ceRNA network development and tumor-infiltrating immune cell analysis in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Li Chen ◽  
Weijie Zou ◽  
Lei Zhang ◽  
Huijuan Shi ◽  
Zhi Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Expression Patterns ◽  
Cell Types ◽  
High Morbidity ◽  
Clinical Prognosis ◽  
Cerna Network ◽  
Kaplan Meier

Abstract Background: Hepatocellular carcinoma is among the primary causes of cancer deaths globally. Despite efforts to understand liver cancer, its high morbidity and mortality remain high. Herein, we constructed two nomograms based on ceRNA networks and invading immune cells to describe the molecular mechanisms along with the clinical prognosis of HCC patients.Methods: RNA maps of tumors and normal samples were downloaded from TCGA. HTseq counts and fragments per megapons per thousand bases were read from 421 samples, including 371 tumor samples and 50 normal samples. We established a ceRNA network based on differential gene expression in normal versus tumor subjects. CIBERSORT was employed to differentiate 22 immune cell types according to tumor transcriptomes. Kaplan-Meier along with Cox proportional hazard analyses were employed to determine the prognosis-linked factors. Nomograms were constructed based on prognostic immune cells and ceRNAs. We employed ROC (Receiver operating characteristic) and calibration curve analyses to estimate these nomogram. Results: The difference analysis found 2028 mRNAs, 128 miRNAs, and 136 lncRNAs to be significantly differentially expressed in tumor samples relative to normal samples. We set up a ceRNA network containing 21 protein-coding mRNAs, 12 miRNAs, and 3 lncRNAs. In kaplan-Meier analysis, 21 of the 36 ceRNAs were considered significant. Of the 22 cell types, resting dendritic cell levels were markedly different in tumor samples versus normal controls. Calibration and ROC curve analysis of the ceRNA network, as well as immune-infiltration of tumor showed resultful accuracy (three-year survival AUC: 0.691, five-year survival AUC: 0.700; three-years survival AUC: 0.674, five-year survival AUC: 0.694). Our data suggest that Tregs, CD4 T-cells, mast cells, SNHG1, HMMR and hsa-miR-421 are associated with HCC based on ceRNA-immune cells co-expression patterns. Conclusion: On the basis of ceRNA network modeling and immune cell infiltration analysis, our study offers an effective bioinformatics strategy for studying HCC molecular mechanisms and prognosis.

scholarly journals Molecular mechanisms governing circulating immune cell heterogeneity across different species revealed by single cell sequencing

2021 ◽  
Author(s):  
Zhibin Li ◽  
chengcheng Sun ◽  
Fei Wang ◽  
Xiran Wang ◽  
Jiacheng Zhu ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Evolutionary Biology ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
Cell Types ◽  
Genetic Regulatory Networks ◽  
Peripheral Blood Mononuclear

Background: Immune cells play important roles in mediating immune response and host defense against invading pathogens. However, insights into the molecular mechanisms governing circulating immune cell diversity among multiple species are limited. Methods: In this study, we compared the single-cell transcriptomes of 77 957 immune cells from 12 species using single-cell RNA-sequencing (scRNA-seq). Distinct molecular profiles were characterized for different immune cell types, including T cells, B cells, natural killer cells, monocytes, and dendritic cells. Results: The results revealed the heterogeneity and compositions of circulating immune cells among 12 different species. Additionally, we explored the conserved and divergent cellular cross-talks and genetic regulatory networks among vertebrate immune cells. Notably, the ligand and receptor pair VIM-CD44 was highly conserved among the immune cells. Conclusions: This study is the first to provide a comprehensive analysis of the cross-species single-cell atlas for peripheral blood mononuclear cells (PBMCs). This research should advance our understanding of the cellular taxonomy and fundamental functions of PBMCs, with important implications in evolutionary biology, developmental biology, and immune system disorders

scholarly journals Tumor infiltrating immune cells (TIICs) as a biomarker for prognosis benefits in patients with osteosarcoma

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Ying Chen ◽  
Bo Zhao ◽  
Xiaohu Wang
Keyword(s):  
Risk Score ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Cell Types ◽  
Difficult Problem ◽  
Model Base ◽  
Model Based ◽  
Kaplan Meier ◽  
Score Model

