Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature

Abstract Background Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer. Methods We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses. Results A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group (p = 1.215e − 06 in the training set; p = 0.0069 in the validation set; p = 1.233e − 07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR = 1.432; 95% CI 1.204–1.702, p < 0.001), validation set (HR = 1.162; 95% CI 1.004–1.345, p = 0.044), and whole set (HR = 1.240; 95% CI 1.128–1.362, p < 0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. Conclusions We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

Download Full-text

Immune-Related LncRNAs as Predictors of Survival in Breast Cancer: A Prognostic Signature

10.21203/rs.3.rs-31519/v1 ◽

2020 ◽

Author(s):

wei ma ◽

fangkun zhao ◽

xinmiao yu ◽

shu guan ◽

huandan suo ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Curve Analysis ◽

Time Dependent ◽

Training Set ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Validation Set

Abstract Background: Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancers development and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Methods: We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separatedinto training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate and multivariate Cox regression analyses. Results: A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group( p= 1.215e−06 in the training set; p =0.0069 in the validation set; p =1.233e−07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set, 0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR= 1.432; 95% CI 1.204−1.702, p <0.001), validation set (HR= 1.162; 95% CI 1.004−1.345, p = 0.044), and whole set (HR=1.240; 95% CI 1.128−1.362, p <0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways. Conclusions: We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

Download Full-text

Immune-related lncRNAs as predictors of survival in breast cancer: a prognostic signature

10.21203/rs.3.rs-31519/v2 ◽

2020 ◽

Author(s):

wei ma ◽

fangkun zhao ◽

xinmiao yu ◽

shu guan ◽

huandan suo ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Curve Analysis ◽

Time Dependent ◽

Training Set ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Validation Set

Abstract Background: Breast cancer is a highly heterogeneous disease, this poses challenges for classification and management. Long non-coding RNAs play acrucial role in the breast cancersdevelopment and progression, especially in tumor-related immune processes which have become the most rapidly investigated area. Therefore, we aimed at developing an immune-related lncRNA signature to improve the prognosis prediction of breast cancer.Methods: We obtained breast cancer patient samples and corresponding clinical data from The Cancer Genome Atlas (TCGA) database. Immune-related lncRNAs were screened by co-expression analysis of immune-related genes which were downloaded from the Immunology Database and Analysis Portal (ImmPort). Clinical patient samples were randomly separated into training and testing sets. In the training set, univariate Cox regression analysis and LASSO regression were utilized to build a prognostic immune-related lncRNA signature. The signature was validated in the training set, testing set, and whole cohorts by the Kaplan–Meier log-rank test, time-dependent ROC curve analysis, principal component analysis, univariate andmultivariate Cox regression analyses.Results:A total of 937 immune- related lncRNAs were identified, 15 candidate immune-related lncRNAs were significantly associated with overall survival (OS). Eight of these lncRNAs (OTUD6B-AS1, AL122010.1, AC136475.2, AL161646.1, AC245297.3, LINC00578, LINC01871, AP000442.2) were selected for establishment of the risk prediction model. The OS of patients in the low-risk group was higher than that of patients in the high-risk group(p=1.215e−06 in the training set; p=0.0069 in the validation set; p=1.233e−07 in whole cohort). The time-dependent ROC curve analysis revealed that the AUCs for OS in the first, eighth, and tenth year were 0.812, 0.81, and 0.857, respectively, in the training set,0.615, 0.68, 0.655 in the validation set, and 0.725, 0.742, 0.741 in the total cohort. Multivariate Cox regression analysis indicated the model was a reliable and independent indicator for the prognosis of breast cancer in the training set (HR= 1.432; 95% CI 1.204−1.702, p<0.001), validation set (HR= 1.162; 95% CI 1.004−1.345, p = 0.044), and whole set (HR=1.240; 95% CI 1.128−1.362, p<0.001). GSEA analysis revealed a strong connection between the signature and immune-related biological processes and pathways.Conclusions:We constructed and verified a robust signature of 8 immune-related lncRNAs for the prediction of breast cancer patient survival.

