DNA Methylation Biomarkers Research Articles

Altered DNA methylome profiles of blood leukocytes in Chinese patients with mild cognitive impairment and Alzheimer's disease.

Objective: DNA methylation plays a potential role in the pathogenesis of Alzheimer's disease (AD). However, little is known about the global changes of blood leukocyte DNA methylome profiles from Chinese patients with mild cognitive impairment (MCI) and with AD, or the specific DNA methylation-based signatures associated with MCI and AD. In this study, we sought to dissect the characteristics of blood DNA methylome profiles in MCI- and AD-affected Chinese patients with the aim of identifying novel DNA methylation biomarkers for AD. Methods: In this study, we profiled the DNA methylome of peripheral blood leukocytes from 20 MCI- and 20 AD-affected Chinese patients and 20 cognitively healthy controls (CHCs) with the Infinium Methylation EPIC BeadChip array. Results: We identified significant alterations of the methylome profiles in MCI and AD blood leukocytes. A total of 2,582 and 20,829 CpG sites were significantly and differentially methylated in AD and MCI compared with CHCs (adjusted p < 0.05), respectively. Furthermore, 441 differentially methylated positions (DMPs), aligning to 213 unique genes, were overlapped by the three comparative groups of AD versus CHCs, MCI versus CHCs, and AD versus MCI, of which 6 and 5 DMPs were continuously hypermethylated and hypomethylated in MCI and AD relative to CHCs (adjusted p < 0.05), respectively, such as FLNC cg20186636 and AFAP1 cg06758191. The DMPs with an area under the curve >0.900, such as cg18771300, showed high potency for predicting MCI and AD. In addition, gene ontology and pathway enrichment results showed that these overlapping genes were mainly involved in neurotransmitter transport, GABAergic synaptic transmission, signal release from synapse, neurotransmitter secretion, and the regulation of neurotransmitter levels. Furthermore, tissue expression enrichment analysis revealed a subset of potentially cerebral cortex-enriched genes associated with MCI and AD, including SYT7, SYN3, and KCNT1. Conclusion: This study revealed a number of potential biomarkers for MCI and AD, also highlighted the presence of epigenetically dysregulated gene networks that may engage in the underlying pathological events resulting in the onset of cognitive impairment and AD progression. Collectively, this study provides prospective cues for developing therapeutic strategies to improve cognitive impairment and AD course.

Whole-genome bisulfite sequencing identified the key role of the Src family tyrosine kinases and related genes in systemic lupus erythematosus.

As a multisystemic autoimmune illness, the basic mechanisms behind the pathophysiology of systemic lupus erythematosus (SLE) remain poorly understood. We aimed to investigate the possible significance of SLE's DNA methylation and gainfurtherinsightinto potential SLE-related biomarkers and therapeutic targets. We used whole genome bisulfite sequencing (WGBS) method to analyze DNA methylation in 4 SLE patients and 4 healthy people. 702 differentially methylated regions (DMRs) were identified, and 480 DMR-associated genes were annotated. We found the majority of the DMR-associated elements were enriched in repeat and gene bodies. The top 10 hub genes identified were LCK, FYB, PTK2B, LYN, CTNNB1, MAPK1, GNAQ, PRKCA, ABL1, and CD247. Compared to the control group, LCK and PTK2B had considerably decreased levels of mRNA expression in the SLE group. Receiver operating characteristic (ROC) curve suggested that LCK and PTK2B may be potential candidate biomarkers to predict SLE. Our study improved comprehension of the DNA methylation patterns of SLE and identified potential biomarkers and therapeutic targets for SLE.

Epigenetic Changes Associated with Osteosarcoma: A Comprehensive Review.

Osteosarcoma is the most common malignant primary bone tumor in children and adolescents. While clinical outcomes have improved, the 5-year survival rate is only around 60% if discovered early and can require debilitating treatments, such as amputations. A better understanding of the disease could lead to better clinical outcomes for patients with osteosarcoma. One promising avenue of osteosarcoma research is in the field of epigenetics. This research investigates changes in genetic expression that occur above the genome rather than in the genetic code itself. The epigenetics of osteosarcoma is an active area of research that is still not fully understood. In a narrative review, we examine recent advances in the epigenetics of osteosarcoma by reporting biomarkers of DNA methylation, histone modifications, and non-coding RNA associated with disease progression. We also show how cancer tumor epigenetic profiles are being used to predict and improve patient outcomes. The papers in this review cover a large range of epigenetic target genes and pathways that modulate many aspects of osteosarcoma, including but not limited to metastases and chemotherapy resistance. Ultimately, this review will shed light on the recent advances in the epigenetics of osteosarcoma and illustrate the clinical benefits of this field of research.

