scholarly journals SELE gene as a characteristic prognostic biomarker of colorectal cancer

2021 ◽  
Vol 49 (4) ◽  
pp. 030006052110043
Author(s):  
Na Li ◽  
Honghe Xiao ◽  
Jiangli Shen ◽  
Ximin Qiao ◽  
Fenjuan Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Expression Profiles ◽  
Immunohistochemical Analysis ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Clinical Value ◽  
Cox Regression Analysis

Objective To investigate the expression and clinical value of the E-selectin gene ( SELE) in colorectal cancer (CRC). Methods Using gene expression profiles and clinicopathological data for patients with CRC from The Cancer Genome Atlas, and tumor and adjacent normal tissues from 31 patients with CRC from Xianyang Central Hospital, we studied the correlation between SELE gene expression and clinical parameters using Kaplan–Meier and Cox proportional hazards regression analyses. Results Higher expression of SELE was significantly associated with a poorer prognosis and shorter survival in patients with CRC. The median expression level of SELE was significantly higher in CRC tissues compared with healthy adjacent tissue. Cox regression analysis showed that the prognosis of CRC was significantly correlated with the expression of SELE. Immunohistochemical analysis also showed that positive expression of E-selectin increased significantly in line with increasing TNM stage. Conclusion: This study confirmed that SELE gene expression is an independent prognostic factor in patients with CRC.

scholarly journals Construction of a Pseudogene‐associated ceRNA Network and Identification of Prognostic Pseudogene Biomarkers for Colorectal Cancer

2021 ◽  
Author(s):  
Zhuoqi Li ◽  
Jing Zhou ◽  
Liankun Gu ◽  
Baozhen Zhang
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Colon Adenocarcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cerna Network ◽  
Kaplan Meier

Abstract Colorectal cancer (CRC) is one of the most common and deadly malignant carcinomas. Many long noncoding RNAs (lncRNA) have been reported to play an important role in the tumorigenesis of CRC by interacting with miRNAs and influencing the expression of some mRNAs through a competing endogenous RNA (ceRNA) network. Pseudogenes are one kind of lncRNA and can act as RNA sponges for miRNAs and regulate gene expression via ceRNA networks, but there are few studies about pseudogenes in CRC. In this study, total of 31 differentially expressed (DE) pseudogenes, 17 DE miRNAs and 152 DE mRNAs were identified by analyzing the expression profiles of colon adenocarcinoma (COAD) obtained from The Cancer Genome Atlas (TCGA). And a ceRNA network was constructed based on these RNAs. Kaplan–Meier analysis showed that 7 pseudogenes, 4 miRNAs and 30 mRNAs were significantly associated with overall survival. Then multivariate Cox regression analysis on the ceRNA-related DE pseudogenes was performed and a 5-pseudogene signature with the greatest prognostic value for CRC was identified. What’s more, the results were validated by the Gene Expression Omnibus (GEO) database, and quantitative real‐time PCR (qRT‐PCR) in 113 pairs of CRC tissues. In conclusion, this study provides a pseudogene-associated ceRNA network and 7 prognostic pseudogene biomarkers, and a 5-pseudogene prognostic risk signature that may be useful to predict the survival of CRC patients.

scholarly journals Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Xin Qiu ◽  
Qin-Han Hou ◽  
Qiu-Yue Shi ◽  
Hai-Xing Jiang ◽  
Shan-Yu Qin
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Pancreatic Cancer ◽  
Cancer Progression ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Cox Regression Analysis

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

scholarly journals Effect of Health Care Provider Delays on Short-Term Outcomes in Patients With Colorectal Cancer: Multicenter Population-Based Observational Study

10.2196/15911 ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. e15911
Author(s):  
Ahmed Abdulaal ◽  
Chanpreet Arhi ◽  
Paul Ziprin
Keyword(s):  
Colorectal Cancer ◽  
Observational Study ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Diagnostic Delay ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Treatment Delays ◽  
Cox Regression Analysis ◽  
Kaplan Meier

