Although various treatments are currently being developed, lung cancer still has a very high mortality rate. Moreover, while various strategies for the diagnosis and treatment of lung cancer are being used in clinical settings, in many cases, lung cancer does not respond to treatment and presents reducing survival rates. Cancer nanotechnology, also known as nanotechnology in cancer, is a relatively new topic of study that brings together scientists from a variety of fields, including chemistry, biology, engineering, and medicine. The use of lipid-based nanocarriers to aid drug distribution has already had a significant impact in several scientific fields. Lipid-based nanocarriers have been demonstrated to help stabilize therapeutic compounds, overcome barriers to cellular and tissue absorption, and improve in vivo drug delivery to specific target areas. For this reason, lipid-based nanocarriers are being actively researched and used for lung cancer treatment and vaccine development. This review discusses the improvements in drug delivery achieved with lipid-based nanocarriers, the obstacles that still exist with in vivo applications, and the current clinical and experimental applications of lipid-based nanocarriers in lung cancer treatment and management.

Induction of antigen-specific immune activation by the maturation of dendritic cells (DCs) is a strategy used for cancer immunotherapy. In this study, we find that FimH, which is an Escherichia coli adhesion portion, induces toll-like receptor 4-dependent and myeloid differentiation protein 2-independent DC maturation in mice in vivo. A combined treatment regimen with FimH and antigen promotes antigen-specific immune activation, including proliferation of T cells, production of IFN-γ and TNF-α, and infiltration of effector T cells into tumors, which consequently inhibits tumor growth in mice in vivo against melanoma and carcinoma. In addition, combined therapeutic treatment of anti-PD-L1 antibodies and FimH treatment efficiently inhibits CT26 tumor growth in BALB/c mice. Finally, FimH promotes human peripheral blood DC activation and syngeneic T-cell proliferation and activation. Taken together, these findings demonstrate that FimH can be a useful adjuvant for cancer immunotherapy.

Motivation: Cancer classification based on gene expression profiles has provided insight on the causes of cancer and cancer treatment. Recently, machine learning-based approaches have been attempted in downstream cancer analysis to address the large differences in gene expression values, as determined by single-cell RNA sequencing (scRNA-seq).Results: We designed cancer classifiers that can identify 21 types of cancers and normal tissues based on bulk RNA-seq as well as scRNA-seq data. Training was performed with 7398 cancer samples and 640 normal samples from 21 tumors and normal tissues in TCGA based on the 300 most significant genes expressed in each cancer. Then, we compared neural network (NN), support vector machine (SVM), k-nearest neighbors (kNN) and random forest (RF) methods. The NN performed consistently better than other methods. We further applied our approach to scRNA-seq transformed by kNN smoothing and found that our model successfully classified cancer types and normal samples.Availability and implementation: Cancer classification by neural network.Supplementary information: Supplementary data are available at Bioinformatics online.