Low UGP2 Expression Is Associated with Tumour Progression and Predicts Poor Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a malignant tumour associated with a high mortality rate and poor prognosis worldwide. Uridine diphosphate-glucose pyrophosphorylase 2 (UGP2), a key enzyme in glycogen biosynthesis, has been reported to be associated with the occurrence and development of various cancer types. However, its diagnostic value and prognostic value in HCC remain unclear. The present study observed that UGP2 expression was significantly downregulated at both the mRNA and protein levels in HCC tissues. Receiver operating characteristic (ROC) curve analysis revealed that UGP2 may be an indicator for the diagnosis of HCC. In addition, Kaplan-Meier and Cox regression multivariate analyses indicated that UGP2 is an independent prognostic factor of overall survival (OS) in patients with HCC. Furthermore, gene set enrichment analysis (GSEA) suggested that gene sets negatively correlated with the survival of HCC patients were enriched in the group with low UGP2 expression levels. More importantly, a significant correlation was identified between low UGP2 expression and fatty acid metabolism. In summary, the present study demonstrates that UGP2 may contribute to the progression of HCC, indicating a potential therapeutic target for HCC patients.

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Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5970085 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Honglan Guo ◽

Qinqiao Fan

Keyword(s):

Hepatocellular Carcinoma ◽

Roc Curve ◽

Cox Regression ◽

Enrichment Analysis ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

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Increased Expression Together with the Gene Regulation Network of NEIL3 Predicts Poor Prognosis and Correlates with Immune Infiltrates in Hepatocellular Carcinoma

10.21203/rs.2.23392/v1 ◽

2020 ◽

Author(s):

Bo Hu ◽

Xiao-Bo Yang ◽

Xinting Sang

Keyword(s):

Hepatocellular Carcinoma ◽

Gene Regulation ◽

Poor Prognosis ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Immune Gene ◽

Data Set ◽

Gene Markers

Abstract Background: Abnormal Nei endonuclease VIII-like 3 (NEIL3)expression is associated with carcinogenesis. Methods: We used sequencing data from the Cancer Genome Atlas database, analyzed NEIL3 expression, gene regulation networks and the correlation with immune infiltrates in hepatocellular carcinoma (HCC). Clinicopathologic characteristics associated with overall survival in TCGA patients using Cox regression and the Kaplan-Meier method. Gene Set Enrichment Analysis was performed using TCGA data set. LinkedOmics was used to identify differential gene expression with NEIL3 and to analyze Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways. Gene enrichment analysis examined target networks of kinases and transcription factors.Correlations between NEIL3 expression and cancer immune infiltrates and immune gene markers were analyzed by TIMER and GEPIA. Results: We found that overexpressed NEIL3 predicted poor prognosis. Functional network analysis suggested that NEIL3 regulates the DNA replication and cell cycle signaling via pathways involving several cancer-related kinases and E2F Transcription Factor 1.NEIL3 was also found to be associated with the infiltration of several immune cells. Conclusions: Our results demonstrate that data mining efficiently reveals information about NEIL3 expression, potential regulatory networks and the relationship with immune infiltration in HCC, laying a foundation for further study of the role of NEIL3 in carcinogenesis.

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Abnormal TACC3 Expression is an Independent Prognostic Biomarker in Lung Carcinoma

10.21203/rs.3.rs-121095/v1 ◽

2020 ◽

Author(s):

Dongwei He ◽

Xiaoyan Fan ◽

Yulong Zhang ◽

You Li

Keyword(s):

Risk Factor ◽

Lung Carcinoma ◽

Independent Risk Factor ◽

Cox Regression ◽

Coiled Coil ◽

Enrichment Analysis ◽

Diagnostic Value ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Roc Curve Analysis

