scholarly journals Identification of Core Genes Related to Progression and Prognosis of Hepatocellular Carcinoma and Small-Molecule Drug Predication

2021 ◽  
Vol 12 ◽  
Author(s):  
Nan Jiang ◽  
Xinzhuo Zhang ◽  
Dalian Qin ◽  
Jing Yang ◽  
Anguo Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Gene Expression Signature ◽  
Cox Regression Analysis ◽  
Small Molecule Drugs

BackgroundHepatocellular carcinoma (HCC) is one of the most leading causes of cancer death with a poor prognosis. However, the underlying molecular mechanisms are largely unclear, and effective treatment for it is limited. Using an integrated bioinformatics method, the present study aimed to identify the key candidate prognostic genes that are involved in HCC development and identify small-molecule drugs with treatment potential.Methods and ResultsIn this study, by using three expression profile datasets from Gene Expression Omnibus database, 1,704 differentially expressed genes were identified, including 671 upregulated and 1,033 downregulated genes. Then, weighted co-expression network analysis revealed nine modules are related with pathological stage; turquoise module was the most associated module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analyses (KEGG) indicated that these genes were enriched in cell division, cell cycle, and metabolic related pathways. Furthermore, by analyzing the turquoise module, 22 genes were identified as hub genes. Based on HCC data from gene expression profiling interactive analysis (GEPIA) database, nine genes associated with progression and prognosis of HCC were screened, including ANLN, BIRC5, BUB1B, CDC20, CDCA5, CDK1, NCAPG, NEK2, and TOP2A. According to the Human Protein Atlas and the Oncomine database, these genes were highly upregulated in HCC tumor samples. Moreover, multivariate Cox regression analysis showed that the risk score based on the gene expression signature of these nine genes was an independent prognostic factor for overall survival and disease-free survival in HCC patients. In addition, the candidate small-molecule drugs for HCC were identified by the CMap database.ConclusionIn conclusion, the nine key gene signatures related to HCC progression and prognosis were identified and validated. The cell cycle pathway was the core pathway enriched with these key genes. Moreover, several candidate molecule drugs were identified, providing insights into novel therapeutic approaches for HCC.

scholarly journals PSMC2 Regulates Cell Cycle Progression Through the p21/Cyclin D1 Pathway and Predicts a Poor Prognosis in Human Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yiwei Liu ◽  
Hairong Chen ◽  
Xiangcheng Li ◽  
Feng Zhang ◽  
Lianbao Kong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Cell Proliferation ◽  
Cyclin D1 ◽  
Cell Cycle Progression ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Luciferase Reporter ◽  
Cycle Progression ◽  
Cox Regression Analysis

Proteasome 26S subunit ATPase 2 (PSMC2) plays a pathogenic role in various cancers. However, its function and molecular mechanism in hepatocellular carcinoma (HCC) remain unknown. In this study, tissue microarray (TMA) analysis showed that PSMC2 is highly expressed in HCC tumors and correlates with poor overall and disease-free survival in HCC patients. Multivariate Cox regression analysis revealed that PSMC2 is an independent prognostic factor for HCC patients. Furthermore, our results showed that PSMC2 knockdown inhibited cell proliferation and suppressed tumorigenesis in vivo. Knockdown of PSMC2 increased the expression of p21 and therefore decreased the expression of cyclin D1. Dual-luciferase reporter assays indicated that depletion of PSMC2 significantly enhanced the promoter activity of p21. Importantly, PSMC2 knockdown-induced phenotypes were also rescued by downregulation of P21. Taken together, our data suggest that PSMC2 promotes HCC cell proliferation and cell cycle progression through the p21/cyclin D1 signaling pathway and could be a promising diagnostic and therapeutic target for HCC patients.

scholarly journals A Prognostic Autophagy-Related Gene Pair Signature and Small-Molecule Drugs for Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
ZeBing Song ◽  
GuoPei Zhang ◽  
Yang Yu ◽  
ShaoQiang Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Small Molecule ◽  
Risk Score ◽  
Cox Regression ◽  
Cancer Genome ◽  
Related Gene ◽  
Prognostic Signature ◽  
Aberrant Expression ◽  
Cox Regression Analysis ◽  
Small Molecule Drugs

