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Identification of protein signatures for lung cancer subtypes based on BPSO method

Author

Listed:
  • Jihan Wang
  • Hanping Wang
  • Jing Xu
  • Qiying Song
  • Baozhen Zhou
  • Jingbo Shangguan
  • Mengju Xue
  • Yangyang Wang

Abstract

The objective of this study was to identify protein biomarkers that can distinguish between LUAD and LUSC, critical for personalized treatment plans. The proteomic profiling data of LUAD and LUSC samples from TCPA database, along with phenotype and survival information from TCGA database were downloaded and preprocessed for analysis. We used BPSO feature selection method and identified 10 candidate protein biomarkers that have better classifying performance, as analyzed by t-SNE and PCA algorithms. To explore the causalities among these proteins and their associations with tumor subtypes, we conducted the PCStable algorithm to construct a regulatory network. Results indicated that 4 proteins, MIG6, CD26, NF2, and INPP4B, were directly linked to the lung cancer subtypes and may be useful in guiding therapeutic decision-making. Besides, spearman correlation, Cox proportional hazard model and Kaplan-Meier curve was employed to validate the biological significance of the candidate proteins. In summary, our study highlights the importance of protein biomarkers in the classification of lung cancer subtypes and the potential of computational methods for identifying key biomarkers and understanding their underlying biological mechanisms.

Suggested Citation

  • Jihan Wang & Hanping Wang & Jing Xu & Qiying Song & Baozhen Zhou & Jingbo Shangguan & Mengju Xue & Yangyang Wang, 2023. "Identification of protein signatures for lung cancer subtypes based on BPSO method," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-16, December.
    • Handle: RePEc:plo:pone00:0294243
    DOI: 10.1371/journal.pone.0294243
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    References listed on IDEAS

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    1. Arnab Kumar Maity & Anirban Bhattacharya & Bani K. Mallick & Veerabhadran Baladandayuthapani, 2020. "Bayesian data integration and variable selection for pan‐cancer survival prediction using protein expression data," Biometrics, The International Biometric Society, vol. 76(1), pages 316-325, March.
    2. Lê, Sébastien & Josse, Julie & Husson, François, 2008. "FactoMineR: An R Package for Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i01).
    3. Dao, Phong B., 2022. "On Wilcoxon rank sum test for condition monitoring and fault detection of wind turbines," Applied Energy, Elsevier, vol. 318(C).
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