scholarly journals Prognostic implications of CDC45 expression in hepatocellular carcinoma

2020 ◽  
Author(s):  
Chen Yang ◽  
Shufang Xie ◽  
Yi Wu ◽  
Guoqing Ru ◽  
Xianglei He ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cytochrome P450 ◽  
Dna Replication ◽  
Prognostic Marker ◽  
Expression Profiles ◽  
Chemical Carcinogenesis ◽  
Functional Enrichment ◽  
Expression Levels ◽  
Transcriptome Expression ◽  
Tissue Specimens

Abstract Background The outcomes of hepatocellular carcinoma (HCC) remain poor despite dramatic improvements in treatment, and novel prognostic biomarkers are urgently needed to ameliorate the situation. Cell division cycle protein 45 (CDC45) plays a key role in DNA replication, which is considered to be involved in tumorigenesis and has become a new prognostic marker. This study investigated CDC45 expression in tumour tissues and defined its prognostic value in HCC patients. Methods Differential transcriptional and proteomic expression profiles were obtained and validated using multiple datasets. We then used immunohistochemistry (IHC) staining to examine the expression of CDC45 in tumour tissue specimens and compare them with adjacent normal tissue specimens using a constructed tissue microarray (TMA) and analysed the relationship of clinical features and prognosis in HCC patients. Functional enrichment analyses were used to describe significantly involved hallmark pathways of differentially expressed genes (DEGs). Results Contrary to the transcriptome expression level in the database, our results showed that the proteome expression of CDC45 in was evidently downregulated in HCC compared with normal adjacent tissues (P < 0.0001). Although we did not find any differences in terms of vascular invasion, Edmondson grade, lymphatic infiltration, or metastasis between patients with high and low CDC45 expression in our limited number of HCC samples, low expression of CDC45 was significantly correlated with aggressive characteristics, including microvascular invasion (P = 0.046). In addition, a multivariate analysis indicated that CDC45 expression (P = 0.035) was an independent prognostic factor for the overall survival (OS) rate of patients with HCC. Furthermore, patients with low CDC45 expression levels were significantly correlated with inferior OS rates than patients with high CDC45 expression levels (P < 0.05). Functional annotations indicated that CDC45 is involved in the most significant pathways, including the cell cycle, DNA replication, drug metabolism − cytochrome P450, metabolism of xenobiotics by cytochrome P450, and chemical carcinogenesis pathways. Conclusions Our findings showed that a low protein level of CDC45 was associated with a poor prognosis in HCC patients, indicating that CDC45 might be a novel prognostic marker and help identify new therapeutic targets for HCC.

scholarly journals Prognostic implications of cell division cycle protein 45 expression in hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10824
Author(s):  
Chen Yang ◽  
shufang Xie ◽  
Yi Wu ◽  
Guoqing Ru ◽  
Xianglei He ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Division ◽  
Dna Replication ◽  
Chemical Carcinogenesis ◽  
Cell Division Cycle ◽  
Functional Enrichment ◽  
Functional Annotations ◽  
Normal Tissues ◽  
Prognostic Implications ◽  
Tissue Specimens

Background The overall prognosis of hepatocellular carcinoma (HCC) is poor and novel prognostic biomarkers might better monitor the progression of HCC. Cell division cycle protein 45 (CDC45) plays a key role in DNA replication and considered to be involved in tumorigenesis. This study investigated CDC45 expression in tumour tissues and defined its prognostic value in HCC patients. Methods We used immunohistochemistry (IHC) staining to examine the expression of CDC45 in tumour tissue specimens and compare them with adjacent normal tissue specimens using a constructed tissue microarray (TMA) and analyzed how clinical features are related to HCC prognosis. Functional enrichment analyses were used to describe significantly involved hallmark pathways of differentially expressed genes (DEGs, which were screened out according to the high or low expression of CDC45 in tumour tissues). Results Our findings showed that the proteome expression of CDC45 was evidently downregulated in HCC tissues compared with matched normal tissues (P < 0.0001). Although we did not find any differences in terms of vascular invasion, metastasis, lymphatic infiltration, or Edmondson grade between patients with high and low CDC45 expression, low CDC45 expression was significantly correlated with microvascular invasion (P = 0.046). Multivariate analysis indicated that CDC45 expression (P = 0.035) was an independent prognostic factor for the overall survival (OS) rate of HCC patients. Patients with CDC45 expression was positively correlated with OS rates among HCC patients (P < 0.05). Functional annotations indicated that CDC45 is involved in the most significant pathways, including the cell cycle, DNA replication, chemical carcinogenesis and drug metabolism–cytochrome P450 pathways. Discussion Our findings showed that low proteomic level of CDC45 was associated with a poor prognosis in HCC patients, indicating that CDC45 might be a novel prognostic marker.

