scholarly journals Identification of Glycolysis-Related lncRNAs and the Novel lncRNA WAC-AS1 Promotes Glycolysis and Tumor Progression in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Xigang Xia ◽  
Hao Zhang ◽  
Peng Xia ◽  
Yimin Zhu ◽  
Jie Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Expression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Differential Expression Analysis ◽  
Lactate Production ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Prognostic Analysis

BackgroundHigh glycolysis efficiency in tumor cells can promote tumor growth. lncRNAs play an important role in the proliferation, metabolism and migration of cancer cells, but their regulation of tumor glycolysis is currently not well researched.MethodsWe analyzed the co-expression of glycolysis-related genes and lncRNAs in The Cancer Genome Atlas (TCGA) database to screen glycolysis-related lncRNAs. Further prognostic analysis and differential expression analysis were performed. We further analyzed the relationship between lncRNAs and tumor immune infiltration. Since WAC antisense RNA 1 (WAC-AS1) had the greatest effect on the prognosis among all screened lncRNAs and had a larger coefficient in the prognostic model, we chose WAC-AS1 for further verification experiments and investigated the function and mechanism of action of WAC-AS1 in hepatocellular carcinoma.ResultsWe screened 502 lncRNAs that have co-expression relationships with glycolytic genes based on co-expression analysis. Among them, 112 lncRNAs were abnormally expressed in liver cancer, and 40 lncRNAs were related to the prognosis of patients. Eight lncRNAs (WAC-AS1, SNHG3, SNHG12, MSC-AS1, MIR210HG, PTOV1-AS1, AC145207.5 and AL031985.3) were used to established a prognostic model. Independent prognostic analysis (P<0.001), survival analysis (P<0.001), receiver operating characteristic (ROC) curve analysis (AUC=0.779) and clinical correlation analysis (P<0.001) all indicated that the prognostic model has good predictive power and that the risk score can be used as an independent prognostic factor (P<0.001). The risk score and lncRNAs in the model were found to be related to a variety of immune cell infiltration and immune functions. WAC-AS1 was found to affect glycolysis and promote tumor proliferation (P<0.01). WAC-AS1 affected the expression of several glycolysis-related genes (cAMP regulated phosphoprotein 19 (ARPP19), CHST12, MED24 and KIF2A) (P<0.01). Under hypoxic conditions, WAC-AS1 regulated ARPP19 by sponging miR-320d to promote glucose uptake and lactate production (P<0.01).ConclusionWe constructed a model based on glycolysis-related lncRNAs to evaluate the prognostic risk of patients. The risk score and lncRNAs in the model were related to immune cell infiltration. WAC-AS1 can regulate ARPP19 to promote glycolysis and proliferation by sponging miR-320d.

scholarly journals Development and validation of a LRP1B mutation-associated prognostic model for hepatocellular carcinoma

2021 ◽  
Author(s):  
Jian Xu ◽  
Xiaomin Shen ◽  
Bo Zhang ◽  
Rui Su ◽  
Mingxuan Cui ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gene Mutation ◽  
Prognostic Model ◽  
Immune Cell ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Immune Cell Infiltration ◽  
Significant Difference ◽  
Patient Prognosis

Abstract Purpose: To develop a LRP1B gene mutation based prognostic model for hepatocellular carcinoma (HCC) patients risk prediction. Methods: The LRP1B gene mutation rate was calculated from HCC patient samples. Meanwhile, differentially expressed genes according to LRP1B mutant were screened out for prognostic model establishment. Based on this innovative model, HCC patients were categoried into high and low-risk group. The immune status including immune cell infiltration ratio and checkpoints have been explored in two groups. Results: It can be shown here 11 genes demonstrate significant differences according to LRP1B status, which can better predict HCC patient prognosis. The accuracy of the model prediction is evaluated and approved by the AUC value. From the immune cell infiltration ratio analysis, there is a significant difference in the infiltration degree of 7 types of immune cells and 2 immune checkpoints between high and low-risk HCC patients. Meanwhile, LRP1B was tested as a prognostic marker in clinic to predict different stages for HCC with satisfied accurancy. Conclusion: This study has explored a potential prognostic biomarker and developed a novel LRP1B mutation-associated prognostic model for hepatocellular carcinoma, which provides a systematic reference for future better understanding of clinical research.