Download Full-text

A novel ferroptosis-related genes model for prognosis prediction of lung adenocarcinoma

BMC Pulmonary Medicine ◽

10.1186/s12890-021-01588-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Fei Li ◽

Dongcen Ge ◽

Shu-lan Sun

Keyword(s):

Regression Analysis ◽

High Risk ◽

Prediction Model ◽

Cox Regression ◽

Risk Group ◽

Training Set ◽

Cox Regression Analysis ◽

Key Genes ◽

Validation Set ◽

Prognostic Prediction

Abstract Background Ferroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. This study aims to investigate the potential correlation between ferroptosis and the prognosis of lung adenocarcinoma (LUAD). Methods RNA-seq data were collected from the LUAD dataset of The Cancer Genome Atlas (TCGA) database. Based on ferroptosis-related genes, differentially expressed genes (DEGs) between LUAD and paracancerous specimens were identified. The univariate Cox regression analysis was performed to screen key genes associated with the prognosis of LUAD. LUAD patients were divided into the training set and validation set. Then, we screened out key genes and built a prognostic prediction model involving 5 genes using the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation and the multivariate Cox regression analysis. After dividing LUAD patients based on the median level of risk score as cut-off value, the generated prognostic prediction model was validated in the validation set. Moreover, we analyzed the somatic mutations, and estimated the scores of immune infiltration in the high-risk and low-risk groups. Functional enrichment analysis of DEGs was performed as well. Results High-risk scores indicated the worse prognosis of LUAD. The maximum area under curve (AUC) of the training set and the validation set in this study was 0.7 and 0.69, respectively. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of LUAD cases with the survival time of 1, 3 and 5 years was 0.698, 0.71 and 0.73, respectively. In addition, the mutation frequency of LUAD patients in the high-risk group was significantly higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results. Conclusions This study constructs a novel LUAD prognosis prediction model involving 5 ferroptosis-related genes, which can be used as a promising tool for decision-making of clinical therapeutic strategies of LUAD.

Download Full-text

Identification of methylation-driven genes prognosis signature and immune microenvironment in uterus corpus endometrial cancer

Cancer Cell International ◽

10.1186/s12935-021-02038-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

JinHui Liu ◽

ChengJian Ji ◽

Yichun Wang ◽

Cheng Zhang ◽

HongJun Zhu

Keyword(s):

Dna Methylation ◽

Regression Analysis ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Prognostic Index ◽

Curve Analysis ◽

Immune Microenvironment ◽

Roc Curve Analysis ◽

Cox Regression Analysis

Abstract Background Uterus corpus endometrial cancer (UCEC) is the main malignant tumor in gynecology, with a high degree of heterogeneity, especially in terms of prognosis and immunotherapy efficacy. DNA methylation is one of the most important epigenetic modifications. Studying DNA methylation can help predict the prognosis of cancer patients and provide help for clinical treatment. Our research aims to discover whether abnormal DNA methylation can predict the prognosis of UCEC and reflect the patient's tumor immune microenvironment. Patients and methods The clinical data, DNA methylation data, gene expression data and somatic mutation data of UCEC patients were all downloaded from the TCGA database. The MethylMix algorithm was used to integrate DNA methylation data and mRNA expression data. Univariate Cox regression analysis, Multivariate Cox regression analysis, and Lasso Cox regression analysis were used to determine prognostic DNA methylation-driven genes and to construct an independent prognostic index (MDS). ROC curve analysis and Kaplan–Meier survival curve analysis were used to evaluate the predictive ability of MDS. GSEA analysis was used to explore possible mechanisms that contribute to the heterogeneity of the prognosis of UCEC patients. Results 3 differential methylation-driven genes (DMDGs) (PARVG, SYNE4 and CDO1) were considered as predictors of poor prognosis in UCEC. An independent prognostic index was finally established based on 3 DMDGs. From the results of ROC curve analysis and survival curve analysis, MDS showed excellent prognostic ability in TCGA-UCEC. A new nomogram based on MDS and other prognostic clinical indicators has also been successfully established. The C-index of the nomogram for OS prediction was 0.764 (95% CI = 0.702–0.826). GSEA analysis suggests that there were differences in immune-related pathways among patients with different prognosis. The abundance of M2 macrophages and M0 macrophages were significantly enhanced in the high-risk group while T cells CD8, Eosinophils and Neutrophils were markedly elevated in the low-risk group. Meanwhile, patients in the low-risk group had higher levels of immunosuppressant expression, higher tumor mutational burden and immunophenoscore (IPS) scores. Joint survival analysis revealed that 7 methylation-driven genes could be independent prognostic factors for overall survival for UCEC. Conclusion We have successfully established a risk model based on 3 DMDGs, which could accurately predict the prognosis of patients with UCEC and reflect the tumor immune microenvironment.

