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Science Papers Examine Dysregulated Lipids in Lung Cancer, Combined Approach to Identify Meningioma Subtypes

Combining RNA sequencing with machine learning, investigators from Peking University have identified several lipids that are dysregulated in early-stage lung cancer patients and hold potential for the early detection of the disease. In the study, which appears in Science Translational Medicine this week, the researchers performed single-cell RNA sequencing on different early-stage lung cancers, finding that lipid metabolism was broadly dysregulated in different cell types. Untargeted lipidomics was performed in an exploratory cohort of 311 individuals and, through support vector machine algorithm-based and mass spectrum-based feature selection, nine lipids were identified as the features most important for early-stage cancer detection. Based on these lipids, the team developed a liquid chromatography-mass spectrometry-based targeted assay that proved highly effective for detecting early-stage lung cancer in multiple independent cohorts largely composed of nonsmokers with stage 1 adenocarcinomas versus healthy individuals or individuals with benign tumors.

The integration of RNA sequencing, DNA methylation, and cytogenetics represents the most effective means of identifying the different subtypes of meningioma, according to a new study appearing in Science Advances this week. Despite advances in understanding tumors of the central nervous system, meningiomas remain difficult to classify. In the study, a Baylor College of Medicine team evaluated different approaches now being used as alternatives to histopathological grading in meningioma diagnosis: unbiased DNA methylation, RNA-seq, and cytogenetic profiling. They find that each technique performed similarly, but that integrating these methods into one classifier improved overall accuracy. "Our comprehensive profiling of 110 primary meningiomas, with analysis of an additional 255 samples from published datasets, strongly suggests that there are three biologically distinct groups of meningiomas that can be identified best by integrating DNA methylation, RNA-seq, and [chromosomal instability]/cytogenetics," the researchers write.

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