Abstract. Sushi domain containing 2 (SUSD2) has been identified as a gene encoding an 822-amino acid protein, which contains a transmembrane domain and functional domains inherent to adhesion molecules. Previous studies have reported that increased expression of SUSD2 has a critical role in tumorigenesis in human breast cancer. However, to the best of our knowledge, SUSD2 expression status and its correlation with the clinicopathological features of non-small cell lung cancer (NSCLC) have not previously been investigated. In the present study, reverse transcription-quantitative polymerase chain reaction and western blotting were used to evaluate SUSD2 messenger RNA (mRNA) and protein expression in NSCLC and adjacent normal lung tissues. The clinicopathological significance of SUSD2 was investigated by immunohistochemical analysis of an NSCLC tissue microarray. Receiver operating characteristic analysis was used to determine the cut-off score for positive expression of SUSD2. Furthermore, the correlation between SUSD2 expression and the clinicopathological features of NSCLC was analyzed by χ 2 test. The results revealed that SUSD2 mRNA (P<0.0001) and protein (P<0.0001) expression levels were significantly decreased in NSCLC tissues compared with those of adjacent normal tissues. When the SUSD2 positive expression percentage was determined to be >47.5% (area under ROC curve, 0.799; P<0.000), positive expression of SUSD2 was observed in 100% (32/32) of normal lung tissues and 55% (88/160) of NSCLC tissues by immunohistochemistry (χ 2 =21.160; P<0.000). Furthermore, it was demonstrated that the reduced SUSD2 protein levels in cancer tissues were positively correlated with poor histological grade (χ 2 =41.764; P<0.000), advanced clinical stage (χ 2 =10.790; P=0.013), higher pT (χ 2 =9.070; P=0.028) and positive regional lymph node metastasis (χ 2 =15.399; P=0.002).In conclusion, these data suggest that the reduced expression of SUSD2 is associated with the progression of NSCLC and may have a role in the pathogenesis of NSCLC.
Traditional Chinese medicine (TCM) usually plays therapeutic roles on complex diseases in the form of formulas. However, the multicomponent and multitarget characteristics of formulas bring great challenges to the mechanism analysis and secondary development of TCM in treating complex diseases. Modern bioinformatics provides a new opportunity for the optimization of TCM formulas. In this report, a new bioinformatics analysis of a computational network pharmacology model was designed, which takes Chai-Hu-Shu-Gan-San (CHSGS) treatment of depression as the case. In this model, effective intervention space was constructed to depict the core network of the intervention effect transferred from component targets to pathogenic genes based on a novel node importance calculation method. The intervention-response proteins were selected from the effective intervention space, and the core group of functional components (CGFC) was selected based on these intervention-response proteins. Results show that the enriched pathways and GO terms of intervention-response proteins in effective intervention space could cover 95.3 and 95.7% of the common pathways and GO terms that respond to the major functional therapeutic effects. Additionally, 71 components from 1,012 components were predicted as CGFC, the targets of CGFC enriched in 174 pathways which cover the 86.19% enriched pathways of pathogenic genes. Based on the CGFC, two major mechanism chains were inferred and validated. Finally, the core components in CGFC were evaluated by in vitro experiments. These results indicate that the proposed model with good accuracy in screening the CGFC and inferring potential mechanisms in the formula of TCM, which provides reference for the optimization and mechanism analysis of the formula in TCM.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.