KIF11 Promotes Proliferation of Hepatocellular Carcinoma among Patients with Liver Cancers

Hu, Zhandong; Jiang, Ying; Sun, Hongmei; Wang, Jingwen; Zhai, Lili; Yin, Zhiqi; Yan, Jun

doi:10.1155/2021/2676745

Cited by 25 publications

(26 citation statements)

References 35 publications

(17 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is simply described to be closely related to the tumor microenvironment [15][16][17][18]. AURKA and KIF11 are used as a target in many tumors for targeted therapy, and these genes are also microenvironment constituent genes of tumors [19][20][21][22]. CKD1 has not been studied much in glioma.…”

Section: Discussionmentioning

confidence: 99%

LGG subtypes based on the activity changes of immunologic and hallmark gene sets in cancer

Chen¹

2022

Preprint

View full text Add to dashboard Cite

Purpose: When glioma is in WHO grade II-III, glioma is of low grade. Gliomas in this period have the opportunity for surgical treatment and can be treated with a range of targeted therapies to enhance patient survival. Methods: In this study, we downloaded low-grade glioma data from TCGA database and GSEA database. Cancer subtypes were classified by GSVA enrichment method.Results: By GSVA enrichment analysis, we obtain three low-grade gliomas (LGG) cancer subtypes. After further survival prognosis analysis and biological function analysis, we obtained 21 tumor microenvironment gene sets and 17 core genes that affect patients' survival prognosis, and these genes have the potential to become targets for targeted therapies and disease detection.Conclusion: We screened a total of 21 gene sets through a series of enrichment analyses, statistical and prognostic analyses, etc. Among them, 17 core genes were identified, namely: TOP2A, KIF20A, CCNB2, AURKA, KIF11, CDK1, BUB1B, CCNA2, BUB1, CDC20, CDCA8, TPX2, KIF2C, POLA2, POLE2, POLA1 and POLE. TOP2A.

show abstract

Section: Discussionmentioning

confidence: 99%

LGG subtypes based on the activity changes of immunologic and hallmark gene sets in cancer

Chen¹

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Overexpression of KIF11 can lead to abnormal cell division and genomic instability, which are closely related to tumorigenesis (27). Studies have found that abnormal expression of KIF11 is closely related to HCC progression and prognosis, indicating that it could be a biomarker for the prognosis and treatment of HCC (28). PBK is a T cell-derived protein kinase, belonging to the serine/threonine protein kinase, with high expression in the testis and thymus and low expression in normal cells (29).…”

Section: Discussionmentioning

confidence: 99%

Combined screening analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma

Cao¹,

Zhang²,

Zhang³

et al. 2022

J Gastrointest Oncol

View full text Add to dashboard Cite

Background: Methylation plays an important role in hepatocellular carcinoma (HCC) by altering the expression of key genes. The aim of this study was to screen the aberrantly methylated-differentially expressed genes (DEGs) in HCC and elucidate their underlying molecular mechanism.Methods: Gene expression microarrays (GSE101685) and gene methylation microarrays (GSE44909) were selected. DEGs and differentially methylated genes (DMGs) were screened. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the Database for Annotation, Visualization, and Integrated discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to analyze the functional protein-protein interaction (PPI) network. Molecular Complex Detection (MCODE) analysis was performed using the Cytoscape software. Hub genes were verified in The Cancer Genome Atlas (TCGA) database.Results: A total of 80 hypomethylation-high expression genes (Hypo-HGs) were identified. Pathway enrichment analysis showed DNA replication, cell cycle, viral carcinogenesis, and the spliceosome. The top 5 hub genes were minichromosome maintenance complex component 3 (MCM3), checkpoint kinase 1 (CHEK1), kinesin family member 11 (KIF11), PDZ binding kinase (PBK), and Rac GTPase activating protein 1 (RACGAP1). In addition, 189 hypermethylation-low expression genes (Hyper-LGs) were identified. Pathway enrichment analysis indicated enrichment in metabolic pathways, drug metabolismother enzymes, and chemical carcinogenesis. The top 5 hub genes were leukocyte immunoglobulin like receptor B2 (LILRB2), formyl peptide receptor 1 (FPR1), S100 calcium binding protein A9 (S100A9), S100 calcium binding protein A8 (S100A8), and myeloid cell nuclear differentiation antigen (MNDA). The methylation status and mRNA expression of MCM3, CHEK1, KIF11, PBK, and S100A9 were consistent in the TCGA database and significantly correlated with the prognosis of patients.Conclusions: Combined screening of aberrantly methylated-DEGs based on bioinformatic analysis may provide new clues for elucidating the epigenetic mechanism in HCC. Hub genes, including MCM3, CHEK1, KIF11, PBK, and S100A9, may serve as biomarkers for the precise diagnosis of HCC.

show abstract

“…Similar to our results, Huang et al also demonstrate that IGF1 could be the carcinogenic factor and possible target in HC by bioinformatic method and experiments [ 38 ]. Both KIF11 and ZWINT can affect HC progression by mediating cell proliferation [ 39 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

Development an Immune-Related MicroRNA Risk Index in Hepatocellular Carcinoma

Zhou

Wang

et al. 2022

Journal of Oncology

View full text Add to dashboard Cite

Purpose. Hepatocellular carcinoma (HC) has emerged as one of the most prevalent malignancies on a global scale. Recently, immunotherapy has achieved favorable effectiveness in the management of multiple cancers. However, there are limited therapeutic options for advanced HC. As the liver is a special immune organ, we intend to uncover potential and effective immunotherapeutic modalities for HC. Our study was designed to develop specific immune-related miRNAs (IRMs) for outcome assessment and individualized strategies for the management of HC. Methods. The miRNA-seq and survival data of TCGA-LIHC dataset was enrolled into this program. We first collected IRMs from Immune-miR website. Differentially expression analysis was applied to screen aberrantly expressed IRMs. In order to set up an IRM-related index (IRMRI) in HC, we conducted the Cox relevant methods. Next, the statistical approaches (survival curve and ROC curve analyses) were utilized to detect the evaluation capacity of our IRMRI. Subsequently, we obtained the target genes of hub miRNAs from IRMRI through three miRNA-related predictive online tools (miRDB, miRTarBase, and TargetScan websites). Results. Five IRMs were determined to develop the IRMRI. It can effectively segregate all HC cases from two different risk subgroups. We identified a marked discrepancy in survival outcome between the two groups by survival analysis and confirmed the reliability of IRMRI in two testing sets. Moreover, we collected 10 hub target genes (ESR1, IGF1, PDGFRB, JUN, MYC, ZWINT, MAD2L1, TOP2A, KIF11, and CDCA8) which were strongly linked to HC progression and malignant behavior. Conclusion. We screened out five hub IRMs with clinical value and constructed a risk index model in HC, which can precisely assess the risk status and outcome of patients to a certain extent.

show abstract

KIF11 Promotes Proliferation of Hepatocellular Carcinoma among Patients with Liver Cancers

Cited by 25 publications

References 35 publications

LGG subtypes based on the activity changes of immunologic and hallmark gene sets in cancer

LGG subtypes based on the activity changes of immunologic and hallmark gene sets in cancer

Combined screening analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma

Development an Immune-Related MicroRNA Risk Index in Hepatocellular Carcinoma

Contact Info

Product

Resources

About