scholarly journals A Novel Model Based on Necroptosis-Related Genes for Predicting Prognosis of Patients With Prostate Adenocarcinoma

2022 ◽  
Vol 9 ◽  
Author(s):  
Xin-yu Li ◽  
Jian-xiong You ◽  
Lu-yu Zhang ◽  
Li-xin Su ◽  
Xi-tao Yang
Keyword(s):  
Prostate Cancer ◽  
Prognostic Model ◽  
Clinical Information ◽  
Roc Curves ◽  
Prostate Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Bioinformatics Analyses ◽  
High Risk Prostate Cancer ◽  
Good Prediction Accuracy

Background: Necroptosis is a newly recognized form of cell death. Here, we applied bioinformatics tools to identify necroptosis-related genes using a dataset from The Cancer Genome Atlas (TCGA) database, then constructed a model for prognosis of patients with prostate cancer.Methods: RNA sequence (RNA‐seq) data and clinical information for Prostate adenocarcinoma (PRAD) patients were obtained from the TCGA portal (http://tcga-data.nci.nih.gov/tcga/). We performed comprehensive bioinformatics analyses to identify hub genes as potential prognostic biomarkers in PRAD u followed by establishment and validation of a prognostic model. Next, we assessed the overall prediction performance of the model using receiver operating characteristic (ROC) curves and the area under curve (AUC) of the ROC.Results: A total of 5 necroptosis-related genes, namely ALOX15, BCL2, IFNA1, PYGL and TLR3, were used to construct a survival prognostic model. The model exhibited excellent performance in the TCGA cohort and validation group and had good prediction accuracy in screening out high-risk prostate cancer patients.Conclusion: We successfully identified necroptosis-related genes and constructed a prognostic model that can accurately predict 1- 3-and 5-years overall survival (OS) rates of PRAD patients. Our riskscore model has provided novel strategy for the prediction of PRAD patients’ prognosis.

scholarly journals Identification of Key Genes Related to Lung Squamous Cell Carcinoma Using Bioinformatics Analysis

2020 ◽  
Vol 21 (8) ◽  
pp. 2994
Author(s):  
Miaomiao Gao ◽  
Weikaixin Kong ◽  
Zhuo Huang ◽  
Zhengwei Xie
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Bioinformatics Analysis ◽  
Lung Squamous Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Good Prediction Accuracy ◽  
Key Genes

Lung squamous cell carcinoma (LUSC) is often diagnosed at the advanced stage with poor prognosis. The mechanisms of its pathogenesis and prognosis require urgent elucidation. This study was performed to screen potential biomarkers related to the occurrence, development and prognosis of LUSC to reveal unknown physiological and pathological processes. Using bioinformatics analysis, the lung squamous cell carcinoma microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs). Furthermore, PPI and WGCNA network analysis were integrated to identify the key genes closely related to the process of LUSC development. In addition, survival analysis was performed to achieve a prognostic model that accomplished good prediction accuracy. Three hundred and thirty–seven up–regulated and 119 down-regulated genes were identified, in which four genes have been found to play vital roles in LUSC development, namely CCNA2, AURKA, AURKB, and FEN1. The prognostic model contained 5 genes, which were all detrimental to prognosis. The AUC of the established prognostic model for predicting the survival of patients at 1, 3, and 5 years was 0.692, 0.722, and 0.651 in the test data, respectively. In conclusion, this study identified several biomarkers of significant interest for additional investigation of the therapies and methods of prognosis of lung squamous cell carcinoma.

scholarly journals An angiogenesis-related long noncoding RNA signature correlates with prognosis in patients with hepatocellular carcinoma

2021 ◽  
Vol 41 (4) ◽  
Author(s):  
Dengliang Lei ◽  
Yue Chen ◽  
Yang Zhou ◽  
Gangli Hu ◽  
Fang Luo
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Long Noncoding Rna ◽  
Noncoding Rna ◽  
Enrichment Analysis ◽  
Clinical Information ◽  
Roc Curves ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Highly Correlated