scholarly journals Identification of The Critical Genes and miRNAs in Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Jun Wang ◽  
Chuyan Wang ◽  
Liuqing Yang ◽  
Kexin Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Expression Profiles ◽  
Chemical Carcinogenesis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Cancer Pathogenesis ◽  
Target Network ◽  
Mirnas Expression

Abstract Hepatocellular carcinoma (HCC) is a global health problem with a complex etiology and pathogenesis. Microarray data is increasingly being used as a novel and effective method for cancer pathogenesis analysis. To unravel the potential prognosis of HCC, an integrative study of mRNA and miRNA for HCC was conducted. Two microarray datasets (GSE89377 and GSE101685) and two miRNAs expression profiles (GSE112264 and GSE113740) were obtained from Gene Expression Omnibus (GEO) database. A total of 177 DEGs and 80 DEMs were screened out. Functional enrichment of DEGs was proceeded by Clue GO, including GO and KEGG pathway analysis. These genes were significantly enriched in chemical carcinogenesis. PPI network was then established on the STRING platform, and ten hub genes (CDC20, TOP2A, ASPM, NCAPG, AURKA, CYP2E1, HMMR, PRC1, TYMS, and CYP4A11) were visualized via Cytoscape software. Then, a miRNA-target network was established to identify the dysregulated miRNA. A key miRNA (hsa-miR-124-3p) was filtered. Finally, the miRNA-target- transcription factor networks were constructed for hsa-miR-124-3p. The network for hsa-miR-124-3p included two transcription factors (TFs) and five targets. These identified DEGs and DEMs, TFs, targets, and regulatory networks may help advance our understanding the underlying pathogenesis of HCC.

scholarly journals Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Xiaofei Wang ◽  
Jie Qiao ◽  
Rongqi Wang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Expression Profiles ◽  
Univariate Analysis ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

scholarly journals FOS Expression Distinguishes Two Groups of Atypical CML (aCML) Allowing Targeted Therapy

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 3893-3893
Author(s):  
Manja Meggendorfer ◽  
Wencke Walter ◽  
Stephan Hutter ◽  
Wolfgang Kern ◽  
Claudia Haferlach ◽  
...  
Keyword(s):  
Gene Expression ◽  
Myeloproliferative Neoplasms ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Next Generation Sequencing Data ◽  
Employment Equity ◽  
Expression Levels ◽  
Equity Ownership ◽  
Cox 2 ◽  
Fos Expression

