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. 2021 Apr 20;21(1):228.
doi: 10.1186/s12935-021-01938-4.

NEIL3 may act as a potential prognostic biomarker for lung adenocarcinoma

Cui Zhao #  1 , Jian Liu #  2 , Haomiao Zhou  1 , Xin Qian  1 , Hui Sun  3 , Xuewen Chen  4 , Miaosen Zheng  1 , Tingting Bian  3 , Lei Liu  3 , Yifei Liu #  5 , Jianguo Zhang #  6
Affiliations

NEIL3 may act as a potential prognostic biomarker for lung adenocarcinoma

Cui Zhao et al. Cancer Cell Int. .

Abstract

Background: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death. This study aimed to develop and validate reliable prognostic biomarkers and signature.

Methods: Differentially expressed genes were identified based on three Gene Expression Omnibus (GEO) datasets. Based on 1052 samples' data from our cohort, GEO and The Cancer Genome Atlas, we explored the relationship of clinicopathological features and NEIL3 expression to determine clinical effect of NEIL3 in LUAD. Western blotting (22 pairs of tumor and normal tissues), Real-time quantitative PCR (19 pairs of tumor and normal tissues), and immunohistochemical analyses (406-tumor tissues subjected to microarray) were conducted. TIMER and ImmuCellAI analyzed relationship between NEIL3 expression and the abundance of tumor-infiltrating immune cells in LUAD. The co-expressed-gene prognostic signature was established based on the Cox regression analysis.

Results: This study identified 502 common differentially expressed genes and confirmed that NEIL3 was significantly overexpressed in LUAD samples (P < 0.001). Increased NEIL3 expression was related to advanced stage, larger tumor size and poor overall survival (p < 0.001) in three LUAD cohorts. The proportions of natural T regulatory cells and induced T regulatory cells increased in the high NEIL3 group, whereas those of B cells, Th17 cells and dendritic cells decreased. Gene set enrichment analysis indicated that NEIL3 may activate cell cycle progression and P53 signaling pathway, leading to poor outcomes. We identified nine prognosis-associated hub genes among 370 genes co-expressed with NEIL3. A 10-gene prognostic signature including NEIL3 and nine key co-expressed genes was constructed. Higher risk-score was correlated with more advanced stage, larger tumor size and worse outcome (p < 0.05). Finally, the signature was verified in test cohort (GSE50081) with superior diagnostic accuracy.

Conclusions: This study suggested that NEIL3 has the potential to be an immune-related therapeutic target and an independent predictor of LUAD prognosis. We also developed a prognostic signature for LUAD with a precise diagnostic accuracy.

Keywords: Bioinformatics; Immunohistochemistry; Lung adenocarcinoma; NEIL3; Prognostic signature; Real-time quantitative PCR.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Identification of commonly upregulated genes. a Differentially expressed genes (DEGs) of GSE30219, GSE43458, and GSE33532 identified via GEO2R online tools and Venn diagram software. b The 126 overexpressed DEGs in lung adenocarcinoma. c NEIL3 expression in various cancer tissues and normal tissues. *p < 0.05, **p < 0.01, ***p < 0.001
Fig. 2
Fig. 2
NEIL3 was overexpressed in LUAD. a, b The NEIL3 expression in TCGA LUAD cohort and GSE31210 cohort. c NEIL3 protein level in 22 pairs of LUAD tissues and their matched normal tissues were determined by western blotting. d NEIL3 mRNA levels were determined by quantitative real-time polymerase chain reaction (qRT-PCR). e Immunohistochemical staining for NEIL3 in normal lung tissue and lung adenocarcinoma tissues (original magnification ×100). N normal tissue, T tumor tissue
Fig. 3
Fig. 3
Association with NEL3 expression, clinicopathological characteristics, overall survival (OS), and relapse-free survival (RFS). a–c Increased NEIL3 expression was significantly associated with advanced TNM stage and larger tumor size. d–g The Kaplan–Meier plotters based on four datasets showed LUAD patients with high NEIL3 expression have poor OS and RFS. h The multivariate analyses proved the independent predictive ability of TNM stage and NEIL3 expression for LUAD overall survival
Fig. 4
Fig. 4
Association with NEIL3 expression and tumor-infiltrating immune cells (TIICs). a Correlations between NEIL3 expression and immune infiltration levels from TIMER web tool. b Association between clinical outcome and abundance of immune infiltrates or NEIL3 expression in the TIMER “Survival” module. c Proportions of 24 tumor-infiltrating immune cells in high and low NEIL3 expression groups. d Correlation matrix of all 24 immune cell proportions
Fig. 4
Fig. 4
Association with NEIL3 expression and tumor-infiltrating immune cells (TIICs). a Correlations between NEIL3 expression and immune infiltration levels from TIMER web tool. b Association between clinical outcome and abundance of immune infiltrates or NEIL3 expression in the TIMER “Survival” module. c Proportions of 24 tumor-infiltrating immune cells in high and low NEIL3 expression groups. d Correlation matrix of all 24 immune cell proportions
Fig. 5
Fig. 5
Functional enrichment analysis and protein–protein interaction network of NEIL3 and genes co-expressed with NEIL3. a KEGG signaling pathway enrichment analysis of the high and low NEIL3 expression groups via GSEA software. b The PPI network among genes co-expressed with NEIL3. The yellow dots indicate a positive correlation with NEIL3, whereas the blue dots indicate a negative correlation. c, d KEGG enrichment analysis and GO analysis of the co-expressed genes. e The 10 hub genes among the 370 co-expressed genes identified by the Cytoscape software CytoHubba plugin
Fig. 6
Fig. 6
Relation between signature and cancer risk. a Distribution of risk score. b Proportion of deaths was increased in the high risk score group. c Hierarchical clustering of the 10 genes between the low and high risk groups. Red, up-regulated; green, down-regulated. d–g A higher risk score was remarkably associated with shorter overall survival, poorer clinical outcomes, more advanced TNM stage, and larger tumor size (p < 0.05)
Fig. 7
Fig. 7
Predictive performances and verification of signature. A forest plot of univariate (a) and multivariate (b) Cox regression analyses revealed that risk score could be an independent prognostic factor in TCGA cohort. c The area under the curve (AUC) of the multiple receiver operating characteristic (ROC) curves for multiple valuable factors in TCGA cohort. d The Kaplan–Meier plotters of the test cohort (GSE50081 dataset) showed LUAD patients in the high risk score group have poor overall survival. e Multivariate Cox regression analysis of the GSE50081 cohort. f 1-, 3-, and 5-year ROC curves of the GSE50081 cohort
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