Abstract Hepatocellular carcinoma (HCC) is one of the most prevalent and lethal cancers worldwide. Neovascularization is closely related to the malignancy of tumors. We constructed a signature of angiogenesis-related long noncoding RNA (lncRNA) to predict the prognosis of patients with HCC. The lncRNA expression matrix of 424 HCC patients was downloaded from The Cancer Genome Atlas (TCGA). First, gene set enrichment analysis (GSEA) was used to distinguish the differentially expressed genes of the angiogenesis genes in liver cancer and adjacent tissues. Next, a signature of angiogenesis-related lncRNAs was constructed using univariate and multivariate analyses, and receiver operating characteristic (ROC) curves were used to assess the accuracy. The signature and relevant clinical information were used to construct the nomogram. A 5-lncRNA signature was highly correlated with overall survival (OS) in HCC patients and performed well in evaluations using the C-index, areas under the curve, and calibration curves. In summary, the 5-lncRNA model can serve as an accurate signature to predict the prognosis of patients with liver cancer, but its mechanism of action must be further elucidated by experiments.

scholarly journals A Novel Nine Apoptosis-Related Genes Signature Predicting Overall Survival for Kidney Renal Clear Cell Carcinoma and its Associations with Immune Infiltration

2021 ◽  
Vol 8 ◽  
Author(s):  
Yi Wang ◽  
Yinhao Chen ◽  
Bingye Zhu ◽  
Limin Ma ◽  
Qianwei Xing
Keyword(s):  
Overall Survival ◽  
Cell Carcinoma ◽  
Prognostic Model ◽  
Cox Regression ◽  
Clear Cell ◽  
Roc Curves ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Immune Infiltration

Background: This study was designed to establish a sensitive prognostic model based on apoptosis-related genes to predict overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).Methods: Obtaining the expression of apoptosis-related genes and associated clinical parameters from online datasets (The Cancer Genome Atlas, TCGA), their biological function analyses were performed through differently expressed genes. By means of LASSO, unadjusted and adjusted Cox regression analyses, this predictive signature was constructed and validated by internal and external databases (both TCGA and ArrayExpress).Results: A total of nine apoptosis-related genes (SLC27A2, TNFAIP2, IFI44, CSF2, IL4, MDK, DOCK8, WNT5A, APP) were ultimately screened as associated hub genes and utilized to construct a prognosis model. Then our constructed riskScore model significantly passed the validation in both the internal and external datasets of OS (all p < 0.05) and verified their expressions by qRT-PCR. Moreover, we conducted the Receiver Operating Characteristic (ROC), finding the area under the ROC curves (AUCs) were all above 0.70 which indicated that riskScore was a stable independent prognostic factor (p < 0.05). Furthermore, prognostic nomograms were established to figure out the relationship between 1-, 3- and 5-year OS and individual parameters for ccRCC patients. Additionally, survival analyses indicated that our riskScore worked well in predicting OS in subgroups of age, gender, grade, stage, T, M, N0, White (all p < 0.05), except for African, Asian and N1 (p > 0.05). We also explored its association with immune infiltration and applied cMap database to seek out highly correlated small molecule drugs.Conclusion: Our study successfully constructed a prognostic model containing nine hub apoptosis-related genes for ccRCC, helping clinicians predict patients’ OS and making the prognostic assessment more standardized. Future prospective studies are required to validate our findings.

scholarly journals Prognostic Signatures of Alternative Splicing Events in Esophageal Carcinoma Based on TCGA Splice-Seq Data

2021 ◽  
Vol 11 ◽  
Author(s):  
Ping Ye ◽  
Yan Yang ◽  
Liqiang Zhang ◽  
Guixi Zheng
Keyword(s):  
Alternative Splicing ◽  
Esophageal Carcinoma ◽  
Cox Regression ◽  
Donor Site ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Roc Curves ◽  
The Cancer Genome Atlas ◽  
Acceptor Site ◽  
Cox Regression Analysis

An alternative splicing (AS) event is a highly complex process that plays an essential role in post-transcriptional gene expression. Several studies have suggested that abnormal AS events were the primary element in the pathological process of cancer. However, few works are dedicated to the study of AS events in esophageal carcinoma (EC). In the present study, clinical information and RNA-seq data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The percent spliced in (PSI) values of AS events were acquired from the TCGA Splice-seq. A total of 183 EC patients were enrolled in this study, and 2,212 AS events were found significantly associated with the overall survival of these patients by univariate Cox regression analysis. The prognostic signatures based on AS events were built by multivariate Cox analysis. Receiver operating characteristic (ROC) curves displayed that the area under the curve (AUC) of the following prognostic signatures, including exon skip (ES), alternate terminator (AT), alternate acceptor site (AA), alternate promoter (AP), alternate donor site (AD), retained intron (RI), and total events, was greater than 0.8, suggesting that these seven signatures had valuable prognosis prediction capacity. Finally, the risk score of prognostic signatures was indicated as an independent risk factor of survival. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to explore the function of splicing factors (SFs) that were associated with AS events. Also, the interactive network between AS events and SFs identified several hub genes and AS events which need further study. This was a comprehensive study that explored prognosis-related AS events and established valuable prognosis signatures in EC patients. The network of interactions between AS events and SFs might offer novel insights into the fundamental mechanisms of tumorigenesis and progression of EC.

