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. 2022 Nov 10:13:996444.
doi: 10.3389/fgene.2022.996444. eCollection 2022.

A pyroptosis expression pattern score predicts prognosis and immune microenvironment of lung squamous cell carcinoma

Wei Chen  1   2   3 , Min-Yu Wen  1   2   3 , Kai-Bin Yang  1   3 , Li-Tao Zheng  1   2   3 , Xuan Li  1   3
Affiliations

A pyroptosis expression pattern score predicts prognosis and immune microenvironment of lung squamous cell carcinoma

Wei Chen et al. Front Genet. .

Abstract

Pyroptosis has been proved to significantly influence the development of lung squamous cell carcinoma (LUSC). To better predict overall survival (OS) and provide guidance on the selection of therapy for LUSC patients, we constructed a novel prognostic biomarker based on pyroptosis-related genes. The dataset for model construction were obtained from The Cancer Genome Atlas and the validation dataset were obtained from Gene Expression Omnibus. Differential expression genes between different pyroptosis expression patterns were identified. These genes were then used to construct pyroptosis expression pattern score (PEPScore) through weighted gene co-expression network analysis, univariate and multivariate cox regression analysis. Afterward, the differences in molecule and immune characteristics and the effect of different therapies were explored between the subgroups divided by the model. The PEPScore was constructed based on six pyroptosis-related genes (CSF2, FGA, AKAP12, CYP2C18, IRS4, TSLP). Compared with the high-PEPScore subgroup, the low-PEPScore subgroup had significantly better OS, higher TP53 and TTN mutation rate, higher infiltration of T follicular helper cells and CD8 T cells, and may benefit more from chemotherapeutic drugs, immunotherapy and radiotherapy. PEPScore is a prospective prognostic model to differentiate prognosis, molecular and immune microenvironmental features, as well as provide significant guidance for selecting clinical therapies.

