POS0859 DEEP PHENOTYPING OF DERMATOMYOSITIS BASED ON LIPID FERROPTOSIS-RELATED GENES BY MACHINE LEARNING

Background:Dermatomyositis (DM) is an idiopathic inflammatory myopathy with heterogeneous clinical manifestation that raise challenges regarding diagnosis and therapy1. Ferroptosis is a newly discovered form of regulated cell death that is the nexus between metabolism, redox biology, and rheumatic immune diseases2. However, how ferroptosis maintains the balance of lymphocyte T cells and affect disease activity in DM is unclear.Objectives:To investigate an ferroptosis-related multiple gene expression signature for classification by assessing the global gene expression profile, and calculate the lymphocyte T cells status in the different subsets.Methods:Gene expression profiles of skeletal muscle from DM samples were acquired from GEO database. GSE143323 (30 patients and 20 HCs) was selected as the training set. The GSE3307 contained 21 DM patients and was selected as the validation set. The 60 ferroptosis genes were obtained from previous literature3. The intersection of the global gene and ferroptosis genes was considered the set of significant G-Ferroptosis genes for further analysis. The “NMF” (R-package) was applied as an unsupervised clustering method for sample classification by using G-Ferroptosis genes expression microarray data from the training datasets. An ferroptosis score model was constructed. The performance of the ferroptosis genes-based risk score model constructed by the DM training set was validated in the batch-1 and batch-2 DM sets. Normalized ferroptosis genes training data was used to compare the ssGSEA scores of gene sets between the high risk and low risk group. The statistical software package R (version 4.0.3) was used for all analyses. P value < 0.05 were considered statistically significant.Results:We selected 54 significant G-Ferroptosis genes for further analysis in training set. There were 2 distinct subtypes (high-ferroptosis-score groups and low-ferroptosis-score groups) identified in G-Ferroptosis genes cohort which were also identified in validation datasets (Fig.1A, C, D). Metallothionein 1G (MT1G) was a characteristic gene of low-ferroptosis-score group. The characteristic genes of high-ferroptosis-score group were acyl-CoA synthetase family member 2(ACSF2) and aconitase 1(ACO1) (Fig.1B). Patients in high-ferroptosis-score group had a lower level of Tregs compared with that of low-ferroptosis-score patients in both training and validation set (P <0.05, Fig.1E).Conclusion:The biological process of ferroptosis is associated with the lever of Tregs, suggesting the process of ferroptosis may be involved in the disease progression of DM. Identificating ferroptosis-related features for DM might provide a new idea for clinical treatment.References:[1]DeWane ME, Waldman R, Lu J. Dermatomyositis: Clinical features and pathogenesis. Journal of the American Academy of Dermatology 2020;82(2):267-81. doi: 10.1016/j.jaad.2019.06.1309 [published Online First: 2019/07/08].[2]Liang C, Zhang X, Yang M, et al. Recent Progress in Ferroptosis Inducers for Cancer Therapy. Advanced materials (Deerfield Beach, Fla) 2019;31(51):e1904197. doi: 10.1002/adma.201904197 [published Online First: 2019/10/09].[3]Liang JY, Wang DS, Lin HC, et al. A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Hepatocellular Carcinoma. International journal of biological sciences 2020;16(13):2430-41. doi: 10.7150/ijbs.45050 [published Online First: 2020/08/08].Acknowledgements:This project was supported by National Science Foundation of China (82001740).Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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Tumor-Infiltrating T Cells Are Not Predictive of Clinical Outcome in Follicular Lymphoma.

Blood ◽

10.1182/blood.v108.11.824.824 ◽

2006 ◽

Vol 108 (11) ◽

pp. 824-824 ◽

Cited By ~ 2

Author(s):

Wei Yun Z. Ai ◽

Debra Czerwinski ◽

Sandra J. Horning ◽

John Allen ◽

Robert Tibshirani ◽

...

Keyword(s):

