROC AUC of the model was 0.79 with a sensitivity and specificity of 71.9% and 76.6%, respectively. We identified biomarkers potentially useful for BC screening measured through a widespread and low-cost technique. This is the first study reporting circulating miRs for BC detection in a screening setting. Validation in a wider sample is warranted. Trial registration: The Andromeda prospective cohort study protocol was retrospectively registered on 27-11-2015 (NCT02618538).

BackgroundObesity is an established risk factor for several common chronic diseases such as breast and colorectal cancer, metabolic and cardiovascular diseases; however, the biological basis for these relationships is not fully understood. To explore the association of obesity with these conditions, we investigated peripheral blood leucocyte (PBL) DNA methylation markers for adiposity and their contribution to risk of incident breast and colorectal cancer and myocardial infarction.MethodsDNA methylation profiles (Illumina Infinium® HumanMethylation450 BeadChip) from 1941 individuals from four population-based European cohorts were analysed in relation to body mass index, waist circumference, waist-hip and waist-height ratio within a meta-analytical framework. In a subset of these individuals, data on genome-wide gene expression level, biomarkers of glucose and lipid metabolism were also available. Validation of methylation markers associated with all adiposity measures was performed in 358 individuals. Finally, we investigated the association of obesity-related methylation marks with breast, colorectal cancer and myocardial infarction within relevant subsets of the discovery population.ResultsWe identified 40 CpG loci with methylation levels associated with at least one adiposity measure. Of these, one CpG locus (cg06500161) in ABCG1 was associated with all four adiposity measures (P = 9.07×10−8 to 3.27×10−18) and lower transcriptional activity of the full-length isoform of ABCG1 (P = 6.00×10−7), higher triglyceride levels (P = 5.37×10−9) and higher triglycerides-to-HDL cholesterol ratio (P = 1.03×10−10). Of the 40 informative and obesity-related CpG loci, two (in IL2RB and FGF18) were significantly associated with colorectal cancer (inversely, P < 1.6×10−3) and one intergenic locus on chromosome 1 was inversely associated with myocardial infarction (P < 1.25×10−3), independently of obesity and established risk factors.ConclusionOur results suggest that epigenetic changes, in particular altered DNA methylation patterns, may be an intermediate biomarker at the intersection of obesity and obesity-related diseases, and could offer clues as to underlying biological mechanisms.