Abstract:Introduction: Crohn’s disease (CD) is a disease that manifests mainly as chronic inflammation of the gastrointestinal tract, which is still not well understood in terms of its pathogenesis. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) and miRNAs with diagnostic and therapeutic potential in CD.Materials and methods: Three CD datasets (GSE179285, GSE102133, GSE75214) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal … Show more
“…The detailed results are listed in Supplementary Table 2 . Summarizing the results of our gene enrichment analysis and those in similar studies, we found that the results of KEGG analysis were similar to those of previously reported articles, i.e., IL-17 and TNF-α were significantly enriched in CD 11 , 34 ; however, in BP analysis, our study found that a variety of humoral immunities, especially microbial-associated humoral immunity, were significantly enriched in CD, which has not been previously reported in the literature. Figure 3 Functional enrichment analysis of DEGs.…”
Crohn's disease (CD) is a complex autoimmune disorder presumed to be driven by complex interactions of genetic, immune, microbial and even environmental factors. Intrinsic molecular mechanisms in CD, however, remain poorly understood. The identification of novel biomarkers in CD cases based on larger samples through machine learning approaches may inform the diagnosis and treatment of diseases. A comprehensive analysis was conducted on all CD datasets of Gene Expression Omnibus (GEO); our team then used the robust rank aggregation (RRA) method to identify differentially expressed genes (DEGs) between controls and CD patients. PPI (protein‒protein interaction) network and functional enrichment analyses were performed to investigate the potential functions of the DEGs, with molecular complex detection (MCODE) identifying some important functional modules from the PPI network. Three machine learning algorithms, support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were applied to determine characteristic genes, which were verified by ROC curve analysis and immunohistochemistry (IHC) using clinical samples. Univariable and multivariable logistic regression were used to establish a machine learning score for diagnosis. Single-sample GSEA (ssGSEA) was performed to examine the correlation between immune infiltration and biomarkers. In total, 5 datasets met the inclusion criteria: GSE75214, GSE95095, GSE126124, GSE179285, and GSE186582. Based on RRA integrated analysis, 203 significant DEGs were identified (120 upregulated genes and 83 downregulated genes), and MCODE revealed some important functional modules in the PPI network. Machine learning identified LCN2, REG1A, AQP9, CCL2, GIP, PROK2, DEFA5, CXCL9, and NAMPT; AQP9, PROK2, LCN2, and NAMPT were further verified by ROC curves and IHC in the external cohort. The final machine learning score was defined as [Expression level of AQP9 × (2.644)] + [Expression level of LCN2 × (0.958)] + [Expression level of NAMPT × (1.115)]. ssGSEA showed markedly elevated levels of dendritic cells and innate immune cells, such as macrophages and NK cells, in CD, consistent with the gene enrichment results that the DEGs are mainly involved in the IL-17 signaling pathway and humoral immune response. The selected biomarkers analyzed by the RRA method and machine learning are highly reliable. These findings improve our understanding of the molecular mechanisms of CD pathogenesis.
“…The detailed results are listed in Supplementary Table 2 . Summarizing the results of our gene enrichment analysis and those in similar studies, we found that the results of KEGG analysis were similar to those of previously reported articles, i.e., IL-17 and TNF-α were significantly enriched in CD 11 , 34 ; however, in BP analysis, our study found that a variety of humoral immunities, especially microbial-associated humoral immunity, were significantly enriched in CD, which has not been previously reported in the literature. Figure 3 Functional enrichment analysis of DEGs.…”
Crohn's disease (CD) is a complex autoimmune disorder presumed to be driven by complex interactions of genetic, immune, microbial and even environmental factors. Intrinsic molecular mechanisms in CD, however, remain poorly understood. The identification of novel biomarkers in CD cases based on larger samples through machine learning approaches may inform the diagnosis and treatment of diseases. A comprehensive analysis was conducted on all CD datasets of Gene Expression Omnibus (GEO); our team then used the robust rank aggregation (RRA) method to identify differentially expressed genes (DEGs) between controls and CD patients. PPI (protein‒protein interaction) network and functional enrichment analyses were performed to investigate the potential functions of the DEGs, with molecular complex detection (MCODE) identifying some important functional modules from the PPI network. Three machine learning algorithms, support vector machine-recursive feature elimination (SVM-RFE), random forest (RF), and least absolute shrinkage and selection operator (LASSO), were applied to determine characteristic genes, which were verified by ROC curve analysis and immunohistochemistry (IHC) using clinical samples. Univariable and multivariable logistic regression were used to establish a machine learning score for diagnosis. Single-sample GSEA (ssGSEA) was performed to examine the correlation between immune infiltration and biomarkers. In total, 5 datasets met the inclusion criteria: GSE75214, GSE95095, GSE126124, GSE179285, and GSE186582. Based on RRA integrated analysis, 203 significant DEGs were identified (120 upregulated genes and 83 downregulated genes), and MCODE revealed some important functional modules in the PPI network. Machine learning identified LCN2, REG1A, AQP9, CCL2, GIP, PROK2, DEFA5, CXCL9, and NAMPT; AQP9, PROK2, LCN2, and NAMPT were further verified by ROC curves and IHC in the external cohort. The final machine learning score was defined as [Expression level of AQP9 × (2.644)] + [Expression level of LCN2 × (0.958)] + [Expression level of NAMPT × (1.115)]. ssGSEA showed markedly elevated levels of dendritic cells and innate immune cells, such as macrophages and NK cells, in CD, consistent with the gene enrichment results that the DEGs are mainly involved in the IL-17 signaling pathway and humoral immune response. The selected biomarkers analyzed by the RRA method and machine learning are highly reliable. These findings improve our understanding of the molecular mechanisms of CD pathogenesis.
