Evidence map›Paper›PMID 41815151›Full record

ArticleTranslational cancer research2026

A risk score model based on glycosylation-related genes for predicting radioresistance and prognosis of lung adenocarcinoma.

Yihong Chen, Baixia Yang, Xiaogang Zhai, Weidong Shi, Hongyan Qian, Qin Ge

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Article in Translational cancer research, 2026. 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.

Yihong Chen *Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University and Medical School of Nantong University, Nantong, China.
Baixia Yang *Department of Radiotherapy, Nantong Tumor Hospital, Affiliated Cancer Hospital of Nantong University, Nantong, China.
Xiaogang Zhai *Department of Radiotherapy, Nantong Tumor Hospital, Affiliated Cancer Hospital of Nantong University, Nantong, China.
Weidong ShiDepartment of Thoracic Surgery, The Second People Hospital of Nantong, Nantong, China.
Hongyan QianCancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University and Medical School of Nantong University, Nantong, China.
Qin GeCancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University and Medical School of Nantong University, Nantong, China.ORCID https://orcid.org/0000-0001-6735-0097

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6 · The paper itself

Abstract

Background: Radiotherapy resistance (RR) is the main cause of radiotherapy failure in lung cancer patients, and its mechanisms are still unrevealed. Glycosylation, as a type of post-translational modification of proteins, plays a key role in tumor progression. Some studies have shown a strong link between glycosylation and RR. However, the absence of a systematic glycosylation-related genes (GRGs) model to predict radiotherapy efficacy in lung adenocarcinoma (LUAD) patients highlights a significant clinical and research gap. The aim of the research was to investigate the prognostic characteristics of GRGs in LUAD treated with radiotherapy. Methods: RNA sequencing data of LUAD were obtained from The Cancer Genome Atlas (TCGA) database. The expression and prognostic significance of GRGs in patients who underwent radiotherapy were analyzed with bioinformatics tools, and the Gene Expression Omnibus (GEO) database was used for verification. Gene set enrichment analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), etc. were used to analyze the potential mechanism of risk model constructed by GRGs in LUAD. The predictive significance of risk model was investigated by immune infiltration analysis, somatic mutations, and drug susceptibility analysis, etc. Single-cell sequencing and molecular docking were used to find new potential targets for LUAD patients. Finally, our bioinformatics analysis results were verified by wet experiments. Results: GO and KEGG analyses found that glycosylation played a pivotal role in LUAD RR. Forty-four differentially expressed radiotherapy-related glycosylation genes (DERRGGs) were identified in LUAD. Conclusions: This study constructed glycosylation related RiskScore to predict the prognosis of LUAD patients, specifically in the context of radiotherapy. We explored the relationship between radiotherapy efficacy, glycosylation and prognosis of LUAD patients, which provides new ideas for personalized treatment of LUAD patients. And we suggested that tretinoin may be a potential radiotherapy sensitizer for LUAD, providing a foundation for future investigations.

Indexed as

glycosylationLung adenocarcinoma (LUAD)prognosisradiotherapy resistance (RR)tumor immune microenvironment

Identifiers

PMID41815151
PMCPMC12971580

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