Identification of the Immune Cell Infiltration Landscape in Hepatocellular Carcinoma to Predict Prognosis and Guide Immunotherapy

Background: Globally, hepatocellular carcinoma (HCC) is the sixth most frequent malignancy with a high incidence and a poor prognosis. Immune cell infiltration (ICI) underlies both the carcinogenesis and immunogenicity of tumors. However, a comprehensive classification system based on the immune features for HCC remains unknown.Methods: The HCC dataset from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) cohorts was used in this study. The ICI patterns of 571 patients were characterized using two algorithms: the patterns were determined based on the ICI using the ConsensusClusterPlus package, and principal component analysis (PCA) established the ICI scores. Differences in the immune landscape, biological function, and somatic mutations across ICI scores were evaluated and compared, followed by a predictive efficacy evaluation of ICI scores for immunotherapy by the two algorithms and validation using an external immunotherapy cohort.Results: Based on the ICI profile of the HCC patients, three ICI patterns were identified, including three subtypes having different immunological features. Individual ICI scores were determined; the high ICI score subtype was characterized by enhanced activation of immune-related signaling pathways and a significantly high tumor mutation burden (TMB); concomitantly, diminished immunocompetence and enrichment of pathways associated with cell cycle and RNA degradation were found in the low ICI score subtype. Taken together, our results contribute to a better understanding of an active tumor and plausible reasons for its poor prognosis.Conclusion: The present study reveals that ICI scores may serve as valid prognostic biomarkers for immunotherapy in HCC.

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Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.611058 ◽

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.

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Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions

Cancer Cell International ◽

10.1186/s12935-021-02033-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Qiuxian Zheng ◽

Qin Yang ◽

Jiaming Zhou ◽

Xinyu Gu ◽

Haibo Zhou ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cell ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Linear Discriminant ◽

Immune Signature ◽

Immune Signatures

Abstract Background Hepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical outcomes. The HCC microenvironment is important for both tumour carcinogenesis and immunogenicity, but a classification system based on immune signatures has not yet been comprehensively described. Methods HCC datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) were used in this study. Gene set enrichment analysis (GSEA) and the ConsensusClusterPlus algorithm were used for clustering assessments. We scored immune cell infiltration and used linear discriminant analysis (LDA) to improve HCC classification accuracy. Pearson's correlation analyses were performed to assess relationships between immune signature indices and immunotherapies. In addition, weighted gene co-expression network analysis (WGCNA) was applied to identify candidate modules closely associated with immune signature indices. Results Based on 152 immune signatures from HCC samples, we identified four distinct immune subtypes (IS1, IS2, IS3, and IS4). Subtypes IS1 and IS4 had more favourable prognoses than subtypes IS2 and IS3. These four subtypes also had different immune system characteristics. The IS1 subtype had the highest scores for IFNγ, cytolysis, angiogenesis, and immune cell infiltration among all subtypes. We also identified 11 potential genes, namely, TSPAN15, TSPO, METTL9, CD276, TP53I11, SPINT1, TSPO, TRABD2B, WARS2, C9ORF116, and LBH, that may represent potential immunological biomarkers for HCC. Furthermore, real-time PCR revealed that SPINT1, CD276, TSPO, TSPAN15, METTL9, and WARS2 expression was increased in HCC cells. Conclusions The present gene-based immune signature classification and indexing may provide novel perspectives for both HCC immunotherapy management and prognosis prediction.

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Prediction of Hepatocellular Carcinoma Prognosis and Immune Cell Infiltration Using Gene Signature Associated with Inflammatory Response

Computational and Mathematical Methods in Medicine ◽

10.1155/2022/2415129 ◽

2022 ◽

Vol 2022 ◽

pp. 1-24

Author(s):

Bin-Bin Da ◽

Shuai Luo ◽

Ming Huang ◽

Fei Song ◽

Rong Ding ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Inflammatory Response ◽

Immune Cell ◽

Cox Regression ◽

Inflammatory Responses ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Differential Analysis

