Identification of biomarkers correlated with diagnosis and prognosis of endometrial cancer using bioinformatics analysis

2020 ◽  
Vol 121 (12) ◽  
pp. 4908-4921
Author(s):  
Huishan Zhao ◽  
Aihua Jiang ◽  
Mingwei Yu ◽  
Hongchu Bao
Keyword(s):  
Endometrial Cancer ◽  
Bioinformatics Analysis ◽  
Diagnosis And Prognosis

scholarly journals Identification of candidate biomarkers associated with the diagnosis and prognosis of endometrial cancer: a bioinformatics analysis

2021 ◽  
Author(s):  
Changqiang Wei ◽  
Le Huang ◽  
Lingjie Deng ◽  
Huisi Lin ◽  
Weicheng Pan ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Hub Genes ◽  
Diagnosis And Prognosis ◽  
Small Molecule Drugs ◽  
Damage Response ◽  
Common Malignancy

Abstract Background Endometrial cancer (EC) is a common malignancy tumor that seriously threatens the wellbeing and health of women. This study aimed to map the hub genes and potential pathways that may be involved in EC. Methods In our study, three gene expression profiles of GEO and EC data from the TCGA database were analyzed. We performed WGCNA, and Cox regression analyses to screen hub genes and further validated them by using HPA, KM plots, and other databases. We also studied the methylation level by UCSC Xena and mutation of hub genes using Cbioportal . Results We identified 363 differentially expressed genes (DEGs) (146 upregulated and 217 downregulated genes). Pathway analysis revealed that hub genes are mainly related to cell cycle, DNA damage response, EMT, hormone ER, RAS/MAPK, and PI3K/AKT. Finally, we identified five hub genes that are related to the progression and prognosis of EC and screened several relevant small-molecule drugs. Conclusions MTHFD2, KIF4A, TPX2, RPS6KA6, and SIX1 were identified as candidate biomarkers for further basic and clinical research on endometrial cancer.

scholarly journals Identification of Candidate Biomarkers Associated With the Diagnosis and Prognosis of Endometrial Cancer ：A Bioinformatics Analysis Identifies

2020 ◽  
Author(s):  
Changqiang Wei ◽  
Le Huang ◽  
Lingjie Deng ◽  
Huisi Lin ◽  
Weicheng Pan ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Bioinformatics Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Regression Analyses ◽  
Hub Genes ◽  
Diagnosis And Prognosis ◽  
Damage Response ◽  
Common Malignancy

Abstract BackgroundEndometrial cancer (EC) is a common malignancy tumor that seriously threatens the wellbeing and health of women. This study aimed to map the hub genes and potential pathways that may be involved in EC.MethodsIn our study, three gene expression profiles of GEO and EC data from the TCGA database were analyzed. We performed WGCNA, and Cox regression analyses to screen hub genes and further validated them by using HPA, KM plots, and other databases. We also studied the methylation level by UCSC Xena and mutation of hub genes using Cbioportal .ResultsWe identified 363 differentially expressed genes (DEGs) (146 upregulated and 217 downregulated genes) . Pathway analysis revealed that hub genes are mainly related to cell cycle, DNA damage response, EMT, hormone ER, RAS/MAPK, and PI3K/AKT. Finally, we identified five hub genes that are related to the progression and prognosis of EC and screened several relevant small-molecule drugs.ConclusionsMTHFD2, KIF4A, TPX2, RPS6KA6, and SIX1 were identified as candidate biomarkers for further basic and clinical research on endometrial cancer.

Identification of diagnosis and prognosis gene markers in B-ALL with ETV6-RUNX1 fusion by Integrated Bioinformatics Analysis

Gene ◽  
2022 ◽  
pp. 146132
Author(s):  
Hongkai Zhu ◽  
Rong Zhang ◽  
Ruijuan Li ◽  
Zhihua Wang ◽  
Heng Li ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Gene Markers ◽  
Diagnosis And Prognosis

scholarly journals LASSO and Bioinformatics Analysis in the Identification of Key Genes for Prognostic Genes of Gynecologic Cancer

2021 ◽  
Vol 11 (11) ◽  
pp. 1177
Author(s):  
Shao-Hua Yu ◽  
Jia-Hua Cai ◽  
De-Lun Chen ◽  
Szu-Han Liao ◽  
Yi-Zhen Lin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Endometrial Cancer ◽  
Pathway Analysis ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gynecologic Cancer ◽  
Functional Enrichment ◽  
Lasso Regression ◽  
Potential Biomarker ◽  
Study Gene Expression

