Application of a scoring system based on immune gene pairs in predicting the effect of immunotherapy in patients with non-small cell lung cancer

Abstract

The invention discloses an application of a scoring system based on immune gene pairs in predicting the curative effect and prognosis of immunotherapy for patients with non-small cell lung cancer. The scoring system disclosed in the present invention is based on the following four immune-related gene pairs: CCL2 and VEGFA gene, CDK1 and CXCL9 gene, HLA-DOB and LCK gene, IL-12A and TBX21 gene. The IRGP index (IRGPI) calculated based on the immune gene pair scoring system constructed in the present invention is significantly correlated with the progression-free survival of non-small cell lung cancer patients receiving anti-PD‑1 immunotherapy. It shows that using the 4 immune-related gene pairs of the present invention and the scoring system constructed based on these 4 immune-related gene pairs can predict the curative effect and prognosis of non-small cell lung cancer patients receiving anti-PD-1 immunotherapy.

Description

technical field [0001] The invention relates to the application of predicting the immunotherapy effect of non-small cell lung cancer patients based on an immune gene pair scoring system in the field of biomedicine. Background technique [0002] Lung cancer is a malignant tumor with the highest morbidity and mortality in China and even in the world. According to histopathological classification, non-small-cell lung cancer (NSCLC) accounts for about 85% and is the most common histological type. In recent years, immunotherapy targeting the immune checkpoint programmed death-1 (PD-1) and its ligand (PD-1 ligand, PD-L1) has become the most popular tumor therapy after molecular targeted therapy. means to rapidly change the treatment paradigm of NSCLC. Recent clinical trials and real-world studies have demonstrated long-term survival benefits for patients with immunotherapy, but in limited populations. Therefore, there is an urgent need for reliable markers to screen out this pa...

AI Technical Summary

Problems solved by technology

There are also studies that use gene expression characteristics to predict treatment-sensitive patients, but due to the limitation of the analysis platform, the clinical application value is not high

One of the reasons is that traditional research is based on gene expression levels, and these data need to be adequately standardized. However, due to technical bias and biological heterogeneity, there will always be problems such as small sample overfitting and lack of sufficient validation.

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