Dysregulation of autophagy-related genes (ARGs) is related to the prognosis of cancers. However, the aberrant expression of ARGs signature in the prognosis of hepatocellular carcinoma (HCC) remain unclear. Using The Cancer Genome Atlas and the International Cancer Genome Consortium database, 188 common autophagy-related gene pairs (ARGPs) were identified. Through univariate, least absolute shrinkage and selection operator analysis, and multivariate Cox regression analysis, a prognostic signature of the training set was constructed on the basis of 6 ARGPs. Further analysis revealed that the ARGP based signature performed more accurately in overall survival (OS) prediction compared to other published gene signatures. In addition, a high risk of HCC was closely related to CTLA4 upregulation, LC3 downregulation, low-response to axitinib, rapamycin, temsirolimus, docetaxel, metformin, and high-response to bleomycin. Univariate Cox and multivariate Cox analysis revealed that the risk score was an independent prognostic factor for HCC. These results were internally validated in the test and TCGA sets and externally validated in the ICGC set. A nomogram, consisting of the risk score and the TNM stage, performed well when compared to an ideal nomogram. In conclusion, a 6-ARGP-based prognostic signature was identified and validated as an effective predictor of OS of patients with HCC. Furthermore, we recognized six small-molecule drugs, which may be potentially effective in treating HCC.

scholarly journals Identification of a Ferroptosis-Related Signature Model Including mRNAs and lncRNAs for Predicting Prognosis and Immune Activity in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zi-An Chen ◽  
Hui Tian ◽  
Dong-Mei Yao ◽  
Yuan Zhang ◽  
Zhi-Jie Feng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Small Molecule ◽  
Risk Score ◽  
Cox Regression ◽  
Principal Component ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Cox Regression Analysis ◽  
Small Molecule Drugs

BackgroundFerroptosis is a novel form of regulated cell death involved in tumor progression. The role of ferroptosis-related lncRNAs in hepatocellular carcinoma (HCC) remains unclear.MethodsRNA-seq and clinical data for HCC patients were downloaded from The Cancer Genome Atlas (TCGA) Genomic Data Commons (GDC) portal. Bioinformatics methods, including weighted gene coexpression network analysis (WGCNA), Cox regression, and least absolute shrinkage and selection operator (LASSO) analysis, were used to identify signature markers for diagnosis/prognosis. The tumor microenvironment, immune infiltration and functional enrichment were compared between the low-risk and high-risk groups. Subsequently, small molecule drugs targeting ferroptosis-related signature components were predicted via the L1000FWD and PubChem databases.ResultsThe prognostic model consisted of 2 ferroptosis-related mRNAs (SLC1A5 and SLC7A11) and 8 ferroptosis-related lncRNAs (AC245297.3, MYLK-AS1, NRAV, SREBF2-AS1, AL031985.3, ZFPM2-AS1, AC015908.3, MSC-AS1). The areas under the curves (AUCs) were 0.830 and 0.806 in the training and test groups, respectively. Decision curve analysis (DCA) revealed that the ferroptosis-related signature performed better than all pathological characteristics. Multivariate Cox regression analysis showed that the risk score was an independent prognostic factor. The survival probability of low- and high-risk patients could be clearly distinguished by the principal component analysis (PCA) plot. The risk score divided HCC patients into two distinct groups in terms of immune status, especially checkpoint gene expression, which was further supported by the Gene Ontology (GO) biological process, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, several small molecule drugs (SIB-1893, geldanamycin and PD-184352, etc) targeting ferroptosis-related signature components were identified for future reference.ConclusionWe constructed a new ferroptosis-related mRNA/lncRNA signature for HCC patients. The model can be used for prognostic prediction and immune evaluation, providing a reference for immunotherapies and targeted therapies.

