Identification of potential immunotherapeutic targets and prognostic biomarkers in Graves' disease using weighted gene co-expression network analysis
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ABSTRACT:
SUBMITTER: Mi N
PROVIDER: S-EPMC10926144 | biostudies-literature | 2024 Feb
REPOSITORIES: biostudies-literature
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Project description:AimWhole transcriptome analysis was conducted to identify differentially expressed RNAs and regulatory networks associated with papillary thyroid carcinoma (PTC).MethodsA weighted gene co-expression network analysis based on high-throughput sequencing data for six pairs of PTC and adjacent tissue samples was conducted to understand the biological functions and regulatory networks involving long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs).ResultsWe detected 131, 338, 31, and 556 differentially expressed circRNAs, lncRNAs, miRNAs, and mRNAs, respectively. We identified modules that were significantly positively and negatively related to cancer and lymph node metastasis. Gray and turquoise modules were positively correlated with cancer phenotypes (p < 0.05), whereas yellow, brown, and blue modules were negatively correlated with cancer (p < 0.05). Gray module was positively correlated with lateral lymph node metastasis (p = 0.02). Kaplan-Meier analyses revealed that the levels of transmembrane protein 63C (TMEM63C), lysyl oxidase-like 1 (LOXL1), collagen type V alpha 1 chain (COL5A1), ADAM metalloproteinase with thrombospondin type I motif 2 (ADAMTS2), and LysM-domain containing 3 (LYSMD3) were significantly associated with overall survival (p < 0.05). Significant increase in the expression of COL5A1 and LOXL1 in tumor tissues was validated by quantitative real-time polymerase chain reaction (p < 0.05). COL5A1 and LOXL1 promoted PTC cell growth and invasion in vitro.ConclusionsWe identified COL5A1 and LOXL1 as potential prognostic biomarkers, providing new insights into the occurrence and progression of PTC.
| S-EPMC9089218 | biostudies-literature
Project description:Glioma is the most common brain tumor with high mortality. However, there are still challenges for the timely and accurate diagnosis and effective treatment of the tumor. One hundred and twenty-one samples with grades II, III and IV from the Gene Expression Omnibus database were used to construct gene co-expression networks to identify hub modules closely related to glioma grade, and performed pathway enrichment analysis on genes from significant modules. In gene co-expression network constructed by 2345 differentially expressed genes from 121 gene expression profiles for glioma, we identified the black and blue modules that associated with grading. The module preservation analysis based on 118 samples indicates that the two modules were replicable. Enrichment analysis showed that the extracellular matrix genes were enriched for blue module, while cell division genes were enriched for black module. According to survival analysis, 21 hub genes were significantly up-regulated and one gene was significantly down-regulated. What's more, IKBIP, SEC24D, and FAM46A are the genes with little attention among the 22 hub genes. In this study, IKBIP, SEC24D, and FAM46A related to glioma were mentioned for the first time to the current knowledge, which might provide a new idea for us to study the disease in the future. IKBIP, SEC24D and FAM46A among the 22 hub genes identified that are related to the malignancy degree of glioma might be used as new biomarkers to improve the diagnosis, treatment and prognosis of glioma.
| S-EPMC7351128 | biostudies-literature
Project description:Testicular germ cell tumors (TGCTs) are common in young males, and seminoma accounts for a large proportion of TGCTs. However, there are limited records on the exploration of novel biomarkers for seminoma. Hence, we aimed to identify new biomarkers associated with overall survival in seminoma. mRNA-seq and clinical traits of TGCTs were downloaded from UCSC XENA and analyzed by weighted gene co-expression network analysis. After intersection with differentially expressed genes in GSE8607, common genes were subjected to protein-protein interaction (PPI) network construction and enrichment analyses. Then, the top 10 common genes were investigated by Kaplan-Meier (KM) survival analyses and univariate Cox regression analyses. Ultimately, TYROBP, CD68, and ITGAM were considered three prognostic biomarkers in seminoma. Based on correlation analysis between these genes and immune infiltrates, we suggest that the three biomarkers influence the survival of seminoma patients, possibly through regulating the infiltration of immune cells. In conclusion, our study demonstrated that TYROBP, CD68, and ITGAM could be regarded as prognostic biomarkers and therapeutic targets for seminoma patients.
