Background: Oral cancer (OC) is a common and dangerous malignant tumor with a low survival rate. However, the micro level mechanism has not been explained in detail. Methods: Gene and miRNA expression micro array data were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and miRNAs (DE miRNAs) were identified by R software. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of genes and genomes (KEGG) pathway analysis were used to assess the potential molecular mechanisms of DEGs. Cytoscape software was utilized to construct protein–protein interaction (PPI) network and miRNA-gene network. Central genes were screened out with the participation of gene degree, molecular complex detection (MCODE) plugin, and miRNA-gene network. Then, the identified genes were checked by The Cancer Genome Atlas (TCGA) gene expression profile, Kaplan-Meier data, Oncomine, and the Human Protein Atlas database. Receiver operating characteristic (ROC) curve was drawn to predict the diagnostic efficiency of crucial gene level in normal and tumor tissues. Univariate and multivariate Cox regression were used to analyze the effect of dominant genes and clinical characteristics on the overall survival rate of OC patients. Results: Gene expression data of gene expression profiling chip(GSE9844, GSE30784, and GSE74530) were obtained from GEO database, including 199 tumor and 63 non-tumor samples. We identified 298 gene mutations, including 200 upregulated and 98 downregulated genes. GO functional annotation analysis showed that DEGs were enriched in extracellular structure and extracellular matrix containing collagen. In addition, KEGG pathway enrichment analysis demonstrated that the DEGs were significantly enriched in IL-17 signaling pathway and PI3K-Akt signaling pathway. Then, we detected three most relevant modules in PPI network. Central genes (CXCL8, DDX60, EIF2AK2, GBP1, IFI44, IFI44L, IFIT1, IL6, MMP9,CXCL1, CCL20, RSAD2, and RTP4) were screened out with the participation of MCODE plugin, gene degree, and miRNA-gene network. TCGA gene expression profile and Kaplan-Meier analysis showed that high expression of CXCL8, DDX60, IL6, and RTP4 was associated with poor prognosis in OC patients, while patients with high expression of IFI44L and RSAD2 had a better prognosis. The elevated expression of CXCL8, DDX60, IFI44L, RSAD2, and RTP44 in OC was verified by using Oncomine database. ROC curve showed that the mRNA levels of these five genes had a helpful diagnostic effect on tumor tissue. The Human Protein Atlas database showed that the protein expressions of DDX60, IFI44L, RSAD2, and RTP44 in tumor tissues were higher than those in normal tissues. Finally, univariate and multivariate Cox regression showed that DDX60, IFI44L, RSAD2, and RTP44 were independent prognostic indicators of OC. Conclusion: This study revealed the potential biomarkers and relevant pathways of OC from publicly available GEO database, and provided a theoretical basis for elucidating the diagnosis, treatment, and prognosis of OC.

Lung adenocarcinoma is the leading cause of cancer death worldwide. Recently, ubiquitin C-terminal hydrolase L1 (UCHL1) has been demonstrated to be highly expressed in many tumors and plays the role of an oncogene. However, the functional mechanism of UCHL1 is unclear in lung adenocarcinoma progression. We analyzed the differential expression of the UCHL1 gene in lung adenocarcinoma and normal lung tissues, and the correlation between the UCHL1 gene and prognosis was also analyzed by the bioinformatics database TCGA. Meanwhile, we detected and analyzed the expression of UCHL1 and Ki-67 protein in a tissue microarray (TMA) containing 150 patients with lung adenocarcinoma by immunohistochemistry (IHC) and clinicopathological characteristics by TCGA database. In vitro experiments, we knocked down the UCHL1 gene of A549 cells and detected the changes in cell migration, invasion, and apoptosis. At the same time, we analyzed the effect of UCHL1 on anti-tumor drug sensitivity of lung adenocarcinoma by a bioinformatics database. In terms of the detection rate of lung adenocarcinoma indicators, we analyzed the impact of UCHL1 combined with common clinical indicators on the detection rate of lung adenocarcinoma through a bioinformatics database. In this study, the analysis of UCHL1 protein expression in lung adenocarcinoma proved that obviously higher UCHL1 protein level was discovered in lung adenocarcinoma tissues. The expression of UCHL1 was closely related to poor clinical outcomes. Interestingly, a significantly positive correlation between the expression of UCHL1 and Ki-67-indicated UCHL1 was associated with tumor migration and invasion. Through executing loss of function tests, we affirmed that silencing of UCHL1 expression significantly inhibited migration and invasion of lung adenocarcinoma cells in vitro. Furthermore, lung adenocarcinoma cells with silenced UCHL1 showed a higher probability of apoptosis. In terms of the detection rate of lung adenocarcinoma indicators, we discovered UCHL1 could improve the detection rate of clinical lung adenocarcinoma and affect drug sensitivity. In lung adenocarcinoma, UCHL1 promotes tumor migration, invasion, and metastasis by inhibiting apoptosis and has an important impact on the clinical drug treatment of lung adenocarcinoma. In addition, UCHL1 can improve the detection rate of clinical lung adenocarcinoma. Above all, UCHL1 may be a new marker for the diagnosis of lung adenocarcinoma and provide a new target for the treatment of clinical diseases.

