Identification of Two Molecular Subtypes of Hepatocellular Carcinoma Based on Dysregulated Immune LncRNAs

Long non-coding RNAs (lncRNAs) as important regulators of gene expression also have critical functions in immune regulation. This study identified lncRNA modulators of immune-related pathways as biomarkers for hepatocellular carcinoma (HCC). The profile of lncRNA regulation in immune pathways in HCC was comprehensively mapped. To determine lncRNAs with immunomodulatory functions specific to HCC, the enrichment of lncRNAs in a collection of 17 immune functions was calculated applying gene set enrichment analysis (GSEA). Unsupervised clustering of samples were performed in the R package ConsensusClusterPlus to analyze subtype survival and immunological characteristics. The enrichment of 3,134 lncRNA–immune pathway pairs in both diseased and normal samples showed a total of 1,984 immunoregulatory functional lncRNAs specific to HCC only. In addition, 18 immune-related lncRNAs were disordered in HCC and were significantly associated with immune cell infiltration. Functional enrichment analysis indicated that the 18 dysregulated immune lncRNAs were enriched in cytokines, cytokine receptors, TGFb family members, TNF family members, and TNF family member receptor pathways. Two molecular subtypes of hepatocellular carcinoma were identified based on 18 dysregulated immune lncRNAs. Immunological profiling showed that subtype 1 samples with higher levels of cytokine response had a better survival, but subtype 2 samples with higher levels of tumor proliferation had poorer survival. This study identified 18 HCC-specific dysregulated immune lncRNAs and two HCC molecular subtypes with significant prognostic differences and immune characteristics. The current findings help understand the function of lncRNAs and promote the identification of immunotherapy targets.

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Identification of an epithelial-mesenchymal transition-related lncRNA prognostic signature for patients with glioblastoma

Scientific Reports ◽

10.1038/s41598-021-03213-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

XinJie Yang ◽

Sha Niu ◽

JiaQiang Liu ◽

Jincheng Fang ◽

ZeYu Wu ◽

...

Keyword(s):

Immune Cell ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Prognostic Signature ◽

Mesenchymal Transition

AbstractGlioblastoma (GBM) is a strikingly heterogeneous and lethal brain tumor with very poor prognosis. LncRNAs play critical roles in the tumorigenesis of GBM through regulation of various cancer-related genes and signaling pathways. Here, we focused on the essential role of EMT and identified 78 upregulated EMT-related genes in GBM through differential expression analysis and Gene set enrichment analysis (GSEA). A total of 301 EMT-related lncRNAs were confirmed in GBM through Spearman correlation analysis and a prognostic signature consisting of seven EMT-related lncRNAs (AC012615.1, H19, LINC00609, LINC00634, POM121L9P, SNHG11, and USP32P3) was established by univariate and multivariate Cox regression analyses. Significantly, Kaplan–Meier analysis and receiver-operating-characteristic (ROC) curve validated the accuracy and efficiency of the signature to be satisfactory. Quantitative real-time (qRT)-PCR assay demonstrated the expression alterations of the seven lncRNAs between normal glial and glioma cell lines. Functional enrichment analysis revealed multiple EMT and metastasis-related pathways were associated with the EMT-related lncRNA prognostic signature. In addition, we observed the degree of immune cell infiltration and immune responses were significantly increased in high-risk subgroup compared with low-risk subgroup. In conclusion, we established an effective and robust EMT-related lncRNA signature which was expected to predict the prognosis and immunotherapy response for GBM patients.

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SPP1 is a Prognostic Related Biomarker and Correlated with Tumor-Infiltrating Immune Cells in Hepatocellular Carcinoma

10.21203/rs.3.rs-1156469/v1 ◽

2021 ◽

Author(s):

Xiaofeng Wang ◽

Kun Zhang ◽

Li Geng ◽

DongLi Liu

Keyword(s):

Hepatocellular Carcinoma ◽

T Cells ◽

Immune Cell ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Tumor Promoter ◽

