Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis

Abstract

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.

UHPLC-HRMS–based serum lipisdomics reveals novel biomarkers to assist in the discrimination between colorectal adenoma and cancer

“…It is well known that PCs are the most abundant glycerophospholipids in eukaryotic membranes, abnormalities of which are related to tumor cell proliferation and signal transduction ( 24 , 28 ). Metabolic disorders of PCs have been found in various tumors, and some PCs have reportedly shown significant potential as tumor biomarkers ( 24 , 26 , 29 ). In recent studies, the expression of PCs in cancer patients was shown to be decreased ( 30 – 32 ), and this downregulation might be due to overexpression of phospholipase A2 (PLA2) in tumors ( 33 ), which causes hydrolysis of most PCs into FAs and LPCs.…”

Lysophosphatidylcholine: Potential Target for the Treatment of Chronic Pain

“…At present, the accurate detection of LPC relies on metabolomics or lipidomics technology, which provides assays for exploiting the role of LPC in chronic pain. Apart from chronic pain, LPC or LPC species in body fluids such as blood, urine, cerebrospinal fluid, and tissues are uniquely or collectively related to cancer [ 150 , 151 , 152 ], diabetes [ 153 , 154 , 155 , 156 ], coronary atherosclerosis [ 157 ], Alzheimer’s disease [ 158 , 159 ], rheumatoid arthritis [ 83 ], COVID-19 [ 160 ], liver and kidney damage [ 161 , 162 ], etc. Whether LPC is necessary for other chronic pain conditions, such as cancer pain, has not been confirmed.…”

HPGDS is a novel prognostic marker associated with lipid metabolism and aggressiveness in lung adenocarcinoma

“…Even though lipid metabolism pathways in lung cancer are widely studied ( 45 – 47 ), this study is different because it focused on the abnormal lipid metabolism pathways in patients with LUAD and established a more reliable prognostic risk score. We performed biological experiments and confirmed that HPGDS can promote the migration of A549 by upregulating the expression of key lipid metabolism enzymes ACSL1 and ACC, but not HK2 and ACAA1.…”