Disease Markers最新文献

Background: Asthma is one of the most common respiratory diseases and one of the largest burdens of health care resources across the world. This study is aimed at using bioinformatics methods to find effective clinical indicators for asthma and conducting experimental validation.

Methods: We downloaded GSE64913 data and performed differentially expressed gene (DEG) screening. Weighted gene coexpression network analysis (WGCNA) on DEGs was applied to identify key module most associated with asthma for protein-protein interaction (PPI) analysis. According to the degree value, ten genes were obtained and subjected to expression analysis and receiver operating characteristic (ROC) analysis. Next, key genes were screened for expression analysis and immunological analysis. Finally, cell counting kit-8 (CCK-8) and qRT-PCR were also conducted to observe the influence of hub gene on cell proliferation and inflammatory cytokines.

Results: From the GSE64913 dataset, 711 upregulated and 684 downregulated DEGs were found. In WGCNA, the top 10 genes in the key module were examined by expression analysis in asthma, and CYCS was determined as an asthma-related oncogene with a good predictive ability for the prognosis of asthmatic patients. CYCS is significantly associated with immune cells, such as HHLA2, IDO1, TGFBR1, and CCL18 and promoted the proliferation of asthmatic cells in vitro.

Conclusion: CYCS plays an oncogenic role in the pathophysiology of asthma, indicating that this gene may become a novel diagnostic biomarker and promising target of asthma treatment.

Performing cardiopulmonary bypass (CPB) to reduce ischemic injury during surgery is a common approach to cardiac surgery. However, this procedure can lead to systemic inflammation and multiorgan dysfunction. Therefore, elucidating the molecular mechanisms of CPB-induced inflammatory cytokine release is essential as a critical first step in identifying new targets for therapeutic intervention. The GSE143780 dataset which is mRNA sequencing from total circulating leukocytes of the neonatorum was downloaded from the Gene Expression Omnibus (GEO) database. A total of 21 key module genes were obtained by analyzing the intersection of differentially expressed gene (DEG) and gene coexpression network analysis (WGCNA), and then, 4 genes (TRAF3IP2-AS1, PPARGC1B, CD4, and PDLIM5) were further confirmed after the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) screening and were used as hub genes for CPB-induced inflammatory cytokine release in patients with congenital heart defects. The enrichment analysis revealed 21 key module genes mainly related to the functions of developmental cell growth, regulation of monocyte differentiation, regulation of myeloid leukocyte differentiation, ERK1 and ERK2 cascade, volume-sensitive anion channel activity, and estrogen receptor binding. The result of gene set enrichment analysis (GSEA) showed that the hub genes were related to different physiological functions of cells. The ceRNA network established for hub genes includes 3 hub genes (PPARGC1B, CD4, and PDLIM5), 55 lncRNAs, and 34 miRNAs. In addition, 4 hub genes have 215 potential therapeutic agents. Finally, expression validation of the four hub genes revealed that they were all significantly low expressed in the surgical samples than before.

Purpose: Keloid is a type of benign fibrous proliferative tumor characterized by excessive scarring. C1q/TNF-related protein 3 (CTRP3) has been proven to possess antifibrotic effect. Here, we explored the role of CTRP3 in keloid. In the current research, we examined the influence of CTRP3 on keloid fibroblasts (KFs) and investigated the potential molecular mechanism.

Methods: KF tissue specimens and adjacent normal fibroblast (NF) tissues were collected cultured from 10 keloid participants. For the TGF-β1 stimulation group, KFs were processed with human recombinant TGF-β1. Cell transfection of pcDNA3.1-CTRP3 or pcDNA3.1 was performed. The siRNA of CTRP3 (si-CTRP3) or negative control siRNA (si-scramble) was transfected into KFs.

Results: CTRP3 was downregulated in keloid tissues and KFs. CTRP3 overexpression significantly controlled TGF-β1-induced propagation and migration in KFs. Col I, α-SMA, and fibronectin mRNA and protein levels were enhanced by TGF-β1 stimulation, whereas they were inhibited by CTRP3 overexpression. In contrast, CTRP3 knockdown exhibited the opposite effect. In addition, CTRP3 attenuated TGF-β receptors TRI and TRII in TGF-β1-induced KFs. Furthermore, CTRP3 prevented TGF-β1-stimulated nuclear translocation of smad2 and smad3 and suppressed the expression levels of p-smad2 and p-smad3 in KFs.

Conclusion: CTRP3 exerted an antifibrotic role through inhibiting proliferation, migration, and ECM accumulation of KFs via regulating TGF-β1/Smad signal path.

Objective: To investigate the efficacy of butylphthalide combined with edaravone in the treatment of acute ischemic stroke and the effect on serum inflammatory factors.

