Pancreatic cancer is a malignant disease with high mortality and a dismal prognosis. Circulating tumor cell (CTC) detection and characterization have emerged as essential techniques for early detection, prognostication, and liquid biopsy in many solid malignancies. Unfortunately, due to the low EPCAM expression in pancreatic cancer CTCs, no specific marker is available to identify and isolate this rare cell population. This study analyzed single-cell RNA sequencing profiles of pancreatic CTCs from a genetically engineered mouse model (GEMM) and pancreatic cancer patients. Through dimensionality reduction analysis, murine pancreatic CTCs were grouped into three clusters with different biological functions. CLIC4 and GAS2L1 were shown to be overexpressed in pancreatic CTCs in comparison with peripheral blood mononuclear cells (PBMCs). Further analyses of PBMCs and RNA-sequencing datasets of enriched pancreatic CTCs were used to validate the overexpression of GAS2L1 in pancreatic CTCs. A combinatorial approach using both GAS2L1 and EPCAM expression leads to an increased detection rate of CTCs in PDAC in both GEMM and patient samples. GAS2L1 is thus proposed as a novel biomarker of pancreatic cancer CTCs.

Blue light regulates biological function in various cells, such as proliferation, oxidative stress, and cell death. We employed blue light illumination on human umbilical vein endothelial cells utilizing a LED device at 453 nm wavelength and revealed a novel biphasic response on human umbilical vein endothelial cells (HUVECs). The results showed that low fluence blue light irradiation promoted the fundamental cell activities, including cell viability, migration and angiogenesis by activating the angiogenic pathways such as the VEGF signaling pathway. In contrast, high fluence illumination caused the opposite effect on those activities by upregulating pro-apoptotic signaling cascades like ferroptosis, necroptosis and the p53 signaling pathways. Our results provide an underlying insight into photobiomodulation by blue light and may help to implement potential treatment strategies for treating angiogenesis-dependent diseases.

Abdominal aortic aneurysm (AAA) is a prevalent aortic disease that causes high mortality due to asymptomatic gradual expansion and sudden rupture. The underlying molecular mechanisms and effective pharmaceutical therapy for preventing AAA progression have not been fully identified. In this study, we identified the key modules and hub genes involved in AAA growth from the GSE17901 dataset in the Gene Expression Omnibus (GEO) database through the weighted gene co-expression network analysis (WGCNA). Key genes were further selected and validated in the mouse dataset (GSE12591) and human datasets (GSE7084, GSE47472, and GSE57691). Finally, we predicted drug candidates targeting key genes using the Drug–Gene Interaction database. Overall, we identified key modules enriched in the mitotic cell cycle, GTPase activity, and several metabolic processes. Seven key genes (CCR5, ADCY5, ADCY3, ACACB, LPIN1, ACSL1, UCP3) related to AAA progression were identified. A total of 35 drugs/compounds targeting the key genes were predicted, which may have the potential to prevent AAA progression.

To identify miRNAs that are involved in cell migration in human umbilical vein endothelial cells (HUVECs), we employed RNA sequencing under high glucose incubation and text mining within the databases miRWalk and TargetScanHuman using 83 genes that regulate HUVECs migration. From both databases, 307 predicted miRNAs were retrieved. Differentially expressed miRNAs were determined by exposing HUVECs to high glucose stimulation, which significantly inhibited the migratory ability of HUVECs as compared to cells cultured in normal glucose. A total of 35 miRNAs were found as differently expressed miRNAs in miRNA sequencing, and 4 miRNAs, namely miR-21-3p, miR-107, miR-143-3p, and miR-106b-5p, were identified as overlapping hits. These were subjected to hub gene analysis and pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG), identifing 71 pathways which were influenced by all four miRNAs. The influence of all four miRNAs on HUVEC migration was phenomorphologically confirmed. miR21 and miR107 promoted migration in HUVECs while miR106b and miR143 inhibited migration. Pathway analysis also revealed eight shared pathways between the four miRNAs. Protein–protein interaction (PPI) network analysis was then performed to predict the functionality of interacting genes or proteins. This revealed six hub genes which could firstly be predicted to be related to HUVEC migration.

Background: Arteriosclerosis obliterans (ASO) is a major cause of adult limb loss worldwide. Autophagy of vascular endothelial cell (VEC) contributes to the ASO progression. However, the molecular mechanism that controls VEC autophagy remains unclear. In this study, we aimed to explore the role of the GRB2 associated binding protein 1 (GAB1) in regulating VEC autophagy. Methods: In vivo and in vitro studies were applied to determine the loss of adapt protein GAB1 in association with ASO progression. Histological GAB1 expression was measured in sclerotic vascular intima and normal vascular intima. Gain- and loss-of-function of GAB1 were applied in VEC to determine the effect and potential downstream signaling of GAB1. Results: The autophagy repressor p62 was significantly downregulated in ASO intima as compared to that in healthy donor (0.80 vs. 0.20, t = 6.43, P < 0.05). The expression level of GAB1 mRNA (1.00 vs. 0.24, t = 7.41, P < 0.05) and protein (0.72 vs. 0.21, t = 5.97, P < 0.05) was significantly decreased in ASO group as compared with the control group. Loss of GAB1 led to a remarkable decrease in LC3II (1.19 vs. 0.68, t = 5.99, P < 0.05), whereas overexpression of GAB1 significantly led to a decrease in LC3II level (0.41 vs. 0.93, t = 7.12, P < 0.05). Phosphorylation levels of JNK and p38 were significantly associated with gain- and loss-of-function of GAB1 protein. Conclusion: Loss of GAB1 promotes VEC autophagy which is associated with ASO. GAB1 and its downstream signaling might be potential therapeutic targets for ASO treatment.

Background: There is still an unmet need for therapeutic drugs for patients with an abdominal aortic aneurysm (AAA), especially for candidates unsuitable for surgical or interventional repair. Therefore, the purpose of this in silico study is to identify significant genes and regulatory mechanisms in AAA patients to predicate the potential therapeutic compounds for significant genes. Methods: The GSE57691 dataset was obtained from Gene Expression Omnibus (GEO) and used to identify the differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA). The biological function of DEGs was determined using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). AAA-related genes were obtained from the Comparative Toxicogenomics Database (CTD) using the keywords: aortic aneurysm and abdominal. The hub genes in AAA were obtained by overlapping DEGs, WGCNA-based hub genes, and CTD-based genes. The diagnostic values of hub genes were determined using ROC curve analysis. Hereby, a TF-miRNA-hub gene network was constructed based on the miRnet database. Using these data, potential therapeutic compounds for the therapy of AAA were predicted based on the Drug Gene Interaction Database (DGIdb). Results: A total of 218 DEGs (17 upregulated and 201 downregulated) and their biological function were explored; 4093 AAA-related genes were derived by text mining. Three hub modules and 144 hub genes were identified by WGCNA. asparagine synthetase (ASNS), axin-related protein 2 (AXIN2), melanoma cell adhesion molecule (MCAM), and the testis-specific Y-encoded-like protein 1 (TSPYL1) were obtained as intersecting hub genes and the diagnostic values were confirmed with ROC curves. As potential compounds targeting the hub genes, asparaginase was identified as the target compound for ASNS. Prednisolone and abiraterone were identified as compounds targeting TSPYL1. For MCAM and TSPYL1, no potential therapeutic compound could be predicted. Conclusion: Using WGCNA analysis and text mining, pre-existing gene expression data were used to provide novel insight into potential AAA-related protein targets. For two of these targets, compounds could be predicted.