A Novel Pyroptosis-Related Signature for Predicting Prognosis and Indicating Immune Microenvironment Features in Osteosarcoma

Osteosarcoma is a common malignant bone tumor with a propensity for drug resistance, recurrence, and metastasis. A growing number of studies have elucidated the dual role of pyroptosis in the development of cancer, which is a gasdermin-regulated novel inflammatory programmed cell death. However, the interaction between pyroptosis and the overall survival (OS) of osteosarcoma patients is poorly understood. This study aimed to construct a prognostic model based on pyroptosis-related genes to provide new insights into the prognosis of osteosarcoma patients. We identified 46 differentially expressed pyroptosis-associated genes between osteosarcoma tissues and normal control tissues. A total of six risk genes affecting the prognosis of osteosarcoma patients were screened to form a pyroptosis-related signature by univariate and LASSO regression analysis and verified using GSE21257 as a validation cohort. Combined with other clinical characteristics, including age, gender, and metastatic status, we found that the pyroptosis-related signature score, which we named “PRS-score,” was an independent prognostic factor for patients with osteosarcoma and that a low PRS-score indicated better OS and a lower risk of metastasis. The result of ssGSEA and ESTIMATE algorithms showed that a lower PRS-score indicated higher immune scores, higher levels of tumor infiltration by immune cells, more active immune function, and lower tumor purity. In summary, we developed and validated a pyroptosis-related signature for predicting the prognosis of osteosarcoma, which may contribute to early diagnosis and immunotherapy of osteosarcoma.

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Bioinformatics Analysis Reveals an Association Between Cancer Cell Stemness, Gene Mutations, and the Immune Microenvironment in Stomach Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2020.595477 ◽

2020 ◽

Vol 11 ◽

Author(s):

Zaisheng Ye ◽

Miao Zheng ◽

Yi Zeng ◽

Shenghong Wei ◽

Yi Wang ◽

...

Keyword(s):

Immune Cells ◽

Clinical Characteristics ◽

Prognostic Model ◽

Cox Regression ◽

Tumor Therapy ◽

Gene Mutations ◽

The Cancer Genome Atlas ◽

Prognostic Models ◽

Immune Microenvironment ◽

Stomach Adenocarcinoma

Cancer stem cells (CSCs), characterized by infinite proliferation and self-renewal, greatly challenge tumor therapy. Research into their plasticity, dynamic instability, and immune microenvironment interactions may help overcome this obstacle. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena Browser. Tumor purity and infiltrating immune cells in stomach adenocarcinoma (STAD) tissues were predicted using the ESTIMATE R package and CIBERSORT method, respectively. Differentially expressed genes (DEGs) between the high and low mRNAsi groups were used to construct prognostic models with weighted gene co-expression network analysis (WGCNA) and Lasso regression. The association between cancer stemness, gene mutations, and immune responses was evaluated in STAD. A total of 6,739 DEGs were identified between the high and low mRNAsi groups. DEGs in the brown (containing 19 genes) and blue (containing 209 genes) co-expression modules were used to perform survival analysis based on Cox regression. A nine-gene signature prognostic model (ARHGEF38-IT1, CCDC15, CPZ, DNASE1L2, NUDT10, PASK, PLCL1, PRR5-ARHGAP8, and SYCE2) was constructed from 178 survival-related DEGs that were significantly related to overall survival, clinical characteristics, tumor microenvironment immune cells, TMB, and cancer-related pathways in STAD. Gene correlation was significant across the prognostic model, CNVs, and drug sensitivity. Our findings provide a prognostic model and highlight potential mechanisms and associated factors (immune microenvironment and mutation status) useful for targeting CSCs.

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Prognostic prediction model for glioblastoma: a metabolic gene signature and independent external validation

10.21203/rs.3.rs-18209/v1 ◽

2020 ◽

Author(s):

Chuxiang Lei ◽

Wenlin Chen ◽

Yuekun Wang ◽

Binghao Zhao ◽

Penghao Liu ◽

...