Methods: One hundred and sixty patients with acute ischemic stroke who attended the neurovascular intervention department of our hospital from May 2020 to June 2022 were enrolled as study subjects for prospective analysis and were equally divided into a control group and an experimental group using the random number table method, with 80 cases in each group. The control group was treated with edaravone injection, while the experimental group was treated with butylphthalide combined with edaravone. The disease was recorded to compare the efficacy, erythrocyte sedimentation rate, homocysteine, serum inflammatory factors including tumor necrosis factor-α, C-reactive protein and interleukin-6 levels, and the incidence of adverse reactions between the two groups.

Results: The total effective rate of treatment in the experimental group was 90.0% (72/80), while that of the control group was 62.5% (50/80), the total effective rate of the experimental group was significantly higher than that of the control group, and the difference was statistically significant (P < 0.05). After treatment, the erythrocyte sedimentation rate, homocysteine level, and serum TNF-α, CRP, and IL-6 levels of patients in the experimental group improved compared with those before treatment, and the degree of improvement was better than that of the control group, and the difference was statistically significant (P < 0.05). After 3 months of treatment, a comparison of the incidence of adverse reactions in the two groups showed no statistically significant difference between the two groups (P > 0.05).

Conclusion: The treatment of acute ischemic stroke with butylphthalide combined with edaravone has positive significance in improving blood circulation regulation and serum inflammatory factor levels and is reliable and worthy of clinical promotion.

Objective: To investigate the relationship between changes in blood glucose and blood lipid levels and the risk of thyroid cancer in patients with type 2 diabetes mellitus.

Methods: A total of 159 patients with type 2 diabetes who were treated in our hospital between June 2018 and February 2021 were recruited and assigned into the observation group, including 136 patients with type 2 diabetes without thyroid cancer (nonthyroid cancer group) and 23 patients with type 2 diabetes complicated with thyroid cancer (thyroid cancer group), and 120 healthy subjects during the same period were selected as the control group. Glycated hemoglobin (HbAlc), total cholesterol (TC), triacylglycerol (TG), high density lipoprotein cholesterol (HDL-C), and low density lipoprotein cholesterol (LDL-C) were detected and compared. Pearson's method was conducted to analyze the correlation between serum HbAlc level and TC, TG, HDL-C, and LDL-C levels in patients with type 2 diabetes mellitus; multivariate logistic regression analysis was performed to analyze the influencing factors of thyroid cancer in patients with type 2 diabetes mellitus.

Results: The serum HbAlc level and the incidence of thyroid cancer in patients with type 2 diabetes mellitus in the observation group were significantly higher than those in the control group (P < 0.05). The levels of TC, TG, and LDL-C in patients with type 2 diabetes mellitus in the observation group were significantly higher than those in the control group, and the level of HDL-C was significantly lower than that in the control group (P < 0.05). The correlation analysis showed that serum HbAlc levels in patients with type 2 diabetes were positively correlated with TC and TG levels and negatively correlated with HDL-C levels (P < 0.05) and not correlated with LDL-C levels (P > 0.05). Compared with the type 2 diabetes patients without thyroid cancer, the serum HbAlc, TC, and TG levels of the patients with type 2 diabetes mellitus in the thyroid cancer group were significantly higher, and the levels of HDL-C were significantly lower (P < 0.05). There was no significant change in the level of LDL-C (P > 0.05). Multivariate logistic regression analysis showed that serum HbAlc, TC, and TC levels were all risk factors for thyroid cancer in patients with type 2 diabetes mellitus (P < 0.05), while serum HDL-C level was a protective factor for thyroid cancer in patients with type 2 diabetes mellitus (P < 0.05).

Conclusion: Thyroid cancer in type 2 diabetes patients may be linked to elevated levels of blood HbAlc, TC, and TG. HbAlc may raise the risk of thyroid cancer in type 2 diabetes patients by modulating blood lipid levels, which might serve as a marker to assess the risk of thyroid cancer in type 2 diabetes mellitus patients. However, since this study did not conduct in vitro and in vivo experim

