Evidence map›Paper›PMID 41123817›Full record

ArticleCellular oncology (Dordrecht, Netherlands)2025

Machine learning constructs a ferroptosis related signature for predicting prognosis and drug sensitivity in lung cancer.

Zihao Li, Yibing Chen, Benxin Hou, Yanjun Mi, Chunlin Fu, Zhaoyang Han, Qing Tang, Weihong Sun, Qing Xia, Yuan Liao and 1 more

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PubMed Full text at PMC Publisher
In one paragraph

Article in Cellular oncology (Dordrecht, Netherlands), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Zihao Li *MOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, School of Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510631, China.
Yibing Chen *Genetic and Prenatal Diagnosis Center, Department of Gynecology and Obstetrics, First Affiliated Hospital, Zhengzhou University, Zhengzhou, 450052, China.
Benxin Hou *Department of General Surgery, The Third People's Hospital of Hainan Province, Sanya, 572000, China.
Yanjun Mi *Department of Oncology, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, 361000, China.
Chunlin FuMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, School of Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510631, China.
Zhaoyang HanMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, School of Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510631, China.
Qing TangMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, School of Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510631, China.
Weihong SunMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, School of Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510631, China.
Qing XiaDepartment of Oncology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China. xiaqing@shsmu.edu.cn.
Yuan LiaoDepartment of Laboratory Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China. liaoy47@mail.sysu.edu.cn.
Zhengzhi ZouMOE Key Laboratory of Laser Life Science & Institute of Laser Life Science, College of Biophotonics, School of Optoelectronic Science and Engineering, South China Normal University, Guangzhou, 510631, China. zouzhengzhi@m.scnu.edu.cn.ORCID http://orcid.org/0000-0002-1893-7649

Funding

National Natural Science Foundation of China 81972479National Natural Science Foundation of China 82271883Natural Science Foundation of Guangdong Province 2024A1515010310
6 · The paper itself

Abstract

purposeFerroptosis is a novel form of iron-dependent programmed cell death that is associated with the progression of various tumors and cancer treatment responses. However, its role in the clinical treatment of lung cancer, particularly in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), remains poorly understood. This study aims to explore the prognostic value of ferroptosis-related genes in lung cancer and establish a reliable predictive model.

methodsWe collected the GSE4573, TCGA-LUAD, and TCGA-LUSC datasets, comprising a total of 1,271 samples and a ferroptosis gene set of 717 genes. Weighted gene coexpression network analysis (WGCNA) was used to identify ferroptosis-related markers, followed by the application of 101 machine learning algorithms combining 10 different approaches to develop a ferroptosis-related signature (FRS) for lung cancer prognosis.

resultsThe FRS demonstrated superior performance in predicting the survival of lung cancer patients, significantly outperforming traditional TNM and American Joint Committee on Cancer (AJCC) staging systems. External validation using the GSE13213 dataset also confirmed its robustness. Furthermore, the low-risk group exhibited higher immune microenvironment scores, suggesting a more active anti-tumor immune response, while the high-risk group showed elevated cell proliferation, migration, T-cell exclusion, and TIDE scores, indicating a more aggressive tumor phenotype. Additionally, the low-risk group demonstrated higher sensitivity to multiple drugs, including cisplatin, cyclophosphamide, paclitaxel, erlotinib, Niraparib, Rapamycin, Fulvestrant, and Venetoclax, highlighting its potential for guiding personalized treatment strategies.

conclusionThe FRS represents a powerful and clinically relevant tool for predicting the survival of lung cancer patients, offering new insights into personalized treatment and therapeutic decision-making.

Indexed as

Drug Resistance, NeoplasmFerroptosisLung NeoplasmsMachine LearningAntineoplastic AgentsBiomarkers, TumorFemaleGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansPrognosisTumor MicroenvironmentAntineoplastic AgentsBiomarkers, TumorFerroptosisLung cancerMachine learningPrognostic signatureRNA-seq

Identifiers

PMID41123817
PMCPMC12698758

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.