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BACKGROUND:Ferroptosis-related genes have been found to play an important role in the pathogenesis of rheumatoid arthritis.However,there is currently a lack of immune expression of ferroptosis-related signature genes in rheumatoid arthritis and the construction of competing endogenous RNA(CeRNA)interaction networks.Machine learning,as a powerful signature gene selection algorithm based on bioinformatics,can more accurately identify ferroptosis-related signature genes that dominate the pathogenesis of rheumatoid arthritis. OBJECTIVE:To screen ferroptosis-related signature genes in rheumatoid arthritis using bioinformatics and machine learning methods,and to analyze the correlation between ferroptosis-related signature genes and immune infiltration and the construction of CeRNA network of ferroptosis-related signature genes. METHODS:Rheumatoid arthritis-related microarrays were obtained from the GEO database,and ferroptosis-related genes and their differential gene expression were extracted using R language.The differentially expressed genes were screened using machine learning methods.The LASSO regression and SVM-RFE methods were used for signature gene screening,and the genes filtered by both were re-intersected to finally obtain the signature genes in rheumatoid arthritis.Receiver operating characteristic curves were used to assess the accuracy of the screened signature genes for disease diagnosis.Immune infiltration of rheumatoid arthritis and normal synovial tissues was analyzed using the CIBERSORT algorithm,and the correlation between the signature genes and immune cells was analyzed.Finally,the CeRNA network of ferroptosis-related signature genes for rheumatoid arthritis was constructed and the disease signature genes were validated. RESULTS AND CONCLUSION:A total of 150 ferroptosis-related genes in rheumatoid arthritis were obtained,including 55 up-regulated genes and 95 down-regulated genes.GO and KEGG enrichment analyses identified 18 GO significantly correlated entries and 30 KEGG entries respectively,mainly involving metal ion homeostasis,ferric ion homeostasis and oxidative stress response.Machine learning analysis finally identified disease signature genes GABARAPL1 and SAT1.GSEA analysis found that adipocytokine signaling pathway,drug metabolism cytochrome P450,fatty acid metabolism,PPAR signaling pathway,tyrosine metabolism were mainly concentrated when GABARAPL1 was highly expressed,and chemokine signaling pathway,intestinal immune network on IGA production were mainly concentrated when SAT1 was highly expressed.Immune infiltration analysis found that nine immune cells were significantly different in rheumatoid arthritis and normal synovial tissues,in which plasma cells,T-cell CD8,and T-cell follicular helper were highly expressed and the rest were lowly expressed in the disease group.Single gene and immune cell correlation analysis found that GABARAPL1 was positively correlated with dendritic resting cells,activated NK cells,and macrophage M1,with the most significant correlation with dendritic resting cells,while SAT1 was positively correlated with T cell CD4 and γδ T cells and negatively correlated with NK resting cells.GSVA analysis found that SAT1 was upregulated in ascorbic acid and aldehyde metabolism,while downregulated in B-cell receptor signaling pathway,Toll-like receptor signaling pathway,T-cell receptor signaling pathway,and natural killer cell-mediated cytotoxicity.GABARAPL1 showed a down-regulation trend in PPAR signaling pathway,metabolism of nicotinate and nicotinamide,tryptophan metabolism,fatty acid metabolism,and steroid biosynthesis.Sixty long non-code RNAs may play a key role in the development of rheumatoid arthritis.To conclude,the occurrence of rheumatoid arthritis is significantly correlated with the abnormal expression of rheumatoid arthritis-induced ferroptosis-related signature genes,and the signature genes induce disease development via relevant signaling pathways.By analyzing rheumatoid arthritis-related long non-code RNAs-mediated ceRNA networks,potential therapeutic targets and signaling pathways can be identified to further elucidate its pathogenesis and provide a reference basis for subsequent experimental studies.
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Objective:To investigate the differences in the expression profiles of cyclic RNA (circRNA) in peripheral blood mononuclear cells (PBMCs) of rheumatoid arthritis (RA) and its clinical significance.Methods:Venous blood were collected from 4 patients with RA (group T) and 4 healthy subjects (group C). The expression profiles of circRNA in PBMCs of the two groups were detected by Arraystar circRNA microarray, and the differentially expressed circRNA was analyzed by clustering analysis. The binding sites for interaction between differentially expressed circRNA and miRNA were predicted, and functional analysis such as geneontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was performed. quantitative real-time polymerase chain reaction (RT-qPCR) was used to verify the expression of partially differentially expressed circRNA in the two groups of PBMCs, and a circRNA-miRNA-mRNA regulatory network (ceRNA network) was constructed for the target circRNA with significantly differential expression. A receiver operating characteristic curve [receiver operating characteristic curve (ROC)] was established to analyze the potential diagnostic value of target circRNA. SPSS Statistics 23.0 and Graphpad Prism 8.0 were used to analyze the data, and the independent t test was used to analyze the difference between the two groups. Results:① Microarray results showed that, compared with group C, a total of 399 [fold of difference (FC)>1.5, and P<0.05] circRNA were abnormally expressed in PBMCs of group T; including 149 up-regulated and 250 down-regulated. ② Bioinformatics analysis: The prediction of the binding site of circRNA and miRNA suggested that the differentially expressed circRNA in RA might affect the inflammatory response by targeting miR-140-5p, miR-338-5p, and miR-9-5p. GO analysis showed that the differentially expressed circRNA was mainly involved in the intimal-binding organelles, protein metabolism and binding, etc. KEGG pathway analysis showed that most of the involved pathways were related to infection and human immune dysregulation. ③ The results of multi-sample RT-qPCR validation showed that the expression level of hsa_circRNA_009012 in group T was significantly higher than that in group C ( t=-4.417, P<0.01), the expression level of hsa_circRNA_101328 was significantly lower than that in group C ( t=-1.042, P<0.01), and the expression of hsa_circRNA_058230 had no significant change ( t=4.691, P>0.05). ④ ROC curve analysis indicated that hsa_circRNA_009012 had potential value in the diagnosis of RA [area under curve=0.96]. Conclusion:The expression of circRNA in PBMCs of patients with RA is imbalanced, and it may participate in the regulation of the development of RA. Among them, hsa_circRNA_009012 is expected to become a new biological marker for the diagnosis and treatment of RA.
