scholarly journals Identification of Hub Genes Associated With Immune Infiltration and Predict Prognosis in Hepatocellular Carcinoma via Bioinformatics Approaches

2021 ◽  
Vol 11 ◽  
Author(s):  
Huaping Chen ◽  
Junrong Wu ◽  
Liuyi Lu ◽  
Zuojian Hu ◽  
Xi Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Division ◽  
Immune Cell ◽  
Cox Regression ◽  
Close Association ◽  
Functional Enrichment ◽  
Microtubule Assembly ◽  
Hub Genes ◽  
Immune Infiltration

AimsIn the cancer-related research field, there is currently a major need for a greater number of valuable biomarkers to predict the prognosis of hepatocellular carcinoma (HCC). In this study, we aimed to screen hub genes related to immune cell infiltration and explore their prognostic value for HCC.MethodsWe analyzed five datasets (GSE46408, GSE57957, GSE74656, GSE76427, and GSE87630) from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). A protein–protein interaction network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes; then, the hub genes were identified. Functional enrichment of the genes was performed on the Metascape website. Next, the expression of these hub genes was validated in several databases, including Oncomine, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Human Protein Atlas. We explored the correlations between the hub genes and infiltrated immune cells in the TIMER2.0 database. The survival curves were generated in GEPIA2, and the univariate and multivariate Cox regression analyses were performed using TIMER2.0.ResultsThe top ten hub genes [DNA topoisomerase II alpha (TOP2A), cyclin B2 (CCNB2), protein regulator of cytokinesis 1 (PRC1), Rac GTPase-activating protein 1 (RACGAP1), aurora kinase A (AURKA), cyclin-dependent kinase inhibitor 3 (CDKN3), nucleolar and spindle-associated protein 1 (NUSAP1), cell division cycle-associated 5 (CDCA5), abnormal spindle microtubule assembly (ASPM), and non-SMC condensin I complex subunit G (NCAPG)] were identified in subsequent analysis. These genes are most markedly enriched in cell division, suggesting their close association with tumorigenesis. Multi-database analyses validated that the hub genes were upregulated in HCC tissues. All hub genes positively correlated with several types of immune infiltration, including B cells, CD4+ T cells, macrophages, and dendritic cells. Furthermore, these hub genes served as independent prognostic factors, and the expression of these hub genes combing with the macrophage levels could help predict an unfavorable prognosis of HCC.ConclusionIn sum, these hub genes (TOP2A, CCNB2, PRC1, RACGAP1, AURKA, CDKN3, NUSAP1, CDCA5, ASPM, and NCAPG) may be pivotal markers for prognostic prediction as well as potentially work as targets for immune-based intervention strategies in HCC.

scholarly journals Identification of Prognostic Biomarkers and Correlation With Immune Infiltrates in Hepatocellular Carcinoma Based on a Competing Endogenous RNA Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhangya Pu ◽  
Yuanyuan Zhu ◽  
Xiaofang Wang ◽  
Yun Zhong ◽  
Fang Peng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Survival Analysis ◽  
Immune Cell ◽  
Cox Regression ◽  
Quantitative Polymerase Chain Reaction ◽  
Prognostic Biomarkers ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Cerna Network

