Identification of potential functional genes in papillary thyroid cancer by co-expression network analysis.
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ABSTRACT:
SUBMITTER: Ao ZX
PROVIDER: S-EPMC6144229 | biostudies-literature | 2018 Oct
REPOSITORIES: biostudies-literature
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Project description:BackgroundPapillary thyroid carcinoma (PTC), which is the most common endocrine malignancy, has been steadily increasing worldwide in incidence over the years, while mechanisms underlying the pathogenesis and diagnostic for PTC are incomplete. The purpose of this study is to identify potential biomarkers for diagnosis of PTC, and provide new insights into pathogenesis of PTC.MethodsBased on weighted gene co-expression network analysis, Robust Rank Aggregation, functional annotation, GSEA and DNA methylation, were employed for investigating potential biomarkers for diagnosis of PTC.ResultsBlack and turquoise modules were identified in the gene co-expression network constructed by 1807 DEGs that from 6 eligible gene expression profiles of Gene Expression Omnibus database based on Robust Rank Aggregation and weighted gene co-expression network analysis. Hub genes were significantly down-regulated and the expression levels of the hub genes were different in different stages in hub gene verification. ROC curves indicated all hub genes had good diagnostic value for PTC (except for ABCA6 AUC = 89.5%, the 15 genes with AUC > 90%). Methylation analysis showed that hub gene verification ABCA6, ACACB, RMDN1 and TFPI were identified as differentially methylated genes, and the decreased expression level of these genes may relate to abnormal DNA methylation. Moreover, the expression levels of 8 top hub genes were correlated with tumor purity and tumor-infiltrating immune cells. These findings, including functional annotations and GSEA provide new insights into pathogenesis of PTC.ConclusionsThe hub genes and methylation of hub genes may as potential biomarkers provide new insights for diagnosis of PTC, and all these findings may be the direction to study the mechanisms underlying of PTC in the future.
| S-EPMC7161219 | biostudies-literature
Project description:Background The genes and genetic mechanisms underlying the occurrence and progression of papillary thyroid carcinoma (PTC) are still unknown. This study aimed to find candidate genes related to the pathogenesis and progression of PTC. Methods RNA sequencing (RNA-seq) data of PTC and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) database with clinical stage data to form a test, validation, and clinical-stage data matrix. We used the test data set to analyze differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) to find those gene clusters highly correlated with PTC. We then verified the expression of genes in the interested modules using the validation matrix. The quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the reliability of the expression of selected genes. Five key genes (GDF15, LCN2, KCNN4, SH3BGRL3, and MMP2) were used to analyze the connection between gene expression and the American Joint Committee on Cancer (AJCC) stage. The upregulated and downregulated DEGs, along with the modules of interest, were subjected to Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Results We used WGCNA to find two modules of interest, the yellow module, which was positively associated with PTC, and the blue module, which was negatively correlated with PTC. Four genes (GDF15, LCN2, KCNN4, and SH3BGRL3) from the yellow module were determined to be highly expressed in PTC in the test data matrix and were verified in both the validation data matrix and quantitative real-time PCR, which indicated that these four genes were highly correlated with the occurrence of the PTC. Furthermore, these four genes also had a significantly higher expression in the advanced levels of pathological T, N, and AJCC stage, meaning that they were correlated with the progression of PTC. Genes in the yellow module and upregulated DEGs were significantly enriched in three vital signaling pathways, including focal adhesion, extracellular matrix (ECM)-receptor interaction, and the PI3K-Akt signaling pathway. Conclusions Four candidate genes (GDF15, LCN2, KCNN4, and SH3BGRL3) may be potential biomarkers for the PTC’s pathogenesis and may be useful for predicting the disease stage.
