Immune Gene Signature Delineates a Subclass of Papillary Thyroid Cancer with Unfavorable Clinical Outcomes

Papillary thyroid carcinoma (PTC) represents a heterogeneous disease with diverse clinical outcomes highlighting a need to identify robust biomarkers with clinical relevance. We applied non-negative matrix factorization-based deconvolution to publicly available gene expression profiles of thyroid cancers in the Cancer Genome Atlas (TCGA) consortium. Among three metagene signatures identified, two signatures were enriched in canonical BRAF-like and RAS-like thyroid cancers with up-regulation of genes involved in oxidative phosphorylation and cell adhesions, respectively. The third metagene signature representing up-regulation of immune-related genes further segregated BRAF-like and RAS-like PTCs into their respective subgroups of immunoreactive (IR) and immunodeficient (ID), respectively. BRAF-IR PTCs showed enrichment of tumor infiltrating immune cells, tall cell variant PTC, and shorter recurrence-free survival compared to BRAF-ID PTCs. RAS-IR and RAS-ID PTC subtypes included majority of normal thyroid tissues and follicular variant PTC, respectively. Immunopathological features of PTC subtypes such as immune cell fraction, repertoire of T cell receptors, cytolytic activity, and expression level of immune checkpoints such as and PD-L1 and CTLA-4 were consistently observed in two different cohorts. Taken together, an immune-related metagene signature can classify PTCs into four molecular subtypes, featuring the distinct histologic type, genetic and transcriptional alterations, and potential clinical significance.

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Recognition of Tumor-Associated Antigens and Immune Subtypes in Glioma for mRNA Vaccine Development

Frontiers in Immunology ◽

10.3389/fimmu.2021.738435 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shuai Ma ◽

Yixu Ba ◽

Hang Ji ◽

Fang Wang ◽

Jianyang Du ◽

...

Keyword(s):

Gene Expression ◽

Tumor Antigens ◽

Vaccine Development ◽

Expression Profiles ◽

Gene Expression Profiles ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Immune Gene ◽

Interactive Analysis ◽

Genome Atlas

BackgroundAlthough mRNA vaccines have been efficient for combating a variety of tumors, their effectiveness against glioma remains unclear. There is growing evidence that immunophenotyping can reflect the comprehensive immune status and microenvironment of the tumor, which correlates closely with treatment response and vaccination potency. The purpose of this research was to screen for effective antigens in glioma that could be used for developing mRNA vaccines and to further differentiate the immune subtypes of glioma to create an selection criteria for suitable patients for vaccination.MethodsGene expression profiles and clinical data of 698 glioma samples were extracted from The Cancer Genome Atlas, and RNA_seq data of 1018 glioma samples was gathered from Chinese Glioma Genome Atlas. Gene Expression Profiling Interactive Analysis was used to determine differential expression genes and prognostic markers, cBioPortal software was used to verify gene alterations, and Tumor Immune Estimation Resource was used to investigate the relationships among genes and immune infiltrating cells. Consistency clustering was applied for consistent matrix construction and data aggregation, Gene oncology enrichment was performed for functional annotation, and a graph learning-based dimensionality reduction method was applied to describe the subtypes of immunity.ResultsFour overexpressed and mutated tumor antigens associated with poor prognosis and infiltration of antigen presenting cells were identified in glioma, including TP53, IDH1, C3, and TCF12. Besides, four immune subtypes of glioma (IS1-IS4) and 10 immune gene modules were identified consistently in the TCGA data. The immune subtypes had diverse molecular, cellular, and clinical features. IS1 and IS4 displayed an immune-activating phenotype and were associated with worse survival than the other two subtypes, while IS2 and IS3 had lower levels of tumor immune infiltration. Immunogenic cell death regulators and immune checkpoints were also diversely expressed in the four immune subtypes.ConclusionTP53, IDH1, C3, and TCF12 are effective antigens for the development of anti-glioma mRNA vaccines. We found four stable and repeatable immune subtypes of human glioma, the classification of the immune subtypes of glioma may play a crucial role in the predicting mRNA vaccine outcome.

