scholarly journals Identification of IFN-β Associated Genes Signature Predicting Overall Survival for Glioblastoma

2021 ◽  
Author(s):  
Cheng Lijing ◽  
Yuan Meiling ◽  
Li Shu ◽  
Chen Junjing ◽  
Zhong Shupeng ◽  
...  
Keyword(s):  
Overall Survival ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Individual Treatment ◽  
Normal Brain ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Prognosis Prediction

Abstract Background: Brain glioblastoma (GBM) is the most common primary malignant tumor of intracranial tumors. The prognosis of this disease is extremely poor. While the introduction of IFN-β regimen in the treatment of gliomas has significantly improved the outcome of patients, the underlying mechanism remains to be elucidated. Materials and methods: mRNA expression profiles and clinicopathological data were downloaded from TCGA-GBM and GSE83300 data set from the GEO. Univariate Cox regression analysis and lasso Cox regression model established a novel four‐gene IFN-β signature (including PRDX1, SEC61B, XRCC5, and BCL2L2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction (PCR) experiments. Gene Set Enrichment Analyses (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the lncRNAs and IFN-β associated genes. A lncRNA with a correlation coefficient |R2| > 0.3 and P < 0.05 was considered to be an IFN-β associated lncRNA.Results: Patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four‐gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction in GBM.Conclusions: Our study established a novel IFN-β associated genes signature to predict overall survival of GBM, which may help in clinical decision making for individual treatment.

scholarly journals An autophagy-related long non-coding RNA signature for patients with colorectal cancer

2021 ◽  
Author(s):  
Dongyan Zhao ◽  
Xizhen Sun ◽  
Sidan Long ◽  
Shukun Yao
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Cox Regression Analysis

AbstractAimLong non-coding RNAs (lncRNAs) have been identified to regulate cancers by controlling the process of autophagy and by mediating the post-transcriptional and transcriptional regulation of autophagy-related genes. This study aimed to investigate the potential prognostic role of autophagy-associated lncRNAs in colorectal cancer (CRC) patients.MethodsLncRNA expression profiles and the corresponding clinical information of CRC patients were collected from The Cancer Genome Atlas (TCGA) database. Based on the TCGA dataset, autophagy-related lncRNAs were identified by Pearson correlation test. Univariate Cox regression analysis and the least absolute shrinkage and selection operator analysis (LASSO) Cox regression model were performed to construct the prognostic gene signature. Gene set enrichment analysis (GSEA) was used to further clarify the underlying molecular mechanisms.ResultsWe obtained 210 autophagy-related genes from the whole dataset and found 1187 lncRNAs that were correlated with the autophagy-related genes. Using Univariate and LASSO Cox regression analyses, eight lncRNAs were screened to establish an eight-lncRNA signature, based on which patients were divided into the low-risk and high-risk group. Patients’ overall survival was found to be significantly worse in the high-risk group compared to that in the low-risk group (log-rank p = 2.731E-06). ROC analysis showed that this signature had better prognostic accuracy than TNM stage, as indicated by the area under the curve. Furthermore, GSEA demonstrated that this signature was involved in many cancer-related pathways, including TGF-β, p53, mTOR and WNT signaling pathway.ConclusionsOur study constructed a novel signature from eight autophagy-related lncRNAs to predict the overall survival of CRC, which could assistant clinicians in making individualized treatment.

scholarly journals A novel survival model based on a Ferroptosis-related gene signature for predicting overall survival in bladder cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yingchun Liang ◽  
Fangdie Ye ◽  
Chenyang Xu ◽  
Lujia Zou ◽  
Yun Hu ◽  
...  
Keyword(s):  
Overall Survival ◽  
Bladder Cancer ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Risk Groups ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognosis Prediction

