scholarly journals Construction of prognostic predictor by comprehensive analyzing alternative splicing events for Colon adenocarcinoma

2020 ◽  
Author(s):  
Yaqi Qu ◽  
Peng Guo ◽  
Le Zhang ◽  
Lifei Tian
Keyword(s):  
Risk Score ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Multivariable Analysis ◽  
Colon Adenocarcinoma ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Prognostic Predictor ◽  
Incidence And Mortality

Abstract Background Colon adenocarcinoma (COAD) is one of the most common malignant tumors with high incidence and mortality rates worldwide. Reliable prognostic biomarkers are needed to identify to guide clinical practice.Materials and Method Comprehensive gene expression with AS profiles for each patient were downloaded using SpliceSeq database from The Cancer Genome Atlas (TCGA). Cox regression analysis was conducted to screen prognostic AS events. R package “limma” was used to screen different expression genes (DEGs) between normal and tumor samples in COAD cohort. Venn plot analysis was performed between DEGs and prognostic AS events, the DEGs with prognostic AS events occurred(DEGAS) were obtained. Top 30 DEGAS in protein-protein interaction(PPI) analysis were bring in Cox proportional hazards regression to establish prognostic models.Results Totally, 350 patients were included in study. 22,451 AS events were detected. 2,004 AS events form 1439 genes showed as survival-associated AS events significantly. By overlapping the 1439 genes and 6455 DEGs, 211 DEGs with AS events were obtained. After construction of PPI, top 30 hub genes were included in multivariable analysis. Finally, a risk score based on 12 genes which associated with overall survival(OS) was established (P < 0.05). The area under the curve(AUC) is 0.782. And the risk score shows as an independent predictor (P < 0.001).Conclusions By exploring survival-associated AS events, a powerful prognostic predictor was built. We aim at proposing a novel method to provide treatment targets for COAD and guide clinical practice in the future.

scholarly journals Comprehensive Characterization of Alternative mRNA Splicing Events in Glioblastoma: Implications for Prognosis, Molecular Subtypes, and Immune Microenvironment Remodeling

2021 ◽  
Vol 10 ◽  
Author(s):  
Liang Zhao ◽  
Jiayue Zhang ◽  
Zhiyuan Liu ◽  
Yu Wang ◽  
Shurui Xuan ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Cox Model ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Alternative Mrna Splicing

Alternative splicing (AS) of pre-mRNA has been widely reported to be associated with the progression of malignant tumors. However, a systematic investigation into the prognostic value of AS events in glioblastoma (GBM) is urgently required. The gene expression profile and matched AS events data of GBM patients were obtained from The Cancer Genome Atlas Project (TCGA) and TCGA SpliceSeq database, respectively. 775 AS events were identified as prognostic factors using univariate Cox regression analysis. The least absolute shrinkage and selection operator (LASSO) cox model was performed to narrow down candidate AS events, and a risk score model based on several AS events were developed subsequently. The risk score-based signature was proved as an efficient predictor of overall survival and was closely related to the tumor purity and immunosuppression in GBM. Combined similarity network fusion and consensus clustering (SNF-CC) analysis revealed two distinct GBM subtypes based on the prognostic AS events, and the associations between this novel molecular classification and clinicopathological factors, immune cell infiltration, as well as immunogenic features were further explored. We also constructed a regulatory network to depict the potential mechanisms that how prognostic splicing factors (SFs) regulate splicing patterns in GBM. Finally, a nomogram incorporating AS events signature and other clinical-relevant covariates was built for clinical application. This comprehensive analysis highlights the potential implications for predicting prognosis and clinical management in GBM.

scholarly journals Construction of prognostic predictor by comprehensive analyzing alternative splicing events for Colon adenocarcinoma

2020 ◽  
Author(s):  
Yaqi Qu ◽  
Yujia Chen ◽  
Le Zhang ◽  
Lifei Tian
Keyword(s):  
Clinical Practice ◽  
Alternative Splicing ◽  
Risk Score ◽  
Protein Interaction ◽  
Interaction Analysis ◽  
Colon Adenocarcinoma ◽  
Cox Regression Analysis ◽  
Prognostic Predictor ◽  
Protein Protein Interaction ◽  
Incidence And Mortality

