PRC1 and RACGAP1 are Diagnostic Biomarkers of Early HCC and PRC1 Drives Self-Renewal of Liver Cancer Stem Cells

Liao, Shixin; Wang, Kaili; Zhang, Lulu; Shi, Gaoli; Wang, Zhiwei; Chen, Zhenzhen; Zhu, Pingping; He, Qiankun

doi:10.3389/fcell.2022.864051

Cited by 8 publications

(5 citation statements)

References 55 publications

(60 reference statements)

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“…A multidimensional comprehensive HCC treatment strategy based on resection, liver transplantation, radiotherapy, chemotherapy, percutaneous ablation, and immunotherapy for early-stage HCC can achieve promising outcomes ( 32 – 34 ). However, due to the low sensitivity of traditional tumor diagnostic methods and the lack of obvious symptoms of early-stage HCC, most patients are in advanced stages of HCC at the time of diagnosis ( 35 , 36 ). And therapeutic measures to treat advanced HCC are scarce and ineffective ( 37 , 38 ).…”

Section: Discussionmentioning

confidence: 99%

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

et al. 2023

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BackgroundHepatocellular carcinoma (HCC), the third most prevalent cause of cancer-related death, is a frequent primary liver cancer with a high rate of morbidity and mortality. T-cell depletion (TEX) is a progressive decline in T-cell function due to continuous stimulation of the TCR in the presence of sustained antigen exposure. Numerous studies have shown that TEX plays an essential role in the antitumor immune process and is significantly associated with patient prognosis. Hence, it is important to gain insight into the potential role of T cell depletion in the tumor microenvironment. The purpose of this study was to develop a trustworthy TEX-based signature using single-cell RNA-seq (scRNA-seq) and high-throughput RNA sequencing, opening up new avenues for evaluating the prognosis and immunotherapeutic response of HCC patients.MethodsThe International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases were used to download RNA-seq information for HCC patients. The 10x scRNA-seq. data of HCC were downloaded from GSE166635, and UMAP was used for clustering descending, and subgroup identification. TEX-related genes were identified by gene set variance analysis (GSVA) and weighted gene correlation network analysis (WGCNA). Afterward, we established a prognostic TEX signature using LASSO-Cox analysis. External validation was performed in the ICGC cohort. Immunotherapy response was assessed by the IMvigor210, GSE78220, GSE79671, and GSE91061cohorts. In addition, differences in mutational landscape and chemotherapy sensitivity between different risk groups were investigated. Finally, the differential expression of TEX genes was verified by qRT-PCR.Result11 TEX genes were thought to be highly predictive of the prognosis of HCC and substantially related to HCC prognosis. Patients in the low-risk group had a greater overall survival rate than those in the high-risk group, according to multivariate analysis, which also revealed that the model was an independent predictor of HCC. The predictive efficacy of columnar maps created from clinical features and risk scores was strong.ConclusionTEX signature and column line plots showed good predictive performance, providing a new perspective for assessing pre-immune efficacy, which will be useful for future precision immuno-oncology studies.

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Section: Discussionmentioning

confidence: 99%

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

et al. 2023

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“…Similarly, high nuclear RACGAP1 expression is associated with poor survival in patients with colorectal cancer ( 51 ). In HCC, RACGAP1 overexpression promotes disease progression and has been reported to be significantly associated with worse overall survival ( 16 , 17 ). In the present study, knockdown of RACGAP1 significantly suppressed cell proliferation and induced apoptosis, whilst overexpression of RACGAP1 significantly promoted cell proliferation and suppressed apoptosis in Huh-7 and Hep3B cells under hypoxic conditions.…”

Section: Discussionmentioning

confidence: 99%

“…For example, recent studies have reported that upregulation of RACGAP1 can be an indicator of a worse overall survival rate in patients with HCC ( 16 ). The activation of RACGAP1 and demethylation of its promoter region, specifically at the H3K4me2 site, have been reported to promote early recurrence, metastasis and microvascular invasion in HCC ( 17 , 18 ). However, the specific role of RACGAP1 in HCC within a hypoxic microenvironment has not been extensively evaluated.…”

Section: Introductionmentioning

confidence: 99%

HIF‑1α and RACGAP1 promote the progression of hepatocellular carcinoma in a mutually regulatory way