Whole-genome DNA methylation and DNA methylation-based biomarkers in lung squamous cell carcinoma

Exploringearly detection methods through comprehensive evaluation of DNA methylation for lung squamous cell carcinoma (LUSC) patients is of great significance. By using different machine learning algorithms for feature selection and model construction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, five methylation biomarkers in LUSC (along with mapped genes) were identified including cg14823851 (TBX4), cg02772121 (TRIM15), cg10424681 (C6orf201), cg12910906 (ARHGEF4), and cg20181079 (OR4D11), achieving extremely high sensitivity and specificity in distinguishing LUSC from normal samples in independent cohorts. Pyrosequencing assay verified DNA methylation levels, meanwhile qRT-PCR and immunohistochemistry results presented their accordant methylation-related gene expression statuses in paired LUSC and normal lung tissues. The five methylation-based biomarkers proposed in this study have great potential for the diagnosis of LUSC and could guide studies in methylation-regulated tumor development and progression.

Early detection of lung cancer using a panel of circulating cell-free DNA methylation biomarkers.

e20614 Background: Lung cancer remains the leading cause of cancer mortality worldwide. Early detection of lung cancer helps improve treatment and survival. Numerous aberrant DNA methylations have been reported in early-stage lung cancer. Here, we sought to identify novel DNA methylation biomarkers that could potentially be used for noninvasive early diagnosis of lung cancers. Methods: This prospective-specimen collection and retrospective-blinded-evaluation trial enrolled a total of 317 participants (198 tissues and 119 plasmas) comprising healthy controls, patients with lung cancer and benign disease from The First Affiliated Hospital of Soochow University between January 2020 and December 2021. Tissue and plasma samples were subjected to targeted bisulfite sequencing with a lung cancer specific panel targeting 9,307 differential methylation regions (DMRs). DMRs associated with lung cancer were identified by comparing the methylation profiles of tissue samples from patients with lung cancer and benign disease. Markers were selected with minimum redundancy and maximum relevance (mRMR) algorithm. A prediction model for lung cancer diagnosis was built through logistic regression algorithm and validated independently in tissue samples. Furthermore, the performance of this developed model was evaluated in a set of plasma cell-free DNA (cfDNA) samples. Results: We identified 7 DMRs corresponding to 7 differentially methylated genes (DMRs, including HOXB4, HOXA7, HOXD8, ITGA4, ZNF808, PTGER4, and B3GNTL1), highly associated with lung cancer by comparing the methylation profiles of lung cancer and benign nodule tissues. Based on the 7-DMR biomarker panel, we developed a new diagnostic model in tissue samples, termed “7-DMR model”, to distinguish lung cancers from benign diseases, achieving AUCs of 0.966 (95%CI: 0.933-1.000)/0.961 (0.924-1.000), sensitivities of 0.887 (0.824-0.951)/0.922 (0.863-0.980), specificities of 0.938 (0.889-0.986)/1.000 (1.000-1.000), and accuracies of 0.896 (0.835-0.957)/0.938 (0.886-0.991) in the discovery cohort (n = 96) and the independent validation cohort (n = 81), respectively. Furthermore, the 7-DMR model was applied to noninvasive discrimination of lung cancers and non-lung cancers including benign lung diseases and healthy controls in an independent validation cohort of plasma samples (n = 106), yielding an AUC of 0.935 (0.862-1.000), sensitivity of 0.808 (0.733-0.883), specificity of 0.975 (0.945-1.000), and accuracy of 0.934 (0.887-0.981). Conclusions: The 7 novel DMRs could be promising methylation biomarkers that merits further development as a noninvasive test for early detection of lung cancer.

A machine learning–based multidimensional model integrating clinical, radiomics, and cell-free DNA methylation biomarkers for the classification of pulmonary nodules.