To screen key biomarkers of esophageal cancer (ESCA) by bioinformatics and analyze the correlation between key genes and immune infiltration. Expression profile data of ESCA was downloaded from TCGA database, and DEGs in ESCA were screened with R software. After the RNA binding proteins (RBPs) in DEGs were screened, the protein interaction network was constructed using tools such as STRING and Cytoscape and the key genes (HENMT1) were screened. Survival analysis of HENMT1 was performed by Kaplan–Meier method. Functional enrichment analysis of HENMT1 interacting proteins was performed using the DAVID website, and GSEA predicted the signal pathways involved by HENMT1. CIBERSORT algorithm was used to analyze the infiltration of immune cells in ESCA. The expression of HENMT1 in ESCA was detected by immunohistochemistry. A total of 105 RNA binding proteins (RBPs) were differentially expressed in ESCA, and a PPI network was constructed to screen the key gene HENMT1. The expression level of hemmt1 gene was closely related to the infiltration of B cells naive, T cells regulatory (Tregs), neutrophils, T cells CD4 memory activated, master cells resting and dendritic cells resting in ESCA tissues (P < .05). Immunohistochemical results showed that HENMT1 was highly expressed in ESCA tissues and was positively correlated with the expression of MKI67. HENMT1 is related to the occurrence and prognosis of ESCA, and is also related to the infiltration of immune cells in ESCA tissue, which may provide a new idea for the targeted treatment of ESCA.

To preliminary explore the expression pattern, clinical significance, and potential molecular mechanism of EIF3B in lung adenocarcinoma (LUAD), and to prov

To analyze the expression and mechanism of prognosis related differentially expressed genes (DEGs) in hepatocellular carcinoma (HCC), and establish a prognosis risk model of prognosis related DEGs. Transcriptome data and clinical information of 374 HCC samples were downloaded from TCGA data. Kaplan-Meier (KM) survival analysis screened prognostic genes in DEGs. Protein-protein interaction (PPI) network was constructed based on prognostic genes and key genes were screened. Cox regression was used to analyze the key genes and construct the prognostic risk model, calculate the risk score of each patient, divide the patients into low-risk group and high-risk group according to the median risk value, use KM analysis method for survival analysis and draw the survival curve, and use receiver operating characteristic (ROC) curve to evaluate the prognostic risk model, The relationship between risk score and clinicopathological features of HCC patients was analyzed. GEPIA and the human protein atlas (HPA) databases were used for expression verification of model genes. The mirDIP database is used to analyze the regulatory network of model genes. GSCAlite platform is used to analyze the mechanism and drug sensitivity of model genes. A total of 1987 DEGs were extracted from the transcriptome data of HCC and normal samples, of which 258 were related to the prognosis of HCC (P < 0.01). Six key genes (CDK1, CCNA2, BUB1, CDC20, CCNB1 and TOP2A) were screened from the PPI network based on prognostic related genes, and the prognostic risk model was established. Survival analysis showed that the overall survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P < 0.01). The AUC values of 1, 3 and 5 years in the prognostic risk model were 0.716, 0.678 and 0.633. Multivariate Cox regression analysis showed that patient age and patient risk score were independent risk factors for the prognosis of HCC. The model gene is highly expressed in HCC and can promote apoptosis, cell cycle and EMT pathway. In addition, the high expression of model gene produced drug resistance to trametinib, selumetinib and RDEA119 (refametinib). The prognostic risk model based on six prognostic related DEGs is an independent prognostic factor of HCC, which can effectively predict the survival and prognosis of HCC patients.

Kawasaki disease (KD) is a systemic vasculitis primarily affecting children, with coronary artery lesions being its most severe complication. In this study, an optimized mouse KD model was established using the water-soluble extract of Candida albicans (CAWS). Myocardial inflammation and related pathological changes were evaluated using HE staining and Masson trichrome staining. The immunofluorescence technique detected the infiltration of immune cells in cardiac tissue. The expression and localization of VDAC1 protein in myocardial tissue were detected by immunohistochemistry. In vitro, a phagocytic model was established by co-culturing RAW264.7 macrophages with Candida albicans spores, and the formation and function of autophagolysosomes were assessed using LC3 immunofluorescence staining and a Lyso-Tracker Red probe. Through dose screening, it was determined that 8 mg was the optimal modeling dose for inducing coronary artery inflammation, with a moderate mortality rate at this dose. HE staining showed that CAWS injection stably induced coronary artery lesions consistent with the characteristics of human Kawasaki disease in mice. Masson staining confirmed that there was significant collagen fiber deposition around the coronary arteries and aorta in the CAWS group of mice, which closely coincided with the inflammatory area, and a statistically significant difference was observed from the control group at 14 days (p < 0.001). Immunofluorescence revealed that, on the 14th day of modeling, the infiltration of multiple immune cells in the cardiac tissue of the CAWS group had significantly increased (p < 0.001). The immunohistochemical results showed that, on the 28th day of modeling, the expression of VDAC1 protein in the myocardial tissue of the CAWS group was significantly upregulated (p < 0.001). In vitro experiments have shown that in macrophages infected with Candida albicans spores, the formation of autophagolysosomes increases in the early stage, while autophagic flow is blocked in the later stage, suggesting a functional disorder.