Abstract Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors, with high incidence and mortality rates worldwide. Reliable prognostic biomarkers are needed to guide clinical practice. Methods: Comprehensive gene expression with alternative splicing (AS) profiles for each patient were downloaded using the SpliceSeq database from The Cancer Genome Atlas. Cox regression analysis was conducted to screen for prognostic AS events. The R package limma was used to screen differentially expressed genes (DEGs) between normal and tumor samples in the COAD cohort. A Venn plot analysis was performed between DEGs and prognostic AS events, and the DEGs that co-occurred with prognostic AS events (DEGAS) were identified. The top 30 most-connected DEGAS in protein–protein interaction analysis were identified through Cox proportional hazards regression to establish prognostic models. Results: In total, 350 patients were included in the study. A total of 22,451 AS events were detected, of which 2,004 from 1,439 genes were significantly associated with survival time. By overlapping these 1,439 genes with 6,455 DEGs, 211 DEGs with AS events were identified. After construction of the protein–protein interaction network, the top 30 hub genes were included in a multivariate analysis. Finally, a risk score based on 12 genes associated with overall survival was established (P < 0.05). The area under the curve was 0.782. The risk score was an independent predictor (P < 0.001). Conclusions: By exploring survival-associated AS events, a powerful prognostic predictor consisting of 12 DEGAS was built. This study aims to propose a novel method to provide treatment targets for COAD and guide clinical practice in the future.

scholarly journals Identification of the Pyroptosis-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Bixian Luo ◽  
Jianwei Lin ◽  
Wei Cai ◽  
Mingliang Wang
Keyword(s):  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Model Based ◽  
Patient Prognosis

The prognosis of advanced colon adenocarcinoma (COAD) remains poor. However, existing methods are still difficult to assess patient prognosis. Pyroptosis, a lytic and inflammatory process of programmed cell death caused by the gasdermin protein, is involved in the development and progression of various tumors. Moreover, there are no related studies using pyroptosis-related genes to construct a model to predict the prognosis of COAD patients. Thus, in this study, bioinformatics methods were used to analyze the data of COAD patients downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to construct a risk model for the patient prognosis. TCGA database was used as the training set, and GSE39582 downloaded from GEO was used as the validation set. A total of 24 pyroptosis-related genes shown significantly different expression between normal and tumor tissues in COAD and seven genes (CASP4, CASP5, CASP9, IL6, NOD1, PJVK, and PRKACA) screened by univariate and LASSO cox regression analysis were used to construct the risk model. The receiver operating characteristic (ROC) and Kaplan–Meier (K–M curves) curves showed that the model based on pyroptosis-related genes can be used to predict the prognosis of COAD and can be validated by the external cohort well. Then, the clinicopathological factors were combined with the risk score to establish a nomogram with a C-index of 0.774. In addition, tissue validation results also showed that CASP4, CASP5, PRKACA, and NOD1 were differentially expressed between tumor and normal tissues from COAD patients. In conclusion, the risk model based on the pyroptosis-related gene can be used to assess the prognosis of COAD patients well, and the related genes may become the potential targets for treatment.

scholarly journals Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Sihan Chen ◽  
Guodong Cao ◽  
Wei Wu ◽  
Yida Lu ◽  
Xiaobo He ◽  
...  
Keyword(s):  
Cox Regression ◽  
Mechanistic Model ◽  
Single Gene ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Gene Markers ◽  
Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

scholarly journals Five lncRNAs Associated With Prostate Cancer Prognosis Identified by Coexpression Network Analysis

2020 ◽  
Vol 19 ◽  
pp. 153303382096357
Author(s):  
Xiaoyong Gong ◽  
Bobin Ning
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Normal Prostate ◽  
Cox Regression Analysis ◽  
Incidence And Mortality

Prostate cancer (PCa) is a highly malignant tumor, with increasing incidence and mortality rates worldwide. The aim of this study was to identify the prognostic lncRNAs and construct an lncRNA signature for PCa diagnosis by the interaction network between lncRNAs and protein-coding genes (PCGs). The differentially expressed lncRNAs (DElncRNAs) and PCGs (DEPCGs) between PCa and normal prostate tissues were screened from The Cancer Genome Atlas (TCGA) database. The DEPCGs were functionally annotated in terms of the enriched pathways. Weighted gene co-expression network analysis (WGCNA) of 104 PCa samples identified 15 co-expression modules, of which the Turquoise module was negatively correlated with cancer and included 5 key lncRNAs and 47 PCGs. KEGG pathway analyses of the core 47 PCGs showed significant enrichment in classic PCa-related pathways, and overlapped with the enriched pathways of the DEPCGs. LINC00857, LINC00900, LINC00908, LINC00900, SNHG3 and FENDRR were significantly associated with the survival of PCa and have not been reported previously. Finally, Multivariable Cox regression analysis was used to establish a prognostic risk formula, and the patients were accordingly stratified into the low- and high-risk groups. The latter had significantly worse OS compared to the low-risk group (P < 0.01), and the area under the receiver operating characteristic curve (ROC) of 14-year OS was 0.829. The accuracy of our prediction model was determined by calculating the corresponding concordance index (C-index) and risk curves. In conclusion, we established a 5-lncRNA prognostic signature that provides insights into the biological and clinical relevance of lncRNAs in PCa.

