Survival prediction and molecular subtyping of squamous cell lung cancer based on network embedding

Squamous cell lung cancer (SQCLC), which is fatal to humans, is heterogeneous with different genetic and histological features. We used SBMOI, a multi-omics data integration method from previous study, to integrate clinical, gene expression, and somatic mutation data of SQCLC to construct new patien...

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Bibliographic Details
Published in:Scientific reports 2024-11, Vol.14 (1), p.29474-13, Article 29474
Main Authors: Guo, Dingjie, Chen, Jing, Wang, Yixian, Liu, Xin
Format: Article
Language:English
Subjects:
631/114
631/67
Biomarkers, Tumor - genetics
Carcinoma, Squamous Cell - classification
Carcinoma, Squamous Cell - genetics
Carcinoma, Squamous Cell - mortality
Carcinoma, Squamous Cell - pathology
Cell survival
Embedding
Female
Gene expression
Humanities and Social Sciences
Humans
Lung cancer
Lung Neoplasms - classification
Lung Neoplasms - genetics
Lung Neoplasms - mortality
Lung Neoplasms - pathology
Male
Middle Aged
Molecular subtyping
Multi-omics data integration
multidisciplinary
Mutation
Prognosis
ROC Curve
Science
Science (multidisciplinary)
Squamous cell lung cancer
Survival
Survival Analysis
Survival prediction
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Summary:Squamous cell lung cancer (SQCLC), which is fatal to humans, is heterogeneous with different genetic and histological features. We used SBMOI, a multi-omics data integration method from previous study, to integrate clinical, gene expression, and somatic mutation data of SQCLC to construct new patient features. Next, random survival forest (RSF) model and SimpleMKL model were constructed to predict the survival of SQCLC patients, and K-means model was constructed to perform molecular subtyping. The results of the RSF model showed that when the dimension of the patient features were 11 × 364 and the hard threshold was 0.2, we obtained the best results, and the AUC value of the 1-year time-dependent ROC curve was 0.706. The SimpleMKL model, constructed using the same patient features, performed exceptionally well, with 1-year, 5-year, and 10-year survival prediction AUC values of 0.944, 0.947 and 0.950, respectively. We used K-means analysis to identify three SQCLC molecular subtypes with significant survival differences. The patient features constructed by SBMOI were used to effectively predict the survival and molecular subtyping of SQCLC patients. In addition, our study further confirmed the effectiveness in multi-omics data integration task and broad applicability of SBMOI.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-81199-z