scholarly journals Identification of lnc RNAs Related to Prognosis of Patients With Colorectal Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303382096212
Author(s):  
Yuqi Sun ◽  
Peng Peng ◽  
Lanlan He ◽  
Xueren Gao
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
High Expression ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Score Model ◽  
Prognostic Prediction

The purpose of this study was to identify long noncoding RNAs (lncRNAs) related to prognosis of patients with colorectal cancer (CRC) and develop a prognostic prediction model for CRC. Transcriptome data and survival information of CRC patients were downloaded from The Cancer Genome Atlas. The differentially expressed lncRNAs (DElncRNAs) between CRC and normal colorectal tissues were identified by the edgeR package. The association of DElncRNAs expression with prognosis of CRC patients was analyzed by the survival package. A nomogram predicting 3- and 5- year overall survival of CRC patients was drawn by the rms package. A total of 1046 DElncRNAs were identified, including 271 down-regulated and 775 up-regulated lncRNAs in CRC. Multivariate Cox regression analysis showed 10 lncRNAs related to the prognosis of CRC patients. Thereinto high expression of AC004009.1, LHX1-DT, ELFN1-AS1, AL136307.1, AC087379.2, RBAKDN and AC078820.1 was associated with poorer prognosis of CRC patients. High expression of LINC01055, AL590483.1 and AC008514.1 was associated with better prognosis of CRC patients. Furthermore, the risk score model developed based on the 10 lncRNAs could effectively predict overall survival of CRC patients. In conclusion, 10 prognostic biomarkers for CRC were identified, which would be helpful to understand the role of lncRNAs in CRC progression.

scholarly journals Development and Validation of a Unique Gignature of Cancer Stemness-related Genes for Predicting the Overall survival of Colorectal Cancer Patients

2020 ◽  
Author(s):  
Ran Wei ◽  
Jichuan Quan ◽  
Shuofeng Li ◽  
Zhao Lu ◽  
Xu Guan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cancer Stemness ◽  
Poor Overall Survival ◽  
Tissue Samples ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
M6a Rna Methylation

Abstract Background: Cancer stem cells (CSCs), which are characterized by self-renewal and plasticity, are highly correlated with tumor metastasis and drug resistance. To fully understand the role of CSCs in colorectal cancer (CRC), we evaluated the stemness traits and prognostic value of stemness-related genes in CRC.Methods: In this study, the data from 616 CRC patients from The Cancer Genome Atlas (TCGA) were assessed and subtyped based on the mRNA expression-based stemness index (mRNAsi). The correlations of cancer stemness with the immune microenvironment, tumor mutational burden (TMB) and N6-methyladenosine (m6A) RNA methylation regulators were analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to identify the crucial stemness-related genes and modules. Furthermore, a prognostic expression signature was constructed using Lasso-penalized Cox regression analysis. The signature was validated via multiplex immunofluorescence staining of tissue samples in an independent cohort of 48 CRC patients.Results: This study suggests that high mRNAsi scores are associated with poor overall survival in stage Ⅳ CRC patients. Moreover, the levels of TMB and m6A RNA methylation regulators were positively correlated with mRNAsi scores, and low mRNAsi scores were characterized by increased immune activity in CRC. The analysis identified 2 key modules and 34 key genes as prognosis-related candidate biomarkers. Finally, a 3-gene prognostic signature (PARPBP, KNSTRN and KIF2C) was explored together with specific clinical features to construct a nomogram, which was successfully validated in an external cohort. Conclusions: There is a unique correlation between CSCs and the prognosis of CRC patients, and the novel biomarkers related to cell stemness could accurately predict the clinical outcomes of these patients.

Development and validation of the trans-omics model for pancreatic adenocarcinoma

Epigenomics ◽  
2020 ◽  
Author(s):  
Weiguo Huang ◽  
Wanqing Weng ◽  
Boda Wu ◽  
Tingbo Ye ◽  
Zhuo Lin ◽  
...  
Keyword(s):  
Overall Survival ◽  
Pancreatic Adenocarcinoma ◽  
Cox Regression ◽  
Operator Method ◽  
Comprehensive Analysis ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Selection Operator ◽  
Development And Validation

Aim: To develop a trans-omics-based molecular clinicopathological algorithm for predicting pancreatic adenocarcinoma prognosis, we performed a comprehensive analysis of the expression levels of mRNA, DNA methylation and DNA copy number in The Cancer Genome Atlas dataset. Materials & methods: Based on the least absolute shrinkage and selection operator method – COX regression analysis, a trans-omics-based classifier was established to predict overall survival. Nomogram was constructed by combining the classifier band clinical pathological characterization. Results: Based on trans-omics, we developed a 10-gene-based classifier and a molecular-clinicopathologic nomogram for predicting overall survival with satisfactory accuracy. Conclusion: Trans-omics-based classifier and molecule-clinicopathological nomogram based on the classifier can accurately predict the prognosis of pancreatic adenocarcinoma patients

