RNA modifications are crucial for post-transcriptional gene regulation. Research on RNA modifications has become a novel frontier of epitranscriptomics. Up to now, over 170 kinds of modifications have been identified on mRNA and diverse non-coding RNA. Three classes of proteins (writers, erasers, and readers) regulate the addition, removal, and identification of epigenetic marks, thus affecting RNA biological functions. Increasing evidence identifies the dysregulation of RNA modifications in different cancer types and the therapeutic potential of targeting RNA-modifying enzymes. The ability of RNA modifications to improve mRNA stability and translation efficacy and decrease immunogenicity has been exploited for the clinical use of mRNA cancer vaccines. This review aims to shed light on several vital cap, tail, and internal modifications of RNA with a focus on the connection between RNA epigenetic pathways and cancer pathogenesis. We further explore the clinical potential of RNA modifications as dynamic biomarkers for cancer diagnosis, prognosis, and therapeutic response prediction, addressing both technological challenges and translational opportunities. Finally, we analyze the limitations of current studies and discuss the research focus in the future.

Immune system plays a crucial role in the physiological and pathological regulation of the cardiovascular system. The exploration history and milestones of immune system in cardiovascular diseases (CVDs) have evolved from the initial discovery of chronic inflammation in atherosclerosis to large-scale clinical studies confirming the importance of anti-inflammatory therapy in treating CVDs. This progress has been facilitated by advancements in various technological approaches, including multi-omics analysis (single-cell sequencing, spatial transcriptome et al.) and significant improvements in immunotherapy techniques such as chimeric antigen receptor (CAR)-T cell therapy. Both innate and adaptive immunity holds a pivotal role in CVDs, involving Toll-like receptor (TLR) signaling pathway, nucleotide-binding oligomerization domain-containing proteins 1 and 2 (NOD1/2) signaling pathway, inflammasome signaling pathway, RNA and DNA sensing signaling pathway, as well as antibody-mediated and complement-dependent systems. Meanwhile, immune responses are simultaneously regulated by multi-level regulations in CVDs, including epigenetics (DNA, RNA, protein) and other key signaling pathways in CVDs, interactions among immune cells, and interactions between immune and cardiac or vascular cells. Remarkably, based on the progress in basic research on immune responses in the cardiovascular system, significant advancements have also been made in pre-clinical and clinical studies of immunotherapy. This review provides an overview of the role of immune system in the cardiovascular system, providing in-depth insights into the physiological and pathological regulation of immune responses in various CVDs, highlighting the impact of multi-level regulation of immune responses in CVDs. Finally, we also discuss pre-clinical and clinical strategies targeting the immune system and translational implications in CVDs.

The purpose of this review is to evaluate the role of epigenetic-targeting approaches in the management of neuroendocrine neoplasms (NENs), particularly as a priming strategy for subsequent therapies. We explore the molecular basis of epigenetic modifications in NENs, and we review preclinical and clinical studies on DNA methyltransferase and histone deacetylase (HDAC) inhibitors.

DNA methyltransferase and HDAC inhibitors can upregulate SSTR2 expression, thereby improving radioligand uptake and treatment response in NENs. The LANTana study investigates ASTX727 as a strategy to restore SSTR2 expression in metastatic NENs, allowing previously ineligible patients to receive PRRT. Preclinical studies demonstrate that combining epigenetic agents with radiotherapy, chemotherapy, or targeted inhibitors can enhance tumour sensitivity and overcome resistance.

Epigenetic modifications play a crucial role in NENs, influencing tumour progression, therapy resistance, and SSTR2 expression. Epigenetic priming with DNA methyltransferase and HDAC inhibitors can enhance SSTR2 expression, improving the efficacy of PRRT in NENs. The LANTana study and other trials are investigating whether epigenetic-targeting approaches can be integrated into NEN treatment to optimize PRRT and overcome therapeutic limitations.

Vascular disease in aging populations spans a wide range of disorders including strokes, circulation disorders and hypertension. As individuals age, vascular disorders co-occur and hence exert combined effects. In the present study we introduce vascular-related biological stress as a novel biomarker to capture the combined effects of vascular disease burden for more precision in early detection of cognitive changes in aging.

to determine the role of vascular-related biological Stress, DNA methylation-based biological aging and Allostatic Load in the relationship between vascular disorders and major cognitive domains including global cognition, episodic memory and executive function in a representative sample of adults across the age span.

The present study included participants from MIDUS refresher sample. Vascular-related biological stress included: BMI, Average blood pressure, sitting, Waist-hip ratio, Blood hemoglobin A1c percent, Blood dehydroepiandrosterone (ng/mL), Blood fasting insulin levels uIU/mL, Blood serum interleukin-8 (pg/mL), Blood serum interleukin-6 (pg/mL), Blood fasting glucose levels mg/dL and Blood fibrinogen (mg/dL). DNA methylation-based biological age measures included GrimAge2 that was constructed based on DNA methylation surrogate markers for select plasma proteins and smoking-pack years. Allostatic load scores were calculated based on biomarkers commonly used in allostatic load calculations: cortisol (urine), norepinephrine (urine), epinephrine (urine), dopamine (urine), glycosylated hemoglobin (HBA1C, blood), low density lipoprotein (LDL, blood), C-reactive protein (CRP, blood) dehydroepiandrosterone sulfate (DHEAS, blood), high-density lipoprotein (HDL, blood) and systolic blood pressure (average, sitting). Least Absolute Shrinkage and Selection Operator (LASSO) and response models (item and continuous) were used to calculate vascular-related biological stress and theta scores. Four-way decomposition modeling approach was used to calculate the natural direct and indirect effects in the relationship between vascular disease and major cognitive domains.

550 individuals with data on biomarkers, DNA methylation and cognition assessments were included in the present study. Median age was 54 (range = 26, 78) with females representing 48% of the sample. In the relationship between vascular disease and cognition, the overall proportions mediated through vascular-related biological stress (item-response scale) were 0.60 (P = 0.01); 1.1 (P = 0.308); 0.53 (P = 0.002) for global cognition, episodic memory and executive function respectively. The overall proportions mediated through DNA methylation (GrimAge2) were 0.27 (P = 0.002); 0.39 (P = 0.102); 0.20, (P = 0.002) for global cognition, episodic memory and executive function respectively and 0.10 (P = 0.08); 0.09 (P = 0.5); 0.07 (P = 0.18) through allostatic load (sum scores).

Our findings suggest that vascular-related biological stress, DNA methylation and to some extent allostatic load mediate the effects of vascular disease on global cognition and executive function.