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Recently, competing endogenous RNAs (ceRNA) have revealed a significant role in the progression of HCC. Herein, we aimed to construct a ceRNA network to identify potential biomarkers and illustrate its correlation with immune infiltration in HCC.MethodsRNA sequencing data and clinical traits of HCC patients were downloaded from TCGA. The limma R package was used to identify differentially expressed (DE) RNAs. The predicted prognostic model was established using univariate and multivariate Cox regression. A K-M curve, TISIDB and GEPIA website were utilized for survival analysis. Functional annotation was determined using Enrichr and Reactome. Protein-to-protein network analysis was implemented using SRTNG and Cytoscape. Hub gene expression was validated by quantitative polymerase chain reaction, Oncomine and the Hunan Protein Atlas database. Immune infiltration was analyzed by TIMMER, and Drugbank was exploited to identify bioactive compounds.ResultsThe predicted model that was established revealed significant efficacy with 3- and 5-years of the area under ROC at 0.804 and 0.744, respectively. Eleven DEmiRNAs were screened out by a K-M survival analysis. Then, we constructed a ceRNA network, including 56 DElncRNAs, 6 DEmiRNAs, and 28 DEmRNAs. The 28 DEmRNAs were enriched in cancer-related pathways, for example, the TNF signaling pathway. Moreover, six hub genes, CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1, were all overexpressed in HCC tissues and independently correlated with survival rate. Furthermore, expression of hub genes was related to immune cell infiltration in HCC, including B cells, CD8+ T cells, CD4+ T cells, monocytes, macrophages, neutrophils, and dendritic cells.ConclusionThe findings from this study demonstrate that CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1 are closely associated with prognosis and immune infiltration, representing potential therapeutic targets or prognostic biomarkers in HCC.

scholarly journals Identification of Prognostic Biomarkers and Correlation with Immune Infiltrates in Hepatocellular Carcinoma Based on a Competing Endogenous RNA Network

2020 ◽  
Author(s):  
Zhangya Pu ◽  
Yuanyuan Zhu ◽  
Xiaofang Wang ◽  
Yun Zhong ◽  
Fang Peng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Survival Analysis ◽  
Immune Cell ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Prognostic Biomarkers ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Recently, competing endogenous RNAs (ceRNA) have revealed a significant role in the progression of HCC. Herein, we aimed to construct a ceRNA network to identify potential biomarkers and illustrate its correlation with immune infiltration in HCC. Methods: RNA sequencing data and clinical traits of HCC patients were downloaded from TCGA. The limma R package was used to identify differentially expressed (DE) RNAs. The predicted prognostic model was established using univariate and multivariate Cox regression. A K-M curve and GEPIA website were utilized for survival analysis. Functional annotation was determined using Enrichr and Reactome. Protein-to-protein network analysis was implemented using SRTNG and Cytoscape. Hub gene expression was validated by Oncomine and the Hunan Protein Atlas database. Immune infiltration was analyzed by TIMMER, and Drugbank was exploited to identify bioactive compounds. Results: The predicted model that was established revealed significant efficacy with 3- and 5-years of the area under ROC at 0.804 and 0.744, respectively. Eleven DEmiRNAs were screened out by a K-M survival analysis. Then, we constructed a ceRNA network, including 56 DElncRNAs, 6 DEmiRNAs, and 28 DEmRNAs. The 28 DEmRNAs were enriched in cancer-related pathways, for example, the TNF signaling pathway. Moreover, six hub genes, CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1, were all overexpressed in HCC tissues and independently correlated with survival rate. Furthermore, expression of hub genes was related to immune cell infiltration in HCC, including B cells, CD8 + T cells, CD4 + T cells, monocytes, macrophages, neutrophils, and dendritic cells. Conclusions: The findings from this study demonstrate that CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1 are closely associated with prognosis and immune infiltration, representing potential therapeutic targets or prognostic biomarkers in HCC.

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 Identification of Biomarkers Related to Immune Cell Infiltration in Hepatocellular Carcinoma Using Gene Co-Expression Network

2021 ◽  
Vol 27 ◽  
Author(s):  
Wanbang Zhou ◽  
Yiyang Chen ◽  
Ruixing Luo ◽  
Zifan Li ◽  
Guanwei Jiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Tumor Progression ◽  
Protein Interactions ◽  
Immune Cells ◽  
Immune Cell ◽  
Immune Microenvironment ◽  
Hub Genes ◽  
Expression Levels ◽  
Immune Infiltration

