Meta-analysis of microarray datasets identify several chromosome segregation-related cancer/testis genes potentially contributing to anaplastic thyroid carcinoma

Mu, Lan; Qiu, Yulu; Jin, Tong; Yin, Zhou; Mao, Zhiyuan; Zhang, Yongjie

doi:10.7717/peerj.5822

Cited by 13 publications

(10 citation statements)

References 71 publications

(96 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…High KIF2C expression in male patients with esophageal squamous cell carcinoma is associated with worse overall survival, and even with the same pathological TNM stage, patients with high KIF2C expression had a worse outcome (43). Similar results have indicated that KIF2C promotes breast cancer, prostate cancer, and thyroid carcinoma (44)(45)(46). In our study, KIF2C was also upregulated in HCC tissues and was associated with a poor outcome.…”

Section: Discussionsupporting

confidence: 81%

Down regulated oncogene KIF2C inhibits growth, invasion, and metastasis of hepatocellular carcinoma through the Ras/MAPK signaling pathway and epithelial-to-mesenchymal transition

Mo¹,

Fang²,

Zhao³

et al. 2022

Ann Transl Med

View full text Add to dashboard Cite

Background: Hepatocellular carcinoma (HCC) is the leading cause of cancer death. Kinesin family member 2C (KIF2C) has been shown as oncogene in a variety of tumors. However, it's role in HCC remains unclear.Methods: In this study, the expression level of KIF2C in HCC was detected by immunohistochemical staining and RT-PCR, and verified by Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Oncomine database. A curve was established to evaluate the diagnostic efficiency of KIF2C. The effect of KIF2C on HCC was investigated by flow cytometry, Cell Counting Kit-8, Transwell, and the wound-healing assay. We explored the underlying mechanism through epithelial-to-mesenchymal transition (EMT) and transcriptome sequences analysis.Results: KIF2C was overexpression in HCC tissue and related to neoplasm histologic grade (P<0.001), pathology stage (P=0.001), and a dismal prognosis (overall, recurrence-free, and disease-free survival). The diagnostic efficacy of KIF2C was >90% in diagnosing HCC. The HCC cell function experiments showed that KIF2C promoted HCC cell proliferation, migration, invasion, and an accelerated cell cycle, and inhibited apoptosis. Based on western blot analysis and RT-PCR, we found that KIF2C promoted HCC invasion and metastasis through activation of the EMT. Based on transcriptome sequences, we showed that KIF2C promoted HCC through the Ras/MAPK and PI3K/Akt signaling pathway.Conclusions: KIF2C was found to promote the progression of HCC and is anticipated to serve as a biomarker for HCC diagnosis, prognosis, and targeted therapy.

show abstract

Section: Discussionsupporting

confidence: 81%

Down regulated oncogene KIF2C inhibits growth, invasion, and metastasis of hepatocellular carcinoma through the Ras/MAPK signaling pathway and epithelial-to-mesenchymal transition

Mo¹,

Fang²,

Zhao³

et al. 2022

Ann Transl Med

View full text Add to dashboard Cite

show abstract

“…The results showed that high levels of ANLN, CENPF, KIF2C, TPX2, and NDC80 indicated poor prognosis in ATC, suggesting that intervention of these genes might be a promising strategy for ATC treatment. Liu's meta-analysis of microarray datasets identified TRIP13, DLGAP5, HJURP, CDKN3, NEK2, KIF15, TTK, KIF2C, AURKA and TPX2 as cell cycle-related key genes potentially contributing to anaplastic thyroid carcinoma, 34 which were in consistence with some of our results (KIF2C, TPX2).…”