| S-EPMC8798968 | biostudies-literature
Project description:BackgroundThyroid cancer is the most common endocrine malignancy, with a recent global increase of 20% in age-related incidence. Ultrasonography and ultrasonography-guided fine-needle aspiration biopsy (FNAB) are the most widely used diagnostic tests for thyroid nodules; however, it is estimated that up to 25% of thyroid biopsies are cytologically inconclusive. Molecular markers can help guide patient-oriented and targeted treatment of thyroid nodules and thyroid cancer.MethodsDatasets related to papillary thyroid cancer (PTC) or thyroid carcinoma (GSE129562, GSE3678, GSE54958, GSE138042, and GSE124653) were downloaded from the GEO database and analysed using the Limma package of R software. For functional enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes pathway analysis and Gene Ontology were applied to differentially expressed genes (DEGs) using the Metascape website. A protein-protein interaction (PPI) network was built from the STRING database. Gene expression, protein expression, immunohistochemistry, and potential functional gene survival were analysed using the GEPIA website, the Human Protein Atlas website, and the UALCAN website. Potential target miRNAs were predicted using the miRDB and Starbase datasets.ResultsWe found 219 upregulated and 310 downregulated DEGs, with a cut-off of p < 0.01 and ∣log FC | >1.5. The DEGs in papillary thyroid cancer were mainly enriched in extracellular structural organisation. At the intersection of the PPI network and Metascape MCODEs, the hub genes in common were identified as FN1, APOE, CLU, and SDC2. In the targeted regulation network of miRNA-mRNA, the hsa-miR-424-5p was found to synchronously modulate two hub genes. Survival analysis showed that patients with high expression of CLU and APOE had better prognosis.ConclusionsCLU and APOE are involved in the molecular mechanism of papillary thyroid cancer. The hsa-miR-424-5p might have the potential to reverse the processes of papillary thyroid cancer by modulating the hub genes. These are potential targets for the treatment of patients with papillary thyroid cancer.
| S-EPMC7817291 | biostudies-literature
Project description:ObjectiveIn this study, crucial genes and microRNAs (miRNAs) associated with the progression, staging, and prognosis of papillary thyroid cancer (PTC) were identified.MethodsFour PTC datasets, including our own mRNA-sequencing (mRNA-seq) dataset and three public datasets downloaded from Gene Expression Omnibus and The Cancer Genome Atlas, were used to analyze differentially expressed genes (DEGs) and miRNAs (DEMs) between PTC tumor tissues and paired normal tissues (control). Gene ontology (GO) terms and pathways associated with these DEGs were identified, and protein-protein interactions (PPIs) were analyzed. Additionally, an miRNA-mRNA regulatory network was constructed and the functions of DEMs were explored. Finally, miRNAs/mRNAs associated with tumor staging and prognosis were identified. The expression levels of several key genes and miRNAs were validated by qRT-PCR.ResultsNumerous DEGs and DEMs were identified between tumor and control groups in four datasets. The DEGs were significantly enriched in cell adhesion and cancer-related GO terms and pathways. In the constructed PPI network, ITGA2, FN1, ICAM1, TIMP1 and CDH2 were hub proteins. In the miRNA-mRNA negative regulatory networks, miR-204-5p regulated the largest number of target genes, such as TNFRSF12A. miR-146b, miR-204, miR-7-2, and FN1 were associated with tumor stage in PTC, and TNFRSF12A and CLDN1 were related to prognosis.ConclusionsOur results suggested the important roles of ITGA2, FN1, ICAM1, TIMP1 and CDH2 in the progression of PTC. miR-204-5p, miR-7-2, and miR-146b are potential biomarkers for PTC staging and FN1, CLDN1, and TNFRSF12A may serve as markers of prognosis in PTC.
| S-EPMC6230289 | biostudies-literature
Project description:The present study aimed to systematically examine the molecular mechanisms of papillary thyroid cancer (PTC), and identify potential biomarkers and drugs for the treatment of PTC. Two microarray data sets (GSE3467 and GSE3678), containing 16 PTC samples and 16 paired normal samples, were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified using the Linear Models for Microarray Analysis package. Subsequently, the common DEGs were screened for functional and pathway enrichment analysis using the Database for Annotation Visualization and Integrated Discovery. The representative interaction subnetwork was further derived using Molecular Complex Detection software. In addition, the potential drugs for the hub DEGs in the subnetwork were screened from DrugBank and the potential drug?like ligands, which interacted with genes, were selected using MTiOpenScreen. A total of 167 common DEGs, including 77 upregulated and 90 downregulated DEGs, were screened. The common DEGs were associated with the functions of plasma membrane, extracellular matrix, response to steroid hormone stimulus and cell adhesion, and the pathways of tyrosine metabolism and cell adhesion molecules were significantly enriched. A total of eight common DEGs (MET, SERPINA1, LGALS3, FN1, TNFRSF11B, LAMB3 and COL13A1) were involved in the subnetwork. The two drugs, lanoteplase and ocriplasmin, and four drugs, ??mercaptoethanol, recombinant ? 1?antitrypsin, PPL?100 and API, were found for FN1 and SERPINA1, respectively. The common DEGs identified may be potential biomarkers for PCT. FN1 and SERPINA1 may be involved in PTC by regulating epithelial?to?mesenchymal transition and responding to steroid hormone stimuli, respectively. Ocriplasmin, ??mercaptoethanol and recombinant ? 1?antitrypsin may be potential drugs for the treatment of PTC.