The incidence of Nasopharyngeal carcinoma (NPC) is rising in recent years, especially in some non-developed parts of the world. Hence, cost-efficient means for sensitive detection of NPC are vital.

We recruited 646 participants, including healthy individuals, patients with benign nasopharyngeal diseases, and NPC patients for plasma cell-free DNA(cfDNA), which underwent low-depth whole-genome sequencing (WGS) to extract multi-dimensional molecular features, including fragmentation pattern, end motif, copy number variation(CNV), and transcription factors(TF). Based on these features, we employed a machine learning algorithm to build prediction models for NPC detection.

We achieved a sensitivity of 95.8% and a specificity of 99.4% to discriminate NPC patients from healthy individuals.

This study can be a proof-of-concept for these multi-dimensional molecular features to be implemented as a noninvasive approach for the detection and even early detection of NPC.

Cholangiocarcinoma (CCA), also known as bile duct cancer, is a devastating malignancy primarily affecting the biliary tract.

To assess their performance in clinical diagnosis and monitoring of CCA, plasma methylation and circulating tumor cells were detected.

Plasma samples were collected from Hubei Cancer Hospital (n = 156). Plasma DNA was tested to detect SHOX2, HOXA9, SEPTIN9, and RASSF1A methylation using TaqMan PCR. Circulating tumor cells (CTCs) were detected in the peripheral blood of patients using the United States Food and Drug Administration-approved cell search system before and after clinical therapy. The CCA diagnostic value was estimated using the area under the curve. The independent prognosis risk factors for patients with CCA were estimated using Cox and logistic regression analyses.

The sensitivity and specificity of the four DNA plasma methylations exhibited 64.74% sensitivity and 93.88% specificity for detecting CCA. The receiver operating characteristic curve of the combined value for CCA diagnosis in plasma was 0.828 ± 0.032. RASSF1A plasma methylation was related to the prognosis of patients with CCA. We determined the prognostic hazard ratio for CCA using CTC count, tumor stage, methylation, and carbohydrate antigen 19-9 levels as key factors. Our overall survival nomogram achieved a C-index of 0.705 (0.605-0.805).

SHOX2, HOXA9, SEPTIN9, and RASSF1A plasma methylation demonstrated increased sensitivity for diagnosing CCA. RASSF1A plasma methylation and CTCs were valuable predictors to assess CCA prognosis and recurrence.

Detecting DNA rare bases is essential for diagnosing genetic disorders and cancers. However, their low abundance and high structural similarity make selective and sensitive detection challenging. The two-dimensional functionalized carbon material graphdiyne (GDY) holds great promise for enhancing sensor performance due to its excellent electronic properties, biocompatibility, and ease of functionalization. This study employs density functional theory (DFT) to investigate the adsorption behavior of rare bases on GDY and R-GDY (R = B/N) surfaces. Essential factors, including adsorption energy, bandgap, charge transfer, and density of states, are systematically analyzed. Additionally, critical sensor performance metrics, such as deposition time, sensitivity, and selectivity are predicted, providing valuable insights into the potential applications of these materials. The results indicate that while pure GDY can specifically recognize 5-hydroxymethylcytosine, its sensitivity is limited. In contrast, R-GDY stably adsorbs rare bases via π-π interactions, exhibiting good reversibility and moderate charge transfer, which significantly enhance its sensitivity. R-GDY effectively distinguishes between rare bases based on translocation time, making it ideal for the development of efficient and reusable electrochemical biosensors, thus providing a reliable approach for clinical diagnostics.

Emerging evidence suggests that abnormal DNA methylation patterns may play a role in the progression of adolescent idiopathic scoliosis (AIS). However, the mechanisms underlying the influence of DNA methylation on curve severity remain largely unknown.

To characterize the DNA methylation profiles associated with the curve severity of AIS.

Retrospective study with prospectively collected clinical data and blood samples.

A total of 7 AIS monozygotic twin pairs discordant for curve severity were included. Genome-wide methylation profile from blood samples were quantified by Illumina Infinium MethylationEPIC BeadChip (850K chip). Cell type composition of the samples was estimated by RefbaseEWAS method. Differentially methylated CpG sites were identified through comparison between patients with low and high Cobb angle. We also performed a gene-based collapsing analysis using mCSEA by aggregating the CpG sites based on promoter region. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed using clusterProfiler package.

Genome-wide DNA methylation analysis identified multiple differentially methylated positions across the genome. Gene-based collapsing analysis identified 212 differentially methylated genes (FDR adjusted p<.05), most of which (186/212) were hypermethylated in the group with high Cobb angle. Some of the identified genes were well-documented in AIS literature, such as TBX1, PAX3 and LBX1. Functional enrichment analysis revealed that the differentially methylated genes (DMGs) were involved in pattern specification process, skeletal development, muscle function, neurotransmission and several signaling pathways (cAMP, Wnt and prolactin).

The study represents the largest systematic epigenomic analyses of monozygotic twins discordant for curve severity and supports the important role of altered DNA methylation in AIS.

The identified CpG sites provide insight into novel biomarkers predicting curve progression of AIS. Furthermore, the differentially methylated genes and enriched pathways may serve as interventional therapy target for AIS patients.

The association between DNA methylation age acceleration (DNAmAA) and cardiovascular-kidney-metabolic (CKM) syndrome stages and long-term mortality in the population with CKM syndrome remains unclear.

This cohort study included 1889 participants from the National Health and Nutrition Examination Survey (1999-2002) with CKM stages and DNA methylation age data. DNAmAA was calculated as residuals from the regression of DNA methylation age on chronological age. The primary outcome was all-cause mortality, with cardiovascular and noncardiovascular mortality as secondary outcomes. Proportional odds models assessed the associations between DNAmAAs and CKM stages, and Cox proportional hazards regression models estimated the associations between DNAmAAs and mortality. Significant associations were found between DNAmAAs and advanced CKM stages, particularly for GrimAge2Mort acceleration (GrimAA) (odds ratio [OR], 1.547 [95% CI, 1.316-1.819]). Over an average follow-up of 14 years, 1015 deaths occurred. Each 5-unit increase in GrimAA was associated with a 50% increase in all-cause mortality (95% CI, 1.39-1.63), a 77% increase in cardiovascular mortality (95% CI, 1.46-2.15), and a 42% increase in noncardiovascular mortality (95% CI, 1.27-1.59). With the lowest GrimAA tertile as a reference, the highest GrimAA tertile showed hazard ratios of 1.95 (95% CI, 1.56-2.45) for all-cause mortality, 3.06 (95% CI, 2.13-4.40) for cardiovascular mortality, and 1.65 (95% CI, 1.20-2.29) for noncardiovascular mortality. Mediation analysis indicated that GrimAA mediates the association between various exposures (including physical activity, Healthy Eating Index-2015 score, hemoglobin A1c, etc.) and mortality.