scholarly journals Immune Implication of ASF1B Gene in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Tao Meng ◽  
Zhong Tong ◽  
Ming-Ya Yang ◽  
Jing-Jing Wang ◽  
Li-Xin Zhu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Immune Cell ◽  
Risk Group ◽  
External Validation ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Test Set

Abstract Background: Anti-silencing function 1B (ASF1B) has been demonstrated to contribute to tumorigenesis. However, its carcinogenic and immune effects in hepatocellular carcinoma (HCC) have not been reported. This study aimed to identify immune role of ASF1B in HCC.Methods: HCC datasets obtained from The Cancer Genome Atlas (TCGA) database were used to investigate the role of ASF1B gene in HCC, followed by validation using Gene Expression Omnibus (GEO) datasets and Gene Expression Profiling Interactive Analysis (GEPIA) website. CIBERSORT analysis was performed to evaluate immune cell infiltration levels. The TISIDB and cBioPortal network tool were used to seek ASF1B-associated immunomodulators and its co-expressed genes. TCGA cohort was divided into train set and test set according to the ratio of 7:3. Cox regression was used to identify ASF1B-associated prognostic immunomodulators in train set, followed by internal validation using the test set. Based on the median risk-score, HCC patients were divided into high- and low-risk group for the further survival curves and receiver operating characteristic (ROC) analysis, as well as nomogram and calibration curves analysis. Finally, the dataset collected from the GEO was adopted for external validation.Results: ASF1B was over-expressed in TCGA HCC cohort and contributed poor prognosis, which was verified in two GEO datasets (GSE14520 and GSE6764) and GEPIA, as well as Kaplan Meier Plotter network tool. The immune cell infiltration levels were found to be associated with the ASF1B copy numbers and mRNA expression. A total of 78 ASF1B-associated genes were screened out, including 7 immunoinhibitors, 21 immunostimulators and 50 tightly co-expressed genes. Finally, 5 ASF1B-associated genes (TNFSF4, TNFRSF4, KDR, MICB and CST7) were identified to be strongly related to HCC survival. Survival analysis demonstrated that the prognosis of patients in high-risk group was poor. The prognosis predict model, which was established by nomogram based on risk-score, and was validated in both TCGA test set and GEO validated datasets, exerted excellent predictive power in this study.Conclusion: Our findings showed that the ASF1B was associated with HCC immunity. The selected ASF1B-asociated immune markers could be promising biomarkers for the prognosis of HCC.

scholarly journals Development and validation of a LRP1B mutation-associated prognostic model for hepatocellular carcinoma

2021 ◽  
Author(s):  
Jian Xu ◽  
Xiaomin Shen ◽  
Bo Zhang ◽  
Rui Su ◽  
Mingxuan Cui ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gene Mutation ◽  
Prognostic Model ◽  
Immune Cell ◽  
Low Risk ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Immune Cell Infiltration ◽  
Significant Difference

Purpose: To develop a LRP1B gene mutation based prognostic model for hepatocellular carcinoma (HCC) patients risk prediction. Methods: The LRP1B gene mutation rate was calculated from HCC patient samples. Meanwhile, differentially expressed genes according to LRP1B mutant were screened out for prognostic model establishment. Based on this innovative model, HCC patients were categoried into high and low-risk group. The immune status including immune cell infiltration ratio and checkpoints have been explored in two groups. The functions of LRP1B and risk factors in the model were verified using both in vivo and in vitro experiments. Results: It could be demonstrated that LRP1B was a potential negative predictor for HCC patients prognosis with high mutation frequency. The functions of LRP1B was verified with ELISA assay and Quantitative Real-time PCR method based on clinical recruited HCC participants. 11 genes displayed significant differences according to LRP1B status, which could better predict HCC patient prognosis. The functions of these genes were examined using HCC cell line HCCLM3, suggesting they played a pivotal role in determining HCC cell proliferation and apoptosis. From the immune cell infiltration ratio analysis, there was a significant difference in the infiltration degree of 7 types of immune cells and 2 immune checkpoints between high and low-risk HCC patients. Conclusion: This study hypothesized a potential prognostic biomarker and developed a novel LRP1B mutation-associated prognostic model for hepatocellular carcinoma, which provided a systematic reference for future understanding of clinical research.