Download Full-text

Construction on of a Ferroptosis-Related lncRNA-Based Model to Improve the Prognostic Evaluation of Gastric Cancer Patients Based on Bioinformatics

Frontiers in Genetics ◽

10.3389/fgene.2021.739470 ◽

2021 ◽

Vol 12 ◽

Author(s):

Jiahui Pan ◽

Xinyue Zhang ◽

Xuedong Fang ◽

Zhuoyuan Xin

Keyword(s):

Gastric Cancer ◽

Cancer Patients ◽

Roc Curve ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Curve Analysis ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Gastric Cancer Patients

BackgroundGastric cancer is one of the most serious gastrointestinal malignancies with bad prognosis. Ferroptosis is an iron-dependent form of programmed cell death, which may affect the prognosis of gastric cancer patients. Long non-coding RNAs (lncRNAs) can affect the prognosis of cancer through regulating the ferroptosis process, which could be potential overall survival (OS) prediction factors for gastric cancer.MethodsFerroptosis-related lncRNA expression profiles and the clinicopathological and OS information were collected from The Cancer Genome Atlas (TCGA) and the FerrDb database. The differentially expressed ferroptosis-related lncRNAs were screened with the DESeq2 method. Through co-expression analysis and functional annotation, we then identified the associations between ferroptosis-related lncRNAs and the OS rates for gastric cancer patients. Using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a prognostic model based on 17 ferroptosis-related lncRNAs. We also evaluated the prognostic power of this model using Kaplan–Meier (K-M) survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA).ResultsA ferroptosis-related “lncRNA–mRNA” co-expression network was constructed. Functional annotation revealed that the FOXO and HIF-1 signaling pathways were dysregulated, which might control the prognosis of gastric cancer patients. Then, a ferroptosis-related gastric cancer prognostic signature model including 17 lncRNAs was constructed. Based on the RiskScore calculated using this model, the patients were divided into a High-Risk group and a low-risk group. The K-M survival curve analysis revealed that the higher the RiskScore, the worse is the obtained prognosis. The ROC curve analysis showed that the area under the ROC curve (AUC) of our model is 0.751, which was better than those of other published models. The multivariate Cox regression analysis results showed that the lncRNA signature is an independent risk factor for the OS rates. Finally, using nomogram and DCA, we also observed a preferable clinical practicality potential for prognosis prediction of gastric cancer patients.ConclusionOur prognostic signature model based on 17 ferroptosis-related lncRNAs may improve the overall survival prediction in gastric cancer.

