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. 2020 Oct 30:10:550002.
doi: 10.3389/fonc.2020.550002. eCollection 2020.

Using Machine Learning Modeling to Explore New Immune-Related Prognostic Markers in Non-Small Cell Lung Cancer

Jiasheng Xu  1 , Han Nie  2 , Jiarui He  2 , Xinlu Wang  2 , Kaili Liao  3 , Luxia Tu  1 , Zhenfang Xiong  1
Affiliations

Using Machine Learning Modeling to Explore New Immune-Related Prognostic Markers in Non-Small Cell Lung Cancer

Jiasheng Xu et al. Front Oncol. .

Abstract

Objective: To find new immune-related prognostic markers for non-small cell lung cancer (NSCLC).

Methods: We found GSE14814 is related to NSCLC in GEO database. The non-small cell lung cancer observation (NSCLC-OBS) group was evaluated for immunity and divided into high and low groups for differential gene screening according to the score of immune evaluation. A single factor COX regression analysis was performed to select the genes related to prognosis. A prognostic model was constructed by machine learning, and test whether the model has a test efficacy for prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used as a validation dataset for the same validation and prognostic analysis of the model. The coexpression genes of hub genes were obtained by pearson analysis and gene enrichment, function enrichment and protein interaction analysis. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared.

Results: By screening, we found that LYN, C3, COPG2IT1, HLA.DQA1, and TNFRSF17 is closely related to prognosis. After machine learning, we constructed the immune prognosis model from these 5 genes, and the model AUC values were greater than 0.9 at three time periods of 1, 3, and 5 years; the total survival period of the low-risk group was significantly better than that of the high-risk group. The results of prognosis analysis in ACT samples were consistent with OBS groups. The coexpression genes are mainly involved B cell receptor signaling pathway and are mainly enriched in apoptotic cell clearance. Prognostic key genes are highly correlated with PDCD1, PDCD1LG2, LAG3, and CTLA4 immune checkpoints. The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN, C3, and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis.

Conclusion: LYN, C3, COPG2IT1, LA.DQA1, and NFRSF17 may be new immune markers to judge the prognosis of patients with non-small cell lung cancer.

Keywords: immune-related prognostic markers; immunohistochemistry; machine learning; model building; non-small cell lung cancer.

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Figures

Figure 1
Figure 1
(A) Violin distribution of immune scores for OBS samples. (B) Comparison of tumor heterogeneity between high and low score groups. (C) Volcano map of differential genes in OBS samples. (D) Heat map of differential genes in OBS samples. (E) Single factor regression analysis of differential genes to screen for key genes for prognosis.
Figure 2
Figure 2
(A–E) Prognostic analysis of 5 key prognostic genes. (F) GO results of GSEA analysis of five key prognostic genes. (G) KEGG results of GSEA analysis of five key prognostic genes.
Figure 3
Figure 3
(A) The abscissa is the number of patients in OBS group, and the high and low risk groups are divided by the risk score. (B) The abscissa is the number of patients, and the division of high-score and low-risk groups is verified by survival. (C) Heatmap of the expression of five key prognostic genes in high-risk and low-risk patients in OBS group. (D) Comparison of survival analysis between high-risk and low-risk patients; (E) ROC analysis test results of model sensitivity and specificity.
Figure 4
Figure 4
(A) The abscissa is the number of patients in ACT group, and the high and low risk groups are divided by the risk score. (B) The abscissa is the number of patients, and the division of high-score and low-risk groups is verified by survival. (C) Heatmap of the expression of five key prognostic genes in high-risk and low-risk patients in ACT group. (D) Comparison of survival analysis between high-risk and low-risk patients. (E) ROC analysis test results of model sensitivity and specificity.
Figure 5
Figure 5
(A) Hubgene infiltration in 24 immune cells in high-risk and low-risk groups of ACT patients. (B) Functional enrichment results of co-expressed genes of five key prognostic genes. (C) Pathway enrichment results of co-expressed genes for five key prognostic genes. (D–F) Protein-protein interaction network of co-expressed genes of five key prognostic genes.
Figure 6
Figure 6
(A–T) Correlation analysis results of LYN, C3, COPG2IT1, HLA.DQA1, TNFRSF17 with PDCD1, PDCD1LG2, LAG3 and CTLA4 immune checkpoints.
Figure 7
Figure 7
Expression levels of five key prognostic genes in tumor tissues of two groups of patients with NSCLC.
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