&lt;p&gt;Bioinformatics Analysis Identifies a Novel Role of &lt;em&gt;GINS1&lt;/em&gt; Gene in Colorectal Cancer&lt;/p&gt;

Bu, Fanqin; Zhu, Xiaojian; Zhu, Jinfeng; Liu, Zitao; Wu, Ting; Luo, Chen; Kang, Lin; Huang, Jun

doi:10.2147/cmar.s279165

Cited by 18 publications

(18 citation statements)

References 25 publications

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“…GINS1 is a subunit of the GINS complex. Previous studies have demonstrated that GINS1 is upregulated in tumor samples and correlated with poor prognosis ( Bu et al, 2020a ; Bu et al, 2020b ; Li H et al, 2021 ). These studies are consistent with our findings.…”

Section: Discussionmentioning

confidence: 99%

Identification of lncRNA/circRNA-miRNA-mRNA ceRNA Network as Biomarkers for Hepatocellular Carcinoma

et al. 2022

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Background: Hepatocellular carcinoma (HCC) accounts for the majority of liver cancer, with the incidence and mortality rates increasing every year. Despite the improvement of clinical management, substantial challenges remain due to its high recurrence rates and short survival period. This study aimed to identify potential diagnostic and prognostic biomarkers in HCC through bioinformatic analysis.Methods: Datasets from GEO and TCGA databases were used for the bioinformatic analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out by WebGestalt website and clusterProfiler package of R. The STRING database and Cytoscape software were used to establish the protein-protein interaction (PPI) network. The GEPIA website was used to perform expression analyses of the genes. The miRDB, miRWalk, and TargetScan were employed to predict miRNAs and the expression levels of the predicted miRNAs were explored via OncomiR database. LncRNAs were predicted in the StarBase and LncBase while circRNA prediction was performed by the circBank. ROC curve analysis and Kaplan-Meier (KM) survival analysis were performed to evaluate the diagnostic and prognostic value of the gene expression, respectively.Results: A total of 327 upregulated and 422 downregulated overlapping DEGs were identified between HCC tissues and noncancerous liver tissues. The PPI network was constructed with 89 nodes and 178 edges and eight hub genes were selected to predict upstream miRNAs and ceRNAs. A lncRNA/circRNA-miRNA-mRNA network was successfully constructed based on the ceRNA hypothesis, including five lncRNAs (DLGAP1-AS1, GAS5, LINC00665, TYMSOS, and ZFAS1), six circRNAs (hsa_circ_0003209, hsa_circ_0008128, hsa_circ_0020396, hsa_circ_0030051, hsa_circ_0034049, and hsa_circ_0082333), eight miRNAs (hsa-miR-150-5p, hsa-miR-19b-3p, hsa-miR-23b-3p, hsa-miR-26a-5p, hsa-miR-651-5p, hsa-miR-10a-5p, hsa-miR-214-5p and hsa-miR-486-5p), and five mRNAs (CDC6, GINS1, MCM4, MCM6, and MCM7). The ceRNA network can promote HCC progression via cell cycle, DNA replication, and other pathways. Clinical diagnostic and survival analyses demonstrated that the ZFAS1/hsa-miR-150-5p/GINS1 ceRNA regulatory axis had a high diagnostic and prognostic value.Conclusion: These results revealed that cell cycle and DNA replication pathway could be potential pathways to participate in HCC development. The ceRNA network is expected to provide potential biomarkers and therapeutic targets for HCC management, especially the ZFAS1/hsa-miR-150-5p/GINS1 regulatory axis.

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Section: Discussionmentioning

confidence: 99%

Identification of lncRNA/circRNA-miRNA-mRNA ceRNA Network as Biomarkers for Hepatocellular Carcinoma

et al. 2022

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“…COAD is one of the most deadly cancers. For the treatment of CC, some prognostic markers have been found, as well as some models to predict clinical outcomes [ 16 , 17 ]. However, the search for markers or models that can accurately predict prognosis and provide personalized treatment for patients remains critical.…”