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@#Objective To reveal the potential mechanism of cisplatin resistance in non-small cell lung cancer A549 cells by comparing the expression profiles of wild-type A549 cells and cisplatin-resistant A549 cells (A549/DPP) through whole transcriptome sequencing analysis. Methods The cisplatin resistant A549 (A549/DDP) cell line was first established. Then, the whole-transcriptome analysis was conducted both on A549 and A549/DDP cells. Next, the differentially expressed RNAs of lncRNA-seq, circRNA-seq, and miRNA-seq data were identified, respectively, followed by functional enrichment analysis. Finally, a comprehensive analysis based on the whole transcriptome data was performed and the construction of the ceRNA network was carried out. Results A total of 4 517 lncRNA, 123 circRNA, and 145 miRNA were differentially expressed in A549/DDP cells compared with the A549 cell line. These different RNAs were significantly enriched in cancer-related pathways. The ceRNA network contained 12 miRNAs, 4 circRNAs, 23 lncRNAs, and 9 mRNA nodes, of which hsa-miR-125a-5p and hsa-miR-125b-5p were important miRNAs based on the topological analysis. Conclusion Tumor necrosis factor signaling pathway and p53 signaling pathway are involved in A549/DPP resistance. Hsa-miR-125a-5p and hsa-miR-125b-5p may be potential targets for reversing cisplatin resistance.
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@# Objective: To screen differentially expressed lncRNA, miRNA and mRNA in colorectal cancer (CRC) in TCGA database, and to explore their relationship with CRC prognosis and related biological functions. Methods: RNA sequencing (RNA-Seq) data and miRNA-Seq data of CRC samples were downloaded from the TCGAdatabase and analyzed, and differentially expressed lncRNA, miRNA and mRNA were screened by R program. The lncRNA-miRNA-mRNA ceRNA network in CRC was constructed by analyzing and integrating the relationships between differentially expressed RNAs through miRcode, TargetScan and miRTarbase databases.KaplanMeier method was used to analyze the relationship between the expression of lncRNA, miRNA, mRNA in ceRNA network and the survival prognosis of patients.Finally, the signal pathways involved in the occurrence and development of CRC were analyzed by GSEA functional enrichment analysis software. Results: A total of 614 differentially expressed lncRNAs, 244 differentially expressed miRNAs, and 12 672 differentially expressed mRNAs in CRC were identified; a ceRNA network consisting of 139 lncRNAs, 37 miRNAs and 228 mRNAs was constructed;It was found that 58 lncRNAs, 23 miRNAs, and 150 mRNAs were associated with the prognosis of CRC.The results of GSEA enrichment analysis showed that mRNA was mainly involved in signaling pathways such as Notch, Hedgehog and TGF-β. Conclusion: CRC-related ceRNA network was successfully constructed and lncRNAs, miRNAs and mRNAs associated with CRC prognosis were screened. It provides a valuable preliminary basis for further in-depth clinical research and basic experimental research on CRC.
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Objective This study aimed to analyze the differences in the molecular characteristics of transcriptome between esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). Methods We obtained transcriptomic data on ESCC and EAC from the TCGA database, screened differentially expressed mRNAs, lncRNAs and miRNAs in cancer and the adjacent tissues, and constructed a network of ESCC- and EAC-related competitive endogenous RNA (ceRNA). We predicted the target genes and performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on important miRNAs, and compared the molecular features of the transcriptomes between ESCC and EAC. Results The ceRNA network analysis showed that PVT1, LINC00524, miR-204, miR-383, HOXC8 and NTRK2 played important regulatory roles in both ESCC and EAC. Totally, 13 227 regulatory target genes were predicted with miR-204-5p via miRWalk and 232 target genes screened from the miRDB database. GO analysis revealed 38 enrichments, mainly involved in the regulation of cell-matrix adhesion, morphogenesis of cell membrane projection, and β-catenin combination, KEGG analysis showed 4 relevant pathways: the hedgehog, life-regulating, estrogen and relaxin signaling pathways, and survival analysis manifested LINC00261, MLIP-IT1 and LINC00504 as survival-related differentially expressed lncRNAs, hsa-mir-338 as differentially expressed miRNA, but no mRNA in ESCC. Survival-related differentially expressed lncRNAs in EAC included CYP1B1-AS1 and HOTAIR, and differentially expressed mRNAs included IL11, NTRK2, ANGPT2 and PBK. Of the differentially expressed lncRNAs in both ESCC and EAC, 150 (15.4%) were up-regulated and 158 (26.8%) down-regulated; of the miRNAs, 22 (24.2%) up-regulated and 8 (27.6%) down-regulated; and of the mRNAs, 234 (20.5%) up-regulated and 418 (23.7%) down-regulated. Conclusion There are significant molecular differences between ESCC and EAC, and the differentially expressed lncRNA, miRNA and mRNA may provide some new targets and molecular markers for the treatment and prognosis of esophageal carcinoma.