Development and validation of a novel immune-related prognostic signature in lung squamous cell carcinoma patients

Tumour tissue-derived small extracellular vesicles reflect molecular subtypes of bladder cancer

mRNA-based molecular subtypes have implications for bladder cancer prognosis and clinical benefit from certain therapies. Whether small extracellular vesicles (sEVs) can reflect bladder cancer molecular subtypes is unknown. We performed whole transcriptome RNA sequencing for formalin fixed paraffin embedded (FFPE) tumour tissues and sEVs separated from matched tissue explants, urine and plasma in patients with bladder cancer. sEVs were separated using size-exclusion chromatography, and characterized by transmission electron microscopy, nano flow cytometry and western blots, respectively. High yield of sEVs were obtained using approximately 1 g of tissue, incubated with media for 30 min. FFPE tumour tissue and tumour tissue-derived sEVs demonstrated good concordance in molecular subtype classification. All urinary sEVs were classified as luminal subtype, while all plasma sEVs were classified as Ba/Sq subtype, regardless of the molecular subtypes indicated by their matched FFPE tumour tissue. The comparison within urine sEVs, which may exclude the sample type specific background, could pick up the different biology between NMIBC and MIBC, as well as the signature genes related to molecular subtypes. Four candidate sEV-related bladder cancer-specific mRNA biomarkers, FAM71E2, OR4K5, FAM138F and KRTAP26-1, were identified by analysing matched urine sEVs, tumour tissue derived sEVs, and adjacent normal tissue derived sEVs. Compared to sEVs separated from biofluids, tissue-derived sEVs may reflect more tissue- or disease-specific biological features. Urine sEVs are promising biomarkers to be used for liquid biopsy-based molecular subtype classification, but the current algorithm needs to be modified/adjusted. Future work is needed to validate the four new bladder cancer-specific biomarkers in large cohorts.

Gene-Smoking Interaction Analysis for the Identification of Novel Asthma-Associated Genetic Factors

Asthma is a complex heterogeneous disease caused by gene-environment interactions. Although numerous genome-wide association studies have been conducted, these interactions have not been systemically investigated. We sought to identify genetic factors associated with the asthma phenotype in 66,857 subjects from the Health Examination Study, Cardiovascular Disease Association Study, and Korea Association Resource Study cohorts. We investigated asthma-associated gene-environment (smoking status) interactions at the level of single nucleotide polymorphisms, genes, and gene sets. We identified two potentially novel (SETDB1 and ZNF8) and five previously reported (DM4C, DOCK8, MMP20, MYL7, and ADCY9) genes associated with increased asthma risk. Numerous gene ontology processes, including regulation of T cell differentiation in the thymus (GO:0033081), were significantly enriched for asthma risk. Functional annotation analysis confirmed the causal relationship between five genes (two potentially novel and three previously reported genes) and asthma through genome-wide functional prediction scores (combined annotation-dependent depletion, deleterious annotation of genetic variants using neural networks, and RegulomeDB). Our findings elucidate the genetic architecture of asthma and improve the understanding of its biological mechanisms. However, further studies are necessary for developing preventive treatments based on environmental factors and understanding the immune system mechanisms that contribute to the etiology of asthma.