Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated. As omics data grow, artificial intelligence models, particularly deep learning, are prominent in developing more rapid and effective methods to potentially improve lung cancer patient diagnosis, prognosis and treatment strategy. This decade has seen genome-based deep learning models thriving in various lung cancer tasks, including cancer prediction, subtype classification, prognosis estimation, cancer molecular signatures identification, treatment response prediction and biomarker development. In this study, we summarized available data sources for deep-learning-based lung cancer mining and provided an update on recent deep learning models in lung cancer genomics. Subsequently, we reviewed the current issues and discussed future research directions of deep-learning-based lung cancer genomics research.

Glioma is a Center Nervous System (CNS) neoplasm that arises from the glial cells. In a new scheme category of the World Health Organization 2016, lower-grade gliomas (LGGs) are grade II and III gliomas. Following the discovery of suppression of negative immune regulation, immunotherapy is a promising effective treatment method for lower-grade glioma patients. However, the therapy is not effective for all types of LGGs, and tumor mutational burden (TMB) has been shown to be a potential biomarker for the susceptibility and prognosis of immunotherapy in lower-grade glioma patients. Hence, predicting TMB benefits brain cancer patients. In this study, we investigated the correlation between MRI (magnetic resonance imaging)-based radiomic features and TMB in LGG by applying machine learning methods. Six machine learning classifiers were examined on the features extracted from the genetic algorithm. Subsequently, a light gradient boosting machine (LightGBM) succeeded in selecting 11 radiomics signatures for TMB classification. Our LightGBM model resulted in high accuracy of 0.7936, and reached a balance between sensitivity and specificity, achieving 0.76 and 0.8107, respectively. To our knowledge, our study represents the best model for classification of TMB in LGG patients at present.

The role of activated platelets in acute and chronic cardiovascular diseases (CVDs) is well established. Therefore, antiplatelet drugs significantly reduce the risk of severe CVDs. Evodia rutaecarpa (Wu-Chu-Yu) is a well-known Chinese medicine, and rutaecarpine (Rut) is a main bioactive component with substantial beneficial properties including vasodilation. To address a research gap, we investigated the inhibitory mechanisms of Rut in washed human platelets and experimental mice. At low concentrations (1–5 μM), Rut strongly inhibited collagen-induced platelet aggregation, whereas it exerted only a slight or no effect on platelets stimulated with other agonists (e.g., thrombin). Rut markedly inhibited P-selectin expression; adenosine triphosphate release; [Ca²⁺]i mobilization; hydroxyl radical formation; and phospholipase C (PLC)γ2/protein kinase C (PKC), mitogen-activated protein kinase, and phosphoinositide 3-kinase (PI3K)/Akt/glycogen synthase kinase-3β (GSK3β) phosphorylation stimulated by collagen. SQ22536 (an adenylate cyclase inhibitor) or ODQ (a guanylate cyclase inhibitor) did not reverse Rut-mediated antiplatelet aggregation. Rut was not directly responding to vasodilator-stimulated phosphoprotein phosphorylation. Rut significantly increased the occlusion time of fluorescence irradiated thrombotic platelet plug formation. The findings demonstrated that Rut exerts a strong effect against platelet activation through the PLCγ2/PKC and PI3K/Akt/GSK3β pathways. Thus, Rut can be a potential therapeutic agent for thromboembolic disorders.