Abstract BCR-ABL1 negative myeloproliferative neoplasms not only include atypical chronic myeloid leukemia (aCML), but also chronic myelomonocytic leukemia (CMML), chronic neutrophilic leukemia (CNL), and myelodysplastic/myeloproliferative neoplasm, unclassifiable (MDS/MPN, U). Despite the recent advances in characterizing aCML more specifically, based on next generation sequencing data, the differential diagnosis and subsequent treatment decisions remain difficult. Therefore, we analyzed the transcriptome and performed whole genome sequencing (WGS) in a cohort of morphologically defined 231 patients (pts): 49 aCML, 30 CNL, 50 MDS/MPN, U, and 102 CMML all diagnosed according to WHO classification. WGS libraries were prepared with the TruSeq PCR free library prep kit and sequenced on a NovaSeq 6000 or HiSeqX instrument with 100x coverage (Illumina, San Diego, CA). The Illumina tumor/unmatched normal workflow was used for variant calling. To remove potential germline variants, each variant was queried against the gnomAD database, variants with global population frequencies >1% where excluded. For transcriptome analysis total RNA was sequenced on the NovaSeq 6000 with a median of 50 mio. reads per sample. The obtained estimated gene counts were normalized and the resulting log2 counts per million (CPMs) were used as a proxy of gene expression. Unsupervised exploratory analysis techniques, such as principal component analysis (PCA) and hierarchical clustering (HC) were used to identify groups of samples with similar expression profiles. We observed a high similarity between the different entities with CMML being the most distant entity, followed by CNL. MDS/MPN, U and aCML were the most similar entities. Due to high within-group heterogeneity, we found that it was impossible to identify a gene expression signature that separated aCMLs reliably from the other MDS/MPN overlap entities. Surprisingly, PCA as well as HC indicated the existence of two subgroups within the aCMLs. Therefore, we searched for genes with a bimodal-like expression profile. We found that FOS expression levels strongly separated aCMLs into two groups of 16 pts (FOSlow) and 33 pts (FOShi), respectively. Interestingly, FOShi correlated with mutations in SETBP1 (12/33, 36% vs. 3/16, 19%), a known marker typically mutated in aCML (Figure 1a). Addressing the mutational landscape of these two groups (FOShivs.FOSlow) we found that ASXL1 (88% vs. 100%), TET2 (33% vs. 50%), SRSF2 (45% vs. 56%), EZH2 (27% vs. 31%), and NRAS (21% vs. 25%) showed rather similar mutation frequencies. GATA2 (15% vs. 31%) and RUNX1 (18% vs. 38%) mutations were less frequent in FOShi, whereas SETBP1 and CBL (18% vs. 6%) were more frequent in this group. Consistent with known features of SETBP1 mutation this group showed a higher white blood cell count (78 x109/L vs. 52 x109/L) and platelet count (158x109/L vs. 90x109/L), although none of these differences were significant. The two groups were further analyzed for gene expression differences and we found 16 genes with synchronized upregulation within the FOShi group that were differentially expressed (FDR < 0.05, absolute logFC > 1.5) compared to FOSlow. Functional enrichment analysis linked those genes with regulation of cell proliferation (p<0.001), negative regulation of cell death (p<0.001), and the AP-1 complex (p<0.001). Those 16 genes included the transcription factors JUN, FOSB, EGR3, and KLF4, the cancer-related genes DUSP1, RHOB, OSM, TNFRSF10C, and CXCR2, and the FDA approved drug targets JUN, COX-2, and FCGR3B (Figure 1b). JUN/FOS are the main components of the AP-1 complex, a regulator of cell life and death. The upregulation of these genes results in increased proliferation as clinically observed in aCML pts. Furthermore, for these pts a treatment with INFα might result in an anti-proliferative effect by modulation of FOS transcript levels. Further, COX-2 inhibitors might also suppress proliferation and differentiation of leukemia cells. However, for the FOSlow pts these treatments might not be as effective due to the already low expression levels of the respective genes. Since the expression levels of FOShi equal those of MDS/MPN overlap, whereas FOSlow levels are closer to the ones of a healthy control cohort, SETBP1 mutation might be a marker and indicator for pts with high FOS expression and therefore providing further treatment options by targeting specifically the FOS mediated pathways. Disclosures Meggendorfer: MLL Munich Leukemia Laboratory: Employment. Walter:MLL Munich Leukemia Laboratory: Employment. Hutter:MLL Munich Leukemia Laboratory: Employment. Kern:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership. Haferlach:MLL Munich Leukemia Laboratory: Employment, Equity Ownership.

scholarly journals Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jia Wang ◽  
Rui Peng ◽  
Zheng Zhang ◽  
Yixi Zhang ◽  
Yuke Dai ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Primary Liver Cancer ◽  
Interaction Network ◽  
Chemical Carcinogenesis ◽  
Parameter Analysis ◽  
Qrt Pcr ◽  
Key Genes

