2026-10-13T13:15:03Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33809308 2026-09-15T17:22:52Z category_12 category_13 category_14 category_46 category_734 category_811 category_64 category_132 portal_5 item_type_3 month_year_09_2026

<p>Melatonin associated genes.</p> Hongxia Guo (302081) Lixia Liu (447585) Ying Lu (66401) Yuhui Ma (789963) Xiaolu Ren (16941907) Tong Cui (2537890) Cell Biology Genetics Molecular Biology Immunology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Cancer Infectious Diseases superior predictive accuracy receiver operating characteristic predict patient outcomes past 20 years multivariate cox analysis related signature genes key module genes critical module genes )- associated genes significant negative association selected using univariate chosen using univariate immune analysis showed eight model genes nomogram &# 8217 related prognostic model predictive risk model model genes verified using using deseq2 significant relationships prognostic value immune checkpoints immune cells risk model xlink "> study intended provide recommendations immunological studies gse31210 datasets gse31210 dataset gene expression either low dramatic upsurge discovered near combining 1 algorithm may <div> <p>Purpose</p><p>Lung adenocarcinoma (LUAD), the most prevalent lung cancer subtype, has witnessed a dramatic upsurge in frequency over the past 20 years. This study intended to construct a predictive risk model and analyze melatonin (ME)-associated genes in LUAD.</p> <p>Methods</p><p>Differentially expressed genes (DEGs) were found by using DESeq2 to analyze the TCGA-LUAD dataset. ME-DEGs were discovered near the junction, and ME critical module genes were identified by WGCNA. To build a risk model, model genes from ME-DEGs were chosen using univariate and multivariate Cox analysis. The samples were categorized as either low-risk or high-risk. The model was verified using the GSE31210 dataset. Gene expression was confirmed by RT-qPCR, immunological studies were performed, and a nomogram was developed.</p> <p>Results</p><p>The 98 ME-DEGs were obtained by combining 1,052 ME key module genes with 5,449 LUAD-DEGs. Eight model genes were selected using univariate and multivariate cox regression analyses. The risk model, which was constructed using model genes, showed good predictive performance in both the TCGA-LUAD and GSE31210 datasets. Additionally, the nomogram’s superior predictive accuracy for LUAD was confirmed by calibration and receiver operating characteristic (ROC) curves. Additionally, the results of immune analysis showed that ALG3 and FRY had significant relationships with immune cells and immune checkpoints, and that E2F1 had a significant negative association with FRY.</p> <p>Conclusion</p><p>We developed and validated a novel ME-related prognostic model for LUAD. This algorithm may be able to predict patient outcomes and provide recommendations for tailored immunotherapy.</p></div> 2026-09-15T17:22:50Z Dataset Dataset 10.1371/journal.pone.0357584.s001 https://figshare.com/articles/dataset/_p_Melatonin_associated_genes_p_/33809308 CC BY 4.0