Abstract Background The effective treatment and prognosis prediction of bladder cancer (BLCA) remains a medical problem. Ferroptosis is an iron-dependent form of programmed cell death. Ferroptosis is closely related to tumour occurrence and progression, but the prognostic value of ferroptosis-related genes (FRGs) in BLCA remains to be further clarified. In this study, we identified an FRG signature with potential prognostic value for patients with BLCA. Methods The corresponding clinical data and mRNA expression profiles of BLCA patients were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to extract FRGs related to survival time, and a Cox regression model was used to construct a multigene signature. Both principal component analysis (PCA) and single-sample gene set enrichment analysis (ssGSEA) were performed for functional annotation. Results Clinical traits were combined with FRGs, and 15 prognosis-related FRGs were identified by Cox regression. High expression of CISD1, GCLM, CRYAB, SLC7A11, TFRC, ACACA, ZEB1, SQLE, FADS2, ABCC1, G6PD and PGD was related to poor survival in BLCA patients. Multivariate Cox regression was used to construct a prognostic model with 7 FRGs that divided patients into two risk groups. Compared with that in the low-risk group, the overall survival (OS) of patients in the high-risk group was significantly lower (P < 0.001). In multivariate regression analysis, the risk score was shown to be an independent predictor of OS (HR = 1.772, P < 0.01). Receiver operating characteristic (ROC) curve analysis verified the predictive ability of the model. In addition, the two risk groups displayed different immune statuses in ssGSEA and different distributed patterns in PCA. Conclusion Our research suggests that a new gene model related to ferroptosis can be applied for the prognosis prediction of BLCA. Targeting FRGs may be a treatment option for BLCA.

scholarly journals An Immune-Related lncRNA Expression Profile to Improve Prognosis Prediction for Lung Adenocarcinoma: From Bioinformatics to Clinical Word

2021 ◽  
Vol 11 ◽  
Author(s):  
Boxiang Zhang ◽  
Rui Wang ◽  
Kai Li ◽  
Ziyang Peng ◽  
Dapeng Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Pearson Correlation ◽  
Statistical Significance ◽  
Survival Outcome ◽  
Prognostic Signature ◽  
Cox Regression Analysis

BackgroundLung cancer is still the top-ranked cancer-related deaths all over the world. Now immunotherapy has emerged as a promising option for treating lung cancer. Recent evidence indicated that lncRNAs were also key regulators in immune system. We aimed to develop a novel prognostic signature based on the comprehensive analysis of immune-related lncRNAs to predict survival outcome of LUAD patients.MethodsThe gene expression profiles of 491 LUAD patients were downloaded from TCGA. 1047 immune-related lncRNAs were obtained through Pearson correlation analysis of immune genes and lncRNAs using statistical software R language. Univariate and multivariate Cox regression analysis were performed to determine the optimal immune-related lncRNAs prognostic signature (ITGCB-DT, ABALON, TMPO-AS1 and VIM-AS1). Finally, we validated the immune-related lncRNAs prognostic signature in The First Affiliated Hospital of Xi’an Jiaotong University cancer center cohort.ResultsA four immune-related lncRNAs prognostic signature was constructed to predict the survival outcome of LUAD patients. Statistical significance were found that the LUAD patients in high-risk group suffered shorter overall survival than those in low-risk group (P &lt;0.001). ROC curve analysis shown that the four immune-related lncRNAs prognostic signature had the best predictive effect compared with age, gender, AJCC-stage, T stage, N stage, M stage (AUC = 0.756). More importantly, clinical cohort studies proved that the signature could predict the overall survival of LUAD patients with an AUC = 0.714.ConclusionsIn summary, we demonstrated that the novel immune-related lncRNAs signature had the ability to predict the prognosis of LUAD patients, which might serve as potential prognostic biomarkers and guide the individualized treatment strategies for LUAD patients.

scholarly journals Gene expression profile correlates with molecular and clinical features in patients with myelofibrosis

2021 ◽  
Vol 5 (5) ◽  
pp. 1452-1462
Author(s):  
Sebastiano Rontauroli ◽  
Sara Castellano ◽  
Paola Guglielmelli ◽  
Roberta Zini ◽  
Elisa Bianchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Risk ◽  
Clinical Features ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Myeloproliferative Neoplasms ◽  
Expression Profiles ◽  
Prognostic Information ◽  
Cox Regression Analysis