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer and has poor outcomes. However, the potential molecular biological process underpinning the occurrence and development of HCC is still largely unknown. The purpose of this study was to identify the core genes related to HCC and explore their potential molecular events using bioinformatics methods. HCC-related expression profiles GSE25097 and GSE84005 were selected from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) between 306 HCC tissues and 281 corresponding noncancerous tissues were identified using GEO2R online tools. The protein-protein interaction network (PPIN) was constructed and visualized using the STRING database. Gene Ontology (GO) and KEGG pathway enrichment analyses of the DEGs were carried out using DAVID 6.8 and KOBAS 3.0. Additionally, module analysis and centrality parameter analysis were performed by Cytoscape. The expression differences of key genes in normal hepatocyte cells and HCC cells were verified by quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Additionally, survival analysis of key genes was performed by GEPIA. Our results showed that a total of 291 DEGs were identified including 99 upregulated genes and 192 downregulated genes. Our results showed that the PPIN of HCC was made up of 287 nodes and 2527 edges. GO analysis showed that these genes were mainly enriched in the molecular function of protein binding. Additionally, KEGG pathway analysis also revealed that DEGs were mainly involved in the metabolic, cell cycle, and chemical carcinogenesis pathways. Interestingly, a significant module with high centrality features including 10 key genes was found. Among these, CDK1, NDC80, HMMR, CDKN3, and PTTG1, which were only upregulated in HCC patients, have attracted much attention. Furthermore, qRT-PCR also confirmed the upregulation of these five key genes in the normal human hepatocyte cell line (HL-7702) and HCC cell lines (SMMC-7721, MHCC-97L, and MHCC-97H); patients with upregulated expression of these five key genes had significantly poorer survival and prognosis. CDK1, NDC80, HMMR, CDKN3, and PTTG1 can be used as molecular markers for HCC. This finding provides potential strategies for clinical diagnosis, accurate treatment, and prognosis analysis of liver cancer.

scholarly journals Potential lncRNA Biomarkers for HBV-Related Hepatocellular Carcinoma Diagnosis Revealed by Analysis on Coexpression Network

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shunqiang Nong ◽  
Xiaohao Chen ◽  
Zechen Wang ◽  
Guidan Xu ◽  
Wujun Wei ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Functional Annotation ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Coexpression Network ◽  
Support Vector ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Background. Increasing evidence demonstrated that long noncoding RNA (lncRNA) could affect inflammatory tumor immune microenvironment by modulating gene expression and could be used as a biomarker for HBC-related hepatocellular carcinoma (HCC) but still needs further research. The aim of the present study was to determine an lncRNA signature for the diagnosis of HBV-related HCC. Methods. HBV-related HCC expression profiles (GSE55092, GSE19665, and GSE84402) were abstracted from the GEO (Gene Expression Omnibus) data resource, and R package limma and RobustRankAggreg were employed to identify common differentially expressed genes (DEGs). Using machine learning, optimal diagnostic lncRNA molecular markers for HBV-related HCC were identified. The expression of candidate lncRNAs was cross-validated in GSE121248, and an ROC (receiver operating characteristic) curve of lncRNA biomarkers was carried out. Additionally, a coexpression network and functional annotation was built, after which a PPI (protein-protein interaction) network along with module analysis were conducted with the Cytoscape open source software. Result. A total of 38 DElncRNAs and 543 DEmRNAs were identified with a fold change larger than 2.0 and a P value < 0.05. By machine learning, AL356056.2, AL445524.1, TRIM52-AS1, AC093642.1, EHMT2-AS1, AC003991.1, AC008040.1, LINC00844, and LINC01018 were screened out as optional diagnostic lncRNA biosignatures for HBV-related HCC. The AUC (areas under the curve) of the SVM (support vector machine) model and random forest model were 0.957 and 0.904, respectively, and the specificity and sensitivity were 95.7 and 100% and 94.3 and 86.5%, respectively. The results of functional enrichment analysis showed that the integrated coexpressed DEmRNAs shared common cascades in the p53 signaling pathway, retinol metabolism, PI3K-Akt signaling cascade, and chemical carcinogenesis. The integrated DEmRNA PPI network complex was found to be comprised of 87 nodes, and two vital modules with a high degree were selected with the MCODE app. Conclusion. The present study identified nine potential diagnostic biomarkers for HBV-related HCC, all of which could potentially modulated gene expression related to inflammatory conditions in the tumor immune microenvironment. The functional annotation of the target DEmRNAs yielded novel evidence in evaluating the precise functions of lncRNA in HBV-related HCC.