Keyword(s):

Prognostic Model ◽

Validation Cohort ◽

External Validation ◽

Cox Proportional Hazards ◽

The Cancer Genome Atlas ◽

Prognostic Models ◽

Normal Brain ◽

Protective Effects ◽

Lasso Regression ◽

Metabolic Genes

Abstract Background. Glioblastoma (GBM) is the most common primary malignant intracranial tumor and is closely related to metabolic alterations. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods . Transcriptome data were obtained for all patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were filtered, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and we also conducted an independent external validation to examine the model. Results. There were 341 metabolic genes that showed significant differences between normal brain tissues and GBM tissues in both the training and validation cohorts, among which 56 genes were significantly correlated with the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10 , COMT , and GPX2 , with protective effects, as well as OCRL and RRM2 , with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients ( P <0.0001), and this significant result was also observed in the independent external validation cohort ( P <0.001). Conclusions . The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients. Background. Glioblastoma (GBM) is the most common primary malignant intracranial tumor and is closely related to metabolic alterations. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods . Transcriptome data were obtained for all patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were filtered, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and we also conducted an independent external validation to examine the model. Results. There were 341 metabolic genes that showed significant differences between normal brain tissues and GBM tissues in both the training and validation cohorts, among which 56 genes were significantly correlated with the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10 , COMT , and GPX2 , with protective effects, as well as OCRL and RRM2 , with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients ( P <0.0001), and this significant result was also observed in the independent external validation cohort ( P <0.001).Conclusions . The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients.

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Tumor Immune Microenvironment and Its Related miRNAs in Tumor Progression

Frontiers in Immunology ◽

10.3389/fimmu.2021.624725 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yingying Xing ◽

Guojing Ruan ◽

Haiwei Ni ◽

Hai Qin ◽

Simiao Chen ◽

...

Keyword(s):

T Cells ◽

Tumor Progression ◽

Tumor Cells ◽

Immune Cells ◽

Suppressor Cells ◽

Immune Microenvironment ◽

Non Coding Rna ◽

Tumor Immune Microenvironment ◽

Downstream Gene Expression

MiRNA is a type of small non-coding RNA, by regulating downstream gene expression that affects the progression of multiple diseases, especially cancer. MiRNA can participate in the biological processes of tumor, including proliferation, invasion and escape, and exhibit tumor enhancement or inhibition. The tumor immune microenvironment contains numerous immune cells. These cells include lymphocytes with tumor suppressor effects such as CD8+ T cells and natural killer cells, as well as some tumor-promoting cells with immunosuppressive functions, such as regulatory T cells and myeloid-derived suppressor cells. MiRNA can affect the tumor immune microenvironment by regulating the function of immune cells, which in turn modulates the progression of tumor cells. Investigating the role of miRNA in regulating the tumor immune microenvironment will help elucidate the specific mechanisms of interaction between immune cells and tumor cells, and may facilitate the use of miRNA as a predictor of immune disorders in tumor progression. This review summarizes the multifarious roles of miRNA in tumor progression through regulation of the tumor immune microenvironment, and provides guidance for the development of miRNA drugs to treat tumors and for the use of miRNA as an auxiliary means in tumor immunotherapy.

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Role of Immune Cell-Specific Hypermethylation Signatures in Classification and Risk Stratification of Breast Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.674338 ◽

2021 ◽

Vol 8 ◽

Author(s):

Yong Chen ◽

Fada Xia ◽

Bo Jiang ◽

Wenlong Wang ◽

Xinying Li

Keyword(s):

