ZWINT is a Promising Therapeutic Biomarker Associated with the Immune Microenvironment of Hepatocellular Carcinoma

Lin, Tong; Zheng, Lin; Peng, Lisheng

doi:10.2147/ijgm.s340057

Cited by 9 publications

(3 citation statements)

References 62 publications

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“…For example, in pancreatic cancer, ZWINT is induced to highly express by hypoxia, and high expression of ZWINT activates the P53/P21 signaling pathway and promotes ubiquitination and degradation of P53, ultimately leading to the acceleration of cell cycle and proliferation (Chen et al, 2021). ZWINT also accelerates tumor proliferation by promoting cell division and immunosuppression in hepatocellular carcinoma (Lin et al, 2021). However, the tumor functional states in which ZWINT may be involved have not been systematically studied in a pan-cancer perspective.…”

Section: Discussionmentioning

confidence: 99%

ZWINT is a cancer prognosis and immune infiltration-related biomarker from pan-cancer analysis

wang

Chen

et al. 2023

Preprint

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ZWINT plays an important role in regulating the mitotic checkpoint and cell cycle, and is closely associated with tumor proliferation and migration. Recent studies have shown that high ZWINT expression is associated with poor prognosis in patients with lung adenocarcinoma(LUAD) and Glioblastoma (GBM). Previous analyses of ZWINT were limited to a certain type of cancer, but there has not been a systematic pan-cancer study of ZWINT. We used the TCGA (The Cancer Genome Atlas) project and GTEx data (Genotype-Tissue Expression) to analyze ZWINT expression levels and the correlation with cancer survival prognosis. To understand the underlying biological mechanisms of ZWINT and its relevance to immune infiltration, we systematically analyzed ZWINT-associated genetic alterations, immune infiltration and gene enrichment analysis with different bioinformatics methods. Our study showed that ZWINT mRNA expression was elevated in most human tumors and was significantly increased in the early stages of cancer compared to ZWINT expression in normal tissues.ZWINT high expression is significantly correlated with poor prognosis in most tumors. ZWINT is extensively involved in immune infiltration of tumors. Single cell sequencing data showed that ZWINT was significantly associated with cell cycle, DNA repair, DNA damage, and proliferation. Thus, ZWINT expression correlates with prognosis and immune infiltration in tumor patients. ZWINT may be a potential biomarker for prognosis and an important target for tumor immunotherapy.

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Section: Discussionmentioning

confidence: 99%

ZWINT is a cancer prognosis and immune infiltration-related biomarker from pan-cancer analysis

wang

Chen

et al. 2023

Preprint

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“…Silencing OGDHL would induce lipogenesis and affect sorafenib’s chemosensitization effect on HCC cells ( 44 ). ZWINT upregulation showed a significant association with unfavorable survivals and clinicopathological features of HCC patients ( 45 ). CFHR3 overexpression was correlated with a favorable prognosis for HCC patients ( 46 ).…”

Section: Discussionmentioning

confidence: 99%

Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq

Liu

Wang

et al. 2022

Front. Immunol.

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BackgroundStudies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics.MethodsFrom the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)–Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed.ResultsEleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO–Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability.ConclusionTo precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.

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“…A miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed with hub genes, and then miRNAs and TFs were identified. CDT1 [687], ZWINT (ZW10 interacting kinetochore protein) [688], hsa-mir-3613-3p [689], GATA2 [690], RUNX1 [691], SUZ12 [692], NUCKS1 [693], FOXP2 [694], TP53 [695], AHR (aryl hydrocarbon receptor) [696] and TRIM28 [697] were revealed to be correlated with disease outcome in patients with hepatocellular carcinoma. Hsa-mir-199a-5p [698], hsa-mir-22-5p [699], hsamir-338-5p [700], GATA2 [701], RUNX1 [702], SUZ12 [703], TP53 [704] and AHR (aryl hydrocarbon receptor) [705] were revealed to be associated with cardiovascular diseases, but these molecular markers might be novel targets for NAFLD.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics analysis of differentially expressed genes in non alcoholic fatty liver disease using next generation sequencing data

Vastrad¹

2021

Preprint

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Non alcoholic fatty liver disease (NAFLD) is the most common metabolic disease in humans, affecting the majority of individuals. In the current investigation, we aim to identify potential key genes linked with NAFLD through bioinformatics analyses of next generation sequencing (NGS) dataset. NGS dataset of GSE135251 from the Gene Expression Omnibus (GEO) database were retrieved. Differentially expressed genes (DEGs) were obtained by DESeq2 package. g:Profiler database was further used to identify the potential gene ontology (GO) and REACTOME pathways. Protein-protein interaction (PPI) network was constructed using the Hippie interactome database. miRNet and NetworkAnalyst databases were used to establish a miRNA-hub gene regulatory network and TF-hub gene regulatory network for the hub genes. Hub genes were verified based on receiver operating characteristic curve (ROC) analysis. Totally, 951 DEGs were identified including 476 up regulated genes and 475 down regulated genes screened in NAFLD and normal control. GO showed that DEGs were significantly enhanced for signaling and regulation of biological quality. REACTOME pathway analysis revealed that DEGs were enriched in signaling by interleukins and extracellular matrix organization. ESR2, JUN, PTN, PTGER3, CEBPB, IKBKG, HSPA8, SFN, CDKN1A and E2F1 were indicated as hub genes from PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network. Furthermore, ROC analysis revealed that ESR2, JUN, PTN, PTGER3, CEBPB, IKBKG, HSPA8, SFN, CDKN1A and E2F1 might serve as diagnostic biomarkers in NAFLD. The current investigation provided insights into the molecular mechanism of NAFLD that might be useful in further investigations.

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ZWINT is a Promising Therapeutic Biomarker Associated with the Immune Microenvironment of Hepatocellular Carcinoma

Cited by 9 publications

References 62 publications

ZWINT is a cancer prognosis and immune infiltration-related biomarker from pan-cancer analysis

ZWINT is a cancer prognosis and immune infiltration-related biomarker from pan-cancer analysis

Analysis on heterogeneity of hepatocellular carcinoma immune cells and a molecular risk model by integration of scRNA-seq and bulk RNA-seq

Bioinformatics analysis of differentially expressed genes in non alcoholic fatty liver disease using next generation sequencing data

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