Genome-wide methylation and expression profiling identify a novel epigenetic signature in gastrointestinal pan-adenocarcinomas

Epigenomics ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 907-920
Author(s):  
Wei Song ◽  
Jun Ren ◽  
Wen-Jie Wang ◽  
Chun-Tao Wang ◽  
Tao Fu
Keyword(s):  
Overall Survival ◽  
Survival Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Predictive Ability ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Clinicopathologic Factors ◽  
Cancer Genome Atlas

Aim: To identify methylation-driven genes and establish a novel epigenetic signature for gastrointestinal (GI) pan-adenocarcinomas. Materials & methods: Methylation and RNA-seq data for GI adenocarcinomas were downloaded from the Cancer Genome Atlas database. A methylation-driven gene signature was established by multivariate Cox regression analysis. We developed a prognostic nomogram using a combination of methylation-driven gene risk score and clinicopathological variables. A joint survival analysis based on gene expression and methylation was conducted to further investigate the prognostic role of methylation-driven genes. Results: An epigenetic signature was established based on five methylation-driven genes. We also established a prognostic nomogram based on methylation-driven gene risk score and clinicopathologic factors, with a favorable predictive ability. Joint survival analysis revealed that 28 methylation-driven genes could be independent prognostic factors for overall survival for GI adenocarcinomas. Conclusion: An epigenetic signature was established that effectively predicts the overall survival for GI adenocarcinomas across anatomic boundaries.

scholarly journals A long non-coding RNA signature predicts survival for glioblastoma as prognostic biomarkers

2020 ◽  
Author(s):  
Zhenzhe Li ◽  
Zhonghua Lv ◽  
Lei Yu ◽  
Sibin Zhang ◽  
Yingjie Wang ◽  
...  
Keyword(s):  
Noncoding Rna ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Differential Analysis ◽  
Cox Regression Analysis ◽  
Non Coding Rna ◽  
Cancer Genome Atlas ◽  
The Central Nervous System ◽  
Long Non Coding Rna

Abstract Background: Glioblastoma (GBM) is one of the most fatal tumors in the central nervous system. Its prognosis is very poor. There is increasing evidence that long noncoding RNA (lncRNA) participates in the biological process of glioblastoma. Nevertheless, the role of lncRNA in predicting the prognosis of GBM is still uncertain. Methods: In this study, using RNA-Seq and clinical follow-up data of GBM patients from The Cancer Genome Atlas (TCGA), we performed differential analysis of lncRNA, univariable and multivariable Cox regression analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Ontology (GO) analysis.Results: We identified four lncRNAs closely interrelated with survival and prognosis of GBM patients. This lncRNA signature was effective in both the training set and the testing set, and it was independent to clinical factors.Conclusions: Our data suggested that the four lncRNAs could be used as promising biomarkers for predicting prognosis in GBM patients.

scholarly journals The Development of Three-DNA Methylation Signature as a Novel Prognostic Biomarker in Patients with Colorectal Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shu Gong ◽  
Weijian Ye ◽  
Tiankai Liu ◽  
Shaofen Jian ◽  
Wenhua Liu
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Overall Survival ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Methylation Array ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Potential Biomarker ◽  
And Gender

Aims. The prognosis of colorectal cancer (CRC) remains poor. This study aimed to develop and validate DNA methylation-based signature model to predict overall survival of CRC patients. Methods. The methylation array data of CRC patients were retrieved from The Cancer Genome Atlas (TCGA) database. These patients were divided into training and validation datasets. A risk score model was established based on Kaplan-Meier and multivariate Cox regression analysis of training cohort and tested in validation cohort. Results. Among total 14,626 DNA methylation candidate markers, we found that a three-DNA methylation signature (NR1H2, SCRIB, and UACA) was significantly associated with overall survival of CRC patients. Subgroup analysis indicated that this signature could predict overall survival of CRC patients regardless of age and gender. Conclusions. We established a prognostic model consisted of 3-DNA methylation sites, which could be used as potential biomarker to evaluate the prognosis of CRC patients.

scholarly journals Construction of a Novel Prognostic Immune-Related LncRNA Risk Model for Gastric Cancer

2021 ◽  
Author(s):  
Gen-hua Yang
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Cancer Genome Atlas ◽  
Score Model

