Expression of the Topoisomerase II Alpha (TOP2A) Gene in Lung Adenocarcinoma Cells and the Association with Patient Outcomes

Du, Xiaomei; Xue, Zhi-Wen; Lv, Jianning; Wang, Heidou

doi:10.12659/msm.929120

Cited by 21 publications

(19 citation statements)

References 31 publications

(40 reference statements)

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“…It participates in several processes such as chromatid separation, chromosome condensation, and the relief of torsional stress that occurs during DNA replication and transcription. Aberrant expression of TOP2A has been identified in many tumors, including lung adenocarcinoma, hepatocellular carcinoma, adrenocortical carcinoma, neuroblastic tumors, prostate cancer, and breast cancer ( de Resende et al, 2013 ; Jain et al, 2013 ; Moretti et al, 2013 ; Chen et al, 2016 ; Du et al, 2020 ; Zeng et al, 2020 ). TOP2A is overexpressed in ccRCC, and its overexpression promoted the proliferation and migration of ccRCC cells ( Yuan et al, 2018 ; Luo et al, 2019 ; Zhang et al, 2019b ).…”

Section: Discussionmentioning

confidence: 99%

Identification of UBE2I as a Novel Biomarker in ccRCC Based on a Large-Scale CRISPR-Cas9 Screening Database and Immunohistochemistry

Lai

You

et al. 2022

Front. Mol. Biosci.

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Background: The genome-wide CRISPR-cas9 dropout screening has emerged as an outstanding approach for characterization of driver genes of tumor growth. The present study aims to investigate core genes related to clear cell renal cell carcinoma (ccRCC) cell viability by analyzing the CRISPR-cas9 screening database DepMap, which may provide a novel target in ccRCC therapy.Methods: Candidate genes related to ccRCC cell viability by CRISPR-cas9 screening from DepMap and genes differentially expressed between ccRCC tissues and normal tissues from TCGA were overlapped. Weighted gene coexpression network analysis, pathway enrichment analysis, and protein–protein interaction network analysis were applied for the overlapped genes. The least absolute shrinkage and selection operator (LASSO) regression was used to construct a signature to predict the overall survival (OS) of ccRCC patients and validated in the International Cancer Genome Consortium (ICGC) and E-MTAB-1980 database. Core protein expression was determined using immunohistochemistry in 40 cases of ccRCC patients.Results: A total of 485 essential genes in the DepMap database were identified and overlapped with differentially expressed genes in the TCGA database, which were enriched in the cell cycle pathway. A total of four genes, including UBE2I, NCAPG, NUP93, and TOP2A, were included in the gene signature based on LASSO regression. The high-risk score of ccRCC patients showed worse OS compared with these low-risk patients in the ICGC and E-MTAB-1980 validation cohort. UBE2I was screened out as a key gene. The immunohistochemistry indicated UBE2I protein was highly expressed in ccRCC tissues, and a high-level nuclear translocation of UBE2I occurs in ccRCC. Based on the area under the curve (AUC) values, nuclear UBE2I had the best diagnostic power (AUC = 1). Meanwhile, the knockdown of UBE2I can inhibit the proliferation of ccRCC cells.Conclusion: UBE2I, identified by CRISPR-cas9 screening, was a core gene-regulating ccRCC cell viability, which accumulated in the nucleus and acted as a potential novel promising diagnostic biomarker for ccRCC patients. Blocking the nuclear translocation of UBE2I may have potential therapeutic value with ccRCC patients.

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Section: Discussionmentioning

confidence: 99%

Identification of UBE2I as a Novel Biomarker in ccRCC Based on a Large-Scale CRISPR-Cas9 Screening Database and Immunohistochemistry

Lai

You

et al. 2022

Front. Mol. Biosci.