Breast Cancer ◽

Risk Stratification ◽

Immune Cells ◽

Prediction Accuracy ◽

Prognostic Value ◽

Clinical Characteristics ◽

Immune Cell ◽

Cell Types ◽

Clinical Model

Background: Epigenetic regulation, including DNA methylation, plays a major role in shaping the identity and function of immune cells. Innate and adaptive immune cells recruited into tumor tissues contribute to the formation of the tumor immune microenvironment (TIME), which is closely involved in tumor progression in breast cancer (BC). However, the specific methylation signatures of immune cells have not been thoroughly investigated yet. Additionally, it remains unknown whether immune cells-specific methylation signatures can identify subgroups and stratify the prognosis of BC patients.Methods: DNA methylation profiles of six immune cell types from eight datasets downloaded from the Gene Expression Omnibus were collected to identify immune cell-specific hypermethylation signatures (IC-SHMSs). Univariate and multivariate cox regression analyses were performed using BC data obtained from The Cancer Genome Atlas to identify the prognostic value of these IC-SHMSs. An unsupervised clustering analysis of the IC-SHMSs with prognostic value was performed to categorize BC patients into subgroups. Multiple Cox proportional hazard models were constructed to explore the role of IC-SHMSs and their relationship to clinical characteristics in the risk stratification of BC patients. Integrated discrimination improvement (IDI) was performed to determine whether the improvement of IC-SHMSs on clinical characteristics in risk stratification was statistically significant.Results: A total of 655 IC-SHMSs of six immune cell types were identified. Thirty of them had prognostic value, and 10 showed independent prognostic value. Four subgroups of BC patients, which showed significant heterogeneity in terms of survival prognosis and immune landscape, were identified. The model incorporating nine IC-SHMSs showed similar survival prediction accuracy as the clinical model incorporating age and TNM stage [3-year area under the curve (AUC): 0.793 vs. 0.785; 5-year AUC: 0.735 vs. 0.761]. Adding the IC-SHMSs to the clinical model significantly improved its prediction accuracy in risk stratification (3-year AUC: 0.897; 5-year AUC: 0.856). The results of IDI validated the statistical significance of the improvement (p < 0.05).Conclusions: Our study suggests that IC-SHMSs may serve as signatures of classification and risk stratification in BC. Our findings provide new insights into epigenetic signatures, which may help improve subgroup identification, risk stratification, and treatment management.

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Cancer Stemness-Based Prognostic Immune-Related Gene Signatures in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Frontiers in Endocrinology ◽

10.3389/fendo.2021.755805 ◽

2021 ◽

Vol 12 ◽

Author(s):

Na Li ◽

Yalin Li ◽

Peixian Zheng ◽

Xianquan Zhan

Keyword(s):

Immune Cells ◽

Prognostic Model ◽

Lung Squamous Cell Carcinoma ◽

Risk Groups ◽

Gene Signature ◽

Prognostic Models ◽

Cancer Stemness ◽

Immune Related Gene ◽

Tumor Purity ◽

Immune Related Genes

BackgroundCancer stem cells (CSCs) refer to cells with self-renewal capability in tumors. CSCs play important roles in proliferation, metastasis, recurrence, and tumor heterogeneity. This study aimed to identify immune-related gene-prognostic models based on stemness index (mRNAsi) in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), respectively.MethodsX-tile software was used to determine the best cutoff value of survival data in LUAD and LUSC based on mRNAsi. Tumor purity and the scores of infiltrating stromal and immune cells in lung cancer tissues were predicted with ESTIMATE R package. Differentially expressed immune-related genes (DEIRGs) between higher- and lower-mRNAsi subtypes were used to construct prognostic models.ResultsmRNAsi was negatively associated with StromalScore, ImmuneScore, and ESTIMATEScore, and was positively associated with tumor purity. LUAD and LUSC samples were divided into higher- and lower-mRNAsi groups with X-title software. The distribution of immune cells was significantly different between higher- and lower-mRNAsi groups in LUAD and LUSC. DEIRGs between those two groups in LUAD and LUSC were enriched in multiple cancer- or immune-related pathways. The network between transcriptional factors (TFs) and DEIRGs revealed potential mechanisms of DEIRGs in LUAD and LUSC. The eight-gene-signature prognostic model (ANGPTL5, CD1B, CD1E, CNTFR, CTSG, EDN3, IL12B, and IL2)-based high- and low-risk groups were significantly related to overall survival (OS), tumor microenvironment (TME) immune cells, and clinical characteristics in LUAD. The five-gene-signature prognostic model (CCL1, KLRC3, KLRC4, CCL23, and KLRC1)-based high- and low-risk groups were significantly related to OS, TME immune cells, and clinical characteristics in LUSC. These two prognostic models were tested as good ones with principal components analysis (PCA) and univariate and multivariate analyses. Tumor T stage, pathological stage, or metastasis status were significantly correlated with DEIRGs contained in prognostic models of LUAD and LUSC.ConclusionCancer stemness was not only an important biological process in cancer progression but also might affect TME immune cell infiltration in LUAD and LUSC. The mRNAsi-related immune genes could be potential biomarkers of LUAD and LUSC. Evaluation of integrative characterization of multiple immune-related genes and pathways could help to understand the association between cancer stemness and tumor microenvironment in lung cancer.