scholarly journals Potential of Immune-Related Genes as Biomarkers for Diagnosis and Subtype Classification of Preeclampsia

2020 ◽  
Vol 11 ◽  
Author(s):  
Ying Wang ◽  
Zhen Li ◽  
Guiyu Song ◽  
Jun Wang
Keyword(s):  
Differential Expression ◽  
Clinical Outcomes ◽  
Expression Analysis ◽  
Immune Cell ◽  
Differential Expression Analysis ◽  
Cell Infiltration ◽  
Support Vector ◽  
Immune Cell Infiltration ◽  
Consensus Clustering ◽  
Immune Related Genes

ObjectivePreeclampsia is the main cause of maternal mortality due to a lack of diagnostic biomarkers and effective prevention and treatment. The immune system plays an important role in the occurrence and development of preeclampsia. This research aimed to identify significant immune-related genes to predict preeclampsia and possible prevention and control methods.MethodsDifferential expression analysis between normotensive and PE pregnancies was performed to identify significantly changed immune-related genes. Generalized linear model (GLM), random forest (RF), and support vector machine (SVM) models were established separately to screen the most suitable biomarkers for the diagnosis of PE among these significantly changed immune-related genes. The consensus clustering method was used to divide the PE cases into several subgroups to explore the function of the significantly changed immune-related genes in PE.ResultsThirteen significantly changed immune-related genes were obtained by the differential expression analysis. RF was the best model and was used to select the four most important explanatory variables (CRH, PI3, CCL18, and CCL2) to diagnose PE. A nomogram model was constructed to predict PE based on these four variables. The decision curve analysis (DCA) and clinical impact curves revealed that PE patients could significantly benefit from this nomogram. Consensus clustering analysis of the 13 differentially expressed immune-related genes (DIRGs) was used to identify 3 subgroups of PE pregnancies with different clinical outcomes and immune cell infiltration.ConclusionOur study identified four immune-related genes to predict PE and three subgroups of PE with different clinical outcomes and immune cell infiltration. Future studies on the three subgroups may provide direction for individualized treatment of PE patients.

scholarly journals A Comprehensive Prognostic and Immunological Analysis of a New Three-Gene Signature in Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Jun Liu ◽  
Jianjun Lu ◽  
Wenli Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Immune Checkpoint ◽  
Immune Cell ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Cancer Genome ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Immune Checkpoint Molecules

There are few reports on the role of genes associated with the mRNA expression-based stemness index (mRNAsi) in the prognosis and immune regulation of hepatocellular carcinoma (HCC). This study is aimed at analyzing the expression profile and prognostic significance of a new mRNAsi-based three-gene signature in HCC. This three-gene signature was identified by analyzing mRNAsi data from the Cancer Genome Atlas (TCGA) HCC dataset. The prognostic value of the risk score based on the three-gene signature was evaluated by Cox regression and Kaplan-Meier analysis and then verified in the International Cancer Genome Consortium (ICGC) database. Meanwhile, the correlations between the risk score and immune cell infiltration patterns, microsatellite instability (MSI), tumor mutation burden (TMB), immune checkpoint molecules, hypoxia-related genes, immunotherapy response, and compounds targeting the gene signature were explored, respectively. The results showed that compared with normal liver tissues, the mRNAsi score of HCC tissues was significantly increased. PTDSS2, MRPL9, and SOCS were the genes most related to mRNAsi in HCC tissues. Survival analysis results suggested the risk score based on the three-gene signature was an independent predictor of the prognosis for patients with HCC. The nomogram combining the risk score and pathological stage showed a good predictive ability for the overall survival of patients with HCC patients. Meanwhile, the risk score was significantly related to immune cell infiltration patterns, MSI, TMB, several immune checkpoint molecules, and hypoxia-related genes. In addition, the risk score was associated with the immunotherapy response, and fifteen potential therapeutic drugs targeting the three-gene signature were identified. Therefore, we propose to use this three-gene signature including PTDSS2, MRPL9, and SOCS as a potential prognostic biomarker for HCC.