Immune Infiltration ◽

Kaplan Meier ◽

Secreted Phosphoprotein 1

Abstract Background: Secreted phosphoprotein 1 (SPP1) functions as a tumor promoter in varies tumors, but little is known whether it is an actual player on driving immune infiltration in hepatocellular carcinoma. Methods: In this study, we identified the expression of SPP1 by Oncomine, GEPIA and TIMER databases, and assessed SPP1 immumohistochemical staining analysis by The HPA database. We evaluated the clinical outcomes between SPP1 expression and hepatocellular carcinoma patients via Kaplan-Meier Plotter. We also tested the relationship between SPP1 and critical oncogenes by TIMER and GEPIA databases. Then we explored immune infiltration analyses using TIMER and TISIDB datasets. In addition, we performed functional enrichment analyses with Metascape and GeneMANIA databases. Results: We found that SPP1 overexpressed in hepatocellular carcinoma tissues and high SPP1 expression was correlated with shorter OS and PFS survivals in hepatocellular carcinoma patients. SPP1 expression is positive correlation with critical oncogenes related stemness associated genes, cell cycle and proliferation, therapeutic resistance, metastasis, and tumor angiogenesis in hepatocellular carcinoma. Importantly, SPP1 expression was positively correlated with infiltrating levels of CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells. Furthermore, SPP1 expression showed strong correlations with diverse immune hallmark sets in hepatocellular carcinoma. Notably, functional enrichment analysis suggested that SPP1 strong related with immune response. Conclusions: These findings imply that SPP1 is correlated with prognosis and immune cell infiltrating, offering a new potential immunotherapeutic target in hepatocellular carcinoma.

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simplifyEnrichment: an R/Bioconductor package for Clustering and Visualizing Functional Enrichment Results

10.1101/2020.10.27.312116 ◽

2020 ◽

Author(s):

Zuguang Gu ◽

Daniel Hübschmann

Keyword(s):

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

R Package ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Link Type ◽

Real World Datasets ◽

Or Gene ◽

Gene Overlap ◽

Similarity Matrices

AbstractMotivationFunctional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant information that is difficult to summarize. Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters.Resultswe proposed a new method named binary cut for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that binary cut can efficiently cluster functional terms into groups where terms showed more consistent similarities within groups and were more mutually exclusive between groups. We compared binary cut clustering on the similarity matrices from different similarity measurements and we found the semantic similarity worked well with binary cut while the similarity matrices based on gene overlap showed less consistent patterns and they were not recommended to work with binary cut. We implemented the binary cut algorithm into an R package simplifyEnrichment which additionally provides functionalities for visualizing, summarizing and comparing the clusterings.Availability and implementationThe simplifyEnrichment package and the documentations are available at https://bioconductor.org/packages/simplifyEnrichment/. The reports for the analysis of all datasets benchmarked in the paper are available at https://simplifyenrichment.github.io/. The scripts that performed the analysis are available at https://github.com/jokergoo/simplifyEnrichment_manuscript.

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Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.653836 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shixin Xiang ◽

Jing Li ◽

Jing Shen ◽

Yueshui Zhao ◽

Xu Wu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Tumor Microenvironment ◽

Immune Cell ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Tumor Purity

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis.Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER).Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration.Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.

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Identification and Validation of an 6-Metabolism-Related Gene Signature and Its Correlation With Immune Checkpoint in Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.783934 ◽

2021 ◽

Vol 11 ◽

Author(s):

He Ren ◽

Wanjing Li ◽

Xin Liu ◽

Shuliang Li ◽

Hao Guo ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Checkpoint ◽

Immune Cell ◽

Enrichment Analysis ◽

Gene Signature ◽

Clinicopathological Characteristics ◽

Gene Set Enrichment Analysis ◽

Operating Characteristics ◽

Kaplan Meier ◽

Characteristics Analysis

Hepatocellular carcinoma (HCC) is a common malignant tumor with relatively high malignancy and rapid disease progression. Metabolism-related genes (MRGs) are involved in the pathogenesis of HCC. This study explored potential key MRGs and their effect on T-cell immune function in the tumor immune microenvironment to provide new insight for the treatment of HCC. Of 456 differentially expressed MRGs identified from TCGA database, 21 were screened by MCODE and cytoHubba algorithms. From the key module, GAD1, SPP1, WFS1, GOT2, EHHADH, and APOA1 were selected for validation. The six MRGs were closely correlated with survival outcomes and clinicopathological characteristics in HCC. Receiver operating characteristics analysis and Kaplan-Meier plots showed that these genes had good prognostic value for HCC. Gene set enrichment analysis of the six MRGs indicated that they were associated with HCC development. TIMER and GEPIA databases revealed that WFS1 was significantly positively correlated and EHHADH was negatively correlated with tumor immune cell infiltration and immune checkpoint expression. Finally, quantificational real-time polymerase chain reaction (qRT-PCR) confirmed the expression of WFS1 and EHHADH mRNA in our own patients’ cohort samples and four HCC cell lines. Collectively, the present study identified six potential MRG biomarkers associated with the prognosis and tumor immune infiltration of HCC, thus providing new insight into the pathogenesis and treatment of HCC.