Background The United Kingdom has lower survival figures for all types of cancers compared to many European countries despite similar national expenditures on health. This discrepancy may be linked to long diagnostic and treatment delays. Objective The aim of this study was to determine whether delays experienced by patients with colorectal cancer (CRC) affect their survival. Methods This observational study utilized the Somerset Cancer Register to identify patients with CRC who were diagnosed on the basis of positive histology findings. The effects of diagnostic and treatment delays and their subdivisions on outcomes were investigated using Cox proportional hazards regression. Kaplan-Meier plots were used to illustrate group differences. Results A total of 648 patients (375 males, 57.9% males) were included in this study. We found that neither diagnostic delay nor treatment delay had an effect on the overall survival in patients with CRC (χ23=1.5, P=.68; χ23=0.6, P=.90, respectively). Similarly, treatment delays did not affect the outcomes in patients with CRC (χ23=5.5, P=.14). The initial Cox regression analysis showed that patients with CRC who had short diagnostic delays were less likely to die than those experiencing long delays (hazard ratio 0.165, 95% CI 0.044-0.616; P=.007). However, this result was nonsignificant following sensitivity analysis. Conclusions Diagnostic and treatment delays had no effect on the survival of this cohort of patients with CRC. The utility of the 2-week wait referral system is therefore questioned. Timely screening with subsequent early referral and access to diagnostics may have a more beneficial effect.

scholarly journals A Prognostic Nomogram Combining Gene Expression Profiles and TNM Stage Predicts Survival in Gastric Cancer

2021 ◽  
Author(s):  
Jian Huang ◽  
Dongcun Wang ◽  
Xiaoliang Wang ◽  
Xiaoxing Ye ◽  
Jiping Da
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Proportional Hazards ◽  
Expression Profiles ◽  
Tnm Staging ◽  
Cox Proportional Hazards ◽  
Tnm Stage ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
High Morbidity

Abstract BackgroundGastric carcinoma (GC) is a highly aggressive malignancy and is associated with high morbidity and mortality rates around the world, the current tumor-node-metastasis (TNM) staging system is inadequate to predict overall survival (OS) in GC patients. therefore, potential forecasting methods for prognosis are important to investigate.MethodsDifferentially expressed genes (DEGs) were screened using gene expression data from The Cancer Genome Atlas (TCGA). We then construct a risk score signature model by univariate Cox proportional hazards regression (CPHR) analysis, the Kaplan-Meier method（KM）and multivariate CPHR analysis. Using TNM stage, we developed a signature-based nomogram. Finally, we utilize an independent Gene Expression Omnibus dataset (GSE62254) validate the prognostic value of risk score signature model and nomogram.ResultsWe identified five OS-related mRNAs among 1113 mRNAs that were differentially expressed between GC and normal samples in the TCGA dataset. We then constructed a five-mRNA signature model, which efficiently distinguished high-risk from low-risk patient in both cohort, and even viable in the TNM stage-III, gender(male, female) and age(＜65-year-old, ≥65-year-old) subgroups (P<0.05). Utilizing TNM stage, we developed a signature-based nomogram, which performed better than use the TNM stage or five-mRNA signature alone for prognostic prediction in the TCGA and GSE62254 dataset.ConclusionsThese results suggest that both risk signature and nomogram were effective prognostic indicators for patients with GCs, and could potentially be used for individualized management of such patients.

scholarly journals A six-gene-based signature for breast cancer radiotherapy sensitivity prediction

2020 ◽  
Author(s):  
Xing Chen ◽  
Junjie Zheng ◽  
Min ling Zhuo ◽  
Ailong Zhang ◽  
Zhenhui You
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Regression Analysis ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Breast Cancer Radiotherapy

Abstract Background: Breast cancer (BRCA) represents the most common malignancy among women worldwide that with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Methods: we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity prediction. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA tumor samples compared with their paracancerous samples in the training set were identified by using edgeR Bioconductor package followed by dimensionality reduction through autoencoder method and univariate Cox regression analysis to screen genes among DEGs that with significant prognosis significance in patients that were previously treated with radiation. LASSO Cox regression method was applied to screen optimal genes for constructing radiotherapy sensitivity prediction signature. Results: 603 DEGs were obtained in BRCA tumor samples, and seven out of which were retained after univariate cox regression analysis. LASSO Cox regression analysis finally remained six genes based on which the radiotherapy sensitivity prediction model was constructed. The signature was proved to be robust in both training and independent testing sets and an independent marker for BRCA radiotherapy sensitivity prediction. Conclusions: this study should be helpful for BRCA patients’ therapeutics selection and clinical decision.

scholarly journals Identification of a Novel Immune-Related CpG Methylation Signature to Predict Prognosis in Stage II/III Colorectal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Feng Chen ◽  
Lijuan Pei ◽  
Siyao Liu ◽  
Yan Lin ◽  
Xinyin Han ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Stage Ii ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Methylation Patterns

