Xudong Xing

Lung adenocarcinomas (LUAD) that radiologically display as subsolid nodules (SSNs) exhibit more indolent biological behavior than solid LUAD. The transcriptomic features and tumor microenvironment (TME) of SSN remain poorly understood. Here, we performed single-cell RNA sequencing analyses of 16 SSN samples, 6 adjacent normal lung tissues (nLung), and 9 primary LUAD with lymph node metastasis (mLUAD). Approximately 0.6 billion unique transcripts were obtained from 118,293 cells. We found that cytotoxic natural killer/T cells were dominant in the TME of SSN, and malignant cells in SSN undergo a strong metabolic reprogram and immune stress. In SSN, the subtype composition of endothelial cells was similar to that in mLUAD, while the subtype distribution of fibroblasts was more like that in nLung. Our study provides single-cell transcriptomic profiling of SSN and their TME. This resource provides deeper insight into the indolent nature of SSN and will be helpful in advancing lung cancer immunotherapy.

Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism-related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0-18:1, 16:0-18:2, 18:0-18:1, 18:0-18:2, and 16:0-22:6; and triglycerides 16:0-18:1-18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography-mass spectrometry (MS)-based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention.