EXO1 overexpression is associated with poor prognosis of hepatocellular carcinoma patients

Dai, Yaoyao; Tang, Zhuqi; Yang, Zongguo; Zhang, Lan; Deng, Qing; Zhang, Xiaofeng; Yu, Yongchun; Liu, Xing; Zhu, Junfeng

doi:10.1080/15384101.2018.1534511

Cited by 40 publications

(35 citation statements)

References 36 publications

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“…Also, due to its role in DNA replication repair and homology-directed repair, the relationships of EXO1 and BRCA1/2 mutations and its underlying mechanism have become an important focus to be studied (Lemacon et al, 2017). A recent study found that the EXO1 expression level was elevated in hepatocellular carcinoma patients and its overexpression was correlated with larger tumor size, increased lymph node metastasis, and thus proving its potential therapeutic value for hepatocellular carcinoma as a promising prognostic marker (Dai et al, 2018). Our findings were validated by another study to some extent, and they found DEPDC1, EXO1, RRM2 and some proteins had enhanced expression in the ductal carcinoma in situ and invasive ductal carcinoma (Kretschmer et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses

Zhou

Liu

et al. 2020

PeerJ

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Background BRCA1 and BRCA2 genes are currently proven to be closely related to high lifetime risks of breast cancer. To date, the closely related genes to BRCA1/2 mutations in breast cancer remains to be fully elucidated. This study aims to identify the gene expression profiles and interaction networks influenced by BRCA1/2 mutations, so as to reflect underlying disease mechanisms and provide new biomarkers for breast cancer diagnosis or prognosis. Methods Gene expression profiles from The Cancer Genome Atlas (TCGA) database were downloaded and combined with cBioPortal website to identify exact breast cancer patients with BRCA1/2 mutations. Gene set enrichment analysis (GSEA) was used to analyze some enriched pathways and biological processes associated BRCA mutations. For BRCA1/2-mutant breast cancer, wild-type breast cancer and corresponding normal tissues, three independent differentially expressed genes (DEGs) analysis were performed to validate potential hub genes with each other. Protein–protein interaction (PPI) networks, survival analysis and diagnostic value assessment helped identify key genes associated with BRCA1/2 mutations. Results The regulation process of cell cycle was significantly enriched in mutant group compared with wild-type group. A total of 294 genes were identified after analysis of DEGs between mutant patients and wild-type patients. Interestingly, by the other two comparisons, we identified 43 overlapping genes that not only significantly expressed in wild-type breast cancer patients relative to normal tissues, but more significantly expressed in BRCA1/2-mutant breast patients. Based on the STRING database and cytoscape software, we constructed a PPI network using 294 DEGs. Through topological analysis scores of the PPI network and 43 overlapping genes, we sought to select some genes, thereby using survival analysis and diagnostic value assessment to identify key genes pertaining to BRCA1/2-mutant breast cancer. CCNE1, NPBWR1, A2ML1, EXO1 and TTK displayed good prognostic/diagnostic value for breast cancer and BRCA1/2-mutant breast cancer. Conclusion Our research provides comprehensive and new insights for the identification of biomarkers connected with BRCA mutations, availing diagnosis and treatment of breast cancer and BRCA1/2-mutant breast cancer patients.

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Section: Discussionmentioning

confidence: 99%

Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses

Zhou

Liu

et al. 2020

PeerJ

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“…EXO1 has been associated with different types of cancers owning to its mutations, including colon, breast, ovarian, lung, pancreatic, and gastric tract cancer (Bau et al, ; Hansen et al, ; Jin et al, ; Sun, Zheng, & Shen, ). Nevertheless, the overexpression of EXO1 has also been reported in several other cancers associated with poor prognosis, which in part is related to increased DNA repair activity (Axelsen, Lotem, Sachs, & Domany, ; Dai et al, ; de Sousa et al, ; Muthuswami et al, ). In this study, AURKA, CCNB1, CCNF, and EXO1 were significantly upregulated in CRC compared with normal samples, and in CRC patients, the survival rate was positively correlated with the high expression of these genes.…”

Section: Discussionmentioning

confidence: 99%

Identification of novel biomarkers and small molecule drugs in human colorectal cancer by microarray and bioinformatics analysis

