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Background:. Hyperbaric oxygen treatment (HBOT) has been demonstrated to influence the keloid recurrence rate after surgery and to relieve keloid symptoms and other pathological processes in keloids. To explore the mechanism of the effect of HBOT on keloids, tumor immune gene expression and immune cell infiltration were studied in this work. Methods:. From February 2021 to April 2021, HBOT was carried out on keloid patients four times before surgery. Keloid tissue samples were collected and divided into an HBOT group (keloid with HBOT before surgery [HK] group, n = 6) and a non-HBOT group (K group, n = 6). Tumor gene expression was analyzed with an Oncomine Immune Response Research Assay kit. Data were mined with R package. The differentially expressed genes between the groups were compared. Hub genes between the groups were determined and verified with Quantitative Real-time PCR. Immune cell infiltration was analyzed based on CIBERSORT deconvolution algorithm analysis of gene expression and verified with immunohistochemistry (IHC). Results:. Inflammatory cell infiltration was reduced in the HK group. There were 178 upregulated genes and 217 downregulated genes. Ten hub genes were identified, including Integrin Subunit Alpha M (ITGAM), interleukin (IL)-4, IL-6, IL-2, Protein Tyrosine Phosphatase Receptor Type C (PTPRC), CD86, transforming growth factor (TGF), CD80, CTLA4, and IL-10. CD80, ITGAM, IL-4, and PTPRC with significantly downregulated expression were identified. IL-10 and IL-2 were upregulated in the HK group but without a significant difference. Infiltration differences of CD8 lymphocyte T cells, CD4 lymphocyte T-activated memory cells, and dendritic resting cells were identified with gene CIBERSORT deconvolution algorithm analysis. Infiltration levels of CD4 lymphocyte T cell in the HK group were significantly higher than those of the K group in IHC verification. Conclusion:. HBOT affected tumor gene expression and immune cell infiltration in keloids. CD4 lymphocyte T cell, especially activated memory CD4+T, might be the key regulatory immune cell, and its related gene expression needs further study.

Background Primary colorectal cancer (PCRC) is one of the most common malignant tumors in clinic, and is characterized by high heterogeneity occurring between tumors and intracellularly. Therefore, this study aimed to explore potential gene targets for the diagnosis and treatment of PCRC via bioinformatic technology. Methods Gene Expression Omnibus (GEO) was used to download the data used in this study. Differently expressed genes (DEGs) were identified with GEO2R, and the gene set enrichment analysis (GSEA) was implemented for enrichment analysis. Then, the researchers constructed a protein-protein interaction (PPI) network, a significant module, and a hub genes network. Results The GSE81558 dataset was downloaded, and a total of 97 DEGs were found. There were 23 up-regulated DEGs and 74 down-regulated DEGs in the PCRC samples, compared with the control group. The PPI network included a total of 42 nodes and 63 edges. One module network consisted of 11 nodes and 25 edges. Another module network consisted of 4 nodes and 6 edges. The hub genes network was created by cytoHubba using GCG, GUCA2B, CLCA4, ZG16, TMIGD1, GUCA2A, CHGA, PYY, SST, and MS4A12. Conclusions Ten hub genes were found from the genomic samples of patients with PCRC and normal controls by bioinformatics analysis. The hub genes might provide novel ideas and evidence for the diagnosis and targeted therapy of PCRC.

Cardiac-cerebral vascular disease (CCVD), is primarily induced by atherosclerosis, and is a leading cause of mortality. Numerous studies have investigated and attempted to clarify the molecular mechanisms of atherosclerosis; however, its pathogenesis has yet to be completely elucidated. Two expression profiling datasets, GSE43292 and GSE57691, were obtained from the Gene Expression Omnibus (GEO) database. The present study then identified the differentially expressed genes (DEGs), and functional annotation of the DEGs was performed. Finally, an atherosclerosis animal model and neural network prediction model was constructed to verify the relationship between hub gene and atherosclerosis. The results identified a total of 234 DEGs between the normal and atherosclerosis samples. The DEGs were mainly enriched in actin filament, actin binding, smooth muscle cells, and cytokine-cytokine receptor interactions. A total of 13 genes were identified as hub genes. Following verification of animal model, the common DEG, Tropomyosin 2 (TPM2), was found, which were displayed at lower levels in the atherosclerosis models and samples. In summary, DEGs identified in the present study may assist clinicians in understanding the pathogenesis governing the occurrence and development of atherosclerosis, and TPM2 exhibits potential as a promising diagnostic and therapeutic biomarker for atherosclerosis.