3070 Background: Patients with pulmonary nodules undergoing excessive invasive procedures is a pressing clinical problem. We sought to develop a noninvasive, machine learning-based multidimensional tool combining clinical, radiomic, and cell-free DNA (cfDNA) methylation biomarkers for improving accuracy of pulmonary nodules classification. Methods: This prospectively collected and retrospective blinded evaluation trial enrolled a total of 1,276 subjects at 24 hospitals in China. All patients with a 5-30 mm pulmonary nodule at high risk of lung cancer had undergone surgical resection with definitive pathological diagnosis. Clinical information, preoperative peripheral blood, and chest CT scans were collected. The regions of interest (ROIs) containing target nodule on the CT images were automatically segmented by a deep-learning based model. 2,153 radiomics features were extracted from ROIs using PyRadiomics. Based on clinical and radiomics features, four classification models were constructed using LightGBM, Lasso, Random Forest, and Logistic Regression algorithms. Subsequently, the predicted probabilities of the above four models were averaged to obtain a final score of the combined clinical and radiomic biomarkers model (CRBM) in a training set (n=797). Then we integrated CRBM model with our previously established cfDNA methylation model (PulmoSeek; DOI: 10.1172/JCI145973) to create a new combined model using logistic regression (n=201), PulmoSeek Plus V2.0, and verified it independently (n =278). The ROC curves were compared to evaluate the diagnostic performance among the CRBM, PulmoSeek, and PulmoSeek Plus V2.0 model, pathologic diagnosis as the gold standard. Results: The CRBM model achieved AUCs of 0.81(95%CI 0.73-0.90) and 0.80 (0.74-0.86) in the two validation sets (n1=201, n2=278), respectively. In the training set (n=201) and validation set (n=278), the PulmoSeek Plus V2.0 obtained AUCs of 0.93 (0.90-0.97) and 0.91 (0.88-0.95), and accuracies of 0.89 (0.84-0.93) and 0.84 (0.79-0.88), respectively. In the combined set (n=479), when compared with CRBM and PulmoSeek, PulmoSeek Plus V2.0 yielded improved AUCs of 11% and 6%, and accuracies of 6% and 3%, respectively. PulmoSeek Plus V2.0 model for rule-out at the fixed specificity of 50%, had an overall sensitivity of 0.98 (0.96-0.99), PPV of 0.86 (0.82-0.89), and NPV of 0.998 (0.988-1.000, at 5% prevalence). It maintains good diagnostic performance in early-stage lung cancer (0-I, n=328) and 5-10 mm nodules (n=92), with sensitivities of 0.98 (0.96-0.99) and 0.98 (0.92-0.99), respectively. Conclusions: PulmoSeek Plus V2.0, as a novel machine learning-based multidimensional model, improves the accuracy of pulmonary nodules classification, and potentially reduces the unnecessary invasive procedures among individuals with benign nodules. Clinical trial information: NCT03181490 , NCT03651986 .

Methylated DNA biomarkers and incident cancer in the American Cancer Society (ACS) Cancer Prevention Study-3 (CPS-3) cohort.

3004 Background: Multi-cancer early detection (MCED) tests are being developed to complement current single-cancer screening, and are based on next-generation sequencing of cell-free DNA in blood plasma with subsequent analysis of genomic features by machine-learning algorithms. To evaluate the detectability of cancer signals in individuals without known cancer at the time of blood draw, we assessed pre-diagnostic signals using a previously validated locked, methylation-based assay in plasma samples from the ACS CPS-3 cohort. Methods: CPS-3 is a prospective cohort study of cancer incidence and mortality. Participants (N=294,000) donated a blood sample (36 mL) at enrollment (2006-2013) and are followed for cancer outcomes via routine linkages with population-based cancer and death registries. Selected participants diagnosed with cancer within 3 years (Y) of blood donation and an equivalent number of matched non-cancer participants were chosen for processing. Despite differences in sample collection (tube, protocol) and long storage time in freezer, standard requirements for MCED sample quality were applied. Primary outcome: demonstrate that cancer signal was detectable via the MCED test up to 3Y prior to conventional diagnosis. Secondary outcomes: assess detectability based on time from blood draw and other clinical factors. Sampling weights were considered in the statistical analyses. Results: Of 268,726 CPS-3 participants, 2,850 were selected and assayed by cohort sampling, and 2,754 were included in the primary analysis set. CPS-3 cohort demographics and cancer incidence patterns compared to Surveillance, Epidemiology, and End Results suggested that this cohort is younger and healthier than the U.S. population. The test negative rate was 100% among non-cancers. The test positive rate (TPR) stratified by time between blood draw and clinical diagnosis was 2.3% (3Y), 6.0% (2Y), 15.2% (1Y). For eventually fatal cancers, TPR was 5.1% (3Y), 21.6% (2Y), 55.1% (1Y) vs 1.8%, 4.1%, 10.2% for non-fatal cancers. By stage, within 1Y, TPR was 73.7% for Distant at 4-6 months (M) and 94.1% at 1-3M vs 26.7% for Regional at 4-6M and 71.0% at 1-3M. The overall accuracy of predicting cancer signal origin was 95% (1Y). Many cancers were detected 3Y prior to clinical diagnosis: for example, a case of colon/rectum cancer at Regional stage was detected at 991 days. Conclusions: The ACS CPS-3 cohort includes persons at lower-than-average risk of cancer but offered a biospecimen repository with sufficient plasma samples to assess the preclinical detectability of cancer signals by retrospective evaluation. Rapid changes in detectability over time underscore the short preclinical time scales for aggressive cancers to develop, and therefore to be detected. These insights into cancer biology reinforce the importance of pre-diagnostic samples in the study of cancer signals for early detection.