Gene Expression ◽

Flow Cytometry ◽

Overall Survival ◽

T Cells ◽

Clinical Outcome ◽

Follicular Lymphoma ◽

Clinical Features ◽

Training Set ◽

Hla Dr ◽

Validation Set

Abstract Background: Follicular lymphoma (FL) has variable clinical outcomes. It has been suspected that tumor-infiltrating immune cells affect the biology and outcome of this disease. Using gene expression profiling and immunoperoxide tissue staining techniques, T cells and macrophages have been related to the survival outcome in some studies, but not others. In the current study, we used flow cytometry to analyze T cells and their subsets in follicular lymphoma biopsy specimens and determined whether these cell populations correlated with clinical features and outcomes. Methods: Two hundred and eighty-nine follicular lymphoma patients (pt) presented from 1997 to 2003 underwent an excisional lymph node biopsy prior to any treatment. The median age of pt at diagnosis was 45.7 yrs, median follow-up was 8.6 yrs for living pts, and median survival was 15.7 yrs. All but 8 patients had stage III/IV disease, 5 had stage I/II, and 3 were unknown. The histological grades were: 162 (56%) grade 1, 112 (39%) grade 2, 13 (4.5%) grade 3 and 2 (0.5%) unknown. Among the 289 patients, 41(17%) had low FLIPI score, 150 (63%) intermediate, 48 (20%) high and 50 unknown. All biopsies were analyzed for CD20, CD3, CD4, CD8 and HLA-DR expression by single-parameter flow cytometry. The 289 pts were divided into a training set of 147 and a validation set of 142, stratified by age and era of diagnosis. We used these two factors to stratify the pts because age at diagnosis is the most important prognostic factor for survival, and, in our data set, the era of diagnosis had an impact on survival and on the time from diagnosis to first treatment. For our analysis, we began with the training set and used the percentages of each immune cell population as a continuous variable in a univariate analysis in relation to clinical features and outcomes. We chose 8 phenotypic variables: CD20, CD3, CD4, CD8, HLA-DR, CD4/CD3 ratio, CD8/CD3 ratio, and activated T cells [defined as (HLA-DR-CD20)/CD3]. Five parameters were used as clinical endpoints: overall survival, FLIPI score at diagnosis, the time from diagnosis to first treatment (defined as the time from the first treatment to second treatment), response to CVP as the first treatment and the duration of the benefit from the first treatment (defined as the time interval between initiation of first treatment and initiation of second treatment). Results: The number of pt evaluable for each of the outcome parameters was as follows: 289 for time to first treatment and for overall survival, 239 for FLIPI scores, 164 for response to CVP and 129 for duration of the benefit from the first treatment., Of the 8 variables tested in the training set, only CD4/CD3 ratio and CD8/CD3 ratio were marginally significant for the survival endpoint, with p 0.034 and 0.088, respectively. None of the variables was significant for any of the other endpoints. A multivariate analysis yielded CD4/CD3 as the only significant predictor for survival. When CD4/CD3 was tested in the validation set, it yielded a p value of 0.48. Conclusion: We find no evidence that the percentage of tumor-infiltrating T cells or their subsets is predictive of clinical outcome in follicular lymphoma. Any gene expression signature involving T cells that does relate to clinical outcome could therefore be a property of the activity of the cells rather than a simple reflection of their numbers.

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Faculty Opinions recommendation of Distinct gene-expression profiles associated with the susceptibility of pathogen-specific CD4 T cells to HIV-1 infection.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717980795.793471790 ◽

2013 ◽

Author(s):

Paul Goepfert ◽

Anju Bansal

Keyword(s):

Gene Expression ◽

T Cells ◽

Cd4 T Cells ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Distinct Gene ◽

Hiv 1

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HIV-1 Infection Transcriptomics: Meta-Analysis of CD4+ T Cells Gene Expression Profiles

Viruses ◽

10.3390/v13020244 ◽

2021 ◽

Vol 13 (2) ◽

pp. 244 ◽

Cited By ~ 1

Author(s):

Antonio Victor Campos Coelho ◽

Rossella Gratton ◽

João Paulo Britto de Melo ◽

José Leandro Andrade-Santos ◽

Rafael Lima Guimarães ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Differentially Expressed Genes ◽

Cd4 T Cells ◽

Expression Profiles ◽

Meta Analysis ◽

Differentially Expressed ◽

Rna Seq ◽

Infected Cells ◽

Hiv 1

HIV-1 infection elicits a complex dynamic of the expression various host genes. High throughput sequencing added an expressive amount of information regarding HIV-1 infections and pathogenesis. RNA sequencing (RNA-Seq) is currently the tool of choice to investigate gene expression in a several range of experimental setting. This study aims at performing a meta-analysis of RNA-Seq expression profiles in samples of HIV-1 infected CD4+ T cells compared to uninfected cells to assess consistently differentially expressed genes in the context of HIV-1 infection. We selected two studies (22 samples: 15 experimentally infected and 7 mock-infected). We found 208 differentially expressed genes in infected cells when compared to uninfected/mock-infected cells. This result had moderate overlap when compared to previous studies of HIV-1 infection transcriptomics, but we identified 64 genes already known to interact with HIV-1 according to the HIV-1 Human Interaction Database. A gene ontology (GO) analysis revealed enrichment of several pathways involved in immune response, cell adhesion, cell migration, inflammation, apoptosis, Wnt, Notch and ERK/MAPK signaling.