| S-EPMC5355717 | biostudies-literature
Project description:Background: Differentiated thyroid cancer (DTC) is the most common type of thyroid cancer. Many of them can relapse to dedifferentiated thyroid cancer (DDTC) and exhibit different gene expression profiles. The underlying mechanism of dedifferentiation and the involved genes or pathways remained to be investigated. Methods: A discovery cohort obtained from patients who received surgical resection in the Fudan University Shanghai Cancer Center (FUSCC) and two validation cohorts derived from Gene Expression Omnibus (GEO) database were used to screen out differentially expressed genes in the dedifferentiation process. Weighted gene co-expression network analysis (WGCNA) was constructed to identify modules highly related to differentiation. Gene Set Enrichment Analysis (GSEA) was used to identify pathways related to differentiation, and all differentially expressed genes were grouped by function based on the GSEA and literature reviewing data. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to control the number of variables in each group. Next, we used logistic regression to build a gene signature in each group to indicate differentiation status, and we computed receiver operating characteristic (ROC) curve to evaluate the indicative performance of each signature. Results: A total of 307 upregulated and 313 downregulated genes in poorly differentiated thyroid cancer (PDTC) compared with papillary thyroid cancer (PTC) and normal thyroid (NT) were screened out in FUSCC cohort and validated in two GEO cohorts. WGCNA of 620 differential genes yielded the seven core genes with the highest correlation with thyroid differentiation score (TDS). Furthermore, 395 genes significantly correlated with TDS in univariate logistic regression analysis were divided into 11 groups. The areas under the ROC curve (AUCs) of the gene signature of group transcription and epigenetic modification, signal and substance transport, extracellular matrix (ECM), and metabolism in the training set [The Cancer Genome Atlas (TCGA) cohort] and validation set (combined GEO cohort) were both >0.75. The gene signature based on group transcription and epigenetic modification, cilia formation and movement, and proliferation can reflect the patient's disease recurrence state. Conclusion: The dedifferentiation of DTC is affected by a variety of mechanisms including many genes. The gene signature of group transcription and epigenetic modification, signal and substance transport, ECM, and metabolism can be used as biomarkers for DDTC.
| S-EPMC8113627 | biostudies-literature
Project description:BackgroundPapillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analyse the gene expression profiles of the disease and established a new model for the correlation.MethodsThe thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 types of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated.ResultsWe identify two subtypes in PTC based on the expression of ferroptosis related genes, with the proportion of cluster 1 significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. The mutations of Braf and Nras are detected as the major mutations of cluster 1 and 2, respectively. Subsequent analyses of TME immune cells infiltration indicated cluster 1 is remarkably richer than cluster 2. The risk score of THCA is in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data shows that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer.ConclusionsOur study finds seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we construct the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.
| S-EPMC8314640 | biostudies-literature
Project description:Papillary thyroid carcinoma (PTC) variants exhibit different prognosis, but critical characteristics of PTC variants that contribute to differences in pathogenesis are not well-known. This study aims to characterize dysregulated immune-associated and cancer-associated genes in three PTC subtypes to explore how the interplay between cancer and immune processes causes differential prognosis. RNA-sequencing data from The Cancer Genome Atlas (TCGA) were used to identify dysregulated genes in each variant. The dysregulation profiles of the subtypes were compared using functional pathways clustering and correlations to relevant clinical variables, genomic alterations, and microRNA regulation. We discovered that the dysregulation profiles of classical PTC (CPTC) and the tall cell variant (TCPTC) are similar and are distinct from that of the follicular variant (FVPTC). However, unique cancer or immune-associated genes are associated with clinical variables for each subtype. Cancer-related genes MUC1, FN1, and S100-family members were the most clinically relevant in CPTC, while APLN and IL16, both immune-related, were clinically relevant in FVPTC. RAET-family members, also immune-related, were clinically relevant in TCPTC. Collectively, our data suggest that dysregulation of both cancer and immune associated genes defines the gene expression landscapes of PTC variants, but different cancer or immune related genes may drive the phenotype of each variant.
