Prognostic Implications of Pyroptosis-Related Gene Signatures in Lung Squamous Cell Carcinoma

Identification and validation of pyroptosis patterns with a novel quantification system for the prediction of prognosis in lung squamous cell carcinoma

Background

The role of pyroptosis in lung squamous cell carcinoma (LUSC) remains unclear. This study aimed to screen pyroptosis-related genes (PRGs) and construct a model to investigate the immune infiltration, gene mutations, and immune response of patients of LUSC.

Methods

We conducted a comprehensive evaluation of pyroptosis patterns in patients with LUSC with 51 PRGs. Pyroptosis-related clusters were identified using consistency clustering algorithm. Differences in the biologic and clinical characteristics between the clusters were analyzed. Cox regression analysis was performed to screen for differentially expressed genes (DEGs) related to prognosis, and a principal component analysis (PCA) algorithm was used to construct a model based on these genes. The pyroptosis score was calculated for each tumor sample, and the samples were classified into high- and low-score groups based on the score. The disparities in survival, single-nucleotide variation (SNV), copy number variation (CNV), and immunotherapy response between high-score and low-score groups were analyzed.

Results

A total of 51 PRGs were used to classify LUSC samples into three pyroptosis clusters with significant differences in survival (P=0.005). Based on the 390 DEGs between the three clusters, two distinct pyroptosis gene clusters were identified by secondary clustering, with significant differences in prognosis (P=0.005). A pyroptosis scoring model was established to evaluate the regulatory patterns of PRGs, and patients were stratified into two groups with high and low scores, using the median pyroptosis score as the cutoff. The survival analyses indicated that patients with high scores had worse prognoses in The Cancer Genome Atlas (TCGA)-LUSC cohort (P=0.002), which was further supported by the analysis of the GSE37745 (P=0.006) and GSE135222 datasets (P=0.02).

Conclusions

The quantification of pyroptosis patterns was found to be important in predicting prognosis and devising personalized treatment strategies in patients with LUSC.

Pyroptosis and Inflammasome-Related Genes-NLRP3, NLRC4 and NLRP7 Polymorphisms Were Associated with Risk of Lung Cancer

Background

Cancer development and tumor immune microenvironment remodeling are closely linked to pyroptosis and inflammasome activation. However, little information is available in single nucleotide polymorphisms (SNPs) in pyroptosis and inflammasome-related genes in patients with lung cancer. This study aims to evaluate the associations between pyroptosis-related gene (NLRP3, NLRC4, and NLRP7) polymorphisms and the risk of lung cancer.

Methods

The MassARRAY platform was used to genotype six SNPs of the NLRP3, NLRC4, and NLRP7 genes in 660 lung cancer cases and 660 controls.

Results

Individuals with rs35829419-A, rs385076-C, and rs775882-T alleles exhibited a higher risk of lung cancer (p < 0.01), while rs212704-T appears protective (p = 0.006). The rs35829419-AA, rs385076-TC/CC, and rs775882-CT/TT genotypes were associated with various degrees of elevated risk of lung cancer (p<0.02), whereas rs212704-TT was associated with a reduced risk of the disease (p=0.014). Genetic models analysis showed that rs35829419, rs385076, and rs775882 was associated with an increased risk of lung cancer, while rs212704 was related to a reduced risk in all three models (p < 0.05). The four SNPs remained significant in smoker and nonsmoker subgroups (p < 0.05). However, rs35829419 was correlated with risk of adenocarcinoma and small cell lung cancer, and rs212704 was only protective for squamous cell carcinoma. The rs385076 and rs775882 were associated with all three pathological types (p < 0.01).

Conclusion

Besides providing candidate markers for identification of high-risk populations and early prevention of the disease, our research also provided new insight into anti-tumor strategies targeting inflammasomes and pyroptosis.

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

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.

