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. 2022 Aug 9;7(3):498-509.
doi: 10.1016/j.idm.2022.07.009. eCollection 2022 Sep.

A Naive Bayes model on lung adenocarcinoma projection based on tumor microenvironment and weighted gene co-expression network analysis

Zhiqiang Ye  1 , Pingping Song  2 , Degao Zheng  3 , Xu Zhang  2 , Jianhong Wu  4
Affiliations

A Naive Bayes model on lung adenocarcinoma projection based on tumor microenvironment and weighted gene co-expression network analysis

Zhiqiang Ye et al. Infect Dis Model. .

Abstract

Based on the lung adenocarcinoma (LUAD) gene expression data from the cancer genome atlas (TCGA) database, the Stromal score, Immune score and Estimate score in tumor microenvironment (TME) were computed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm. And gene modules significantly related to the three scores were identified by weighted gene co-expression network analysis (WGCNA). Based on the correlation coefficients and P values, 899 key genes affecting tumor microenvironment were obtained by selecting the two most correlated modules. It was suggested through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis that these key genes were significantly involved in immune-related or cancer-related terms. Through univariate cox regression and elastic network analysis, genes associated with prognosis of the LUAD patients were screened out and their prognostic values were further verified by the survival analysis and the University of ALabama at Birmingham CANcer (UALCAN) database. The results indicated that eight genes were significantly related to the overall survival of LUAD. Among them, six genes were found differentially expressed between tumor and control samples. And immune infiltration analysis further verified that all the six genes were significantly related to tumor purity and immune cells. Therefore, these genes were used eventually for constructing a Naive Bayes projection model of LUAD. The model was verified by the receiver operating characteristic (ROC) curve where the area under curve (AUC) reached 92.03%, which suggested that the model could discriminate the tumor samples from the normal accurately. Our study provided an effective model for LUAD projection which improved the clinical diagnosis and cure of LUAD. The result also confirmed that the six genes in the model construction could be the potential prognostic biomarkers of LUAD.

Keywords: Lung adenocarcinoma; Naive Bayes model; Prognostic biomarkers; Tumor microenvironment; Weighted gene co-expression network analysis.

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Conflict of interest statement

The authors declare there is no conflict of interest.

Figures

Fig. 1
Fig. 1
Correlation between the three kinds of scores and survival of LUAD patients.
Fig. 2
Fig. 2
Correlation between clinical features and Stromal score or Immune score.
Fig. 3
Fig. 3
Construction and module analysis of co-expression network.
Fig. 4
Fig. 4
Results of enrichment analysis of the 899 key genes.
Fig. 5
Fig. 5
Survival analysis results of the 13 selected genes (ARG2, CCL3L3, CCR2, CD74, CDCP1, CTSL, ERO1B, FURIN, HLA-DRB5, KCTD12, MERTK, MS4A7 and PIK3CG).
Fig. 6
Fig. 6
Differential expression analysis of the prognostic genes based on TCGA database (A) and UACLAN database (B).
Fig. 7
Fig. 7
Immune infiltration analysis of CD74, CDCP1, FURIN, HLA-DRB5, KCTD12 and PIK3CG.
Fig. 8
Fig. 8
PCA result of the two kinds of samples of the dataset (panel A) and ROC curve of the Naive Bayes model with AUC (panel B).
See this image and copyright information in PMC

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