| S-EPMC7588113 | biostudies-literature
Project description:BackgroundPancreatic cancer is one of the principal causes of tumor-related death worldwide. CXC chemokines, a subfamily of functional chemotactic peptides, affect the initiation of tumor cells and clinical outcomes in several human malignant tumors. However, the specific biological functions and clinical significance of CXC chemokines in pancreatic cancer have not been clarified.MethodsBioinformatics analysis tools and databases, including ONCOMINE, GEPIA2, the Human Protein Atlas, DAVID, GeneMANIA, cBioPortal, STRING, DGidb, MethSurv, TRRUST, SurvExpress, SurvivalMeth, and TIMER, were utilized to clarify the clinical significance and biological functions of CXC chemokine in pancreatic cancer.ResultsExcept for CXCL11/12, the transcriptional levels of other CXC chemokines in PAAD tissues were significantly elevated, and the expression level of CXCL16 was the highest among these CXC chemokines. Our findings also suggested that all of the CXC chemokines were linked to tumor-immune dysfunction involving the abundance of immune cell infiltration, and the Cox proportional hazard model confirmed that dendritic and CXCL3/5/7/8/11/17 were significantly associated with the clinical outcome of PAAD patients. Furthermore, increasing expressions of CXCL5/9/10/11/17 were related to unfavorable overall survival (OS), and only CXCL17 was a prognostic factor for disease-free survival (DFS) in PAAD patients. The expression pattern and prognostic power of CXC chemokines were further validated in the independent GSE62452 dataset. For the prognostic value of single CpG of DNA methylation of CXC chemokines in patients with PAAD, we identified 3 CpGs of CXCL1, 2 CpGs of CXCL2, 2 CpGs of CXCL3, 3 CpGs of CXCL4, 10 CpGs of CXCL5, 1 CpG of CXCL6, 1 CpG of CXCL7, 3 CpGs of CXCL12, 3 CpGs of CXCL14, and 5 CpGs of CXCL17 that were significantly associated with prognosis in PAAD patients. Moreover, the prognostic value of CXC chemokine signature in PAAD was explored and tested in two independent cohort, and results indicated that the patients in the low-risk group had a better OS compared with the high-risk group. Survival analysis of the DNA methylation of CXC chemokine signature demonstrated that PAAD patients in the high-risk group had longer survival times.ConclusionsThese findings reveal the novel insights into CXC chemokine expression and their biological functions in the pancreatic cancers, which might serve as accurate prognostic biomarkers and suitable immunotherapeutic targets for patients with pancreatic cancer.
| S-EPMC8419473 | biostudies-literature
Project description:The study aimed to seek potential biomarkers for acute myocardial infarction (AMI) detection and treatment.The dataset GSE48060 was used, consisting of 52 peripheral blood samples (31 AMI samples and 21 normal controls). By limma package, differentially expressed genes (DEGs) between 2 kinds of samples were identified, followed by enrichment analysis, subpathway analysis, protein-protein interaction (PPI) network analysis, and transcription factor network (TFN) analysis. Weighted gene co-expression network analysis was used to further extract key modules relating to AMI, followed by enrichment and TFN analyses. Expression validation was performed via meta-analysis of 2 datasets, GSE22229 and GSE29111.A set of 428 DEGs in AMI were screened out, and the upregulated toll-like receptor (TLR) family genes (TLR1, TLR2, and TLR10) were enriched in wound response, immune response and inflammatory response functions, and downregulated genes (GBP5, CXCL5, GZMA, CCL5, and CCL4) were correlated with immune response. CCL5, GZMA, GZMB, TLR2, and formyl peptide receptor 1 (FPR1) were predicted as crucial nodes in the PPI network. Signal transducer and activator of transcription 1 (STAT1) was the key transcription factor (TF) with multiple targets. The grey module was highly related to AMI. Genes in this module were closely related to regulation of macrophage activation, and spermatogenic leucine zipper 1 (SPZ1) was identified as a TF. Expressions of TLR2 and FPR1 were confirmed via the integrated matrix.Several potential biomarkers for AMI detection were identified, such as GZMB, GBP5, FPR1, TLR2, STAT1, and SPZ1. They might exert their functions via regulation of immune and inflammation responses. Genes in grey module play significant roles in AMI via regulation of macrophage activation.