Abstract Background: Abnormal expression of transforming acidic coiled-coil protein 3 (TACC3) correlates with tumorigenesis of many human malignancies. However, the expression pattern of TACC3 and its clinical significance have not been well characterized in lung carcinoma (LUAD) so far. Objective: To investigate the association of TACC3 expression level with the clinicopathological characteristics and prognosis of LUAD patients.Methods: In the study, based on Oncomine, Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and The Cancer Genome Atlas (TCGA) databases, the expression of TACC3 was determined in LUAD patients. Further, the expression of TACC3 was established using qRT-PCR in LUAD patients. Results: Our results showed that TACC3 was significantly overexpressed in LUAD tumors compared with non-tumors in the above public databases (all p<0.01). A receiver operating characteristic (ROC) curve analysis suggested that TACC3 may have diagnostic value in LUAD patients (normal vs tumor: AUC = 0.940). Kaplan-Meier analysis further demonstrated that high TACC3 expression in tumors was significantly associated with worse overall survival (OS) in LUAD patients (all p<0.01). In addition, Univariate and multivariate Cox regression analyses showed that TACC3 was an independent risk factor for OS among LUAD patients (HR = 1.02, 95% CI: 1.01-1.04, p = 0.00823; HR=1.43, 95% CI: 1.17-1.70, p <0.001). Finally, using gene set enrichment analysis (GSEA 3.0), we found that a series of potential pathways related to TACC3 were highly enriched with the high TACC3 expression phenotype (p = 0.024, p = 0.003, respectively). Conclusions: The present study provides evidence that TACC3 expression is upregulated in tumors and may be an independent risk factor for prognosis in LUAD patients.

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Overexpressed XRCC2 as an independent risk factor for poor prognosis in glioma patients

Molecular Medicine ◽

10.1186/s10020-021-00316-0 ◽

2021 ◽

Vol 27 (1) ◽

Author(s):

Zhendong Liu ◽

Wang Zhang ◽

Xingbo Cheng ◽

Hongbo Wang ◽

Lu Bian ◽

...

Keyword(s):

Risk Factor ◽

Expression Pattern ◽

Poor Prognosis ◽

Independent Risk Factor ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Cellular Mechanisms ◽

Molecular Features ◽

Meta Analyses

Abstract Background XRCC2, a homologous recombination-related gene, has been reported to be associated with a variety of cancers. However, its role in glioma has not been reported. This study aimed to find out the role of XRCC2 in glioma and reveal in which glioma-specific biological processes is XRCC2 involved based on thousands of glioma samples, thereby, providing a new perspective in the treatment and prognostic evaluation of glioma. Methods The expression characteristics of XRCC2 in thousands of glioma samples from CGGA and TCGA databases were comprehensively analyzed. Wilcox or Kruskal test was used to analyze the expression pattern of XRCC2 in gliomas with different clinical and molecular features. The effect of XRCC2 on the prognosis of glioma patients was explored by Kaplan–Meier and Cox regression. Gene set enrichment analysis (GSEA) revealed the possible cellular mechanisms involved in XRCC2 in glioma. Connectivity map (CMap) was used to screen small molecule drugs targeting XRCC2 and the expression levels of XRCC2 were verified in glioma cells and tissues by RT-qPCR and immunohistochemical staining. Results We found the overexpression of XRCC2 in glioma. Moreover, the overexpressed XRCC2 was associated with a variety of clinical features related to prognosis. Cox and meta-analyses showed that XRCC2 is an independent risk factor for the poor prognosis of glioma. Furthermore, the results of GSEA indicated that overexpressed XRCC2 could promote malignant progression through involved signaling pathways, such as in the cell cycle. Finally, doxazosin, quinostatin, canavanine, and chrysin were identified to exert anti-glioma effects by targeting XRCC2. Conclusions This study analyzed the expression pattern of XRCC2 in gliomas and its relationship with prognosis using multiple datasets. This is the first study to show that XRCC2, a novel oncogene, is significantly overexpressed in glioma and can lead to poor prognosis in glioma patients. XRCC2 could serve as a new biomarker for glioma diagnosis, treatment, and prognosis evaluation, thus bringing new insight into the management of glioma.

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

PeerJ ◽

10.7717/peerj.11273 ◽

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.

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The diagnostic and prognostic value of H2AFY in hepatocellular carcinoma

10.21203/rs.3.rs-17889/v2 ◽

2020 ◽

Author(s):