Download Full-text

A Novel Ferroptosis-Related Genes Model for Prognosis Prediction of Lung Adenocarcinoma

10.21203/rs.3.rs-354347/v1 ◽

2021 ◽

Author(s):

Fei Li ◽

Dongcen Ge ◽

Shu-lan Sun

Keyword(s):

Regression Analysis ◽

High Risk ◽

Prediction Model ◽

Lung Adenocarcinoma ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Validation Set

Abstract Background. Ferroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. The aim of this study is to investigate the relationship between ferroptosis and the prognosis of lung adenocarcinoma (LUAD).Methods. RNA-seq data was collected from the LUAD dataset of The Cancer Genome Altas (TCGA) database. We used ferroptosis-related genes as the basis, and identify the differential expression genes (DEGs) between cancer and paracancer. The univariate Cox regression analysis were used to screen the prognostic-related genes. We divided the patients into training and validation sets. Then, we screened out key genes and built a 5 genes prognostic prediction model by the applications of the least absolute shrinkage and selection operator (LASSO) 10-fold cross-validation and the multi-variate Cox regression analysis. We divided the cases by the median value of risk score and validated this model in the validation set. Meanwhile, we analyzed the somatic mutations, and estimated the score of immune infiltration in the high- and low-risk groups, as well as performed functional enrichment analysis of DEGs.Results. The result revealed that the high-risk score triggered the worse prognosis. The maximum area under curve (AUC) of the training set and the validation set of in this study was 0.7 and 0.69. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of cases with survival time of 1, 3 and 5 years are 0.698, 0.71 and 0.73. In addition, the mutation frequency of patients in the high-risk group was higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results.Conclusion. This study constructed a novel LUAD prognosis prediction model base on 5 ferroptosis-related genes, which can provide a prognostic evaluation tool for the clinical therapeutic decision.

Download Full-text

Construction and Validation of a Prognostic Risk Model for Triple Negative Breast Cancer Based on Autophagy-Related Genes

10.21203/rs.3.rs-1141994/v1 ◽

2021 ◽

Author(s):

Cheng Yan ◽

Qingling Liu ◽

Ruoling Jia

Keyword(s):

Breast Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Risk Model ◽

Cox Regression ◽

Risk Group ◽

Lasso Regression ◽

Cox Regression Analysis

Abstract Background: Autophagy plays an important role in triple negative breast cancer (TNBC). However, the prognostic value of autophagy-related genes (ARGs) in TNBC remains unknown. In this study, we established a survival model to evaluate the prognosis of TNBC patients using ARGs signature.Methods: A total of 222 autophagy-related genes were downloaded from The Human Autophagy Database. The RNA-sequencing data and corresponding clinical data of TNBC were obtained from the TCGA database. Differential gene expression of ARGs (DE-ARGs) between normal samples and TNBC samples was determined by the EdgeR software package. Then, univariate Cox, Lasso, and multivariate Cox regression analyses were performed. According to the Lasso regression results based on univariate Cox, we identified a prognostic signature for overall-survival (OS), which was further validated by using GEO cohort. We also found an independent prognostic marker that can predict the clinicopathological features of TNBC. Furthermore, a nomogram was drawn to predict the survival probability of TNBC patients, which could help in clinical decision for TNBC treatment. Finally, we validated the requirement of a ARG in our model for TNBC cell survival and metastasis.Results: There are 43 differentially expressed ARGs (DE-ARGs) were identified between normal and tumor samples. A risk model for OS using CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74 and VAMP3 by Lasso regression analysis was established based on univariate Cox regression analysis. Overall survival of TNBC patients was significantly shorter in the high-risk group than in the low-risk group for both the training and validation cohorts. Using the Kaplan-Meier curves and ROC curves, we demonstrated the accuracy of the prognostic model. Multivariate Cox regression analysis was used to verify risk score as independent predictor. Then a nomogram was proposed to predict 1-, 3-, and 5-year survival for TNBC patients. The calibration curves showed great accuracy of the model for survival prediction. Finally, we found that depletion of EIF4EBP1, one of ARGs in our model, significantly reduced cell proliferation and metastasis of TNBC cells. Conclusion: An autophagy-related prognosis model in TNBCs was constructed using ARGs signature containing CDKN1A, CTSD, CTSL, EIF4EBP1, TMEM74 and VAMP3. It could serve as an independent prognostic biomarker in TNBC.