scholarly journals Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer

Biomolecules ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 524 ◽  
Author(s):  
Ken Asada ◽  
Kazuma Kobayashi ◽  
Samuel Joutard ◽  
Masashi Tubaki ◽  
Satoshi Takahashi ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Patient Survival ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Support Vector ◽  
Rna Expression ◽  
Bioinformatics Analyses ◽  
Expression Levels ◽  
Individual Level ◽  
Omics Analysis

Lung cancer is one of the leading causes of death worldwide. Therefore, understanding the factors linked to patient survival is essential. Recently, multi-omics analysis has emerged, allowing for patient groups to be classified according to prognosis and at a more individual level, to support the use of precision medicine. Here, we combined RNA expression and miRNA expression with clinical information, to conduct a multi-omics analysis, using publicly available datasets (the cancer genome atlas (TCGA) focusing on lung adenocarcinoma (LUAD)). We were able to successfully subclass patients according to survival. The classifiers we developed, using inferred labels obtained from patient subtypes showed that a support vector machine (SVM), gave the best classification results, with an accuracy of 0.82 with the test dataset. Using these subtypes, we ranked genes based on RNA expression levels. The top 25 genes were investigated, to elucidate the mechanisms that underlie patient prognosis. Bioinformatics analyses showed that the expression levels of six out of 25 genes (ERO1B, DPY19L1, NCAM1, RET, MARCH1, and SLC7A8) were associated with LUAD patient survival (p < 0.05), and pathway analyses indicated that major cancer signaling was altered in the subtypes.

Expression patterns and a prognostic model of m6A-associated regulators in prostate adenocarcinoma

2020 ◽  
Vol 14 (18) ◽  
pp. 1717-1731
Author(s):  
Song Ou-Yang ◽  
Ji-Hong Liu ◽  
Qin-Zhang Wang
Keyword(s):  
Prognostic Model ◽  
Cox Regression ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Prostate Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Consensus Clustering ◽  
Cox Regression Analysis ◽  
Clinical Biomarkers

Aim: To study the expression patterns and prognostic value of the m6A-associated regulators in prostate adenocarcinoma (PRAD). Materials & methods: The mRNA expression and clinical data were downloaded from ‘The Cancer Genome Atlas database’. The m6A-associated variants were downloaded from m6AVar database, and combined with 14 common m6A regulators for subsequent analysis. One-way analysis of variance, univariate Cox regression analysis and least absolute shrinkage and selection operator algorithm were successively applied to obtain the ultimate regulators and prognostic model. Finally, consensus clustering, protein–protein interaction (PPI) and enrichment analysis were performed. Result: Nine regulators were obtained. PRAD patients could be classified into two risk groups and subclasses with significant survival differences by the prognostic model and consensus clustering, respectively. Conclusion: All these nine regulators were related to prognosis in PRAD, and could be used as clinical biomarkers.

scholarly journals Construction of a Risk Score Prognosis Model for Hepatocellular Carcinoma Based on Pyroptosis Related Genes

2021 ◽  
Author(s):  
Xin-yu Li ◽  
Li-xin Su ◽  
Ming-zhe Wen ◽  
Jian-xiong You ◽  
xi-tao Yang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Prognostic Model ◽  
Operating Characteristic ◽  
Clinical Information ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Model Based ◽  
Prognosis Model ◽  
Cancer Genome Atlas

Abstract Background: In this study, a prognostic model based on pyroptosis-related genes was established to predict overall survival (OS) in patients with hepatocellular carcinoma(HCC).Methods: The gene expression data and clinical information of HCC patients were acquired from The Cancer Genome Atlas (TCGA). Using bioinformatics analysis, this predictive signature was constructed and validated. The performance of predictive signature was assessed by the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). Results: A total of 3 pyroptosis-related genes (BAK1, GSDME, and NOD2) were used to construct a survival prognostic model, and experimental validation performed using an experimental cohort. The prognosis model exhibited good performance based on the AUC (AUC: 0.826 at 1 years, 0.796 at 3 years, 0.867 at 5 years). The calibration plots showed excellent calibration.Conclusion: In this study, a novel prognostic model based on three pyroptosis-related genes is constructed and used to predict the prognosis of HCC patients. The model can accurately and conveniently predict the 1- 3-and 5-year OS of HCC patients.