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Gene Expression in RET/PTC3 and E7 Transgenic Mouse Thyroids: RET/PTC3 But Not E7 Tumors Are Partial and Transient Models of Human Papillary Thyroid Cancers

Endocrinology ◽

10.1210/en.2008-0531 ◽

2008 ◽

Vol 149 (10) ◽

pp. 5107-5117 ◽

Cited By ~ 14

Author(s):

Agnès Burniat ◽

Ling Jin ◽

Vincent Detours ◽

Natacha Driessens ◽

Jean-Christophe Goffard ◽

...

Keyword(s):

Gene Expression ◽

Fusion Gene ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Human Thyroid ◽

Matrix Remodeling ◽

Biological Processes ◽

Papillary Thyroid ◽

Thyroid Cancers ◽

Transient Models

We studied gene expression profiles in two mouse models of human thyroid carcinoma: the Tg-RET/PTC3 (RP3) and Tg-E7 mice. RP3 fusion gene is the most frequent mutation found in the first wave post-Chernobyl papillary thyroid cancers (PTCs). E7 is an oncoprotein derived from the human papillomavirus 16 responsible for most cervical carcinoma in women. Both transgenic mice develop thyroid hyperplasia followed by solid differentiated carcinoma in older animals. To understand the different steps leading to carcinoma, we analyzed thyroid gene expression in both strains at different ages by microarray technology. Important biological processes were differentially regulated in the two tumor types. In E7 thyroids, cell cycle was the most up-regulated process, an observation consistent with the huge size of these tumors. In RP3 thyroids, contrary to E7 tumors, several human PTC characteristics were observed: overexpression of many immune-related genes, regulation of human PTC markers, up-regulation of EGF-like growth factors and significant regulation of angiogenesis and extracellular matrix remodeling-related genes. However, similarities were incomplete; they did not concern the overall gene expression and were not conserved in old animals. Therefore, RP3 tumors are partial and transient models of human PTC. They constitute a good model, especially in young animals, to study the respective role of the biological processes shared with human PTC and will allow testing drugs targeting these validated variables.

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Investigation of Genetic Determinants of Glioma Immune Phenotype by Integrative Immunogenomic Scale Analysis

Frontiers in Immunology ◽

10.3389/fimmu.2021.557994 ◽

2021 ◽

Vol 12 ◽

Author(s):

Binghao Zhao ◽

Yuekun Wang ◽

Yaning Wang ◽

Congxin Dai ◽

Yu Wang ◽

...

Keyword(s):

Immune Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Clinicopathological Characteristics ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Immune Phenotype ◽

Immune Phenotypes ◽

Glioma Microenvironment ◽

Genome Atlas

The immunosuppressive mechanisms of the surrounding microenvironment and distinct immunogenomic features in glioblastoma (GBM) have not been elucidated to date. To fill this gap, useful data were extracted from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, GSE43378, GSE23806, and GSE12907. With the ssGSEA method and the ESTIMATE and CIBERSORT algorithms, four microenvironmental signatures were used to identify glioma microenvironment genes, and the samples were reasonably classified into three immune phenotypes. The molecular and clinical features of these phenotypes were characterized via key gene set expression, tumor mutation burden, fraction of immune cell infiltration, and functional enrichment. Exhausted CD8+ T cell (GET) signature construction with the predictive response to commonly used antitumor drugs and peritumoral edema assisted in further characterizing the immune phenotype features. A total of 2,466 glioma samples with gene expression profiles were enrolled. Tumor purity, ESTIMATE, and immune and stromal scores served as the 4 microenvironment signatures used to classify gliomas into immune-high, immune-middle and immune-low groups, which had distinct immune heterogeneity and clinicopathological characteristics. The immune-H phenotype had higher expression of four immune signatures; however, most checkpoint molecules exhibited poor survival. Enriched pathways among the subtypes were related to immunity. The GET score was similar among the three phenotypes, while immune-L was more sensitive to bortezomib, cisplatin, docetaxel, lapatinib, and rapamycin prescriptions and displayed mild peritumor edema. The three novel immune phenotypes with distinct immunogenetic features could have utility for understanding glioma microenvironment regulation and determining prognosis. These results contribute to classifying glioma subtypes, remodeling the immunosuppressive microenvironment and informing novel cancer immunotherapy in the era of precision immuno-oncology.