scholarly journals High Expression Levels of SLC38A1 Are Correlated with Poor Prognosis and Defective Immune Infiltration in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yun Liu ◽  
Yong Yang ◽  
Linna Jiang ◽  
Hongrui Xu ◽  
Junwei Wei
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Death ◽  
Programmed Cell Death ◽  
Protein Expression ◽  
Prognostic Significance ◽  
Functional Enrichment ◽  
High Expression ◽  
Expression Levels ◽  
Immune Infiltration ◽  
Mrna And Protein Expression

Solute Carrier Family 38 Member 1 (SLC38A1) is a principal transporter of glutamine and plays a crucial role in the transformation of neoplastic cells. However, the correlation between SLC38A1 expression, prognosis, and immune infiltration in hepatocellular carcinoma (HCC) has yet to be elucidated. We used two independent patient cohorts, namely, a Cancer Genome Atlas (TCGA) cohort and a Clinical Proteomic Tumor Analysis Consortium (CPTAC) cohort, to analyze the role of SLC38A1 in HCC at the mRNA and protein levels, respectively. In these two cohorts, SLC38A1 mRNA and protein expression levels were higher in HCC tissues than in adjacent nontumor tissues. Both SLC38A1 mRNA and protein expression were positively associated with clinicopathological characteristics (clinical stage, T stage, pathological grade, tumor size, and tumor thrombus), were negatively associated with survival, and were independent prognostic factors in HCC patients. Functional enrichment analyses further indicated that SLC38A1 was involved in multiple pathways related to amino acid metabolism, tumors, and immunity. High expression levels of SLC38A1 were inversely proportional to CD8+ T cells and directly proportional to macrophages M0, neutrophils, programmed cell death-1/programmed cell death ligand 1 (PD-1/PD-L1), and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). Moreover, we used immunohistochemical analysis of tissue samples and other online databases to further validate the expression levels and prognostic significance of SLC38A1 in HCC. Collectively, our study demonstrated that the upregulated expression of SLC38A1 was related to an unfavorable prognosis and defective immune infiltration in HCC.

scholarly journals m6A RNA Methylation Regulators Elicit Malignant Progression and Predict Clinical Outcome in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wenli Li ◽  
Jun Liu ◽  
Zhanzhong Ma ◽  
Xiaofeng Zhai ◽  
Binbin Cheng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cell ◽  
Clinical Outcome ◽  
Cox Regression ◽  
Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Rna Methylation ◽  
M6a Rna Methylation

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide, and N6-methyladenosine (m6A) is a predominant internal modification of RNA in various cancers. We obtained the expression profiles of m6A-related genes for HCC patients from the International Cancer Genome Consortium and The Cancer Genome Atlas datasets. Most of the m6A RNA methylation regulators were confirmed to be differentially expressed among groups stratified by clinical characteristics and tissues. The clinical factors (including stage, grade, and gender) were correlated with the two subgroups (cluster 1/2). We identified an m6A RNA methylation regulator-based signature (including METTL3, YTHDC2, and YTHDF2) that could effectively stratify a high-risk subset of these patients by univariate and LASSO Cox regression, and receiver operating characteristic (ROC) analysis indicated that the signature had a powerful predictive ability. Immune cell analysis revealed that the genes in the signature were correlated with B cell, CD4 T cell, CD8 T cell, dendritic cell, macrophage, and neutrophil. Functional enrichment analysis suggested that these three genes may be involved in genetic and epigenetic events with known links to HCC. Moreover, the nomogram was established based on the signature integrated with clinicopathological features. The calibration curve and the area under ROC also demonstrated the good performance of the nomogram in predicting 3- and 5-year OS in the ICGC and TCGA cohorts. In summary, we demonstrated the vital role of m6A RNA methylation regulators in the initial presentation and progression of HCC and constructed a nomogram which would predict the clinical outcome and provide a basis for individualized therapy.

scholarly journals Identification of Flap endonuclease 1 as a potential core gene in hepatocellular carcinoma by integrated bioinformatics analysis