The detection of multiple cancer types with an extended set of methylation and protein markers.

3040 Background: Cancer is the 2nd leading cause of mortality in the US, with an estimated 1.96M new cases expected in 2023. With guideline recommended screening unavailable for most cancers, multicancer early detection (MCED) tests in development may extend the reach of cancer screening. As part of an ongoing program examining multiple biomarker classes in cancer early detection, we examined the feasibility of combining blood DNA methylation and cancer protein markers for the detection of multiple cancer types. Methods: 236 samples from subjects with cancer (13 tumor organ types) were retrospectively collected and gender/age matched with 146 unscreened non-cancer control samples. Plasma and serum were used for cfDNA methylation analysis of 36 differentially methylated regions and 8 cancer-associated protein markers in a randomized, blinded fashion. Methylated DNA markers (MDMs) were assayed using target enrichment long-probe quantitative-amplified signal assays. Protein markers were assayed with commercial kits. Normalized methylation values/protein concentrations were used to develop a logistic regression model for cancer detection. The model was built with 5 random 0.7/0.3 training/validation splits, keeping case/control, cancer types, gender, and age distributions similar to the original data set. Models for MDMs and protein markers were created, and a combined model including both MDMs and protein markers was also evaluated for performance improvement. Results: A reduced subset of MDM and protein markers was identified to have robust detection of several cancer types. At 99% specificity, overall sensitivity for cancer detection was 68% (95% CI: 62%-74%) for MDMs, 43% (37%-50%) for proteins, and 75% (69%-80%) for MDMs and proteins combined. Conclusions: Building on promising results with mutation and protein biomarker analysis (Science 369:6499, 2020) this pilot study demonstrates the potential of combining DNA methylation and protein biomarkers and contributes to a larger scale effort to develop a multi-biomarker class MCED test (Ann Oncol 2022 Vol. 33:S575). [Table: see text]

The application value of circulating tumor DNA methylation biomarkers in early diagnosis of non-small cell lung cancer.

The application value of circulating tumor DNA methylation biomarkers in early diagnosis of non-small cell lung cancer.

Blood DNA methylation biomarkers for cognitive decline in older adults with type 2 diabetes

AbstractBackgroundIndividuals with type 2 diabetes (T2D) are at particularly high risk for cognitive decline and dementia. Finding early, pre‐clinical biomarkers for detecting and tracking cognitive impairment and dementia in this population is important for personalized medicine approach. Our study aims to find blood DNA methylation biomarkers for cognitive decline among elderly T2D patients.MethodTwo groups of 30 T2D subjects each, from the Israel Diabetes and Cognitive Decline (IDCD) cohort, were included. One group (converters) was cognitively normal at baseline, yet at 36‐54 months follow‐up deteriorated from a Clinical Dementia Rating scale CDR = 0 (no dementia) to CDR≥0.5 (questionable dementia or frank dementia). The control group (non‐converters) was cognitively normal at baseline and maintained normal cognition during the follow‐up period (CDR = 0). We performed an epigenome‐wide association study, using the Illumina Infinium MethylationEPIC microarray, and searched for differentially methylated positions (DMPs) with significant methylation alterations (≥ 2 fold and FDR corrected p value &lt;0.05) between the baseline and the follow‐up time points for each of the two groups.ResultParticipants were 65‐83 years of age (mean‐73.6 ± 4.1), 72% were men. The mean hemoglobin A1C value was 6.9 ± 0.8%, and T2D duration was 9.4 ± 4.7 years. Converters and non‐converters did not differ in any of these characteristics. Results show significant changes in multiple DMPs among the converters, but only in a few DMPs in the non‐converters. Gene ontology analyses indicates primarily involvement of central nervous system‐related pathways, mainly neurodevelopmental disorders.ConclusionThese preliminary results, employing a high‐throughput approach for methylation profile in blood, suggest methylomic alterations as a potential biomarker for cognitive decline in T2D elderly. A priori identification of patients at risk for cognitive decline may lead to early intervention in modifiable risk factors in this population, which is highly susceptible to dementia.

DNA methylation biomarkers distinguishing early-stage prostate cancer from benign prostatic hyperplasia.