Abstract Myelofibrosis (MF) belongs to the family of classic Philadelphia-negative myeloproliferative neoplasms (MPNs). It can be primary myelofibrosis (PMF) or secondary myelofibrosis (SMF) evolving from polycythemia vera (PV) or essential thrombocythemia (ET). Despite the differences, PMF and SMF patients are currently managed in the same way, and prediction of survival is based on the same clinical and genetic features. In the last few years, interest has grown concerning the ability of gene expression profiles (GEPs) to provide valuable prognostic information. Here, we studied the GEPs of granulocytes from 114 patients with MF, using a microarray platform to identify correlations with patient characteristics and outcomes. Cox regression analysis led to the identification of 201 survival-related transcripts characterizing patients who are at high risk for death. High-risk patients identified by this gene signature displayed an inferior overall survival and leukemia-free survival, together with clinical and molecular detrimental features included in contemporary prognostic models, such as the presence of high molecular risk mutations. The high-risk group was enriched in post-PV and post-ET MF and JAK2V617F homozygous patients, whereas pre-PMF was more frequent in the low-risk group. These results demonstrate that GEPs in MF patients correlate with their molecular and clinical features, particularly their survival, and represent the proof of concept that GEPs might provide complementary prognostic information to be applied in clinical decision making.

scholarly journals Identification and Validation of Angiogenesis-Related Gene Expression for Predicting Prognosis in Patients With Ovarian Cancer

2022 ◽  
Vol 11 ◽  
Author(s):  
Yue Wang ◽  
Bao Xuan Li ◽  
Xiang Li
Keyword(s):  
Ovarian Cancer ◽  
Overall Survival ◽  
Regression Analysis ◽  
Risk Score ◽  
Clinical Decision Making ◽  
Cox Regression ◽  
Risk Group ◽  
Cancer Genome ◽  
Risk Scores ◽  
Cox Regression Analysis

Ovarian cancer (OC) is a highly heterogeneous disease with different cellular origins reported; thus, precise prognostic strategies and effective new therapies are urgently needed for patients with OC. A growing number of studies have shown that most malignancies have intensive angiogenesis and rapid growth. Therefore, angiogenesis plays an important role in the development of tumor metastasis. However, the prognostic value of angiogenesis-related genes (ARGs) in OC remains to be further elucidated. In this study, the expression data and corresponding clinical data from patients with OC and normal control samples were downloaded with UCSC XENA. A total of 1,960 differentially expressed ARGs were screened and functionally annotated through Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate Cox regression analysis was performed to identify ARGs associated with prognosis. New ARGs signatures (including ESM1, CXCL13, TPCN2, PTPRD, FOXO1, and ELK3) were constructed for the prediction of overall survival (OS) in OC based on the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. Patients were divided based on their median risk score. In the The Cancer Genome Atlas (TCGA) training dataset, the survival analysis showed that overall survival was lower in the high-risk group than that in the low-risk group (p &lt; 0.0001). The International Cancer Genome Consortium (ICGC) database was used for validation, and the receiver operating characteristic (ROC) curves showed good performance. Univariate and multivariate Cox analyses were conducted to identify independent predictors of OS. The nomogram, including the risk score, age, stage, grade, and position, can not only show good predictive ability but also can explore the correlation analysis based on ARGs for immunogenicity, immune components, and immune phenotypes with risk score. Risk scores were correlated strongly with the type of immune infiltration. Furthermore, homologous recombination defect (HRD), NtAIscore, LOH score, LSTm score, stemness index (mRNAsi), and stromal cells were significantly correlated with risk score. The present study suggests that the novel signature constructed from six ARGs may serve as effective prognostic biomarkers for OC and contribute to clinical decision making and personalized prognostic monitoring of OC.

scholarly journals A six-microRNA signature can better predict overall survival of patients with esophagus adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7353 ◽  
Author(s):  
Tian Lan ◽  
Yunyan Lu ◽  
Zunqiang Xiao ◽  
Haibin Xu ◽  
Junling He ◽  
...  
Keyword(s):  
Overall Survival ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Pathway Enrichment Analysis ◽  
Mirna Signature ◽  
Training Set ◽  
Data Set ◽  
Cox Regression Analysis

Background The microRNAs (miRNAs) have been validated as prognostic markers in many cancers. Here, we aimed at developing a miRNA-based signature for predicting the prognosis of esophagus adenocarcinoma (EAC). Methods The RNA-sequencing data set of EAC was downloaded from The Cancer Genome Atlas (TCGA). Eighty-four patients with EAC were classified into a training set and a test set randomly. Using univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO), we identified prognostic factors and constructed a prognostic miRNA signature. The accuracy of the signature was evaluated by the receiver operating characteristic (ROC) curve. Result In general, in the training set, six miRNAs (hsa-mir-425, hsa-let-7b, hsa-mir-23a, hsa-mir-3074, hsa-mir-424 and hsa-mir-505) displayed good prognostic power as markers of overall survival for EAC patients. Relative to patients in the low-risk group, those assigned to the high-risk group according to their risk scores of the designed miRNA model displayed reduced overall survival. This 6-miRNA model was validated in test and entire set. The area under curve (AUC) for ROC at 3 years was 0.959, 0.840, and 0.868 in training, test, and entire set, respectively. Molecular functional analysis and pathway enrichment analysis indicated that the target messenger RNAs associated with 6-miRNA signature were closely related to several pathways involved in carcinogenesis, especially cell cycle. Conclusion In summary, a novel 6-miRNA expression-based prognostic signature derived from the EAC data of TCGA was constructed and validated for predicting the prognosis of EAC.