GrimAA may serve as a valuable biomarker for assessing CKM stages and mortality risk in individuals with CKM syndrome, thereby informing personalized management strategies.

Accumulating evidence suggests that genetic and epigenetic biomarkers hold potential for enhancing the early detection and monitoring of breast cancer (BC). Epigenetic alterations of the Homeobox A2 (HOXA2) gene have recently garnered significant attention in the clinical management of various malignancies. However, the precise role of HOXA2 in breast tumorigenesis has remained elusive. To address this point, we conducted high-throughput RNA sequencing and DNA methylation array studies on laser-microdissected human BC samples, paired with normal tissue samples. Additionally, we performed comprehensive in silico analyses using large public datasets: TCGA and METABRIC. The diagnostic performance of HOXA2 was calculated by means of receiver operator characteristic curves. Its prognostic significance was assessed through immunohistochemical studies and Kaplan-Meier Plotter database interrogation. Moreover, we explored the function of HOXA2 and its role in breast carcinogenesis through in silico, in vitro, and in vivo investigations. Our work revealed significant hypermethylation and downregulation of HOXA2 in human BC tissues. Low HOXA2 expression correlated with increased BC aggressiveness and unfavorable patient survival outcomes. Suppression of HOXA2 expression significantly heightened cell proliferation, migration, and invasion in BC cells, and promoted tumor growth in mice. Conversely, transgenic HOXA2 overexpression suppressed these cellular processes and promoted apoptosis of cancer cells. Interestingly, a strategy of pharmacological demethylation successfully restored HOXA2 expression in malignant cells, reducing their neoplastic characteristics. Bioinformatics analyses, corroborated by in vitro experimentations, unveiled a novel implication of HOXA2 in the lipid metabolism of BC. Specifically, depletion of HOXA2 leaded to a concomitantly decreased expression of PPARγ and its target CIDEC, a master regulator of lipid droplet (LD) accumulation, thereby resulting in reduced LD abundance in BC cells. In summary, our study identifies HOXA2 as a novel prognosis-relevant tumor suppressor in the mammary gland.

Allele-specific DNA methylation reflects genetic variation and parentally-inherited changes, and is involved in gene regulation and pathologies. Yet, our knowledge of this phenomenon is largely limited to blood. Here we present a comprehensive atlas of allele-specific DNA methylation using deep whole-genome sequencing across 39 normal human cell types. We identified 325k regions, covering 6% of the genome and 11% of CpGs, that show a bimodal distribution of methylated and unmethylated molecules. In 34k of these regions, genetic variations at individual alleles segregate with methylation patterns, validating allele-specific methylation. We also identified 460 regions showing parental allele-specific methylation, the majority of which are novel, as well as 78 regions associated with known imprinted genes. Surprisingly, sequence-dependent and parental allele-dependent methylation is often restricted to specific cell types, revealing unappreciated variation of allele-specific methylation across the human body. Finally, we validate tissue-specific, maternal allele-specific methylation of CHD7, offering a potential mechanism for the paternal bias in the inheritance mode of CHARGE syndrome associated with this gene. The atlas provides a resource for studying allele-specific methylation and regulatory mechanisms underlying imprinted expression in specific human cell types.

Recurrent pregnancy loss (RPL) is characterized by the occurrence of three or more consecutive spontaneous pregnancy losses before 20-24 weeks of gestation. Despite significant progress in the investigation of the biological pathways associated with unexplained RPL, the precise molecular mechanisms remain elusive. Recent advances in multi-omics approaches have identified numerous biomarkers that offer potential avenues for understanding the underlying complexities of RPL. The aim of this comprehensive literature review was to investigate the functional roles of these candidate markers and explore the possible key mechanisms that may contribute to RPL. We also aimed to elucidate the functional networks predicted by omics analyses, which hold promise for providing invaluable insights into novel diagnostic and therapeutic strategies for women experiencing RPL. Furthermore, this review expands on clinical implications and possible applications, highlighting those currently moving towards clinical use and ongoing studies developing in this direction.

Cell division cycle 6 (CDC6) is a member of the AAA+ ATPase family and has chaperone-like activity. Many studies have shown that CDC6 plays an important role in cancer development and progression.

Explored CDC6 mRNA and protein expression in normal human tissues and tumors using TCGA, GTEx, and HPA. The role of CDC6 in cancer was analyzed using multiple web platforms and software, including R, cBioPortal, UALCAN, SangerBox and others. Finally, CCK-8, EdU assays and Transwell assays were used to verify the effects of CDC6 knockdown on HCC cell proliferation, migration, and invasion.

CDC6 expression was upregulated in most cancers and was associated with poorer prognosis. RNA methylation may play an important role in CDC6 epigenetic modification. CDC6 was significantly positively associated with CD4+ Th2 cells and MDSC in a variety of tumors. Furthermore, immunomodulatory genes are strongly associated with CDC6 expression in most tumor types. CDC6 has higher predictive value than B. Clonality and TMB, and its expression is significantly positively correlated with TMB/MSI and DNAss/RNAss, and is closely related to cell cycle events. Down-regulation of CDC6 can inhibit proliferation, migration and invasion of HCC cells.

CDC6 is associated with the occurrence and progression of multiple cancer types by regulating the cell cycle. It holds promise as a diagnostic and prognostic biomarker for cancer, and offers potential in immunomodulatory and targeted therapies.

Nuclease-deactivated Cas (dCas) proteins can be used to recruit epigenetic effectors, and this class of epigenetic editing technologies has revolutionized the ability to synthetically control the mammalian epigenome and transcriptome. DNA methylation is one of the most important and well-characterized epigenetic modifications in mammals, and while many different forms of dCas-based DNA methyltransferases (dCas-DNMTs) have been developed for programmable DNA methylation, these tools are frequently poorly tolerated and/or lowly expressed in mammalian cell types. Further, the use of dCas-DNMTs has largely been restricted to cell lines, which limits mechanistic insights in karyotypically normal contexts and hampers translational utility in the longer term. Here, we extend previous insights into the rational design of the catalytic core of the mammalian DNMT3A methyltransferase and test three dCas9-DNMT3A/3L variants across different human cell lines and in primary donor-derived human T cells. We find that mutations within the catalytic core of DNMT3A stabilize the expression of dCas9-DNMT3A/3L fusion proteins in Jurkat T cells without sacrificing DNA methylation or gene-silencing performance. We also show that these rationally engineered mutations in DNMT3A alter DNA methylation profiles at loci targeted with dCas9-DNMT3A/3L in cell lines and donor-derived human T cells. Finally, we leverage the transcriptionally repressive effects of dCas9-DNMT3A/3L variants to functionally link the expression of a key immunomodulatory transcription factor to cytokine secretion in donor-derived T cells. Overall, our work expands the synthetic biology toolkit for epigenetic editing and provides a roadmap for the use of engineered dCas-based DNMTs in primary mammalian cell types.