DNA methylation markers are considered robust diagnostic features in various cancer types, as epigenetic marks are commonly altered during cancer progression. Differentiation between benign prostatic hyperplasia (BPH) and early-stage prostate cancer (PCa) is clinically difficult, relying on the information of the patient's symptoms or levels of prostate-specific antigen. A total of 42 PCa patients and 11 BPH patients were recruited. Genomic DNA was purified from tissues and used for the library preparation of the target-enriched methylome with enzymatic conversion and a Twist 85 Mbp EM-seq panel. Paired-end sequencing (150bp) was performed using NovaSeq 6000 or NextSeq 550. After quality control, including adapter trimming and de-duplication of raw sequencing data, differential methylation patterns were analyzed between the BPH and PCa groups. We report DNA methylation patterns existing between BPH and PCa. The major finding is that broad hypermethylation occurred at genic loci in PCa tissues as compared to the BPH. Gene ontology analysis suggested that hypermethylation of genic loci involved in chromatin and transcriptional regulation is involved in cancer progression. We also compared PCa tissues with high Gleason scores to tissues with low Gleason scores. The high-Gleason PCa tissues showed hundreds of focal differentially methylated CpG sites corresponding to genes functioning in cancer cell proliferation or metastasis. This suggests that dissecting early-to-advanced-grade cancer stages requires an in-depth analysis of differential methylation at the single CpG site level. Our study reports that enzymatic methylome sequencing data can be used to distinguish PCa from BPH and advanced PCa from early-stage PCa. The stage-specific methylation patterns in this study will be valuable resources for diagnostic purposes as well as further development of liquid biopsy approaches for the early detection of PCa.

DNA methylation clock DNAmFitAge shows regular exercise is associated with slower aging and systemic adaptation

DNAmPhenoAge, DNAmGrimAge, and the newly developed DNAmFitAge are DNA methylation (DNAm)-based biomarkers that reflect the individual aging process. Here, we examine the relationship between physical fitness and DNAm-based biomarkers in adults aged 33–88 with a wide range of physical fitness (including athletes with long-term training history). Higher levels of VO2max (ρ = 0.2, p = 6.4E − 4, r = 0.19, p = 1.2E − 3), Jumpmax (p = 0.11, p = 5.5E − 2, r = 0.13, p = 2.8E − 2), Gripmax (ρ = 0.17, p = 3.5E − 3, r = 0.16, p = 5.6E − 3), and HDL levels (ρ = 0.18, p = 1.95E − 3, r = 0.19, p = 1.1E − 3) are associated with better verbal short-term memory. In addition, verbal short-term memory is associated with decelerated aging assessed with the new DNAm biomarker FitAgeAcceleration (ρ: − 0.18, p = 0.0017). DNAmFitAge can distinguish high-fitness individuals from low/medium-fitness individuals better than existing DNAm biomarkers and estimates a younger biological age in the high-fit males and females (1.5 and 2.0 years younger, respectively). Our research shows that regular physical exercise contributes to observable physiological and methylation differences which are beneficial to the aging process. DNAmFitAge has now emerged as a new biological marker of quality of life.

Association of DNA methylation signatures with premature ageing and cardiovascular death in patients with end-stage kidney disease: a pilot epigenome-wide association study

ABSTRACT Patients with end-stage kidney disease (ESKD) display features of premature aging. There is strong evidence that changes in DNA methylation (DNAm) contribute to age-related pathologies; however, little is known about their association with premature aging and cardiovascular mortality in patients with ESKD. We assayed genome-wide DNAm in a pilot case-control study of 60 hemodialysis patients with (n=30, cases) and without (n=30, controls) a fatal cardiovascular event. DNAm was profiled on the Illumina EPIC BeadChip. Four established DNAm clocks (i.e., Horvath-, Hannum-, Pheno-, and GrimAge) were used to estimate epigenetic age (DNAmAge). Epigenetic age acceleration (EAA) was derived as the residuals of regressing DNAmAge on chronological age (chroAge), and its association with cardiovascular death was examined using multivariable conditional logistic regression. An epigenome-wide association study (EWAS) was performed to identify differentially methylated CpGs associated with cardiovascular death. All clocks performed well at predicting chroAge (correlation between DNAmAges and chroAge of r=0.76-0.89), with GrimAge showing the largest deviation from chroAge (a mean of +21.3 years). There was no significant association of EAAs with cardiovascular death. In the EWAS, a CpG (cg22305782) in the FBXL19 gene had the strongest association with cardiovascular death with significantly lower DNAm in cases vs. controls (P FDR=2.0x10−6). FBXL19 is involved in cell apoptosis, inflammation, and adipogenesis. Overall, we observed more accelerated aging in patients with ESKD, although there was no significant association of EAAs with cardiovascular death. EWAS suggests a potential novel DNAm biomarker for premature cardiovascular mortality in ESKD.

