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Machine Learning-Driven Biomarker for Predicting Lymph Node Metastasis and Prognosis in Esophagogastric Junction Adenocarcinoma

Machine Learning-Driven Biomarker for Predicting Lymph Node Metastasis and Prognosis in Esophagogastric Junction Adenocarcinoma

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500112823
Enrollment
Unknown
Registered
2025-11-19
Start date
2025-11-20
Completion date
Unknown
Last updated
2025-11-24

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Esophagogastric Junction Adenocarcinoma

Interventions

Observation group of adenocarcinoma of the esophagogastric junction:None

Sponsors

West China Hospital, Sichuan University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 90 Years

Inclusion criteria

Inclusion criteria: 1. Participants with histopathologically confirmed adenocarcinoma of the esophagogastric junction (AEG); 2. Underwent surgical intervention; 3. Had complete clinical medical records and follow-up data.

Exclusion criteria

Exclusion criteria: 1. Cases with incomplete or missing clinicopathological data. 2. Participants diagnosed with other histological subtypes (e.g., squamous cell carcinoma, neuroendocrine carcinoma); 3. Death within 30 days after surgery.

Design outcomes

Primary

MeasureTime frame
The mRNA expression of multiple genes (ALG3, KIFC1, LSR, ERCC2, NMB);Immunohistochemical scores of the multi-gene panel (ALG3, KIFC1, LSR, ERCC2, NMB);Postoperative survival status and survival time;Baseline characteristics (including age, sex, T stage, N stage, M stage, TNM stage, tumor differentiation grade, tumor location, and histological type, etc.);—

Countries

China

Contacts

Public ContactJiankun Hu

West China Hospital, Sichuan University

hujkwch@126.com+86 182 0819 2080

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026