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Early Response of CD8+ T Cells in COVID-19 Patients

Journal of Personalized Medicine ◽

10.3390/jpm11121291 ◽

2021 ◽

Vol 11 (12) ◽

pp. 1291

Author(s):

Deni Ramljak ◽

Martina Vukoja ◽

Marina Curlin ◽

Katarina Vukojevic ◽

Maja Barbaric ◽

...

Keyword(s):

Gene Expression ◽

Immune Response ◽

T Cells ◽

Mrna Expression ◽

Cd8 T Cells ◽

Expression Profiles ◽

Expression Patterns ◽

Effector Memory ◽

Healthy Controls ◽

Ifn Γ

Healthy and controlled immune response in COVID-19 is crucial for mild forms of the disease. Although CD8+ T cells play important role in this response, there is still a lack of studies showing the gene expression profiles in those cells at the beginning of the disease as potential predictors of more severe forms after the first week. We investigated a proportion of different subpopulations of CD8+ T cells and their gene expression patterns for cytotoxic proteins (perforin-1 (PRF1), granulysin (GNLY), granzyme B (GZMB), granzyme A (GZMA), granzyme K (GZMK)), cytokine interferon-γ (IFN-γ), and apoptotic protein Fas ligand (FASL) in CD8+ T cells from peripheral blood in first weeks of SARS-CoV-2 infection. Sixteen COVID-19 patients and nine healthy controls were included. The absolute counts of total lymphocytes (p = 0.007), CD3+ (p = 0.05), and CD8+ T cells (p = 0.01) in COVID-19 patients were significantly decreased compared to healthy controls. In COVID-19 patients in CD8+ T cell compartment, we observed lower frequency effector memory 1 (EM1) (p = 0.06) and effector memory 4 (EM4) (p < 0.001) CD8+ T cells. Higher mRNA expression of PRF1 (p = 0.05) and lower mRNA expression of FASL (p = 0.05) at the fifth day of the disease were found in COVID-19 patients compared to healthy controls. mRNA expression of PRF1 (p < 0.001) and IFN-γ (p < 0.001) was significantly downregulated in the first week of disease in COVID-19 patients who progressed to moderate and severe forms after the first week, compared to patients with mild symptoms during the entire disease course. GZMK (p < 0.01) and FASL (p < 0.01) mRNA expression was downregulated in all COVID-19 patients compared to healthy controls. Our results can lead to a better understanding of the inappropriate immune response of CD8+ T cells in SARS-CoV2 with the faster progression of the disease.

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Phenotypic and clonal stability of antigen-inexperienced memory-like T cells across the genetic background, hygienic status, and aging

10.1101/2020.09.01.277004 ◽

2020 ◽

Author(s):

Alena Moudra ◽

Veronika Niederlova ◽

Jiri Novotny ◽

Lucie Schmiedova ◽

Jan Kubovciak ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

Genetic Background ◽

Expression Profiles ◽

Bacterial Colonization ◽

Gene Expression Profiles ◽

Inbred Strains ◽

Microbial Colonization ◽

Independent Manner ◽

Different Genetic Background

AbstractAntigen-inexperienced memory-like T (AIMT) cells are functionally unique T cells representing one of the two largest subsets of murine CD8+ T cells. However, differences between laboratory inbred strains, insufficient data from germ-free mice, a complete lack of data from feral mice, and unclear relationship between AIMT cells formation during aging represent major barriers for better understanding of their biology. We performed a thorough characterization of AIMT cells from mice of different genetic background, age, and hygienic status by flow cytometry and multi-omics approaches including analyses of gene expression, TCR repertoire, and microbial colonization. Our data showed that AIMT cells are steadily present in mice independently of their genetic background and hygienic status. Despite differences in their gene expression profiles, young and aged AIMT cells originate from identical clones. We identified that CD122 discriminates two major subsets of AIMT cells in a strain-independent manner. Whereas thymic CD122LOW AIMT cells (innate memory) prevail only in young animals with high thymic IL-4 production, peripheral CD122HIGH AIMT cells (virtual memory) dominate in aged mice. Co-housing with feral mice changed the bacterial colonization of laboratory strains, but had only minimal effects on the CD8+ T-cell compartment including AIMT cells.

