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. 2021 Dec;12(1):1273-1285.
doi: 10.1080/21655979.2021.1911211.

The screening of immune-related biomarkers for prognosis of lung adenocarcinoma

Zhonghui Liu  1 , Dan Sun  1 , Qing Zhu  2 , Xinmin Liu  1
Affiliations

The screening of immune-related biomarkers for prognosis of lung adenocarcinoma

Zhonghui Liu et al. Bioengineered. 2021 Dec.

Abstract

Lung adenocarcinoma (LUAD) accounts for a frequently seen non-small cell lung cancer (NSCLC) histological subtype, and it is associated with dismal prognostic outcome. However, the benefits of traditional treatment are still limited, and the efficacies of immunotherapy are quite different. Therefore, it is of great significance to identify novel immune-related therapeutic targets in lung adenocarcinoma. In this study, we identified a set of immune-related biomarkers for prognosis of lung adenocarcinoma, which could provide new ideas for immunotherapy of lung adenocarcinoma. Datasets related to LUAD were filtered from the GEO database. The appropriate packages were used to identify differentially expressed genes (DEGs) and to carry out enrichment analysis, followed by the construction of prognostic biomarkers. The Kaplan-Meier (K-M) curves were plotted to analyze patient survival based on hub genes. Associations between the expression of selected biomarkers and six types of tumor-infiltrating immune cells were evaluated based on the online tool TIMER. After analyzing five GEO datasets(GSE32867, GSE46539, GSE63459, GSE75037 and GSE116959), we discovered altogether 67 DEGs, among which, 15 showed up-regulation while 52 showed down-regulation. Enrichments of integrated DEGs were identified in the ontology categories. CAV1, CFD, FMO2 and CLEC3B were eventually selected as independent prognostic biomarkers, they were correlated with clinical outcomes of LUAD patients. Moreover, a positive correlation was observed between biomarker expression and all different types of immune infiltration, and the expression level of the four biomarkers was all positively related to macrophage.

Keywords: Lung adenocarcinoma (LUAD); bioinformatics analysis; immune infiltration.

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Conflict of interest statement

No potential conflict of interest was reported by the author(s).

Figures

None
Graphical abstract
Figure 1.
Figure 1.
Differentially expressed genes between the two groups of samples in each dataset. (a) GSE32867, (b) GSE46539, (c) GSE63459, (d) GSE75037, (e) GSE116959. Red and green dots stand for up-regulated and down-regulated genes, respectively, while black dots stand for insignificant genes
Figure 2.
Figure 2.
The RRA-based identification of potent DEGs. Heatmap showing the 20 most significantly up-regulated and down-regulated DEGs identified from the GEO series. The rows and columns stand for DEGs and datasets, respectively. The change in color from red to green indicated the change from up-regulation to down-regulation. Numbers within the box are the logarithmic FCs
Figure 3.
Figure 3.
Enrichments of integrated DEGs are identified in the ontology categories. (a) Ten most significant BP terms. (b) Ten most significant CC terms. (c) Ten most significant MF terms. (d) Three most significantly enriched KEGG pathways. (e) The top 20 enriched DisGeNET terms. (f) The top 6 enriched PaGenBase terms. (g) The top 5 enriched TRRUST terms
Figure 4.
Figure 4.
Construction of biomarkers with prognostic value. (a) Forest plot showing the eventual model established using the forward stepwise strategy. (b) Associations of the expression of biomarkers with survival for LUAD cases. (c) Nomogram for predicting 1-, 3-, and 5-year survival. (d) Calibration curve of the nomogram that predicted 3-year OS. (e) Calibration curve of the nomogram that predicted 5-year OS
Figure 5.
Figure 5.
Biomarker validation. (a) Differential expression between tumor and matched non-carcinoma samples. (b) Expression in LUAD samples with different tumor stages. (c) Association between expression and overall survival time. (a) CAV1. (b) CFD. (c) CLEC3B. (d) FMO2
Figure 6.
Figure 6.
Relationship between the expression of biomarkers and immune infiltration degrees in LUAD. (a) CAV1. (b) CFD. (c) CLEC3B. (d) FMO2. P < 0.05 denotes significance
Figure 7.
Figure 7.
Correlations between biomarker expression and tumor infiltration with different calculation methods
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