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Effect of Aging-Related Genes on the Prognosis of Colon Cancer

10.21203/rs.3.rs-984927/v1 ◽

2021 ◽

Author(s):

Yushu Liu ◽

Jiantao Gong ◽

Yanyi Huang ◽

Qunguang Jiang

Keyword(s):

Colon Cancer ◽

Cancer Patients ◽

Clinical Characteristics ◽

Cox Regression ◽

Validation Cohort ◽

External Validation ◽

Predictive Ability ◽

Lasso Regression ◽

Internal Validation ◽

Cox Regression Analysis

Abstract Background:Colon cancer is a common malignant cancer with high incidence and poor prognosis. Cell senescence and apoptosis are important mechanisms of tumor occurrence and development, in which aging-related genes(ARGs) play an important role. This study aimed to establish a prognostic risk model based on ARGs for diagnosis and prognosis prediction of colon cancer .Methods: We downloaded transcriptome data and clinical information of colon cancer patients from the Cancer Genome Atlas(TCGA) database and the microarray dataset(GSE39582) from the Gene Expression Omnibus(GEO) database. Univariate COX, least absolute shrinkage and selection operator(LASSO) regression algorithm and multivariate COX regression analysis were used to construct a 6-ARG prognosis model and calculated the riskScore. The prognostic signatures is validated by internal validation cohort and external validation cohort(GSE39582).In addition, functional enrichment pathways and immune microenvironment of aging-related genes(ARGs) were also analyzed. We also analyzed the correlation between rsikScore and clinical features and constructed a nomogram based on riskScore. We are the first to construct prognostic nomogram based on ARGs.Results: Through univariate COX,LASSO regression algorithm and multivariate COX regression analysis,6 prognostic ARGs (PDPK1,RAD52,GSR,IL7,BDNF and SERPINE1) were screened out and riskScore was constructed. We have verified that riskScore has good prognostic value in both internal validation cohort and external validation cohort. Pathway enrichment and immunoanalysis of ARGs provide a direction for the treatment of colon cancer patients. We also found that riskScore was closely related to the clinical characteristics of patients. Based on riskScore and related clinical features, we constructed a nomogram, which has good predictive performance.Conclusion: The 6-ARG prognostic signature we constructed has a certain clinical predictive ability. Its riskScore is also closely related to clinical characteristics, and nomogram based on this has stronger predictive ability than a single indicator. ARGs and the nomogram we constructed may provide a promising treatment for colon cancer patients.

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Role of Epigenetic Regulation in Plasticity of Tumor Immune Microenvironment

Frontiers in Immunology ◽

10.3389/fimmu.2021.640369 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yunkai Yang ◽

Yan Wang

Keyword(s):

Immune Cells ◽

Noncoding Rna ◽

Immune Escape ◽

Genetic Regulation ◽

Epigenetic Alterations ◽

Immune Microenvironment ◽

Post Translational Modifications ◽

Human Genes ◽

Tumor Immune Microenvironment

The tumor immune microenvironment (TIME), an immunosuppressive niche, plays a pivotal role in contributing to the development, progression, and immune escape of various types of cancer. Compelling evidence highlights the feasibility of cancer therapy targeting the plasticity of TIME as a strategy to retrain the immunosuppressive immune cells, including innate immune cells and T cells. Epigenetic alterations, such as DNA methylation, histone post-translational modifications, and noncoding RNA-mediated regulation, regulate the expression of many human genes and have been reported to be accurate in the reprogramming of TIME according to vast majority of published results. Recently, mounting evidence has shown that the gut microbiome can also influence the colorectal cancer and even extraintestinal tumors via metabolites or microbiota-derived molecules. A tumor is a kind of heterogeneous disease with specificity in time and space, which is not only dependent on genetic regulation, but also regulated by epigenetics. This review summarizes the reprogramming of immune cells by epigenetic modifications in TIME and surveys the recent progress in epigenetic-based cancer clinical therapeutic approaches. We also discuss the ongoing studies and future areas of research that benefits to cancer eradication.