Section: Discussionsupporting

confidence: 90%

<p>Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis</p>

Wang

Guo

et al. 2020

CMAR

View full text Add to dashboard Cite

The conventional interventions of anaplastic thyroid carcinoma (ATC) patients are mainly through surgery, chemotherapy, and radiotherapy; however, it is hardly to improve survival rate. We aimed to investigate the differential expressed genes (DEGs) between ATC and normal thyroid gland through bioinformatics analysis of the microarray datasets and find new potential therapeutic targets for ATC. Methods: Microarray datasets GSE9115, GSE29265, GSE33630, GSE53072, and GSE65144 were downloaded from Gene Expression Omnibus (GEO) database. Compared with the normal tissue, GEO2R was conducted to screen the DEGs in each chip under the condition of |log FC| > l, adjusted P-values (adj. P) < 0.05. The Retrieval of Interacting Genes (STRING) database was used to calculate PPI networks of DEGs with a combined score >0.4 as the cutoff criteria. The hub genes in the PPI network were visualized and selected according to screening conditions in Cytoscape software. In addition, the novel genes in ATC were screened for survival analysis using Kaplan-Meier plotter from those hub genes and validated by RT-qPCR. Results: A total of 284 overlapping DEGs were obtained, including 121 upregulated and 161 downregulated DEGs. A total of 232 DEGs were selected by STRING database. The 50 hub genes in the PPI network were chosen according to three screening conditions. In addition, the Kaplan-Meier plotter database confirmed that high expressions of ANLN, CENPF, KIF2C, TPX2, and NDC80 were negatively correlated with poor overall survival of ATC patients. Finally, RT-qPCR experiments showed that KIF2C and CENPF were significantly upregulated in ARO cells and CAL-62 cells when compared to Nthy-ori 3-1 cells, TPX2 was upregulated only in CAL-62 cells, while ANLN and NDC80 were obviously decreased in ARO cells and CAL-62 cells. Conclusion: Our study suggested that CENPF, KIF2C, and TPX2 might play a significant role in the development of ATC, which could be further explored as potential biomarkers for the treatment of ATC.

show abstract

“…By constructing a gene co-expression network and identifying related gene clusters, the correlation between gene modules and phenotypes can be calculated based on phenotypic information, and the most relevant gene modules can be found. Numerous potential biomarkers have been identified to date based on WGCNA for sequencing data [14][15][16]. For example, Tang et al identified five genes as prognostic biomarkers for breast cancer, and Qiu et al revealed several genes associated with the development of breast cancer for further basic and clinical research [14,17].…”

Section: Introductionmentioning

confidence: 99%

Identification of five hub genes as monitoring biomarkers for breast cancer metastasis in silico

et al. 2019

View full text Add to dashboard Cite

Background: Breast cancer is one of the most common endocrine cancers among females worldwide. Distant metastasis of breast cancer is causing an increasing number of breast cancer-related deaths. However, the potential mechanisms of metastasis and candidate biomarkers remain to be further explored. Results: The gene expression profiles of GSE102484 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to screen for the most potent gene modules associated with the metastatic risk of breast cancer, and a total of 12 modules were identified based on the analysis. In the most significant module (R 2 = 0.68), 21 network hub genes (MM > 0.90) were retained for further analyses. Next, protein-protein interaction (PPI) networks were used to further explore the biomarkers with the most interactions in gene modules. According to the PPI networks, five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) were identified as key genes associated with breast cancer progression. Furthermore, the prognostic value and differential expression of these genes were validated based on data from The Cancer Genome Atlas (TCGA) and Kaplan-Meier (KM) Plotter. Receiver operating characteristic (ROC) curve analysis revealed that the mRNA expression levels of these five hub genes showed excellent diagnostic value for breast cancer and adjacent tissues. Moreover, these five hub genes were significantly associated with worse distant metastasis-free survival (DMFS) in the patient cohort based on KM Plotter. Conclusion: Five hub genes (TPX2, KIF2C, CDCA8, BUB1B, and CCNA2) associated with the risk of distant metastasis were extracted for further research, which might be used as biomarkers to predict distant metastasis of breast cancer.

show abstract

Meta-analysis of microarray datasets identify several chromosome segregation-related cancer/testis genes potentially contributing to anaplastic thyroid carcinoma

Cited by 13 publications

References 71 publications

Down regulated oncogene KIF2C inhibits growth, invasion, and metastasis of hepatocellular carcinoma through the Ras/MAPK signaling pathway and epithelial-to-mesenchymal transition

Down regulated oncogene KIF2C inhibits growth, invasion, and metastasis of hepatocellular carcinoma through the Ras/MAPK signaling pathway and epithelial-to-mesenchymal transition

<p>Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis</p>

Identification of five hub genes as monitoring biomarkers for breast cancer metastasis in silico

Contact Info

Product

Resources

About