A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model

Wei Hu,Na Sun,Yueping Shen,Xuanli Chen,Jiadong Chu,Nengjun Yi

doi:10.1038/s41598-021-03645-6

Abstract

There have been few investigations of cancer prognosis models based on Bayesian hierarchical models. In this study, we used a novel Bayesian method to screen mRNAs and estimate the effects of mRNAs on the prognosis of patients with lung adenocarcinoma. Based on the identified mRNAs, we can build a prognostic model combining mRNAs and clinical features, allowing us to explore new molecules with the potential to predict the prognosis of lung adenocarcinoma. The mRNA data (n = 594) and clinical data (n = 470) for lung adenocarcinoma were obtained from the TCGA database. Gene set enrichment analysis (GSEA), univariate Cox proportional hazards regression, and the Bayesian hierarchical Cox proportional hazards model were used to explore the mRNAs related to the prognosis of lung adenocarcinoma. Multivariate Cox proportional hazard regression was used to identify independent markers. The prediction performance of the prognostic model was evaluated not only by the internal cross-validation but also by the external validation based on the GEO dataset (n = 437). With the Bayesian hierarchical Cox proportional hazards model, a 14-gene signature that included CPS1, CTPS2, DARS2, IGFBP3, MCM5, MCM7, NME4, NT5E, PLK1, POLR3G, PTTG1, SERPINB5, TXNRD1, and TYMS was established to predict overall survival in lung adenocarcinoma. Multivariate analysis demonstrated that the 14-gene signature (HR 3.960, 95% CI 2.710–5.786), T classification (T1, reference; T3, HR 1.925, 95% CI 1.104–3.355) and N classification (N0, reference; N1, HR 2.212, 95% CI 1.520–3.220; N2, HR 2.260, 95% CI 1.499–3.409) were independent predictors. The C-index of the model was 0.733 and 0.735, respectively, after performing cross-validation and external validation, a nomogram was provided for better prediction in clinical application. Bayesian hierarchical Cox proportional hazards models can be used to integrate high-dimensional omics information into a prediction model for lung adenocarcinoma to improve the prognostic prediction and discover potential targets. This approach may be a powerful predictive tool for clinicians treating malignant tumours.

Highlights

There have been few investigations of cancer prognosis models based on Bayesian hierarchical models
We found that the following 14 genes were significantly related to patient survival: CPS1, CTPS2, DARS2, IGFBP3, MCM5, MCM7, NME4, NT5E, PLK1, POLR3G, PTTG1, SERPINB5, TXNRD1 and TYMS (Fig. 3A)
The results showed that the mRNA expression of CTPS2 was dramatically increased in lung adenocarcinoma samples compared with normal lung samples (P < 0.001, Fig. 5A)

Summary

Introduction

There have been few investigations of cancer prognosis models based on Bayesian hierarchical models. Bayesian hierarchical Cox proportional hazards models can be used to integrate high-dimensional omics information into a prediction model for lung adenocarcinoma to improve the prognostic prediction and discover potential targets. With the development of molecular technologies, we have the opportunity to integrate high-dimensional omics information into a prediction model of lung adenocarcinoma to improve its prognostic prediction ability, discover potential therapeutic targets and guide clinical treatment This has become a new strategy to predict the prognosis of patients with lung adenocarcinoma[6,7,8]. Bayesian statistics is a kind of statistical inference based on population, sample, and prior information In this context, Yi et al combined Bayesian statistics with the classical LASSO Cox regression model and constructed a new prediction model, the Bayesian hierarchical Cox proportional hazards model, which obtained a higher C-index and had better s tability[12]. The Bayesian hierarchical Cox proportional hazards model has not been applied to the prognosis and prediction of high-dimensional omics in lung adenocarcinoma

Methods

Results

Conclusion