Hepatocellular carcinoma (HCC) is a common cancer with poor prognosis. Due to the lack of effective biomarkers and its complex immune microenvironment, the effects of current HCC therapies are not ideal. In this study, we used the GSE57957 microarray data from Gene Expression Omnibus database to construct a co-expression network. The weighted gene co-expression network analysis and CIBERSORT algorithm, which quantifies cellular composition of immune cells, were used to identify modules related to immune cells. Four hub genes (EFTUD2, GAPDH, NOP56, PA2G4) were identified by co-expression network and protein-protein interactions network analysis. We examined these genes in TCGA database, and found that the four hub genes were highly expressed in tumor tissues in multiple HCC groups, and the expression levels were significantly correlated with patient survival time, pathological stage and tumor progression. On the other hand, methylation analysis showed that the up-regulation of EFTUD2, GAPDH, NOP56 might be due to the hypomethylation status of their promoters. Next, we investigated the correlations between the expression levels of four hub genes and tumor immune infiltration using Tumor Immune Estimation Resource (TIMER). Gene set variation analysis suggested that the four hub genes were associated with numerous pathways that affect tumor progression or immune microenvironment. Overall, our results showed that the four hub genes were closely related to tumor prognosis, and may serve as targets for treatment and diagnosis of HCC. In addition, the associations between these genes and immune infiltration enhanced our understanding of tumor immune environment and provided new directions for the development of drugs and the monitoring of tumor immune status.

scholarly journals Identification of Potentially Functional Circular RNAs hsa_circ_0070934 and hsa_circ_0004315 as Prognostic Factor of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Pejman Morovat ◽  
Saman Morovat ◽  
Arash M. Ashrafi ◽  
Shahram Teimourian
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Prognostic Factor ◽  
Sequence Data ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Ppi Network ◽  
Hub Genes ◽  
Cerna Network

Abstract Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide, which has a high mortality rate and poor treatment outcomes with yet unknown molecular basis. It seems that gene expression plays a pivotal role in the pathogenesis of the disease. Circular RNAs (circRNAs) can interact with microRNAs (miRNAs) to regulate gene expression in various malignancies by acting as competitive endogenous RNAs (ceRNAs). However, the potential pathogenesis roles of the ceRNA network among circRNA/miRNA/mRNA in HCC are unclear. In this study, first, the HCC circRNA expression data were obtained from three Gene Expression Omnibus microarray datasets (GSE164803, GSE94508, GSE97332), and the differentially expressed circRNAs (DECs) were identified using R limma package. Also, the liver hepatocellular carcinoma (LIHC) miRNA and mRNA sequence data were retrieved from TCGA, and differentially expressed miRNAs (DEMIs) and mRNAs (DEGs) were determined using the R DESeq2 package. Second, CSCD website was used to uncover the binding sites of miRNAs on DECs. The DECs' potential target miRNAs were revealed by conducting an intersection between predicted miRNAs from CSCD and downregulated DEMIs. Third, some related genes were uncovered by intersecting targeted genes predicted by miRWalk and targetscan online tools with upregulated DEGs. The ceRNA network was then built using the Cytoscape software. The functional enrichment and the overall survival time of these potential targeted genes were analyzed, and a PPI network was constructed in the STRING database. Network visualization was performed by Cytoscape, and ten hub genes were detected using the CytoHubba plugin tool. Four DECs (hsa_circ_0000520, hsa_circ_0008616, hsa_circ_0070934, hsa_circ_0004315) were obtained and six miRNAs (hsa-miR-542-5p, hsa-miR-326, hsa-miR-511-5p, hsa-miR-195-5p, hsa-miR-214-3p, and hsa-miR-424-5p) which are regulated by the above DECs were identified. Then 543 overlapped genes regulated by six miRNAs mentioned above were predicted. Functional enrichment analysis showed that these genes are mostly associated with cancer regulation functions. Ten hub genes (TTK،AURKB, KIF20A، KIF23، CEP55، CDC6، DTL، NCAPG، CENPF، PLK4) have been screened from the PPI network of the 204 survival-related genes. KIF20A, NCAPG, TTK, PLK4, and CDC6 were selected for the highest significant p-values. In the end, a circRNA-miRNA-mRNA regulatory axis was established for five final selected hub genes. This study implies the potential pathogenesis of the obtained network and proposes that the two DECs (has_circ_0070934 and has_circ_0004315) may be important prognostic factor for HCC.