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“…TOP2A , a cycle-dependent protein, is involved in a variety of cell biological processes, such as DNA replication, chromatin condensation, chromosome separation, and chromosome structure maintenance ( Chen et al, 2015 ). TOP2A may be a prognostic biomarker and potential therapeutic target for patients with LUAD ( Du et al, 2020 ). We found that the expression levels of UBE2C, CHEK1 , and BIRC5 in tumor samples were not significantly different from adjacent normal samples, which may be due to the small sample size.…”

Section: Discussionmentioning

confidence: 99%

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

Liu

Liao

et al. 2022

PeerJ

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Background Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis. Methods In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues. Results A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues. Conclusions The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

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“…The enzyme maintains the topological state of chromosomes during DNA replication and transcription, catalyzes the instantaneous break and reconnection of two double-helical DNA strands, thereby changing DNA and topological structure, and controlling cell cycle progression. TOP2A is abnormally expressed in various tumors [24][25][26] and is used in cancer treatment [27][28][29][30] . Double-strand break (DSB) is the most common type of genetic toxicity of DNA damage, and a single unrepaired DSB can trigger cell apoptosis.…”

Section: Discussionmentioning

confidence: 99%

Identification of Biomarkers Related To The Diagnosis And Prognosis of Thyroid Cancer Through Bioinformatics Analysis

Xie

Yin

Pan

et al. 2021

Preprint

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Background: Thyroid cancer is the most common malignant tumor of the head and neck. In recent years, the incidence of thyroid cancer (THCA) worldwide has rapidly increased and shows a trend in the younger generation. This study attempted to screen key genes and potential prognostic biomarkers for thyroid cancer using bioinformatics analysis.Methods: This study attempted to screen key genes and potential prognostic biomarkers for thyroid cancer using bioinformatics analysis. 101 cases of thyroid cancer and 78 cases of normal thyroid tissue were collected from three Gene Expression Omnibus (GEO) databases, then we identified the differentially expressed genes (DEGs) and conducted downstream analyses. Moreover, we screened hub genes by constructing a protein‐protein interaction (PPI) network. Finally, we assessed the expression level of hub genes in thyroid cancer tissue and its normal tissue using GEPIA and qRT-PCR respectively. Results: 159 upregulated and 251 downregulated genes were determined after gene integration of these three GEO data sets. Through PPI analysis, we consider the top 20 DEGs with high connectivity as the hub genes of THCA. After that, this study verified 20 central genes through the GEPIA database and found that only four hub genes (TOP2A, FN1, TIMP1, and MMP9) had significantly higher expression levels in thyroid cancer tissues than in normal thyroid tissues. We further analyzed the correlation between these four hub genes and the prognosis of patients with thyroid cancer, which suggests that FN1, MMP9, TIMP1 help assess the prognosis of patients with thyroid cancer. We performed GSEA analysis on these 4 hub genes simultaneously, found that the high expression of these 4 hub genes enriched the "cell cycle." Subsequently, we collected thyroid cancer tissue specimens, verified these four hub gene expression levels by RT-PCR, and found that only FN1 and TIMP1 genes in thyroid cancer tissues had significantly higher mRNA levels than normal tissues. Conclusions: Our research has identified 20 hub genes that may be related to the occurrence and development of thyroid cancer through multiple gene expression profile data sets and a series of comprehensive bioinformatics analyses. Further database and tissue validation analysis revealed that only 2 hub genes may be considered as potential prognostic biomarkers, including FN1 and TIMP1. In addition, these two hub genes are involved in the cell cycle, suggesting that they may play a role in the occurrence and development of thyroid cancer.

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Expression of the Topoisomerase II Alpha (TOP2A) Gene in Lung Adenocarcinoma Cells and the Association with Patient Outcomes

Cited by 21 publications

References 31 publications

Identification of UBE2I as a Novel Biomarker in ccRCC Based on a Large-Scale CRISPR-Cas9 Screening Database and Immunohistochemistry

Identification of UBE2I as a Novel Biomarker in ccRCC Based on a Large-Scale CRISPR-Cas9 Screening Database and Immunohistochemistry

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

Identification of Biomarkers Related To The Diagnosis And Prognosis of Thyroid Cancer Through Bioinformatics Analysis

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