Project description:Lung cancer is the deadliest cancer worldwide. In this study, we obtained RNA-sequencing data from 61 lung cancer samples. We hope that this data can improve the understanding of this disease.
Project description:Lung cancer is the leading cause of cancer mortality and early detection is the key to improve 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 early-stage lung cancer and found lipid metabolism was broadly dysregulated in different cell types and glycerophospholipid metabolism is the most significantly altered lipid metabolism-related pathway. Untargeted lipidomics were detected in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum-based feature selection, we have identified nine lipids as the most important detection features and developed a LC-MS-based targeted assay utilizing 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 CT and a prospective clinical cohort containing 109 participants, this assay reached over 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization-mass spectrometry imaging assay confirmed the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. Thus, this method, designated as Lung Cancer Artificial Intelligence Detector (LCAID), may be used for early detection of lung cancer or large-scale screening of high-risk populations in cancer prevention.
