A pyroptosis-related gene model and its correlation with the microenvironment of lung adenocarcinoma: A bioinformatics analysis and experimental verification

Identification of a novel ADCC-related gene signature for predicting the prognosis and therapy response in lung adenocarcinoma

BACKGROUND

Previous studies have largely neglected the role of ADCC in LUAD, and no study has systematically compiled ADCC-associated genes to create prognostic signatures.

METHODS

In this study, 1564 LUAD patients, 2057 NSCLC patients, and more than 5000 patients with various cancer types from diverse cohorts were included. R package ConsensusClusterPlus was utilized to classify patients into different subtypes. A number of machine-learning algorithms were used to construct the ADCCRS. GSVA and ClusterProfiler were used for enrichment analyses, and IOBR was used to quantify immune cell infiltration level. GISTIC2.0 and maftools were used to analyze the CNV and SNV data. The Oncopredict package was used to predict drug information based on the GDSC1. Three immunotherapy cohorts were used to evaluate patient response to immunotherapy. The Seurat package was used to process single-cell data, the AUCell package was used to calculate cells' geneset activity scores, and the Scissor algorithm was used to identify ADCCRS-associated cells.

RESULTS

Through unsupervised clustering, two distinct subtypes of LUAD were identified, each exhibiting distinct clinical characteristics. The ADCCRS, consisted of 16 genes, was constructed by integrated machine-learning methods. The prognostic power of ADCCRS was validated in 28 independent datasets. Further, ADCCRS shows better predictive abilities than 102 previously published signatures in predicting LUAD patients' survival. A nomogram incorporating ADCCRS and clinical features was constructed, demonstrating high predictive performance. ADCCRS positively correlates with patients' gene mutation, and integrated analysis of bulk and single-cell transcriptome data revealed the association of ADCCRS with TME modulators. Cells representing high-ADCCRS phenotype exhibited more malignant features. LUAD patients with high ADCCRS levels exhibited sensitivity to chemotherapy and targeted therapy, while displaying resistance to immunotherapy. In pan-cancer analysis, ADCCRS still exhibited significant prognostic value and was found to be a risk factor for most cancer patients.

CONCLUSIONS

ADCCRS offers a critical prognostic insight for patients with LUAD, shedding light on the tumor microenvironment and forecasting treatment responsiveness.

Exploring the multifaceted effects of Interleukin-1 in lung cancer: From tumor development to immune modulation

Lung cancer, acknowledged as one of the most fatal cancers globally, faces limited treatment options on an international scale. The success of clinical treatment is impeded by challenges such as late diagnosis, restricted treatment alternatives, relapse, and the emergence of drug resistance. This predicament has led to a saturation point in lung cancer treatment, prompting a rapid shift in focus towards the tumor microenvironment (TME) as a pivotal area in cancer research. Within the TME, Interleukin-1 (IL-1) is abundantly present, originating from immune cells, tissue stromal cells, and tumor cells. IL-1's induction of pro-inflammatory mediators and chemokines establishes an inflammatory milieu influencing tumor occurrence, development, and the interaction between tumors and the host immune system. Notably, IL-1 expression in the TME exhibits characteristics such as staging, tissue specificity, and functional pluripotency. This comprehensive review aims to delve into the impact of IL-1 on lung cancer, encompassing aspects of occurrence, invasion, metastasis, immunosuppression, and immune surveillance. The ultimate goal is to propose a novel treatment approach, considering the intricate dynamics of IL-1 within the TME.