ABSTRACT
Hepatocellular carcinoma (HCC) is a common type of liver cancer and one of the highly lethal diseases worldwide. Hypoxia plays an important role in the development and prognosis of HCC. This study aimed to construct a new hypoxia-related prognosis signature and investigate its potential ceRNA axes in HCC. RNA profiles and hypoxia genes were downloaded, respectively, from the Cancer Genome Atlas hepatocellular carcinoma database and Gene Set Enrichment Analysis website. Cox regression analyses were performed to select the prognostic genes and construct the risk model. The ENCORI database was applied to build the lncRNA-miRNA-mRNA prognosis-related network. The TIMER and CellMiner databases were employed to analyze the association of gene expression in ceRNA with immune infiltration and drug sensitivity, respectively. Finally, the co-expression analysis was carried out to construct the potential lncRNA/miRNA/mRNA regulatory axes. We obtained a prognostic signature including eight hypoxia genes (ENO2, KDELR3, PFKP, SLC2A1, PGF, PPFIA4, SAP30, and TKTL1) and further established a hypoxia-related prognostic ceRNA network including 17 lncRNAs, six miRNAs, and seven mRNAs for hepatocellular carcinoma. Then, the analysis of immune infiltration and drug sensitivity showed that gene expression in the ceRNA network was significantly correlated with the infiltration abundance of multiple immune cells, the expression level of immune checkpoints, and drug sensitivity. Finally, we identified three ceRNA regulatory axes (SNHG1/miR-101-3p/PPFIA4, SNHG1/miR-101-3p/SAP30, and SNHG1/miR-101-3p/TKTL1) associated with the progression of HCC under hypoxia. Here, we constructed a prognosis gene signature and a ceRNA network related to hypoxia for hepatocellular carcinoma. Among the ceRNA network, six highly expressed lncRNAs (AC005540.1, AC012146.1, AC073529.1, AC090772.3, AC138150.2, AL390728.6) and one highly expressed mRNA (PPFIA4) were the potential biomarkers of hepatocellular carcinoma which we firstly reported. The three predicted hypoxia-related regulatory axes may play a vital role in the progression of hepatocellular carcinoma.
Subject(s)
Carcinoma, Hepatocellular , Liver Neoplasms , MicroRNAs , RNA, Long Noncoding , Humans , Carcinoma, Hepatocellular/genetics , Carcinoma, Hepatocellular/pathology , RNA, Long Noncoding/genetics , RNA, Long Noncoding/metabolism , Liver Neoplasms/genetics , Liver Neoplasms/pathology , Prognosis , Gene Expression Regulation, Neoplastic , Gene Regulatory Networks , MicroRNAs/genetics , MicroRNAs/metabolism , RNA, Messenger/genetics , RNA, Messenger/metabolism , Hypoxia/genetics , Transketolase/geneticsABSTRACT
Although lung cancer treatment strategies have improved in recent years, the 5-year overall survival of non-small cell lung cancer (NSCLC) remains less than 15%. Chemotherapy is considered the most promising option in the comprehensive treatment of NSCLC. Fucoxanthin (FX) is a natural product derived from brown algae and has extensive applications in medicine. Previous studies reported that FX effectively inhibits the growth of NSCLC cells in vitro and in vivo. However, the mechanism underlying the anti-NSCLC effect of FX remains unknown. In this study, NSCLC cell lines and a xenograft nude mouse model were used to examine the anti-NSCLC activities of FX in vitro and in vivo. Network pharmacology analysis and inhibitors or activators of the PI3K/Akt signaling pathway were used to explore the anti-NSCLC mechanisms of FX. The results indicated that FX could inhibit proliferation, migration, and invasion, arrest cell cycle at the G0/G1 phase, and induce apoptosis of NSCLC cells in vitro. Additionally, FX suppressed tumor growth in vivo. The PI3K/Akt signaling pathway was found to be involved in the anti-NSCLC activity of FX. In conclusion, FX inhibits malignant biological behaviors of NSCLC by suppressing the phosphorylation of both PI3K and AKT, and subsequently inactivating PI3K/AKT signaling pathway.
Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Animals , Carcinoma, Non-Small-Cell Lung/pathology , Cell Line, Tumor , Cell Proliferation , Humans , Lung Neoplasms/pathology , Mice , Phosphatidylinositol 3-Kinases/metabolism , Proto-Oncogene Proteins c-akt/metabolism , Signal Transduction , XanthophyllsABSTRACT
BACKGROUND: Diabetes has significant effects on bone metabolism. Both type 1 and type 2 diabetes can cause osteoporotic fracture. However, it remains challenging to diagnose osteoporosis in type 2 diabetes by bone mineral density which lacks regular changes. Seen another way, osteoporosis can be ascribed to the imbalance of bone metabolism, which is closely related to diabetes as well. METHODS: Here, to assist clinicians in diagnosing osteoporosis in type 2 diabetes, an efficient and simple SVM (support vector machine) model was established based on different combinations of biochemical indexes, which were collected from patients who did the test of bone turn-over markers (BTMs) from January 2016 to March 2018 in the department of endocrine, Zhongda Hospital affiliated to Southeast University. The classification was done based on a software package of machine learning in Python. The classification performance was measured by SKLearn program incorporated in the Python software package and compared with the clinical diagnostic results. RESULTS: The predicting accuracy rate of final model was above 88%, with feature combination of sex, age, BMI (body mass index), TP1NP (total procollagen I N-terminal propeptide) and OSTEOC (osteocalcin). CONCLUSIONS: Experimental results show that the model showed an anticipant result for early detection and daily monitoring on type 2 diabetic osteoporosis.