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Identification and Validation of a Predictive Immune-related Genes Signature for the Prognosis of Cholangiocarcinoma

10.21203/rs.3.rs-59448/v1 ◽

2020 ◽

Author(s):

Rui Zhang ◽

Chen Chen ◽

Qi Li ◽

Jialu Fu ◽

Dong Zhang ◽

...

Keyword(s):

Gene Expression ◽

T Cells ◽

High Risk ◽

Risk Score ◽

Risk Model ◽

Predictive Accuracy ◽

Expression Profiles ◽

Gene Expression Profiles ◽

The Cancer Genome Atlas ◽

Immune Related Genes

Abstract Background: Immune-related genes (IRGs) play a crucial role in the initiation and progression of cholangiocarcinoma (CCA). However, immune signatures have rarely been used to predict prognosis of CCA. The aim of this study was to construct a novel model for CCA to predict survival based on IRGs expression data.Methods: The gene expression profiles and clinical data of CCA patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database were integrated to establish and validate prognostic IRG signatures. Differentially expressed immune-related genes were screened. Univariate and multivariate Cox analysis were performed to identify prognostic IRGs, and the risk model that predicts outcomes was constructed. Furthermore, receiver operating characteristic (ROC) and Kaplan-Meier curve were plotted to examine predictive accuracy of the model, and a nomogram was constructed based on IRGs signature, combining with other clinical characteristics. Finally, CIBERSORT was used to analyze the association of immune cells infiltration with risk score.Results: We identified that 223 IRGs were signiﬁcantly dysregulated in patients with CCA, among which five IRGs (AVPR1B, CST4, TDGF1, RAET1E and IL9R) were identified as robust indicators for overall survival (OS), and a prognostic model was built based on the IRGs signature. Meanwhile, patients with high risk had worse OS in training and validation cohort, and the area under the ROC was 0.898 and 0.846, respectively. Nomogram demonstrated that immune risk score contributed much more points than other clinicopathological variables, with a C-index of 0.819 (95% CI, 0.727-0.911). Finally, we found that IRGs signature was positively correlated with the proportion of CD8+ T cells, neurophils and T gamma delta, while negatively with that of CD4+ memory resting T cells.Conclusions: We established and validated an effective five IRGs-based prediction model for CCA, which could accurately classify patients into groups with low and high risk of poor prognosis.

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Graft-Versus-Host Disease–Free Antitumoral Signature After Allogeneic Donor Lymphocyte Injection Identified by Proteomics and Systems Biology

JCO Precision Oncology ◽

10.1200/po.18.00365 ◽

2019 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Xiaowen Liu ◽

Zongliang Yue ◽

Yimou Cao ◽

Lauren Taylor ◽

Qing Zhang ◽

...

Keyword(s):

T Cells ◽

Systems Biology ◽

Graft Versus Host Disease ◽

Acute Gvhd ◽

Hematopoietic Cell Transplantation ◽

Training Set ◽

Activated T Cells ◽

Graft Versus Host ◽

Single Cell Profiling ◽

Validation Set

PURPOSE As a tumor immunotherapy, allogeneic hematopoietic cell transplantation with subsequent donor lymphocyte injection (DLI) aims to induce the graft-versus-tumor (GVT) effect but often also leads to acute graft-versus-host disease (GVHD). Plasma tests that can predict the likelihood of GVT without GVHD are still needed. PATIENTS AND METHODS We first used an intact-protein analysis system to profile the plasma proteome post-DLI of patients who experienced GVT and acute GVHD for comparison with the proteome of patients who experienced GVT without GVHD in a training set. Our novel six-step systems biology analysis involved removing common proteins and GVHD-specific proteins, creating a protein-protein interaction network, calculating relevance and penalty scores, and visualizing candidate biomarkers in gene networks. We then performed a second proteomics experiment in a validation set of patients who experienced GVT without acute GVHD after DLI for comparison with the proteome of patients before DLI. We next combined the two experiments to define a biologically relevant signature of GVT without GVHD. An independent experiment with single-cell profiling in tumor antigen–activated T cells from a patient with post–hematopoietic cell transplantation relapse was performed. RESULTS The approach provided a list of 46 proteins in the training set, and 30 proteins in the validation set were associated with GVT without GVHD. The combination of the two experiments defined a unique 61-protein signature of GVT without GVHD. Finally, the single-cell profiling in activated T cells found 43 of the 61 genes. Novel markers, such as RPL23, ILF2, CD58, and CRTAM, were identified and could be extended to other antitumoral responses. CONCLUSION Our multiomic analysis provides, to our knowledge, the first human plasma signature for GVT without GVHD. Risk stratification on the basis of this signature would allow for customized treatment plans.

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