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Identification and Validation of an Immune-Related eRNA Prognostic Signature for Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.657051 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shenglan Cai ◽

Xingwang Hu ◽

Ruochan Chen ◽

Yiya Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cells ◽

Cox Regression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Roc Curves ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Immune Genes ◽

Normal Tissues

BackgroundEnhancer RNAs (eRNAs) are intergenic long non-coding RNAs (lncRNAs) that participate in the progression of malignancies by targeting tumor-related genes and immune checkpoints. However, the potential role of eRNAs in hepatocellular carcinoma (HCC) is unclear. In this study, we aimed to construct an immune-related eRNA prognostic model that could be used to prospectively assess the prognosis of patients with HCC.MethodsGene expression profiles of patients with HCC were downloaded from The Cancer Genome Atlas (TCGA). The eRNAs co-expressed from immune genes were identified as immune-related eRNAs. Cox regression analyses were applied in a training cohort to construct an immune-related eRNA signature (IReRS), that was subsequently used to analyze a testing cohort and combination of the two cohorts. Kaplan-Meier and receiver operating characteristic (ROC) curves were used to validate the predictive effect in the three cohorts. Gene Set Enrishment Analysis (GSEA) computation was used to identify an IReRS-related signaling pathway. A web-based cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) computation was used to evaluate the relationship between the IReRS and infiltrating immune cells.ResultsA total of sixty-four immune-related eRNAs (IReRNAs) was identified in HCC, and 14 IReRNAs were associated with overall survival (OS). Five IReRNAs were used for constructing an immune-related eRNA signature (IReRS), which was shown to correlate with poor survival and to be an independent prognostic biomarker for HCC. The GSEA results showed that the IReRS was correlated to cancer-related and immune-related pathways. Moreover, we found that IReRS was correlated to infiltrating immune cells, including CD8+ T cells and M0 macrophages. Finally, differential expressions of the five risk IReRNAs in tumor tissues vs. adjacent normal tissues and their prognostic values were verified, in which the AL445524.1 may function as an oncogene that affects prognosis partly by regulating CD4-CLTA4 related genes.ConclusionOur results suggest that the IReRS could serve as a biomarker for predicting prognosis in patients with HCC. Additionally, it may be correlated to the tumor immune microenvironment and could also be used as a biomarker in immunotherapy for HCC.

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

Frontiers in Oncology ◽

10.3389/fonc.2020.586019 ◽

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.

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Identification of tumor antigens and immune subtypes of pancreatic adenocarcinoma for mRNA vaccine development

Molecular Cancer ◽

10.1186/s12943-021-01310-0 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Xing Huang ◽

Gang Zhang ◽

Tianyu Tang ◽

Tingbo Liang

Keyword(s):

Pancreatic Adenocarcinoma ◽

Tumor Antigens ◽

Vaccine Development ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Clinical Information ◽