Chen¹,

Wang

Shen

et al. 2019

Molec Gen & Gen Med

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Background Colorectal cancer (CRC) is one of the most common malignant tumors. In the present study, the expression profile of human multistage colorectal mucosa tissues, including healthy, adenoma, and adenocarcinoma samples was downloaded to identify critical genes and potential drugs in CRC. Methods Expression profiles, GSE33113 and GSE44076, were integrated using bioinformatics methods. Differentially expressed genes (DEGs) were analyzed by R language. Functional enrichment analyses of the DEGs were performed using the Database for Annotation, visualization, and integrated discovery (DAVID) database. Then, the search tool for the retrieval of interacting genes (STRING) database and Cytoscape were used to construct a protein–protein interaction (PPI) network and identify hub genes. Subsequently, survival analysis was performed among the key genes using Gene Expression Profiling Interactive Analysis (GEPIA). Connectivity Map (CMap) was used to query potential drugs for CRC. Results A total of 428 upregulated genes and 751 downregulated genes in CRC were identified. The functional changes of these DEGs were mainly associated with cell cycle, oocyte meiosis, DNA replication, p53 signaling pathway, and progesterone‐mediated oocyte maturation. A PPI network was identified by STRING with 482 nodes and 2,368 edges. Survival analysis revealed that high mRNA expression of AURKA, CCNB1, CCNF, and EXO1 was significantly associated with longer overall survival. Moreover, CMap predicted a panel of small molecules as possible adjuvant drugs to treat CRC. Conclusion Our study found key dysregulated genes involved in CRC and potential drugs to combat it, which may provide novel insights and potential biomarkers for prognosis, as well as providing new CRC treatments.

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“…CDKN3 has been correlated with poor tumor differentiation and advanced tumor stage by promoting cell proliferation in HCC (37). HOXD9 acts as a tumor promoter to drive metastasis by regulating ZEB1 in HCC (38), and EXO1 overexpression is associated with poor prognosis in HCC patients (39); however, there have been few reports regarding the effect of NEIL3 on HCC. Two selected genes show different trends in comparison with TCGA data, likely due to race and sample size, which needs further con rmation using larger datasets.…”

Section: Discussionmentioning

confidence: 99%

Development of a Prognostic Gene Signature For Hepatocellular Carcinoma

Luo

et al. 2020

Preprint

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Background: Accurate prediction of overall survival is important for prognosis and the assignment of appropriate personalized clinical treatment in hepatocellular carcinoma (HCC) patients. The aim of the present study was to establish an optimal gene model for the independent prediction of prognosis associated with common clinical patterns.Methods: Gene expression profiles and the corresponding clinical information of the LIHC cohort were obtained from The Cancer Genome Atlas. Differentially expressed genes were found using the R package “limma”. Subsequently, a prognostic gene signature was developed using the LASSO Cox regression model. Kaplan–Meier, log-rank, and receiver operating characteristic (ROC) analyses were performed to verify the predictive accuracy of the prognostic model. Finally, a nomogram and calibration plot were created using the “rms” package.Results: Differentially expressed genes were screened with threshold criteria (FDR < 0.01 and |log FC|>3) and 563 differentially expressed genes were obtained, including 448 downregulated and 115 upregulated genes. Using the LASSO Cox regression model, a prognostic gene signature was developed based on nine genes,IQGAP3, BIRC5, PTTG1, STC2, CDKN3, PBK, EXO1, NEIL3, and HOXD9, the expression levels of which were quantitated using RT-qPCR. According to the risk scores, patients were separated into high-risk and low-risk groups. Patients with lower risk scores generally had a better survival rate than those with higher risk scores. The mortality rate in the high-risk group was 42.02%, while that in the low-risk group was 12.50%. Results of the log-rank test showed significant differences in mortality between the two groups (HR: 4.86; 95% CI: 2.72–8.69; P = 1.01E-08). Subsequently, we assessed the prognostic accuracy of the gene signature using an ROC curve and the results show good sensitivity and specificity, with an average area under the curve (AUC) of 0.81 at 5 years (P < 0.01). Following multivariate adjustment for conventional clinical patterns, the prognostic gene signature remained a powerful and independent factor (HR: 4.70; 95% CI: 2.61–8.38; P = 2.06E-07), confirming its robust predictive ability of overall survival in HCC patients. Finally, a nomogram was established based on the gene signature and four clinicopathological features, which demonstrated an advantageous discriminating ability with the potential to facilitate clinical decision-making in HCC.Conclusion: Our prognostic gene signature can be used as a combined biomarker for the independent prediction of overall survival in HCC patients. Moreover, we created a nomogram that can be used to infer prognosis and aid individualized decisions regarding treatment and surveillance.

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EXO1 overexpression is associated with poor prognosis of hepatocellular carcinoma patients

Cited by 40 publications

References 36 publications

Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses

Differentially expressed genes and key molecules of BRCA1/2-mutant breast cancer: evidence from bioinformatics analyses

Identification of novel biomarkers and small molecule drugs in human colorectal cancer by microarray and bioinformatics analysis

Development of a Prognostic Gene Signature For Hepatocellular Carcinoma

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