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

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.

Assessment of an Exhaled Breath Test Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry to Detect Lung Cancer.

Importance: Exhaled breath is an attractive option for cancer detection. A sensitive and reliable breath test has the potential to greatly facilitate diagnoses and therapeutic monitoring of lung cancer. Objective: To investigate whether the breath test is able to detect lung cancer using the highly sensitive high-pressure photon ionization time-of-flight mass spectrometry (HPPI-TOFMS). Design, Setting, and Participants: This diagnostic study was conducted with a prospective-specimen collection, retrospective-blinded evaluation design. Exhaled breath samples were collected before surgery and detected by HPPI-TOFMS. The detection model was constructed by support vector machine (SVM) algorithm. Patients with pathologically confirmed lung cancer were recruited from Peking University People's Hospital, and healthy adults without pulmonary noncalcified nodules were recruited from Aerospace 731 Hospital. Data analysis was performed from August to October 2020. Exposures: Breath testing and SVM algorithm. Main Outcomes and Measures: The detection performance of the breath test was measured by sensitivity, specificity, accuracy, and area under the receiver-operating characteristic curve (AUC). Results: Exhaled breath samples were from 139 patients with lung cancer and 289 healthy adults, and all breath samples were collected and tested. Of all participants, 228 (53.27%) were women and the mean (SD) age was 57.0 (11.4) years. After clinical outcomes were ascertained, all participants were randomly assigned into the discovery data set (381 participants) and the blinded validation data set (47 participants). The discovery data set was further broken into a training set (286 participants) and a test set (95 participants) to construct and test the detection model. The detection model reached a mean (SD) of 92.97% (4.64%) for sensitivity, 96.68% (2.21%) for specificity, and 95.51% (1.93%) for accuracy in the test set after 500 iterations. In the blinded validation data set (47 participants), the model revealed a sensitivity of 100%, a specificity of 92.86%, an accuracy of 95.74%, and an AUC of 0.9586. Conclusions and Relevance: This diagnostic study's results suggest that a breath test with HPPI-TOFMS is feasible and accurate for lung cancer detection, which may be useful for future lung cancer screenings.

A fluorometric aptasensor for patulin based on the use of magnetized graphene oxide and DNase I-assisted target recycling amplification.

A fluorometric patulin (PAT) assay is presented that is based on the use of magnetic reduced graphene oxide (rGO) and DNase I. The fluorescence of the PAT aptamer labelled with 6-carboxyfluorescein (FAM) is quenched by magnetized reduced graphene oxide (rGO-Fe3O4) due to fluorescence resonance energy transfer (FRET). However, in the presence of PAT, the labelled aptamer is stripped off from rGO-Fe3O4. The rGO-Fe3O4 is then magnetically separated so that the fluorescence of free labelled PAT aptamer is restored. DNase I cannot hydrolyze the aptamer on rGO-Fe3O4, but it can cleave the free aptamer-PAT complex. This will release FAM and PAT which can undergo a number of additional cycles to trigger the cleavage of abundant aptamer. Recycling of DNase I-assisted target therefore leads to a strong amplification of fluorescence and consequently to an assay with low limit of detection. The detection limit for PAT is as low as 0.28 µg L-1 which is about 13 times lower than that without using DNase I. The method offers a new approach towards rapid, sensitive and selective detection based on an aptamer. Conceivably, it has a wide scope in that it may be applied to numerous other analytes if appropriate aptamers are available. Abstract Schematic of a fluorometric assay based on the use of magnetic graphene oxide and DNase I. It was applied to the determination of patulin. DNase I was introduced for recycling amplification. The detection limit is about 13 times lower than that without using DNase I. Figure a contains poor quality of text in image. Otherwise, please provide replacement figure file.Thank you. I will provide the figure file.