Atherosclerosis (AS) is a potential inducer of numerous cardio-cerebrovascular diseases. However, little research has investigated the expression of TPM2 in human atherosclerosis samples. A total of 34 clinical samples were obtained, including 17 atherosclerosis and 17 normal artery samples, between January 2018 and April 2021. Bioinformatics analysis was applied to explore the potential role of TPM2 in atherosclerosis. Immunohistochemistry, immunofluorescence, and western blotting assays were used to detect the expression of TPM2 and α-SMA proteins. The mRNA expression levels of TPM2 and α-SMA were detected using RT-qPCR. A neural network and intima-media thickness model were constructed. A strong relationship existed between the intima-media thickness and relative protein expression of TPM2 (P0.001, R=-0.579). The expression of TPM2 was lower in atherosclerosis than normal artery (P0.05). Univariate logistic regression showed that TPM2 (OR=0.150, 95% CI: 0.026-0.868, P=0.034) had clear correlations with atherosclerosis. A neural network model was successfully constructed with a relativity of 0.94434. TPM2 might be an independent protective factor for arteries, and one novel biomarker of atherosclerosis.

Objective To identify key genes involved in occurrence and development of retinoblastoma. Methods The microarray dataset, GSE5222, was downloaded from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between unilateral and bilateral retinoblastoma were identified and functional enrichment analysis performed. The protein–protein interaction (PPI) network was constructed and analysed by STRING and Cytoscape. Results DEGs were mainly associated with activation of cysteine-type endopeptidase activity involved in apoptotic process and small molecule catabolic process. Seven genes (WAS, GNB3, PTGER1, TACR1, GPR143, NPFF and CDKN2A) were identified as HUB genes. Conclusion Our research provides more understanding of the mechanisms of the disease at a molecular level and may help in the identification of novel biomarkers for retinoblastoma.

Polycystic ovary syndrome (PCOS) is a common female infertility, which may be caused by excessive androgen, but its mechanism remains unknown. Transsexuals are women who take androgen drugs for a long time, and gradually have male signs. Their ovaries may have received high concentrations of androgen, which leads to the failure of ovarian reproductive function. Therefore, we searched the relevant data of PCOS and transsexuals in gene expression omnibus database, used limma package to identify the most similarly genes, and then analyzed the possible mechanism of PCOS through gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. Then, the protein-protein interaction network was constructed by searching the String database, and the top 5 hub genes were identified by the cytohubba plug-in of Cytoscape. Finally, ubiquitin conjugating enzyme E2 E1 (UBE2E1), ubiquitin C (UBC), transcription elongation factor B subunit 1 (TCEB1), ubiquitin conjugating enzyme E2 N (UBE2N), and ring finger protein 7 (RNF7) genes were identified as the most similarly expressed genes between PCOS and Transsexuals. They may cause the ubiquitination of androgen receptor and eventually lead to sinus follicular growth arrest. In conclusion, 5 Central genes were identified in PCOS and transsexuals. These genes can be used as targets for early diagnosis or treatment of PCOS.

