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. 2021 Nov 24;21(1):1267.
doi: 10.1186/s12885-021-09006-w.

Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer

Xiaotao Li  1   2   3 , Shi Fu  1   2   3 , Yinglong Huang  1   2   3 , Ting Luan  1   2   3 , Haifeng Wang  4   5   6 , Jiansong Wang  7   8   9
Affiliations

Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer

Xiaotao Li et al. BMC Cancer. .

Abstract

Background: Bladder cancer (BC) is one of the most common malignancies and has a relatively poor outcome worldwide. In this study, we attempted to construct a novel metabolism-related gene (MRG) signature for predicting the survival probability of BC patients.

Methods: First, differentially expressed MRGs between BC and normal samples were identified and used to construct a protein-protein interaction (PPI) network and perform mutation analysis. Next, univariate Cox regression analysis was utilized to select prognostic genes, and multivariate Cox regression analysis was applied to establish an MRG signature for predicting the survival probability of BC patients. Moreover, Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive capability of the MRG signature. Finally, a nomogram based on the MRG signature was established to better predict the survival of BC.

Results: In the present study, 27 differentially expressed MRGs were identified, most of which presented mutations in BC patients, and LRP1 showed the highest mutation rate. Next, an MRG signature, including MAOB, FASN and LRP1, was established by using univariate and multivariate Cox regression analysis. Furthermore, survival analysis indicated that BC patients in the high-risk group had a dramatically lower survival probability than those in the low-risk group. Finally, Cox regression analysis showed that the risk score was an independent prognostic factor, and a nomogram integrating age, pathological tumor stage and risk score was established and presented good predictive ability.

Conclusion: We successfully constructed a novel MRG signature to predict the prognosis of BC patients, which might contribute to the clinical treatment of BC.

Keywords: Bladder cancer; GEO; Metabolism-related gene; Prognosis; TCGA.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Identification of Metabolism-related DEGs. A B The volcano plots of DEGs in normal samples compared to BC samples in GSE13507 and TCGA database (B). C The venn diagram of MRGs, DEGs in GSE13507 and DEGs in TCGA database
Fig. 2
Fig. 2
The results of GO Functional Annotation and KEGG Pathway Enrichment Analysis. A The enriched biological processes by metabolism-related DEGs. B The enriched cellular components by metabolism-related DEGs. C The enriched molecular functions by metabolism-related DEGs. D The enriched KEGG pathways by metabolism-related DEGs
Fig. 3
Fig. 3
PPI network genetic variation for metabolism-related DEGs. A The PPI network of 27 metabolism-related DEGs. B The bar diagrams showed the interactions of each gene and other genes. C The mutation frequency of 23 metabolism-related DEGs in 414 BC samples from the TCGA database
Fig. 4
Fig. 4
Identification of prognostic metabolism-related DEGs. A Univariate Cox regression analysis identified 5 prognostic metabolism-related DEGs. B Multivariate Cox regression analysis reserved 3 prognostic metabolism-related DEGs for establishing the prognostic MRG signature
Fig. 5
Fig. 5
Assessing the efficiencies of the prognostic MRG signature in the training set and validation set. A B C The Kaplan-Meier survival curves of the training set (A), the testing set (B) and the validation set (C). D E F The distribution of risk scores and the survival status of patients in the training set (D), the testing set (E) and the validation set (F), and each dot represents a BC patient. G H I ROC curves of the training set (G), the testing set (H) and the validation set (I) showed the performance for predicting the 1-year, 3-year and 5-year OS
Fig. 6
Fig. 6
Construction of a nomogram for better predicting the 1-year, 3-year and 5-year OS of patients in the training set. A B Univariate (A) and multivariate (B) Cox regression analyses identified independent prognostic factors in training set. C Nomogram based on the age, pathological tumor stage and risk score was established in the training set. D The calibration curve showed the predictive efficiency of nomogram in the training set
Fig. 7
Fig. 7
The expression levels of MAOB, FASN and LRP1. A TCGA database. B GSE13507. C Clinical samples
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