In various neurodevelopmental disorders (NDDs), sets of differential methylation marks (referred to as DNA methylation signatures or episignatures) are syndrome-specific and useful in evaluating the pathogenicity of detected genetic variants. These signatures have generally been tested using methylation arrays, requiring additional experimental and evaluation costs. As an alternative, long-read sequencing can simultaneously and accurately evaluate genetic and epigenetic changes. In addition, genome-wide DNA methylation profiling with more complete sets of CpG using long-read sequencing (than methylation arrays) may provide alternative but more comprehensive DNA methylation signatures, which have yet to be adequately investigated.

Nine and seven cases of molecularly diagnosed Sotos syndrome and ATR-X syndrome, respectively, were sequenced using nanopore long-read sequencing, together with 22 controls. Genome-wide differential DNA methylation analysis was performed. Among these differential DNA methylation sites, a single-locus DNA methylation mark at part of the NSD1 CpG island (CpGi) was subsequently studied in an additional 22 cases with a NSD1 point mutation or a 5q35 submicroscopic deletion involving NSD1. To investigate the potential utility of a single-locus DNA methylation test at NSD1 CpGi for differential diagnosis, nine cases with NSD1-negative clinically overlapping overgrowth intellectual disability syndromes (OGIDs) were also tested.

Long-read sequencing enabled the successful extraction of two sets of differential methylation marks unique to each of Sotos syndrome and ATR-X syndrome, referred to as long-read-based DNA methylation signatures (LR-DNAm signatures), as alternatives to reported DNA methylation signatures (obtained by methylation array). Additionally, we found that a part, but not all, of the NSD1 CpGi were hypomethylated compared with the level in controls in both cases harboring NSD1 point mutations and those with a 5q35 submicroscopic deletion. This difference in methylation is specific to Sotos syndrome and lacking in other OGIDs.

Simultaneous evaluation of genetic and epigenetic alterations using long-read sequencing may improve the discovery of DNA methylation signatures, which may in turn increase the diagnostic yields. As an example of the outcomes of these analyses, we propose that a single-locus DNA methylation test at NSD1 CpGi may streamline the molecular diagnosis of Sotos syndrome, regardless of the type of NSD1 aberration.

Alzheimer's disease (AD) brains are characterized by neuropathologic and biochemical changes that are highly variable across individuals. Capturing epigenetic factors that associate with this variability can reveal novel biological insights into AD pathophysiology. We conducted an epigenome-wide association study of DNA methylation (DNAm) in 472 AD brains with neuropathologic measures (Braak stage, Thal phase, and cerebral amyloid angiopathy score) and brain biochemical levels of five proteins (APOE, amyloid-β (Aβ)40, Aβ42, tau, and p-tau) core to AD pathogenesis. Using a novel regional methylation (rCpGm) approach, we identified 5,478 significant associations, 99.7% of which were with brain tau biochemical measures. Of the tau-associated rCpGms, 93 had concordant associations in external datasets comprising 1,337 brain samples. Integrative transcriptome-methylome analyses uncovered 535 significant gene expression associations for these 93 rCpGms. Genes with concurrent transcriptome-methylome perturbations were enriched in oligodendrocyte marker genes, including known AD risk genes such as BIN1 , myelination genes MYRF, MBP and MAG previously implicated in AD, as well as novel genes like LDB3 . We further annotated the top oligodendrocyte genes in an additional 6 brain single cell and 2 bulk transcriptome datasets from AD and two other tauopathies, Pick's disease and progressive supranuclear palsy (PSP). Our findings support consistent rCpGm and gene expression associations with these tauopathies and tau-related phenotypes in both bulk brain tissue and oligodendrocyte clusters. In summary, we uncover the integrative epigenomic landscape of AD and demonstrate tau-related oligodendrocyte gene perturbations as a common potential pathomechanism across different tauopathies.

Tumor treatment has undergone revolutionary changes with the development of immunotherapy, especially immune checkpoint inhibitors. Because not all patients respond positively to immune therapeutic agents, and severe immune-related adverse events (irAEs) are frequently observed, the development of the biomarkers evaluating the response of a patient is key for the application of immunotherapy in a wider range. Recently, various multi-omics features measured by high-throughput technologies, such as tumor mutation burden (TMB), gene expression profiles, and DNA methylation profiles, have been proved to be sensitive and accurate predictors of the response to immunotherapy. A large number of predictive models based on these features, utilizing traditional machine learning or deep learning frameworks, have also been proposed. In this review, we aim to cover recent advances in predicting tumor immunotherapy response using multi-omics features. These include new measurements, research cohorts, data sources, and predictive models. Key findings emphasize the importance of TMB, neoantigens, MSI, and mutational signatures in predicting ICI responses. The integration of bulk and single-cell RNA sequencing has enhanced our understanding of the tumor immune microenvironment and enabled the identification of predictive biomarkers like PD-L1 and IFN-γ signatures. Public datasets and machine learning models have also improved predictive tools. However, challenges remain, such as the need for large and diverse clinical datasets, standardization of multi-omics data, and model interpretability. Future research will require collaboration among researchers, clinicians, and data scientists to address these issues and enhance cancer immunotherapy precision.

Lung cancer, a leading cause of cancer-related deaths worldwide, is primarily linked to smoking, tobacco use, air pollution, and exposure to hazardous chemicals. Genetic alterations, particularly in oncogenes like RAS, EGFR, MYC, BRAF, HER, and P13K, can lead to metabolic changes in cancer cells. These cells often rely on glycolysis for energy production, even in the presence of oxygen, a phenomenon known as aerobic glycolysis. This metabolic shift, along with other alterations, contributes to cancer cell growth and survival. To develop effective therapies, it's crucial to understand the genetic and metabolic changes that drive lung cancer. This review aims to identify specific genes associated with these metabolic alterations and screen phytochemicals for their potential to target these genes. By targeting both genetic and metabolic pathways, we hope to develop innovative therapeutic approaches to combat lung cancer.

