A Novel Pyroptosis-Associated Long Non-coding RNA Signature Predicts Prognosis and Tumor Immune Microenvironment of Patients With Breast Cancer

Background: Pyroptosis, a kind of programmed cell death characterized by the rupture of cell membranes and the release of inflammatory substances, plays an important role in the occurrence and development of cancer. However, few studies focus on the pyroptosis-associated long non-coding RNAs (lncRNAs) in breast cancer (BC). The prognostic value of pyroptosis-associated lncRNAs and their relationship with tumor microenvironment (TME) in BC remain unclear. The purpose of this study was to explore the prognostic role of pyroptosis-associated lncRNAs and their relationship with TME in BC.Methods: The transcriptome data and clinical data of female BC patients were downloaded from The Cancer Genome Atlas (TCGA) database. A total of 937 patients were randomly assigned to either training set or validation set. A pyroptosis-associated lncRNA signature was constructed in the training set and verified in the validation set. Functional analysis and immune microenvironment analysis related to pyroptosis-associated lncRNAs were performed. A nomogram based on the risk score and clinical characteristics was established.Results: A 9-pyroptosis-associated lncRNA signature was constructed to separate BC patients into two risk groups. High-risk patients had poorer prognosis than low-risk patients. The risk score was proven to be an independent prognostic factor by multivariate Cox regression analysis. Function analysis and immune microenvironment analysis showed that low-risk BC tended to be an immunologically “hot” tumor. A nomogram was constructed with risk score and clinical characteristics. Receiver operating characteristic curve (ROC) analysis demonstrated credible predictive power of the nomogram. The area under time-dependent ROC curve (AUC) reached 0.880 at 1 year, 0.804 at 3 years, and 0.769 at 5 years in the training set, and 0.799 at 1 year, 0.794 at 3 years, and 0.728 at 5 years in the validation set.Conclusion: We identified a novel pyroptosis-associated lncRNA signature that was an independent prognostic indicator for BC patients. Pyroptosis-associated lncRNAs had potential relationship with the immune microenvironment and might be therapeutic targets for BC patients.

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An Algorithm Combining Clinical Characteristics and Day 7 Biomarker Concentrations Predicts Future Graft-Versus-Host Disease Following Related Donor Hematopoietic Cell Transplantation

Blood ◽

10.1182/blood.v120.21.463.463 ◽

2012 ◽

Vol 120 (21) ◽

pp. 463-463 ◽

Cited By ~ 1

Author(s):

Andrew C. Harris ◽

James L.M. Ferrara ◽

Thomas M. Braun ◽

Daniel R. Couriel ◽

Sung W. Choi ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Clinical Characteristics ◽