Background. Pathological changes of the adrenal gland and the possible underlying molecular mechanisms are currently unclear in the case of atherosclerosis (AS) combined with chronic stress (CS). Methods. New Zealand white rabbits were used to construct a CS and AS animal model. Proteomics and bioinformatics were employed to identify hub proteins in the adrenal gland related to CS and AS. Hub proteins were detected using immunohistochemistry, immunofluorescence assays, and Western blotting. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to analyze the expression of genes. In addition, a neural network model was constructed. The quantitative relationships were inferred by cubic spline interpolation. Enzymatic activity of mitochondrial citrate synthase and OGDH was detected by the enzymatic assay kit. Function of citrate synthase and OGDH with knockdown experiments in the adrenal cell lines was performed. Furthermore, target genes-TF-miRNA regulatory network was constructed. Coimmunoprecipitation (IP) assay and molecular docking study were used to detect the interaction between citrate synthase and OGDH. Results. Two most significant hub proteins (citrate synthase and OGDH) that were related to CS and AS were identified in the adrenal gland using numerous bioinformatic methods. The hub proteins were mainly enriched in mitochondrial proton transport ATP synthase complex, ATPase activation, and the AMPK signaling pathway. Compared with the control group, the adrenal glands were larger and more disordered, irregular, and necrotic in the AS+CS group. The expression of citrate synthase and OGDH was higher in the AS+CS group than in the control group, both at the protein and mRNA levels ( P < 0.05 ). There were strong correlations among the cross-sectional areas of adrenal glands, citrate synthase, and OGDH ( P < 0.05 ) via Spearman’s rho analysis, receiver operating characteristic curves, a neural network model, and cubic spline interpolation. Enzymatic activity of citrate synthase and OGDH increased under the situation of atherosclerosis and chronic stress. Through the CCK8 assay, the adrenal cell viability was downregulated significantly after the knockdown experiment of citrate synthase and OGDH. Target genes-TF-miRNA regulatory network presented the close interrelations among the predicted microRNA, citrate synthase and OGDH. After Coimmunoprecipitation (IP) assay, the result manifested that the citrate synthase and OGDH were coexpressed in the adrenal gland. The molecular docking study showed that the docking score of optimal complex conformation between citrate synthase and OGDH was -6.15 kcal/mol. Conclusion. AS combined with CS plays a significant role on the hypothalamic–pituitary–adrenal (HPA) axis, promotes adrenomegaly, increases the release of glucocorticoid (GC), and might enhance ATP synthesis and energy metabolism in the body through citrate synthase and OGDH gene targets, providing a potential research direction for future related explorations into this mechanism.

Rhabdomyosarcoma (RMS) is a common malignant soft tissue sarcoma, which is the third most common soft tissue sarcoma after malignant fibrohistoma and liposarcoma. The discovery of potential postbiomarkers could lead to early and more effective treatment measures to reduce the mortality of RMS. The discovery of biomarker is expected to be the direction of targeted therapy, providing a new direction for the precise treatment of RMS. Gene Expression Omnibus database was used to download the tow gene profiles, GSE28511 and GSE135517. GEO2R was applied to identify differently expressed genes (DEGs) between RMS and normal group. Database for Annotation, Visualization and Integrated Discovery and Metascape can perform the enrichment analysis for the DEGs. Protein-protein interaction network was constructed, and the hub genes was identified by the Cytoscape. Expression and overall survival analysis of hub genes were performed. A total of 15 common DEGs were screened between RMS and normal tissues. The enrichment analysis here showed that the DEGs mainly enriched in the muscle filament sliding, myofibril, protein complex, sarcomere, myosin complex, nuclear chromosome, and tight junction. The 6 hub genes (DNA Topoisomerase II Alpha, Insulin Like Growth Factor 2, HIST1H4C, Cardiomyopathy Associated 5, Myosin Light Chain 2 [MYL2], Myosin Heavy Chain 2) were identified. Compared with the normal tissues, MYL2 were down-regulated in the RMS tissues. RMS patients with low expression level of MYL2 had poorer overall survival times than those with high expression levels (P

Background: Gastric cancer (GC) is the fifth most common cancer in the world and the second leading cause of cancer death. Gastric cancer screening is one of the effective strategies to reduce the mortality. If we use gastric cancer biomarkers with good clinical effect instead of gastroscopy screening, it can achieve early detection, early diagnosis, early treatment. Methods: We download four gene expression profiling datasets of gastric cancer (GSE118916, GSE54129, GSE103236, GSE112369), which obtained from the Gene Expression Omnibus (GEO) database. The DEGs between gastric cancer and adjacent normal tissues were detect to explore genes that may play a key role in gastric cancer. GO and KEGG analyses of overlap genes were performed by the Metascape online database, the protein-protein interaction (PPI) network was analyzed by the STRING online database, and we obtained the hub genes of PPI network via using the Cytoscape software. The survival curve analysis was performed by km-plotter and the stage plots of hub genes was performed by the GEPIA online database. We selected CDH3, LEF1 and MMP7 as candidate biomarkers to construct a back propagation neural network model. Results: This study shows that MMP7, CDH3 and LEF1 are highly expressed in gastric cancer tissues. Conclusions: The joint prediction of the MMP7, CDH3 and LEF1 is helpful for the early diagnosis of cancer.