Abstract Background and AimStudies have recently shown that immune-related lncRNAs play a vital role in the occurrence and development of human malignancies. However, the study in gastric cancer (GC) remains unclear. Here, we aimed to identify immune-related lncRNAs and construct a risk score model to predict the prognosis of GC patients.Methods:RNA expression data and clinical characteristics of GC were download from The Cancer Genome Atlas (TCGA) database. Immune genes were obtained from the Molecular Signatures Database (MSigDB). Immune-related lncRNAs were acquired by correlation coefficient between the immune genes and lncRNAs using “limma R” package and Cytoscape 3.6.1. The risk score model was constructed by univariate and multivariate Cox regression, and its prognostic value was verified in TCGA cohort. Results:A total of 146 immune-related lncRNAs were obtained compared 375 GC samples with 32 normal samples. A five immune-related lncRNA (AP001528.2, LINC02542, LINC02526, PVT1 and LINC01094) risk score model was constructed to predict prognosis of GC patients by Cox regression analysis. Moreover, GC patients with higher risk score had a poorer overall survival than that with lower risk score (P<0.001). Furthermore, ROC analysis revealed that the risk score model had the best predictive effect compared with clinicopathological features during 5 years followed-up (AUC = 0.679). Indeed, PCA analysis showed that the patients in the low- and high- group were significantly distinguished in different directions based on the risk score model. Conclusion:This study indicated that a five immune-related lncRNA risk score model possessed a satisfactory predictive prognosis, which might be potential prognostic biomarkers and immunotherapy targets for GC patients in future.

scholarly journals A 16-CpG-based Prognostic Signature for Colorectal Cancer

2020 ◽  
Author(s):  
Shuo Chen ◽  
Yan Wang ◽  
Lin Zhang ◽  
Mingyue Xu ◽  
Boxue Wang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Prediction Model ◽  
Risk Score ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Cancer Genome Atlas ◽  
Prognostic Prediction ◽  
Survival Risk ◽  
Differential Methylation Analysis

Abstract Background: To develop a CpG-based prognostic prediction model to provide survival risk prediction for colorectal cancer. Differential methylation analysis was performed on 309 colorectal cancer and 38 adjacent cancer specimens from the Cancer Genome Atlas (TCGA). Results: 2113 hypermethylation sites as well as 723 hypomethylation sites were screened out and 16 related CpG methylation loci were further identified. The risk score was calculated based on the methylation sites identified and utilized as an independent prognostic variable for multivariate Cox regression prediction model, which was further optimized by the independent prognostic factors (including stage and risk score). Conclusion: This study has identified several potential prognostic biomarkers and established a CpG-based prognostic prediction model for colorectal cancer, which provides a valuable reference for future clinical research.

Prognostic role of aberrant mTOR activation in patients with stage II and III colorectal cancer

2020 ◽  
Vol 14 (12) ◽  
pp. 1127-1137
Author(s):  
Tong-Tong Zhang ◽  
Yi-Qing Zhu ◽  
Hong-Qing Cai ◽  
Jun-Wen Zheng ◽  
Jia-Jie Hao ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Stage Ii ◽  
Poor Prognostic Factor ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Potential Biomarker ◽  
Risk Predictor

Aim: This study aimed to develop an effective risk predictor for patients with stage II and III colorectal cancer (CRC). Materials & methods: The prognostic value of p-mTOR (Ser2448) levels was analyzed using Kaplan–Meier survival analysis and Cox regression analysis. Results: The levels of p-mTOR were increased in CRC specimens and significantly correlated with poor prognosis in patients with stage II and III CRC. Notably, the p-mTOR level was an independent poor prognostic factor for disease-free survival and overall survival in stage II CRC. Conclusion: Aberrant mTOR activation was significantly associated with the risk of recurrence or death in patients with stage II and III CRC, thus this activated proteins that may serve as a potential biomarker for high-risk CRC.

scholarly journals Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sheng Zheng ◽  
Zizhen Zhang ◽  
Ning Ding ◽  
Jiawei Sun ◽  
Yifeng Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Efficiency ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

Identification of a lncRNA prognostic signature-related to stem cell index and its significance in colorectal cancer

2021 ◽  
Author(s):  
Xiao-Cheng Wang ◽  
Ya Liu ◽  
Fei-Wu Long ◽  
Liang-Ren Liu ◽  
Chuan-Wen Fan
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Prognostic Markers ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Free Survival ◽  
Cancer Genome Atlas ◽  
Bioinformatic Approaches ◽  
Cell Phenotypes ◽  
The Relationship

Background: The relationship between long noncoding RNAs (lncRNAs) and the mRNA stemness index (mRNAsi) in colorectal cancer (CRC) is still unclear. Materials & methods: The mRNAsi, mRNAsi-related lncRNAs and their clinical significance were analyzed by bioinformatic approaches in The Cancer Genome Atlas (TCGA)-COREAD dataset. Results: mRNAsi was negatively related to pathological features but positively related to overall survival and recurrence-free survival in CRC. A five mRNAsi-related lncRNAs prognostic signature was further developed and showed independent prognostic factors related to overall survival in CRC patients, due to the five mRNAsi-related lncRNAs involved in several pathways of the cancer stem cells and malignant cancer cell phenotypes. Conclusion: The present study highlights the potential roles of mRNAsi-related lncRNAs as alternative prognostic markers.

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