Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Liu, Lu; He, Chen; Zhou, Qing; Wang, Ganlu; Lv, Zhiwu; Liu, Jintao

doi:10.1002/jcp.28932

Cited by 54 publications

(37 citation statements)

References 38 publications

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“…knockdown reversed the phenomenon [18]. Recent studies on PTC showed that LPAR5 was associated with progression and overall survival in thyroid cancer, which is consistent with our study [19][20][21]. Tissue factor pathway inhibitor (TFPI), is the endogenous inhibitor of tissue factor induced blood coagulation, and its expression had been demonstrated in smooth muscle cells, monocytes, plate lets and several breast cancer cell lines [22][23][24].…”

Section: Discussionsupporting

confidence: 89%

Integrated bioinformatics analysis and screening hub genes in papillary thyroid cancer

Fan

Dong

et al. 2020

Preprint

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Abstract Background With the increasing incidence, papillary thyroid cancer (PTC) is receiving more and more attention, but the pathogenesis of which is still not completely elucidated. The purpose of this study was to explore key biomarkers and new therapeutic targets in PTC. Methods GEO2R and Venn online software were used for differential gene screening analysis. Hub genes were screened via STRING and Cytoscape, following Gene Ontology and KEGG enrichment analysis. Finally, survival analysis and expression validation were performed via UALCAN online software and immunohistochemistry. Results We screened 334 consistently differentially expressed genes (DEGs), composed of 136 upregulated genes and 198 downregulated genes. Gene ontology enrichment analysis suggested that DEGs mainly enriched in the cancer-related pathways and functions. PPI network visualization was performed to select 17 upregulated and 13 downregulated DEGs. Finally, the expression verification and overall survival analysis conducted in the Gene Expression Profiling Interactive Analysis Tool (GEPIA) and UALCAN showed that LPAR5, TFPI and ENTPD1 were related to the development of PTC and the prognosis of PTC patients, and the expression of LPAR5 was verified by tissue chip. Conclusions In summary, the hub genes and pathways identified in the present study not only provided new biomarkers for PTC, but also will be useful for elucidating the pathogenesis of PTC.

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Section: Discussionsupporting

confidence: 89%

Integrated bioinformatics analysis and screening hub genes in papillary thyroid cancer

Fan

Dong

et al. 2020

Preprint

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“…Shaohua Zhan et al identi ed FN1 as a novel prognostic biomarker associated with sporadic medullary thyroid cancer pathophysiological changes [29]. Furthermore, some studies found FN1 was highly expressed in TC from a series of data sets, but they did not offer a survival analysis of FN1 [30][31][32][33]. In this study, we demonstrated FN1 could be associated with TC occurrence and worse DFS, which may be through the PI3K-Akt signaling pathway and ECM-receptor interaction pathway.…”

Section: Discussionmentioning

confidence: 65%

“…FN1 has also turned out to be associated with the ECM changes [23]. Numerous studies demonstrated that it plays an important role in various cancers, such as oral squamous cell carcinoma [24], nasopharyngeal carcinoma [25], ovarian cancer [26], renal cancer [27], and thyroid cancer [28][29][30][31][32][33]. In the thyroid cancer, Sponziello M et al demonstrated that the overexpression of FN1 mediated thyroid tumor cell migration and invasion by 86 patients [28].…”

Section: Discussionmentioning

confidence: 99%

Identification of Candidate Biomarkers Related to Diagnosis and Prognosis of Thyroid Cancer via Transcriptomic Profile Analysis

Li¹,

Jiang²,

Feng³

et al. 2020

Preprint

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BackgroundThe detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Compared with other cancers, the gene profile plays a more prominent role in the diagnosis and treatment of TC. MethodsFour datasets from Gene Expression Omnibus (GEO) was used to perform transcriptomic profile analysis. The overlapping differentially expressed genes (DEGs) were analyzed by R package “limma” and “RobustRankAggreg”. Then the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from Gene Expression Profiling Interactive Analysis (GEPIA) database, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes.ResultsA total of 80 DEGs (34 upregulated and 46 downregulated) were identified. Then FN1, TIMP1, ITGA2, and KIT were considered hub genes, which had a high degree of connectivity in the PPI network. GEPIA identified that FN1, TIMP1, and ITGA2 were upregulated, and KIT was downregulated. Upregulations of FN1 expression (P=0.024) and ITGA2 expression (P=0.029) and downregulation of KIT expression (P=0.012) increased risks of decreased DFS for patients. IHC showed that the expression of FN1, TIMP1, and ITGA2 protein were upregulated, while the expression of KIT protein was downregulated in the TC clinical specimens. Besides, five hub genes were enriched in the PI3K-Akt signaling pathway and ECM-receptor interaction.ConclusionsIn summary, these hub genes were potential biomarkers of diagnosis and prognosis of TC.

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“…Recently, the robust rank aggregation (RRA) has been speci cally designed to compare several ranked gene lists and identi ed commonly overlapping genes to reduce or get rid of these mistakes [5]. Numerous studies adopted the RRA method to select and lter differentially expressed microRNA or mRNA pro les on multiple datasets [6][7][8]. However, there is a little study on identifying DEGs using the RRA method in HCC, especially on screening prognostic gene signatures.…”

Section: Introductionmentioning

confidence: 99%

Identification and Validation of a Reliable Prognostic Eleven-Genes Signature for Hepatocellular Carcinoma

Liu

Wang

2020

Preprint

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Background: Hepatocellular carcinoma (HCC) is a common malignant tumor with high mortality and mortality. Although advances in early diagnosis, disease management and treatment of HCC, the outcomes remain unsatisfactory. This study aimed to identify the reliable prognostic biomarkers based integrated bioinformatics analysis to predict and improve the survival of HCC patients. Methods: The gene expression or transcriptome profiles and survival of HCC were acquired from the Gene Expression Omnibus database (GEO) and the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened out by the limma or edgeR package in the R software. Univariate, LASSO and multivariate Cox regression analyses were conducted to explore survival-related signature. Subsequently, a prognostic model and nomogram composed of prognostic signature were constructed for assessing overall survival (OS). Kaplan-Meier analysis, receiver operating characteristic (ROC) curve and stratified analysis were performed to confirm the prognostic performance of the prognostic model.Results: Compared with nontumor samples, 451 reliable DEGs were identified using the robust rank aggregation and overlap validation. Eleven survival-related DEGs were selected for the construction of a risk evaluation model, which could efficiently distinguish high-risk patients from low-risk patients and even be feasible in the subgroups of stages and age. Further analyses suggested the positive and independent prognostic performance of the model compared to other clinical characteristics (P< 0.05, ROC > 0.7). Finally, a prognostic nomogram composed of the model was constructed for assessing the overall survival, and Harrell’s concordance index and calibration curves demonstrated its efficient predictive performance. Conclusion: The predictive model and nomogram will contribute directly to further clinical applications in the individualized survival prediction, the improvement of treatment strategies and more accurate management for patients with HCC.

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Identification of key genes and pathways of thyroid cancer by integrated bioinformatics analysis

Cited by 54 publications

References 38 publications

Integrated bioinformatics analysis and screening hub genes in papillary thyroid cancer

Integrated bioinformatics analysis and screening hub genes in papillary thyroid cancer

Identification of Candidate Biomarkers Related to Diagnosis and Prognosis of Thyroid Cancer via Transcriptomic Profile Analysis

Identification and Validation of a Reliable Prognostic Eleven-Genes Signature for Hepatocellular Carcinoma

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