Skip to main page content
U.S. flag
See More

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Feb;181(3):362-374.
doi: 10.1111/bph.16257. Epub 2023 Nov 23.

Transcriptome-level discovery of survival-associated biomarkers and therapy targets in non-small-cell lung cancer

Balázs Győrffy  1
Affiliations
Free article

Transcriptome-level discovery of survival-associated biomarkers and therapy targets in non-small-cell lung cancer

Balázs Győrffy. Br J Pharmacol. 2024 Feb.
Free article

Abstract

Background and purpose: Survival rate of patients with lung cancer has increased by over 60% in the recent two decades. With longer survival, the identification of genes associated with survival has emerged as an issue of utmost importance to uncover the most promising biomarkers and therapeutic targets.

Experimental approach: An integrated database was set up by combining multiple independent datasets with clinical data and transcriptome-level gene expression measurements. Univariate and multivariate survival analyses were performed to identify genes with higher expression levels linked to shorter survival. The strongest genes were filtered to include only those with known druggability.

Key results: The entire database includes 2852 tumour specimens from 17 independent cohorts. Of these, 2227 have overall survival data and 1256 samples have progression-free survival time. The most significant genes associated with survival were MIF, UBC and B2M in lung adenocarcinoma and ANXA2, CSNK2A2 and KRT18 in squamous cell carcinoma. We also aimed to reveal the best druggable targets in non-smokers lung cancer. The three most promising hits in this cohort were MDK, THY1 and PADI2. The established lung cancer cohort was added to the Kaplan-Meier plotter (https://www.kmplot.com) enabling the validation of future gene expression-based biomarkers in both the present and yet unexamined subgroups of patients.

Conclusions and implications: In this study, we established a comprehensive database of transcriptome-level data for lung cancer. The database can be utilized to identify and rank the most promising biomarkers and therapeutic targets for different subtypes of lung cancer.

Keywords: cox regression; gene arrays; gene expression; pharmacology; prognosis; survival.

PubMed Disclaimer

References

REFERENCES

    1. Alcaraz-Sanabria, A., Cabañas Morafraile, E., Fernández-Hinojal, G., Velasco, G., Pérez-Segura, P., Pandiella, A., Győrffy, B., & Ocaña, A. (2022). Transcriptomic mapping of non-small cell lung cancer K-RAS p.G12C mutated tumors: Identification of surfaceome targets and immunologic correlates. Frontiers in Immunology, 12, 786069. https://doi.org/10.3389/fimmu.2021.786069
    1. Alexander, S. P., Fabbro, D., Kelly, E., Mathie, A., Peters, J. A., Veale, E. L., Armstrong, J. F., Faccenda, E., Harding, S. D., Pawson, A. J., Southan, C., Davies, J. A., Beuve, A., Brouckaert, P., Bryant, C., Burnett, J. C., Farndale, R. W., Friebe, A., Garthwaite, J., … Waldman, S. A. (2021). THE CONCISE GUIDE TO PHARMACOLOGY 2021/22: Catalytic receptors. British Journal of Pharmacology, 178(S1), S264-S312. https://doi.org/10.1111/bph.15541
    1. Alexander, S. P., Fabbro, D., Kelly, E., Mathie, A., Peters, J. A., Veale, E. L., Armstrong, J. F., Faccenda, E., Harding, S. D., Pawson, A. J., Southan, C., Davies, J. A., Boison, D., Burns, K. E., Dessauer, C., Gertsch, J., Helsby, N. A., Izzo, A. A., Koesling, D., … Wong, S. S. (2021). THE CONCISE GUIDE TO PHARMACOLOGY 2021/22: Enzymes. British Journal of Pharmacology, 178(S1), S313-S411. https://doi.org/10.1111/bph.15542
    1. Alexander, S. P., Kelly, E., Mathie, A., Peters, J. A., Veale, E. L., Armstrong, J. F., Faccenda, E., Harding, S. D., Pawson, A. J., Southan, C., Buneman, O. P., Cidlowski, J. A., Christopoulos, A., Davenport, A. P., Fabbro, D., Spedding, M., Striessnig, J., Davies, J. A., Ahlers-Dannen, K. E., … Zolghadri, Y. (2021). THE CONCISE GUIDE TO PHARMACOLOGY 2021/22: Introduction and Other Protein Targets. British Journal of Pharmacology, 178(S1), S1-S26. https://doi.org/10.1111/bph.15537
    1. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B: Methodological, 57, 289-300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

Publication types

MeSH terms