scholarly journals Comprehensive Analysis of lncRNAs Related to the Prognosis of Esophageal Cancer Based on ceRNA Network and Cox Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Chao Li ◽  
Wu Yao ◽  
Congcong Zhao ◽  
Guo Yang ◽  
Jingjing Wei ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Target Genes ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Transcriptome Profiling ◽  
Assessment Model ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Cox Regression Analysis ◽  
Cerna Network

Background. Esophageal cancer is one of the most deadly malignant tumors. Among the common malignant tumors in the world, esophageal cancer is ranked seventh, which has a high mortality rate. Long noncoding RNAs (lncRNAs) play an important role in the occurrence and development of various tumors. lncRNAs can competitively bind microRNAs (miRNAs) with mRNA, which can regulate the expression level of the encoded gene at the posttranscriptional level. This regulatory mechanism is called the competitive endogenous RNA (ceRNA) hypothesis, and ceRNA has important research value in tumor-related research. However, the regulation of lncRNAs is less studied in the study of esophageal cancer. Methods. The Cancer Genome Atlas (TCGA) database was used to download transcriptome profiling data of esophageal cancer. Gene expression quantification data contains 160 cancer samples and 11 normal samples. These data were used to identify differentially expressed lncRNAs and mRNAs. miRNA expression data includes 185 cancer samples and 13 normal samples. The differentially expressed RNAs were identified using the edgeR package in R software. Then, the miRcode database was used to predict miRNAs that bind to lncRNAs. MiRTarBase, miRDB, and TargetScan databases were used to predict the target genes of miRNAs. Cytoscape software was used to draw ceRNA network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using DAVID 6.8. Finally, multifactor cox regression was used to screen lncRNAs related to prognosis. Results. We have screened 1331 DElncRNAs, 3193 DEmRNAs, and 162 DEmiRNAs. Among them, the ceRNA network contains 111 lncRNAs, 11 miRNAs, and 63 DEmRNAs. Finally, we established a prediction model containing three lncRNAs through multifactor Cox regression analysis. Conclusions. Our research screened out three independent prognostic lncRNAs from the ceRNA network and constructed a risk assessment model. This is helpful to understand the regulatory role of lncRNAs in esophageal cancer.

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

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.

scholarly journals Identification of a Pyroptosis-Related lncRNA Risk Model for Predicting Prognosis and Immune Response in Colon Adenocarcinoma

2022 ◽  
Author(s):  
Yuying Tan ◽  
Liqing Lu ◽  
Xujun Liang ◽  
Yongheng Chen
Keyword(s):  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Colon Adenocarcinoma ◽  
Sequencing Data ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Cerna Network ◽  
Kaplan Meier

Abstract Background: Colon adenocarcinoma (COAD) is one of the most common malignant tumors and diagnosed at an advanced stage with poor prognosis in the world. Pyroptosis is involved in the initiation and progression of tumors. This research focused on constructing a pyroptosis-related ceRNA network to generate a reliable risk model for risk prediction and immune infiltration analysis of COAD.Methods: Transcriptome data, miRNA-sequencing data and clinical information were downloaded from the TCGA database. Firstly, differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) were identified to construct a pyroptosis-related ceRNA network. Secondly, a pyroptosis-related lncRNA risk model was developed applying univariate Cox regression analysis and least absolute shrinkage and selection operator method (LASSO) regression analysis. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to functionally annotate RNAs contained in the ceRNA network. In addition, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox regression, and nomogram were applied to validate this risk model. Finally, the relationship of this risk model with immune cells and immune checkpoint blockade (ICB) related genes were analyzed.Results: Totally 5373 DEmRNAs, 1159 DElncRNAs and 355 DEmiRNAs were identified. A pyroptosis-related ceRNA regulatory network containing 132 lncRNAs, 7miRNAs and 5 mRNAs was constructed and a ceRNA-based pyroptosis-related risk model including 11 lncRNAs was built. Tumor tissues were classified into high- and low- risk groups according to the median risk score. Kaplan-Meier analysis showed that the high-risk group had a shorter survival time; ROC analysis, independent prognostic analysis and nomogram further indicated the risk model was a significant independent prognostic factor which had excellent ability to predict patients’ risk. Moreover, immune infiltration analysis indicated that the risk model was related to immune infiltration cells (i.e., B cells naïve, T cells follicular helper, Macrophages M1, etc.) and ICB-related genes (i.e., PD-1, CTLA4, HAVCR2, etc).Conclusions: This pyroptosis-related lncRNA risk model possessed good prognostic value and the ability to predict the outcome of ICB immunotherapy in COAD.