The aim of this study is to identify potential biomarkers for early diagnosis of gynecologic cancer in order to improve survival. Cervical cancer (CC) and endometrial cancer (EC) are the most common malignant tumors of gynecologic cancer among women in the world. As the underlying molecular mechanisms in both cervical and endometrial cancer remain unclear, a comprehensive and systematic bioinformatics analysis is required. In our study, gene expression profiles of GSE9750, GES7803, GES63514, GES17025, GES115810, and GES36389 downloaded from Gene Expression Omnibus (GEO) were utilized to analyze differential gene expression between cancer and normal tissues. A total of 78 differentially expressed genes (DEGs) common to CC and EC were identified to perform the functional enrichment analyses, including gene ontology and pathway analysis. KEGG pathway analysis of 78 DEGs indicated that three main types of pathway participate in the mechanism of gynecologic cancer such as drug metabolism, signal transduction, and tumorigenesis and development. Furthermore, 20 diagnostic signatures were confirmed using the least absolute shrink and selection operator (LASSO) regression with 10-fold cross validation. Finally, we used the GEPIA2 online tool to verify the expression of 20 genes selected by the LASSO regression model. Among them, the expression of PAMR1 and SLC24A3 in tumor tissues was downregulated significantly compared to the normal tissue, and found to be statistically significant in survival rates between the CC and EC of patients (p < 0.05). The two genes have their function: (1.) PAMR1 is a tumor suppressor gene, and many studies have proven that overexpression of the gene markedly suppresses cell growth, especially in breast cancer and polycystic ovary syndrome; (2.) SLC24A3 is a sodium–calcium regulator of cells, and high SLC24A3 levels are associated with poor prognosis. In our study, the gene signatures can be used to predict CC and EC prognosis, which could provide novel clinical evidence to serve as a potential biomarker for future diagnosis and treatment.

The effect of marital status on endometrial cancer-related diagnosis and prognosis: a Surveillance Epidemiology and End Results database analysis

2019 ◽  
Vol 15 (34) ◽  
pp. 3963-3976 ◽  
Author(s):  
Jia Dong ◽  
Qinjin Dai ◽  
Fan Zhang
Keyword(s):  
Endometrial Cancer ◽  
Marital Status ◽  
Cox Regression ◽  
Significant Prognostic Factor ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis ◽  
Kaplan Meier ◽  
Risk Of Mortality ◽  
The Relationship ◽  
End Results

Aim: Marital status has been proved a significant prognostic factor for diagnosis and prognosis in various cancers, but the effect in endometrial cancer (EMC) is controversial. The research was designed to clarify the relationship between marital status and EMC. Methods: We identified 39,387 patients with EMC between 2004 and 2010 from the Surveillance Epidemiology and End Results database. Patients were categorized into four groups according to marital status. We used the logistic regression, the Kaplan–Meier method and Cox regression analysis to analyze the effect of marital status on EMC-related diagnosis and prognosis. Results: The study suggests that marriage benefits the diagnosis and prognosis of EMC. Widowed and unmarried patients had higher risk of mortality than other marital status.

scholarly journals TENT4A Non-Canonical Poly(A) Polymerase Regulates DNA-Damage Tolerance via Multiple Pathways That Are Mutated in Endometrial Cancer

2021 ◽  
Vol 22 (13) ◽  
pp. 6957
Author(s):  
Umakanta Swain ◽  
Gilgi Friedlander ◽  
Urmila Sehrawat ◽  
Avital Sarusi-Portuguez ◽  
Ron Rotkopf ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Dna Polymerase ◽  
Mrna Stability ◽  
Antisense Rna ◽  
Bioinformatics Analysis ◽  
Dna Polymerases ◽  
Dna Lesions ◽  
Dna Damage Tolerance ◽  
Translesion Dna Synthesis ◽  
Cancer Genomes

TENT4A (PAPD7) is a non-canonical poly(A) polymerase, of which little is known. Here, we show that TENT4A regulates multiple biological pathways and focuses on its multilayer regulation of translesion DNA synthesis (TLS), in which error-prone DNA polymerases bypass unrepaired DNA lesions. We show that TENT4A regulates mRNA stability and/or translation of DNA polymerase h and RAD18 E3 ligase, which guides the polymerase to replication stalling sites and monoubiquitinates PCNA, thereby enabling recruitment of error-prone DNA polymerases to damaged DNA sites. Remarkably, in addition to the effect on RAD18 mRNA stability via controlling its poly(A) tail, TENT4A indirectly regulates RAD18 via the tumor suppressor CYLD and via the long non-coding antisense RNA PAXIP1-AS2, which had no known function. Knocking down the expression of TENT4A or CYLD, or overexpression of PAXIP1-AS2 led each to reduced amounts of the RAD18 protein and DNA polymerase h, leading to reduced TLS, highlighting PAXIP1-AS2 as a new TLS regulator. Bioinformatics analysis revealed that TLS error-prone DNA polymerase genes and their TENT4A-related regulators are frequently mutated in endometrial cancer genomes, suggesting that TLS is dysregulated in this cancer.

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