Abstract
Purpose
The updated guidelines highlight gene expression-based multigene panel as a critical tool to assess overall survival (OS) and improve treatment for lung adenocarcinoma (LUAD) patients. Nevertheless, genome-wide expression signatures are still limited in real clinical utility because of insufficient data utilization, a lack of critical validation, and inapposite machine learning algorithms.
Methods
2330 primary LUAD samples were enrolled from 11 independent cohorts. Seventy-six algorithm combinations based on ten machine learning algorithms were applied. A total of 108 published gene expression signatures were collected. Multiple pharmacogenomics databases and resources were utilized to identify precision therapeutic drugs.
Results
We comprehensively developed a robust machine learning-derived genome-wide expression signature (RGS) according to stably OS-associated RNAs (OSRs). RGS was an independent risk element and remained robust and reproducible power by comparing it with general clinical parameters, molecular characteristics, and 108 published signatures. RGS-based stratification possessed different biological behaviors, molecular mechanisms, and immune microenvironment patterns. Integrating multiple databases and previous studies, we identified that alisertib was sensitive to the high-risk group, and RITA was sensitive to the low-risk group.
Conclusion
Our study offers an appealing platform to screen dismal prognosis LUAD patients to improve clinical outcomes by optimizing precision therapy.
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Data availability
The original data presented in the study are freely available from the following websites: UCSC Xena database, https://xenabrowser.net/datapages/. GEO, https://www.ncbi.nlm.nih.gov/geo/. CBioPortal, https://www.cbioportal.org/. CancerSEA, http://biocc.hrbmu.edu.cn/CancerSEA/. PRISM, https://depmap.org/portal/download/. CCLE, https://sites.broadinstitute.org/ccle/. CMap database, https://clue.io/.
Abbreviations
- LUAD:
-
Lung adenocarcinoma
- TNM:
-
Tumor-node-metastasis
- OS:
-
Overall survival
- ctDNA:
-
Circulating tumor DNA
- NGS:
-
Next-generation sequencing
- RGS:
-
Robust machine learning-derived genome-wide expression signature
- FPKM:
-
Fragments per kilobase of million
- TPM:
-
Trans per million
- GEO:
-
Gene Expression Omnibus
- OSRs:
-
OS-associated RNAs
- Enet:
-
Elastic network
- RSF:
-
Random survival forest
- GBM:
-
Generalized boosted regression modeling
- plsRcox:
-
Partial least-squares regression for Cox
- survival-SVM:
-
Survival support vector machine
- SuperPC:
-
Supervised principal components
- C-index:
-
Concordance index
- K–M:
-
Kaplan–Meier
- AUC:
-
Area under the ROC curve
- EMT:
-
Epithelial–mesenchymal transition
- TMB:
-
Tumor mutation burden
- KEGG:
-
Kyoto Encyclopedia of Genes and Genomes
- GO:
-
Gene Ontology
- MSI:
-
Microsatellite instability
- FGA:
-
Fraction of genome altered
- FGG:
-
Fraction of genome gained
- FGL:
-
Fraction of the genome lost
- ICBs:
-
Immune checkpoint blockers
- PRISM:
-
Profiling relative inhibition simultaneously in mixtures
- CTRP:
-
Cancer Therapeutics Response Portal
- ssGSEA:
-
Single-sample gene set enrichment analysis
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Funding
This work was supported by the Henan Province Medical Research Project, Henan, China (No. LHGJ20190388).
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XG contributed to the study design and data analysis. XG contributed to manuscript writing. XH contributed to project oversight and manuscript revisiting. HX and SW collected samples and generated data. WY offers the funding. WY, YZ, LL, LW, ZX, YB, SL, and LFL contributed to manuscript revisiting. All authors read and approved the final manuscript.
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Ge, X., Xu, H., Weng, S. et al. Systematic analysis of transcriptome signature for improving outcomes in lung adenocarcinoma. J Cancer Res Clin Oncol 149, 8951–8968 (2023). https://doi.org/10.1007/s00432-023-04814-y
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DOI: https://doi.org/10.1007/s00432-023-04814-y