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Peer-reviewedCC BY 4.0PLOS One

Prognostic value of melatonin-related signature genes in lung adenocarcinoma

  • Hongxia Guo1
  • Lixia Liu2
  • Ying Lu3
  • Yuhui Ma4
  • Xiaolu Ren5
  • Tong Cui
5 affiliations
  1. 1Department of Thoracic-cancer, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
  2. 2Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
  3. 3Department of Traditional Chinese Medicine, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
  4. 4Department of Radiotherapy, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
  5. 5Department of Radiotherapy, Shanxi Province Cancer Hospital/ Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi, China
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Abstract

Purpose: 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. Methods: 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. Results: 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. Conclusion: 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.

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Cite

BibTeXRIS
BibTeX
@article{guo2026prognostic,
  title = {{Prognostic value of melatonin-related signature genes in lung adenocarcinoma}},
  author = {Hongxia Guo and Lixia Liu and Ying Lu and Yuhui Ma and Xiaolu Ren and Tong Cui},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0357584},
  url = {https://doi.org/10.1371/journal.pone.0357584},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Prognostic value of melatonin-related signature genes in lung adenocarcinoma
AU  - Hongxia Guo
AU  - Lixia Liu
AU  - Ying Lu
AU  - Yuhui Ma
AU  - Xiaolu Ren
AU  - Tong Cui
PY  - 2026
DA  - 2026/09/15
JO  - PLOS One
DO  - 10.1371/journal.pone.0357584
UR  - https://doi.org/10.1371/journal.pone.0357584
AB  - Purpose: 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. Methods: 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. Results: 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. Conclusion: 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.
SN  - 1932-6203
ER  -