xuyang ma ◽

Ying Ding ◽

Li Zeng

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Poor Prognosis ◽

Prognostic Value ◽

Clinical Characteristics ◽

Cox Regression ◽

Normal Liver ◽

Diagnostic Value ◽

The Relationship ◽

Diagnostic And Prognostic Value

Abstract Background: The potential correlation between H2AFY (also known as MacroH2A1) and the clinical characteristics of hepatocellular carcinoma (HCC) patients was analysed through gene expression profiles and clinical data in The Cancer Genome Atlas (TCGA) database, and the diagnostic and prognostic value of H2AFY in HCC was discussed. Methods: The gene expression data of HCC and the corresponding clinical characteristics of HCC patients were downloaded from the TCGA database. The differences in H2AFY in normal liver tissues and HCC were analysed. The relationship between H2AFY and clinical characteristics was analysed by Wilcoxon signed-rank test, logistic regression and Kruskal-Wallis test. The Kaplan-Meier method and the Cox regression method were used to analyse the relationship between overall survival and clinical characteristics of the patients. An ROC curve was used to predict the diagnostic value of H2AFY in HCC. Gene set enrichment analysis (GSEA) was used to analyse the pathway enrichment of H2AFY. Result: Compared with normal liver tissues, H2AFY was significantly highly expressed in HCC. H2AFY was positively correlated with the age, clinical stage, G stage (grade) and T stage (tumor stage) of liver cancer patients. Higher H2AFY expression predicted a poor prognosis in HCC patients. Cox regression analysis suggested that H2AFY was an independent risk factor for the prognosis of HCC patients. The ROC curve suggested that H2AFY had certain diagnostic value in HCC. GSEA suggested that H2AFY was correlated with lipid metabolism and a variety of tumour pathways. Conclusion: Our study showed that H2AFY was significantly overexpressed in HCC. H2AFY may be a potential diagnostic and prognostic marker for HCC, and high expression of H2AFY predicts a poor prognosis in patients with HCC.

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Decreased DHRS1 expression is a novel predictor of poor survival in patients with hepatocellular carcinoma

Biomarkers in Medicine ◽

10.2217/bmm-2021-0041 ◽

2021 ◽

Vol 15 (15) ◽

pp. 1319-1331

Author(s):

Li Li ◽

Hui-Jing Situ ◽

Wen-Cheng Ma ◽

Xuan Liu ◽

Lu-Lu Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Aberrant Expression ◽

Normal Tissues ◽

Multiple Data ◽

Kaplan Meier ◽

Survival Gene ◽

Mrna And Protein Expression

Aim: To investigate the effect of aberrant expression of DHRS1 on hepatocellular carcinoma (HCC). Materials & methods: Kaplan–Meier and Cox regression analyses were performed to evaluate the correlation between DHRS1 and overall survival. Gene set enrichment analysis was performed to explore the potential function of DHRS1 in HCC. Results: Multiple data analysis revealed that DHRS1 mRNA and protein expression level were remarkably lower in HCC than that in normal tissues. In survival analysis, patients with low DHRS1 expression presented a poorer prognosis, and was an independent risk factor for HCC. Conclusion: Decreased DHRS1 expression may be a potential predictor of poor prognosis in HCC.

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High Level of METTL7B Indicates Poor Prognosis of Patients and Is Related to Immunity in Glioma

Frontiers in Oncology ◽

10.3389/fonc.2021.650534 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yujia Xiong ◽

Mingxuan Li ◽

Jiwei Bai ◽

Yutao Sheng ◽

Yazhuo Zhang

Keyword(s):

Poor Prognosis ◽

Immune Cell ◽

Characteristic Curve ◽

Glioma Cells ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Immune Checkpoints ◽

Roc Curve Analysis ◽

Cox Analysis ◽

High Level

Glioma is the most common primary intracranial malignant tumor in adults. Although there have been many efforts on potential targeted therapy of glioma, the patient’s prognosis remains dismal. Methyltransferase Like 7B (METTL7B) has been found to affect the development of a variety of tumors. In this study, we collected RNA-seq data of glioma in CGGA and TCGA, analyzed them separately. Then, Kaplan-Meier survival analysis, univariate and multivariate Cox analysis, and receiver operating characteristic curve (ROC curve) analysis were used to evaluate the effect of METTL7B on prognosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA) enrichment analyses were used to identify the function or pathway associated with METTL7B. Moreover, the ESTIMATE algorithm, Cibersort algorithm, Spearman correlation analysis, and TIMER database were used to explore the relationship between METTL7B and immunity. Finally, the role of METTL7B was explored in glioma cells. We found that METTL7B is highly expressed in glioma, and high expression of METTL7B in glioma is associated with poor prognosis. In addition, there were significant differences in immune scores and immune cell infiltration between the two groups with different expression levels of METTL7B. Moreover, METTL7B was also correlated with immune checkpoints. Knockdown of METTL7B revealed that METTL7B promoted the progression of glioma cells. The above results indicate that METTL7B affects the prognosis of patients and is related to tumor immunity, speculating that METTL7B may be a new immune-related target for the treatment of glioma.