Section: Discussionmentioning

confidence: 99%

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma

Yang

Peng

et al. 2022

Computational Intelligence and Neuroscience

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Background. A risk assessment model for prognostic prediction of colon adenocarcinoma (COAD) was established based on weighted gene co-expression network analysis (WGCNA). Methods. From the Cancer Genome Atlas (TCGA) database, RNA-seq data and clinical data of COAD patients were retrieved. After screening of differentially expressed genes (DEGs), WGCNA was performed to identify gene modules and screen those associated with COAD progression. Then, via protein-protein interaction (PPI) network construction of module genes, hub genes were obtained, which were then subjected to the least absolute shrinkage and selection operator (LASSO) and Cox regression to build a hub gene-based prognostic scoring model. The receiver operating characteristic curve (ROC curve) was plotted for the optimal cutoff (OCO) of the risk score, based on which, patients were assigned to high or low-risk groups. Areas under the ROC curve (AUCs) were calculated, and model performance was visualized using Kaplan–Meier (KM) survival curves and verified in the external dataset GSE29621. Finally, the model’s independent prognostic value was evaluated by univariate and multivariate Cox regression analyses, and a nomogram was built. Results. Totally 2840 DEGs were screened from COAD dataset of TCGA, including 1401 upregulated ones and 1439 downregulated ones, which were divided into 10 modules by WGCNA. The eigenvalue of the black module was found to have a high correlation with COAD progression. PPI interaction networks were constructed for genes in the black module, and 34 hub genes were obtained by using the MCODE plug-in. A LASSO-Cox regression approach was utilized to analyze the hub genes, and a prognostic risk score model based on the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) was constructed. KM analysis identified shorter overall lower survival in the high-risk group. The model was verified to have favorable predictive ability through training set and validation set. The nomogram, composed of tumor node metastasis (TNM) staging and risk score, was of good predictability. Conclusions. The COAD prognostic risk model constructed upon the signatures of 9 genes (CHEK1, DEPDC1B, FANCI, MCM10, NCAPG, PARPBP, PLK4, RAD51AP1, and RFC4) can effectively predict the survival status of COAD patients.

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“…The involvement of GINS subunits in malignancies has been reported in recent studies. For example, high expression of GINS1 promotes drug resistance in leukemia cells (Hsieh et al 2020) and help predicts clinical outcomes of colorectal cancer patients (Bu et al 2020b). On the other hand, high GINS4 results in tumorigenesis of human bladder cancer, colorectal cancer, and hepatocellular carcinoma (Yamane et al 2016;Rong et al 2020;Zhang et al 2021).…”

Section: Discussionmentioning

confidence: 99%

GINS Complex Subunit 2 Facilitates Gastric Adenocarcinoma Proliferation and Indicates Poor Prognosis

Huang

Zeng

Gao

et al. 2021

Tohoku J. Exp. Med.

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Gastric cancer is the one of the most lethal malignancies of digestive system. Identifying molecular biomarkers is invaluable in help predicting clinical outcomes and developing targeted chemotherapies. GINS complex subunit 2 (GINS2) plays an essential role in the initiation and elongation of DNA replication. Although there have been studies revealing the prognostic significance of GINS2 in breast cancer and lung cancer, its involvement and function in gastric cancer need to be elucidated. We retrospectively enrolled a cohort of gastric adenocarcinoma patients after surgical resection (n = 123). By analyzing the mRNA and protein levels of GINS2 in tissue samples, we found that GINS2 presented a higher expression in tumor tissues than in adjacent normal stomach tissues. Besides, GINS2 level was positively correlated with tumor size and gastric adenocarcinoma tumor stage, implying its potential role as a tumor promoter. Univariate and multivariate analyses identified that patients with lower GINS2 showed a better overall survival compared to those with higher GINS2 expression. In addition, cellular and xenograft experiments confirmed the role of GINS2 in facilitating tumor proliferation both in vitro and in vivo. To our knowledge, this is the initial finding on GINS2 in promoting gastric adenocarcinoma progression. In conclusion, our study revealed a pro-oncogenic role of GINS2 in gastric cancer.

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<p>Bioinformatics Analysis Identifies a Novel Role of <em>GINS1</em> Gene in Colorectal Cancer</p>

Cited by 18 publications

References 25 publications

Identification of lncRNA/circRNA-miRNA-mRNA ceRNA Network as Biomarkers for Hepatocellular Carcinoma

Identification of lncRNA/circRNA-miRNA-mRNA ceRNA Network as Biomarkers for Hepatocellular Carcinoma

Identification of 9 Gene Signatures by WGCNA to Predict Prognosis for Colon Adenocarcinoma

GINS Complex Subunit 2 Facilitates Gastric Adenocarcinoma Proliferation and Indicates Poor Prognosis

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