Iron homeostasis landscape informs patient survival and neoadjuvant chemotherapy response in cancer

Abstract Background Cancer cells frequently alter iron metabolism by increasing uptake or decreasing export, promoting oncogenesis and proliferation. However, the quantitative relationship between iron homeostasis and cancer remains underexplored. Methods and results We simplified these alterations into measurable changes in the expression levels of iron transport-related genes. We investigated the expression of iron transport-related genes by comparing genomic data of 478 human cancer cell lines to 105 normal tissue samples, as well as analyzing clinically paired 6903 tumor to 709 normal samples. We revealed that cancer cells exhibited increased expression of transferrin receptor 1 (TFR1) or CD44 for iron uptake and decreased expression of ferroportin for iron efflux to favor iron accumulation. Using machine learning, we identified TFR1, CD44, and ferroportin as the top three features consistently associated most with patient survival. We developed a cancer-specific iron risk score (CIRS) using parametric time-to-event models based on RNA-seq data from TFR1, CD44, and ferroportin in 4723 patients spanning 15 cancers. The CIRS, cross-validated in five external datasets, significantly stratified survival risk for various cancers, and predicted chemotherapy response in two independent breast cancer pharmacogenomic datasets. Conclusions Our study enhances the understanding of the relationship between iron homeostasis, survival, and chemotherapy response, laying the groundwork for iron-targeted therapies.

Authors

Institutions

Publication Details

Journal
BioData Mining
Published
2026-07-24
DOI
https://doi.org/10.1186/s13040-026-00589-3
Primary Topic
Iron Metabolism and Disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Iron homeostasis landscape informs patient survival and neoadjuvant chemotherapy response in cancer

Xiaoyu Yan, Kangna Cao, Xu Steven Xu, Xiaoqing Fan
BioData Mining
Iron Metabolism and Disorders
article

Iron homeostasis landscape informs patient survival and neoadjuvant chemotherapy response in cancer

Xiaoyu Yan, Kangna Cao, Xu Steven Xu, Xiaoqing Fan
article en

Abstract

Abstract Background Cancer cells frequently alter iron metabolism by increasing uptake or decreasing export, promoting oncogenesis and proliferation. However, the quantitative relationship between iron homeostasis and cancer remains underexplored. Methods and results We simplified these alterations into measurable changes in the expression levels of iron transport-related genes. We investigated the expression of iron transport-related genes by comparing genomic data of 478 human cancer cell lines to 105 normal tissue samples, as well as analyzing clinically paired 6903 tumor to 709 normal samples. We revealed that cancer cells exhibited increased expression of transferrin receptor 1 (TFR1) or CD44 for iron uptake and decreased expression of ferroportin for iron efflux to favor iron accumulation. Using machine learning, we identified TFR1, CD44, and ferroportin as the top three features consistently associated most with patient survival. We developed a cancer-specific iron risk score (CIRS) using parametric time-to-event models based on RNA-seq data from TFR1, CD44, and ferroportin in 4723 patients spanning 15 cancers. The CIRS, cross-validated in five external datasets, significantly stratified survival risk for various cancers, and predicted chemotherapy response in two independent breast cancer pharmacogenomic datasets. Conclusions Our study enhances the understanding of the relationship between iron homeostasis, survival, and chemotherapy response, laying the groundwork for iron-targeted therapies.

BioData Mining
Chinese University of Hong Kong (HK), Genmab (United States) (US)
Openalex Percentile: Top 9%
Iron Metabolism and Disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.