scholarly journals Identification and validation of a prognostic 5-protein signature for biochemical recurrence following radical prostatectomy in prostate cancer

2020 ◽  
Author(s):  
Xiangkun Wu ◽  
Wenjie Li ◽  
Daojun Lv ◽  
Yongda Liu ◽  
Di Gu
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Biochemical Recurrence ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Medical Decision ◽  
Cox Regression Analysis ◽  
Protein Signature

Abstract Background : Biochemical recurrence (BCR) is considered as an indicator for prostate cancer (PCa)-specific recurrence and mortality. However, lack of effective prediction model to assess the prognosis of patients for optimization of treatment. The aim of this work was to construct a protein-based nomogram that could predict BCR for PCa.Materials and methods: Univariate Cox regression analysis was conducted to identify candidate proteins from the Cancer Genome Atlas (TCGA) database. LASSO Cox regression was further conducted to pick out the most significant prognostic proteins and formulate the proteins signature for predicting BCR. Additionally, a nomogram was constructed by multivariate Cox proportional hazards regression.Results: We established a 5‐protein-based signature which was well used to identify PCa patients into high‐ and low‐risk groups. Kaplan-Meier analysis demonstrated patients with higher BCR generally had significantly worse survival than those with lower BCR (p<0.0001). Time-dependent receiver operating characteristic curve expounded that ours signature had excellent prognostic efficiency for 1‐, 3‐ and 5‐year BCR (area under curve in training set: 0.691, 0.797, 0.808 and 0.74, 0.739, 0.82 in the test set). Univariable and multivariate Cox regression analysis showed that this 5‐protein signature was an independent of several clinical signatures including age, Gleason score, T stage, N status, PSA and residual tumor. Moreover, a nomogram was constructed and calibration plots confirmed the its predictive value in 3-, 5- and 10-year BCR overall survival.Conclusion: Our study identified a 5-protein-based signature and constructed a prognostic nomogram that reliably predicts BCR in prostate cancer. The findings might be of paramount importance in tumor prognosis and medical decision-making.

scholarly journals Identification and Validation of a Prognostic Prediction Model of m6A Regulator-Related LncRNAs in Hepatocellular Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Chen Jin ◽  
Rui Li ◽  
Tuo Deng ◽  
Jialiang Li ◽  
Yan Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prediction Model ◽  
Risk Score ◽  
Cox Regression ◽  
Pearson Correlation ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Prediction Ability ◽  
Cox Regression Analysis

Hepatocellular carcinoma (HCC) is a highly invasive malignancy prone to recurrence, and patients with HCC have a low 5-year survival rate. Long non-coding RNAs (lncRNAs) play a vital role in the occurrence and development of HCC. N6-methyladenosine methylation (m6A) is the most common modification influencing cancer development. Here, we used the transcriptome of m6A regulators and lncRNAs, along with the complete corresponding clinical HCC patient information obtained from The Cancer Genome Atlas (TCGA), to explore the role of m6A regulator-related lncRNA (m6ARlnc) as a prognostic biomarker in patients with HCC. The prognostic m6ARlnc was selected using Pearson correlation and univariate Cox regression analyses. Moreover, three clusters were obtained via consensus clustering analysis and further investigated for differences in immune infiltration, immune microenvironment, and prognosis. Subsequently, nine m6ARlncs were identified with Lasso-Cox regression analysis to construct the prognostic signature m6A-9LPS for patients with HCC in the training cohort (n = 226). Based on m6A-9LPS, the risk score for each case was calculated. Patients were then divided into high- and low-risk subgroups based on the cutoff value set by the X-tile software. m6A-9LPS showed a strong prognosis prediction ability in the validation cohort (n = 116), the whole cohort (n = 342), and even clinicopathological stratified survival analysis. Combining the risk score and clinical characteristics, we established a nomogram for predicting the overall survival (OS) of patients. To further understand the mechanism underlying the m6A-9LPS-based classification of prognosis differences, KEGG and GO enrichment analyses, competitive endogenous RNA (ceRNA) network, chemotherapeutic agent sensibility, and immune checkpoint expression level were assessed. Taken together, m6A-9LPS could be used as a precise prediction model for the prognosis of patients with HCC, which will help in individualized treatment of HCC.

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