Development and Validation of Prognostic Nomograms Predicting Recurrence and Survival in Patients with Solitary Hepatocellular Carcinoma Following Curative Liver Resection

2021 ◽  
Author(s):  
Xinxin Chen ◽  
Wenxia Qiu ◽  
Xuekun Xie ◽  
Zefeng Chen ◽  
Zhiwei Han ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Resection ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Clinicopathological Characteristics ◽  
Tumor Diameter ◽  
Cox Regression Analysis ◽  
Tumor Capsule ◽  
Calibration Curves ◽  
Solitary Hepatocellular Carcinoma

Abstract Background: This work was designed to establish and verify our nomograms integrating clinicopathological characteristics with hematological biomarkers to predict both disease-free survival (DFS) and overall survival (OS) in solitary hepatocellular carcinoma (HCC) patients following hepatectomy.Methods: We scrutinized the data retrospectively from 414 patients with a clinicopathological diagnosis of solitary HCC from Guangxi Medical University Cancer Hospital (Nanning, China) between January 2004 and December 2012. Following the random separation of the samples in a 7:3 ratio into the training set and validation set, the former set was assessed by Cox regression analysis to develop two nomograms to predict the 1-year and 3-year DFS and OS (3-years and 5-years). This was followed by discrimination and calibration estimation employing Harrell’s C-index (C-index) and calibration curves, while the internal validation was also assessed.Results: In the training cohort, the tumor diameter, tumor capsule, macrovascular invasion, and alpha-fetoprotein (AFP) were included in the DFS nomogram. Age, tumor diameter, tumor capsule, macrovascular invasion, microvascular invasion, and aspartate aminotransferase (AST) were included in the OS nomogram. The C-index was 0.691 (95% CI: 0.644-0.738) for the DFS-nomogram and 0.713 (95% CI: 0.670-0.756) for the OS-nomogram. The survival probability calibration curves displayed a fine agreement between the predicted and observed ranges in both data sets. Conclusion: Our nomograms combined clinicopathological features with hematological biomarkers to emerge effective in predicting the DFS and OS in solitary HCC patients following curative liver resection. Therefore, the potential utility of our nomograms for guiding individualized treatment clinically and monitor the recurrence monitoring in these patients.

scholarly journals Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11273
Author(s):  
Lei Yang ◽  
Weilong Yin ◽  
Xuechen Liu ◽  
Fangcun Li ◽  
Li Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Overall Survival ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Hub Genes ◽  
Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

scholarly journals Prognostic Value and Regulatory Network of Immune-Related Genes in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Xiang Zhou ◽  
Keying Zhang ◽  
Fa Yang ◽  
Chao Xu ◽  
Jianhua Jiao ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Cox Regression ◽  
Wilcoxon Test ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Hepatocellular carcinoma (HCC) is a disease with higher morbidity, mortality, and poor prognosis in the whole world. Understanding the crosslink between HCC and the immune system is essential for people to uncover a few potential and valuable therapeutic strategies. This study aimed to reveal the correlation between HCC and immune-related genes and establish a clinical evaluation model. Methods: We had analyzed the clinical information consisted of 373 HCC and 49 normal samples from the cancer genome atlas (TCGA). The differentially expressed genes (DEGs) were selected by the Wilcoxon test and the immune-related differentially expressed genes (IRDEGs) in DEGs were identified by matching DEGs with immune-related genes downloaded from the ImmPort database. Furthermore, the univariate Cox regression analysis and multivariate Cox regression analysis were performed to construct a prognostic risk model. Then, twenty-two types of tumor immune-infiltrating cells (TIICs) were downloaded from Tumor Immune Estimation Resource (TIMER) and were used to construct the correlational graphs between the TIICs and risk score by the CIBERSORT. Subsequently, the transcription factors (TFs) were gained in the Cistrome website and the differentially expressed TFs (DETFs) were achieved. Finally, the KEGG pathway analysis and GO analysis were performed to further understand the molecular mechanisms between DETFs and PDIRGs.Results: In our study, 5839 DEGs, 326 IRDEGs, and 31 prognosis-related IRDEGs (PIRDEGs) were identified. And 8 optimal PIRDEGs were employed to construct a prognostic risk model by multivariate Cox regression analysis. The correlation between risk genes and clinical characterizations and TIICs has verified that the prognostic model was effective in predicting the prognosis of HCC patients. Finally, several important immune-related pathways and molecular functions of the eight PIRDEGs were significantly enriched and there was a distinct association between the risk IRDEGs and TFs. Conclusion: The prognostic risk model showed a more valuable predicting role for HCC patients, and produced many novel therapeutic targets and strategies for HCC.

