ArticleFrontiers in oncology2024
Evaluation of a three-gene methylation model for correlating lymph node metastasis in postoperative early gastric cancer adjacent samples.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
33 citing papers in PubMed.
- Acidic tumor microenvironment-modulated nanoparticle potentiates gastric cancer photoimmunotherapy.Journal of advanced research · 2026Article
- Riding the Wave of Progress: Examining the Current Landscape and Future Potential of MicroRNAs in Cancer Gene Therapy.Current gene therapy · 2026Review
- The role of telomeres in leukemic stem cells function.Regenerative therapy · 2025Review
- Regulation of the MAPK/ERK Pathway by miRNA-27b in Gastric Cancer: Diagnostic Implications and Therapeutic Potential of Aloin.Asia-Pacific journal of clinical oncology · 2025Article
- A novel four-serum marker model for early detection and therapeutic monitoring of breast cancer.Scientific reports · 2025Article
- Multistage feature selection and stacked generalization model for cancer detection.Scientific reports · 2025Article
- [A multi-molecular predictive model for lymph node metastasis in papillary thyroid carcinoma based on machine learning algorithms].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2025Article
- Review
- Review
- The oncogenic role of FOXM1 in hepatocellular carcinoma: molecular mechanisms, clinical significance, and therapeutic potentials.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Development of a CTL Epitope-Based Vaccine Targeting Tumor Antigens in Esophageal Cancer: An Immunoinformatic Study.Iranian journal of biotechnology · 2025Article
- Roles of anoikis in hepatocellular carcinoma therapy and the assessment of anoikis-regulatory molecules as therapeutic targets.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.Tomography (Ann Arbor, Mich.) · 2025Article
- Thiazole derivatives in cancer therapy: mechanistic insights, bioactivity, and future perspective.Future medicinal chemistry · 2025Review
- In silico and in vitro assessment of TP53, ATM, RAD51, and BAX genes in gastric cancer and their contribution to radiotherapy resistance.Hereditas · 2025Article
- Autoimmune gastritis detection from preprocessed endoscopy images using deep transfer learning and moth flame optimization.Scientific reports · 2025Article
- A hybrid XAI-driven deep learning framework for robust GI tract disease diagnosis.Scientific reports · 2025Article
- MSC-derived extracellular vesicles: Precision miRNA delivery for overcoming cancer therapy resistance.Regenerative therapy · 2025Review
- In vitro and in silico evaluation of fluorinated diphenylamine chalcone derivatives as potential antimalarial and anticancer agents.Scientific reports · 2025Article
- A robust chronic obstructive pulmonary disease classification model using dragonfly optimized kernel extreme learning machine.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lymph node metastasis (LNM) has a profound impact on the treatment and prognosis of early gastric cancer (EGC), yet the existing evaluation methods lack accuracy. Recent research has underscored the role of precancerous lesions in tumor progression and metastasis. The objective of this study was to utilize the previously developed EGC LNM prediction model to further validate and extend the analysis in paired adjacent tissue samples. Methods: We evaluated the model in a monocentric study using Methylight, a methylation-specific PCR technique, on postoperative fresh-frozen EGC samples (n = 129) and paired adjacent tissue samples (n = 129). Results: The three-gene methylation model demonstrated remarkable efficacy in both EGC and adjacent tissues. The model demonstrated excellent performance, with areas under the curve (AUC) of 0.85 and 0.82, specificities of 85.1% and 80.5%, sensitivities of 83.3% and 73.8%, and accuracies of 84.5% and 78.3%, respectively. It is noteworthy that the model demonstrated superior performance compared to computed tomography (CT) imaging in the adjacent tissue group, with an area under the curve (AUC) of 0.86 compared to 0.64 (p < 0.001). Furthermore, the model demonstrated superior diagnostic capability in these adjacent tissues (AUC = 0.82) compared to traditional clinicopathological features, including ulceration (AUC = 0.65), invasional depth (AUC = 0.66), and lymphovascular invasion (AUC = 0.69). Additionally, it surpassed traditional models based on these features (AUC = 0.77). Conclusion: The three-gene methylation prediction model for EGC LNM is highly effective in both cancerous and adjacent tissue samples in a postoperative setting, providing reliable diagnostic information. This extends its clinical utility, particularly when tumor samples are scarce, making it a valuable tool for evaluating LNM status and assisting in treatment planning.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.