DNA methylation is an epigenetic mechanism that regulates gene expression and is implicated in diseases such as cancer and atherosclerosis. However, traditional clinical methods for detecting DNA methylation often lack sensitivity and specificity, making early diagnosis challenging. Nanomaterials offer a solution with their unique properties, enabling highly sensitive photochemical and electrochemical detection techniques. These advanced methods enhance the accuracy and efficiency of identifying DNA methylation patterns, providing a powerful tool for early diagnosis and treatment of methylation-related diseases. This review summarizes nanomaterial-based techniques, categorized into electrochemical and photochemical methods for developing next-generation biosensors for DNA methylation. Electrochemical approaches based on nanostructured or nanomaterial-modified electrodes can detect methylation through electrical signals and can directly identify methylation sites via ionic current changes based on nanopore sequencing. Photochemical methods based on nanoparticles allow for optical detection through colorimetry, fluorescence, surface plasmon resonance, and Raman spectroscopy. Nanotechnology-implemented methodologies enable ultrasensitive and selective biosensors as point-of-care platforms for DNA methylation analysis, thereby advancing epigenetic research and clinical diagnostics.

Phospholipase C epsilon 1 (PLCε1) can hydrolyze phosphatidylinositol-4,5-bisphosphate and phosphatidylinositol-4-phosphate at the plasma membrane and perinuclear membrane in the cardiovascular system, producing lipid-derived second messengers. These messengers are considered prominent triggers for various signal transduction processes. Notably, diverse cardiac phenotypes have been observed in cardiac-specific and global Plce1 knockout mice under conditions of pathological stress. It is well established that the cardiac-specific Plce1 knockout confers cardioprotective benefits. Therefore, the development of tissue/cell-specific targeting approaches is critical for advancing therapeutic interventions.

This review aims to distill the foundational biology and functional significance of PLCε1 in cardiovascular diseases, as well as to explore potential avenues for research and the development of novel therapeutic strategies targeting PLCε1.

Cardiovascular diseases remain the leading cause of morbidity and mortality worldwide, with incidence rates escalating annually. A comprehensive understanding of the multifaceted role of PLCε1 is essential for enhancing the diagnosis, management, and prognostic assessment of patients suffering from cardiovascular diseases.

Renal cell carcinoma (RCC) is a common malignancy, with patients frequently diagnosed at an advanced stage due to the absence of sufficiently sensitive detection technologies, significantly compromising patient survival and quality of life. Advances in cell-free DNA (cfDNA) methylation profiling using liquid biopsies offer a promising non-invasive diagnostic option, but robust biomarkers for early detection are current not available. This study aimed to identify methylation biomarkers for RCC and establish a DNA methylation signature-based prognostic model for this disease.

High-throughput methylation sequencing was performed on peripheral blood samples obtained from 49 primarily Stage I RCC patients and 44 healthy controls. Comparative analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods were employed to identify RCC methylation signatures.Subsequently, methylation markers-based diagnostic and prognostic models for RCC were independently trained and validated using random forest and Cox regression methodologies, respectively.

Comparative analysis revealed 864 differentially methylated CpG islands (DMCGIs), 96.3% of which were hypermethylated. Using a training set from The Cancer Genome Atlas (TCGA) dataset of 443 early-stage RCC tumors and matched normal tissues, we applied LASSO regression and identified 23 methylation signatures. We then constructed a random forest-based diagnostic model for early-stage RCC and validated the model using two independent datasets: a TCGA set of 460 RCC tumors and controls, and a blood sample set from our study of 15 RCC cases and 29 healthy controls. For Stage I RCC tissue, the model showed excellent discrimination (AUC-ROC: 0.999, sensitivity: 98.5%, specificity: 100%). Blood sample validation also yielded commendable results (AUC-ROC: 0.852, sensitivity: 73.9%, specificity: 89.7%). Further analysis using Cox regression identified 7 of the 23 DMCGIs as prognostic markers for RCC, allowing the development of a prognostic model with strong predictive power for 1-, 3-, and 5-year survival (AUC-ROC > 0.7).

Our findings highlight the critical role of hypermethylation in RCC etiology and progression, and present these identified biomarkers as promising candidates for diagnostic and prognostic applications.

DNA methylation is mainly catalyzed by three DNA methyltransferase (DNMT) proteins in mammals. Usually DNMT1 is considered the primary DNMT for maintenance DNA methylation, whereas DNMT3A and DNMT3B function in de novo DNA methylation. Interestingly, we found DNMT3A and DNMT3B exerted maintenance and de novo DNA methylation in postimplantation mouse embryos. Together with DNMT1, they maintained DNA methylation at some pluripotent genes and lineage marker genes. Germline-derived DNA methylation at the imprinting control regions (ICRs) is stably maintained in embryos. DNMT1 maintained DNA methylation at most ICRs in postimplantation embryos. Surprisingly, DNA methylation was increased at five ICRs after implantation, and two DNMT3 proteins maintained the newly acquired DNA methylation at two of these five ICRs. Intriguingly, DNMT3A and DNMT3B maintained preexisting DNA methylation at four other ICRs, similar to what we found in embryonic stem cells before. These results suggest that DNA methylation is more dynamic than originally thought during embryogenesis including the ICRs of the imprinted regions. DNMT3A and DNMT3B exert both de novo and maintenance DNA methylation functions after implantation. They maintain large portions of newly acquired DNA methylation at variable degrees across the genome in mouse embryos, together with DNMT1. Furthermore, they contribute to maintenance of preexisting DNA methylation at a subset of ICRs as well as in the CpG islands and certain lineage marker gene. These findings may have some implications for the important roles of DNMT proteins in development and human diseases.

The human genome consists of 23 chromosome pairs (22 autosomes and one pair of sex chromosomes), with 46 chromosomes in a normal cell. In the interphase nucleus, the 2 m long nuclear DNA is assembled with proteins forming chromatin. The typical mammalian cell nucleus has a diameter between 5 and 15 μm in which the DNA is packaged into an assortment of chromatin assemblies. The human brain has over 3000 cell types, including neurons, glial cells, oligodendrocytes, microglial, and many others. Epigenetic processes are involved in directing the organization and function of the genome of each one of the 3000 brain cell types. We refer to epigenetics as the study of changes in gene function that do not involve changes in DNA sequence. These epigenetic processes include histone modifications, DNA modifications, nuclear RNA, and transcription factors. In the interphase nucleus, the nuclear DNA is organized into different structures that are permissive or a hindrance to gene expression. In this chapter, we will review the epigenetic mechanisms that give rise to cell type-specific gene expression patterns.