| S-EPMC6721495 | biostudies-literature
Project description:ObjectiveTo investigate the differential expression gene modules and hub genes associated with Alzheimer's disease (AD) by weighted gene co-expression network analysis (WGCNA) and annotate the biological functions of these modules.MethodsWe downloaded transcriptome sequencing data from the GEO database, and according to the correlation of the genes, a gene co-expression network was constructed with the parameter setting of β=8 and a correlation coefficient threshold of 0.85. Pearson correlation test was used to calculate the correlation between the module genes and clinical traits to screen the gene modules significantly associated with AD and identify the hub genes according to the connectivity within modules. GO functional enrichment analysis and KEGG pathway analysis were used to annotate the functions of the modules. A cell model of AD was established in SH-SY5Y cells by Aβ1-42 treatment, and the mRNA expression levels of the hub genes were compared between the Aβ1-42-treated cells and the control cells.ResultsTen gene co-expression modules were constructed based on the correlations of gene expression, in which the brown (r=0.66, P < 0.001) and turquoise modules (r=-0.68, P < 0.001) were significantly correlated with the AD group. Forty-eight genes were identified as the hub genes in the co-expression network. Function annotation revealed that the genes in both modules were mainly enriched in DNA damage and repair pathways and metabolism-related pathways. Differential expression analysis of the genes revealed that the genes DNASE1, TEKT2 and MTSS1L were highly expressed while ACP2, LANCL2 and GMPR2 were lowly expressed in AD group. The results of cell experiment confirmed the up-regulation of DNASE1, TEKT2 and MTSS1L genes and the down-regulation of ACP2, LANCL2, and GMPR2 in Aβ1-42-treated SH-SY5Y cells (P < 0.01).ConclusionThe brown and turquoise modules are closely correlated with AD. The hub genes including MTSS1L, GMPR2, ACP2, ACTG1 and LANCL2 selected from the modules may participate in AD pathogenesis by regulating DNA damage and repair.
| S-EPMC8752417 | biostudies-literature
Project description:BACKGROUND: Thyroid carcinoma is the most common endocrine malignancy and a common cancer among the malignancies of head and neck. Noninvasive and convenient biomarkers for diagnosis of papillary thyroid carcinoma (PTC) as early as possible remain an urgent need. The aim of this study was to discover and identify potential protein biomarkers for PTC specifically. METHODS: Two hundred and twenty four (224) serum samples with 108 PTC and 116 controls were randomly divided into a training set and a blind testing set. Serum proteomic profiles were analyzed using SELDI-TOF-MS. Candidate biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays. RESULTS: A total of 3 peaks (m/z with 9190, 6631 and 8697 Da) were screened out by support vector machine (SVM) to construct the classification model with high discriminatory power in the training set. The sensitivity and specificity of the model were 95.15% and 93.97% respectively in the blind testing set. The candidate biomarker with m/z of 9190 Da was found to be up-regulated in PTC patients, and was identified as haptoglobin alpha-1 chain. Another two candidate biomarkers (6631, 8697 Da) were found down-regulated in PTC and identified as apolipoprotein C-I and apolipoprotein C-III, respectively. In addition, the level of haptoglobin alpha-1 chain (9190 Da) progressively increased with the clinical stage I, II, III and IV, and the expression of apolipoprotein C-I and apolipoprotein C-III (6631, 8697 Da) gradually decreased in higher stages. CONCLUSION: We have identified a set of biomarkers that could discriminate PTC from non-cancer controls. An efficient strategy, including SELDI-TOF-MS analysis, HPLC purification, MALDI-TOF-MS trace and LC-MS/MS identification, has been proved successful.
| S-EPMC2761863 | biostudies-literature