Wu,

Xu,

et al. 2023

Mol Med Rep

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Hypoxia, a condition characterized by low oxygen levels, serves an important role in the progression of hepatocellular carcinoma (HCC). However, the precise molecular mechanisms underlying hypoxia-induced HCC progression are yet to be fully elucidated. The present study assessed the involvement of two key factors, hypoxia-inducible factor-1α (HIF-1α) and Rac GTPase activating protein 1 (RACGAP1), in HCC development under hypoxic conditions. HIF-1α and RACGAP1 genes were overexpressed and knocked down in Hep3B and Huh7 cells using lentiviral transduction and the levels of HIF-1α and RACGAP1 in the cells were assessed using quantitative PCR, western blotting and immunofluorescence. Co-immunoprecipitation experiments were performed to evaluate the interaction between HIF-1α and RACGAP1. Subsequently, the proliferation, apoptosis, migration and invasion of Hep3B and Huh7 cells were assessed using the Cell Counting Kit-8 assay, flow cytometry, Transwell assay and migration experiments. The expression levels of HIF-1α and RACGAP1 in normal and HCC tumor samples were analyzed utilizing the Gene Expression Profiling Interactive Analysis database. Furthermore, correlations between HIF-1α/RACGAP1 gene expression levels and patient survival outcomes were evaluated using the Kaplan-Meier plotter. Knockdown of HIF-1α resulted in a significant decrease in RACGAP1 expression, whilst overexpression of HIF-1α resulted in a significant increase in RACGAP1 expression. Moreover, overexpression and knockdown of RACGAP1 had the same effect on HIF-1α expression. Additionally, it was demonstrated that HIF-1α and RACGAP1 interacted directly within a complex. Overexpression of HIF-1α or RACGAP1 significantly increased proliferation, invasion and migration, and significantly decreased the proportion of apoptotic Hep3B and Huh7 cells. Conversely, knockdown of HIF-1α or RACGAP1 significantly decreased proliferation, invasion and migration, and significantly increased the proportion of apoptotic Hep3B and Huh7 cells. In addition, the combined knockdown or overexpression of HIF-1α and RACGAP1 had a more pronounced effect on HCC cell migration compared with knockdown of HIF-1α alone. Furthermore, there was a significant positive correlation between the expression levels of HIF-1α and RACGAP1 in HCC tissues and patients with HCC and upregulation of both HIF-1α and RACGAP1 demonstrated a lower overall survival probability. In conclusion, HIF-1α and RACGAP1 may synergistically contribute to the development of HCC, highlighting their potential as valuable targets for HCC therapy.

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“…These studies have shown a significant association between RACGAP1 expression and worse prognosis in various human cancer types, including BC. 114,115 The gene RACGAP1 has been identified as a potential oncogene in many types of human cancers. This is a component of the centralspindlin that is necessary for the activation of cytokinesis and looks to be a member of the Rho GTPaseactivating protein family.…”

Section: Hmmrmentioning

confidence: 99%

Identification of prognostic biomarkers of invasive ductal carcinoma by an integrated bioinformatics approach

Marrugo-Padilla,

Márquez-Lázaro,

Álviz-Amador

2023

F1000Res

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Background: Invasive ductal carcinoma (IDC) is the most common type of breast cancer (BC) worldwide. Nowadays, due to its heterogeneity and high capacity for metastasis, it is necessary to discover novel diagnostic and prognostic biomarkers. Therefore, this study aimed to identify novel candidate prognostic genes for IDC using an integrated bioinformatics approach. Methods: Three expression profile data sets were obtained from GEO (GSE29044, GSE3229, and GSE21422), from which differentially expressed genes (DEGs) were extracted for comparative transcriptome analysis of experimental groups (IDC versus control). Next, STRING was utilized to construct a protein interaction network with the shared DEGs, and MCODE and cytoHubba were used to identify the hub genes, which were then characterized using functional enrichment analysis in DAVID and KEGG. Finally, using the Kaplan-Meier tracer database, we determined the correlation between the expression of hub genes and overall survival in BC. Results: We identified seven hub genes (Kinesin-like protein KIF23 [KIF23], abnormal spindle-like microcephaly [ASPM]-associated protein [ASPMAP], Aurora kinase A [AURKA], Rac GTPase-activating protein 1 [RACGAP1], centromere protein F [CENPF], hyaluronan-mediated motility receptor [HMMR], and protein regulator of cytokinesis 1 [PRC1]), which were abundant in microtubule binding and tubulin binding, pathways linked to fundamental cellular structures including the mitotic spindle, spindle, microtubule, and spindle pole. The role of these genes in the pathophysiology of IDC is not yet well characterized; however, they have been associated with other common types of BC, modulating pathways such as Wnt/β-catenin, the epithelial-to-mesenchymal transition (EMT) process, chromosomal instability (CIN), PI3K/AKT/mTOR, and BRCA1 and BRCA2, playing an important role in its progression and being associated with a poor prognosis, thus representing a way to improve our understanding of the process of tumorigenesis and the underlying molecular events of IDC. Conclusions: Genes identified may lead to the discovery of new prognostic targets for IDC.

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PRC1 and RACGAP1 are Diagnostic Biomarkers of Early HCC and PRC1 Drives Self-Renewal of Liver Cancer Stem Cells

Cited by 8 publications

References 55 publications

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

T-cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing

HIF‑1α and RACGAP1 promote the progression of hepatocellular carcinoma in a mutually regulatory way

Identification of prognostic biomarkers of invasive ductal carcinoma by an integrated bioinformatics approach

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