Genetic Alterations ◽

Immune Checkpoints ◽

Immune Gene ◽

Mutation Burden

Abstract Background Although mRNA vaccines have been effective against multiple cancers, their efficacy against pancreatic adenocarcinoma (PAAD) remains undefined. Accumulating evidence suggests that immunotyping can indicate the comprehensive immune status in tumors and their immune microenvironment, which is closely associated with therapeutic response and vaccination potential. The aim of this study was to identify potent antigens in PAAD for mRNA vaccine development, and further distinguish immune subtypes of PAAD to construct an immune landscape for selecting suitable patients for vaccination. Methods Gene expression profiles and clinical information of 239 PAAD datasets were extracted from ICGC, and RNA-Seq data of 103 samples were retrieved from TCGA. GEPIA was used to calculate differential expression levels and prognostic indices, cBioPortal program was used to compare genetic alterations, and TIMER was used to explore correlation between genes and immune infiltrating cells. Consensus cluster was used for consistency matrix construction and data clustering, DAVID was used for functional annotation, and graph learning-based dimensional reduction was used to depict immune landscape. Results Six overexpressed and mutated tumor antigens associated with poor prognosis and infiltration of antigen presenting cells were identified in PAAD, including ADAM9, EFNB2, MET, TMOD3, TPX2, and WNT7A. Furthermore, five immune subtypes (IS1-IS5) and nine immune gene modules of PAAD were identified that were consistent in both patient cohorts. The immune subtypes showed distinct molecular, cellular and clinical characteristics. IS1 and IS2 exhibited immune-activated phenotypes and correlated to better survival compared to the other subtypes. IS4 and IS5 tumors were immunologically cold and associated with higher tumor mutation burden. Immunogenic cell death modulators, immune checkpoints, and CA125 and CA199, were also differentially expressed among the five immune subtypes. Finally, the immune landscape of PAAD showed a high degree of heterogeneity between individual patients. Conclusions ADAM9, EFNB2, MET, TMOD3, TPX2, and WNT7A are potent antigens for developing anti-PAAD mRNA vaccine, and patients with IS4 and IS5 tumors are suitable for vaccination.

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

10.1101/2021.09.15.460430 ◽

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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Landscape of Immune Cell Infiltration in Cervical Cancer

10.21203/rs.3.rs-584271/v1 ◽

2021 ◽

Author(s):

Shasha Shi ◽

Fu Peng ◽

Chenghao Yu

Keyword(s):

Cervical Cancer ◽

Immune Cell ◽

Checkpoint Inhibitors ◽

Expression Profiles ◽

The United States ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Checkpoints ◽

Immune Cell Infiltration ◽

Cancer Tissues

Abstract BackgroundCervical cancer is a life-threatening cancer among women. It is the second most prevalent malignant tumor in women. It ranks high in cancer deaths among women worldwide, including in the United States. Immune checkpoint inhibitors have emerged as an important therapeutic approach to treat several cancers, including cervical cancer. Notably, the development and progress of cervical cancer may be related to sustained immune response. This underlines the need to clarify immune cell infiltration (ICI) in cervical cancer tissues. MethodsIn this study, disease-related information of 964 cervical cancer patients was first retrieved from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. We utilized bioinformatics data to analyze the expression profiles of immune genes in cervical cancer tissues. ResultsPatients were divided into high and low groups according to ICI score. High ICI scores corresponded with activation of immune signaling pathways and high tumor mutation burden (TMB), which was related to better prognosis of G1-2 cervical cancer. In addition, most immune checkpoints and immuno-related genes such as CD274, CD8A, CXCL10, etc. were over-expressed in the high ICI group. ConclusionsThis study demonstrated that ICI score can accurately predict the prognosis of cervical cancer. Understanding ICI patterns will deepen our understanding of tumor microenvironment (TME) of cervical cancer, which may create the foundation for the development of efficient immunotherapeutic strategies against the cancer.