With the increasing incidence of colorectal cancer (CRC) and continued difficulty in treating it using immunotherapy, there is an urgent need to identify an effective immune-related biomarker associated with the survival and prognosis of patients with this disease. DNA methylation plays an essential role in maintaining cellular function, and changes in methylation patterns may contribute to the development of autoimmunity, aging, and cancer. In this study, we aimed to identify a novel immune-related methylated signature to aid in predicting the prognosis of patients with CRC. We investigated DNA methylation patterns in patients with stage II/III CRC using datasets from The cancer genome atlas (TCGA). Overall, 182 patients were randomly divided into training (n = 127) and test groups (n = 55). In the training group, five immune-related methylated CG sites (cg11621464, cg13565656, cg18976437, cg20505223, and cg20528583) were identified, and CG site-based risk scores were calculated using univariate Cox proportional hazards regression in patients with stage II/III CRC. Multivariate Cox regression analysis indicated that methylated signature was independent of other clinical parameters. The Kaplan–Meier analysis results showed that CG site-based risk scores could significantly help distinguish between high- and low-risk patients in both the training (P = 0.000296) and test groups (P = 0.022). The area under the receiver operating characteristic curve in the training and test groups were estimated to be 0.771 and 0.724, respectively, for prognosis prediction. Finally, stratified analysis results suggested the remarkable prognostic value of CG site-based risk scores in CRC subtypes. We identified five methylated CG sites that could be used as an efficient overall survival (OS)-related biomarker for stage II/III CRC patients.

scholarly journals Effect of Health Care Provider Delays on Short-Term Outcomes in Patients With Colorectal Cancer: Multicenter Population-Based Observational Study (Preprint)

2019 ◽  
Author(s):  
Ahmed Abdulaal ◽  
Chanpreet Arhi ◽  
Paul Ziprin
Keyword(s):  
Colorectal Cancer ◽  
Observational Study ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Diagnostic Delay ◽  
Population Based ◽  
Cox Proportional Hazards ◽  
Treatment Delays ◽  
Cox Regression Analysis ◽  
Kaplan Meier

BACKGROUND The United Kingdom has lower survival figures for all types of cancers compared to many European countries despite similar national expenditures on health. This discrepancy may be linked to long diagnostic and treatment delays. OBJECTIVE The aim of this study was to determine whether delays experienced by patients with colorectal cancer (CRC) affect their survival. METHODS This observational study utilized the Somerset Cancer Register to identify patients with CRC who were diagnosed on the basis of positive histology findings. The effects of diagnostic and treatment delays and their subdivisions on outcomes were investigated using Cox proportional hazards regression. Kaplan-Meier plots were used to illustrate group differences. RESULTS A total of 648 patients (375 males, 57.9% males) were included in this study. We found that neither diagnostic delay nor treatment delay had an effect on the overall survival in patients with CRC (χ<sup>2</sup><sub>3</sub>=1.5, <i>P</i>=.68; χ23=0.6, <i>P</i>=.90, respectively). Similarly, treatment delays did not affect the outcomes in patients with CRC (χ<sup>2</sup><sub>3</sub>=5.5, <i>P</i>=.14). The initial Cox regression analysis showed that patients with CRC who had short diagnostic delays were less likely to die than those experiencing long delays (hazard ratio 0.165, 95% CI 0.044-0.616; <i>P</i>=.007). However, this result was nonsignificant following sensitivity analysis. CONCLUSIONS Diagnostic and treatment delays had no effect on the survival of this cohort of patients with CRC. The utility of the 2-week wait referral system is therefore questioned. Timely screening with subsequent early referral and access to diagnostics may have a more beneficial effect.

scholarly journals The Prognostic Significance of PDE7B in Cytogenetically Normal Acute Myeloid Leukemia

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Ling Cao ◽  
Weilong Zhang ◽  
Xiaoni Liu ◽  
Ping Yang ◽  
Jing Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Acute Myeloid Leukemia ◽  
Myeloid Leukemia ◽  
Proportional Hazards ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Gene Expression Profiles ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Acute Myeloid

AbstractAcute myeloid leukemia (AML) is a malignant hematological disease in which nearly half have normal cytogenetics. We have tried to find some significant molecular markers for this part of the cytogenetic normal AML, which hopes to provide a benefit for the diagnosis, molecular typing and prognosis prediction of AML patients. In the present study, we calculated and compared the gene expression profiles of cytogenetically normal acute myeloid leukemia (CN-AML) patients in database of The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and dataset Vizome (a total of 632 CN-AML samples), and we have demonstrated a correlation between PDE7B gene and CN-AML. Then we proceeded to a survival analysis and prognostic risk analysis between the expression levels of PDE7B gene and CN-AML patients. The result showed that the event-free survival (EFS) and overall survival (OS) were significantly shorter in CN-AML patients with high PDE7B levels in each dataset. And we detected a significantly higher expression level of PDE7B in the leukemia stem cell (LSC) positive group. The Cox proportional hazards regression model showed that PDE7B is an independent risk predictor for CN-AML. All results indicate that PDE7B is an unfavorable prognostic factor for CN-AML.

