Major depression disorder (MDD) has become increasingly common in patients with ovarian cancer, which complicates the treatment course. The microRNA (miRNA)-mRNA regulation network may help elucidate the potential mechanism of MDD in ovarian cancer. The differentially expressed microRNAs (DEmiRs) and mRNAs (DEmRNAs) were therefore identified from the GSE61741, GSE58105 and GSE9116 ovarian cancer datasets using GEO2R. The target genes of the DEmiRs were then obtained using the TargetScan, microRNAorg, microT-CDS, miRDB and miRTarBase prediction tools. The DAVID program was used to identify the KEGG pathways of target genes, and the core genes of major depressive disorder (MDD) were identified using the Kaplan-Meier Plotter for ovarian cancer. A total of 5 DEmiRs (miR-23b-3p, miR-33b-3p, miR-1265, miR-933 and miR-629-5p) were obtained from GSE61741 and GSE58105. The target genes of these DEmiRs were enriched in pathways that were considered high risk for developing MDD in ovarian cancer. A total of 11 risk genes were selected from these pathways as the core genes in the miRNA-mRNA network of MDD in ovarian cancer, and eventually identified the following 12 miRNA-mRNAs pairs: miR-629-5p-FGF1, miR-629-5p-AKT3, miR-629-5p-MAGI2, miR-933-BDNF, miR-933-MEF2A, miR-23b-3p-TJP1, miR-23b-3p-JMJD1, miR-23b-3p-APAF1, miR-23b-3p-CAB39, miR-1265-CDKN1B, miR-33b-3p-CDKN1B, and miR-33b-3p-F2R. These results may provide novel insights into the mechanisms of developing MDD in ovarian cancer patients.
Objective
To explore the association of CD40 gene single nucleotide polymorphisms (SNPs) and haplotypes with the susceptibility to systemic lupus erythematosus (SLE), as well as the association of serum levels and genotypes of CD40 with the occurrence of SLE.
Methods
A multiplex PCR single-base extension assay (PCR-SBE) and DNA sequencing were performed to analyze 4 SNPs of the CD40 gene, including rs1883832 C/T, rs13040307 C/T, rs752118 C/T and rs3765459 G/A, in 205 patients with SLE (SLE group) and 220 healthy human controls (control group). Enzyme-linked immunosorbent assay (ELISA) was conducted to measure serum levels of CD40 in these subjects.
Results
Compared with the control group, the SLE group showed significantly increased serum levels of CD40(P < 0.05). There were significant differences in genotype and allele frequencies of the SNP rs1883832 C/T in the CD40 gene between the SLE group and control group (all P < 0.01). Relative risk analysis showed that the risk of developing SLE in rs1883832 T allele carriers was 1.517 times that in rs1883832 C allele carriers (OR = 1.517, 95% CI: 1.157-1.990, P = 0.003). Moreover, serum levels of CD40 were significantly higher in rs1883832 T allele carriers than in rs1883832 C allele carriers (P < 0.01). The risk of developing SLE was significantly increased in TCCA haplotype carriers compared with the healthy controls (OR = 2.322, 95% CI: 1.181 -4.564, P = 0.012).
Conclusion
The CD40 gene rs1883832 C/T polymorphism and its TCCA haplotype were both associated with the occurrence of SLE, and the rs1883832 T allele may be a gene predisposing to SLE.
Key words:
Lupus erythematosus, systemic; Antigens, CD40; Polymorphism, single nucleotide; Haplotypes; Genotype; Alleles
Growing evidence suggests that colorectal cancer (CRC) should be considered a heterogeneous disease. The right side (RCC) and left side (LCC) colorectal cancer have different clinical characteristics and immune landscapes. The aim of this study was to analyze differential expression and prognostic correlation of immune-related factors between RCC and LCC.The gene expression profile and clinical characteristics of CRC patients were retrieved from The Cancer Genome Atlas data portal (n=525). Using a deconvolution algorithm, immune cell infiltration in RCC and LCC based on the RNA-seq data was analyzed. Differentially expressed genes (DEGs) were obtained by performing differential gene expression analysis. Immune-related DEGs were derived by the intersection with immune-related factors downloaded from the IMMPORT database. To further validate the findings, we applied immunohistochemical (IHC) staining of a CRC tissue microarray (TMA). The distribution of immune cells in RCC and LCC and changes in the expression of immune molecules on their membranes were verified. The expression levels of circulating cytokines were measured by flow cytometry to detect the cytokines secreted by immune cells in RCC and LCC. Furthermore, to reveal the prognostic value of differential immune factors on RCC and LCC patients, survival analysis based on mRNA levels using TCGA cohort and survival analysis using protein levels was performed using our CRC patients.The infiltration of immune cells differed between RCC and LCC, the infiltration degree of macrophages M0 was significantly higher in LCC, while the infiltration degree of differentiated macrophages M1 and M2, CD4+ T and CD8+ T cells was significantly higher in RCC. The expression of related molecules by immune cells also differed between RCC and LCC. The expression of 7 genes in RCC was higher than that in LCC, which were CCR5, CD209, CD8A, HCK, HLA-DPB1, HLA-DQA1, HLA-DRA, respectively. Meanwhile, the expression of 2 genes in LCC was higher than in RCC, which were IL-34 and PROCR. Patients with RCC having high expression of HLA-DQA1 mRNA or proteins had better survival and LCC patients with high expression of IL 34 mRNA or protein had better survival.In this study, we comprehensively compared differences in immune cells and regulating factors between left and right colorectal cancer. Different expression patterns and their effects on survival were identified. The analysis of immune-related factors may provide a theoretical basis for precise immunotherapy of RCC and LCC.