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Overexpression of SKA3 correlates with poor prognosis in female early breast cancer

PeerJ ◽

10.7717/peerj.12506 ◽

2021 ◽

Vol 9 ◽

pp. e12506

Author(s):

Yue Zhong ◽

Zhenjie Zhuang ◽

Peiju Mo ◽

Mandi Lin ◽

Jiaqian Gong ◽

...

Keyword(s):

Breast Cancer ◽

Early Breast Cancer ◽

Poor Prognosis ◽

Cox Regression ◽

Molecular Subtype ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Chi Square ◽

Exact Test ◽

Chi Square Test

Background Spindle and kinetochore associated complex subunit 3 (SKA3) plays an important role in tumorigenesis and the progression of various tumors. But the relationship between SKA3 and early breast cancer remains unclear. The study aimed to explore the prognostic significance of SKA3 in breast cancer. Methods In the study, SKA3 expression was initially assessed using the Oncomine database and The Cancer Genome Atlas database (TCGA). Then, we presented validation results for RT-qPCR (quantitative reverse transcription PCR) and ELISA (enzyme-linked immunosorbent assay). The relationship between clinical characteristics and SKA3 expression was assessed by Chi-square test and Fisher’s exact test. Kaplan–Meier method and Cox regression analysis were conducted to evaluate the prognostic value of SKA3. Gene set enrichment analysis (GSEA) was performed to screen biological pathways using the TCGA dataset. Besides, single sample gene set enrichment analysis (ssGSEA) was utilized to identify immune infiltration cells about SKA3. Results SKA3 mRNA was expressed at high levels in breast cancer tissues compared with normal tissues. Chi-square test and Fisher’s exact test showed SKA3 expression was related to age, tumor (T) classification, node (N) classification, tumor-node-metastasis (TNM) stage, estrogen receptor (ER), progesterone receptor (PR), molecular subtype, and race. RT-qPCR results showed that SKA3 expression was overexpressed in ER, PR status, and molecular subtype in Chinese people. Kaplan–Meier curves implicated that high SKA3 expression was related to a poor prognosis in female early breast cancer patients. Cox regression models showed that high SKA3 expression could be used as an independent risk factor for female early breast cancer. Four signaling pathways were enriched in the high SKA3 expression group, including mTORC1 signaling pathway, MYC targets v1, mitotic spindle, estrogen response early. Besides, the SKA3 expression level was associate with infiltrating levels of activated CD4 T cells and eosinophils in breast cancer. Conclusion High SKA3 expression correlates with poor prognosis and immune infiltrates in breast cancer. SKA3 may become a biomarker for the prognosis of breast cancer.

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Identification and Validation of Tumour Microenvironment-Based Immune Related Signature for Hepatocellular Carcioma: Immunotherapeutic Implications

10.21203/rs.3.rs-142371/v1 ◽

2021 ◽

Author(s):

Hong Yu ◽

Shao Wang ◽

Tao Zhou ◽

Jia Sun ◽

Tian Qi ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Treatment Outcomes ◽

Predictive Model ◽

Cox Regression ◽

Survival Rates ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Patient Treatment ◽

Cox Regression Analysis

Abstract Background: Even though treatment outcomes for hepatocellular carcinoma patients have significantly improved, prognostic clinical evaluation remains a substantial challenge due to the heterogeneity and complexity of cancer. Accumulating evidence has revealed that the tumor immune microenvironment is critical for progression and prognosis of hepatocellular carcinoma. A powerful predictive model could assist physicians to better monitor patient treatment outcomes and improve overall survival rates. Therefore, we introduced tumor immune-related genes into a model that could be used for patient risk classification. Results: First, the Single-sample gene set enrichment analysis (ssGSEA) and Weighted gene co-expression networks construction (WGCNA) methods were applied to identify highly associated immunity genes. Following this, a multi-immune-related gene-based signature determined by The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to determine risk stratification. In addition, this predictive model was evaluated according to its performance as a prognostic model in the training and testing datasets. Furthermore, tumor mutation burden and biological enrichment analysis were applied to reveal the potential mechanisms through which the gene signature functions. Conclusion: In conclusion, our four-gene signature model may be clinically applied in hepatocellular carcinoma patients at high risk of mortality for personalized therapy.

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