Acute Gvhd ◽

Low Risk ◽

Training Set ◽

Predictive Algorithm ◽

Risk Patients ◽

Related Donor ◽

Validation Set

Abstract Abstract 463 We have recently described an algorithm combining biomarkers and clinical characteristics, such as severity, that, when assessed at the onset of GVHD symptoms, predicts outcomes such as response to treatment and non-relapse mortality [Levine, Blood, 2012]. This approach maximizes the information (both laboratory and clinical) utilized to develop the predictive algorithm. We have now evaluated whether a combination of pre-HCT clinical characteristics and GVHD biomarkers measured early post-HCT can predict the future occurrence of GVHD with sufficient accuracy and lead time to facilitate the preemptive treatment of a large majority of patients at high risk prior to the onset of GVHD symptoms. We studied 393 patients who received a first allogeneic HCT from related donors between 2001 and 2011 at the University of Michigan whose clinical data and plasma samples were collected prospectively on an IRB-approved protocol. The cumulative incidence of grade 2–4 GVHD by day 100 was 31% (n=121; biopsy proven in 91%); at onset of symptoms (median = day 40), GVHD affected skin alone in 40 pts (33%), GI tract alone in 48 (40%), liver alone in three (2%), and multiple organs in 30 (25%). We divided patients randomly into a training set (n=264) to develop an algorithm to predict acute GVHD using clinical and biomarker parameters by logistic regression, and a validation set (n=129) to test the algorithm. There were no statistically significant differences in pre-HCT clinical parameters between the two sets. We measured a panel of 4 informative GVHD biomarkers (IL-2Rα, TNFR1, elafin, and REG3α) on plasma samples taken at day 7 after HCT by ELISA. We postulated that day 7 biomarker concentrations would be most accurate in predicting early acute GVHD, and we therefore used grade 2–4 GVHD onset in the first two months post-HCT as the predictive endpoint. We developed three predictive models for GVHD in the training set: one model used 5 validated clinical characteristics alone (patient age, graft source [BM], HLA-match, GVHD prophylaxis, myeloablative conditioning [MA], use of TBI [Jagasia, Blood 2012]); the second used 4 biomarker concentrations alone; and the third used the combination of all 9 clinical and biomarker parameters. We postulated that an algorithm with a specificity of 50% or higher would be useful, so we compared all three models at a specificity of 50% for their respective sensitivities. The 5 pre-HCT characteristics alone provided 51% sensitivity; 4 biomarkers alone provided 66% sensitivity; and the combined 9 clinical and biomarker parameters provided 77% sensitivity (Table; combined vs clinical model, p<0.001; vs biomarker model, p=0.07). We then tested the sensitivity of the combined model in the validation set and found it to be 75%. The similar sensitivities in the two sets allowed us to combine them for further analyses. We used the algorithm to classify patients as “high risk” (n=211) or “low risk” (n=182) for the occurrence of acute GVHD. Patients with a high risk panel were much more likely to develop grade 2–4 GVHD (Figure 1A; cumulative 38% vs 20%, p<0.001) which developed almost one month earlier than in the low risk group (median day of onset = 39 vs 65). The greater incidence of acute GVHD in high risk patients resulted in significantly greater non-relapse mortality (NRM) by day 180 post-HCT (12% vs 3%; p=0.001; Figure 1B). The relapse rate was identical in both groups (24%) and thus overall survival was significantly better in the low risk group (84% vs 73%, p=0.004). At one year post-HCT, the differences between high and low risk patients remained significant for NRM (17% vs 6%, p<0.001) and overall survival (61% vs 72%, p=0.01). In conclusion, the best algorithm to predict acute GVHD in recipients of related donor HCT combined a panel of 4 biomarkers at day 7 after HCT and 5 pre-HCT clinical characteristics. The algorithm successfully stratified patients into high and low risk groups for acute GVHD, six month NRM and overall survival. We hypothesize the use of such an algorithm one week after HCT may permit preemptive treatment of patients who are at greatest risk early in their transplant course. We are currently analyzing whether biomarkers at a later time point will identify patients at greatest risk for late-onset GVHD. Table. Combined predictive algorithm Score = -3.57 + 0.54xAge–16.83xBM + 1.35xMismatch - 0.08xGVHD prophy + 0.35xMA + 0.47xTBI + 0.37xlogIL2Rα – 0.06xlogTNFR1 – 0.12xlogElafin – 0.03xlogREG3α Disclosures: No relevant conflicts of interest to declare.

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Genome-Wide Identification of a Novel Autophagy-Related Signature for Colorectal Cancer

Dose-Response ◽

10.1177/1559325819894179 ◽

2019 ◽

Vol 17 (4) ◽

pp. 155932581989417 ◽

Cited By ~ 6

Author(s):

Zhi Huang ◽

Jie Liu ◽

Liang Luo ◽

Pan Sheng ◽

Biao Wang ◽

...

Keyword(s):

Colorectal Cancer ◽

Signaling Pathway ◽

Risk Score ◽

Low Risk ◽

Training Data ◽

The Cancer Genome Atlas ◽

Training Set ◽

Data Set ◽

Validation Set ◽

Cox Analysis

Background: Plenty of evidence has suggested that autophagy plays a crucial role in the biological processes of cancers. This study aimed to screen autophagy-related genes (ARGs) and establish a novel a scoring system for colorectal cancer (CRC). Methods: Autophagy-related genes sequencing data and the corresponding clinical data of CRC in The Cancer Genome Atlas were used as training data set. The GSE39582 data set from the Gene Expression Omnibus was used as validation set. An autophagy-related signature was developed in training set using univariate Cox analysis followed by stepwise multivariate Cox analysis and assessed in the validation set. Then we analyzed the function and pathways of ARGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, a prognostic nomogram combining the autophagy-related risk score and clinicopathological characteristics was developed according to multivariate Cox analysis. Results: After univariate and multivariate analysis, 3 ARGs were used to construct autophagy-related signature. The KEGG pathway analyses showed several significantly enriched oncological signatures, such as p53 signaling pathway, apoptosis, human cytomegalovirus infection, platinum drug resistance, necroptosis, and ErbB signaling pathway. Patients were divided into high- and low-risk groups, and patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both training set and validation set. Furthermore, the nomogram for predicting 3- and 5-year OS was established based on autophagy-based risk score and clinicopathologic factors. The area under the curve and calibration curves indicated that the nomogram showed well accuracy of prediction. Conclusions: Our proposed autophagy-based signature has important prognostic value and may provide a promising tool for the development of personalized therapy.

