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A CD8+ T cell transcription signature predicts prognosis in autoimmune disease.

ABSTRACT: Autoimmune diseases are common and debilitating, but their severe manifestations could be reduced if biomarkers were available to allow individual tailoring of potentially toxic immunosuppressive therapy. Gene expression-based biomarkers facilitating such tailoring of chemotherapy in cancer, but not autoimmunity, have been identified and translated into clinical practice. We show that transcriptional profiling of purified CD8(+) T cells, which avoids the confounding influences of unseparated cells, identifies two distinct subject subgroups predicting long-term prognosis in two autoimmune diseases, antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), a chronic, severe disease characterized by inflammation of medium-sized and small blood vessels, and systemic lupus erythematosus (SLE), characterized by autoantibodies, immune complex deposition and diverse clinical manifestations ranging from glomerulonephritis to neurological dysfunction. We show that the subset of genes defining the poor prognostic group is enriched for genes involved in the interleukin-7 receptor (IL-7R) pathway and T cell receptor (TCR) signaling and those expressed by memory T cells. Furthermore, the poor prognostic group is associated with an expanded CD8(+) T cell memory population. These subgroups, which are also found in the normal population and can be identified by measuring expression of only three genes, raise the prospect of individualized therapy and suggest new potential therapeutic targets in autoimmunity.

SUBMITTER: McKinney EF

PROVIDER: S-EPMC3504359 | biostudies-literature | 2010 May

REPOSITORIES: biostudies-literature

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A CD8+ T cell transcription signature predicts prognosis in autoimmune disease.

McKinney Eoin F EF Lyons Paul A PA Carr Edward J EJ Hollis Jane L JL Jayne David R W DR Willcocks Lisa C LC Koukoulaki Maria M Brazma Alvis A Jovanovic Vojislav V Kemeny D Michael DM Pollard Andrew J AJ Macary Paul A PA Chaudhry Afzal N AN Smith Kenneth G C KG

Nature medicine 20100418 5

Autoimmune diseases are common and debilitating, but their severe manifestations could be reduced if biomarkers were available to allow individual tailoring of potentially toxic immunosuppressive therapy. Gene expression-based biomarkers facilitating such tailoring of chemotherapy in cancer, but not autoimmunity, have been identified and translated into clinical practice. We show that transcriptional profiling of purified CD8(+) T cells, which avoids the confounding influences of unseparated cel ...[more]

PMID: 20400961

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Project description:BackgroundColorectal cancer (CRC) ranks among the most prevalent malignancies affecting the gastrointestinal tract. The infiltration of CD8+ T cells significantly influences the prognosis and progression of tumor patients.MethodsThis study establishes a CRC immune risk model based on CD8+ T cell-related genes. CD8+ T cell-related genes were identified through Weighted Gene Co-expression Network Analysis (WGCNA), and the enriched gene sets were annotated via Gene Ontology (GO) and Reactome pathway analysis. Employing machine learning methods, including the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Random Forest (RF), we identified nine genes associated with CD8+ T-cell infiltration. The infiltration levels of immune cells in CRC tissues were assessed using the ssGSEA algorithm.ResultsThese genes provide a foundation for constructing a prognostic model. The TCGA-CRC sample model's prediction scores were categorized, and the prediction models were validated through Cox regression analysis and Kaplan-Meier curve analysis. Notably, although CRC tissues with higher risk scores exhibited elevated levels of CD8+ T-cell infiltration, they also demonstrated heightened expression of immune checkpoint genes. Furthermore, comparison of microsatellite instability (MSI) and gene mutations across the immune subgroups revealed notable gene variations, particularly with APC, TP53, and TNNT1 showing higher mutation frequencies. Finally, the predictive model's efficacy was corroborated through the use of Tumor Immune Dysfunction and Exclusion (TIDE), Immune Profiling Score (IPS), and immune escape-related molecular markers. The predictive model was validated through an external cohort of CRC and the Bladder Cancer Immunotherapy Cohort. CLRN3 expression levels in tumor and adjacent normal tissues were assessed using quantitative real-time polymerase chain reaction (qRT-PCR) and western blot. Subsequent in vitro and in vivo experiments demonstrated that CLRN3 knockdown significantly attenuated the malignant biological behavior of CRC cells, while overexpression had the opposite effect.ConclusionsThis study presents a novel prognostic model for CRC, providing a framework for enhancing the survival rates of CRC patients by targeting CD8+ T-cell infiltration.

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Project description:BackgroundThe prognosis of oral squamous cell carcinoma (OSCC) patients is difficult to predict or describe due to its high-level heterogeneity and complex aetiologic factors. Ferroptosis is a novel form of iron-dependent cell death that is closely related to tumour growth and progression. This study aims to clarify the predictive value of ferroptosis-related genes (FRGs) on the overall survival(OS) of OSCC patients.MethodsThe mRNA expression profile of FRGs and clinical information of patients with OSCC were collected from the TCGA database. Candidate differentially expressed ferroptosis-related genes (DE-FRGs) were identified by analysing differences between OSCC and adjacent normal tissues. A gene signature of prognosis-related DE-FRGs was established by univariate Cox analysis and LASSO analysis in the training set. Patients were then divided into high- and low-risk groups according to the cut-off value of risk scores, A nomogram was constructed to quantify the contributions of gene signature and clinical parameters to OS. Then several bioinformatics analyses were used to verify the reliability and accuracy of the model in the validation set. Finally, single-sample gene set enrichment analysis (ssGSEA) was also performed to reveal the underlying differences in immune status between different risk groups.ResultsA prognostic model was constructed based on 10 ferroptosis-related genes. Patients in high-risk group had a significantly worse OS (p < 0.001). The gene signature was verified as an independent predictor for the OS of OSCC patients (HR > 1, p < 0.001). The receiver operating characteristic curve displayed the favour predictive performance of the risk model. The prediction nomogram successfully quantified each indicator's contribution to survival and the concordance index and calibration plots showed its superior predictive capacity. Finally, ssGSEA preliminarily indicated that the poor prognosis in the high-risk group might result from the dysregulation of immune status.ConclusionThis study established a 10-ferroptosis-releated gene signature and nomogram that can be used to predict the prognosis of OSCC patients, which provides new insight for future anticancer therapies based on potential FRG targets.

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A CD8+ T cell transcription signature predicts prognosis in autoimmune disease.

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A CD8+ T cell transcription signature predicts prognosis in autoimmune disease.

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