Dataset Information

Identification of differentially expressed genes and signaling pathways in papillary thyroid cancer: a study based on integrated microarray and bioinformatics analysis.

ABSTRACT:

Background

The techniques of DNA microarray and bioinformatic analysis have exhibited efficiency in identifying dysregulated gene expression in human cancers. In this study, we used integrated bioinformatics analysis to improve our understanding of the pathogenesis of papillary thyroid cancer (PTC).

Methods

In this study, we integrated four Gene Expression Omnibus (GEO) datasets, GSE33630, GSE35570, GSE60542 and GSE29265, including 136 normal samples and 157 PTC specimens. The contents of the four datasets are based on GPL570, an Affymetrix Human Genome U133 Plus 2.0 array. Gene ontology (GO) analysis was used to identify characteristic the biological attributes of differentially expressed genes (DEGs) between PTC and normal samples. GO annotation was performed on the DEGs obtained, and the process relied on the DAVID online tool. Kyoto Encyclopedia of Genes and Genomes (KEGG) approach enrichment analyses were adopted to obtain the basic functions of the DEGs. The KOBAS online analysis database was used to complete DEG KEGG pathway comparison and analysis. The search tool (STRING) database was mainly used to search for interacting genes and complete the construction of protein-protein interaction (PPI) networks.

Results

Five hundred-ninety DEGs were consistently expressed in the four datasets; 327 of them were upregulated, while 263 were downregulated. Ten DEGs, including five upregulated (ENTPD1, THRSP, KLK10, ADAMTS9, MIR31HG) and five downregulated (SCARA5, EPHB1, CHRDL1, LOC440934, FOXP2) genes, were randomly selected for q-PCR in our own tissue samples to validate the integrated data. The most highly enriched GO terms were extracellular exosome (GO:0070062), cell adhesion (GO:0070062), positive regulation of gene expression (GO:0010628), and extracellular matrix (ECM) organization (GO:0030198). KEGG pathway analysis was performed, and it was found that abnormally expressed genes effectively participated in pathways such as tyrosine metabolism, complement and coagulation cascades, cell adhesion molecules (CAMs), transcriptional misregulation and ECM-receptor interaction pathways.

Conclusions

Five hundred-ninety DEGs were identified in PTC by integrated microarray analysis. The GO and KEGG analyses presented here suggest that the DEGs were enriched in extracellular exosome, tyrosine metabolism, CAMs, complement and coagulation cascades, transcriptional misregulation and ECM-receptor interaction pathways. Functional studies of PTC should focus on these pathways.

SUBMITTER: Sun T

PROVIDER: S-EPMC7944059 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of differentially expressed genes and signaling pathways in papillary thyroid cancer: a study based on integrated microarray and bioinformatics analysis.

Sun Tuanqi T Guan Qing Q Wang Yunjun Y Qian Kai K Sun Wenyu W Ji Qinghai Q Wu Yi Y Guo Kai K Xiang Jun J

Gland surgery 20210201 2

<h4>Background</h4>The techniques of DNA microarray and bioinformatic analysis have exhibited efficiency in identifying dysregulated gene expression in human cancers. In this study, we used integrated bioinformatics analysis to improve our understanding of the pathogenesis of papillary thyroid cancer (PTC).<h4>Methods</h4>In this study, we integrated four Gene Expression Omnibus (GEO) datasets, GSE33630, GSE35570, GSE60542 and GSE29265, including 136 normal samples and 157 PTC specimens. The con ...[more]

PMID: 33708546

Similar Datasets

Project description:Background: Gastric cancer (GC) has a high mortality rate in cancer-related deaths worldwide. Currently, the pathogenesis of gastric cancer progression remains unclear. Here, we identified several vital candidate genes related to gastric cancer development and revealed the potential pathogenic mechanisms using integrated bioinformatics analysis. Methods: Two microarray datasets from Gene Expression Omnibus (GEO) database integrated. Limma package was used to analyze differentially expressed genes (DEGs) between GC and matched normal specimens. DAVID was utilized to conduct Gene ontology (GO) and KEGG enrichment analysis. The relative expression of OLFM4, IGF2BP3, CLDN1 and MMP1were analyzed based on TCGA database provided by UALCAN. Western blot and quantitative real time PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines, respectively. Results: We downloaded the expression profiles of GSE103236 and GSE118897 from the Gene Expression Omnibus (GEO) database. Two integrated microarray datasets were used to obtain differentially expressed genes (DEGs), and bioinformatics methods were used for in-depth analysis. After gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments analysis, we identified 61 DEGs in common, of which the expression of 34 genes were elevated and 27 genes were decreased. GO analysis displayed that the biological functions of DEGs mainly focused on negative regulation of growth, fatty acid binding, cellular response to zinc ion and calcium-independent cell-cell adhesion. KEGG pathway analysis demonstrated that these DEGs mainly related to the Wnt and tumor signaling pathway. Interestingly, we found 4 genes were most significantly upregulated in the DEGs, which were OLFM4, IGF2BP3, CLDN1 and MMP1. Then, we confirmed the upregulation of these genes in STAD based on sample types. In the final, western blot and qRT-PCR assay were performed to determine the protein and mRNA levels of OLFM4, IGF2BP3, CLDN1 and MMP1 in GC tissues and cell lines. Conclusion: In our study, using integrated bioinformatics to screen DEGs in gastric cancer could benefit us for understanding the pathogenic mechanism underlying gastric cancer progression. Meanwhile, we also identified four significantly upregulated genes in DEGs from both two datasets, which might be used as the biomarkers for early diagnosis and prevention of gastric cancer.

Project description:Background:The mortality rate associated with ovarian cancer ranks the highest among gynecological malignancies. However, the cause and underlying molecular events of ovarian cancer are not clear. Here, we applied integrated bioinformatics to identify key pathogenic genes involved in ovarian cancer and reveal potential molecular mechanisms. Results:The expression profiles of GDS3592, GSE54388, and GSE66957 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 115 samples, including 85 cases of ovarian cancer samples and 30 cases of normal ovarian samples. The three microarray datasets were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichments of DEGs were performed by DAVID and KOBAS online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed from the STRING database. A total of 190 DEGs were identified in the three GEO datasets, of which 99 genes were upregulated and 91 genes were downregulated. GO analysis showed that the biological functions of DEGs focused primarily on regulating cell proliferation, adhesion, and differentiation and intracellular signal cascades. The main cellular components include cell membranes, exosomes, the cytoskeleton, and the extracellular matrix. The molecular functions include growth factor activity, protein kinase regulation, DNA binding, and oxygen transport activity. KEGG pathway analysis showed that these DEGs were mainly involved in the Wnt signaling pathway, amino acid metabolism, and the tumor signaling pathway. The 17 most closely related genes among DEGs were identified from the PPI network. Conclusion:This study indicates that screening for DEGs and pathways in ovarian cancer using integrated bioinformatics analyses could help us understand the molecular mechanism underlying the development of ovarian cancer, be of clinical significance for the early diagnosis and prevention of ovarian cancer, and provide effective targets for the treatment of ovarian cancer.

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.

Dataset Information

Identification of differentially expressed genes and signaling pathways in papillary thyroid cancer: a study based on integrated microarray and bioinformatics analysis.

Background

Methods

Results

Conclusions

Publications

Identification of differentially expressed genes and signaling pathways in papillary thyroid cancer: a study based on integrated microarray and bioinformatics analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets