scholarly journals A liquid biopsy-based approach identifies myeloid cells, STAT3 and arginase-1 as predictors of glioma risk score and patients' survival

2021 ◽  
Author(s):  
Paola Del Bianco ◽  
Laura Pinton ◽  
Sara Magri ◽  
Stefania Canè ◽  
Elena Masetto ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Suppression ◽  
Prognostic Model ◽  
Cox Regression ◽  
Myeloid Cells ◽  
Response To Therapy ◽  
High Grade ◽  
Diagnostic Score ◽  
Arginase 1

Abstract Background Although gliomas are strictly confined to the central nervous system, their negative influence over the immune system can extend to peripheral circulation. The immune suppression exerted by myeloid cells is capable of affecting both response to therapy and disease outcome. Here we analyzed the expansion of several myeloid parameters in the blood of low- and high-grade gliomas and assessed their relevance as biomarkers of disease and clinical outcome. Methods Peripheral blood was obtained from 134 low- and high-grade glioma patients before surgery and treatment. Myeloid cell subsets such as total CD14+, CD14+/p-STAT3+, CD14+/PD-L1+, CD15+ cells and 4 myeloid derived suppressor cell (MDSC) subsets, were evaluated by multiparametric flow cytometry. Arginase-1 (ARG1) quantity and activity was determined in the plasma. Principal component analysis was performed to define correlations between myeloid markers. Multivariable logistic regression model was used to obtain a diagnostic score to discriminate glioma patients from healthy controls, and between each glioma grade. A glioblastoma prognostic model was determined by multiple Cox regression using clinical and myeloid parameters. Results In the blood of glioma patients, changes in myeloid parameters associated with immune suppression were identified and allowed us to define a diagnostic score calculating the risk of being a glioma patient, that included CD15+ cells, MDSC1, MDSC3, p-STAT3 and ARG1 activity. Of note, the same parameters, together with age, can also be used to calculate the risk score in differentiating each glioma grade. Finally, a prognostic model for glioblastoma patients stemmed out from a Cox multiple analysis, highlighting the role of MDSC, p-STAT3 and ARG1 activity together with clinical parameters in predicting the patient outcome. Conclusions This work emphasizes the role of systemic immune suppression carried out by myeloid cells in gliomas. The identification of biomarkers associated with immune landscape, diagnosis and outcome of glioblastoma patients lays the ground for their clinical use for stratification and follow up.

scholarly journals Myeloid Diagnostic and Prognostic Markers of Immune Suppression in the Blood of Glioma Patients

2022 ◽  
Vol 12 ◽  
Author(s):  
Paola Del Bianco ◽  
Laura Pinton ◽  
Sara Magri ◽  
Stefania Canè ◽  
Elena Masetto ◽  
...  
Keyword(s):  
Immune Suppression ◽  
Prognostic Model ◽  
Cox Regression ◽  
Myeloid Cells ◽  
Response To Therapy ◽  
High Grade ◽  
Multivariable Logistic Regression Model ◽  
Diagnostic Score ◽  
Arginase 1

BackgroundAlthough gliomas are confined to the central nervous system, their negative influence over the immune system extends to peripheral circulation. The immune suppression exerted by myeloid cells can affect both response to therapy and disease outcome. We analyzed the expansion of several myeloid parameters in the blood of low- and high-grade gliomas and assessed their relevance as biomarkers of disease and clinical outcome.MethodsPeripheral blood was obtained from 134 low- and high-grade glioma patients. CD14+, CD14+/p-STAT3+, CD14+/PD-L1+, CD15+ cells and four myeloid-derived suppressor cell (MDSC) subsets, were evaluated by flow cytometry. Arginase-1 (ARG1) quantity and activity was determined in the plasma. Multivariable logistic regression model was used to obtain a diagnostic score to discriminate glioma patients from healthy controls and between each glioma grade. A glioblastoma prognostic model was determined by multiple Cox regression using clinical and myeloid parameters.ResultsChanges in myeloid parameters associated with immune suppression allowed to define a diagnostic score calculating the risk of being a glioma patient. The same parameters, together with age, permit to calculate the risk score in differentiating each glioma grade. A prognostic model for glioblastoma patients stemmed out from a Cox multiple analysis, highlighting the role of MDSC, p-STAT3, and ARG1 activity together with clinical parameters in predicting patient’s outcome.ConclusionsThis work emphasizes the role of systemic immune suppression carried out by myeloid cells in gliomas. The identification of biomarkers associated with immune landscape, diagnosis, and outcome of glioblastoma patients lays the ground for their clinical use.

scholarly journals Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongsheng He ◽  
Shengyin Liao ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

scholarly journals N6-Methyladenosine RNA Methylation Regulators Contribute to Malignant Progression and Survival Prediction in Chronic Lymphocytic Leukemia

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 1738-1738 ◽  
Author(s):  
Ya Zhang ◽  
Xiaosheng Fang ◽  
Na Chen ◽  
Xiao Lv ◽  
Xueling Ge ◽  
...  
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Lymphocytic Leukemia ◽  
Functional Enrichment ◽  
Biological Mechanism ◽  
Regression Analyses ◽  
Rna Methylation ◽  
Kaplan Meier ◽  
M6a Rna Methylation

Introduction N6-methyladenosine (m6A) RNA methylation is the most abundant epitranscriptomic modification, dynamically installed by the m6A methyltransferases (termed as "writers"), reverted by the demethylases (termed as "erasers"), and recognized by m6A binding proteins (termed as "readers"). Emerging evidence suggests that m6A RNA methylation regulates RNA stability, and participates in the pathogenesis of multiple diseases including cancers. Nevertheless, the role of m6A RNA methylation in chronic lymphocytic leukemia (CLL) remains to be unveiled. Herein, we hypothesized that m6A RNA methylation contributed to the tumorigenesis and maintenance of CLL. Moreover, the risk-prediction model integrated with the m6A regulators could serve as a novel and effective prognostic indicator in CLL. This study aimed to identify robust m6A RNA methylation-associated fingerprints for risk stratification in patients with CLL. Methods A total of 714 de novo CLL patients from 4 cohorts (China, Spain, Germany and Italy) were enrolled with informed consents. EpiQuik m6A RNA methylation colorimetric quantification assay was utilized to assess m6A RNA methylation levels. LASSO Cox regression algorithm was performed to calculate m6A RNA methylation-associated risk score (short for "m6A risk score") in R software. Besides, Kaplan-Meier survival analysis with log-rank test, univariate and multivariate Cox regression analyses and ROC curve analysis of overall survival (OS) were conduct to explore the prognostic value of m6A signature in CLL. Furthermore, RNA-seq, MeRIP-seq, Ribo-seq, functional enrichment analyses in silico and preclinical experiments ex vivo were applied to confirm the biological mechanism of the m6A regulators in CLL. Results In the present study, we performed a comprehensive analysis to dissect the role of m6A RNA methylation regulators in CLL. Compared with normal B cells from healthy donors, obvious decreased level of m6A RNA methylation was observed in primary CLL cells (p<0.01; Figure 1A). In addition, down-regulated m6A RNA methylation was also detected in CLL cell lines MEC1 and EHEB (p<0.05; Figure 1A). Then, we further investigated the association of the m6A RNA methylation regulators with clinical outcomes of CLL patients. By LASSO Cox regression analysis in 486 CLL patients, the m6A risk score was established with the coefficients of fourteen m6A regulators at the minimum lambda value of 0.00892 (Figure 1B-C). Based on the median risk score as the cut-off value, a clear distribution pattern was delineated in CLL patients (Figure 1D). Kaplan-Meier curves showed stratified high-risk patients presented significantly shorter OS versus the low-risk group (HR=4.477, p<0.001; Figure 2A). Besides, m6A risk score also predicts inferior prognosis in stable subgroup (HR=3.097, p=0.037; Figure 2B), and progressed/ relapsed subgroup (HR=3.325, p=0.001; Figure 2C). Moreover, univariate, multivariate cox regression analyses and ROC curve confirmed high m6A risk score as an independent survival predictor in CLL patients (p<0.001; Figure 2D-E). Thereafter, the clinicopathological relevance and underlying mechanism of m6A risk score were explored. Significant elevated m6A risk score was detected in patients with unfavorable treatment responses compared with stable status (p<0.001; Figure 3A). Furthermore, CLL patients with advanced Binet stage, positive ZAP-70 and unmutated IGHV present increased m6A risk score (p<0.05; Figure 3B-C). Intriguingly, we also observed the significantly negative correlation between highrisk score and 13q14 deletion, in accordance with patients' inferior outcome (p=0.047; Figure 3D). Moreover, Pearson correlation analysis, STRING interactive network and functional enrichment analyses deciphered that the m6A regulators exerted crucial roles in CLL progression potentially via modulating RNA metabolism and oncogenic pathways (Figure 4A-C). Conclusion To date, our study provides evidence for the first time that reduced m6A RNA methylation contributes to the tumorigenesis of CLL. Distinct m6A risk scoreis demonstrated as an efficient tool facilitating prognosis evaluation in CLL patients. However, validation of the signature in more independent cohorts are warranted. Further interrogations will be elucidated on the biological mechanism of m6A regulators, highlighting insights into pathogenesis and therapy strategy of CLL. Disclosures No relevant conflicts of interest to declare.

scholarly journals Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Xiaohong - Liu ◽  
Qian - Xu ◽  
Zi-Jing - Li ◽  
Bin - Xiong
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Metabolic Reprogramming ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract BackgroundMetabolic reprogramming is an important hallmark in the development of malignancies. Numerous metabolic genes have been demonstrated to participate in the progression of hepatocellular carcinoma (HCC). However, the prognostic significance of the metabolic genes in HCC remains elusive. MethodsWe downloaded the gene expression profiles and clinical information from the GEO, TCGA and ICGC databases. The differently expressed metabolic genes were identified by using Limma R package. Univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) Cox regression analysis were utilized to uncover the prognostic significance of metabolic genes. A metabolism-related prognostic model was constructed in TCGA cohort and validated in ICGC cohort. Furthermore, we constructed a nomogram to improve the accuracy of the prognostic model by using the multivariate Cox regression analysis.ResultsThe high-risk score predicted poor prognosis for HCC patients in the TCGA cohort, as confirmed in the ICGC cohort (P < 0.001). And in the multivariate Cox regression analysis, we observed that risk score could act as an independent prognostic factor for the TCGA cohort (HR (hazard ratio) 3.635, 95% CI (confidence interval)2.382-5.549) and the ICGC cohort (HR1.905, 95%CI 1.328-2.731). In addition, we constructed a nomogram for clinical use, which suggested a better prognostic model than risk score.ConclusionsOur study identified several metabolic genes with important prognostic value for HCC. These metabolic genes can influence the progression of HCC by regulating tumor biology and can also provide metabolic targets for the precise treatment of HCC.