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Immune Landscape of Thyroid Cancers: New Insights

Frontiers in Endocrinology ◽

10.3389/fendo.2020.637826 ◽

2021 ◽

Vol 11 ◽

Author(s):

Elisa Menicali ◽

Martina Guzzetti ◽

Silvia Morelli ◽

Sonia Moretti ◽

Efisio Puxeddu

Keyword(s):

Thyroid Cancer ◽

Immune System ◽

Immune Cell ◽

The Cancer Genome Atlas ◽

Immune Checkpoints ◽

Thyroid Tumors ◽

Thyroid Cancers ◽

Immune Destruction

Immune system plays a key role in cancer prevention as well as in its initiation and progression. During multistep development of tumors, cells must acquire the capability to evade immune destruction. Both in vitro and in vivo studies showed that thyroid tumor cells can avoid immune response by promoting an immunosuppressive microenvironment. The recruitment of immunosuppressive cells such as TAMs (tumor-associated macrophages), TAMCs (tumor-associated mast cells), MDSC (myeloid-derived suppressor cells), TANs (tumor-associated neutrophils) and Tregs (regulatory T cells) and/or the expression of negative immune checkpoints, like PD-L1 (programmed death-ligand 1), CTLA-4 (cytotoxic T-lymphocyte associated protein 4), and/or immunosuppressive enzymes, as IDO1 (indoleamine 2,3-dioxygenase 1), are just some of the mechanisms that thyroid cancer cells exploit to escape immune destruction. Some authors systematically characterized immune cell populations and soluble mediators (chemokines, cytokines, and angiogenic factors) that constitute thyroid cancer microenvironment. Their purpose was to verify immune system involvement in cancer growth and progression, highlighting the differences in immune infiltrate among tumor histotypes. More recently, some authors have provided a more comprehensive view of the relationships between tumor and immune system involved in thyroid carcinogenesis. The Cancer Genome Atlas (TCGA) delivered a large amount of data that allowed to combine information on the inflammatory microenvironment with gene expression data, genetic and clinical-pathological characteristics, and differentiation degree of papillary thyroid carcinoma (PTC). Moreover, using a new sensitive and highly multiplex analysis, the NanoString Technology, it was possible to divide thyroid tumors in two main clusters based on expression of immune-related genes. Starting from these results, the authors performed an immune phenotype analysis that allowed to classify thyroid cancers in hot, cold, or intermediate depending on immune infiltration patterns of the tumor microenvironment. The aim of this review is to provide a comprehensive and updated view of the knowledge on immune landscape of thyroid tumors. Understanding interactions between tumor and microenvironment is crucial to effectively direct immunotherapeutic approaches in the treatment of thyroid cancer, particularly for those not responsive to conventional therapies.

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Analysis of Immune Gene Expression Subtypes Reveals Osteosarcoma Immune Heterogeneity

Journal of Oncology ◽

10.1155/2021/6649412 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Ben Wan ◽

Renxian Wang ◽

Jingjun Nie ◽

Yuyang Sun ◽

Bowen Zhang ◽

...

Keyword(s):

Gene Expression ◽

Immune Cell ◽

Structure Learning ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Poor Response ◽

Immune Gene ◽

Immune Gene Expression ◽

Tumor Immune Microenvironment ◽

Graph Structure Learning

Background. Osteosarcoma (OS) patients have a poor response to immunotherapy due to the sheer complexity of the immune system and the nuances of the tumor-immune microenvironment. Methodology. To gain insights into the immune heterogeneity of OS, we identified robust clusters of patients based on the immune gene expression profiles of OS patients in the TARGET database and assessed their reproducibility in an independent cohort collected from the GEO database. The association of comprehensive molecular characterization with reproducible immune subtypes was accessed with ANOVA. Furthermore, we visualized the distribution of individual patients in a tree structure by the graph structure learning-based dimensionality reduction algorithm. Results. We found that 87 OS samples can be divided into 5 immune subtypes, and each of them was associated with distinct clinical outcomes. The immune subtypes also demonstrated widely different patterns in tumor genetic aberrations, tumor-infiltrating, immune cell composition, and cytokine profiles. The immune landscape of OS uncovered the significant intracluster heterogeneity within each subtype and depicted a continuous immune spectrum across patients. Conclusion. The established five immune subtypes in our study suggested immune heterogeneity in OS patients and may provide optimal individual immunotherapy for patients exhibiting OS.

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