Integrating Bulk and Single-Cell Transcriptomics with Machine Learning Reveals a Heme Metabolism-Based Panel for Lung Adenocarcinoma Chemotherapy Resistance.

Journal: International journal of molecular sciences
Published Date: May 14, 2025

Abstract

Lung adenocarcinoma (LUAD) is a leading cause of cancer-related mortality, with heme metabolism playing a critical role in tumor progression and treatment resistance. This study investigates the clinical implications of heme metabolism in LUAD, focusing on its link to ferroptosis and drug sensitivity. Using multi-omics data from TCGA-LUAD, GEO databases, and a single-cell RNA-seq cohort, we identified two molecular subtypes based on heme metabolism-related genes. We further developed a prognostic panel, termed the heme metabolism risk score (HMRS), using LASSO and multivariate Cox regression analyses. The HMRS panel effectively stratified patients into high- and low-risk groups, with high-risk patients showing enhanced tumor proliferation, suppressed ferroptosis, and resistance to chemotherapy. Single-cell analysis revealed elevated heme metabolism risk in epithelial cells correlated with tumor progression. Drug sensitivity predictions were validated in platinum-based chemotherapy cohorts, confirming HMRS as a robust prognostic tool. ABCC2 was identified as a key regulator of ferroptosis and cisplatin resistance, with in vitro experiments demonstrating that ABCC2 knockdown enhanced cisplatin-induced ferroptosis. These findings highlight HMRS as a critical tool for patient stratification and ABCC2 as a promising therapeutic target to overcome cisplatin resistance.

Authors

  • Lin Zhao
    c Key Laboratory of Birth Defects and Related Diseases of Women and Children (Ministry of Education) , West China Second University Hospital Sichuan University , Chengdu , China.
  • Haibo Han
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Clinical Laboratory, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Xuantong Zhou
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Tongyang Gong
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Yuge Zhu
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Bufan Xiao
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Thoracic Surgery II, Peking University Cancer Hospital & Institute, Beijing, China.
  • Shuchang Liu
    College of Science, Zhejiang University of Technology, Hangzhou, Zhejiang, 310014, China.
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.
  • Nan Wu
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, National Center of Excellence for Computational Drug Abuse Research, Drug Discovery Institute, Departments of Computational Biology and Structural Biology, School of Medicine , University of Pittsburgh , Pittsburgh , Pennsylvania 15261 , United States.

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