scholarly journals Multi-Omic Analyses of the m5C Regulator ALYREF Reveal Its Essential Roles in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Chen Xue ◽  
Yalei Zhao ◽  
Ganglei Li ◽  
Lanjuan Li
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Cancer Tissue ◽  
Hub Genes ◽  
Specific Expression ◽  
Tissue Samples ◽  
Expression Levels ◽  
Advanced Tumor

The ALYREF protein acts as a crucial epigenetic regulator in several cancers. However, the specific expression levels and functional roles of ALYREF in cancers are largely unknown, including for hepatocellular carcinoma (HCC). In a pan-cancer tissue analysis that included HCC, we assessed the expression of ALYREF compared to normal tissues using The Cancer Genome Atlas database. Associations between ALYREF gene expression and the clinical characteristics of HCC patient samples were assessed using the UALCAN database. Kaplan-Meier plots were performed to assess HCC patient prognosis, and the TIMER database was used to explore associations between ALYREF expression and immune-cell infiltrations. The same methods were used to assess eIF4A3 expression in HCC patient samples. In addition, ALYREF- and elF4A3-related differentially expressed genes (DEGs) were determined using LinkedOmics, associated protein functionalities were predicted for positively associated DEGs, and both the TargetScan and miRDB databases were used to predict potential upstream miRNAs for control of ALYREF and eIF4A3 expression. We found that ALYREF gene expression was dysregulated in several cancers and was significantly elevated in HCC patient tissue samples and HCC cell lines. The overexpression of ALYREF was significantly related to both advanced tumor-node-metastasis stages and poor HCC prognosis. Furthermore, we found that eIF4A3 expression was significantly correlated with ALYREF expression, and that upregulated eIF4A3 was significantly associated with poor HCC patient outcomes. In the protein-protein interaction network, we identified eight hub genes based on the positively associated DEGs in common between ALYREF and eIF4A3, and the high expression levels of these hub genes were positively associated with patient clinical outcomes. In addition, we identified miR-4666a-5p and miR-6124 as potential regulators of ALYREF and eIF4A3 expression. These findings suggest that increased ALYREF expression may function as a novel biomarker for both HCC diagnosis and prognosis predictions.

scholarly journals Identification of CDC20 As an Immune Infiltration-Correlated Prognostic Biomarker in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Chen Xiong ◽  
Zhihuai Wang ◽  
Guifu Wang ◽  
Chi Zhang ◽  
Shengjie Jin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Cox Regression ◽  
Cancer Genome ◽  
Regression Analyses ◽  
Ubiquitin Protein Ligase ◽  
Immune Infiltration

Abstract Hepatocellular carcinoma (HCC) is a malignancy with a poor prognosis. Some E3 ubiquitin-protein ligases play essential roles in HCC development. We aimed to explore a hub E3 ubiquitin-protein ligase gene and verify its association with prognosis and immune cell infiltration in HCC. We identified cell division cycle 20 (CDC20) as a hub E3 ubiquitin-protein ligase in HCC by determining the intersecting genes in a protein-protein interaction (PPI) network of differentially expressed genes (DEGs) in HCC data from the International Cancer Genome Consortium (ICGC) and 919 E3 ubiquitin-protein ligase genes from the Integrated annotations for Ubiquitin and Ubiquitin-like Conjugation Database (IUUCD). DEGs and their correlations with clinicopathological features were explored in The Cancer Genome Atlas (TCGA), ICGC, and Gene Expression Omnibus (GEO) databases via the Wilcoxon signed-rank test. The prognostic value of CDC20 was illustrated by Kaplan-Meier (K-M) curves and Cox regression analyses. Subsequently, the correlation between CDC20 and immune infiltration was demonstrated via the Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA). CDC20 expression was significantly higher in HCC than in normal tissues (all P < 0.05). K-M curves and Cox regression analyses showed that high CDC20 expression predicted a poor prognosis and might be an independent risk factor for HCC prognosis (P < 0.05). Additionally, the TIMER and GEPIA results indicated that CDC20 is correlated with the immune infiltration of CD8 + T cells, T cells (general), monocytes, and exhausted T cells. This research revealed the potential prognostic value of CDC20 in HCC and demonstrated that CDC20 might be an immune-associated therapeutic target in HCC because of its correlation with immune infiltration.