Novel urine-based DNA methylation biomarkers for urothelial bladder carcinoma detection in patients with hematuria

ABSTRACT Background Urothelial bladder carcinoma (UBC) is usually detected during work-up for hematuria. Cystoscopy and/or contrast-enhanced imaging are the gold standard tools for UBC diagnosis, despite limited by being invasive, expensive and low yield in small flat tumors. Objectives To assess the diagnostic performance of urine-based DNA methylation of six genes (GATA4, P16, P14, APC, CDH1 and CD99) for UBC detection in patients with hematuria. Patients and methods Voided urine was collected from consecutive patients presented with hematuria for urine cytology and DNA methylation assay of the assigned genes using methylation-specific Polymerase Chain Reaction (PCR). Further assessment by office cystoscopy and imaging with subsequent inpatient cystoscopic biopsy for positive findings was done. The diagnostic characteristics of DNA methylation and urine cytology were assessed based on its capability to predict UBC. Results We included 246 patients in the study with identified macroscopic hematuria in 204 (82.9%) patients. Positive cytology was found in 78 (31.7%) patients. DNA methylation of GATA4, P16, P14, APC, CDH1 and CD99 genes was identified in 127 (51.6%), 52 (21.1%), 117 (47.6%), 106 (43.1%), 90 (36.6%) and 71 (28.9%) patients, respectively. The sensitivity of the assigned genes for UBC detection ranges from 35% (95%CI: 31–39) to 83% (95%CI: 79–87). Optimal specificity (SP) (100%) was noted for P16, APC and CDH1 genes. While for the other genes (GATA4, P14 and CD99), the SP was 95% (95%CI: 92–98), 96% (95%CI: 92–99) and 97% (95%CI: 93–99), respectively. On multivariate logistic regression analysis, all genes exclusively demonstrated independent prediction of UBC. On receiver operator characteristic (ROC) analysis, all tested genes methylation showed superior area under the curve (AUC) when compared to urine cytology. Conclusions We have developed a novel urine-based DNA methylation assay for detection of UBC in patients with hematuria with superior diagnostic performance and independent predictive capacity over urine cytology.

Accurate detection of early-stage lung cancer using a panel of circulating cell-free DNA methylation biomarkers

BackgroundLung cancer remains the leading cause of cancer mortality worldwide. Early detection of lung cancer helps improve treatment and survival. Numerous aberrant DNA methylations have been reported in early-stage lung cancer. Here, we sought to identify novel DNA methylation biomarkers that could potentially be used for noninvasive early diagnosis of lung cancers.MethodsThis prospective-specimen collection and retrospective-blinded-evaluation trial enrolled a total of 317 participants (198 tissues and 119 plasmas) comprising healthy controls, patients with lung cancer and benign disease between January 2020 and December 2021. Tissue and plasma samples were subjected to targeted bisulfite sequencing with a lung cancer specific panel targeting 9,307 differential methylation regions (DMRs). DMRs associated with lung cancer were identified by comparing the methylation profiles of tissue samples from patients with lung cancer and benign disease. Markers were selected with minimum redundancy and maximum relevance algorithm. A prediction model for lung cancer diagnosis was built through logistic regression algorithm and validated independently in tissue samples. Furthermore, the performance of this developed model was evaluated in a set of plasma cell-free DNA (cfDNA) samples.ResultsWe identified 7 DMRs corresponding to 7 differentially methylated genes (DMGs) including HOXB4, HOXA7, HOXD8, ITGA4, ZNF808, PTGER4, and B3GNTL1 that were highly associated with lung cancer by comparing the methylation profiles of lung cancer and benign nodule tissue. Based on the 7-DMR biomarker panel, we developed a new diagnostic model in tissue samples, termed “7-DMR model”, to distinguish lung cancers from benign diseases, achieving AUCs of 0.97 (95%CI: 0.93-1.00)/0.96 (0.92-1.00), sensitivities of 0.89 (0.82–0.95)/0.92 (0.86–0.98), specificities of 0.94 (0.89–0.99)/1.00 (1.00–1.00), and accuracies of 0.90 (0.84–0.96)/0.94 (0.89–0.99) in the discovery cohort (n = 96) and the independent validation cohort (n = 81), respectively. Furthermore, the 7-DMR model was applied to noninvasive discrimination of lung cancers and non-lung cancers including benign lung diseases and healthy controls in an independent validation cohort of plasma samples (n = 106), yielding an AUC of 0.94 (0.86-1.00), sensitivity of 0.81 (0.73–0.88), specificity of 0.98 (0.95-1.00), and accuracy of 0.93 (0.89–0.98).ConclusionThe 7 novel DMRs could be promising methylation biomarkers that merits further development as a noninvasive test for early detection of lung cancer.Graphical abstract

Evaluation of DNA methylation biomarkers ASCL1 and LHX8 on HPV-positive self-collected samples from primary HPV-based screening