CpG islands (CGIs) are rare, interspersed DNA sequences, which possess a significant deviation from background genomic distribution by exhibiting patterns of GC-rich and CpG-rich sequence, the density of which provides a good classification feature for long noncoding RNA (lncRNA) promoters. By reviewing previous CpG-related studies, we consider that the transcription regulation of about half of the human genes, mostly housekeeping (HK) genes, involves CGIs, their methylation states, CpG spacing, and other chromosomal parameters. However, the precise CGI definition and positioning of CGIs within gene structures, as well as specific CGI-associated regulatory mechanisms, all remain to be elucidated at individual gene and gene family levels, together with consideration of species and lineage specificity. Although previous studies have already classified CGIs into high-CpG (HCGI), intermediate-CpG (ICGI), and low-CpG (LCGI) densities based on CpG density variation, the correlation between CGI density and gene expression regulation, such as co-regulation of CGIs and TATA-box on HK genes, is not clear. Here, we introduce such a problem-solving protocol for human genome annotation, which is based on a combination of GTEx, JBLA, and GO analysis. Next, we discuss why CGI-associated genes are most likely regulated by HCGI and tend to be HK genes; The HCGI/TATA± and LCGI/TATA± combinations show different GO enrichment, whereas the ICGI/TATA± combination is less characteristic than LCGI/TATA± based on GO enrichment analysis.

This study aimed to explore the potential of HIPK3 DNA methylation as a biomarker for rheumatoid arthritis (RA) by examining its methylation levels in various rheumatic immune diseases and analyzing its correlation with clinical indicators.

We recruited 368 participants, including patients with RA, ankylosing spondylitis (AS), GOUT, psoriatic arthritis (PSA), sjogren's syndrome (SS), systemic lupus erythematosus (SLE), dermatomyositis (DM), and healthy controls (HC), and MethylTargetTM targeted region methylation sequencing technology was employed to analyze the DNA methylation levels of HIPK3 (cg15692052). We analyzed the HIPK3 methylation levels and correlated them with common clinical indicators. Transcriptome data from the GEO dataset (GSE93272) were also analyzed to assess HIPK3 mRNA expression levels in RA patients. Statistical analyses included Spearman correlation, One-Way ANOVA, Kruskal-Wallis tests, and ROC curve analysis.

HIPK3 methylation levels were significantly lower in the RA, SLE, and DM groups compared to the HC group (P < 0.05). RA subgroups based on RF and CCP statuses also showed lower HIPK3 methylation levels compared to the HC group (P < 0.05). Analysis of the GEO dataset revealed significantly elevated HIPK3 mRNA expression in RA patients (P < 0.05). A negative correlation was found between HIPK3 methylation levels and ESR (r = -0.14, P = 7.1e-3), CRP (r = -0.25, P = 4.1e-7), RF (r = -0.18, P = 5.2e-4), and DAS28-CRP (r = -0.12, P = 0.04) in the RA group. ROC analysis indicated that HIPK3 methylation levels had high diagnostic value for RA (AUC = 0.742), SLE (AUC = 0.701), and DM (AUC = 0.948), especially in distinguishing seronegative RA (AUC = 0.612, 0.747, 0.836) and differentiating RA from AS (AUC = 0.788).

The study suggests that HIPK3 hypomethylation in peripheral blood is associated with RA and correlated with inflammatory markers, such as CRP. HIPK3 methylation levels have potential as a novel biomarker for RA diagnosis and predicting inflammation trends, showing particular promise in differentiating RA from other types of arthritis. Key Points • MethylTargetTM targeted region methylation sequencing technology was employed to analyze the DNA methylation levels of HIPK3 (cg15692052). • HIPK3 methylation levels have potential as a novel biomarker for RA diagnosis and predicting inflammation trends.

DNA methylation is an epigenetic modification process that can alter the functionality of a genome. It has been reported to be a key regulator of fruit ripening. In this study, the DNA methylation changes of CpG islands of ethylene signaling genes regulated by 1-methylcyclopropene (1-MCP) during ripening and senescence of tomato fruit were detected. The results showed that the 1-MCP treatment decreased the accumulation of lycopene, maintained the content of vitamin C, and delayed the ripening and senescence of tomato fruit. The quantitative real-time PCR and bisulfite sequencing analysis showed that 1-MCP treatment changed the expression and the DNA methylation level of CpG islands related to the ethylene signaling pathway genes, among which the DNA methylation change of LeEIN3 was the most significant. Compared with the control, 1-MCP treatment increased the DNA methylation level of the CpG island of the LeEIN3 gene, reduced the expression of LeEIN3 in tomato fruit, and was involved in 1-MCP delaying the postharvest senescence of tomato fruit. The results indicated that DNA methylation changes of ethylene signaling genes were involved in ethylene synthesis and signal transduction and played an important role in the regulation of 1-methylcyclopropene, delaying postharvest ripening and senescence of tomato fruit.

Pancreatic adenocarcinoma (PDAC) exhibits a complex microenvironment with diverse cell populations influencing patient prognosis. Single-cell RNA sequencing (scRNA-seq) was used to identify prognosis-related cell types, and DNA methylation (DNAm)-based models were developed to predict outcomes based on their cellular characteristics.

We integrated scRNA-seq, bulk data, and clinical information to identify key cell populations associated with prognosis. The TCGA dataset was used for validation, and cell composition was inferred from DNAm data. Prognostic models were constructed based on cell-type-specific DNAm markers, and genomic features were compared across risk groups. Nomograms were created to assess treatment responses in different risk levels.

Epithelial and T cells were major prognostic factors. Genomic analysis showed that epithelial cells in PDAC followed a malignant trajectory. DNAm data from TCGA confirmed the association of higher epithelial and T cell proportions with worse prognosis. Prognostic models based on DNAm markers of these cells effectively predicted patient survival, especially 5-year overall survival (AUC = 0.834). High-risk group with epithelial cell model showed altered pathways (tight junctions, NOTCH, and P53 signaling), while high-risk group with T cell model had changes in glycolysis, hypoxia, and NOTCH signaling, with more KRAS or TP53 mutations. Low-risk groups in the T cell model displayed stronger antitumor immune responses. Treatment predictions and nomograms were developed for clinical use.

scRNA-seq and DNAm data integration enabled the creation of predictive models based on epithelial and T cell-specific methylation patterns, offering robust prognosis prediction for PDAC patients.

Age-dependent changes in DNA methylation allow chronological and biological age inference, but the underlying mechanisms remain unclear. Using ultra-deep sequencing of >300 blood samples from healthy individuals, we show that age-dependent DNA methylation changes are regional and occur at multiple adjacent CpG sites, either stochastically or in a coordinated block-like manner. Deep learning analysis of single-molecule patterns in two genomic loci achieved accurate age prediction with a median error of 1.46-1.7 years on held-out human blood samples, dramatically improving current epigenetic clocks. Factors such as gender, BMI, smoking and other measures of biological aging do not affect chronological age inference. Longitudinal 10-year samples revealed that early deviations from epigenetic age are maintained throughout life and subsequent changes faithfully record time. Lastly, the model inferred chronological age from as few as 50 DNA molecules, suggesting that age is encoded by individual cells. Overall, DNA methylation changes in clustered CpG sites illuminate the principles of time measurement by cells and tissues, and facilitate medical and forensic applications.