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gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler

F1000Research ◽

10.12688/f1000research.24956.2 ◽

2020 ◽

Vol 9 ◽

pp. 709 ◽

Cited By ~ 1

Author(s):

Liis Kolberg ◽

Uku Raudvere ◽

Ivan Kuzmin ◽

Jaak Vilo ◽

Hedi Peterson

Keyword(s):

Gene List ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Automated Analysis ◽

R Package ◽

Biological Data ◽

Functional Enrichment ◽

Link Type ◽

Functional Profiling ◽

Rest Api

g:Profiler (https://biit.cs.ut.ee/gprofiler) is a widely used gene list functional profiling and namespace conversion toolset that has been contributing to reproducible biological data analysis already since 2007. Here we introduce the accompanying R package, gprofiler2, developed to facilitate programmatic access to g:Profiler computations and databases via REST API. The gprofiler2 package provides an easy-to-use functionality that enables researchers to incorporate functional enrichment analysis into automated analysis pipelines written in R. The package also implements interactive visualisation methods to help to interpret the enrichment results and to illustrate them for publications. In addition, gprofiler2 gives access to the versatile gene/protein identifier conversion functionality in g:Profiler enabling to map between hundreds of different identifier types or orthologous species. The gprofiler2 package is freely available at the CRAN repository.

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The Role of Circular RNA CDR1as/ciRS-7 in Regulating Tumor Microenvironment: A Pan-Cancer Analysis

Biomolecules ◽

10.3390/biom9090429 ◽

2019 ◽

Vol 9 (9) ◽

pp. 429 ◽

Cited By ~ 24

Author(s):

Zou ◽

Zheng ◽

Deng ◽

Yang ◽

Xie ◽

...

Keyword(s):

Tumor Microenvironment ◽

Signaling Pathway ◽

Malignant Tumors ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Circular Rna ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Receptor Interaction

Circular RNA CDR1as/ciRS-7 functions as an oncogenic regulator in various cancers. However, there has been a lack of systematic and comprehensive analysis to further elucidate its underlying role in cancer. In the current study, we firstly performed a bioinformatics analysis of CDR1as among 868 cancer samples by using RNA-seq datasets of the MiOncoCirc database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), gene set enrichment analysis (GSEA), CIBERSORT, Estimating the Proportion of Immune and Cancer cells (EPIC), and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were applied to investigate the underlying functions and pathways. Functional enrichment analysis suggested that CDR1as has roles associated with angiogenesis, extracellular matrix (ECM) organization, integrin binding, and collagen binding. Moreover, pathway analysis indicated that it may regulate the TGF-β signaling pathway and ECM-receptor interaction. Therefore, we used CIBERSORT, EPIC, and the ESTIMATE algorithm to investigate the association between CDR1as expression and the tumor microenvironment. Our data strongly suggest that CDR1as may play a specific role in immune and stromal cell infiltration in tumor tissue, especially those of CD8+ T cells, activated NK cells, M2 macrophages, cancer-associated fibroblasts (CAFs) and endothelial cells. Generally, systematic and comprehensive analyses of CDR1as were conducted to shed light on its underlying pro-cancerous mechanism. CDR1as regulates the TGF-β signaling pathway and ECM-receptor interaction to serve as a mediator in alteration of the tumor microenvironment.

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Integrated Analyses Identify Immune-Related Signature Associated with Qingyihuaji Formula for Treatment of Pancreatic Ductal Adenocarcinoma Using Network Pharmacology and Weighted Gene Co-Expression Network

Journal of Immunology Research ◽

10.1155/2020/7503605 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Xiang Qian ◽

Zhuo Chen ◽

Sha Sha Chen ◽

Lu Ming Liu ◽

Ai Qin Zhang

Keyword(s):

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Ductal Adenocarcinoma ◽

Network Pharmacology ◽

Active Ingredients ◽

Kaplan Meier ◽

Human Protein Atlas

The study aimed to clarify the potential immune-related targets and mechanisms of Qingyihuaji Formula (QYHJ) against pancreatic cancer (PC) through network pharmacology and weighted gene co-expression network analysis (WGCNA). Active ingredients of herbs in QYHJ were identified by the TCMSP database. Then, the putative targets of active ingredients were predicted with SwissTargetPrediction and the STITCH databases. The expression profiles of GSE32676 were downloaded from the GEO database. WGCNA was used to identify the co-expression modules. Besides, the putative targets, immune-related targets, and the critical module genes were mapped with the specific disease to select the overlapped genes (OGEs). Functional enrichment analysis of putative targets and OGEs was conducted. The overall survival (OS) analysis of OGEs was investigated using the Kaplan-Meier plotter. The relative expression and methylation levels of OGEs were detected in UALCAN, human protein atlas (HPA), Oncomine, DiseaseMeth version 2.0 and, MEXPRESS database, respectively. Gene set enrichment analysis (GSEA) was conducted to elucidate the key pathways of highly-expressed OGEs further. OS analyses found that 12 up-regulated OGEs, including CDK1, PLD1, MET, F2RL1, XDH, NEK2, TOP2A, NQO1, CCND1, PTK6, CTSE, and ERBB2 that could be utilized as potential diagnostic indicators for PC. Further, methylation analyses suggested that the abnormal up-regulation of these OGEs probably resulted from hypomethylation, and GSEA revealed the genes markedly related to cell cycle and proliferation of PC. This study identified CDK1, PLD1, MET, F2RL1, XDH, NEK2, TOP2A, NQO1, CCND1, PTK6, CTSE, and ERBB2 might be used as reliable immune-related biomarkers for prognosis of PC, which may be essential immunotherapies targets of QYHJ.