Determination of a prediction model for therapeutic response and prognosis based on chemokine signaling-related genes in stage I–III lung squamous cell carcinoma

Background: The chemokine signaling pathway plays an essential role in the development, progression, and immune surveillance of lung squamous cell carcinoma (LUSC). Our study aimed to systematically analyze chemokine signaling-related genes (CSRGs) in LUSC patients with stage I–III disease and develop a prediction model to predict the prognosis and therapeutic response.

Methods: A total of 610 LUSC patients with stage I–III disease from three independent cohorts were included in our study. Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses were used to develop a CSRG-related signature. GSVA and GSEA were performed to identify potential biological pathways. The ESTIMATE algorithm, ssGSEA method, and CIBERSORT analyses were applied to explore the correlation between the CSRG signature and the tumor immune microenvironment. The TCIA database and pRRophetic algorithm were utilized to predict responses to immunochemotherapy and targeted therapy.

Results: A signature based on three CSRGs (CCL15, CXCL7, and VAV2) was developed in the TCGA training set and validated in the TCGA testing set and GEO external validation sets. A Kaplan–Meier survival analysis revealed that patients in the high-risk group had significantly shorter survival than those in the low-risk group. A nomogram combined with clinical parameters was established for clinical OS prediction. The calibration and DCA curves confirmed that the prognostic nomogram had good discrimination and accuracy. An immune cell landscape analysis demonstrated that immune score and immune-related functions were abundant in the high-risk group. Interestingly, the proportion of CD8 T-cells was higher in the low-risk group than in the high-risk group. Immunotherapy response prediction indicated that patients in the high-risk group had a better response to CTLA-4 inhibitors. We also found that patients in the low-risk group were more sensitive to first-line chemotherapeutic treatment and EGFR tyrosine kinase inhibitors. In addition, the expression of genes in the CSRG signature was validated by qRT‒PCR in clinical tumor specimens.

Conclusion: In the present study, we developed a CSRG-related signature that could predict the prognosis and sensitivity to immunochemotherapy and targeted therapy in LUSC patients with stage I–III disease. Our study provides an insight into the multifaceted role of the chemokine signaling pathway in LUSC and may help clinicians implement optimal individualized treatment for patients.

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Background: An imbalance in the redox homeostasis has been reported in multiple cancers and is associated with a poor prognosis of disease. However, the prognostic value of redox-related genes in non-small-cell lung cancer (NSCLC) remains unclear.

Methods: RNA sequencing data, DNA methylation data, mutation, and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Redox-related differentially expressed genes (DEGs) were used to construct the prognostic signature using least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan–Meier survival curve and receiver operator characteristic (ROC) curve analyses were applied to validate the accuracy of the gene signature. Nomogram and calibration plots of the nomogram were constructed to predict prognosis. Pathway analysis was performed using gene set enrichment analysis. The correlations of risk score with tumor stage, immune infiltration, DNA methylation, tumor mutation burden (TMB), and chemotherapy sensitivity were evaluated. The prognostic signature was validated using GSE31210, GSE26939, and GSE68465 datasets. Real-time polymerase chain reaction (PCR) was used to validate dysregulated genes in NSCLC.

Results: A prognostic signature was constructed using the LASSO regression analysis and was represented as a risk score. The high-risk group was significantly correlated with worse overall survival (OS) (p < 0.001). The area under the ROC curve (AUC) at the 5-year stage was 0.657. The risk score was precisely correlated with the tumor stage and was an independent prognostic factor for NSCLC. The constructed nomogram accurately predicted the OS of patients after 1-, 3-, and 5-year periods. DNA replication, cell cycle, and ECM receptor interaction were the main pathways enriched in the high-risk group. In addition, the high-risk score was correlated with higher TMB, lower methylation levels, increased infiltrating macrophages, activated memory CD4⁺ T cells, and a higher sensitivity to chemotherapy. The signature was validated in GSE31210, GSE26939, and GSE68465 datasets. Real-time PCR validated dysregulated mRNA expression levels in NSCLC.

Conclusions: A prognostic redox-related gene signature was successfully established in NSCLC, with potential applications in the clinical setting.