scholarly journals Three Immune-Associated Subtypes of Diffuse Glioma Differ in Immune Infiltration, Immune Checkpoint Molecules, and Prognosis

2020 ◽  
Vol 10 ◽  
Author(s):  
Quanwei Zhou ◽  
Xuejun Yan ◽  
Weidong Liu ◽  
Wen Yin ◽  
Hongjuan Xu ◽  
...  
Keyword(s):  
Immune Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Immune Checkpoints ◽  
Signal Pathways ◽  
Diffuse Glioma ◽  
Immune Infiltration ◽  
Immune Related Genes

Diffuse glioma is one of the most prevalent malignancies of the brain, with high heterogeneity of tumor-infiltrating immune cells. However, immune-associated subtypes of diffuse glioma have not been determined, nor has the effect of different immune-associated subtypes on disease prognosis and immune infiltration of diffuse glioma patients. We retrieved the expression profiles of immune-related genes from The Cancer Genome Atlas (TCGA) (n = 672) and GSE16011 (n = 268) cohorts and used them to identify subtypes of diffuse glioma via Consensus Cluster Plus analysis. We used the limma, clusterProfiler, ESTIMATE, and survival packages of R for differential analysis, functional enrichment, immune and stromal score evaluation respectively in three subtypes, and performed log-rank tests in immune subtypes of diffuse glioma. The immune-associated features of diffuse glioma in the two cohorts were characterized via bioinformatic analyses of the mRNA expression data of immune-related genes. Three subtypes (C1–3) of diffuse glioma were identified from TCGA data, and were verified using the GSE16011 cohort. We then evaluated their immune characteristics and clinical features. Our mRNA profiling analyses indicated that the different subtypes of diffuse glioma presented differential expression profile of specific genes and signal pathways in the TCGA cohort. Patients with subtype C1, who were mostly diagnosed with grade IV glioma, had poorer outcomes than patients with subtype C2 or C3. Subtype C1 was characterized by a higher degree of immune cell infiltration as estimated by GSVA, and more frequent wildtype IDH1. By contrast, subtype C3 included more grade II and IDH1-mutated glioma, and was associated with more infiltration of CD4+T cells. Most subtype C2 had the features between subtypes C1 and C3. Meanwhile, immune checkpoints and their ligand molecules, including PD1/(PD-L1/PDL2), CTLA4/(CD80/CD86), and B7H3/TLT2, were significantly upregulated in subtype C1 and downregulated in subtype C3. In addition, patients with subtype C1 exhibited more frequent gene mutations. Univariate and multivariate Cox regression analyses revealed that diffuse glioma subtype was an effective, independent, and better prognostic factor. Therefore, we established a novel immune-related classification of diffuse glioma, which provides potential immunotherapy targets for diffuse glioma.