scholarly journals Construction of a Prognostic Signature in Ewing’s Sarcoma: Based On Metabolism-Related Genes

2021 ◽  
Author(s):  
Bo Wu ◽  
Dong Zhu ◽  
Bo Yu ◽  
Yuanyuan Hou ◽  
Hongyu Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Ewing’S Sarcoma ◽  
Cox Regression ◽  
Ewing's Sarcoma ◽  
Risk Group ◽  
Expression Profiles ◽  
High Risk Group ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Cox Analysis

Abstract Objective: By combining the expression profiles of metabolism-related genes (MRGS) with clinical information, the expression quantities of MRGS and the influence on development and prognosis were systematically analyzed, so as to provide a theoretical basis for the clinical study on the prognosis of Ewing's sarcoma.Methods: MRGs expression profiles of 64 patients with Ewing's sarcoma were obtained from the GEO dataset. Univariate Cox regression analysis was used to identify metabolization-related differentially expressed genes (DEGs) related with prognosis in Ewing's sarcoma patients. Then, multivariate Cox analysis was used to calculate novel prognostic markers based on metabolism-related DEGs. Finally, the new prognostic index was verified on the basis of the prognostic models.Results: Univariate Cox regression analysis identified 20 metabolization-related DEGs, 9 of which were significantly associated with Ewing's sarcoma patients' overall survival. Subsequently, we used nine metabolism-related DEGs to construct metabolism-related prognostic signature for patients with Ewing's sarcoma. Based on the 9 DEGs regression coefficient, we put forward the formula of each patient's risk score, and then divided the patients into high-risk group and low-risk group. The results indicated that the survival rate and survival time were higher in the low-risk group and lower in the high-risk group. Multivariate Cox analysis showed that risk score index was indeed an independent prognostic factor for Ewing's sarcoma. In addition, the area under the receiver operating characteristic (ROC) curve for overall survival was 0.985. And a nomogram model was established.Conclusion: The experimental results suggest that the 9 metabolism-related DEGs marker may be effective in predicting the prognosis of Ewing's sarcoma to some extent, helping to individualize treatment of patients at different risks.

scholarly journals Immune prognostic implications of PSMD14 and its associated genes signatures in hepatocellular carcinoma

2020 ◽  
Author(s):  
Chuan Tian ◽  
Mubalake Abudoureyimu ◽  
Xinrong Lin ◽  
Hao Zhou ◽  
Xiaoyuan Chu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Clinicopathologic Characteristics ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Prognostic Implications

Abstract Background PSMD14 played a vital roles initiation and progression of hepatocellular carcinoma (HCC). However, PSMD14 and its-related genes for the immune prognostic implications of HCC patients have rarely been analyzed. Therefore, we aimed to explore gene signatures and immune prognostic values of PSMD14 and its-related genes in HCC. Method Analyzed the expression of PSMD14 in multiple databases, and clinicopathologic characteristics associated with PSMD14 overall survival using Wilcoxon signed-ranktest, logistic and Cox regression, Kaplan-Meier method. An immune prognostic signature (including RBM45, PSMD1, OLA1, CCT6A, LCAT and IVD) was constructed and validated using the co-expression and cox regression analyses in TCGA, ICGC and TIMER datasets. Gene Set Enrichment Analysis (GSEA) was performed using TCGA data set. Results Increased PSMD14 expression in HCC was significantly associated with poor prognosis and clinicopathologic characteristics (grade, histologic stage, surgical approach and T stage, all p-values < 0.05). A total of six PSMD14-related genes were detected, which markedly related to overall survival and immune infiltrating levels in HCC patients. Using cox regression analysis, the PSMD14 and its-related genes were found to be an independent prognostic factor for HCC survival. Calibration curves confirmed good consistency between clinical nomogram prediction and actual observation. Immune prognostic model suggests that patients in the high‐risk group shown significantly poorer survival than patients in the low‐risk group. Conclusion We screened potential immune prognostic genes and constructed and verified a novel PSMD14-based prognostic model of HCC, which provides new potential prognostic biomarkers and therapeutic targets and lays a theoretical foundation for immunotherapy of HCC.