Introduction Cardiac-cerebral vascular diseases (CCVDs) are global health problems due to the characteristic of high mortality. It is found that atherosclerosis (AS), a main cause of CCVDs, is significantly relevant to the change of intimal and media thickness. Neutrophil count (NEU) and neutrophil-lymphocyte ratio (N/L) are recognized possible risk factors for atherosclerosis (AS). However, there are few studies on the separate relationship between carotid intimal thickness, media thickness and NEU, N/L. This study explored the respective effects of NEU and N/L on AS and intimal, media thickness. Materials and methods The χ2, Spearman's rho test, and multiple linear regression were implemented to analyze the relevance between blood parameters and intimal-media thickness. The potential factors, affecting non-depression time (NDT), is identified by univariate Cox regression. ROC curve was performed to determine the ability of blood parameters to predict intimal-media thickness. Immunohistochemistry was implemented. Results and conclusion Based on χ2, Spearman's rho test and multiple linear regression, NEU is related with intimal thickness (P

Objective Atherosclerosis (AS) is the main pathological basis of ischemic cardio-cerebrovascular diseases, and the intimal thickness (IT) of large arteries is regarded as a powerful evaluation indicator for AS. We established an effective neural network model for automatic prediction of the IT and analyzed the high-risk warning indicators of IT. Methods The weight of the left adrenal (WLA) was evaluated. The serum interleukin-6 (IL-6) concentration was measured by enzyme-linked immunosorbent assay. The statistical methods included neural network modeling, a cubic spline interpolation algorithm, Spearman’s rho test, and linear fit. Results Thirty-seven rabbits were classified into a control group (n = 11), high-fat diet group (n = 13), and high-fat diet plus chronic stress group (n = 13). The neural network model was successfully established and verified by comparing the predicted IT with the actual IT. The high-risk warning indicator of IT was identified as follows: 0.445 g

Objective Chronic stress may lead to augmented incidence rates of coronary and cerebrovascular diseases associated with atherosclerosis. However, few studies have focused on the effect of chronic stress on atherosclerosis plaque formation. Therefore, this study was designed to directly evaluate how chronic stress affects atherosclerosis. Methods Thirty rabbits were divided into three groups: the control group, balloon-injury operation + high-fat diet model group, and chronic stress + balloon-injury operation + high-fat diet model group. Physical and social stress were induced, and proteomic methods were applied to identify specific markers. Results After protein determination, the chronic stress + balloon-injury operation + high-fat diet model group exhibited significant upregulation of the following apoptosis-related proteins: UBE2K, caspase 3, caspase 9, BAX, P53, and FAS. In particular, real-time polymerase chain reaction showed that the protein expression of caspase 9 was significantly downregulated in the stress group compared with the non-stress groups. However, the other proteins showed significantly increased expression in the stress group. Conclusion Chronic stress may promote cell apoptosis in the physiopathologic process of atherosclerosis.

Pancreatic cancer (PC) whose mortality is comparable to morbidity is a highly fatal disease. Early approaches of diagnosis and treatment for PC are quite limited, so it is of great urgency to figure out the exact tumorigenesis and development mechanism of PC. To identify the related molecular markers of pancreatic oncogenesis, we downloaded three microarray datasets (GSE63111, GSE101448, and GSE107610) from Gene Expression Omnibus (GEO) database. The common differentially expressed genes (DEGs) among them were identified, and the corresponding function enrichment analyses were accomplished. The protein-protein interaction network was conducted by Search Tool for the Retrieval of Interacting Genes (STRING), and the corresponding module analysis was accomplished by Cytoscape. There were 55 DEGs found in total. The molecular function and biological processes (BP) of these DEGs mainly include cytokinesis, mitotic nuclear division, cell division, cell proliferation, microtubule-based movement, and mineral absorption. Among the 55 DEGs, 14 hub genes were further confirmed and it was concluded that they mainly function in mitotic cytokinesis, microtubule-based movement, mitotic chromosome condensation, and mitotic spindle assembly from the BP analysis. The survival analysis showed that all the 14 hub genes, especially nucleolar and spindle associated protein 1 and abnormal spindle microtubule assembly, may involve in the tumorigenesis and development of PC. And they might be used as new biomarkers for auxiliary diagnosis and potential targets for immunotherapy of PC.

