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.

The machine learning–aided serum lipidomics approach could be used to help early diagnosis of patients with PDAC.

Most malignant brain gliomas (MBGs) are associated with dismal outcomes, mainly due to their late diagnosis. Current diagnostic methods for MBGs are based on imaging and histological examination, which limits their early detection. Here, we aimed to identify reliable plasma lipid biomarkers for non-invasive diagnosis for MBGs. Untargeted lipidomic analysis was firstly performed using a discovery cohort (n=107). The data were processed by a support vector machine (SVM)-based discriminating model to retrieve a panel of candidate biomarkers. Then, a targeted quantification method was developed, and the SVM-based diagnostic model was constructed using a training cohort (n=750) and tested using a test cohort (n=225). Finally, the performance of the diagnostic model was further evaluated in an independent validation cohort (n=920) enrolled from multiple medical centers. A panel of 11 plasma lipids was identified as candidate biomarkers with an accuracy of 0.999. The diagnostic model developed achieved a high performance in distinguishing MBGs patients from normal controls with an area under the receiver-operating characteristic curve (AUC) of 0.9877 and 0.9869 in the training and test cohorts, respectively. In the validation cohort, the 11 lipid panel still achieved an accuracy of 0.9641 and an AUC of 0.9866. The present study demonstrates the applicability and robustness of utilizing a machine learning algorithm to analyze lipidomic data for efficient and reliable biomarker screening. The 11 lipid biomarkers show great potential for the non-invasive diagnosis of MBGs with high throughput. A full list of funding bodies that contributed to this study can be found in the Acknowledgments section.

Sulfonylurea resistance has been found in Japan, and four of the nine resistant weeds are in the genus Lindernia (Scrophulariaceae). To develop appropriate control methods for these resistant weeds, a rapid method to diagnose sulfonylurea-resistant weeds was improved and applied to common falsepimpernel (L. procumbens), two varieties of low falsepimpernel (L. dubia var. dubia and var. major), and azetogarashi (L. micrantha). Illumination is crucial for diagnosis in Lindernia spp., and treatment with 7.5 or 75 ng ai/ml thifensulfuron-methyl produced clear results.

EG5 (KIF11) is a member of the kinesin-like protein family involved in centrosome separation and bipolar spindle formation. When a cell enters mitosis, CDK1 phosphorylates EG5 at Thr926 and promotes EG5 localization on the mitotic spindle which drives bipolar spindle formation. EG5 provides power for spindle movement and thus controls the dynamics of spindle assembly. However, little is known about EG5 regulation or how EG5 detaches from the spindle upon mitotic exit. In this study we identify EG5 as a novel substrate of PP2A phosphatase, and we show that the PP2A/B55α complex plays an important role in mitotic exit by a mechanism involving EG5. The PP2A/B55α complex physically associates with the EG5 C-terminal tail domain and dephosphorylates EG5 at Thr926 that enables mitotic exit. Conversely PP2A knockdown cells show a high level of phospho-EG5 in late metaphase, which is associated with a delay in mitotic exit. These phenotypic features are similar to those induced by EG5/T926D transfection that mimics phosphorylated EG5 status. Our results argue that PP2A controls mitotic exit through EG5 dephosphorylation. Lack of PP2A leads to abnormal EG5 activation, resulting in delay of mitotic exit.