scholarly journals Ferroptosis-Related Gene Signature and Patterns of Immune Infiltration Predict the Overall Survival in Patients With Lung Adenocarcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Yuxuan Wang ◽  
Weikang Chen ◽  
Minqi Zhu ◽  
Lei Xian
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Prognosis Prediction

Background: Lung adenocarcinoma (LUAD) is a malignant tumor with high heterogeneity and poor prognosis. Ferroptosis, a form of regulated cell-death–related iron, has been proven to trigger inflammation-associated immunosuppression in the tumor microenvironment, which promotes tumor growth. Therefore, the clinical prognostic value of ferroptosis-related genes in LUAD needs to be further explored.Method: In this study, we downloaded the mRNA expression profiles and corresponding clinical data of LUAD patients from the Cancer Genome Atlas database. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct ferroptosis-related gene signature. Based on these, we established the nomograms for prognosis prediction and validated the model in the GSE72094 dataset. The cell type was identified using the CIBERSORT algorithm for estimating relative subsets of RNA transcripts, which was then used to screen significant tumor immune-infiltrating cells associated with the LUAD prognosis prediction model. Subsequently, we applied co-expression analysis to reveal the relationship between ferroptosis-related genes and significant immune cells.Results: The univariate COX regression analysis showed that 20 genes were associated with the overall survival (OS) as prognostic differentially expressed genes (DEGs) (FDR &lt;0.05). Patients were divided into two risk groups using a 13-gene signature, with the high-risk group having a significantly worse OS than their low-risk counterparts (p &lt; 0.001). We used receiver operating characteristic (ROC) curve analysis to confirm the predictive capacity of the signature. Besides, we identified seven pairs of ferroptosis-related genes and tumor-infiltrating immune cells associated with the prognosis of LUAD patients.Conclusion: In this study, we construct a ferroptosis-related gene signature that can be used for prognostic prediction in LUAD. In addition, we reveal a potential connection between ferroptosis and tumor-infiltrating immune cells.

scholarly journals A novel RNA binding protein-associated prognostic model to predict overall survival in hepatocellular carcinoma patients

2020 ◽  
Author(s):  
Ye Liu ◽  
Zhixiang Qin ◽  
Hai Yang ◽  
Yang Gu ◽  
Kun Li
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Risk Group ◽  
Differentially Expressed ◽  
Data Set ◽  
Cox Regression Analysis

Abstract Background Hepatocellular carcinoma (HCC) represents one of the deadliest malignancies worldwide. Despite significant advances in diagnosis and treatment, the mortality rate from HCC persists at a substantial level. This research strives to establish a prognostic model based on the RNA binding proteins (RBPs) that can predict HCC patients’ OS. Methods There was an RNA-seq data set derived from the Cancer Genome Atlas (TCGA) databank which was included in our research as well as a Microarray data set (GSE14520). The differentially expressed RBPs between HCC and normal tissues were investigated in TCGA dataset. Subsequently, the TCGA data set was randomly split into a training and a testing cohort. The prognostic model of the training cohort was developed by applying univariate Cox regression and lasso Cox regression analyses and multivariate Cox regression analysis. In order to evaluate the prognostic value of the model, a comprehensive survival assessment was conducted. Results A total of 133 differentially expressed RBPs were identified. Five RBPs (RPL10L, EZH2, PPARGC1A, ZNF239, IFIT1) were used to construct the model. The model accurately predicted the prognosis of liver cancer patients in both the TCGA cohort and the GSE14520 validation cohort. HCC patients could be assigned into a high-risk group and a low-risk group by this model, and the overall survival of these two groups was significantly different. Furthermore, the risk scores obtained by our model were highly correlated with immune cell infiltration. . Conclusions Five RBPs-related prognostic models were constructed and validated to predict OS reliably in HCC individuals. It helps to identify patients at high risk of mortality with the risk prediction score, which optimizes personalized therapeutic decision-making.

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