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Integrated bioinformatics analysis of differentially expressed genes and immune cell infiltration characteristics in Esophageal Squamous cell carcinoma.

ABSTRACT: Esophageal squamous cell carcinoma (ESCC) is a life-threatening thoracic tumor with a poor prognosis. The role of molecular alterations and the immune microenvironment in ESCC development has not been fully elucidated. The present study aimed to elucidate key candidate genes and immune cell infiltration characteristics in ESCC by integrated bioinformatics analysis. Nine gene expression datasets from the Gene Expression Omnibus (GEO) database were analysed to identify robust differentially expressed genes (DEGs) using the robust rank aggregation (RRA) algorithm. Functional enrichment analyses showed that the 152 robust DEGs are involved in multiple processes in the tumor microenvironment (TME). Immune cell infiltration analysis based on the 9 normalized GEO microarray datasets was conducted with the CIBERSORT algorithm. The changes in macrophages between ESCC and normal tissues were particularly obvious. In ESCC tissues, M0 and M1 macrophages were increased dramatically, while M2 macrophages were decreased. A robust DEG-based protein-protein interaction (PPI) network was used for hub gene selection with the CytoHubba plugin in Cytoscape. Nine hub genes (CDA, CXCL1, IGFBP3, MMP3, MMP11, PLAU, SERPINE1, SPP1 and VCAN) had high diagnostic efficiency for ESCC according to receiver operating characteristic (ROC) curve analysis. The expression of all hub genes except MMP3 and PLAU was significantly related to macrophage infiltration. Univariate and multivariate regression analyses showed that a 7-gene signature constructed from the robust DEGs was useful for predicting ESCC prognosis. Our results might facilitate the exploration of potential targeted TME therapies and prognostic evaluation in ESCC.

SUBMITTER: Feng Z

PROVIDER: S-EPMC8371051 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPostmenopausal osteoporosis is a systemic metabolic disorder typified by an imbalance in bone turnover, where bone resorption supersedes bone formation. This imbalance primarily arises from a decline in bone mass induced by estrogen deficiency, and an elevated risk of fractures resulting from degradation of bone microstructure. Despite recognizing these changes, the precise causative factors and potential molecular pathways remain elusive. In this study, we aimed to identify differentially expressed genes (DEGs), associated pathways, and the role of immune infiltration in osteoporosis, leveraging an integrated bioinformatics approach to shed light on potential underlying molecular mechanisms.MethodsWe retrieved the expression profiles of GSE230665 from the Gene Expression Omnibus database, comprising 15 femur samples, including 12 postmenopausal osteoporosis samples and 3 normal controls. From the aggregated microarray datasets, we derived differentially expressed genes (DEGs) for further bioinformatics analysis. We used WGCNA, analyzed DEGs, PPI, and conducted GO analysis to identify pivotal genes. We then used the CIBERSORT method to explore the degree of immune cell infiltration within femur specimens affected by postmenopausal osteoporosis. To probe into the relationship between pivotal genes and infiltrating immune cells, we conducted correlation analysis.ResultsWe identified a total of 12,204 DEGs. Among these, 12,157 were up-regulated, and 47 were down-regulated. GO and KEGG pathway analyses indicated that these DEGs predominantly targeted cellular protein localization activity and associated signaling pathways. The protein-protein interaction network highlighted four central hub-genes: RPL31, RPL34, EEF1G, and BPTF. Principal component analysis indicated a positive correlation between the expression of these genes and resting NK cells (as per CIBERSORT). In contrast, the expression of RPL31, RPL34, and EEF1G showed a negative correlation with T cells (gamma delta per CIBERSORT).ConclusionsImmune infiltration plays a role in the development of osteoporosis.