scholarly journals Association Between Tumor Mutation Burden and Prognosis in Lower-Grade Glioma: An Exploratory Study 

2021 ◽  
Author(s):  
Di Cao ◽  
Jun Wang ◽  
Yan Lin ◽  
Guangwei Li
Keyword(s):  
Immune Cell ◽  
Differential Expression Analysis ◽  
Cell Infiltration ◽  
Lower Grade ◽  
Immune Cell Infiltration ◽  
Hub Genes ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
Lower Grade Glioma ◽  
Genome Atlas

Abstract Background: The therapeutic efficacy of immune checkpoint inhibitor therapy is highly influenced by tumor mutation burden (TMB). The relationship between TMB and prognosis in lower-grade glioma is still unclear. We aimed to explore the relationships and mechanisms between them in lower-grade glioma.Methods: We leveraged somatic mutation data from The Cancer Genome Atlas (TCGA). Clinical cases were divided into high- and low-TMB groups based on the median of TMB. Infiltrating immune cells were analyzed using CIBERSORT and differential expression analysis between the prognostic groups performed. The key genes were identified as intersecting between immune-related genes. Cox regression and survival analysis were performed on the intersecting genes. A total of 7 hub genes were identified. The effect of somatic copy number alterations (SCNA) of the hub genes on immune cell infiltration was analyzed using TIMER, which was used to determine the risk factors and immune infiltration status in LGG. Subsequently, based on hub genes, a TMB Prognosis Index (TMBPI) model was constructed to predict the risk in LGG patients. Besides, this model was validated using data from TCGA and Chinese Glioma Genome Atlas (CGGA).Results: High-TMB favored worse prognosis (P＜0.001) and macrophage infiltration was an independent risk factor (P＜0.001). In the high-TMB group (P=0.033, P=0.009), the proportion of macrophages M0 and M2 increased and monocytes decreased (P=0.006). Besides, the SCNA of the hub genes affected the level of immune cell infiltration by varying degrees among which IGF2BP3, NPNT, and PLA2G2A had a significant impact. The AUC of the ROC curve at 1-, 3- and 5-years were all above 0.74.Conclusions: This study implies that high-TMB correlated with unfavorable prognosis in lower-grade glioma. And high-TMB may have an impact on prognosis by changing tumor microenvironment, caused by the SCNAs of genes. The TMBPI model accurately predicted prognosis in LGG patients.

scholarly journals Glucose-6-Phosphate Dehydrogenase is a Prognostic Biomarker and Correlated with Immune Infiltrates in Hepatocellular Carcinoma

2022 ◽  
Author(s):  
Yang Bu ◽  
Kejun Liu ◽  
Yiming Niu ◽  
Ji Hao ◽  
Lei Cui ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Liver Cancer ◽  
Immune Cell ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Cell Infiltration ◽  
Immune Cell Infiltration ◽  
Potential Biomarker ◽  
Glucose 6 Phosphate Dehydrogenase