It has been demonstrated that the inflammatory response influences cancer development and can be used as a prognostic biomarker in various tumors. However, the relevance of genes associated with inflammatory responses in hepatocellular carcinoma (HCC) remains unknown. The Cancer Genome Atlas (TCGA) database was analyzed using weighted gene coexpression network analysis (WGCNA) and differential analysis to discover essential inflammatory response-related genes (IFRGs). Cox regression studies, both univariate and multivariate, were employed to develop a prognostic IFRGs signature. Additionally, Gene Set Enrichment Analysis (GSEA) was used to deduce the biological function of the IFRGs signature. Finally, we estimated immune cell infiltration using a single sample GSEA (ssGSEA) and x-cell. Our results revealed that, among the major HCC IFRGs, two (DNASE1L3 and KLKB1) were employed to create a predictive IFRG signature. The IFRG signature could correctly predict overall survival (O.S) as per Kaplan-Meier time-dependent roc curves analysis. It was also linked to pathological tumor stage and T stage and might be used as a prognostic predictor in HCC. GSEA analysis concluded that the IFRG signature might influence the immune response in HCC. Immunological cell infiltration and immune checkpoint molecule expression differed in the high-risk and low-risk groups. As a result of our findings, DNASILE may play a role in the tumor microenvironment. However, more research is necessary to confirm the role of DNASE1L3 and KLKB1.

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Characteristics of the Immune Cell Infiltration Landscape in Gastric Cancer to Assistant Immunotherapy

Frontiers in Genetics ◽

10.3389/fgene.2021.793628 ◽

2022 ◽

Vol 12 ◽

Author(s):

Chenlu Li ◽

Jingjing Pan ◽

Yinyan Jiang ◽

Yan Yu ◽

Zhenlin Jin ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Signaling Pathways ◽

Poor Prognosis ◽

Immune Cell ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Therapeutic Responses

Background: Gastric cancer (GC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable indices especially immunotherapy-associated parameters that can predict the therapeutic responses to immunotherapy of GC patients.Methods: Gene expression profile of 854 GC patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets (GSE84433) with their corresponding clinical and somatic mutation data. Based on immune cell infiltration (ICI) levels, molecular clustering classification was performed to identify subtypes and ICI scores in GC patients. After functional enrichment analysis of subtypes, we further explored the correlation between ICI scores and Tumor Mutation Burden (TMB) and the significance in clinical immunotherapy response.Results: Three subtypes were identified based on ICI scores with distinct immunological and prognostic characteristics. The ICI-cluster C, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration and up-regulation of PD-L1. ICI scores were identified through using principal component analysis (PCA) and the low ICI scores were consistent with the increased TMB and the immune-activating signaling pathways. Contrarily, the high-ICI score cluster was involved in the immunosuppressive pathways, such as TGF-beta, MAPK and WNT signaling pathways, which might be responsible for poor prognosis of GC. External immunotherapy and chemotherapy cohorts validated the patients with lower ICI scores exhibited significant therapeutic responses and clinical benefits.Conclusion: This study elucidated that ICI score could sever as an effective prognostic and predictive indicator for immunotherapy in GC. These findings indicated that the systematic assessment of tumor ICI landscapes and identification of ICI scores have crucial clinical implications and facilitate tailoring optimal immunotherapeutic strategies.

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Identification of Immune Cell Infiltration Landscape and Their Prognostic Significance in Uveal Melanoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.713569 ◽

2021 ◽

Vol 9 ◽

Author(s):

Han Zhao ◽

Yun Chen ◽

Peijun Shen ◽

Lan Gong

Keyword(s):

Gene Expression ◽

Uveal Melanoma ◽

Immune Cell ◽

Prognostic Significance ◽

Principal Component ◽

Clinical Information ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Cell Infiltration

Uveal melanoma (UVM) is the most common primary intraocular cancer in adults. Increasing evidence has demonstrated that immune cell infiltration (ICI) is crucial in predicting patient outcomes and therapeutic efficacy. Thus, describing the immune cell infiltrative landscape of UVM tumors may yield a novel prognostic marker and provide direction for immunotherapeutic selection. In this study, the gene expression data and clinical information of UVM patients were obtained from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. The ICI landscape of UVM was analyzed using the CIBERSORT and ESTIMATE algorithms. Two ICI phenotypes were defined, and the ICI scores were calculated by using principal component analysis algorithms. We found that a subtype with high ICI scores had poorer prognosis and increased expression levels of immune checkpoint-related genes. This study demonstrates that ICI scores are an independent prognostic biomarker and highlights their value in predicting immunotherapeutic outcomes.