Project description:To identify candidate key genes and miRNAs associated with esophageal squamous cell carcinoma (ESCC) development and prognosis, the gene expression profiles and miRNA microarray data including GSE20347, GSE38129, GSE23400, and GSE55856 were downloaded from the Gene Expression Omnibus (GEO) database. Clinical and survival data were retrieved from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs) was analyzed via DAVID, while the DEG-associated protein-protein interaction network (PPI) was constructed using the STRING database. Additionally, the miRNA target gene regulatory network and miRNA coregulatory network were constructed, using the Cytoscape software. Survival analysis and prognostic model construction were performed via the survival (version 2.42-6) and rbsurv R packages, respectively. The results showed a total of 2575, 2111, and 1205 DEGs, and 226 differentially expressed miRNAs (DEMs) were identified. Pathway enrichment analyses revealed that DEGs were mainly enriched in 36 pathways, such as the proteasome, p53, and beta-alanine metabolism pathways. Furthermore, 448 nodes and 1144 interactions were identified in the PPI network, with MYC having the highest random walk score. In addition, 7 DEMs in the microarray data, including miR-196a, miR-21, miR-205, miR-194, miR-103, miR-223, and miR-375, were found in the regulatory network. Moreover, several reported disease-related miRNAs, including miR-198a, miR-103, miR-223, miR-21, miR-194, and miR-375, were found to have common target genes with other DEMs. Survival analysis revealed that 85 DEMs were related to prognosis, among which hsa-miR-1248, hsa-miR-1291, hsa-miR-421, and hsa-miR-7-5p were used for a prognostic survival model. Taken together, this study revealed the important roles of DEGs and DEMs in ESCC development, as well as DEMs in the prognosis of ESCC. This will provide potential therapeutic targets and prognostic predictors for ESCC.

Project description:BackgroundImmunotherapy has achieved remarkable efficacy in treating oesophageal squamous cell carcinoma (ESCC). However, this treatment has limited efficacy in some patients. An increasing number of evidence suggested that immune cells within the tumour microenvironment (TME) are strongly related to immunotherapy response and patient prognosis. Thus, the landscape of immune cell infiltration (ICI) in ESCC needs to be mapped.MethodsIn the study, the ICI pattern in 206 cases of ESCC was characterised by two algorithms, namely, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). The ICI score of each specimen was calculated by principal component analysis (PCA) according to ICI signature genes A (ICISGA) and B (ICISGB). The prognostic difference was evaluated by using the Kaplan-Meier method. The related pathways of ICI score were investigated by applying gene set enrichment analysis (GSEA). The R packages of 'regplot', 'timeROC' and 'rms' were applied for the construction of nomogram model.ResultThree TME subtypes were identified with no prognostic implication. A total of 333 differentially expressed genes (DEGs) among immune subtypes were determined, among which ICISGA and ICISGB were identified. Finally, ICI scores were constructed, and the patients were grouped into high or low ICI score group. Compared with the low ICI score group, the high ICI score group had better prognosis. GSEA revealed that the high ICI score group referred to multiple signalling pathways, including B cell receptor, Fc gamma R-mediated phagocytosis, NOD-like receptor and TGF-β signalling pathways. In addition, the nomogram model was constructed to evaluate 1-, 3- and 5-year probability of death in an ESCC patient. The ROC and calibration curves indicated that the model has a good discrimination ability.ConclusionWe depicted a comprehensive ICI landscape in ESCC. ICI score may be used as a predictor of survival rate, which may be helpful for guiding immunotherapy in the future.

Project description:Aberrant methylated genes (DMGs) play an important role in the etiology and pathogenesis of esophageal squamous cell carcinoma (ESCC). In this study, we aimed to integrate three cohorts profile datasets to ascertain aberrant methylated-differentially expressed genes and pathways associated with ESCC by comprehensive bioinformatics analysis. We downloaded data of gene expression microarrays (GSE20347, GSE38129) and gene methylation microarrays (GSE52826) from the Gene Expression Omnibus (GEO) database. Aberrantly differentially expressed genes (DEGs) were obtained by GEO2R tool. The David database was then used to perform Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses on selected genes. STRING and Cytoscape software were used to construct a protein-protein interaction (PPI) network, then the modules in the PPI networks were analyzed with MCODE and the hub genes chose from the PPI networks were verified by Oncomine and TCGA database. In total, 291 hypomethylation-high expression genes and 168 hypermethylation-low expression genes were identified at the screening step, and finally found six mostly changed hub genes including KIF14, CDK1, AURKA, LCN2, TGM1, and DSG1. Pathway analysis indicated that aberrantly methylated DEGs mainly associated with the P13K-AKT signaling, cAMP signaling and cell cycle process. After validation in multiple databases, most hub genes remained significant. Patients with high expression of AURKA were associated with shorter overall survival. To summarize, we have identified six feasible aberrant methylated-differentially expressed genes and pathways in ESCC by bioinformatics analysis, potentially providing valuable information for the molecular mechanisms of ESCC. Our data combined the analysis of gene expression profiling microarrays and gene methylation profiling microarrays, simultaneously, and in this way, it can shed a light for screening and diagnosis of ESCC in future.

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Integrated bioinformatics analysis of differentially expressed genes and immune cell infiltration characteristics in Esophageal Squamous cell carcinoma.

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