Identification of KHSRP-Regulated RNAs in Esophageal Cancer by Integrated Bioinformatics Analysis

Chen, Ling; Zhao, Tiejun

doi:10.1089/cbr.2020.3745

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Through the ROC and survival analysis, we screened the top 5 core genes with better diagnostic efficiency and higher prognostic value for EOC. We further analyzed whether 5 core genes' expression is related to the level of different immune checkpoint proteins through the Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/detail.php) [12]. Then, the types of immune checkpoint proteins that are positively correlated with gene expression were selected and shown in picture.…”

Section: Pearson Correlation Analysis Between 5 Core Genes' Expressiomentioning

confidence: 99%

Identification of potential markers for differentiating epithelial ovarian cancer from ovarian low malignant potential tumors through integrated bioinformatics analysis

Hao

Zhao

et al. 2021

J Ovarian Res

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Background Epithelial ovarian cancer (EOC), as a lethal malignancy in women, is often diagnosed as advanced stages. In contrast, intermediating between benign and malignant tumors, ovarian low malignant potential (LMP) tumors show a good prognosis. However, the differential diagnosis of the two diseases is not ideal, resulting in delays or unnecessary therapies. Therefore, unveiling the molecular differences between LMP and EOC may contribute to differential diagnosis and novel therapeutic and preventive policies development for EOC. Methods In this study, three microarray data (GSE9899, GSE57477 and GSE27651) were used to explore the differentially expressed genes (DEGs) between LMP and EOC samples. Then, 5 genes were screened by protein–protein interaction (PPI) network, receiver operating characteristic (ROC), survival and Pearson correlation analysis. Meanwhile, chemical-core gene network construction was performed to identify the potential drugs or risk factors for EOC based on 5 core genes. Finally, we also identified the potential function of the 5 genes for EOC through pathway analysis. Results Two hundred thirty-four DEGs were successfully screened, including 81 up-regulated genes and 153 down-regulated genes. Then, 5 core genes (CCNB1, KIF20A, ASPM, AURKA, and KIF23) were identified through PPI network analysis, ROC analysis, survival and Pearson correlation analysis, which show better diagnostic efficiency and higher prognostic value for EOC. Furthermore, NetworkAnalyst was used to identify top 15 chemicals that link with the 5 core genes. Among them, 11 chemicals were potential drugs and 4 chemicals were risk factors for EOC. Finally, we found that all 5 core genes mainly regulate EOC development via the cell cycle pathway by the bioinformatic analysis. Conclusion Based on an integrated bioinformatic analysis, we identified potential biomarkers, risk factors and drugs for EOC, which may help to provide new ideas for EOC diagnosis, condition appraisal, prevention and treatment in future.

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Section: Pearson Correlation Analysis Between 5 Core Genes' Expressiomentioning

confidence: 99%

Identification of potential markers for differentiating epithelial ovarian cancer from ovarian low malignant potential tumors through integrated bioinformatics analysis

Hao

Zhao

et al. 2021

J Ovarian Res

View full text Add to dashboard Cite

show abstract

“…Through the ROC and survival analysis, we screened the top 5 core genes with better diagnostic e ciency and higher prognostic value for EOC. We further analyzed whether 5 core genes' expression is related to the level of different immune checkpoint proteins through the Gene Expression Pro ling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/detail.php) ) [20]. Then, the types of immune checkpoint proteins that are positively correlated with gene expression were selected and shown in picture.…”

Section: Pearson Correlation Analysis Between 5 Core Genes' Expression and The Level Of Different Immune Checkpoint Proteinsmentioning

confidence: 99%

Identification of Potential Markers for Differentiating Epithelial Ovarian Cancer from Ovarian Low Malignant Potential Tumors through Integrated Bioinformatics Analysis

Hao

Zhao

et al. 2020

Preprint

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Background: Epithelial ovarian cancer (EOC) is one of the most deadly female malignancies and is often diagnosed in advanced stages. In contrast, ovarian low malignant potential (LMP) tumors with favorable prognosis are intermediate between benign and malignant tumors. However, the current accuracy in distinguishing these diseases is unsatisfactory, leading to delays or unnecessary treatments. Therefore, unveiling the molecular differences between LMP and EOC and identifying useful molecular markers may increase the accuracy of diagnosis and also provide a rational basis for the development of new therapeutic and preventive strategies for EOC. Methods: In this study, three microarray data (GSE9899, GSE57477 and GSE27651) were integrated to explore the differentially expressed genes (DEGs) between LMP and EOC samples. Then, we performed Gene Ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis of these DEGs. Furthermore, 5 core genes were identified by protein–protein interaction (PPI) network analysis, receiver operating characteristic (ROC) analysis, survival and Pearson correlation analysis. Meanwhile, we also identified the potential function of these 5 genes in EOC through KEGG pathway enrichment analysis. Finally, chemical-core gene network construction was performed to identify the potential drugs or risk factors for EOC.Results: A total of 234 DEGs were successfully screened, including 81 upregulated genes and 153 downregulated genes. KEGG-pathway analysis indicated that the upregulated DEGs were mainly enriched in Cell cycle and Oocyte meiosis, whereas the downregulated DEGs were enriched in Huntington's disease. As for GO analysis, the upregulated DEGs were mainly associated with Protein binding, Nucleoplasm and Nucleus, whereas the downregulated DEGs were highly enriched in Cilium, Microtubule, and Motile cilium. In addition, 5 core genes (CCNB1, KIF20A, ASPM, AURKA, and KIF23) were identified through protein–protein interaction (PPI) network analysis, ROC analysis, survival and Pearson correlation analysis, which show better diagnostic efficiency and higher prognostic value for EOC. Furthermore, we identified the potential function of these 5 genes in EOC through KEGG pathway enrichment analysis and found that all 5 core genes were enriched in “DNA replication”, “Mismatch repair”, “Fanconi anemia pathway”, “Cell cycle”, “Homologous recombination” and “Nucleotide excision repair”, and “DNA replication” was the key player in them all. Finally, NetworkAnalyst was used to identify top 15 chemicals that link with the 5 core genes. Among them, 11 chemicals were potential drugs and 4 chemicals were risk factors for EOC.Conclusions: Based on an integrated analysis, we identified potential biomarkers, risk factors and drugs for EOC, which may open a new direction for EOC diagnosis, condition appraisal, prevention and treatment in future.

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Identification of KHSRP-Regulated RNAs in Esophageal Cancer by Integrated Bioinformatics Analysis

Cited by 2 publications

References 25 publications

Identification of potential markers for differentiating epithelial ovarian cancer from ovarian low malignant potential tumors through integrated bioinformatics analysis

Identification of potential markers for differentiating epithelial ovarian cancer from ovarian low malignant potential tumors through integrated bioinformatics analysis

Identification of Potential Markers for Differentiating Epithelial Ovarian Cancer from Ovarian Low Malignant Potential Tumors through Integrated Bioinformatics Analysis

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