Immune Landscape and Classification in Lung Adenocarcinoma Based on a Novel Cell Cycle Checkpoints Related Signature for Predicting Prognosis and Therapeutic Response

Abstract

Lung adenocarcinoma (LUAD) is one of the most common malignancies with the highest mortality globally, and it has a poor prognosis. Cell cycle checkpoints play a central role in the entire system of monitoring cell cycle processes, by regulating the signalling pathway of the cell cycle. Cell cycle checkpoints related genes (CCCRGs) have potential utility in predicting survival, and response to immunotherapies and chemotherapies. To examine this, based on CCCRGs, we identified two lung adenocarcinoma subtypes, called cluster1 and cluster2, by consensus clustering. Enrichment analysis revealed significant discrepancies between the two subtypes in gene sets associated with cell cycle activation and tumor progression. In addition, based on Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we have developed and validated a cell cycle checkpoints-related risk signature to predict prognosis, tumour immune microenvironment: (TIME), immunotherapy and chemotherapy responses for lung adenocarcinoma patients. Results from calibration plot, decision curve analysis (DCA), and time-dependent receiver operating characteristic curve (ROC) revealed that combining age, gender, pathological stages, and risk score in lung adenocarcinoma patients allowed for a more accurate and predictive nomogram. The area under curve for lung adenocarcinoma patients with 1-, 3-, 5-, and 10-year overall survival was: 0.74, 0.73, 0.75, and 0.81, respectively. Taken together, our proposed 4-CCCRG signature can serve as a clinically useful indicator to help predict patients outcomes, and could provide important guidance for immunotherapies and chemotherapies decision for lung adenocarcinoma patients.

Application of an angiogenesis-related genes risk model in lung adenocarcinoma prognosis and immunotherapy

“…The findings revealed that RS strongly predicts outcomes in LUAD patients with various clinical characteristics, particularly in early LUAD patients ( Figures 7A–F ). In order to demonstrate the predictive performance of our model, we compared it with other models based on the TCGA-LUAD database, such as the published gene prediction models of Duan et al (2021) , Zhang et al (2022) , Yang et al (2022) , Xu and Chen, (2021) , Gong et al (2022) . Our model has the greatest c-index of nomogram compared to previously published lung cancer models, as indicated by the findings.…”

Identification of endoplasmic reticulum stress-related signature characterizes the tumor microenvironment and predicts prognosis in lung adenocarcinoma

“…It is particularly important to identify new biomarkers of prognosis and develop more accurate prognostic models to predict the survival of patients with lung cancer to guide the treatment strategy. Previously reported prognostic models for ferroptosis-related genes, cell-cyclecheckpoints-related genes and aging-related-genes have exhibited promising results in predicting the prognosis in LUAD [14][15][16] . However, these biomarkers still have some limitations in terms of predictive power, and more accurate prognostic models are needed.…”

ML Classification of Cancer Types Using High Dimensional Gene Expression Microarray Data

“…Lung adenocarcinoma (LUAD) is a common form of lung cancer which also gets detected in the middle/late stages and therefore is hard to treat [45]. Yang et al (2022) used a dataset of gene expression profiles from 515 tumor samples and 59 normal tissues and split the dataset into two significantly different clusters; they further showed that using age, gender, pathological stages, and risk score as predictors of LUAD increased the prediction accuracy measures [46]. Liu, Lei, Zhang, and Wang (2022) used cluster analysis on enrichment scores of 12 stemness signatures to identify three LUAD subtypes, St-H, St-M and St-L for six different datasets [47].…”