Non-small cell lung cancer (NSCLC) is heterogeneous. Molecular subtyping based on the gene expression profiles is an effective technique for diagnosing and determining the prognosis of NSCLC patients.
Here, we downloaded the NSCLC expression profiles from The Cancer Genome Atlas and the Gene Expression Omnibus databases. ConsensusClusterPlus was used to derive the molecular subtypes based on long-chain noncoding RNA (lncRNA) associated with the PD-1-related pathway. The LIMMA package and least absolute shrinkage and selection operator (LASSO)-Cox analysis were used to construct the prognostic risk model. The nomogram was constructed to predict the clinical outcomes, followed by decision curve analysis (DCA) to validate the reliability of this nomogram.
We discovered that PD-1 was strongly and positively linked to the T-cell receptor signaling pathway. Furthermore, we identified two NSCLC molecular subtypes yielding a significantly distinctive prognosis. Subsequently, we developed and validated the 13-lncRNA-based prognostic risk model in the four datasets with high AUC values. Patients with low-risk showed a better survival rate and were more sensitive to PD-1 treatment. Nomogram construction combined with DCA revealed that the risk score model could accurately predict the prognosis of NSCLC patients.
This study demonstrated that lncRNAs engaged in the T-cell receptor signaling pathway played a significant role in the onset and development of NSCLC, and that they could influence the sensitivity to PD-1 treatment. In addition, the 13 lncRNA model was effective in assisting clinical treatment decision-making and prognosis evaluation.
Citation: Hejian Chen, Shuiyu Xu, Yuhong Zhang, Peifeng Chen. Systematic analysis of lncRNA gene characteristics based on PD-1 immune related pathway for the prediction of non-small cell lung cancer prognosis[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 9818-9838. doi: 10.3934/mbe.2023430
Non-small cell lung cancer (NSCLC) is heterogeneous. Molecular subtyping based on the gene expression profiles is an effective technique for diagnosing and determining the prognosis of NSCLC patients.
Here, we downloaded the NSCLC expression profiles from The Cancer Genome Atlas and the Gene Expression Omnibus databases. ConsensusClusterPlus was used to derive the molecular subtypes based on long-chain noncoding RNA (lncRNA) associated with the PD-1-related pathway. The LIMMA package and least absolute shrinkage and selection operator (LASSO)-Cox analysis were used to construct the prognostic risk model. The nomogram was constructed to predict the clinical outcomes, followed by decision curve analysis (DCA) to validate the reliability of this nomogram.
We discovered that PD-1 was strongly and positively linked to the T-cell receptor signaling pathway. Furthermore, we identified two NSCLC molecular subtypes yielding a significantly distinctive prognosis. Subsequently, we developed and validated the 13-lncRNA-based prognostic risk model in the four datasets with high AUC values. Patients with low-risk showed a better survival rate and were more sensitive to PD-1 treatment. Nomogram construction combined with DCA revealed that the risk score model could accurately predict the prognosis of NSCLC patients.
This study demonstrated that lncRNAs engaged in the T-cell receptor signaling pathway played a significant role in the onset and development of NSCLC, and that they could influence the sensitivity to PD-1 treatment. In addition, the 13 lncRNA model was effective in assisting clinical treatment decision-making and prognosis evaluation.
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Hejian Chen, Shuiyu Xu, Yuhong Zhang, Peifeng Chen. Systematic analysis of lncRNA gene characteristics based on PD-1 immune related pathway for the prediction of non-small cell lung cancer prognosis[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 9818-9838. doi: 10.3934/mbe.2023430
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