STARD5

Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe.

We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes.

Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR.

By fusing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.

Triphenyltin (TPT), a kind of persistent pollutant, is prevalent in the aquatic environment and could pose a threat to coral reef fish. However, little is known about the toxicity of TPT on coral reef fish, especially regarding the representative characteristics of body coloration. Therefore, this study chose the clownfish (Amphiprion ocellaris) in order to investigate the effects of TPT exposure on its carotenoid-based body coloration under the environmentally relevant concentrations (0, 1, 10 and 100 ng/L). After TPT exposure for 60 d, the carotenoid contents were decreased and histological damage in the liver was found, shown as nuclear pyknosis and shift, lipid deposition and fibrotic tissue hyperplasia. Liver transcriptomic analysis showed that TPT exposure interfered with oxidative phosphorylation and fatty acid metabolism pathways, which related to carotenoids uptake and metabolism. Furthermore, TPT exposure led to oxidative damage in the liver, which is responsible for the changes in the antioxidant capacity of enzymes, including GSH, MDA, POD, CAT and T-SOD. TPT exposure also affected the genes (Scarb1, CD36, Stard3 and Stard5) related to carotenoid absorption and transport, as well as the genes (GstP1 and Bco2) related to carotenoid deposition and decomposition. Taken together, our results demonstrate that TPT influenced carotenoid-based coloration in coral reef fish by disrupting carotenoid metabolism, which complements the ecotoxicological effects and toxic mechanisms of TPT and provides data for the body color biology of coral reef fishes.

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, with a high mortality and poor survival rate. Abnormal tumor metabolism is considered a hallmark of HCC and is a potential therapeutic target. This study aimed to identify metabolism-related biomarkers to evaluate the prognosis of patients with HCC.

The Cancer Genome Atlas (TCGA) database was used to explore differential metabolic pathways based on high and low epithelial-mesenchymal transition (EMT) groupings. Genes in differential metabolic pathways were obtained for HCC metabolism-related molecular subtype analysis. Differentially expressed genes (DEGs) from the three subtypes were subjected to Lasso Cox regression analysis to construct prognostic risk models. Stard5 expression in HCC patients was detected by western blot and immunohistochemistry (IHC), and the role of Stard5 in the metastasis of HCC was investigated by cytological experiments.

Unsupervised clustering analysis based on metabolism-related genes revealed three subtypes in HCC with differential prognosis. A risk prognostic model was constructed based on 11 genes (STARD5, FTCD, SCN4A, ADH4, CFHR3, CYP2C9, CCL14, GADD45G, SOX11, SCIN, and SLC2A1) obtained by LASSO Cox regression analysis of the three subtypes of DEGs. We validated that the model had a good predictive power. In addition, we found that the high-risk group had a poor prognosis, higher proportion of Tregs, and responded poorly to chemotherapy. We also found that Stard5 expression was markedly decreased in HCC tissues, which was associated with poor prognosis and EMT. Knockdown of Stard5 contributed to the invasion and migration of HCC cells. Overexpression of Stard5 inhibited EMT in HCC cells.

We developed a new model based on 11 metabolism-related genes, which predicted the prognosis and response to chemotherapy or immunotherapy for HCC. Notably, we demonstrated for the first time that Stard5 acted as a tumor suppressor by inhibiting metastasis in HCC.