Chemotherapy is not recommended for patients with deficient mismatch repair (dMMR) in colorectal cancer (CRC); therefore, assessing the status of MMR is crucial for the selection of subsequent treatment. This study is aimed at building predictive models to accurately and rapidly identify dMMR. A retrospective analysis was performed at Wuhan Union Hospital between May 2017 and December 2019 based on the clinicopathological data of patients with CRC. The variables were subjected to collinearity, least absolute shrinkage and selection operator (LASSO) regression, and random forest (RF) feature screening analyses. Four sets of machine learning models (extreme gradient boosting (XGBoost), support vector machine (SVM), naive Bayes (NB), and RF) and a conventional logistic regression (LR) model were built for model training and testing. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive performance of the developed models. In total, 2279 patients were included in the study and were randomly divided into either the training or test group. Twelve clinicopathological features were incorporated into the development of the predictive models. The area under curve (AUC) values of the five predictive models were 0.8055 for XGBoost, 0.8174 for SVM, 0.7424 for NB, 8584 for RF, and 0.7835 for LR (Delong test, P value < 0.05). The results showed that the RF model exhibited the best recognition ability and outperformed the conventional LR method in identifying dMMR and proficient MMR (pMMR). Our predictive models based on routine clinicopathological data can significantly improve the diagnostic performance of dMMR and pMMR. The four machine learning models outperformed the conventional LR model.

Background: The most prevalent malignant tumor in a human brain nervous system is called glioma. Peptide is a compound formed by the peptide bond of α-amino acids, and the development of polypeptide drugs has been widely used in many fields. We plan to investigate the underlying peptides with clinical value in glioma.

Method: Based on public databases, we targeted the common genes between glioma differentially expressed genes (DEGs) and peptide genes related to glioma prognosis. Then, these common genes were analyzed by LASSO-Cox analysis, prognostic risk model, and nomogram to identify key prognostic peptide genes and the target gene in this study. Next, the mechanism of target gene in glioma was explored by bioinformatics analysis and functional experiments.

Results: We obtained a total of 26 overlapping genes for the following study. After that, 6 independent prognostic factors (REPIN1, PSD3, RDX, CDK4, FANCI, and ARHGEF9) were obtained and applied to construct the prognostic nomogram, and ARHGEF9 was the target gene in the study. Next, peptide ARHGEF9 was found to inhibit glioma cell development. Through Spearman's correlation analysis, ARHGEF9 had a close relation with PI3K/AKT/mTOR pathway. In functional experiments, peptide ARHGEF9 could suppress the protein expressions of p-PIK3K, p-AKT and p-mTOR, while IGF-1 could reverse this effect.

Conclusion: This study identifies 6 new prognostic biomarkers for glioma patients. Among them, peptide ARHGEF9 gene is an inhibitory gene functioning by targeting PI3K/AKT/mTOR pathway.

Emerging evidence has revealed the significant roles of nicotinamide n-methyltransferase (NNMT) in cancer initiation, development, and progression; however, a pan-cancer analysis of NNMT has not been conducted. In this study, we first thoroughly investigated the expression and prognostic significance of NNMT and the relationship between NNMT and the tumor microenvironment using bioinformatic analysis. NNMT was significantly increased and associated with poor prognosis in many common cancers. NNMT expression correlated with the infiltration levels of cancer-associated fibroblasts and macrophages in pan-cancer. Function enrichment analysis discovered that NNMT related to cancer-promoting and immune pathways in various common cancers, such as colon adenocarcinoma, head and neck squamous cell carcinoma, ovarian serous cystadenocarcinoma, and stomach adenocarcinoma. NNMT expression was positively correlated with tumor-associated macrophages (TAMs), especially M2-like TAMs. The results suggest that NNMT might be a new biomarker for immune infiltration and poor prognosis in cancers, providing new direction on therapeutics of cancers.

Background: Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is a common malignant gynecological cancer. The ceRNA networks play important roles in many tumors, while RILPL2-related ceRNA network has been seldom studied in CESC.

Methods: All CESC data was obtained from TCGA database. Differentially expressed RNAs and predicted target RNAs were cross analyzed to construct ceRNA network. RNA and clinicopathological characteristics' influence on overall survival (OS) were determined by univariate and multivariate Cox regression analyses. Lasso regression was used to construct the prediction model. Coexpression analysis was performed to explore the association of gene expression with CESC. This was followed by an experimental validation based on these results.

Results: Between high and low RILPL2 expression CESC patients, totally 1227 DEmRNAs, 39 DEmiRNAs, and 1544 DElncRNAs were identified. After multiple cross analyses, 1 miRNA hsa-miR-1293, 20 mRNAs, and 43 lncRNAs were maintained to construct ceRNA network. CADM3-AS1, LINC00092, and ZNF667-AS1 in ceRNA network were significantly associated with the OS of CESC patients, and patients with low expression of these lncRNAs had worse prognosis. Significant lower expressions of these lncRNAs were also observed in CESC cell line compared with normal cell line.

Conclusion: Low expressions of CADM3-AS1, LINC00092, and ZNF667-AS1 in ceRNA network were probably promising poor prognostic biomarkers for CESC patients. The genes show a prospective research area for CESC-targeted treatment in the future.