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Identification of a Novel Glycolysis-Related Signature to Predict the Prognosis of Patients with Breast Cancer

10.21203/rs.3.rs-720991/v1 ◽

2021 ◽

Author(s):

Menglin He ◽

Cheng Hu ◽

Jian Deng ◽

Hui Ji ◽

Weiqian Tian

Keyword(s):

Breast Cancer ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Gene Signature ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Expression Levels ◽

Incidence And Mortality

Abstract Background: Breast cancer (BC) is a kind of cancer with high incidence and mortality in female. Conventional clinical characteristics are far from accurate to predict individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC. Methods: We analyzed the data of a training cohort from the TCGA database and a validation cohort from GEO database. After the applications of GSEA and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed. The signature contains AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Then, we constructed a risk score formula to classify the patients into high and low-risk groups based on the expression levels of six-gene in patients. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Next, a nomogram was developed to predict the outcomes of patients with risk score and clinical features in 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues.Results: We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in high-risk group showed poor prognosis than that in low-risk group. The AUC values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues except AK3. Conclusion: We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate and obvious prediction and might be used to expand the prediction methods in clinical.

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A Ferroptosis-Related Signature Robustly Predicts Clinical Outcomes and Associates With Immune Microenvironment for Thyroid Cancer

Frontiers in Medicine ◽

10.3389/fmed.2021.637743 ◽

2021 ◽

Vol 8 ◽

Author(s):

Mingqin Ge ◽

Jie Niu ◽

Ping Hu ◽

Aihua Tong ◽

Yan Dai ◽

...

Keyword(s):

Thyroid Cancer ◽

High Risk ◽

Risk Score ◽

Immune Cell ◽

Cox Regression ◽

Low Risk ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Immune Microenvironment ◽

Cox Regression Analysis

Objective: This study aimed to construct a prognostic ferroptosis-related signature for thyroid cancer and probe into the association with tumor immune microenvironment.Methods: Based on the expression profiles of ferroptosis-related genes, a LASSO cox regression model was established for thyroid cancer. Kaplan-Meier survival analysis was presented between high and low risk groups. The predictive performance was assessed by ROC. The predictive independency was validated via multivariate cox regression analysis and stratified analysis. A nomogram was established and verified by calibration curves. The enriched signaling pathways were predicted via GSEA. The association between the signature and immune cell infiltration was analyzed by CIBERSORT. The ferroptosis-related genes were validated in thyroid cancer tissues by immunohistochemistry and RT-qPCR.Results: A ferroptosis-related eight gene model was established for predicting the prognosis of thyroid cancer. Patients with high risk score indicated a poorer prognosis than those with low risk score (p = 1.186e-03). The AUCs for 1-, 2-, and 3-year survival were 0.887, 0.890, and 0.840, respectively. Following adjusting other prognostic factors, the model could independently predict the prognosis (p = 0.015, HR: 1.870, 95%CI: 1.132–3.090). A nomogram combining the signature and age was constructed. The nomogram-predicted probability of 1-, 3-, and 5-year survival approached the actual survival time. Several ferroptosis-related pathways were enriched in the high-risk group. The signature was distinctly associated with the immune cell infiltration. After validation, the eight genes were abnormally expressed between thyroid cancer and control tissues.Conclusion: Our findings established a prognostic ferroptosis-related signature that was associated with the immune microenvironment for thyroid cancer.

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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.

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A Ferroptosis and Pyroptosis Molecular Subtype-Related Signature Applicable for Prognosis and Immune Microenvironment Estimation in Hepatocellular Carcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.761839 ◽

2021 ◽

Vol 9 ◽

Author(s):

Junyu Huo ◽

Jinzhen Cai ◽

Ge Guan ◽

Huan Liu ◽

Liqun Wu

Keyword(s):