BackgroundHost-cell DNA methylation analysis can be used to triage women with high-risk human papillomavirus (HPV)-positive self-collected cervicovaginal samples, but current data are restricted to under-/never-screened women and referral populations. This study evaluated triage performance in women who were offered primary HPV self-sampling for cervical cancer screening.MethodsSelf-collected samples from 593 HPV-positive women who participated in a primary HPV self-sampling trial (IMPROVE study; NTR5078), were tested for the DNA methylation markers ASCL1 and LHX8 using quantitative multiplex methylation-specific PCR (qMSP). The diagnostic performance for CIN3 and cervical cancer (CIN3 + ) was evaluated and compared with that of paired HPV-positive clinician-collected cervical samples.ResultsSignificantly higher methylation levels were found in HPV-positive self-collected samples of women with CIN3 + than control women with no evidence of disease (P values <0.0001). The marker panel ASCL1/LHX8 yielded a sensitivity for CIN3 + detection of 73.3% (63/86; 95% CI 63.9–82.6%), with a corresponding specificity of 61.1% (310/507; 95% CI 56.9–65.4%). The relative sensitivity for detecting CIN3+ was 0.95 (95% CI 0.82–1.10) for self-collection versus clinician-collection, and the relative specificity was 0.82 (95% CI 0.75–0.90).ConclusionsThe ASCL1/LHX8 methylation marker panel constitutes a feasible direct triage method for the detection of CIN3 + in HPV-positive women participating in routine screening by self-sampling.

Integrative single-cell multiomics analyses dissect molecular signatures of intratumoral heterogeneities and differentiation states of human gastric cancer.

Human gastric cancer is a highly lethal disease, but the underlying multiomic molecular signatures remain largely unclear. Here, we performed multi-regional sampling, parallel single-cell multiomics sequencing and integrated analyses of human gastric cancer. We identified common transcriptomic alterations of gastric cancer cells, such as aberrant down-regulation of genes associated with normal stomach function and up-regulation of KRT7, PI3, S100A4, etc. Surprisingly, aberrant and prevalent up-regulation of genes highly expressed in normal colorectal epithelial cells were also identified in cancer cells, which may be partially regulated by promoter chromatin accessibility and DNA methylation levels. We revealed the single-cell DNA methylome landscape of gastric cancer, and identified candidate DNA methylation biomarkers, such as hypermethylated promoters of TMEM240 and HAGLROS, and hypomethylated promoters of TRPM2-AS and HRH1. Additionally, the relationships between genetic lineages, DNA methylation and transcriptomic clusters were systematically revealed at single-cell level. We showed that DNA methylation heterogeneities were mainly among different genetic lineages of cancer cells. Moreover, we found that DNA methylation levels of cancer cells with poorer differentiation states tend to be higher than those of cancer cells with better differentiation states in the primary tumor within the same patient, although still lower than in normal gastric epithelial cells. Cancer cells with poorer differentiation states also prevalently down-regulated MUC1 expression and immune-related pathways, and had poor infiltration of CD8+ T cells. Our study dissected the molecular signatures of intratumoral heterogeneities and differentiation states of human gastric cancer using integrative single-cell multiomics analyses.

Distinct CSF biomarker-associated DNA methylation in Alzheimer’s disease and cognitively normal subjects

BackgroundGrowing evidence has demonstrated that DNA methylation (DNAm) plays an important role in Alzheimer’s disease (AD) and that DNAm differences can be detected in the blood of AD subjects. Most studies have correlated blood DNAm with the clinical diagnosis of AD in living individuals. However, as the pathophysiological process of AD can begin many years before the onset of clinical symptoms, there is often disagreement between neuropathology in the brain and clinical phenotypes. Therefore, blood DNAm associated with AD neuropathology, rather than with clinical data, would provide more relevant information on AD pathogenesis.MethodsWe performed a comprehensive analysis to identify blood DNAm associated with cerebrospinal fluid (CSF) pathological biomarkers for AD. Our study included matched samples of whole blood DNA methylation, CSF Aβ42, phosphorylated tau181 (pTau181), and total tau (tTau) biomarkers data, measured on the same subjects and at the same clinical visits from a total of 202 subjects (123 CN or cognitively normal, 79 AD) in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort. To validate our findings, we also examined the association between premortem blood DNAm and postmortem brain neuropathology measured on a group of 69 subjects in the London dataset.ResultsWe identified a number of novel associations between blood DNAm and CSF biomarkers, demonstrating that changes in pathological processes in the CSF are reflected in the blood epigenome. Overall, the CSF biomarker-associated DNAm is relatively distinct in CN and AD subjects, highlighting the importance of analyzing omics data measured on cognitively normal subjects (which includes preclinical AD subjects) to identify diagnostic biomarkers, and considering disease stages in the development and testing of AD treatment strategies. Moreover, our analysis revealed biological processes associated with early brain impairment relevant to AD are marked by DNAm in the blood, and blood DNAm at several CpGs in the DMR on HOXA5 gene are associated with pTau181 in the CSF, as well as tau-pathology and DNAm in the brain, nominating DNAm at this locus as a promising candidate AD biomarker.ConclusionsOur study provides a valuable resource for future mechanistic and biomarker studies of DNAm in AD.

