Identification of Biomarkers for Lung Adenocarcinoma With Qi Deficiency and Phlegm Dampness

• Published in: The clinical respiratory journal Vol. 18; no. 8; pp. e13812 - n/a
• Main Authors: Chen, Jiabin, Wang, Sheng, Yang, Qiaolei, Zhang, Yongjun, Shen, Jianfei, Chai, Kequn
• Format: Journal Article
• Language: English
• Published: England John Wiley & Sons, Inc 01.08.2024; John Wiley and Sons Inc; Wiley
• Subjects: Adenocarcinoma of Lung - diagnosis; Adenocarcinoma of Lung - genetics; Aged; biomarker; Biomarkers; Biomarkers, Tumor - blood; Biomarkers, Tumor - genetics; Biomarkers, Tumor - metabolism; Cancer; Case-Control Studies; Female; Humans; Leukocytes, Mononuclear - metabolism; lung adenocarcinoma; Lung Neoplasms - diagnosis; Lung Neoplasms - genetics; Male; Medicine, Chinese Traditional - methods; Middle Aged; Original; PPAR gamma - genetics; PPAR gamma - metabolism; Qi deficiency and phlegm dampness; RNA‐seq; ROC Curve; traditional Chinese medicine; traditional Chinese medicine; biomarker; Qi deficiency and phlegm dampness; RNA‐seq; lung adenocarcinoma
• ISSN: 1752-6981; 1752-699X; 1752-699X
• DOI: 10.1111/crj.13812

ABSTRACT Background Qi deficiency and phlegm dampness (QPD) is one of the most common traditional Chinese medicine (TCM) syndromes in lung adenocarcinoma (LUAD). This study aimed to identify syndrome‐specific biomarkers for LUAD with QPD syndrome. Methods Peripheral blood mononuclear cells (PBMCs) from LUAD patients with QPD, LUAD patients with non‐QPD (N‐QPD), and healthy control (H) were collected and analyzed with RNA‐seq to identify differentially expressed genes (DEGs). The area under the receiver operator characteristic curve (AUC) of each DEG was calculated, and the top 10 highest AUC DEGs were validated by qRT‐PCR. Logistic regression analysis was used to develop a diagnostic model evaluated with AUC. Results A total of 135 individuals were enrolled in this study (training set: 15 QPD, 15 N‐QPD, 15 H; validation set: 30 QPD, 30 N‐QPD, 30 H). A total of 1480 DEGs were identified between QPD and N‐QPD. The qRT‐PCR results showed that the expression of DDR2 was downregulated, and PPARG was upregulated, which was in line with the finding of the training set. We developed a diagnostic model with these two genes. The AUC of the diagnostic model in the training cohort and validation cohort was 0.891 and 0.777, respectively. Conclusions We identified the two genes (DDR2 and PPARG) as syndrome‐specific biomarkers for LUAD with QPD syndrome and developed a novel diagnostic model, which may help to improve the accuracy and sensibility of clinical diagnosis and provide a new target for natural drug treatment of LUAD. The DDR2 and PPARG were identified as potential biomarkers for LUAD with QPD syndrome. The practical diagnostic model based on DDR2 and PPARG enriched the diagnostic methods of TCM syndrome differentiation, which may help to improve the accuracy and sensibility of clinical diagnosis. The practical diagnostic model may provide a new target for natural drug treatment of LUAD.