Objective. To explore the role of Chinese prescriptions in non-small cell lung cancer (NSCLC) and provide references for the application of herbs and prescriptions. Methods. Randomized and quasirandomized controlled clinical trials on Chinese herbal medicine in the treatment of NSCLC were collected from seven databases to establish a database of prescriptions on NSCLC. Data-mining analyses were performed by RStudio (v4.0.3) software. Results. A total of 970 prescriptions were obtained from 945 included studies, involving 7 syndromes and 428 herbs. The main patterns of NSCLC included qi deficiency pattern, yin deficiency pattern, blood deficiency pattern, kidney deficiency pattern, heat toxin pattern, phlegm-dampness pattern, and blood stasis pattern. High-frequency herbs on NSCLC were Astragali Radix (Huangqi), Atractylodis Macrocephalae Rhizome (Baizhu), Glycyrrhizae Radix Rhizome (Gancao), Poria (Fuling), Ophiopogonis Radix (Maidong), Hedyotidis Diffusae Herba (Baihuasheshecao), Codonopsis Radix (Dangshen), and Glehniae Radix (Beishashen). The properties of the herbs were mainly cold, warm, and mild. The flavors of the herbs were mainly sweet, bitter, and pungent. The main meridian tropisms were Lung Meridian of Hand-Taiyin, Spleen Meridian of Foot-Taiyin, and Stomach Meridian of Foot-Yangming. Conclusion. Applying clearing and tonifying method by targeting the lung and spleen was the most frequently used therapy in the treatment of NSCLC. This study offered a glimpse of unique views of traditional Chinese medicine on NSCLC and may benefit the treatment of NSCLC.
The most common type of lung cancer tissue is lung adenocarcinoma. The TCGA‐LUAD cohort retrieved from the TCGA dataset was considered the internal training cohort, while GSE68465 and GSE13213 datasets from the GEO database were used as the external test cohort. The TCGA‐LUAD cohort was classified into two immune subtypes using single‐sample gene set enrichment analysis of the immune gene set and unsupervised clustering analysis. The ESTIMATE algorithm, the CIBERSORT algorithm, and HLA family expression levels again validated the reliability of this typing. We performed Venn analysis using immune‐related genes from the immport dataset and differentially expressed genes from the subtypes to retrieve differentially expressed immune genes (DEIGs). In addition, DEIGs were used to construct a prognostic model with the least absolute shrinkage and selection operator regression analysis. A reliable risk model consisting of 11 DEIGs, including S100P, INHA, SEMA7A, INSL4, CD40LG, AGER, SERPIND1, CD1D, CX3CR1, SFTPD, and CD79A, was then built, and its reliability was further confirmed by ROC curve and calibration plot analysis. The high‐risk score subgroup had a poor prognosis and a lower tumour immune dysfunction and exclusion score, indicating a greater likelihood of anti‐PD‐1/cytotoxic T lymphocyte antigen 4 benefit.
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