Abstract Background: Glucose-6-phosphate dehydrogenase (G6PD) plays an important role in the metabolic and immunological aspects of tumors. In hepatocellular carcinoma (HCC), the alteration of tumor microenvironment influences recurrence and metastasis. We extracted G6PD-related data from public databases of HCC tissues and used a bioinformatics approach to explore the correlation between G6PD expression and clinicopathological features and prognosis of immune cell infiltration in HCC.Methods: We extract G6PD expression information from TCGA and GEO databases in liver cancer tissues and normal tissues, validated by immunohistochemistry, and the correlation between G6PD expression and clinical features is analyzed, and the clinical significance of G6PD in liver cancer is assessed by Kaplan-Meier, Cox regression and prognostic line graph models. Functional enrichment analysis is performed by protein-protein interaction (PPI) network, GO/KEGG, GSEA and G6PD-associated differentially expressed genes (DEGs). TIMER and ssGSEA packages are used to assess the correlation between expression and the level of immune cell infiltration.Results: Our results show that G6PD expression is significantly upregulated in hepatocellular carcinoma tissues (P < 0.001). G6PD expression is associated with histological grade, pathological stage, T-stage, vascular infiltration and AFP level (P < 0.05); HCC patients in the low G6PD expression group had longer overall survival and better prognosis compared with the high G6PD expression group (P < 0.05). The level of G6PD expression also affects the levels of macrophages, unactivated dendritic cells, B cells, and follicular helper T cells in the tumor microenvironment.Conclusion: High expression of G6PD is a potential biomarker for poor prognosis of hepatocellular carcinoma, and G6PD may be a target for immunotherapy of HCC.

scholarly journals Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhuomao Mo ◽  
Daiyuan Liu ◽  
Dade Rong ◽  
Shijun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
Cox Regression ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Immune Cell Infiltration ◽  
Immunosuppressive Microenvironment

Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC.Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score.Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts.Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.

scholarly journals Significant correlation between HSPA4 and prognosis and immune regulation in hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12315
Author(s):  
Bing-Bing Shang ◽  
Jun Chen ◽  
Zhi-Guo Wang ◽  
Hui Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dna Methylation ◽  
Roc Curve ◽  
Immune Regulation ◽  
Immune Cell ◽  
Genetic Alterations ◽  
Cell Infiltration ◽  
Immune Checkpoints ◽  
Immune Cell Infiltration ◽  
Cancer Stage

Background Hepatocellular carcinoma (HCC) is an inflammation-associated tumor involved in immune tolerance and evasion in the immune microenvironment. Heat shock proteins (HSPs) are involved in the occurrence, progression, and immune regulation of tumors. Therefore, HSPs have been considered potential therapeutic targets. Here, we aimed to elucidate the value of HSP family A (Hsp70) member 4 (HSPA4) in the diagnosis and predicting prognosis of HCC, and its relationship with immune cell infiltration, immune cell biomarkers, and immune checkpoints. Gene mutation, DNA methylation, and the pathway involved in HCC were also analyzed. Methods The gene expression omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to compare HSPA4 expression, and the results were confirmed by immunohistochemical staining of clinical samples. R package was used to analyze the correlation between HSPA4 and cancer stage, and to establish receiver operating characteristic (ROC) curve of diagnosis, time-dependent survival ROC curve, and a nomogram model. cBioPortal and MethSurv were used to identify genetic alterations and DNA methylation, and their effect on prognosis. The Tumor Immune Estimation Resource (TIMER) was used to analyze immune cell infiltration, immune cell biomarkers, and immune checkpoints. The STRING database was used to analyze protein–protein interaction network information. Gene Ontology (GO) analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the functions of HSPA4 and its functional partner genes. Results Overexpression of HSPA4 was identified in 25 cancers. Overexpression of HSPA4 considerably correlated with cancer stage and alpha-fetoprotein (AFP) level in HCC. Patients with higher HSPA4 expression showed poorer prognosis. HSPA4 expression can accurately identify tumor from normal tissue (AUC = 0.957). The area under 1-, 3-, and 5-year survival ROCs were above 0.6. The HSPA4 genetic alteration rate was 1.3%. Among the 14 DNA methylation CpG sites, seven were related to the prognosis of HCC. HSPA4 was positively related to immune cell infiltration and immune checkpoints (PD-1 and CTLA-4) in HCC. The KEGG pathway enrichment analysis revealed HSPA4 enrichment in antigen processing and presentation together with HSPA8 and HSP90AA1. We verified the value of HSPA4 in the diagnosis and predicting prognosis of HCC. HSPA4 may not only participate in the occurrence and progression but also the immune regulation of HCC. Therefore, HSPA4 can be a potential diagnostic and prognostic biomarker and a therapeutic target for HCC.

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