Download Full-text

Global longitudinal strain and chronic kidney disease prognostic impact on acute coronary syndrome

European Heart Journal ◽

10.1093/ehjci/ehaa946.1622 ◽

2020 ◽

Vol 41 (Supplement_2) ◽

Author(s):

A.R Morgado Gomes ◽

D Campos ◽

C Saleiro ◽

J Gameiro Lopes ◽

J.P Sousa ◽

...

Keyword(s):

Regression Analysis ◽

Roc Curve ◽

Cox Regression ◽

Left Ventricular ◽

Curve Analysis ◽

Left Ventricular Ejection ◽

Roc Curve Analysis ◽

Ventricular Ejection Fraction ◽

Cox Regression Analysis ◽

Coronary Syndrome

Abstract Background Impaired left ventricular ejection fraction (LVEF) and chronic kidney disease (CKD) have been associated with poorer outcomes in acute coronary syndrome (ACS). Increasing evidence on global left ventricular longitudinal strain (GLS) suggests superiority over left ventricular ejection fraction (LVEF) in risk stratification. Methods This study was based on a retrospective analysis of consecutive patients admitted to a Coronary Care Unit between 2009 and 2016. Baseline characteristics and echocardiographic parameters, including LVEF, were assessed. For each patient, a two-dimensional speckle tracking of the left ventricle was assessed and average GLS was calculated using 2, 3 and 4-chamber views. Blood creatinine was measured during hospital stay and used to estimate glomerular filtration rate (GFR) with Modification of Diet in Renal Disease (MDRD) equation. A cox regression analysis was performed to determine mortality prediction value of average GLS, LVEF and GFR in this population. Receiver operating characteristic (ROC) curve analysis was conducted and area under the curve (AUC) was estimated. Results A total of 85 patients (66.7±12.7 years old; 78.8% males) were enrolled. LVEF mean was 49.4±9.8% and average GLS was −16.0±4.0%. GFR median was 80.0±48.9 ml/min/1.73m2. In cox regression analysis, worse average GLS was associated with greater mortality (HR 0.721; 95% CI 0.599–0.867; P=0.001). GFR was inversely related to death (HR 0.967; 95% CI 0.944–0.991, P=0.008). In cox regression analysis using average GLS and GFR as covariates, both proved to be independent predictors of mortality (for average GLS, HR 0.748; 95% CI 0.610–0.918, P=0.005; for GFR, HR 0.974; 95% CI 0.949–0.999; P=0.044). The AUC of average GLS to predict mortality was 0.78 (P<0.001, sensitivity 50.7% and specificity 100%) and for average GLS and GFR combined was 0.85 (P<0.001, sensitivity 84.0% and specificity 77.8%). Although LVEF proved to be a mortality predictor, the AUC obtained by ROC curve analysis was inferior to average GLS, with statistical significance (P=0.043). Conclusions GLS and CKD proved to be independent predictors of mortality in ACS patients. GLS showed superiority when compared to LVEF in risk stratification and in the future it might replace LVEF. The model combining GLS and GFR emphasized the increased risk of CKD patients and how they should be seen as high-risk patients. ROC curve analysis Funding Acknowledgement Type of funding source: None

Download Full-text

Multiplex Immunoassay of Cytokines for The Short-Term Prognosis Predictive Value of Patients With Acute-on-Chronic Liver Failure

10.21203/rs.3.rs-154337/v1 ◽

2021 ◽

Author(s):

Yongyuan Zheng ◽

Genglin Zhang ◽

Lina Wu ◽

Jing Xiong ◽

Lu Wang ◽

...