The risks of obesity and epigenetic age acceleration (EAA) have drawn widespread attention. Lipid accumulation product (LAP) is a simple and reliable indicator of obesity; however, the relationship between LAP and EAA remains unclear.

Data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2002 were used. The EAA was assessed using a self-administered questionnaire in the database. LAP was calculated based on triglycerides and waist circumference. The association between LAP and EAA was analyzed using logistic regression models, subgroup analysis, and smooth curve fitting.

A total of 1796 participants were included in the study, of whom 1055 had EAA. After adjusting for relevant covariates, participants with EAA generally had higher LAP levels than those without EAA (258.1 vs. 244.6). Logistic regression analysis showed that individuals in the highest LAP quartile (Q4) were more likely to have EAA than those in the lowest quartile (Q1) (OR = 1.77; 95% CI 1.31-2.39; P < 0.001). The area under the curve of the adjusted logistic regression analysis was 0.706.

This research indicates that elevated LAP levels are independently linked to an increased risk of EAA, and early intervention to reduce high LAP levels is necessary to mitigate the progression of EAA.

The analysis of circulating cell-free DNA (cfDNA) holds immense promise as a non-invasive diagnostic tool across various human conditions. However, extracting biological insights from cfDNA fragments entails navigating complex and diverse bioinformatics methods, encompassing not only DNA sequence variation, but also epigenetic characteristics like nucleosome footprints, fragment length, and methylation patterns.

We introduce Liquid Biopsy Feature extract (LBFextract), a comprehensive package designed to streamline feature extraction from cfDNA sequencing data, with the aim of enhancing the reproducibility and comparability of liquid biopsy studies. LBFextract facilitates the integration of preprocessing and postprocessing steps through alignment fragment tags and a hook mechanism. It incorporates various methods, including coverage-based and fragment length-based approaches, alongside two novel feature extraction methods: an entropy-based method to infer TF activity from fragmentomics data and a technique to amplify signals from nucleosome dyads. Additionally, it implements a method to extract condition-specific differentially active TFs based on these features for biomarker discovery. We demonstrate the use of LBFextract for the subtype classification of advanced prostate cancer patients using coverage signals at transcription factor binding sites from cfDNA. We show that LBFextract can generate robust and interpretable features that can discriminate between different clinical groups. LBFextract is a versatile and user-friendly package that can facilitate the analysis and interpretation of liquid biopsy data.

LBFextract is freely accessible at https://github.com/Isy89/LBF. It is implemented in Python and compatible with Linux and Mac operating systems. Code and data to reproduce these analyses have been uploaded to 10.5281/zenodo.10964406.

Maternal caloric restriction during pregnancy significantly impacts kidney development, influencing susceptibility to chronic kidney disease in adulthood. This study explores DNA methylation changes in nephron progenitor cells resulting from caloric restriction and their implications for kidney health. Global DNA hypomethylation is observed in nephron progenitors from caloric-restricted embryos, with specific genomic regions displaying distinct methylation patterns, including hypomethylation and hypermethylation. Differentially methylated regions exhibit enhanced chromatin accessibility, indicating biological relevance. Hypomethylated regions are enriched for genes associated with developmental processes, reflecting changes in gene expression and highlighting their functional relevance in kidney development. The study also reveals that supplementing methionine, an essential amino acid, restores disrupted DNA methylation patterns, particularly in enhancer regions, emphasizing methionine's critical role in regulating nephron progenitor cell epigenetics and ensuring proper kidney development. The intricate relationship between maternal nutrition, dynamic DNA methylation, and kidney development is highlighted, emphasizing the enduring impact of early-life nutritional challenges on kidney function. This research elucidates epigenetic mechanisms as mediators for the lasting effects of maternal caloric restriction on kidney health. The study contributes valuable insights into the origins of chronic kidney diseases during early developmental stages, offering potential interventions to mitigate adverse outcomes.NEW & NOTEWORTHY Our study establishes a direct link between maternal caloric restriction, DNA methylation patterns in nephron progenitor cells, and kidney development. We reveal consistent alterations in methylation patterns, coupled with corresponding shifts in the expression of genes related to kidney development and cell proliferation. Methionine supplementation emerges as a promising intervention, effectively restoring disrupted DNA methylation patterns. These findings pave the way for potential therapeutics, optimizing kidney development and mitigating the burden of chronic kidney disease in adulthood.

CYP3As are important drug-metabolizing enzymes in the liver. The causes for large inter-person variability in CYP3A expression/activity remain poorly understood. DNA methylation broadly regulates gene expression and the developmental transition from fetal CYP3A7 to adult CYP3A4, and CpG methylation upstream of the CYP3A4 promoter is associated with its expression. However, because non-promoter CYP3A regulatory regions remain largely uncharacterized, how DNA methylation influences CYP3A expression has yet to be fully explored. We recently identified a distal regulatory region (DRR) that controls the expression of CYP3A4, CYP3A5, and CYP3A7. Here, we investigated the relationship between CYP3A expression and the methylation status of 16 CpG sites within the DRR in 70 liver samples. We found significant associations between DRR methylation and the expression of CYP3A5 and CYP3A7 but not CYP3A4, indicating differential CYP3A regulation by the DRR. Also, we observed a dynamic reduction in DRR DNA methylation during the differentiation of induced pluripotent stem cells to hepatocytes, which correlated with increased CYP3A expression. We then evaluated the relative contribution of genetic variants, TFs, and DRR DNA methylation on CYP3A expression in liver samples. Our results reinforce the DRR as a CYP3A regulator and suggest that DNA methylation may impact CYP3A-mediated drug metabolism.

