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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Gastrointestinal Cancers: Hepato Pancreatic Biliary Cancers
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1509170
This article is part of the Research Topic Innovative Approaches in Chemotherapy and Immunotherapy for Gastroenteropancreatic Neuroendocrine Carcinoma View all articles

Machine Learning Based Predictive Model and Genetic Mutation Landscape for High-grade Colorectal Neuroendocrine Carcinoma: A SEER database analysis with external validation

Provisionally accepted
Ruixin Wu Ruixin Wu Sihao Chen Sihao Chen Yi He Yi He Ya Li Ya Li Song Mu Song Mu Aishun Jin Aishun Jin *
  • Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China

The final, formatted version of the article will be published soon.

    Background: High-grade colorectal neuroendocrine carcinoma (HCNEC) is a rare but aggressive subset of neuroendocrine tumors. This study was designed to construct a risk model based on comprehensive clinical and mutational genomics data to facilitate clinical decision making. Methods: A retrospective analysis was conducted using data from the Surveillance, Epidemiology, and End Results (SEER) database, spanning 2000 to 2019. The external validation cohort was sourced from two tertiary hospitals in Southwest China. Independent factors influencing both overall survival (OS) and cancer-specific survival (CSS) were identified using LASSO, Random Forest, and XGBoost regression techniques. Molecular data with the most common mutations in CNEC were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Results: In this prognostic analysis, the data from 714 participants with HCNEC were evaluated. The median OS for the cohort was 10 months, whereas CSS was 11 months. Six variables (M stage, LODDS, Nodes positive, Surgery, Radiotherapy, and Chemotherapy) were screened as key prognostic indicators. The machine learning model showed reliable performance across multiple evaluation dimensions. The most common mutations of CNEC identified in the COSMIC database were TP53, KRAS, and APC. Conclusions: In this study, a refined machine learning predictive model was developed to assess the prognosis of HCNEC accurately and we briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.

    Keywords: High-grade colorectal neuroendocrine carcinoma (HCNEC), machine learning, prognosis, SEER, COSMIC, Genetic Mutation Landscape

    Received: 10 Oct 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Wu, Chen, He, Li, Mu and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Aishun Jin, Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.