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

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

Frontiers in Genetics ◽

10.3389/fgene.2021.684349 ◽

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.

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

Technology in Cancer Research & Treatment ◽

10.1177/1533033820962120 ◽

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.

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Epigenetic signature predicts overall survival clear cell renal cell carcinoma

Cancer Cell International ◽

10.1186/s12935-020-01640-x ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Yejinpeng Wang ◽

Liang Chen ◽

Lingao Ju ◽

Kaiyu Qian ◽

Xinghuan Wang ◽

...

Keyword(s):

Dna Methylation ◽

Renal Cell Carcinoma ◽

Overall Survival ◽

Cell Carcinoma ◽

Renal Cell ◽

Cox Regression ◽

Clear Cell ◽

The Cancer Genome Atlas ◽

Cell Renal Cell Carcinoma ◽

Cox Regression Analysis

Abstract Background Recently, increasing study have found that DNA methylation plays an important role in tumor, including clear cell renal cell carcinoma (ccRCC). Methods We used the DNA methylation dataset of The Cancer Genome Atlas (TCGA) database to construct a 31-CpG-based signature which could accurately predict the overall survival of ccRCC. Meanwhile, we constructed a nomogram to predict the prognosis of patients with ccRCC. Result Through LASSO Cox regression analysis, we obtained the 31-CpG-based epigenetic signature which were significantly related to the prognosis of ccRCC. According to the epigenetic signature, patients were divided into two groups with high and low risk, and the predictive value of the epigenetic signature was verified by other two sets. In the training set, hazard ratio (HR) = 13.0, 95% confidence interval (CI) 8.0–21.2, P < 0.0001; testing set: HR = 4.1, CI 2.2–7.7, P < 0.0001; entire set: HR = 7.2, CI 4.9–10.6, P < 0.0001, Moreover, combined with clinical indicators, the prediction of 5-year survival of ccRCC reached an AUC of 0.871. Conclusions Our study constructed a 31-CpG-based epigenetic signature that could accurately predicted overall survival of ccRCC and staging progression of ccRCC. At the same time, we constructed a nomogram, which may facilitate the prediction of prognosis for patients with ccRCC.

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

10.21203/rs.3.rs-620279/v1 ◽

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.

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Development and Validation of a Robust Ferroptosis-Related Prognostic Signature to Predict Overall Survival in Clear Cell Renal Cell Carcinoma

10.21203/rs.3.rs-230598/v1 ◽

2021 ◽

Author(s):

Yen-ting Lin ◽

Can-Xuan Li ◽

Jie Chen

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Roc Curve ◽

Prognostic Value ◽

Cox Regression ◽

Expression Patterns ◽

The Cancer Genome Atlas ◽

Entire Group ◽

Cox Regression Analysis ◽

Kaplan Meier

Abstract Background: Ferroptosis is a novel defined type of programmed cell death (PCD) with widespread functions involved in physical conditions or multiple diseases including malignancies. However, the relationship between ccRCC and ferroptosis-related regulators remains poorly known. Herein, we investigate the prognostic values and potential mechanisms of ferroptosis-related genes (FRGs) in ccRCC.Methods: Ferroptosis-related genes were obtained from FerrDb database, GeneCards database and previously published literatures. The gene expression profile of ferroptosis-related regulators and corresponding clinicopathological information were downloaded from The Cancer Genome Atlas (TCGA). Differentially expressed ferroptosis-related genes (DE-FRGs) were screened between ccRCC specimens and noncancerous specimens. Among these genes, prognostic DE-FRGs were identified using univariate COX analysis and LASSO regression analysis. Further multivariate COX regression was employed to identify prognosis-related hub DE-FRGs and establish a prognostic model. Results: We identified seven hub genes (HMGCR, MT1G, BID, EIF4A1, FOXM1, TFAP2C and CHAC1) from the DE-FRGs using univariate Cox regression analysis, LASSO and multivariate Cox regression analysis, and used them to establish a novel clinical predictive model in the TCGA train cohort (n = 374). Subsequently, we assessed the prognostic value of the model. Survival analysis showed that high-risk patients had a reduced overall survival (OS), the time-dependent receiver operating characteristic (ROC) curve analysis confirmed the signature's diagnostic performance. Additionally, multivariate Cox regression analysis suggested that the risk score was an independent prognostic factor. Additionally, we verified the prognostic performance of the risk model in the testing cohort (n=156), and the entire group (n=530) using Kaplan-Meier curve and ROC curve analyses. Functional analysis indicated that several carcinogenic pathways were enriched, and tumor-infiltrating immune cell abundances, and the expression levels of immunosuppressive molecules were different between two risk groups. Finally, external databases (ONCMINE, GEPIA, HPA, Kaplan-Meier plotter and cbioportal) were used to confirm the expression patterns, prognostic value, and genetic mutations of 7 hub FRGs in ccRCC.Conclusions: Collectively, we successfully constructed a novel ferroptosis-related risk signature that was significantly associated with the prognosis of ccRCC.

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

BMC Gastroenterology ◽

10.1186/s12876-021-01734-4 ◽

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.

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Identification and validation of immunotypes for clear cell RCC prognosis

10.21203/rs.3.rs-847352/v1 ◽

2021 ◽

Author(s):

Zhaolin Yang ◽

Jiale Zhou ◽

Yizheng Xue ◽

Yu Zhang ◽

Kaijun Zhou ◽

...

Keyword(s):

Overall Survival ◽

Cox Regression ◽

Clear Cell ◽

Survival Rates ◽

Prognostic Index ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis ◽

Immune Genes ◽

Metastasis Model

Abstract Purpose To develop an immunotype-based prognostic model for predicting the overall survival (OS) of patients with clear cell renal carcinoma (ccRCC). We explored novel immunotypes of patients with ccRCC, particularly those associated with overall survival. A risk-metastasis model was constructed by integrating the immunotypes with immune genes and used to test the accuracy of the immunotype model. Patients and Methods Patient cohort data were obtained from The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, Renji database, and Surveillance, Epidemiology, and End Results (SEER) database. We employed the R software to select 3 immune cells and construct an immunotype-based prediction model. Immune genes selected using random Forest Algorithm were validated by immunohistochemistry (IHC). The H&L risk-metastasis model was constructed to assess the accuracy of the immunotype model through Multivariate COX regression analysis. Result Patients with ccRCC were categorized into immunotype H subgroup and immunotype L subgroup based on the overall survival rates. The immunotypes were found to be the independent prognostic index for ccRCC prognosis. As such, we constructed a new immunotypes-based SSIGN model. Three immune genes associated with difference between immunotype H and L were identified. An H&L risk-metastasis model was constructed to evaluate the accuracy of the immunotype model. Compared to the W-Risk-metastasis model which did not incorporate immunotypes, the H&L risk-metastasis model was more precise in predicting the survival of ccRCC patients. Conclusion The established immunotype model can effectively predict the survival of ccRCC patients. Except for mast cells, T cells and macrophages are positively associated with the overall survival of patients. The three immune genes identified, herein, can predict the survival rate of ccRCC patients, and expression of these immune genes is strongly linked to poor survival. The new SSIGN model provides an accurate tool for predicting the survival of ccRCC patients. H&L risk-metastasis model can effectively predict the risk of tumor metastasis.

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