The purpose of the present study was to detect novel glycolysis-related gene signatures of prognostic values for patients with clear cell renal cell carcinoma (ccRCC).Glycolysis-related gene sets were acquired from the Molecular Signatures Database (V7.0). Gene Set Enrichment Analysis (GSEA) software (4.0.3) was applied to analyze glycolysis-related gene sets. The Perl programming language (5.32.0) was used to extract glycolysis-related genes and clinical information of patients with ccRCC. The receiver operating characteristic curve (ROC) and Kaplan-Meier curve were drawn by the R programming language (3.6.3).The four glycolysis-related genes (B3GAT3, CENPA, AGL, and ALDH3A2) associated with prognosis were identified using Cox proportional regression analysis. A risk score staging system was established to predict the outcomes of patients with ccRCC. The patients with ccRCC were classified into the low-risk group and high-risk group.We have successfully constructed a risk staging model for ccRCC. The model has a better performance in predicting the prognosis of patients, which may have positive reference value for the treatment and curative effect evaluation of ccRCC.
Abstract Epithelial ovarian carcinoma (EOC) is the most lethal gynecologic malignancy. However, the molecular mechanisms remain unclear. In this study, we found that miR-146b was downregulated in EOC and its expression level was negatively correlated with the pathological staging. Follow-up functional experiments illustrated that overexpression of miR-146b significantly inhibited cell migration and invasion, and increased cell proliferation, but it also improved the response to chemotherapeutic agents. Mechanistically, we demonstrated that miR-146b exerted its function mainly through inhibiting F-box and leucine-rich repeat protein 10 (FBXL10), and upregulated the Cyclin D1, vimentin (VIM), and zona-occludens-1 (ZO-1) expression in EOC. These findings indicate that miR-146b–FBXL10 axis is an important epigenetic regulation pathway in EOC. Low miR-146b may contribute to cancer progression from primary stage to advanced stage, and may be the promising therapeutic target of EOC.
Abstract The present study aimed to investigate the protective effects of ganoderic acid A (GAA) on lipopolysaccharide (LPS)-induced acute lung injury. In mouse model of LPS-induced acute lung injury, we found that GAA led to significantly lower lung wet-to-dry weight ratio and lung myeloperoxidase activity, and attenuated pathological damages. In addition, GAA increased superoxide dismutase activity, but decreased malondialdehyde content and proinflammatory cytokines levels in the bronchoalveolar lavage fluid. Mechanistically, GAA reduced the activation of Rho/ROCK/NF-κB pathway to inhibit LPS-induced inflammation. In conclusion, our study suggests that GAA attenuates acute lung injury in mouse model via the inhibition of Rho/ROCK/NF-κB pathway.
Preeclampsia (PE) is characterized by gestational hypertension and proteinuria, and is a leading cause of maternal death and perinatal morbidity globally. Although the exact cause of PE remains unclear, several studies have suggested a role for abnormal expression of multiple genes. The aim of the present study was to identify key genes and related pathways, and to screen for drugs that regulate these genes for potential PE therapy. The GSE60438 dataset was acquired from the Gene Expression Omnibus database to analyze differentially expressed genes (DEGs). By constructing a protein‑protein interaction network and performing reverse transcription‑quantitative PCR verification, proteasome 26S subunit, non‑ATPase 14, prostaglandin E synthase 3 and ubiquinol‑cytochrome c reductase core protein 2 were identified as key genes in PE. In addition, PE was found to be associated with 'circadian rhythm', 'fatty acid metabolism', 'DNA damage response detection of DNA damage', 'regulation of DNA repair' and 'endothelial cell development'. Through connectivity map analysis of DEGs, furosemide and droperidol were suggested to be therapeutic drugs that may target the hub genes for PE treatment. Results analysis of GSEA were included in the discussion section of this article. In conclusion, the current study identified novel key genes associated with the onset of PE and potential drugs for PE treatment.
Background: Renal cell carcinoma (RCC) was one of the most common malignant cancers in the urinary system. Clear cell carcinoma (ccRCC) is the most common pathological type, accounting for approximately 80% of RCC. The lack of accurate and effective prognosis prediction methods has been a weak link in ccRCC treatment. Co-stimulatory molecules played the main role in increasing anti-tumor immune response, which determined the prognosis of patients. Therefore, the main objective of the present study was to explore the prognostic value of Co-stimulatory molecules genes in ccRCC patients. Methods: The TCGA database was used to get gene expression and clinical characteristics of patients with ccRCC. A total of 60 Co-stimulatory molecule genes were also obtained from TCGA-ccRCC, including 13 genes of the B7/ CD28 Co-stimulatory molecules family and 47 genes of the TNF family. In the TCGA cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to generate a multigene signature. R and Perl programming languages were used for data processing and drawing. Real-time PCR was used to verify the expression of differentially expressed genes. Results: The study's initial dataset included 539 ccRCC samples and 72 normal samples. The 13 samples have been eliminated. According to FDR<0.05, there were differences in the expression of 55 Co-stimulatory molecule genes in ccRCC and normal tissues. LASSO Cox regression analysis results indicated that 13 risk genes were optimally used to construct a prognostic model of ccRCC. The patients were divided into a high-risk group and a low-risk group. Those in the high-risk group had significantly lower OS (Overall Survival rate) than patients in the low-risk group. Receiver operating characteristic (ROC) curve analysis confirmed the predictive value of the prognosis model of ccRCC (AUC>0.7). There are substantial differences in immune cell infiltration between high and low-risk groups. Functional analysis revealed that immune-related pathways were enriched, and immune status was different between the two risk groups. Real-time PCR results for genes were consistent with TCGA DEGs. Conclusion: By stratifying patients with all independent risk factors, the prognostic score model developed in this study may improve the accuracy of prognosis prediction for patients with ccRCC.
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