Gene Risk Signature of m6A Regulators, KIAA1429, YTHDF1, YTHDF2, METTL3 and Its Relationship with Immune Infiltration in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Jingjing Guo ◽  
Liwei Wang ◽  
Hanfeng Xu ◽  
Chuandong Zhu ◽  
Jianning Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Clinical Information ◽  
High Risk Group ◽  
Consensus Clustering ◽  
Immune Infiltration ◽  
Infiltrating Cells ◽  
Cancer Tissues ◽  
The Relationship

Abstract Background: Hepatocellular carcinoma (HCC) is a tumor of the digestive system with high mortality. N6-methyladenosine (m6A) is one of the most common forms of mRNA modification. Tumor neoantigens play an important role in anti-tumor immune response and predict the clinical response of immunotherapy. There are few studies on the relationship between m6A regulators and immune infiltrating cells. The objective of this study was to determine the gene expression and prognostic value of m6A regulators in hepatocellular carcinoma. Further, we explored the relationship between m6A regulators and immune-infiltrating cells.Methods: 13 m6A regulators expressions in 374 cancer tissues and 50 normal tissues were analyzed using RNA-seq data and clinical information in the TCGA database. Survival package was used to analyze the survival of patients in the two groups, and the corresponding clinical characteristics and gene expression were analyzed using univariate and multivariate Cox regression. We evaluated the expression of KIAA1429, YTHDF1, YTHDF2, and METTL3 in HCC and its correlation with TIICs using TIMER and CIBERSORT.Results: Most m6A regulators had higher expression levels in 374 cancer tissues. We confirmed two groups of HCC patients using a consensus clustering method. The prognosis of the cluster 1 group was poor compared with that of the cluster 2 group. The m6A regulators, KIAA1429, YTHDF1, YTHDF2, and METTL3 are highly expressed in the high-risk group of HCC. Furthermore, it was found that four m6A regulators (KIAA1429, YTHDF1, YTHDF2, and METTL3) are closely related to the clinicopathological characteristics and poor prognostic marker of hepatocellular carcinoma. We analyzed the correlation between KIAA1429, YTHDF1, YTHDF2, and METTL3 expression and the level of immune invasion of HCC.Conclusion: KIAA1429, YTHDF1, YTHDF2, and METTL3 as m6A regulators may regulate the tumor microenvironment through tumor immune infiltration cells to exert immune anti-tumor effects. KIAA1429, YTHDF1, YTHDF2, and METTL3 as molecular markers providing a new target for therapy of HCC in the further.

scholarly journals Construction of a solid Cox model for AML patients based on multiomics bioinformatic analysis

2021 ◽  
Author(s):  
Lei Gao ◽  
Fu Li ◽  
Jiao Cai ◽  
Jia Liu ◽  
Xi Zhang ◽  
...  
Keyword(s):  
Immune Cell ◽  
Cox Regression ◽  
Cox Model ◽  
Expression Profiles ◽  
Bioinformatic Analysis ◽  
The Cancer Genome Atlas ◽  
Clinical Samples ◽  
Immune Gene ◽  
Hub Genes ◽  
Immune Infiltration

Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy. The bone marrow (BM) microenvironment in AML plays an important role in leukemogenesis, drug resistance and leukemia relapse. In this study, we aimed to identify reliable immune-related biomarkers for AML prognosis by multiomics analysis. We obtained expression profiles from The Cancer Genome Atlas (TCGA) database and constructed a LASSO-Cox regression model to predict the prognosis of AML using multiomics bioinformatic analysis data. This was followed by independent validation of the model in the GSE106291 (n=251), GSE12417 (n=163) and GSE37642 (n=137) datasets and mutated genes in clinical samples for predicting overall survival (OS). Molecular docking was performed to predict the most optimal ligands to these hub genes. The single-cell RNA sequence dataset GSE116256 was used to clarify the expression of the hub genes in different immune cell types. According to their significant differences in immune gene signatures and survival trends, we concluded that the immune infiltration-lacking subtype (IL type) is associated with better prognosis than the immune infiltration-rich subtype (IR type). Using the LASSO model, we built a classifier based on 5 hub genes to predict the prognosis of AML (risk score = -0.086×ADAMTS3 + 0.180×CD52 + 0.472×CLCN5 - 0.356×HAL + 0.368×ICAM3). In summary, we constructed a prognostic model of AML using integrated multiomics bioinformatic analysis that could serve as a therapeutic classifier.

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