scholarly journals Integrated Analysis of Methylation-driven Genes and Pretreatment Prognostic Factors in Patients with Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Dongsheng He ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
Mengxing You ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background: The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients.Methods: The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than -0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test.Results: In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage.Conclusion: We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

scholarly journals Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Pu Wu ◽  
Jinyuan Shi ◽  
Wei Sun ◽  
Hao Zhang
Keyword(s):  
Thyroid Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Cox Regression ◽  
Checkpoint Inhibitors ◽  
Risk Groups ◽  
Low Risk ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear. Objective This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival. Methods A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort. Results A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues. Conclusion In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

scholarly journals Human Nuclear Receptors (NRs) Genes have Prognostic Significance in Hepatocellular Carcinoma Patients

2020 ◽  
Author(s):  
Guangtao Sun ◽  
Kejian Sun ◽  
Chao Shen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Nuclear Receptors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Prognostic Signature ◽  
Term Survival ◽  
Cox Regression Analysis

Abstract Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality in the world. Human nuclear receptors (NRs) have been identified to closely related to various cancer. However, the prognostic significance of NRs on HCC patients has not been studied in detail.Method: We downloaded the mRNA profiles and clinical information of 371 HCC patients from TCGA database and analyzed the expression of 48 NRs. The consensus clustering analysis with the mRNA levels of 48 NRs was performed by the "ConsensusClusterPlus". The Univariate cox regression analysis was performed to predict the prognostic significance of NRs on HCC. The risk score was calculated by the prognostic model constructed based on eight optimal NRs which were selected. Then Multivariate Cox regression analysis was performed to determine whether the risk score is an independent prognostic signature. Finally, the nomogram based on multiple independent prognostic factors including risk score and TNM Stage was used to predict the long-term survival of HCC patients.Results: NRs could effectively separate HCC samples with different prognosis. The prognostic model constructed based on the eight optimal NRs (NR1H3, ESR1, NR1I2, NR2C1, NR6A1, PPARD, PPARG and VDR) could effectively predict the prognosis of HCC patients as an independent prognostic signature. Moreover, the nomogram was constructed based on multiple independent prognostic factors including risk score and TNM Stage and could better predict the long-term survival for 3- and 5-year of HCC patients.Conclusion: Our results provided novel evidences that NRs could act as the potential prognostic signatures for HCC patients.

scholarly journals Integrated analysis of the functions and prognostic values of RNA-binding proteins in neuroblastoma

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260876
Author(s):  
Jun Yang ◽  
Jiaying Zhou ◽  
Cuili Li ◽  
Shaohua Wang
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Risk Model ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Differentially Expressed ◽  
Lasso Regression

Background Neuroblastoma (NB) is the most common solid tumor in children. NB treatment has made significant progress; however, given the high degree of heterogeneity, basic research findings and their clinical application to NB still face challenges. Herein, we identify novel prognostic models for NB. Methods We obtained RNA expression data of NB and normal nervous tissue from TARGET and GTEx databases and determined the differential expression patterns of RNA binding protein (RBP) genes between normal and cancerous tissues. Lasso regression and Cox regression analyses identified the five most important differentially expressed genes and were used to construct a new prognostic model. The function and prognostic value of these RBPs were systematically studied and the predictive accuracy verified in an independent dataset. Results In total, 348 differentially expressed RBPs were identified. Of these, 166 were up-regulated and 182 down-regulated RBPs. Two hubs RBPs (CPEB3 and CTU1) were identified as prognostic-related genes and were chosen to build the prognostic risk score models. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using proportional hazards regression model. A five gene prognostic model: Risk score = (-0.60901*expCPEB3)+(0.851637*expCTU1) was built. Based on this model, the overall survival of patients in the high-risk subgroup was lower (P = 2.152e-04). The area under the curve (AUC) of the receiver-operator characteristic curve of the prognostic model was 0.720 in the TARGET cohort. There were significant differences in the survival rate of patients in the high and low-risk subgroups in the validation data set GSE85047 (P = 0.1237e-08), with the AUC 0.730. The risk model was also regarded as an independent predictor of prognosis (HR = 1.535, 95% CI = 1.368–1.722, P = 2.69E-13). Conclusions This study identified a potential risk model for prognosis in NB using Cox regression analysis. RNA binding proteins (CPEB3 and CTU1) can be used as molecular markers of NB.

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