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A novel prognostic model predicts overall survival in patients with nasopharyngeal carcinoma based on clinical features and blood biomarkers

10.21203/rs.3.rs-54154/v1 ◽

2020 ◽

Author(s):

Changchun Lai ◽

Chunning Zhang ◽

Hualiang Lv ◽

Hanqing Huang ◽

Xia Ke ◽

...

Keyword(s):

Clinical Features ◽

Risk Score ◽

Prognostic Model ◽

Validation Cohort ◽

Predictive Accuracy ◽

Tnm Staging ◽

Clinical Treatment ◽

Lasso Regression ◽

Model Risk ◽

Ebv Dna

Abstract Background: To develop and validate a novel prognostic model to estimate overall survival (OS) in nasopharyngeal carcinoma (NPC) patients based on clinical features and blood biomarkers. And assess its incremental value to TNM staging system, clinical treatment, and Epstein-Barr virus DNA (EBV DNA) for individual OS estimation.Methods: We retrospectively analyzed 519 consecutive NPC. A prognostic model was generated by using the Lasso regression model in training cohort (n = 346). Then comparison of predictive accuracy between the novel prognostic model, TNM staging, clinical treatment, and EBV DNA using concordance index (C-index), time-dependent ROC (tdROC), and decision curve analysis (DCA). Subsequently, a nomogram for OS incorporating the prognostic model, TNM staging and clinical treatment was built. Finally, we stratified patients into high- risk and low-risk groups according to the model risk score, and the survival time of these two groups was analyzed using Kaplan–Meier survival plots. All the results were validated in the independent validation cohort (n = 173).Results: Using the Lasso regression, a prognostic model was established consisting of 13 variables with respect to patient prognosis. The C-index, tdROC and DCA all showed the prognostic model had good predictive accuracy and discriminatory power than TNM staging, clinical treatment and EBV DNA in training cohort. Nomogram consisting of the prognostic model, TNM staging, clinical treatment and EBV DNA shown some superior net benefit. According to the model risk score, we split the patients into two subgroups: low- risk (risk score ≤ -1.423) and high-risk (risk score > -1.423). There had significant differences in OS between the two subgroups of patients. In the validation cohort, similar results were obtained.Conclusions: The proposed novel prognostic model based on clinical features and serological markers represents a promising signature for estimating OS in NPC patients.

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SHMT2 is Associated with Tumor Purity, CD8+ T Immune Cells Infiltration, and a Novel Therapeutic Target in Four Different Human Cancers

Current Molecular Medicine ◽

10.2174/1566524022666220112142409 ◽

2022 ◽

Vol 22 ◽

Author(s):

Muhammad Usman ◽

Yasir Hameed ◽

Mukhtiar Ahmad ◽

Muhammad Junaid Iqbal ◽

Aghna Maryam ◽

...

Keyword(s):