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Hepatitis B ◽

Cox Regression ◽

Curve Analysis ◽

Chronic Liver Failure ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Multiplex Immunoassay ◽

Kaplan Meier

Abstract Background: Since the systemic inflammation has been found to be associated with disease progression and mortality in patients with hepatitis B virus (HBV)-related acute-on-chronic liver failure (HBV-ACLF), the objective of this study was to detect inflammatory factors in ACLF patients by a Luminex-based multiplex immunoassay system for high throughput screening of the cytokine with the most prognostic value.Methods: Luminex-based multiplex immunoassay technology was used to determine the concentrations of 48 cytokines in total at once in serum samples from 40 patients with HBV-ACLF, 30 patients with chronic hepatitis B (CHB) and 25 healthy volunteers as normal controls (NC). Then, the receiver operating characteristic (ROC) curve analysis was applied to evaluate the prognostic prediction accuracy. Besides, Kaplan–Meier curves was used to analyze survival, while the Cox regression analysis to determine the mortality predictors.Results: The level of IL-6, IL-10, IL-15, IL-18, M-CSF, IP-10 and CXCL9 were significantly higher in patients with HBV-ACLF than in either patients with CHB or NC subjects, while the level of EGF, PDGF-AA, PDGF-AB/BB, MDC and sCD40L were significantly lower. The concentrations of IL-6, CXCL9, and IL-15 was higher in non-surviving patients with HBV-ACLF than in surviving patients while MDC was lower. Increased serum IL-6 was positively correlated with disease severity. The ROC curve analysis showed that IL-6 and CXCL-9 accurately predicted 90-day survival in patients with HBV-ACLF, with an accuracy equivalent to those of the Model for End-Stage Liver Disease (MELD), MELD-Na. Kaplan–Meier analysis showed an association between the increase in serum concentration of IL-6 as well as CXCL9 and poor overall survival in patients with HBV-ACLF. Moreover, the multivariate Cox regression analysis showed that only serum IL-6 was an independent predictor of overall survival in patients with HBV-ACLF.Conclusion: Although HBV-related ACLF patients have significantly increased serum levels of multiple cytokines, only serum IL-6 levels could be an independent prognostic biomarker in patients with HBV-ACLF.

Download Full-text

A Novel Framework to Predict Breast Cancer Prognosis Using Immune-Associated LncRNAs

Frontiers in Genetics ◽

10.3389/fgene.2020.634195 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhijian Huang ◽

Chen Xiao ◽

Fushou Zhang ◽

Zhifeng Zhou ◽

Liang Yu ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Immune Cells ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Comprehensive Analysis ◽

Cancer Prognosis ◽

Clinicopathological Factors ◽

Cox Regression Analysis

Background: Breast cancer (BC) is one of the most frequently diagnosed malignancies among females. As a huge heterogeneity of malignant tumor, it is important to seek reliable molecular biomarkers to carry out the stratification for patients with BC. We surveyed immune- associated lncRNAs that may be used as potential therapeutic targets in BC.Methods: LncRNA expression data and clinical information of BC patients were downloaded from the TCGA database for a comprehensive analysis of candidate genes. A model consisting of immune-related lncRNAs enriched in BC cancerous tissues was established using the univariate Cox regression analysis and the iterative Lasso Cox regression analysis. The prognostic performance of this model was validated in two independent cohorts (GSE21653 and BC-KR), and compared with known prognostic biomarkers. A nomogram that integrated the immune-related lncRNA signature and clinicopathological factors was constructed to accurately assess the prognostic value of this signature. The correlation between the signature and immune cell infiltration in BC was also analyzed.Results: The Kaplan-Meier analysis showed that the OS of Patients in the low-risk group had significantly better survival than those in the high-risk group, Clinical subgroup analysis showed that the predictive ability was independent of clinicopathological factors. Univariate/multivariate Cox regression analysis showed immune lncRNA signature is an important prognostic factor and an independent prognostic marker. In addition, GSEA and GSVA analysis as well as comprehensive analysis of immune cells showed that the signature was significantly correlated with the infiltration of immune cells.Conclusion: We successfully constructed an immune-associated lncRNA signature that can accurately predict BC prognosis.

Download Full-text