Evidence map›Paper›PMID 41073893›Full record

ArticleBMC gastroenterology2025

Identification and tissue-level validation of ferroptosis-related genes in small intestinal neuroendocrine neoplasms based on machine learning.

Chuang Lv, Zhiqiang Liu, Chengcheng Tong, Le Liu, Ziyue Dou, Xi Chen

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Article in BMC gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Chuang Lv *Department of Gastroenterology, First Affiliated Hospital of Anhui Medical University, Hefei, China.
Zhiqiang Liu *Department of Gastroenterology, First Affiliated Hospital of Anhui Medical University, Hefei, China.
Chengcheng TongDepartment of Gastroenterology, First Affiliated Hospital of Anhui Medical University, Hefei, China.
Le LiuDepartment of Gastroenterology, First Affiliated Hospital of Anhui Medical University, Hefei, China.
Ziyue DouDepartment of Gastroenterology, First Affiliated Hospital of Anhui Medical University, Hefei, China.
Xi ChenDepartment of Gastroenterology, First Affiliated Hospital of Anhui Medical University, Hefei, China. ayfychenxi@163.com.

Funding

the National Natural Science Foundation of China 82073947
6 · The paper itself

Abstract

backgroundSmall intestinal neuroendocrine neoplasms (SI-NENs), a subgroup of neuroendocrine tumors originating from neuroendocrine cells in the small intestine, present significant therapeutic challenges, and their relationship with ferroptosis-a regulated form of cell death driven by iron-dependent lipid peroxidation-remains poorly understood. This study aimed to identify the ferroptosis-related genes in SI-NENs and evaluate their clinical relevance.

methodsThe RNA sequencing data of SI-NENs patients were retrieved from two public datasets (GSE65286 and GSE98894). Machine learning (LASSO regression, a regularization method for selecting key features by reducing redundant genes, and random forest, an ensemble learning algorithm that builds multiple decision trees to improve prediction accuracy) was adopted to identify the core ferroptosis-related genes in SI-NENs. An external cohort consisting of 10 health volunteers and 14 SI-NENs patients was enrolled and intestinal mucosal biopsies were obtained for validation.

resultsCDCA3, CDC25A, CYP4F8, and MYB were identified as the core ferroptosis-related genes in SI-NENs patients via machine learning. In the external cohort, immunofluorescence (IF) staining confirmed a significantly decreased expression of CYP4F8 and CDCA3 in patients with SI-NENs compared with the health control. Moreover, in G2/3 patients, the expression levels of CYP4F8 and CDCA3 were significantly decreased compared with G1 patients. In contrast, no significant difference in the expression of the four core ferroptosis genes was found across the different tumor locations, the age stratifications, or the abdominal pain statuses. In addition, a statically negative correlation of the expression of CYP4F8 was further detected concerning the tumor diameter and the expression of Ki67.

conclusionFour core ferroptosis-related genes (CDCA3, CDC25A, CYP4F8, and MYB) were identified in SI-NENs patients, which may aid in developing the diagnostic biomarkers and therapeutic targets in the clinic.

Indexed as

FerroptosisIntestinal NeoplasmsMachine LearningNeuroendocrine TumorsAdultAgedCell Cycle ProteinsFemaleGene Expression Regulation, NeoplasticHumansIntestine, SmallMaleMiddle AgedProto-Oncogene Proteins c-mybCell Cycle ProteinsProto-Oncogene Proteins c-mybBiomarkersFerroptosis-related genesMachine learningSmall intestinal neuroendocrine neoplasms

Identifiers

PMID41073893
PMCPMC12512264

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