scholarly journals Repurposing of KLF5 activates a cell cycle signature during the progression from a precursor state to Oesophageal Adenocarcinoma

2020 ◽  
Author(s):  
Connor Rogerson ◽  
Samuel Ogden ◽  
Edward Britton ◽  
Yeng Ang ◽  
Andrew D. Sharrocks ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Prognostic Significance ◽  
Gene Expression Signature ◽  
Chromatin Accessibility ◽  
Oesophageal Adenocarcinoma ◽  
Cell Cycle Gene ◽  
A Cell

AbstractOesophageal adenocarcinoma (OAC) is one of the most common causes of cancer deaths and yet compared to other common cancers, we know relatively little about the underlying molecular mechanisms. Barrett’s oesophagus (BO) is the only known precancerous precursor to OAC, but our understanding about the specific events leading to OAC development is limited. Here, we have integrated gene expression and chromatin accessibility profiles of human biopsies of BO and OAC and identified a strong cell cycle gene expression signature in OAC compared to BO. Through analysing associated chromatin accessibility changes, we have implicated the transcription factor KLF5 in the transition from BO to OAC. Importantly, we show that KLF5 expression is unchanged during this transition, but instead, KLF5 is redistributed across chromatin in OAC cells to directly regulate cell cycle genes specifically in OAC. Our findings have potential prognostic significance as the survival of patients with high expression of KLF5 target genes is significantly lower. We have provided new insights into the gene expression networks in OAC and the mechanisms behind progression to OAC, chiefly the repurposing of KLF5 for novel regulatory activity in OAC.

Identification of Four Key Biomarkers and Small Molecule Drugs Innasopharyngeal Carcinoma by Weighted Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Xi Pan ◽  
Jian-Hao Liu
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Therapeutic Goals ◽  
Small Molecule Drugs

Abstract Background Nasopharyngeal carcinoma (NPC) is a heterogeneous carcinoma that the underlying molecular mechanisms involved in the tumor initiation, progression, and migration are largely unclear. The purpose of the present study was to identify key biomarkers and small-molecule drugs for NPC screening, diagnosis, and therapy via gene expression profile analysis. Methods Raw microarray data of NPC were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to screen out the potential differentially expressed genes (DEGs). The key modules associated with histology grade and tumor stage was identified by using weighted correlation network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the key module were performed to identify potential mechanisms. Candidate hub genes were obtained, which based on the criteria of module membership (MM) and high connectivity. Then we used receiver operating characteristic (ROC) curve to evaluate the diagnostic value of hub genes. The Connectivity map database was further used to screen out small-molecule drugs of hub genes. Results A total of 430 DEGs were identified based on two GEO datasets. The green gene module was considered as key module for the tumor stage of NPC via WGCNA analysis. The results of functional enrichment analysis revealed that genes in the green module were enriched in regulation of cell cycle, p53 signaling pathway, cell part morphogenesis. Furthermore, four DEGs-related hub genes in the green module were considered as the final hub genes. Then ROC revealed that the final four hub genes presented with high areas under the curve, suggesting these hub genes may be diagnostic biomarkers for NPC. Meanwhile, we screened out several small-molecule drugs that have provided potentially therapeutic goals for NPC. Conclusions Our research identified four potential prognostic biomarkers and several candidate small-molecule drugs for NPC, which may contribute to the new insights for NPC therapy.