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Improved cancer biomarkers identification using network-constrained infinite latent feature selection

PLoS ONE ◽

10.1371/journal.pone.0246668 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246668

Author(s):

Lihua Cai ◽

Honglong Wu ◽

Ke Zhou

Keyword(s):

Feature Selection ◽

Expression Profiles ◽

Biological Significance ◽

Feature Selection Method ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Gene Sets ◽

Significant Gene

Identifying biomarkers that are associated with different types of cancer is an important goal in the field of bioinformatics. Different researcher groups have analyzed the expression profiles of many genes and found some certain genetic patterns that can promote the improvement of targeted therapies, but the significance of some genes is still ambiguous. More reliable and effective biomarkers identification methods are then needed to detect candidate cancer-related genes. In this paper, we proposed a novel method that combines the infinite latent feature selection (ILFS) method with the functional interaction (FIs) network to rank the biomarkers. We applied the proposed method to the expression data of five cancer types. The experiments indicated that our network-constrained ILFS (NCILFS) provides an improved prediction of the diagnosis of the samples and locates many more known oncogenes than the original ILFS and some other existing methods. We also performed functional enrichment analysis by inspecting the over-represented gene ontology (GO) biological process (BP) terms and applying the gene set enrichment analysis (GSEA) method on selected biomarkers for each feature selection method. The enrichments analysis reports show that our network-constraint ILFS can produce more biologically significant gene sets than other methods. The results suggest that network-constrained ILFS can identify cancer-related genes with a higher discriminative power and biological significance.

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Investigation of Candidate Genes and Mechanism Associated with Immune Cell during the Progression of Glioblastoma based on Bioinformatics

10.21203/rs.3.rs-117485/v1 ◽

2020 ◽

Author(s):

Hui Xie ◽

Xiao-hui Ding ◽

Ce Yuan ◽

Jin-jiang Li ◽

Zhao-yang Li ◽

...

Keyword(s):

Immune Cell ◽

Risk Model ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Normal Group ◽

Functional Enrichment ◽

M2 Macrophage ◽

Survival Prognosis ◽

Cerna Network ◽

Tumor Group

Abstract Background: This study aimed to investigate the molecular mechanism of immune cell infiltration during the development of glioblastoma multiforme (GBM), and explore the potential immune cell associated prognostic genes for GBM. Methods: Gene expression data and corresponding clinical data of GBM samples (tumor group) and normal samples (normal group) in TCGA-GBM and GTEx dataset were downloaded. The differentially expression analysis was performed on samples between two groups. Based on tumor immune microenvironment analysis, the immune-related RNAs (lncRNAs and mRNAs) were further explored. Then, functional enrichment analysis, ceRNA network, risk prediction model and prognosis investigation were performed. Finally, the results of survival prognosis of key genes were tested by additional datasets. Results: A total of 4989 up-regulated genes and 2349 down-regulated genes were revealed between tumor group and normal group. M2 macrophages accounted for the largest proportion of tumor infiltrates immune cells in GBM, and was related to the prognosis of GBM patients. Totally 168 mRNAs (KIF18B) and 5 lncRNAs were related to infiltration of M2 Macrophage, of which 25 mRNAs and 5 lncRNAs forms a ceRNA network through 37 miRNAs (eg., miR-6849-3p). These genes were mainly assembled in functions like signal release. A risk model based on 5 mRNAs (such as FOX4 and ELFN2) and lncRNA PR11-161H23.5 was constructed. Verification test showed that all 5 mRNAs were significantly associated with OS prognosis.Conclusions: M2 Macrophage infiltration might participate in tumorigenesis of GBM via RP11-161H23.5-miR6849-3p-KIF18B ceRNA interaction. Furthermore, mRNAs such as FOX4 and ELFN2 might be potential prognostic markers for GBM patients.

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