Abstract Background Osteosarcoma is a rare malignant bone tumor in adolescents and children. Poor prognosis has always been a difficult problem for patients with osteosarcoma. Recent studies have shown that tumor infiltrating immune cells (TIICs) are associated with the clinical outcome of osteosarcoma patients. The aim of our research was to construct a risk score model based on TIICs to predict the prognosis of patients with osteosarcoma. Methods CIBERSORTX algorithm was used to calculate the proportion of 22 TIIC types in osteosarcoma samples. Kaplan-Meier curves were drawn to investigate the prognostic value of 22 TIIC types. Forward stepwise approach was used to screen a minimal set of immune cell types. Multivariate Cox PHR analysis was performed to construct an immune risk score model. Results Osteosarcoma samples with CIBERSORTX output p value less than 0.05 were selected for research. Kaplan-Meier curves indicated that naive B cells (p = 0.047) and Monocytes (p = 0.03) in osteosarcoma are associated with poor prognosis. An immune risk score model was constructed base on eight immune cell types, and the ROC curve showed that the immune risk score model is reliable in predicting the prognosis of patients with osteosarcoma (AUC = 0.724). Besides, a nomogram model base on eight immune cell types was constructed to predict the survival rate of patients with osteosarcoma. Conclusions TIICs are closely related to the prognosis of osteosarcoma. The immune risk score model based on TIICs is reliable in predicting the prognosis of osteosarcoma.

scholarly journals The Effect and Regulatory Mechanism of High Mobility Group Box-1 Protein on Immune Cells in Inflammatory Diseases

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1044
Author(s):  
Yun Ge ◽  
Man Huang ◽  
Yong-ming Yao
Keyword(s):  
Immune Cells ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Therapeutic Potential ◽  
Inflammatory Diseases ◽  
Critical Role ◽  
High Mobility ◽  
Cell Types ◽  
High Mobility Group ◽  
High Mobility Group Protein

High mobility group box-1 protein (HMGB1), a member of the high mobility group protein superfamily, is an abundant and ubiquitously expressed nuclear protein. Intracellular HMGB1 is released by immune and necrotic cells and secreted HMGB1 activates a range of immune cells, contributing to the excessive release of inflammatory cytokines and promoting processes such as cell migration and adhesion. Moreover, HMGB1 is a typical damage-associated molecular pattern molecule that participates in various inflammatory and immune responses. In these ways, it plays a critical role in the pathophysiology of inflammatory diseases. Herein, we review the effects of HMGB1 on various immune cell types and describe the molecular mechanisms by which it contributes to the development of inflammatory disorders. Finally, we address the therapeutic potential of targeting HMGB1.

scholarly journals Genetic expression and mutational profile analysis in different pathologic stages of hepatocellular carcinoma patients

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xingjie Gao ◽  
Chunyan Zhao ◽  
Nan Zhang ◽  
Xiaoteng Cui ◽  
Yuanyuan Ren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Random Forest ◽  
Mouse Model ◽  
Decision Tree ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
High Expression ◽  
Clinical Prognosis ◽  
Analytical Approaches

Abstract Background The clinical pathologic stages (stage I, II, III-IV) of hepatocellular carcinoma (HCC) are closely linked to the clinical prognosis of patients. This study aims at investigating the gene expression and mutational profile in different clinical pathologic stages of HCC. Methods Based on the TCGA-LIHC cohort, we utilized a series of analytical approaches, such as statistical analysis, random forest, decision tree, principal component analysis (PCA), to identify the differential gene expression and mutational profiles. The expression patterns of several targeting genes were also verified by analyzing the Chinese HLivH060PG02 HCC cohort, several GEO datasets, HPA database, and diethylnitrosamine-induced HCC mouse model. Results We identified a series of targeting genes with copy number variation, which is statistically associated with gene expression. Non-synonymous mutations mainly existed in some genes (e.g.,TTN, TP53, CTNNB1). Nevertheless, no association between gene mutation frequency and pathologic stage distribution was detected. The random forest and decision tree modeling analysis data showed a group of genes related to different HCC pathologic stages, including GAS2L3 and SEMA3F. Additionally, our PCA data indicated several genes associated with different pathologic stages, including SNRPA and SNRPD2. Compared with adjacent normal tissues, we observed a highly expressed level of GAS2L3, SNRPA, and SNRPD2 (P = 0.002) genes in HCC tissues of our HLivH060PG02 cohort. We also detected the high expression pattern of GAS2L3, SEMA3F, SNRPA, and SNRPD2 in the datasets of GSE102079, GSE76427, GSE64041, GSE121248, GSE84005, and the qPCR assay using diethylnitrosamine-induced HCC mouse model. Moreover, SEMA3F and SNRPD2 protein were highly stained in the HCC tissues of the HPA database. The high expression level of these four genes was associated with the poor survival prognosis of HCC cases. Conclusions Our study provides evidence regarding the gene expression and mutational profile in different clinical pathologic stages of TCGA HCC cases. Identifying four targeting genes, including GAS2L3, SNRPA, SNRPD2, and SEMA3F, offers insight into the molecular mechanisms associated with different prognoses of HCC.

scholarly journals Whole Blood Transcriptome Analysis Reveals the Correlation between Specific Immune Cells and Septicemic Melioidosis