Coexpression of PDGFR-Alpha, PDGFR-Beta and VEGF as a Prognostic Factor in Patients with Hepatocellular Carcinoma

2011 ◽  
Vol 26 (2) ◽  
pp. 108-116 ◽  
Author(s):  
Li Chen ◽  
Yan Shi ◽  
Cheng-ying Jiang ◽  
Li-xin Wei ◽  
Ya-li Lv ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Growth Factor ◽  
Proportional Hazards Model ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Prognostic Significance ◽  
Disease Free Survival ◽  
Hazard Ratio ◽  
Cox Proportional Hazards Model ◽  
Cox Regression Analysis

Aims To evaluate the prognostic value of vascular endothelial growth factor (VEGF), platelet-derived growth factor receptor-alpha (PDGFR-α) and beta (PDGFR-β) expression in patients with hepatocellular carcinoma (HCC). Methods The expression of PDGFR-α, PDGFR-β and VEGF in 63 HCC patients who underwent curative resection was examined by immunohistochemistry (IHC). The correlations between the expression of these biomarkers and the clinicopathological characteristics were analyzed. Patient survival was analyzed by univariate analysis and Cox proportional hazards model. Results Univariate survival analysis showed that PDGFR-α or PDGFR-β overexpression was of no prognostic significance in predicting disease-free survival (DFS) and overall survival (OS) (p>0.05), while VEGF overexpression and PDGFR-α/PDGFR-β/VEGF coexpression were significantly correlated with worse DFS and poorer OS in HCC patients (P<0.05). More importantly, PDGFR-α/PDGFR-β/VEGF coexpression was an independent prognostic marker for poor survival as indicated by multivariate Cox regression analysis (DFS, hazard ratio 3.122, p=0.001; OS, hazard ratio 4.260, p=0.000). Conclusions Coexpression of PDGFR-α, PDGFR-β and VEGF could be considered an independent prognostic biomarker for predicting DFS and OS in HCC patients. This result could be used to identify patients at a higher risk of tumor recurrence and poor prognosis, and help to select therapeutic schemes for the treatment of HCC.

scholarly journals PD-L1+NEUT, Foxp3+Treg, and NLR as New Prognostic Marker with Low Survival Benefits Value in Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110458
Author(s):  
Lin Zhou ◽  
Jing Wang ◽  
Shao-cheng Lyu ◽  
Li-chao Pan ◽  
Xian-jie Shi ◽  
...  
Keyword(s):  
Risk Factors ◽  
Hepatocellular Carcinoma ◽  
Peripheral Blood ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Rank Test ◽  
Cancer Tissue ◽  
Cox Regression Analysis ◽  
Poorly Differentiated ◽  
Independent Risk Factors

Background: This presented study was aimed to evaluate the diagnostic and prognostic value of PD-L1+Neutrophils (PD-L1+NEUT) and neutrophil to lymphocyte ratio (NLR) based on our previous experience of Foxp3+Treg in transplantation. Methods: the NLR cutoff value of 1.79 was used to include 136 cases from the 204 patients with hepatocellular carcinoma (HCC) confirmed by clinical pathology, which were divided into highly-moderately and poorly differentiated HCC groups. The expressions of PD-L1+NEUT and Foxp3+Treg in peripheral blood and cancer tissue were detected with flow cytometry, meanwhile, PD-L1 and Foxp3 expressed in carcinoma and para-carcinoma tissues were marked by immunohistochemistry. Survival rates, including overall survival and disease-free survival, were calculated by the Kaplan–Meier curve and evaluated with the log-rank test. Finally, Cox risk regression model was used to analyze the independent risk factors for prognostic survival. Results: The level of PD-L1+NEUT, Foxp3+Treg, and NLR in peripheral blood of patients with poorly differentiated HCC were significantly increased (all P < .001). Both PD-L1+NEUT and NLR were positively correlated with Foxp3+Treg ( r = 0.479, P = .0017; r = 0.58, P < .0001). The level of PD-L1+NEUT and Foxp3+Treg as well as PD-L1 and Foxp3 in cancer tissue and patients with poorly differentiated HCC were obviously increased (all P < .01), respectively. Cox regression analysis indicated that PD-L1+NEUT, NLR, and Foxp3+Treg were independent risk factors for the prognosis ( P = .000, .000, .006) with a RR and 95%CI of 2.704-(2.155-3.393), 3.139-(2.361-4.173), 1.409-(1.105-1.798), respectively. Conclusion: PD-L1+NEUT, NLR, and Foxp3+Treg are independent risk factors for prognosis which maybe new marker of lower survival benefits.

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