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7619 ◽  
Author(s):  
Chuanfei Li ◽  
Feng Qin ◽  
Hao Hong ◽  
Hui Tang ◽  
Xiaoling Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Expression Profiles ◽  
Core Gene ◽  
Functional Enrichment ◽  
Potential Core ◽  
Potential Biomarker ◽  
Cancer Tissues ◽  
Core Genes

Hepatocellular carcinoma (HCC) is a common yet deadly form of malignant cancer. However, the specific mechanisms involved in HCC diagnosis have not yet fully elucidated. Herein, we screened four publically available Gene Expression Omnibus (GEO) expression profiles (GSE14520, GSE29721, GSE45267 and GSE60502), and used them to identify 409 differentially expressed genes (DEGs), including 142 and 267 up- and down-regulated genes, respectively. The DAVID database was used to look for functionally enriched pathways among DEGs, and the STRING database and Cytoscape platform were used to generate a protein-protein interaction (PPI) network for these DEGs. The cytoHubba plug-in was utilized to detect 185 hub genes, and three key clustering modules were constructed with the MCODE plug-in. Gene functional enrichment analyses of these three key clustering modules were further performed, and nine core genes including BIRC5, DLGAP5, DTL, FEN1, KIAA0101, KIF4A, MCM2, MKI67, and RFC4, were identified in the most critical cluster. Subsequently, the hierarchical clustering and expression of core genes in TCGA liver cancer tissues were analyzed using the UCSC Cancer Genomics Browser, and whether elevated core gene expression was linked to a poor prognosis in HCC patients was assessed using the GEPIA database. The PPI of the nine core genes revealed an interaction between FEN1, MCM2, RFC4, and BIRC5. Furthermore, the expression of FEN1 was positively correlated with that of three other core genes in TCGA liver cancer tissues. FEN1 expression in HCC and other tumor types was assessed with the FIREBROWSE and ONCOMINE databases, and results were verified in HCC samples and hepatoma cells. FEN1 levels were also positively correlated with tumor size, distant metastasis and vascular invasion. In conclusion, we identified nine core genes associated with HCC development, offering novel insight into HCC progression. In particular, the aberrantly elevated FEN1 may represent a potential biomarker for HCC diagnosis and treatment.

scholarly journals RCC1 Expression as a Prognostic Marker in Colorectal Liver Oligometastases

2021 ◽  
Vol 27 ◽  
Author(s):  
Yuxiang Deng ◽  
Long Yu ◽  
Yujie Zhao ◽  
Jianhong Peng ◽  
Yanbo Xu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prognostic Marker ◽  
Expression Profiles ◽  
Chromatin Condensation ◽  
Gene Expression Profiles ◽  
Nucleotide Exchange ◽  
Expression Levels ◽  
Independent Prognostic Marker ◽  
Normal Tissues ◽  
Nucleotide Exchange Factor

Introduction: Regulator of chromatin condensation 1 (RCC1) is a major guanine-nucleotide exchange factor for Ran GTPase, and it plays key roles in various biological processes. Previous studies have found that RCC1 may play a role in the development of tumors, but little is known about the relationship between RCC1 and colorectal liver oligometastases (CLOs).Methods: One hundred and twenty-nine pairs of matched human CLO samples, including both primary tumor and its liver metastasis specimens, were subjected to immunohistochemistry to determine the location and expression levels of RCC1. Associations between RCC1 and survival as well as gene expression profiling were explored.Results: In this study, we first observed that RCC1 was mildly increased in CLO tumor tissues compared with normal tissues, and the localization was primarily nuclear. In addition, our study found that high RCC1 expression in liver oligometastases was an independent prognostic marker for unfavorable recurrence-free survival and overall survival (p = 0.036 and p = 0.016). Gene expression profiles generated from microarray analysis showed that RCC1 was involved in pathways including “Myc targets,” “E2F targets” and “DNA repair” pathways.Conclusion: Our data indicated that RCC1 was expressed mainly in the nucleus, and strong and significant associations were found between RCC1 expression levels and the survival of CLO patients. These findings indicated that RCC1 may play a role in CLO development.

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