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

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.

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

10.21203/rs.3.rs-29488/v1 ◽

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.

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SELE gene as a characteristic prognostic biomarker of colorectal cancer

Journal of International Medical Research ◽

10.1177/03000605211004386 ◽

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.

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

BioMed Research International ◽

10.1155/2020/3497810 ◽

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.

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

Biomarkers in Medicine ◽

10.2217/bmm-2020-0141 ◽

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.

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

10.21203/rs.3.rs-125323/v1 ◽

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.

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

Bioscience Reports ◽

10.1042/bsr20201427 ◽

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.

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Identification and Validation of a Prognostic 5-Protein Signature for Biochemical Recurrence Following Radical Prostatectomy for Prostate Cancer

Frontiers in Surgery ◽

10.3389/fsurg.2021.665115 ◽

2021 ◽

Vol 8 ◽

Author(s):

Daojun Lv ◽

Zanfeng Cao ◽

Wenjie Li ◽

Haige Zheng ◽

Xiangkun Wu ◽

...

Keyword(s):

Prostate Cancer ◽

Survival Analysis ◽

Biochemical Recurrence ◽

Cox Regression ◽

Characteristic Curve ◽

The Cancer Genome Atlas ◽

Medical Decision ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Protein Signature

Background: Biochemical recurrence (BCR) is an indicator of prostate cancer (PCa)-specific recurrence and mortality. However, there is a lack of an effective prediction model that can be used to predict prognosis and to determine the optimal method of treatment for patients with BCR. Hence, the aim of this study was to construct a protein-based nomogram that could predict BCR in PCa.Methods: Protein expression data of PCa patients was obtained from The Cancer Proteome Atlas (TCPA) database. Clinical data on the patients was downloaded from The Cancer Genome Atlas (TCGA) database. Lasso and Cox regression analyses were conducted to select the most significant prognostic proteins and formulate a protein signature that could predict BCR. Subsequently, Kaplan–Meier survival analysis and Cox regression analyses were conducted to evaluate the performance of the prognostic protein-based signature. Additionally, a nomogram was constructed using multivariate Cox regression analysis.Results: We constructed a 5-protein-based prognostic prediction signature that could be used to identify high-risk and low-risk groups of PCa patients. The survival analysis demonstrated that patients with a higher BCR showed significantly worse survival than those with a lower BCR (p < 0.0001). The time-dependent receiver operating characteristic curve showed that the signature had an 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). Univariate and multivariate analyses indicated that this 5-protein signature could be used as independent prognosis marker for PCa patients. Moreover, the concordance index (C-index) confirmed the predictive value of this 5-protein signature in 3, 5, and 10-year BCR overall survival (C-index: 0.764, 95% confidence interval: 0.701–0.827). Finally, we constructed a nomogram to predict BCR of PCa.Conclusions: Our study identified a 5-protein-based signature and constructed a nomogram that could reliably predict BCR. The findings might be of paramount importance for the prediction of PCa prognosis and medical decision-making.Subjects: Bioinformatics, oncology, urology.

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

Technology in Cancer Research & Treatment ◽

10.1177/1533033820963578 ◽

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.

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

Interactive Journal of Medical Research ◽

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.

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Development of a novel prognostic signature for predicting the overall survival of bladder cancer patients

Bioscience Reports ◽

10.1042/bsr20194432 ◽

2020 ◽

Vol 40 (6) ◽

Author(s):

Huamei Tang ◽

Lijuan Kan ◽

Tong Ou ◽

Dayang Chen ◽

Xiaowen Dou ◽

...

Keyword(s):

Dna Methylation ◽

Overall Survival ◽

Bladder Cancer ◽

Cancer Patients ◽

Cox Regression ◽

Test Group ◽

Training Group ◽

The Cancer Genome Atlas ◽

Methylation Site ◽

Cox Regression Analysis

Abstract Background: Bladder cancer is one of the most common malignancies. So far, no effective biomarker for bladder cancer prognosis has been identified. Aberrant DNA methylation is frequently observed in the bladder cancer and holds considerable promise as a biomarker for predicting the overall survival (OS) of patients. Materials and methods: We downloaded the DNA methylation and transcriptome data for bladder cancer from The Cancer Genome Atlas (TCGA), a public database, screened hypo-methylated and up-regulated genes, similarly, hyper-methylation with low expression genes, then retrieved the relevant methylation sites. Cox regression analysis was used to identify a nine-methylation site signature of a training group. Predictive ability was validated in a test group by receiver operating characteristic (ROC) analysis. Results: We identified nine bladder cancer-specific methylation sites as potential prognostic biomarkers and established a risk score system based on the methylation site signature to evaluate the OS. The performance of the signature was accurate, with area under curve was 0.73 in the training group and 0.71 in the test group. Taking clinical features into consideration, we constructed a nomogram consisting of the nine-methylation site signature and patients’ clinical variables, and found that the signature was an independent risk factor. Conclusions: Overall, the significant nine methylation sites could be novel prediction biomarkers, which could aid in treatment and also predict the overall survival likelihoods of bladder cancer patients.

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