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. 2022 Sep 30:2022:7116080.
doi: 10.1155/2022/7116080. eCollection 2022.

Construction of Prognostic Risk Model for Small Cell Lung Cancer Based on Immune-Related Genes

Feng Deng  1 , Feng Tao  1 , Zhili Xu  1 , Jun Zhou  1 , Xiaowei Gong  1 , Ruhu Zhang  1
Affiliations

Construction of Prognostic Risk Model for Small Cell Lung Cancer Based on Immune-Related Genes

Feng Deng et al. Comput Math Methods Med. .

Abstract

Small cell lung cancer (SCLC) is a highly invasive and fatal malignancy. Research at the present stage implied that the expression of immune-related genes is associated with the prognosis in SCLC. Accordingly, it is essential to explore effective immune-related molecular markers to judge prognosis and treat SCLC. Our research obtained SCLC dataset from Gene Expression Omnibus (GEO) and subjected mRNAs in it to differential expression analysis. Differentially expressed mRNAs (DEmRNAs) were intersected with immune-related genes to yield immune-related differentially expressed genes (DEGs). The functions of these DEGs were revealed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Thereafter, we categorized 3 subtypes of immune-related DEGs via K-means clustering. Kaplan-Meier curves analyzed the effects of 3 subtypes on SCLC patients' survival. Single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE validated that the activation of different immune gene subtypes differed significantly. Finally, an immune-related-7-gene assessment model was constructed by univariate-Lasso-multiple Cox regression analyses. Riskscores, Kaplan-Meier curves, receiver operating characteristic (ROC) curves, and independent prognostic analyses validated the prognostic value of the immune-related-7-gene assessment model. As suggested by GSEA, there was a prominent difference in cytokine-related pathways between high- and low-risk groups. As the analysis went further, we discovered a statistically significant difference in the expression of human leukocyte antigen (HLA) proteins and costimulatory molecules expressed on the surface of CD274, CD152, and T lymphocytes in different groups. In a word, we started with immune-related genes to construct the prognostic model for SCLC, which could effectively evaluate the clinical outcomes and offer guidance for the treatment and prognosis of SCLC patients.

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Conflict of interest statement

The authors report no conflict of interest.

Figures

Figure 1
Figure 1
The screening and functional enrichment of immune-related DEGs. (a) Volcano plot of DEGs in SCLC samples and healthy samples in GEO dataset. Red represents significantly upregulated genes and green represents significantly downregulated genes. (b) Venn diagram of DEGs and immune-related genes. (c) GO and (d) KEGG analyses of immune-related DEGs.
Figure 2
Figure 2
Construction and assessment of SCLC subtypes based on immune-related DEGs. (a) Diagram of consensus cumulative distribution function (CDF). (b) Plot of the relative area under the CDF curve. (c) K-means cluster analysis of SCLC samples. (d) Survival analysis of different immune subtypes. (e) Heatmap of tumor microenvironment in different subtypes. (f) Stromal score (red = subtype-1, blue = subtype-2, and green = subtype-3), (g) immune scores (red = subtype-1, blue = subtype-2, and green = subtype-3), (h) ESTIMATE scores (red = subtype-1, blue = subtype-2, and green = subtype-3), and (i) Tumor purity scores for different subtypes (red = subtype-1, blue = subtype-2, and green = subtype-3). ns: not significant; ∗∗P < 0.01; ∗∗∗P < 0.001.
Figure 3
Figure 3
Construction of a prognostic risk assessment model for SCLC patient. (a) Trajectory plot of the gene coefficients with the increasing log (λ) value in the Lasso model. (b) The optimal log (λ) was selected according to the partial likelihood deviance in the Lasso model. (c) Forest plot of 7 genes obtained from multivariate Cox regression analysis. (d) ROC curves of the GSE60052 data sample risk score in both groups. (e) Survival analysis of GSE60052 dataset by the Riskscore model. (f) Distribution of scores, (g) survival status, and (h) the heatmap of 7 genes in both groups.
Figure 4
Figure 4
GSEA for high- and low-risk groups.
Figure 5
Figure 5
Analysis of tumor immune characteristics in high- and low-risk groups. Box plots of (a) HLA, (b) PD-L1, (c) PD-1, (d) CTLA4, and (e) CD28 in high- (red) and low-risk (blue) groups. ns: not significant; ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001.
Figure 6
Figure 6
Correlation analysis between Riskscore and clinical factors in SCLC patients. (a) Forest plot of univariate and (b) multivariate Cox regression analyses based on clinical information, Riskscore, and overall survival.
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