scholarly journals Identification of a competing endogenous RNA network associated with prognosis of pancreatic adenocarcinoma

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Wanqing Weng ◽  
Zhongjing Zhang ◽  
Weiguo Huang ◽  
Xiangxiang Xu ◽  
Boda Wu ◽  
...  
Keyword(s):  
Pancreatic Adenocarcinoma ◽  
Cox Regression ◽  
Expression Patterns ◽  
Operator Method ◽  
Clinical Information ◽  
Normal Subjects ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Good Prediction ◽  
Prediction Function

Abstract Background Emerging evidence suggests that competing endogenous RNAs plays a crucial role in the development and progress of pancreatic adenocarcinoma (PAAD). The objective was to identify a new lncRNA-miRNA-mRNA network as prognostic markers, and develop and validate a multi-mRNAs-based classifier for predicting overall survival (OS) in PAAD. Methods Data on pancreatic RNA expression and clinical information of 445 PAAD patients and 328 normal subjects were downloaded from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Genotype-Tissue Expression (GTEx). The weighted correlation network analysis (WGCNA) was used to analyze long non-coding RNA (lncRNA) and mRNA, clustering genes with similar expression patterns. MiRcode was used to predict the sponge microRNAs (miRNAs) corresponding to lncRNAs. The downstream targeted mRNAs of miRNAs were identified by starBase, miRDB, miRTarBase and Targetscan. A multi-mRNAs-based classifier was develop using least absolute shrinkage and selection operator method (LASSO) COX regression model, which was tested in an independent validation cohort. Results A lncRNA-miRNA-mRNA co-expression network which consisted of 60 lncRNAs, 3 miRNAs and 3 mRNAs associated with the prognosis of patients with PAAD was established. In addition, we constructed a 14-mRNAs-based classifier based on a training cohort composed of 178 PAAD patients, of which the area under receiver operating characteristic (AUC) in predicting 1-year, 3-year, and 5-year OS was 0.719, 0.806 and 0.794, respectively. The classifier also shown good prediction function in independent verification cohorts, with the AUC of 0.604, 0.639 and 0.607, respectively. Conclusions A novel competitive endogenous RNA (ceRNA) network associated with progression of PAAD could be used as a reference for future molecular biology research.

scholarly journals Understanding of prognosis in non-metastatic prostate cancer: a randomised comparative study of clinician estimates measured against the PREDICT prostate prognostic model

2019 ◽  
Vol 121 (8) ◽  
pp. 715-718 ◽  
Author(s):  
David R. Thurtle ◽  
Valerie Jenkins ◽  
Paul D. Pharoah ◽  
Vincent J. Gnanapragasam
Keyword(s):  
Prostate Cancer ◽  
Metastatic Prostate Cancer ◽  
Prognostic Model ◽  
Clinical Information ◽  
Clinical Tool ◽  
Treatment Benefit ◽  
Term Survival ◽  
Radical Treatment ◽  
Online Portal ◽  
Model Predictions

Abstract PREDICT Prostate is an individualised prognostic model that provides long-term survival estimates for men diagnosed with non-metastatic prostate cancer (www.prostate.predict.nhs.uk). In this study clinician estimates of survival were compared against model predictions and its potential value as a clinical tool was assessed. Prostate cancer (PCa) specialists were invited to participate in the study. 190 clinicians (63% urologists, 17% oncologists, 20% other) were randomised into two groups and shown 12 clinical vignettes through an online portal. Each group viewed opposing vignettes with clinical information alone, or alongside PREDICT Prostate estimates. 15-year clinician survival estimates were compared against model predictions and reported treatment recommendations with and without seeing PREDICT estimates were compared. 155 respondents (81.6%) reported counselling new PCa patients at least weekly. Clinician estimates of PCa-specific mortality exceeded PREDICT estimates in 10/12 vignettes. Their estimates for treatment survival benefit at 15 years were over-optimistic in every vignette, with mean clinician estimates more than 5-fold higher than PREDICT Prostate estimates. Concomitantly seeing PREDICT Prostate estimates led to significantly lower reported likelihoods of recommending radical treatment in 7/12 (58%) vignettes, particularly in older patients. These data suggest clinicians overestimate cancer-related mortality and radical treatment benefit. Using an individualised prognostic tool may help reduce overtreatment.

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