Plasma Cell‐Free DNA Concentration and Fragmentomes Predict Neoadjuvant Chemotherapy Response in Cervical Cancer Patients DOI Creative Commons
Ting Peng, Haiqiang Zhang,

Lingguo Li

et al.

Advanced Science, Journal Year: 2024, Volume and Issue: 11(43)

Published: Sept. 25, 2024

Abstract Cervical cancer remains one of the most lethal gynecological malignancies. However, biomarkers for more precise patient care are an unmet need. Herein, concentration 285 plasma cell‐free DNA (cfDNA) samples analyzed from 84 cervical patients and clinical significance cfDNA fragmentomic characteristics across neoadjuvant chemotherapy (NACT) treatment. Patients with poor NACT response exhibit a significantly greater escalation in levels following initial cycle treatment, comparison to favorable response. Distinctive end motif profiles promoter coverages observed between differing responses. Notably, DNASE1L3 analysis further demonstrates intrinsic association resistance. The ratios show good discriminative capacity predicting non‐responders responders (area under curve (AUC) > 0.8). In addition, transcriptional start sites (TSS) around promoters discern alteration biological processes associated resistance reflect potential value These findings predictive may optimize treatment selection, minimize unnecessary assist establishing personalized strategies patients.

Language: Английский

Evaluation of a biomarker for amyotrophic lateral sclerosis derived from a hypomethylated DNA signature of human motor neurons DOI Creative Commons
Calum Harvey, Aga Nowak, Sai Zhang

et al.

BMC Medical Genomics, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 14, 2025

Abstract Amyotrophic lateral sclerosis (ALS) lacks a specific biomarker, but is defined by relatively selective toxicity to motor neurons (MN). As others have highlighted, this offers an opportunity develop sensitive and biomarker based on detection of DNA released from dying MN within accessible biofluids. Here we performed whole genome bisulfite sequencing (WGBS) iPSC-derived neurologically normal individuals. By comparing methylation with atlas tissue derived MN-specific signature hypomethylated genomic regions, which accords genes important for function. Through simulation optimised the selection regions in plasma CSF cell-free (cfDNA). However, show that MN-derived not detectable via WGBS cfDNA. In support our experimental finding, theoretically relative sparsity lower sets limit proportion cfDNA below threshold WGBS. Our findings are ongoing development ALS biomarkers. The could be usefully combined more methods perhaps study instead plasma. Indeed demonstrate neuronal-derived CSF. work relevant all diseases featuring death rare cell-types.

Language: Английский

Citations

0
Circulating cell-free DNA methylation profiles as noninvasive multiple sclerosis biomarkers: A proof-of-concept study DOI Creative Commons
Hailu Fu, Kevin Huang, Wen Zhu

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 20, 2025

Abstract In multiple sclerosis (MS), there is a critical need for non-invasive biomarkers to concurrently classify disease subtypes, evaluate disability severity, and predict long-term progression. this proof-of-concept study, we performed low-coverage whole-genome bisulfite sequencing (WGBS) on 75 plasma cell-free DNA (cfDNA) samples assessed the clinical utility of cfDNA methylation as single assay distinguishing MS patients from non-MS controls, identifying estimating predicting trajectories. We identified thousands differentially methylated CpGs hundreds regions (DMRs) that significantly distinguished separated stratified severity levels. These DMRs were highly enriched in immunologically neurologically relevant regulatory elements ( e.g., active promoters enhancers) contained motifs associated with neuronal function T-cell differentiation. To distinguish subtypes groups, achieved area-under-the-curve (AUC) values ranging 0.67 0.81 using 0.70 0.82 inferred tissue-of-origin patterns methylation, outperforming benchmark neurofilament light chain (NfL) glial fibrillary acidic protein (GFAP) same cohort. Finally, linear mixed-effects model “prognostic regions” where baseline levels progression predicted future (AUC=0.81) within 4-year evaluation window. As plan generate higher-depth WGBS data validation independent cohorts, present findings suggest potential circulating profiles promising noninvasive diagnosis prognosis.

Language: Английский

Citations

0
Recent advance in single-molecule detection and imaging DOI
Han Yun,

Weijie Tong,

Fei Ma

et al.

TrAC Trends in Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown, P. 118252 - 118252

Published: April 1, 2025

Language: Английский

Citations

0
A Computational Framework for Analysis of cfDNA Fragmentation Profiles DOI Creative Commons
Zhong Wee Poh, Guanhua Zhu, Pui‐Mun Wong

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: April 18, 2025

Abstract Circulating cell-free DNA (cfDNA) has emerged as a promising non-invasive medium for studying tumor molecular profiles. Non-random fragmentation patterns in plasma cfDNA, particularly around nucleosome-depleted regions (NDRs) near transcription start sites (TSS), have been shown to reflect epigenetic regulation and gene expression. In this study, coverage profiles of the NDR were utilized derive an score, which was subsequently used proxy inferring To reduce transcript-to-transcript variability enhance clarity these expression-associated signals, we implement method GC-bias correction cfDNA samples. A computational framework (NDRDiff) then developed enable comparative analyses score across different sample groups. The preserved overall trend signal while improving separation expression levels, demonstrated by comparisons healthy donor samples with matched blood RNA-seq data. Validation on simulated dataset showed that NDRDiff achieved area under precision–recall curve (AUPRC) 0.916, outperforming standard t-test (AUPRC 0.777). When applied comparison metastatic colorectal cancer (mCRC) identified 531 differential (DNS) genes facilitated clear between two These DNS found correlate fraction estimates (down-regulated genes: Pearson R = 0.89, p < 0.05; up-regulated –0.88, 0.05) included CLDN4, BIN2, IRAG2, exhibit strong associations or cell signatures. Gene set enrichment analysis further revealed colon other gastrointestinal tissue Collectively, findings underscore potential NDR-based minimally invasive tool monitoring tumor-related features cancer.