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ke Xu ◽  
Dahua Xu ◽  
Hua Pei ◽  
Yunfan Quan ◽  
Jun Liu ◽  
...  
Keyword(s):  
Immune Response ◽  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Immune Cell ◽  
Expression Patterns ◽  
Multivariate Logistic Regression Analysis ◽  
Cell Types ◽  
Host Immune System ◽  
Resting Cells

Melioidosis is a serious infectious disease caused by the environmental Gram-negative bacillus Burkholderia pseudomallei. It has been shown that the host immune system, mainly comprising various types of immune cells, fights against the disease. The present study was to specify correlation between septicemic melioidosis and the levels of multiple immune cells. First, the genes with differential expression patterns between patients with septicemic melioidosis (B. pseudomallei) and health donors (control/healthy) were identified. These genes being related to cytokine binding, cell adhesion molecule binding, and MHC relevant proteins may influence immune response. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed 23 enriched immune response pathways. We further leveraged the microarray data to investigate the relationship between immune response and septicemic melioidosis, using the CIBERSORT analysis. Comparison of the percentages of 22 immune cell types in B. pseudomallei vs. control/healthy revealed that those of CD4 memory resting cells, CD8+ T cells, B memory cells, and CD4 memory activated cells were low, whereas those of M0 macrophages, neutrophils, and gamma delta T cells were high. The multivariate logistic regression analysis further revealed that CD8+ T cells, M0 macrophages, neutrophils, and naive CD4+ cells were strongly associated with the onset of septicemic melioidosis, and M2 macrophages and neutrophils were associated with the survival in septicemic melioidosis. Taken together, these data point to a complex role of immune cells on the development and progression of melioidosis.

scholarly journals The construction and analysis of tumor-infiltrating immune cells and ceRNA networks in metastatic adrenal cortical carcinoma

2020 ◽  
Vol 40 (3) ◽  
Author(s):  
Runzhi Huang ◽  
Ziqi Liu ◽  
Tingli Tian ◽  
Dianwen Song ◽  
Penghui Yan ◽  
...  
Keyword(s):  
Immune Cells ◽  
Immune Cell ◽  
Expression Patterns ◽  
Adrenal Cortical Carcinoma ◽  
Immune Gene ◽  
Cell Type ◽  
Primary Tumors ◽  
Rna Transcripts ◽  
Cerna Network ◽  
Competitive Endogenous Rna

Abstract Purpose: To construct and analyze tumor-infiltrating immune cell and ceRNA (competitive endogenous RNA) networks in metastatic adrenal cortical carcinoma (ACC). Methods: A ceRNA network was established to identify the ceRNAs involved in metastasis of ACC based on 92 samples from TCGA, including 18 cases of metastasis and 74 cases of non-metastatic primary tumors. And the algorithm “cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT)” was used to quantify the proportion of immune cells in ACC. In addition, predictive nomograms based on the types of important immune cells or ceRNAs were constructed to predict ACC prognosis. Moreover, we evaluated the relationships between metastatic ACC-specific immune cells and ceRNA networks to identify the potential immune gene characteristics. Results: Ten prognostic biomarkers were identified as key members of the ceRNA network and three tumor-infiltrating immune cells were identified by CIBERSORT algorithm. Some important co-expression patterns between immune cells and ceRNAs network indicate significant correlation between Macrophages M0 and hsa-miR-130b-3p (P < 0.001), Macrophages M0 and H2AFX (P = 0.003). Conclusions: The present study inferred that the metastasis-related ceRNAs of H2AFX, hsa-miR-130b-3p and Macrophages M0 might play important roles in ACC metastasis.

scholarly journals The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Lugang Deng ◽  
Zhi Qu ◽  
Peixi Wang ◽  
Nan Liu
Keyword(s):  
Immune Cells ◽  
Proportional Hazards ◽  
Immune Cell ◽  
Clear Cell ◽  
Cell Types ◽  
Renal Clear Cell Carcinoma ◽  
Cox Proportional Hazards ◽  
Tissue Samples ◽  
Cerna Network ◽  
Proportional Hazards Regression

Abstract Purpose Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patients’ quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear. Methods We constructed a ceRNA network associated with KIRC by analyzing the long noncoding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from the Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “CIBERSORT”. Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism. Results We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [Area Under Curves (AUCs) of 1, 3 and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747 and 0.772; AUCs of 1, 3 and 5-year survivals in nomogram based on immune cells: 0.603, 0.642 and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells. Conclusions Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

scholarly journals The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Lugang Deng ◽  
Zhi Qu ◽  
Peixi Wang ◽  
Nan Liu
Keyword(s):  
Immune Cells ◽  
Proportional Hazards ◽  
Immune Cell ◽  
Clear Cell ◽  
Cell Types ◽  
Renal Clear Cell Carcinoma ◽  
Cox Proportional Hazards ◽  
Tissue Samples ◽  
Cerna Network ◽  
Proportional Hazards Regression

AbstractBackgroundKidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patients’ quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear.MethodsWe constructed a ceRNA network associated with KIRC by analyzing the long noncoding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from the Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts”. Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism.ResultsWe established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [Area Under Curves (AUCs) of 1, 3 and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747 and 0.772; AUCs of 1, 3 and 5-year survivals in nomogram based on immune cells: 0.603, 0.642 and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells.ConclusionsBased on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

scholarly journals UCP1 modulates Immune Infiltration Level and Survival Outcome in Ovarian Cancer Patients

2021 ◽  
Author(s):  
Jinfa Huang ◽  
Guilian Wang ◽  
Kedan Liao ◽  
Kaixian Deng
Keyword(s):  
Ovarian Cancer ◽  
Molecular Mechanisms ◽  
Immune Cell ◽  
Critical Role ◽  
Clinical Prognosis ◽  
Immune Infiltration ◽  
Interactive Analysis ◽  
Kaplan Meier ◽  
Genes Expression ◽  
Ucp1 Expression

Abstract BackgroundThe uncoupling proteins (UCPs) are critical genes associated with tumorigenesis and chemoresistance. However, little is known about the molecular mechanism of the UCPs in ovarian cancer (OV). Material and methodsUCPs expression analysis was conducted using Gene Expression Profiling Interactive Analysis (GEPIA), and its potential in clinical prognosis was analyzed using Kaplan- Meier analyses. The influence of UCPs on immune infiltration was analyzed by TIMER. In addition, the correlation between UCPs expression and molecular mechanisms was investigated by TIMER and Cancer Single-cell State Atlas (CancerSEA). ResultsUCP1, UCP2, UCP3 and UCP5 expression levels correlated with a favorable prognosis and tumor progression. Moreover, UCP1 expression correlated to several immune cell markers and regulated tumorigenesis, such as tumor invasion, EMT, metastasis and DNA repair. In addition, UCP1 potentially involved in genes expression of SNAI2, MMP2, BRCA1 and PARP1. ConclusionsThese results implied a critical role of UCP1 in the prognosis and immune infiltration of ovarian cancer. In addition, UCP1 expression participated in regulating multiple oncogenes and tumorigenesis.

scholarly journals The Construction and Analysis of ceRNA Network and Immune Infiltration in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Lugang Deng ◽  
Peixi Wang ◽  
Zhi Qu ◽  
Nan Liu
Keyword(s):  
Immune Cells ◽  
Proportional Hazards ◽  
Immune Cell ◽  
Clear Cell ◽  
Cell Types ◽  
Renal Clear Cell Carcinoma ◽  
Cox Proportional Hazards ◽  
Tissue Samples ◽  
Cerna Network ◽  
Proportional Hazards Regression

Background: Kidney renal clear cell carcinoma (KIRC) has the highest invasion, mortality and metastasis of the renal cell carcinomas and seriously affects patient’s quality of life. However, the composition of the immune microenvironment and regulatory mechanisms at transcriptomic level such as ceRNA of KIRC are still unclear.Methods: We constructed a ceRNA network associated with KIRC by analyzing the long non-coding RNA (lncRNA), miRNA and mRNA expression data of 506 tumor tissue samples and 71 normal adjacent tissue samples downloaded from The Cancer Genome Atlas (TCGA) database. In addition, we estimated the proportion of 22 immune cell types in these samples through “The Cell Type Identification by Estimating Relative Subsets of RNA Transcripts.” Based on the ceRNA network and immune cells screened by univariate Cox analysis and Lasso regression, two nomograms were constructed to predict the prognosis of patients with KIRC. Receiver operating characteristic curves (ROC) and calibration curves were employed to assess the discrimination and accuracy of the nomograms. Consequently, co-expression analysis was carried out to explore the relationship between each prognostic gene in a Cox proportional hazards regression model of ceRNA and each survival-related immune cell in a Cox proportional hazards regression model of immune cell types to reveal the potential regulatory mechanism.Results: We established a ceRNA network consisting of 12 lncRNAs, 25 miRNAs and 136 mRNAs. Two nomograms containing seven prognostic genes and two immune cells, respectively, were successfully constructed. Both ROC [area under curves (AUCs) of 1, 3, and 5-year survival in the nomogram based on ceRNA network: 0.779, 0.747, and 0.772; AUCs of 1, 3, and 5-year survivals in nomogram based on immune cells: 0.603, 0.642, and 0.607] and calibration curves indicated good accuracy and clinical application value of both models. Through co-correlation analysis between ceRNA and immune cells, we found both LINC00894 and KIAA1324 were positively correlated with follicular helper T (Tfh) cells and negatively correlated with resting mast cells.Conclusion: Based on the ceRNA network and tumor-infiltrating immune cells, we constructed two nomograms to predict the survival of KIRC patients and demonstrated their value in improving the personalized management of KIRC.

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