“…According to the regulatory network, we found that miR-5698, miR-224-5p, and miR-4709-3p regulate multiple key hub genes identified by using cytoHubba’s MCC algorithm ( 31 , 32 ). These miRNAs are presumed to be the key miRNAs for regulating LUAD.…”
Background
Lung adenocarcinoma (LUAD) is the most common type of lung cancer, has a high incidence rate and is a serious threat to human health. However, the pathogenesis of LUAD is still unclear. Further research on the pathogenesis of LUAD may provide targets for the early diagnosis and treatment of LUAD.
Methods
First, a transcriptome analysis was conducted to sequence the messenger RNA (mRNA) and micro RNA (miRNA) of the LUAD and adjacent control tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were then performed for the functional annotation. Next, a differential miRNA-differential mRNA regulatory network was then constructed, and the function of the mRNAs in the network was analyzed and the key regulatory molecules (the hub molecules) were identified. Cytohubba was then used to analyze the top 20 hub molecules in the total miRNA-mRNA network, and the miRNAs regulating the top 20 hub genes (of which 2 were upregulated and 18 were downregulated). Finally, the key molecules were identified.
Results
By analyzing the function of the mRNA molecules in the regulatory network, we found that the immune response was inhibited, as were the movement and adhesion of immune-related cells; however, cell tumorigenesis, body death, and tumor cell proliferation were activated. The functions of the 20 hub molecules were mainly related to cytotoxicity, cell exosmosis, and cell adhesion mediated by immune cells. Further, we found that miR-5698, miR224-5p, and miR4709-3P regulate multiple key genes (e.g.,
PECAM1
,
CX3CR1
,
KLRD1
, and
CXCL12
), and may be the key miRNAs regulating LUAD.
Conclusions
Immune response, cell tumorigenesis, and tumor cell proliferation play central roles in the overall regulatory network. miR-5698, miR224-5p, and miR4709-3p may be important biomarkers for the occurrence and development of LUAD and have great potential in the prognosis of LUAD patients and the development of new therapeutic targets.
“…Gene ontology (GO) analysis of DEGs in Histone-H and Histone-L groups was conducted using clusterProfiler R package (version 4.0.5). 17 GO enrichment analyses contain biological process (BP), cellular component (CC), and molecular function (MF). FDR < 0.05 was considered to determine significant enrichment.…”
IntroductionProstate cancer is a common cancer among male population. The aberrant expression of histone modifiers has been identified as a potential driving force in numerous cancer types. However, the mechanism of histone modifiers in the development of prostate cancer remains unknown.MethodsExpression profiles and clinical data were obtained from GSE70769, GSE46602, and GSE67980. Seruat R package was utilized to calculate the gene set enrichment of the histone modification pathway and obtain the Histone score. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were employed to identify marker genes with prognostic value. Kaplan–Meier survival analysis was conducted to assess the efficacy of the prognostic model. In addition, microenvironment cell populations counter (MCPcounter), single‐sample gene set enrichment analysis (ssGSEA), and xCell algorithms were employed for immune infiltration analysis. Drug sensitivity prediction was performed using oncoPredict R package.ResultsWe screened differentially expressed genes (DEGs) between Histone‐high score (Histone‐H) and Histone‐low score (Histone‐L) groups, which were enriched in RNA splicing and DNA‐binding transcription factor binding pathways. We retained four prognostic marker genes, including TACC3, YWHAH, TAF1C and TTLL5. The risk model showed significant efficacy in stratification of the prognosis of prostate cancer patients in both internal and external cohorts (p < .0001 and p = .032, respectively). In addition, prognostic gene YWHAH was infiltrated in abundance of fibroblasts and highly correlated with Entinostat_1593 drug sensitivity score and the value of risk score.ConclusionWe innovatively developed a histone modification‐related prognostic model with high prognostic potency and identified YWHAH as possible diagnostic and therapeutic biomarkers for prostate cancer. It provides novel insights to address prostate cancer and enhance clinical outcomes, thereby opening up a new avenue for customized treatment alternatives.
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