| S-EPMC5708914 | biostudies-literature
Project description:Background: Burn injury is a life-threatening disease that does not have ideal biomarkers. Therefore, this study first applied weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening methods to identify pivotal genes and diagnostic biomarkers associated with the skin burn process. Methods: After obtaining transcriptomic datasets of burn patient skin and normal skin from Gene Expression Omnibus (GEO) and performing differential analysis and functional enrichment, WGCNA was used to identify hub gene modules associated with burn skin processes in the burn patient peripheral blood sample dataset and determine the correlation between modules and clinical features. Enrichment analysis was performed to identify the functions and pathways of key module genes. Differential analysis, WGCNA, protein-protein interaction analysis, and enrichment analysis were utilized to screen for hub genes. Hub genes were validated in two other GEO datasets, tested by immunohistochemistry for hub gene expression in burn patients, and receiver operating characteristic curve analysis was performed. Finally, we constructed the specific drug activity, transcription factors, and microRNA regulatory network of the five hub genes. Results: A total of 1,373 DEGs in GSE8056 were obtained, and the top 5 upregulated genes were S100A12, CXCL8, CXCL5, MMP3, and MMP1, whereas the top 5 downregulated genes were SCGB1D2, SCGB2A2, DCD, TSPAN8, and KRT25. DEGs were significantly enriched in the immunity, epidermal development, and skin development processes. In WGCNA, the yellow module was identified as the most closely associated module with tissue damage during the burn process, and the five hub genes (ANXA3, MCEMP1, MMP9, S100A12, and TCN1) were identified as the key genes for burn injury status, which consistently showed high expression in burn patient blood samples in the GSE37069 and GSE13902 datasets. Furthermore, we verified using immunohistochemistry that these five novel hub genes were also significantly elevated in burn patient skin. In addition, MCEMP1, MMP9, and S100A12 showed perfect diagnostic performance in the receiver operating characteristic analysis. Conclusion: In conclusion, we analyzed the changes in genetic processes in the skin during burns and used them to identify five potential novel diagnostic markers in blood samples from burn patients, which are important for burn patient diagnosis. In particular, MCEMP1, MMP9, and S100A12 are three key blood biomarkers that can be used to identify skin damage in burn patients.
| S-EPMC8762241 | biostudies-literature
Project description:BackgroundGlioblastoma (GBM) is the most common malignant tumor in the central system with a poor prognosis. Due to the complexity of its molecular mechanism, the recurrence rate and mortality rate of GBM patients are still high. Therefore, there is an urgent need to screen GBM biomarkers to prove the therapeutic effect and improve the prognosis.ResultsWe extracted data from GBM patients from the Gene Expression Integration Database (GEO), analyzed differentially expressed genes in GEO and identified key modules by weighted gene co-expression network analysis (WGCNA). GSE145128 data was obtained from the GEO database, and the darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r = - 0.62, p = 0.01). We performed enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to reveal the interaction activity in the selected modules. Then Kaplan-Meier survival curve analysis was used to extract genes closely related to GBM prognosis. We used Kaplan-Meier survival curves to analyze the 139 genes in the darkturquoise module, identified four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM. Low expression of DARS/GDI2/TRUB1 and high expression of P4HA2 had a poor prognosis. Finally, we used tumor genome map (TCGA) data, verified the characteristics of hub genes through Co-expression analysis, Drug sensitivity analysis, TIMER database analysis and GSVA analysis. We downloaded the data of GBM from the TCGA database, the results of co-expression analysis showed that DARS/GDI2/P4HA2/TRUB1 could regulate the development of GBM by affecting genes such as CDC73/CDC123/B4GALT1/CUL2. Drug sensitivity analysis showed that genes are involved in many classic Cancer-related pathways including TSC/mTOR, RAS/MAPK.TIMER database analysis showed DARS expression is positively correlated with tumor purity (cor = 0.125, p = 1.07e-02)), P4HA2 expression is negatively correlated with tumor purity (cor =-0.279, p = 6.06e-09). Finally, GSVA analysis found that DARS/GDI2/P4HA2/TRUB1 gene sets are closely related to the occurrence of cancer.ConclusionWe used two public databases to identify four valuable biomarkers for GBM prognosis, namely DARS/GDI2/P4HA2/TRUB1, which have potential clinical application value and can be used as prognostic markers for GBM.