Keywords: TCGA; immune microenvironment; lung squamous cell carcinoma; prognosis; pyroptosis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Abstract graphical representation for comprehensive characterization of PEPScore subgroups in LUSC.
FIGURE 2
FIGURE 2
Exploration of two different expression patterns of pyroptosis-related molecules. (A) Heatmap depicts the association between pyroptosis-related genes expression levels and essential cancer signaling pathways. The percentage is the total proportion of tumors in which a gene has an influence on the pathway among the 32 cancer types (number of inhibited or activated cancer types/32 *100%). Pyroptosis genes that have a role (inhibit or activate) in at least five cancer types are included in this heatmap. The percentage of tumors in which a pathway may be inhibited by specified genes is represented by “pathway inhibit” (blue), whereas activation is represented by “pathway activate” (red). (B) The correlation between the pyroptosis-related genes in LUSC and essential cancer signaling pathways. The dotted line indicates inhibition, whereas the solid line indicates activation. (C) t-SNE plot shows two different pyroptosis expression patterns represented by the expression of pyroptosis-related genes. (D) Kaplan-Meier curves for the OS between two distinct pyroptosis expression patterns. (E) Univariate Cox analysis to explore the prognosis value of each pyroptosis-related genes for LUSC.
FIGURE 3
FIGURE 3
Prognostic value and the characteristics of different PEPScore subgroups. (A) The forest plot depicts the result of univariate Cox analysis on 21 pyroptosis-related hub genes. (B) Kaplan-Meier survival analysis and ROC curves for patients in the TCGA cohort to identify the prognostic power of the PEPScore. (C and D) Kaplan-Meier survival analysis and ROC curves for patients in the GSE30219 and GSE73403 cohort to validate the prognostic power of the PEPScore. (E) ROC curve showing the specificity and sensitivity for PEPScore to predict the pyroptosis expression patterns. (F) The co-expression network of the pyroptosis-related genes in the high-PEPScore subgroup and low-PEPScore subgroup.
FIGURE 4
FIGURE 4
Comprehensive analysis of molecular and tumor-microenvironmental characteristics in PEPScore subgroups. (A) GO and KEGG analysis for revealing the potential regulatory mechanisms underlying the difference of PEPScore in different subgroups. A total of 821 DEGs were obtained from differential expression analysis between high- and low-PEPScore subgroups. (B) GSEA used on the HALLMARK gene sets to explore the potential mechanism underlying the difference of PEPScore in different subgroups. (C) Top 20 mutated molecules in the LUSC patients in TCGA database of different PEPScore subgroups. Each column represents an individual and the mutated genes are arranged by mutation frequency. The color block indicates mutation type, the number on the right shows the mutation percentage, and the figure above shows the TMB. (D) TMB calculation to access the quality and quantity of gene mutations in two PEPScore subgroups. (E) The Kaplan-Meier curves with the log-rank test show significant differences in OS between high and low TMB subgroups. The cut-off value of TMB was 2.105, which was calculated by R package of “survminer”. (F) The Kaplan-Meier curves with the log-rank test show significant differences in OS among LUSC patients with different PEPScore and TMB.
FIGURE 5
FIGURE 5
The landscape of the TME and the characteristics of different PEPScore subgroups. (A) The proportions of immune cells in the two PEPScore subgroups. The thick line in the box indicates the median value, whereas the dispersed dots indicate an outlier. The upper and bottom border of the box reflects the 25th and 75th percentiles. Asterisk denotes the p-value (*: p < 0.05, **: p < 0.01, and ***: p < 0.001). (B) PEPScore categorization and TEM cell proportions for 495 patients in the TCGA dataset. Patient annotations include gender, stage, race, age, smoking, and neoadjuvant treatment.
FIGURE 6
FIGURE 6
PEPScore predicts drug sensitivity. (A) The heatmap presents the different expressions of common drug targets for LUSC patients in high-PEPScore and low-PEPScore subgroups. Asterisk denotes the p-value (*: p < 0.05, **: p < 0.01, and ***: p < 0.001). (B) The difference in IC50 of the common chemotherapeutic drugs between high- and low-PEPScore subgroups. (C) The Wilcoxon test shows the difference in TIDE, MSI, TIS and T cell exclusion and dysfunction scores in high- and low-PEPScore subgroups. The p-value is indicated by asterisk (****p < 0.0001). (D) ROC curve analysis of the predictive value of the PEPScore, TIDE and TIS. (E) The difference in Radiotherapy index (RSI) between high- and low-PEPScore subgroups.
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References

    1. Chen J., Tao Q., Lang Z., Gao Y., Jin Y., Li X., et al. (2022). Signature construction and molecular subtype identification based on pyroptosis-related genes for better prediction of prognosis in hepatocellular carcinoma. Oxid. Med. Cell. Longev. 2022, 4494713. 10.1155/2022/4494713 - DOI - PMC - PubMed
    1. Dong J., Hu Z., Wu C., Guo H., Zhou B., Lv J., et al. (2012). Association analyses identify multiple new lung cancer susceptibility loci and their interactions with smoking in the Chinese population. Nat. Genet. 44 (8), 895–899. 10.1038/ng.2351 - DOI - PMC - PubMed
    1. Eschrich S. A., Pramana J., Zhang H., Zhao H., Boulware D., Lee J. H., et al. (2009). A gene expression model of intrinsic tumor radiosensitivity: Prediction of response and prognosis after chemoradiation. Int. J. Radiat. Oncol. Biol. Phys. 75 (2), 489–496. 10.1016/j.ijrobp.2009.06.014 - DOI - PMC - PubMed
    1. Fu J., Li K., Zhang W., Wan C., Zhang J., Jiang P., et al. (2020). Large-scale public data reuse to model immunotherapy response and resistance. Genome Med. 12 (1), 21. 10.1186/s13073-020-0721-z - DOI - PMC - PubMed
    1. Furuya M. Y., Asano T., Sumichika Y., Sato S., Kobayashi H., Watanabe H., et al. (2018). Tofacitinib inhibits granulocyte-macrophage colony-stimulating factor-induced NLRP3 inflammasome activation in human neutrophils. Arthritis Res. Ther. 20 (1), 196. 10.1186/s13075-018-1685-x - DOI - PMC - PubMed