Epilepsy, affecting approximately 1% of the global population, manifests as recurring seizures and is heavily influenced by genetic factors. Recent advancements in genetic technologies have revolutionized our understanding of epilepsy's genetic landscape. Key studies, such as the discovery of mutations in ion channels (e.g., SCN1A and SCN2A), neurotransmitter receptors (e.g., GABRA1), and synaptic proteins (e.g., SYNGAP1, KCNQ2), have illuminated critical pathways underlying epilepsy susceptibility and pathogenesis. Genome-wide association studies (GWAS) have identified specific genetic variations linked to epilepsy risk, such as variants near SCN1A and PCDH7, enhancing diagnostic accuracy and enabling personalized treatment strategies. Moreover, epigenetic mechanisms, including DNA methylation (e.g., MBD5), histone modifications (e.g., HDACs), and non-coding RNAs (e.g., miR-134), play pivotal roles in altering gene expression and synaptic plasticity, contributing to epileptogenesis. These discoveries offer promising avenues for therapeutic interventions aimed at improving outcomes for epilepsy patients. Genetic testing has become essential in clinical practice, facilitating precise diagnosis and tailored management approaches based on individual genetic profiles. Furthermore, insights into epigenetic regulation suggest novel therapeutic targets for developing more effective epilepsy treatments. In summary, this review highlights significant progress in understanding the genetic and epigenetic foundations of epilepsy. By integrating findings from key studies and specifying genes involved in epigenetic modifications, we underscore the potential for advanced therapeutic strategies in this complex neurological disorder, emphasizing the importance of personalized medicine approaches in epilepsy management.

Ferroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation, has emerged as a critical pathway in cancer biology. This review delves into the epigenetic mechanisms that modulate ferroptosis in cancer cells, focusing on how DNA methylation, histone modifications, and non-coding RNAs influence the expression and function of essential genes involved in this process. By unraveling the complex interplay between these epigenetic mechanisms and ferroptosis, the article sheds light on novel gene targets and functional insights that could pave the way for innovative cancer treatments to enhance therapeutic efficacy and overcome resistance in cancer therapy.

DNA methylation is crucial for chromatin structure, transcription regulation and genome stability, defining cellular identity. Aberrant hypermethylation of CpG-rich regions is common in cancer, influencing gene expression. However, the specific contributions of individual epigenetic modifications to tumorigenesis remain under investigation. In hepatocellular carcinoma (HCC), DNA methylation alterations are documented as in other tumor types. We aimed to identify hypermethylated CpGs in HCC, assess their specificity across other tumor types, and investigate their impact on gene expression. To this end, public methylomes from HCC, other liver diseases, and 27 tumor types as well as expression data from TCGA-LIHC and GTEx were analyzed. This study identified 39 CpG sites that were hypermethylated in HCC compared to control liver tissue, and were located within promoter, gene bodies, and intergenic CpG islands. Notably, these CpGs were predominantly unmethylated in healthy liver tissue and other normal tissues. Comparative analysis with 27 other tumors revealed both common and HCC-specific hypermethylated CpGs. Interestingly, the HCC-hypermethylated genes showed minimal expression in the different healthy tissues, with marginal changes in the level of expression in the corresponding tumors. These findings confirm previous evidence on the limited influence of DNA hypermethylation on gene expression regulation in cancer. It also highlights the existence of mechanisms that allow the selection of tissue-specific methylation marks in normally unexpressed genes during carcinogenesis. Overall, our study contributes to demonstrate the complexity of cancer epigenetics, emphasizing the need of better understanding the interplay between DNA methylation, gene expression dynamics, and tumorigenesis.

Circulating tumor DNA (ctDNA) methylation, an innovative liquid biopsy biomarker, has emerged as a promising tool in early cancer diagnosis, monitoring, and prognosis prediction. As a noninvasive approach, liquid biopsy overcomes the limitations of traditional tissue biopsy. Among various biomarkers, ctDNA methylation has garnered significant attention due to its high specificity and early detection capability across diverse cancer types. Despite its immense potential, the clinical application of ctDNA methylation faces substantial challenges pertaining to sensitivity, specificity, and standardization. In this review, we begin by introducing the basic biology and common detection techniques of ctDNA methylation. We then explore recent advancements and the challenges faced in the clinical application of ctDNA methylation in liquid biopsies. This includes progress in early screening and diagnosis, identification of clinical molecular subtypes, monitoring of recurrence and minimal residual disease (MRD), prediction of treatment response and prognosis, assessment of tumor burden, and determination of tissue origin. Finally, we discuss the future perspectives and challenges of ctDNA methylation detection in clinical applications. This comprehensive overview underscores the vital role of ctDNA methylation in enhancing cancer diagnostic accuracy, personalizing treatments, and effectively monitoring disease progression, providing valuable insights for future research and clinical practice.

The hypothalamus plays an important role in aging, but it remains unclear regarding the underlying epigenetics and whether this hypothalamic basis can help address aging-related diseases. Here, by comparing mouse hypothalamus with two other limbic system components, we show that the hypothalamus is characterized by distinctively high-level DNA methylation during young age and by the distinct dynamics of DNA methylation and demethylation when approaching middle age. On the other hand, age-related DNA methylation in these limbic system components commonly and sensitively applies to genes in hypothalamic regulatory pathways, notably oxytocin (OXT) and gonadotropin-releasing hormone (GnRH) pathways. Middle age is associated with transcriptional declines of genes which encode OXT, GnRH and signaling components, which similarly occur in an Alzheimer's disease (AD)-like model. Therapeutically, OXT-GnRH combination is substantially more effective than individual peptides in treating AD-like disorders in male 5×FAD model. In conclusion, the hypothalamus is important for modeling age-related DNA methylation and developing hypothalamic strategies to combat AD.

Recent studies have underscored the biological significance of RNA modifications in tumorigenicity and progression. However, the potential roles of RNA modifications in immune regulation and the formation of the tumor microenvironment (TME) in head and neck squamous carcinoma (HNSC) remain unclear.

We collected 199 untreated HNSC samples and clinicopathological data from Fujian Provincial Cancer Hospital. MeRIP-seq and RNA-seq were performed to generate methylation and gene expression profiles, respectively. Consensus molecular subtyping was employed to identify prognosis-related genes and RNA modification patterns in HNSC. Experiments confirmed the potential oncogenic behavior influenced by key genes. Molecular subtypes were identified through consensus clustering and validated using external cohort validation sets.

Among the RNA modification-related genes, IGF2BP1 emerged as the most prognostic. HNSC patients were categorized into high and low IGF2BP1 expression groups. High-expressing patients exhibited poorer survival and reduced chemosensitivity, coupled with increased tumor mutational burden, low PD-L1 expression, and limited immune cell infiltration, indicative of aggressive disease. Analysis revealed two distinct RNA modification patterns associated with IGF2BP1 expression: biosynthetically intense type (BIT) and oncogenically active type (OAT), each characterized by distinct clinical features, outcomes, and biological pathways. In an independent immunotherapy cohort, BIT patients displayed enhanced immune responses and sustained clinical benefits.

This study highlights the crucial link between RNA modification and TME diversity. Evaluating RNA modification in tumors improves our understanding of TME features and supports the development of effective immunotherapy strategies.