scholarly journals A retrospective analysis of the role of proton pump inhibitors in colorectal cancer disease survival

2016 ◽  
Vol 23 (6) ◽  
pp. 583 ◽  
Author(s):  
C. Graham ◽  
C. Orr ◽  
C.S. Bricks ◽  
W.M. Hopman ◽  
N. Hammad ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Univariate Analysis ◽  
Retrospective Chart Review ◽  
Cox Proportional Hazards ◽  
Lymph Node Status ◽  
Cancer Disease ◽  
Cox Regression Analysis ◽  
Tumour Characteristics

BackgroundProton pump inhibitors (ppis) are a commonly used medication. A limited number of studies have identified a weak-to-moderate association between ppi use and colorectal cancer (crc) risk, but none to date have identified an effect of ppi use on crc survival. We therefore postulated that an association between ppi use and crc survival might potentially exist.Methods We performed a retrospective chart review of 1304 crc patients diagnosed from January 2005 to December 2011 and treated at the Cancer Centre of Southeastern Ontario. Kaplan–Meier analysis and Cox proportional hazards regression models were used to evaluate overall survival (os).Results We identified 117 patients (9.0%) who were taking ppis at the time of oncology consult. Those taking a ppi were also more often taking asa or statins (or both) and had a statistically significantly increased rate of cardiac disease. No identifiable difference in tumour characteristics was evident in the two groups, including tumour location, differentiation, lymph node status, and stage. Univariate analysis identified a statistically nonsignificant difference in survival, with those taking a ppi experiencing lesser 1-year (82.1% vs. 86.7%, p = 0.161), 2-year (70.1% vs. 76.8%, p = 0.111), and 5-year os (55.2% vs. 62.9%, p = 0.165). When controlling for patient demographics and tumour characteristics, multivariate Cox regression analysis identified a statistically significant effect of ppi in our patient population (hazard ratio: 1.343; 95% confidence interval: 1.011 to 1.785; p = 0.042).Conclusions Our results suggest a potential adverse effect of ppi use on os in crc patients. These results need further evaluation in prospective analyses.

Survival stratification for colorectal cancer via multi-omics integration using an autoencoder-based model

2021 ◽  
pp. 153537022110650
Author(s):  
Hu Song ◽  
Chengwei Ruan ◽  
Yixin Xu ◽  
Teng Xu ◽  
Ruizhi Fan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Target Genes ◽  
Proportional Hazards ◽  
Expression Profiles ◽  
Principal Component ◽  
Good Prognosis ◽  
Cox Proportional Hazards ◽  
The Cancer Genome Atlas ◽  
Prognosis Group

Prognosis stratification in colorectal cancer helps to address cancer heterogeneity and contributes to the improvement of tailored treatments for colorectal cancer patients. In this study, an autoencoder-based model was implemented to predict the prognosis of colorectal cancer via the integration of multi-omics data. DNA methylation, RNA-seq, and miRNA-seq data from The Cancer Genome Atlas (TCGA) database were integrated as input for the autoencoder, and 175 transformed features were produced. The survival-related features were used to cluster the samples using k-means clustering. The autoencoder-based strategy was compared to the principal component analysis (PCA)-, t-distributed random neighbor embedded (t-SNE)-, non-negative matrix factorization (NMF)-, or individual Cox proportional hazards (Cox-PH)-based strategies. Using the 175 transformed features, tumor samples were clustered into two groups (G1 and G2) with significantly different survival rates. The autoencoder-based strategy performed better at identifying survival-related features than the other transformation strategies. Further, the two survival groups were robustly validated using “hold-out” validation and five validation cohorts. Gene expression profiles, miRNA profiles, DNA methylation, and signaling pathway profiles varied from the poor prognosis group (G2) to the good prognosis group (G1). miRNA–mRNA networks were constructed using six differentially expressed miRNAs (let-7c, mir-34c, mir-133b, let-7e, mir-144, and mir-106a) and 19 predicted target genes. The autoencoder-based computational framework could distinguish good prognosis samples from bad prognosis samples and facilitate a better understanding of the molecular biology of colorectal cancer.

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