Language: Английский

Citations

0
DNA methylation in breast cancer: early detection and biomarker discovery through current and emerging approaches DOI Creative Commons

Melissa Hum,

Ann S. G. Lee

Journal of Translational Medicine, Journal Year: 2025, Volume and Issue: 23(1)

Published: April 23, 2025

Breast cancer remains one of the most common cancers in women worldwide. Early detection is critical for improving patient outcomes, yet current screening methods have limitations. Therefore, there a pressing need more sensitive and specific approaches to detect breast its earliest stages. Liquid biopsy has emerged as promising non-invasive method early management. DNA methylation, an epigenetic alteration that often precedes genetic changes, been observed precancerous or stages, making it valuable biomarker. This review explores role methylation potential developing blood-based tests. We discuss advancements methods, recent discoveries biomarkers from both single-omics multi-omics integration studies, machine learning enhancing diagnostic accuracy. Challenges future directions are also addressed. Although challenges remain, advances continue enhance clinical methylation-based biomarkers. Ongoing research crucial further refine these improve outcomes.

Language: Английский

Citations

0
Beyond Biomarkers: Machine Learning-Driven Multiomics for Personalized Medicine in Gastric Cancer DOI Open Access
J. Daniel,

Canfeng Fan,

Tomoya Sano

et al.

Journal of Personalized Medicine, Journal Year: 2025, Volume and Issue: 15(5), P. 166 - 166

Published: April 24, 2025

Gastric cancer (GC) remains one of the leading causes cancer-related mortality worldwide, with most cases diagnosed at advanced stages. Traditional biomarkers provide only partial insights into GC’s heterogeneity. Recent advances in machine learning (ML)-driven multiomics technologies, including genomics, epigenomics, transcriptomics, proteomics, metabolomics, pathomics, and radiomics, have facilitated a deeper understanding GC by integrating molecular imaging data. In this review, we summarize current landscape ML-based integration for GC, highlighting its role precision diagnosis, prognosis prediction, biomarker discovery achieving personalized medicine.

Language: Английский

Citations

0
cfDecon: Accurate and Interpretable Methylation-Based Cell Type Deconvolution for Cell-Free DNA DOI
Yixuan Wang, Jiayi Li,

Jingqi Li

et al.

Lecture notes in computer science, Journal Year: 2025, Volume and Issue: unknown, P. 307 - 311

Published: Jan. 1, 2025

Language: Английский

Citations

0
Mining nucleic acid “omics” to boost liquid biopsy in cancer DOI Creative Commons
Ann Tivey, Rebecca Lee, Alexandra Clipson

et al.

Cell Reports Medicine, Journal Year: 2024, Volume and Issue: 5(9), P. 101736 - 101736

Published: Sept. 1, 2024

Language: Английский

Citations

3
EpiGePT: a pretrained transformer-based language model for context-specific human epigenomics DOI Creative Commons
Zijing Gao, Qiao Liu, Wanwen Zeng

et al.

Genome biology, Journal Year: 2024, Volume and Issue: 25(1)

Published: Dec. 18, 2024

Abstract The inherent similarities between natural language and biological sequences have inspired the use of large models in genomics, but current struggle to incorporate chromatin interactions or predict unseen cellular contexts. To address this, we propose EpiGePT, a transformer-based model designed for predicting context-specific human epigenomic signals. By incorporating transcription factor activities 3D genome interactions, EpiGePT outperforms existing methods signal prediction tasks, especially cell-type-specific long-range interaction predictions genetic variant impacts, advancing our understanding gene regulation. A free online service is available at http://health.tsinghua.edu.cn/epigept .

Language: Английский

Citations

3
Computational deconvolution of DNA methylation data from mixed DNA samples DOI Creative Commons
Maísa R. Ferro Dos Santos, Edoardo Giuili, Andries De Koker

et al.

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(3)

Published: March 27, 2024

Abstract In this review, we provide a comprehensive overview of the different computational tools that have been published for deconvolution bulk DNA methylation (DNAm) data. Here, refers to estimation cell-type proportions constitute mixed sample. The paper reviews and compares 25 methods (supervised, unsupervised or hybrid) developed between 2012 2023 strengths limitations each approach. Moreover, in study, describe impact platform used generation data (including microarrays sequencing), applied pre-processing steps reference dataset on performance. Next reference-based methods, also examine require only partial datasets no set at all. guidelines use specific dependent type availability.

Language: Английский

Citations

2