Glioblastoma (GBM) is a malignant tumor of the central nervous system with high mortality rates. Gene expression profiling may determine the chemosensitivity of GBMs. However, the molecular mechanisms underlying GBM remain to be determined. To screen the novel key genes in its occurrence and development, two glioma databases, GSE122498 and GSE104291, were analyzed in the present study. Bioinformatics analyses were performed using the Database for Annotation, Visualization and Integrated Discovery, the Search Tool for the Retrieval of Interacting Genes, Cytoscape, cBioPortal, and Gene Expression Profiling Interactive Analysis softwares. Patients with recurrent GBM showed worse overall survival rate. Overall, 341 differentially expressed genes (DEGs) were authenticated based on two microarray datasets, which were primarily enriched in ‘cell division’, ‘mitotic nuclear division’, ‘DNA replication’, ‘nucleoplasm’, ‘cytosol, nucleus’, ‘protein binding’, ‘ATP binding’, ‘protein C-terminus binding’, ‘the cell cycle’, ‘DNA replication’, ‘oocyte meiosis’ and ‘valine’. The protein-protein interaction network was composed of 1,799 edges and 237 nodes. Its significant module had 10 hub genes, and CDK1, BUB1B, NDC80, NCAPG, BUB1, CCNB1, TOP2A, DLGAP5, ASPM and MELK were significantly associated with carcinogenesis and the development of GBM. The present study indicated that the DEGs and hub genes, identified based on bioinformatics analyses, had significant diagnostic value for patients with GBM.

Adamantinomatous craniopharyngioma (ACP) is a congenital epithelial tumor in the sellar region with benign histological manifestation but invasive. Currently, surgery is the main treatment for it, but its recurrence rate is high. Therefore, it is of great importance to explore the mechanism of occurrence and development of ACP and to identify new molecules. One gene expression profile, GSE94349, was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by the limma package. Gene set enrichment analysis was used to make enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, we performed the construction and analysis of the protein-protein interaction (PPI) network and significant module. The analysis of the GSE94349 dataset identified 109 DEGs, consisting of 80 upregulated genes and 29 downregulated genes in ACP samples compared with normal brain tissues. Functional and pathway enrichment analysis of DEGs provided a comprehensive overview of some major pathophysiological mechanisms in ACP: RNA polymerase II promoter, glutamate receptor binding, and so on. A total of 10 hub genes of DEGs were obtained from the PPI network, which provided potential therapeutic targets for the ACP. In summary, there were DEGs between ACP tissues and normal brain tissues, which may be involved in the mechanisms of occurrence and development of ACP, especially via the regulation of RNA polymerase II promoter and glutamate receptor binding. Key genes in DEGs could serve as new research targets for the diagnosis and treatment of ACP.

The prevalence of overweight–obesity has increased sharply among undergraduates worldwide. In 2016, approximately 52% of adults were overweight–obese. This cross-sectional study aimed to investigate the prevalence of overweight–obesity and explore in depth the connection between eating habits and overweight–obesity among Chinese undergraduates. The study population included 536 undergraduates recruited in Shijiazhuang, China, in 2017. They were administered questionnaires for assessing demographic and daily lifestyle characteristics, including sex, region, eating speed, number of meals per day, and sweetmeat habit. Anthropometric status was assessed by calculating the body mass index (BMI). The determinants of overweight–obesity were investigated by the Pearson χ2 test, Spearman rho test, multivariable linear regression, univariate/multivariate logistic regression, and receiver operating characteristic curve analysis. The prevalence of undergraduate overweight–obesity was 13.6%. Sex [male vs female, odds ratio (OR): 1.903; 95% confidence interval (95% CI): 1.147–3.156], region (urban vs rural, OR: 1.953; 95% CI: 1.178–3.240), number of meals per day (3 vs 2, OR: 0.290; 95% CI: 0.137–0.612), and sweetmeat habit (every day vs never, OR: 4.167; 95% CI: 1.090–15.933) were significantly associated with overweight–obesity. Eating very fast was positively associated with overweight–obesity and showed the highest OR (vs very slow/slow, OR: 5.486; 95% CI: 1.622–18.553). However, the results of multivariate logistic regression analysis indicated that only higher eating speed is a significant independent risk factor for overweight/obesity (OR: 17.392; 95% CI, 1.614–187.363; P = .019). Scoremeng = 1.402 × scoresex + 1.269 × scoreregion + 19.004 × scoreeatin speed + 2.546 × scorenumber of meals per day + 1.626 × scoresweetmeat habit and BMI = 0.253 × Scoremeng + 18.592. These 2 formulas can help estimate the weight status of undergraduates and predict whether they will be overweight or obese.