Abstract
Purpose
This study aims to establish a risk prediction model based on prognosis-related genes (PRGs) and clinicopathological factors, and investigate the biological activities of PRGs in lung adenocarcinoma (LUAD).
Methods
Risk score signatures were developed by employing multiple algorithms and their amalgamations. A predictive model for overall survival was established through the integration of risk score signatures and several clinicopathological parameters. A comprehensive single-cell atlas, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were used to investigate the biological activities of prognosis-related genes in LUAD.
Results
A risk prediction model was established based on 16 PRGs, exhibiting robust performance in predicting overall survival. The single-cell analysis revealed that epithelial cells were primarily associated with worse survival of LUAD, and PRGs were predominantly enriched in malignant epithelial cells and influenced epithelial cell growth and progression. Furthermore, GSEA and GSVA analysis showed that PRGs were involved in tumor pathways such as epithelial-mesenchymal transition, hypoxia and KRAS_UP, and high GSVA scores are correlated with worse outcome in LUAD patients.
Conclusions
The constructed risk prediction model in this study offers clinicians a valuable tool for tailoring treatment strategies of LUAD and provides a comprehensive interpretation on the biological activities of PRGs in LUAD.
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Data availability
The datasets utilized in this study are available in online repositories. The specific repository/repositories and corresponding accession number(s) can be found in the article or Supplementary Material.
Abbreviations
- AUC:
-
Area under the curve
- GO:
-
Gene ontology
- GSEA:
-
Gene set enrichment analysis
- GSVA:
-
Gene set variation analysis
- KEGG:
-
Kyoto encyclopedia of genes and genomes
- OS:
-
Overall survival
- scRNA-seq:
-
Single-cell RNA sequencing
- TME:
-
Tumor microenvironment
- LUAD:
-
Lung adenocarcinoma
- TCGA:
-
The Cancer Genome Atlas
- GEO:
-
Gene expression omnibus
- UMAP:
-
Uniform manifold approximation and projection
- PRGs:
-
Prognosis-related genes
- GBM:
-
Generalized boosted regression modeling
- EMT:
-
Epithelial–mesenchymal transition
- DCA:
-
Decision curve analysis
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Acknowledgements
We would like to express our gratitude to the researchers who generously shared the original data of open single-cell and bulk RNA sequencing in public databases. Additionally, we extend our appreciation to the providers of open-source R packages, whose contributions were instrumental in our analysis.
Funding
This work was supported by grants from the National Natural Science Foundation of China (81773159 and 81871203).
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The study was designed by YM, SJY, and LYQ. YM and SJY conducted the analysis. YM, SJY, TXL, ZXY, and TDC performed the validation in the independent cohort. YM, SJY, TXL, and ZXY contributed to the preparation of figures and tables. YM and SJY drafted the manuscript. LYQ, SJY, and YY provided revisions to the manuscript. All authors reviewed and approved the final version of the manuscript.
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Yi, M., Shi, J., Tan, X. et al. Integration and deconvolution methodology deciphering prognosis-related signatures in lung adenocarcinoma. J Cancer Res Clin Oncol 149, 16441–16460 (2023). https://doi.org/10.1007/s00432-023-05403-9
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DOI: https://doi.org/10.1007/s00432-023-05403-9