Association of Race and Poverty Status With DNA Methylation–Based Age

The Dunedin Pace of Aging Calculated From the Epigenome (DunedinPACE) measure is a newly constructed DNA methylation (DNAm) biomarker associated with morbidity, mortality, and adverse childhood experiences in several cohorts with European ancestry. However, there are few studies of the DunedinPACE measure among socioeconomically and racially diverse cohorts with longitudinal assessments. To investigate the association of race and poverty status with DunedinPACE scores in a socioeconomically diverse middle-aged cohort of African American and White participants. This longitudinal cohort study used data from the Healthy Aging in Neighborhoods of Diversity Across the Life Span (HANDLS) study. HANDLS is a population-based study of socioeconomically diverse African American and White adults aged 30 to 64 years at baseline in Baltimore, Maryland, with follow-up approximately every 5 years. The current study was restricted to 470 participants with blood samples at 2 time points: August 14, 2004, to June 22, 2009 (visit 1), and June 23, 2009, to September 12, 2017 (visit 2). Genome-wide DNAm was assessed at visit 1 (chronological age, 30-64 years) and visit 2. Data were analyzed from March 18, 2022, to February 9, 2023. DunedinPACE scores were estimated for each participant at 2 visits. DunedinPACE scores are values scaled to a mean of 1, interpretable with reference to a rate of 1 year of biological aging per 1 year of chronological aging. Linear mixed-model regression analysis was used to examine the trajectories of DunedinPACE scores by chronological age, race, sex, and poverty status. Among 470 participants, the mean (SD) chronological age at visit 1 was 48.7 (8.7) years. Participants were balanced by sex (238 [50.6%] were men and 232 [49.4%] were women), race (237 [50.4%] African American and 233 [49.6%] White), and poverty status (236 [50.2%] living below poverty level and 234 [49.8%] living above poverty level). The mean (SD) time between visits was 5.1 (1.5) years. Overall, the mean (SD) DunedinPACE score was 1.07 (0.14), representing a 7% faster pace of biological aging than chronological aging. Linear mixed-effects regression analysis revealed an association between the 2-way interaction between race and poverty status (White race and household income below poverty level: β = 0.0665; 95% CI, 0.0298-0.1031; P < .001) and significantly higher DunedinPACE scores and an association between quadratic age (age squared: β = -0.0113; 95% CI, -0.0212 to -0.0013; P = .03) and significantly higher DunedinPACE scores. In this cohort study, household income below poverty level and African American race were associated with higher DunedinPACE scores. These findings suggest that the DunedinPACE biomarker varies with race and poverty status as adverse social determinants of health. Consequently, measures of accelerated aging should be based on representative samples.

Aberrant methylation in neurofunctional gene serves as a hallmark of tumorigenesis and progression in colorectal cancer

BackgroundDNA methylation is one of the most promising biomarkers in predicting the prognosis of colorectal cancer (CRC). We aimed to develop a DNA methylation biomarker that could evaluate the prognosis of CRC.MethodsA promising DNA methylation biomarker was developed by hypermethylated genes in cancer tissue that were identified from Illumina EPIC methylation arrays. A cohort comprising 30 pairs of snap-frozen tumor tissue and adjacent normal tissue was used for correlation analysis between the methylation and expression status of the marker. The other cohort comprising 254 formalin-fixed paraffin-embedded (FFPE) tumor tissue from 254 CRC patients was used for prognosis analysis.ResultsRegulating synaptic membrane exocytosis 2 (RIMS2) was hypermethylated and lowly expressed in CRC comparing to adjacent normal tissue. Hypermethylation of RIMS2 in CRC was correlated with less frequent KRAS mutant and high differentiation. RIMS2 promoter methylation showed independent predictive value for survival outcome (P = 0.015, HR 1.992, 95% CI [(1.140–3.48)]), and a combination of RIMS2 methylation with KRAS status could predict prognosis better.ConclusionsRIMS2 is frequently hypermethylated in CRC, which can silence the expression of RIMS2. RIMS2 methylation is a novel biomarker for predicting the prognosis of CRC.