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. 2021 Jun 25;7(1):161.
doi: 10.1038/s41420-021-00557-2.

Identification of the pyroptosis‑related prognostic gene signature and the associated regulation axis in lung adenocarcinoma

Wanli Lin  1 , Ying Chen  1 , Bomeng Wu  1 , Ying Chen  1 , Zuwei Li  2
Affiliations

Identification of the pyroptosis‑related prognostic gene signature and the associated regulation axis in lung adenocarcinoma

Wanli Lin et al. Cell Death Discov. .

Abstract

Lung adenocarcinoma (LUAD) remains the most common deadly disease and has a poor prognosis. Pyroptosis could regulate tumour cell proliferation, invasion, and metastasis, thereby affecting the prognosis of cancer patients. However, the role of pyroptosis-related genes (PRGs) in LUAD remains unclear. In our study, comprehensive bioinformatics analysis was performed to construct a prognostic gene model and ceRNA network. The correlations between PRGs and tumour-immune infiltration, tumour mutation burden, and microsatellite instability were evaluated using Pearson's correlation analysis. A total of 23 PRGs were upregulated or downregulated in LUAD. The genetic mutation variation landscape of PRG in LUAD was also summarised. Functional enrichment analysis revealed that these 33 PRGs were mainly involved in pyroptosis, the NOD-like receptor signalling pathway, and the Toll-like receptor signalling pathway. Prognosis analysis indicated a poor survival rate in LUAD patients with low expression of NLRP7, NLRP1, NLRP2, and NOD1 and high CASP6 expression. A prognostic PRG model constructed using the above five prognostic genes could predict the overall survival of LUAD patients with medium-to-high accuracy. Significant correlation was observed between prognostic PRGs and immune-cell infiltration, tumour mutation burden, and microsatellite instability. A ceRNA network was constructed to identify a lncRNA KCNQ1OT1/miR-335-5p/NLRP1/NLRP7 regulatory axis in LUAD. In conclusion, we performed a comprehensive bioinformatics analysis and identified a prognostic PRG signature containing five genes (NLRP7, NLRP1, NLRP2, NOD1, and CASP6) for LUAD patients. Our results also identified a lncRNA KCNQ1OT1/miR-335-5p/NLRP1/NLRP7 regulatory axis, which may also play an important role in the progression of LUAD. Further study needs to be conducted to verify this result.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Landscape of genetic and expression variation of PRG in LUAD.
A The expression of 33 PRG in LUAD and lung tissues, Tumour, red; Normal, blue. The upper and lower ends of the boxes represented the interquartile range of values. The lines in the boxes represented median value. B, C The mutation frequency and classification of 33 PRG in LUAD. D The location of CNV alteration of 33 PRG on 23 chromosomes in the LUAD cohort. E The CNV variation frequency of 33 PRG in the LUAD cohort. The height of the column represented the alteration frequency. ***P < 0.001, PRG pyroptosis‑related gene, LUAD lung adenocarcinoma, SNP single nucleotide polymorphism, INS insertion, DEL deletion.
Fig. 2
Fig. 2. The functional enrichment analysis of PRG in LUAD.
A The enriched item in gene ontology analysis. B The enriched item in Kyoto Encyclopedia of Genes and Genomes analysis. The size of circles represented the number of genes enriched. BP biological process, CC cellular component, MF molecular function, PRG pyroptosis‑related gene.
Fig. 3
Fig. 3. The prognostic value of PRG in LUAD.
The overall survival curve of NLRP7 A NLRP1 B NLRP2 C NOD1 D and CASP6 E in LUAD patients in the high-/low-expression group. PRG pyroptosis‑related gene, LUAD lung adenocarcinoma.
Fig. 4
Fig. 4. Construction of a prognostic PRG model.
A LASSO coefficient profiles of the five PRGs. B Plots of the ten-fold cross-validation error rates. C Distribution of risk score, survival status, and the expression of five prognostic PRGs in LUAD. D, E Overall survival curves for LUAD patients in the high-/low-risk group and the ROC curve of measuring the predictive value. PRG, pyroptosis‑related gene; LUAD, lung adenocarcinoma.
Fig. 5
Fig. 5. Construction of a predictive nomogram.
A, B Hazard ratio and P‐value of the constituents involved in univariate and multivariate Cox regression considering clinical the parameters and five prognostic PRG in LUAD. C, D Nomogram to predict the 1-year, 3-year, and 5-year overall survival rate of LUAD patients. Calibration curve for the overall survival nomogram model in the discovery group. A dashed diagonal line represents the ideal nomogram. PRG pyroptosis‑related gene, LUAD lung adenocarcinoma.
Fig. 6
Fig. 6. The association between five prognostic PRG and immune infiltration (TIMER).
The association between the abundance of immune cells and the expression of CASP6 A NLRP7 B NLRP2 C NOD1 D and NLRP1 E in LUAD. PRG pyroptosis‑related gene, LUAD lung adenocarcinoma.
Fig. 7
Fig. 7. TMB, MSI, and drug-sensitivity analysis of PRG in LUAD.
A–E The correlation between five prognostic PRG and TMB in LUAD. F–J The correlation between five prognostic PRG and MSI in LUAD. K The correlation between five prognostic PRG and CTRP drug sensitivity in LUAD. TMB tumour mutation burden, MSI microsatellite instability, LUAD lung adenocarcinoma, PRG pyroptosis‑related gene, CTRP cancer therapeutics response portal.
Fig. 8
Fig. 8. Construction of ceRNA network.
A Results of miRNA target predicted by mirTarBase and TarBase V.8. The expression B and prognostic value C of miR-335-5p in LUAD. D Results of lncRNA targets predicted by lncBase predicted V.2 and StarBase V2.0. E The network of lncRNA–miRNA–mRNA. F-G The expression lncRNA FTX and lncRNA KCNQ1OT1 in LUAD. H The prognosis value of lncRNA KCNQ1OT1 in LUAD. LUAD lung adenocarcinoma; CeRNA competing endogenous RNA.
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