RESUMO
Objective:To explore the level changes of common laboratory indexes in patients with ischemic stroke (IS) with different infarct sizes and their clinical application value.Methods:The baseline data of 237 patients hospitalized in Lanzhou University Second Hospital from June 2019 to December 2020 and their laboratory indicators within 24 hours of admission were collected. The patients were divided into lacunar group ( n=80) and infarct group ( n=157) according to the infarct area. The experimental indexes and clinical data of the two groups were compared. Binary logistic regression was used to screen the independent influencing factors of infarct size and establish a joint diagnostic model. The receiver operating characteristic (ROC) curve and model calibration chart were used to verify the clinical application value of each index. Results:The levels of cholesterol (CHO)/high density lipoprotein (HDL), low density lipoprotein (LDL)/HDL, neutrophil count, Cystatin C (Cys C), phosphorus (PHOS), indirect bilirubin (IBIL), LDL, apolipoprotein (ApoB), homocysteine (HCY), D-dimer, smoking, drinking, overweight and arterial stenosis in the infarct group were higher than those in the lacunar group (all P<0.05), and the levels of apolipoprotein A Ⅰ (ApoAⅠ)/ApoB, ApoAⅠ and carbon dioxide (CO 2) in the infarct group were lower than those in the lacunar group (all P<0.05). ApoA Ⅰ/ApoB and CO 2 were independent protective factors of infarct size (all P<0.05); Cys C, PHOS and IBIL were independent risk factors of infarct size (all P<0.05). The combined prediction model of CO 2, PHOS, IBIL, ApoA Ⅰ/ApoB and Cys C has good prediction efficiency for infarct area, and the area under the curve (AUC) of combined diagnosis was 0.739. Conclusions:Laboratory indicators are closely related to the infarct size of IS. The model developed in this study have good clinical value, which provides a new basis for IS evaluation and early warning.
RESUMO
OBJECTIVE@#To explore the pathogenesis of gastric cancer through a bioinformatic approach to provide evidence for the prevention and treatment of gastric cancer.@*METHODS@#The differentially expressed genes (DEGs) in gastric cancer and normal gastric mucosa in GSE79973 dataset were analyzed using GEO2R online tool. GO analysis and KEGG pathway enrichment analysis of the DEGs in DAVID database were performed. The protein interaction network was constructed using STRING database, and the key genes (Hub genes) were screened and their functional modules were analyzed using Cytoscape software. The GEPIA database was used to validate the Hub genes, and the Target Scan database was used to predict the microRNAs that regulate the target genes; OncomiR was used to analyze the expressions of the microRNAs in gastric cancer tissues and their relationship with the survival outcomes of the patients.@*RESULTS@#A total of 181 DEGs were identified in gastric cancer, and 10 hub genes were screened by the protein- protein interaction network. Functional analysis showed that these DEGs were involved mainly in protein digestion and absorption, PI3K-Akt signaling pathway, ECM-receptor interaction and platelet activation signal pathway. GEPIA database validation showed that COL1A1 was highly expressed in gastric cancer tissues and was associated with a poor prognosis of patients with gastric cancer. MiR-129-5p was found to bind to the 3'UTR of COL1A1 mRNA, and compared with that in normal tissues, miR-129-5p expression was obviously down-regulated in gastric cancer tissues, and was correlated with the prognosis of the patients.@*CONCLUSIONS@#COL1A1 under regulation by MiR-129-5p is a potential therapeutic target for gastric cancer.