| S-EPMC8781321 | biostudies-literature
Project description:Osteoarthritis (OA) is a joint disease resulting in high rates of disability and low quality of life. The initial site of OA (bone or cartilage) is uncertain. The aim of the current study was to explore biomarkers and pathological processes in subchondral bone samples. The gene expression profile GSE51588 was downloaded from the Gene Expression Omnibus database. Fifty subchondral bone [knee lateral tibial (LT) and medial tibial (MT)] samples from 40 OA and 10 non-OA subjects were analyzed. After data preprocessing, 5439 genes were obtained for weighted gene co-expression network analysis. Highly correlated genes were divided into 19 modules. The yellow module was found to be highly correlated with OA (r = 0.71, p = 1e-08) and the brown module was most associated with the differences between the LT and MT regions (r = 0.77, p = 1e-10). Gene ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment indicated that the yellow module was enriched in a variety of components including proteinaceous extracellular matrix and collagen trimers, involved in protein digestion and absorption, axon guidance, ECM-receptor interaction, and the PI3K-Akt signaling pathway. In addition, the brown module suggests that the differences between the early (LT) and end (MT) stage of OA are associated with extracellular processes and lipid metabolism. Finally, 45 hub genes in the yellow module (COL24A1, COL5A2, COL3A1, MMP2, COL6A1, etc.) and 72 hub genes in the brown module (LIPE, LPL, LEP, SLC2A4, FABP4, ADH1B, ALDH4A1, ADIPOQ, etc.) were identified. Hub genes were validated using samples from cartilage (GSE57218). In summary, 45 hub genes and 72 hub genes in two modules are associated with OA. These hub genes could provide new biomarkers and drug targets in OA. Further studies focusing on subchondral bone are required to validate these hub genes and better understand the pathological process of OA.
| S-EPMC6433881 | biostudies-literature
Project description:BackgroundChromophobe renal cell carcinoma (ChRCC) is the second common subtype of non-clear cell renal cell carcinoma (nccRCC), which accounting for 4-5% of renal cell carcinoma (RCC). However, there is no effective bio-marker to predict clinical outcomes of this malignant disease. Bioinformatic methods may provide a feasible potential to solve this problem.MethodsIn this study, differentially expressed genes (DEGs) of ChRCC samples on The Cancer Genome Atlas database were filtered out to construct co-expression modules by weighted gene co-expression network analysis and the key module were identified by calculating module-trait correlations. Functional analysis was performed on the key module and candidate hub genes were screened out by co-expression and MCODE analysis. Afterwards, real hub genes were filter out in an independent dataset GSE15641 and validated by survival analysis.ResultsOverall 2215 DEGs were screened out to construct eight co-expression modules. Brown module was identified as the key module for the highest correlations with pathologic stage, neoplasm status and survival status. 29 candidate hub genes were identified. GO and KEGG analysis demonstrated most candidate genes were enriched in mitotic cell cycle. Three real hub genes (SKA1, ERCC6L, GTSE-1) were selected out after mapping candidate genes to GSE15641 and two of them (SKA1, ERCC6L) were significantly related to overall survivals of ChRCC patients.ConclusionsIn summary, our findings identified molecular markers correlated with progression and prognosis of ChRCC, which might provide new implications for improving risk evaluation, therapeutic intervention, and prognosis prediction in ChRCC patients.
| S-EPMC6296159 | biostudies-literature
Project description:PurposeAt present, there is a lack of accurate early diagnostic markers for ischemic stroke.MethodsBy using dimensionality reduction cluster analysis, differential expression analysis, weighted co-expression network analysis, protein-protein interaction network analysis, cell heterogeneity and key pathogenic genes were identified in ischemic stroke. Immunomicroenvironment analysis was used to explore the immune landscape and immune associations of key genes in ischemic stroke. The analysis platform we use is R software (version 4.0.5). PCR experiments were used to verify the expression of key genes.ResultsSingle cell sequencing data in ischemic stroke can be annotated as fibroblast cells, pre-B cell CD34, neutrophils cells, bone marrow (BM), keratinocytes, macrophage, neurons and mesenchymal stem cells (MSC). By the intersection of differential expression analysis and WGCNA analysis, 385 genes were obtained. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that these genes were highly correlated with multiple functions and pathways. Protein-protein interaction network analysis revealed that MRPS11 and MRPS12 were key genes, both of which were down-regulated in ischemic stroke. The Pseudo-time series analysis found that the expression of MRPS12 decreased gradually with the differentiation of pre-B cell CD34 cells in ischemic stroke, suggesting that the downregulation of MRPS12 expression may play an important role in ischemic stroke. At last, PCR showed that MRPS11 and MRPS12 were significantly down-regulated in peripheral blood of patients with ischemic stroke.ConclusionsOur study provides a reference for the study of pathogenesis and key targets of ischemic stroke.
| S-EPMC10373980 | biostudies-literature