Immune Cells ◽

Promoter Methylation ◽

In Silico ◽

Human Cancer ◽

Enrichment Analysis ◽

Genetic Alterations ◽

Pathway Enrichment Analysis ◽

Human Cancers ◽

Tumor Purity

Aims: This study was launched to identify the SHMT2 associated Human Cancer subtypes. Background: Cancer is the 2nd leading cause of death worldwide. Previous reports revealed the limited involvement of SHMT2 in human cancer. In the current study, we comprehensively analyzed the role of SHMT2 in 24 major subtypes of human cancers using in silico approach and identified a few subtypes that are mainly associated with SHMT2. Objective:: We aim to comprehensively analyze the role of SHMT2 in 24 major subtypes of human cancers using in silico approach and identified a few subtypes that are mainly associated with SHMT2. Earlier, limited knowledge exists in the medical literature regarding the involvement of Serine Hydroxymethyltransferase 2 (SHMT2) in human cancer. Methods: In the current study, we comprehensively analyzed the role of SHMT2 in 24 major subtypes of human cancers using in silico approach and identified a few subtypes that are mainly associated with SHMT2. Pan-cancer transcriptional expression profiling of SHMT2 was done using UALCAN while further validation was performed using GENT2. For translational profiling of SHMT2, we utilized Human Protein Atlas (HPA) platform. Promoter methylation, genetic alteration, and copy number variations (CNVs) profiles were analyzed through MEXPRESS and cBioPortal. Survival analysis was carried out through Kaplan–Meier (KM) plotter platform. Pathway enrichment analysis of SHMT2 was performed using DAVID, while the gene-drug network was drawn through CTD and Cytoscape. Furthermore, in the tumor microenvironment, a correlation between tumor purity, CD8+ T immune cells infiltration, and SHMT2 expression was accessed using TIMER. Results: SHMT2 was found overexpressed in 24 different subtypes of human cancers and its overexpression was significantly associated with the reduced Overall survival (OS) and Relapse-free survival durations of Breast cancer (BRCA), Kidney renal papillary cell carcinoma (KIRP), Liver hepatocellular carcinoma (LIHC), and Lung adenocarcinoma (LUAD) patients. This implies that SHMT2 plays a significant role in the development and progression of these cancers. We further noticed that SHMT2 was also up-regulated in BRCA, KIRP, LIHC, and LUAD patients of different clinicopathological features. Pathways enrichment analysis revealed the involvement of SHMT2 enriched genes in five diverse pathways. Furthermore, we also explored some interesting correlations between SHMT2 expression and promoter methylation, genetic alterations, CNVs, tumor purity, and CD8+ T immune cell infiltrates. Conclusion: Our results suggested that overexpressed SHMT2 is correlated with the reduced OS and RFS of the BRCA, KIRP, LIHC, and LUAD patients and can be a potential diagnostic and prognostic biomarker for these cancers.

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An Immune Score Model of the Bone Marrow Micro-Environment Predicts Survival in Chronic Lymphocytic Leukaemia

Blood ◽

10.1182/blood-2020-140925 ◽

2020 ◽

Vol 136 (Supplement 1) ◽

pp. 38-38

Author(s):

Yang Liang ◽

Han-ying Huang ◽

Yun Wang ◽

Weida Wang ◽

Huan-xin Lin

Keyword(s):

Bone Marrow ◽

Immune Cells ◽

Prognostic Model ◽

Lymphocytic Leukaemia ◽

Validation Cohort ◽

Immune Signature ◽

Score Model ◽

Immune Related Genes ◽

Immune Score ◽

High Risk Cohort

Background: Most prognostic and predictive models for survival of persons with chronic lymphocytic leukaemia (CLL) are based on clinical and laboratory co-variates and have AUCs of 0.65 to 0.74 indicating considerable inaccuracy. No current prognostic model uses data on immune state of the bone marrow micro-environment to predict survival. Methods: We downloaded relevant data from International Cancer Genome Consortium (CLL-ES; N = 485) and Gene Expression Omnibus (GSE22762; N = 195) with survival data. We used a LASSO Cox regression model to build a survival prediction model based on the expression of immune-related genes in the bone marrow micro-environment. Immune cells corresponding to these immune genes were deduced Using CIBERSORT and compared in cohorts with different survival probabilities. Results: An immune signature based on 9 immune-related genes were constructed in the training cohort (CLL-ES) and tested in a validation cohort (GSE22762). The AUCs of 1, 3 and 5 -year survivals were 0.83 [95% Confidence Interval, 0.63, 0.97], 0.79 [0.67, 0.89] and 0.82 [0.73, 0.90] in training and 0.66 [0.55, 0.79], 0.60 [0.50, 0.69] and 0.66 [0.58, 0.76]in the validation cohort. Subjects in the high-risk cohort identified by immune signature had significantly worse 5-year survival compared with those in low-risk cohort group (training cohort: 91% [87, 95%] vs. 99% [98, 100%], P <0.001; validation cohort: 61% [50, 71%] vs 81% [80, 82%], P = 0.003). Subjects with a low-risk immune signature had a higher proportion of CD4-postive T-cells, activated NK-cells compared with those in the high-risk cohort (p < 0.05). Conclusion: The immune score model of the bone marrow micro-environment we developed accurately predicts survival of persons with CLL. These data suggest a role for immune cells in the bone marrow micro-environment on survival. The immune score we describe can be combined with other prediction models to improve accuracy. Keywords ICGC, GEO, chronic lymphocytic leukemia, immunity, prognostic model Disclosures No relevant conflicts of interest to declare.

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