High Risk ◽

Risk Score ◽

Risk Group ◽

Molecular Subtype ◽

Risk Groups ◽

High Risk Group ◽

Low Risk ◽

Type I ◽

Immune Microenvironment ◽

Risk Patients

Background: Due to the heterogeneity of tumors and the complexity of the immune microenvironment, the specific role of ferroptosis and pyroptosis in hepatocellular carcinoma (HCC) is not fully understood, especially its impact on prognosis.Methods: The training set (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) based on the prognosis-related genes associated with ferroptosis and pyroptosis. The intersecting differentially expressed genes (DEGs) among C1, C2, and C3 were used in univariate Cox and LASSO penalized Cox regression analysis for the construction of the risk score. The median risk score served as the unified cutoff to divide patients into high- and low-risk groups.Results: Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC. As an independent prognostic indicator, the risk score could be applicable for patients with different clinical features tested by subgroup (n = 26) survival analysis. In the high-risk patients with a lower infiltration abundance of activated B cells, activated CD8 T cells, eosinophils, and type I T helper cells and a higher infiltration abundance of immature dendritic cells, the cytolytic activity, HLA, inflammation promotion, and type I IFN response in the high-risk group were weaker. The TP53 mutation rate, TMB, and CSC characteristics in the high-risk group were significantly higher than those in the low-risk group. Low-risk patients have active metabolic activity and a more robust immune response. The high- and low-risk groups differed significantly in histology grade, vascular tumor cell type, AFP, new tumor event after initial treatment, main tumor size, cirrhosis, TNM stage, BCLC stage, and CLIP score.Conclusion: The ferroptosis and pyroptosis molecular subtype-related signature identified and validated in this work is applicable for prognosis prediction, immune microenvironment estimation, stem cell characteristics, and clinical feature assessment in HCC.

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

Journal of Translational Medicine ◽

10.1186/s12967-020-02522-6 ◽

2020 ◽

Vol 18 (1) ◽

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.

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Comprehensive Analysis of Ferroptosis-Related LncRNAs in Breast Cancer Patients Reveals Prognostic Value and Relationship With Tumor Immune Microenvironment

Frontiers in Surgery ◽

10.3389/fsurg.2021.742360 ◽

2021 ◽

Vol 8 ◽

Author(s):

Zhengjie Xu ◽

Suxiao Jiang ◽

Juan Ma ◽

Desheng Tang ◽

Changsheng Yan ◽

...

Keyword(s):

Breast Cancer ◽

T Cells ◽

Regression Analysis ◽

Cox Regression ◽

Comprehensive Analysis ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Immune Microenvironment ◽

Cox Regression Analysis

Background: Breast cancer (BC) is a heterogeneous malignant tumor, leading to the second major cause of female mortality. This study aimed to establish an in-depth relationship between ferroptosis-related LncRNA (FRlncRNA) and the prognosis as well as immune microenvironment of the patients with BC.Methods: We downloaded and integrated the gene expression data and the clinical information of the patients with BC from The Cancer Genome Atlas (TCGA) database. The co-expression network analysis and univariate Cox regression analysis were performed to screen out the FRlncRNAs related to prognosis. A cluster analysis was adopted to explore the difference of immune microenvironment between the clusters. Furthermore, we determined the optimal survival-related FRLncRNAs for final signature by LASSO Cox regression analysis. Afterward, we constructed and validated the prediction models, which were further tested in different subgroups.Results: A total of 31 FRLncRNAs were filtrated as prognostic biomarkers. Two clusters were determined, and C1 showed better prognosis and higher infiltration level of immune cells, such as B cells naive, plasma cells, T cells CD8, and T cells CD4 memory activated. However, there were no significantly different clinical characters between the clusters. Gene Set Enrichment Analysis (GSEA) revealed that some metabolism-related pathways and immune-associated pathways were exposed. In addition, 12 FRLncRNAs were determined by LASSO analysis and used to construct a prognostic signature. In both the training and testing sets, patients in the high-risk group had a worse survival than the low-risk patients. The area under the curves (AUCs) of receiver operator characteristic (ROC) curves were about 0.700, showing positive prognostic capacity. More notably, through the comprehensive analysis of heatmap, we regarded LINC01871, LINC02384, LIPE-AS1, and HSD11B1-AS1 as protective LncRNAs, while LINC00393, AC121247.2, AC010655.2, LINC01419, PTPRD-AS1, AC099329.2, OTUD6B-AS1, and LINC02266 were classified as risk LncRNAs. At the same time, the patients in the low-risk groups were more likely to be assigned to C1 and had a higher immune score, which were consistent with a better prognosis.Conclusion: Our research indicated that the ferroptosis-related prognostic signature could be used as novel biomarkers for predicting the prognosis of BC. The differences in the immune microenvironment exhibited by BC patients with different risks and clusters suggested that there may be a complementary synergistic effect between ferroptosis and immunotherapy.

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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.

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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.

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