{ "abstract": "Screening for gastric cancer (GC) is critical for improving patient survival, yet current clinical methods suffer from invasiveness and limited sensitivity. The aim of this study is to develop a machine learning model for the non-invasive diagnosis of GC using the whole-genome methylation profile of circulating free DNA (cfDNA) in plasma. Plasma cfDNA from 61 patients with GC and 29 healthy controls. First, whole‑genome bisulfite sequencing (WGBS) was performed on 29 paired GC and adjacent normal tissues to identify differentially methylated regions (DMRs). These tissue DMRs were then mapped to plasma cfDNA methylation data, yielding 32 stable DMRs that changed consistently in both tissues and plasma. Using LASSO and Random Forest (RF) algorithms, eight core diagnostic markers were selected. Four machine learning algorithms (Support Vector Machine, RF, K-Nearest Neighbor, Naïve Bayes) were used to build diagnostic models. WGBS revealed global hypomethylation and CpG island hypermethylation in GC, consistent with plasma cfDNA methylation patterns. The RF model built on the eight cfDNA methylation markers (annotated to genes including ATP8A2) performed best, achieving AUC values of 0.978 and 0.938 in the training and independent test sets, respectively, with an accuracy of 88.46%, sensitivity of 94.44%, and specificity of 75.00%. After adjusting for age, the model achieved an AUC of 0.924 on the test set, still demonstrating high discriminatory performance. Functional analysis implicated these markers in pathways such as phagocytosis and axon guidance, and showed significant correlation with immune cell infiltration. Notably, these core DMRs are not covered by the probes on the methylation arrays (450K and EPIC v2), highlighting the advantage of WGBS for novel biomarker discovery. In conclusion, tissue-plasma concordance analysis identified a panel of plasma cfDNA methylation biomarkers with strong diagnostic potential. The resulting RF model provides a promising non-invasive strategy for GC screening. Author summary: In this study, we set out to address the lack of non‑invasive and efficient tools for the detection of gastric cancer. Cancer cells release DNA fragments with specific methylation patterns into the bloodstream. By analyzing plasma samples from patients and healthy individuals, we used machine learning to identify eight key methylation signals from whole‑genome sequencing data. The diagnostic model we built, based on the Random Forest algorithm, performed remarkably well: it distinguished gastric cancer patients from healthy controls with high accuracy in an independent test set, achieving a sensitivity of over 94%. After adjusting for age, the model achieved an AUC of 0.924 on the test set, demonstrating high discriminatory performance. Importantly, the eight methylation markers we identified were overlooked by traditional epigenomic association studies based on microarrays (450K and EPIC v2). This was possible because we employed a technology that captures the entire methylation landscape. Our findings suggest that a simple blood test combined with our machine learning model has the potential to serve as a practical and noninvasive method for screening for gastric cancer. We believe this work brings us one step closer to making precision cancer detection more accessible and less burdensome for patients.", "abstract_shown": true, "affiliations": [ "The First Affiliated Hospital of Guangxi University of Chinese Medicine, Nanning, Guangxi, China", "The Yangtze River Delta Biological Medicine Research and Development Center of Zhejiang Province, Yangtze Delta Region Institution of Tsinghua University, Hangzhou, Zhejiang, China", "Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China", "State Key Laboratory of Targeting Oncology, Guangxi Medical University, Nanning, Guangxi, China" ], "article_type": "Research Article", "arxiv": null, "author_total": 9, "authors": [ { "affs": [ 0 ], "name": "Hailing Ou" }, { "affs": [ 1 ], "name": "Jinli Miao" }, { "affs": [ 2 ], "name": "Fusheng Ling" }, { "affs": [ 3 ], "name": "Jie Gao" }, { "name": "Tingan Wang" }, { "name": "Zhihua Ye" }, { "name": "Chunbo Zhang" }, { "name": "Mingwei Huang" }, { "name": "Yuzhou Qin" } ], "bibtex": "https://research.draconitic.com/paper/5nr5g8qer2yjj.bib", "cited_by": 0, "doi": "10.1371/journal.pgen.1012322", "fulltext_url": "https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1012322", "id": "5nr5g8qer2yjj", "issns": [ "1553-7404", "1553-7390" ], "landing_url": "https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1012322", "language": "eng", "licence": "CC-BY", "licence_label": "CC BY", "licence_source": "article", "licence_url": "https://creativecommons.org/licenses/by/4.0/", "metadata_licence": null, "native_subjects": [ [ "openalex", "1312|Epigenetics and DNA Methylation" ], [ "plos", "/Biology and life sciences/Anatomy/Body fluids/Blood/Blood plasma" ], [ "plos", "/Biology and life sciences/Biochemistry/Biomarkers" ], [ "plos", "/Biology and life sciences/Biochemistry/Nucleic acids/DNA/DNA modification/DNA methylation" ], [ "plos", "/Biology and life sciences/Cell biology/Chromosome biology/Chromatin/Chromatin modification/DNA methylation" ], [ "plos", "/Biology and life sciences/Genetics/DNA/DNA modification/DNA methylation" ], [ "plos", "/Biology and life sciences/Genetics/Epigenetics/Chromatin/Chromatin modification/DNA methylation" ], [ "plos", "/Biology and life sciences/Genetics/Epigenetics/DNA modification/DNA methylation" ], [ "plos", "/Biology and life sciences/Genetics/Gene expression" ], [ "plos", "/Biology and life sciences/Genetics/Gene expression/Chromatin/Chromatin modification/DNA methylation" ], [ "plos", "/Biology and life sciences/Genetics/Gene expression/DNA modification/DNA methylation" ], [ "plos", "/Biology and life sciences/Genetics/Genomics/Genomic medicine/Cancer genomics" ], [ "plos", "/Biology and life sciences/Physiology/Body fluids/Blood/Blood plasma" ], [ "plos", "/Medicine and health sciences/Anatomy/Body fluids/Blood/Blood plasma" ], [ "plos", "/Medicine and health sciences/Diagnostic medicine/Cancer detection and diagnosis" ], [ "plos", "/Medicine and health sciences/Oncology/Basic cancer research/Cancer genomics" ], [ "plos", "/Medicine and health sciences/Oncology/Cancer detection and diagnosis" ], [ "plos", "/Medicine and health sciences/Oncology/Cancers and neoplasms/Gastrointestinal tumors/Gastric cancer" ], [ "plos", "/Physical sciences/Chemistry/Chemical reactions/Methylation" ] ], "openalex": "W7221866716", "pdf_url": "https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1012322&type=printable", "pmcid": "PMC13671515", "pmid": null, "published": "2026-10-01", "publisher": "Public Library of Science", "ris": "https://research.draconitic.com/paper/5nr5g8qer2yjj.ris", "sources": [ { "first_seen": "2026-10-30T12:17:23Z", "native_id": "10.1371/journal.pgen.1012322", "source": "plos", "url": "https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1012322" }, { "first_seen": "2026-10-30T12:18:29Z", "native_id": "PMC:PMC13671515", "source": "europepmc", "url": "https://europepmc.org/article/PMC/PMC13671515" } ], "subjects": [ { "field": "biochemistry", "origin": "plos:gene expression", "subfield": "molecular-biology" }, { "field": "biology", "origin": "plos:genetics", "subfield": "genetics-genomics" }, { "field": "medicine", "origin": "plos:anatomy", "subfield": "physiology" } ], "title": "Genome-Wide DNA methylation profiling of tissue-plasma pairs identifies Novel cfDNA biomarkers for the diagnosis of gastric cancer", "title_text": "Genome-Wide DNA methylation profiling of tissue-plasma pairs identifies Novel cfDNA biomarkers for the diagnosis of gastric cancer", "type": "peer-reviewed", "updated_at": "2026-10-30T12:18:29Z", "url": "https://research.draconitic.com/paper/5nr5g8qer2yjj", "venue": "PLOS Genetics", "versions": [] }