No abstract

The enthusiasm for immune checkpoint inhibitors (ICIs), an efficient tumor treatment model different from traditional treatment, is based on their unprecedented antitumor effect, but the occurrence of immune-related adverse events (irAEs) is an obstacle to the prospect of ICI treatment. IrAEs are a discrete toxicity caused by the nonspecific activation of the immune system and can affect almost all tissues and organs. Currently, research on biomarkers mainly focuses on the gastrointestinal tract, endocrine system, skin and lung. Several potential hypotheses concentrate on the overactivation of the immune system, excessive release of inflammatory cytokines, elevated levels of pre-existing autoantibodies, and presence of common antigens between tumors and normal tissues. This review lists the current biomarkers that might predict irAEs and their possible mechanisms for both nonspecific and organ-specific biomarkers. However, the prediction of irAEs remains a major clinical challenge to screen and identify patients who are susceptible to irAEs and likely to benefit from ICIs.

No abstract

Patients with stage III lung adenocarcinoma (LUAD) have significant survival heterogeneity, meanwhile, CD8+ T cell has a remarkable function in immunotherapy. Therefore, developing novel biomarkers based on CD8+ T cell can help evaluate the prognosis and guide the strategy of immunotherapy for patients with stage III LUAD. Thus, we abstracted twelve datasets from multiple online databases and grouped the stage III LUAD patients into training and validation sets. We then used WGCNA and CIBERSORT, while univariate Cox analysis, LASSO analysis, and multivariate Cox analysis were performed. Subsequently, a novel CD8+ T cell-related classifier including HDFRP3, ARIH1, SMAD2, and UPB1 was developed, which could divide stage III LUAD patients into high- and low-risk groups with distinct survival probability in multiple cohorts (all P < 0.05). Moreover, a robust nomogram including the traditional clinical parameters and risk signature was constructed, and t-ROC, C-index, and calibration curves confirmed its powerful predictive capacity. Besides, we detected the difference in immune cell subpopulations and evaluated the potential benefits of immunotherapy between the two risk subsets. Finally, we verified the correlation between the gene expression and CD8+ T cells included in the model by immunohistochemistry and validated the validity of the model in a real-world cohort. Overall, we constructed a robust CD8+ T cell-related risk model originally which could predict the survival rates in stage III LUAD. What’s more, this model suggested that patients in the high-risk group could benefit from immunotherapy, which has significant implications for accurately predicting the effect of immunotherapy and evaluating the prognosis for patients with stage III LUAD.

No abstract