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

Stem Cells International ◽

10.1155/2021/5546032 ◽

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.

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A Cell Cycle Progression-Derived Gene Signature to Predict Prognosis and Therapeutic Response in Hepatocellular Carcinoma

Disease Markers ◽

10.1155/2021/1986159 ◽

2021 ◽

Vol 2021 ◽

pp. 1-36

Author(s):

Yongfeng Hui ◽

Junzhi Leng ◽

Dong Jin ◽

Di Liu ◽

Genwang Wang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cell Cycle ◽

Cell Cycle Progression ◽

Immune Cell ◽

Gene Signature ◽

Cancer Genome ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Hallmarks Of Cancer ◽

Cycle Progression

Objective. Dysregulation of cell cycle progression (CCP) is one of the hallmarks of cancer. Here, our study is aimed at developing a CCP-derived gene signature for predicting high-risk population of hepatocellular carcinoma (HCC). Methods. Our study retrospectively analyzed the transcriptome profiling and clinical information of HCC patients from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) projects. Uni- and multivariate cox regression models were conducted for identifying which hallmarks of cancer were risk factors of HCC. CCP-derived gene signature was developed with LASSO method. The predictive efficacy was verified by ROC curves and subgroup analyses. A nomogram was then generated and validated by ROC, calibration, and decisive curves. Immune cell infiltration was estimated with ssGSEA method. Potential small molecular compounds were predicted via CTRP and CMap analyses. The response to chemotherapeutic agents was evaluated based on the GDSC project. Results. Among hallmarks of cancer, CCP was identified as a dominant risk factor for HCC prognosis. CCP-derived gene signature displayed the favorable predictive efficacy in HCC prognosis independent of other clinicopathological parameters. A nomogram was generated for optimizing risk stratification and quantifying risk evaluation. CCP-derived signature was in relation to immune cell infiltration, HLA, and immune checkpoint expression. Combining CTRP and CMap analyses, fluvastatin was identified as a promising therapeutic agent against HCC. Furthermore, CCP-derived signature might be applied for predicting the response to doxorubicin and gemcitabine. Conclusion. Collectively, CCP-derived gene signature was a promising marker in prediction of survival outcomes and therapeutic responses for HCC patients.

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Comprehensive analysis of immune-related prognostic genes in the tumour microenvironment of hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-021-08052-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Weike Gao ◽

Luan Li ◽

Xinyin Han ◽

Siyao Liu ◽

Chengzhen Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Proportional Hazards ◽

Immune Cell ◽

Prognostic Index ◽

Tumour Microenvironment ◽

Cox Proportional Hazards ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Potential Biomarker

Abstract Background The mortality rate of hepatocellular carcinoma (HCC) remains high worldwide despite surgery and chemotherapy. Immunotherapy is a promising treatment for the rapidly expanding HCC spectrum. Therefore, it is necessary to further explore the immune-related characteristics of the tumour microenvironment (TME), which plays a vital role in tumour initiation and progression. Methods In this research, 866 immune-related differentially expressed genes (DEGs) were identified by integrating the DEGs of samples from The Cancer Genome Atlas (TCGA)-HCC dataset and the immune-related genes from databases (InnateDB; ImmPort). Afterwards, 144 candidate prognostic genes were defined through weighted gene co-expression network analysis (WGCNA). Results Seven immune-related prognostic DEGs were identified using the L1-penalized least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) model, and the ImmuneRiskScore model was constructed on this basis. The prognostic index of the ImmuneRiskScore model was then validated in the relevant dataset. Patients were divided into high- and low-risk groups according to the ImmuneRiskScore. Differences in the immune cell infiltration of patients with different ImmuneRiskScore values were clarified, and the correlation of immune cell infiltration with immunotherapy biomarkers was further explored. Conclusion The ImmuneRiskScore of HCC could be a prognostic marker and can reflect the immune characteristics of the TME. Furthermore, it provides a potential biomarker for predicting the response to immunotherapy in HCC patients.

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

10.21203/rs.3.rs-1232206/v1 ◽

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.

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

PeerJ ◽

10.7717/peerj.12315 ◽

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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