The Midkine-related ceRNA Network is a Prognostic Marker of Colon Cancer With Perineural Invasion

2021 ◽  
Author(s):  
zhongyu wang ◽  
Zhibin Yang ◽  
xianshuo cheng ◽  
shaojun Fang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
Regulatory Network ◽  
Perineural Invasion ◽  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Joint Analysis ◽  
Cox Regression Analysis ◽  
Cerna Network

Abstract BackgroundPerineural invasion (PNI) is a prominent characteristic of multiple solid tumors and indicates poor prognosis[1, 2]. A growing body of evidence emphasizes the critical role of competitive endogenous RNA(ceRNA) regulatory networks in various human cancers. However, the complexity and behavior characteristics of the ceRNA network in colon cancer with perineural invasion were still unclear. In this study, we aimed to clarify a midkine(MDK)-related ceRNA regulatory network and identify potential prognostic markers associated with colon cancer with perineural invasion.Material and MethodsWe extract the expression profiles of three RNAs (long non-coding RNAs [lncRNAs], microRNAs [miRNAs], and mRNAs) from The Cancer Genome Atlas (TCGA) database. In order to construct the lncRNA-miRNA-mRNA triple regulatory network in colon cancer with perineural invasion, we performed joint analysis in the MDKhigh and MDKlow expression groups, as well as the colon cancer and paracancerous tissue groups. Kaplan-Meier method was used to plot the Overall survival (OS) curves, and the log-rank test was used for testing. Univariate and multivariate Cox regression analysis were used to determine OS-related characteristics.ResultsKCNQ1OT1-miR-454-3p/miR-204-5p-MAP1B ceRNA network related to the prognosis of colon cancer was obtained through bioinformatics analysis. Importantly, we determined the KCNQ1OT1/MAP1B axis in the ceRNA through correlation analysis, and through Cox regression analysis it seems to be a clinical prognostic model. ConclusionIn conclusion, the current study constructing a ceRNA based KCNQ1OT1/MAP1B axis may be a novel important prognostic factor for the diagnosis and prognosis of colon cancer with perineural invasion.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p<0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals Development of prognosis model for colon cancer based on autophagy-related genes

2020 ◽  
Author(s):  
Xu Wang ◽  
Yuanmin Xu ◽  
Ting Li ◽  
Bo Chen ◽  
Wenqi Yang
Keyword(s):  
Colon Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Patient Survival ◽  
Expression Profiles ◽  
Cox Regression Analysis ◽  
Risk Patients

Abstract Background: Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied.Methods: Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and AGRs related to overall patient survival were identified. Cox proportional-hazards models were used to investigate the association between ARG expression profiles and patient prognosis.Results: 20 ARGs were significantly associated with overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p<0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 3- and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians.Conclusion: The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

scholarly journals A novel ferroptosis related gene signature is associated with prognosis in patients with ovarian serous cystadenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhixiang Yu ◽  
Haiyan He ◽  
Yanan Chen ◽  
Qiuhe Ji ◽  
Min Sun
Keyword(s):  
Cox Regression ◽  
Expression Profiles ◽  
Critical Role ◽  
Gene Signature ◽  
Cancer Genome ◽  
Training Data ◽  
Hub Genes ◽  
Validation Data ◽  
Data Set ◽  
Cox Regression Analysis

AbstractOvarian cancer (OV) is a common type of carcinoma in females. Many studies have reported that ferroptosis is associated with the prognosis of OV patients. However, the mechanism by which this occurs is not well understood. We utilized Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) to identify ferroptosis-related genes in OV. In the present study, we applied Cox regression analysis to select hub genes and used the least absolute shrinkage and selection operator to construct a prognosis prediction model with mRNA expression profiles and clinical data from TCGA. A series of analyses for this signature was performed in TCGA. We then verified the identified signature using International Cancer Genome Consortium (ICGC) data. After a series of analyses, we identified six hub genes (DNAJB6, RB1, VIMP/ SELENOS, STEAP3, BACH1, and ALOX12) that were then used to construct a model using a training data set. The model was then tested using a validation data set and was found to have high sensitivity and specificity. The identified ferroptosis-related hub genes might play a critical role in the mechanism of OV development. The gene signature we identified may be useful for future clinical applications.

scholarly journals Prognostic Nomogram for Patients With Pancreatic Ductal Adenocarcinoma of Pancreatic Head After Pancreaticoduodenectomy

2021 ◽  
Vol 15 ◽  
pp. 117955492110241
Author(s):  
Hongkai Zhuang ◽  
Zixuan Zhou ◽  
Zuyi Ma ◽  
Shanzhou Huang ◽  
Yuanfeng Gong ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Perineural Invasion ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Pancreatic Head ◽  
Ductal Adenocarcinoma ◽  
R0 Resection ◽  
Cox Regression Analysis ◽  
Ajcc Stage

Background: The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) of pancreatic head remains poor, even after potentially curative R0 resection. The aim of this study was to develop an accurate model to predict patients’ prognosis for PDAC of pancreatic head following pancreaticoduodenectomy. Methods: We retrospectively reviewed 112 patients with PDAC of pancreatic head after pancreaticoduodenectomy in Guangdong Provincial People’s Hospital between 2014 and 2018. Results: Five prognostic factors were identified using univariate Cox regression analysis, including age, histologic grade, American Joint Committee on Cancer (AJCC) Stage 8th, total bilirubin (TBIL), CA19-9. Using all subset analysis and multivariate Cox regression analysis, we developed a nomogram consisted of age, AJCC Stage 8th, perineural invasion, TBIL, and CA19-9, which had higher C-indexes for OS (0.73) and RFS (0.69) compared with AJCC Stage 8th alone (OS: 0.66; RFS: 0.67). The area under the curve (AUC) values of the receiver operating characteristic (ROC) curve for the nomogram for OS and RFS were significantly higher than other single parameter, which are AJCC Stage 8th, age, perineural invasion, TBIL, and CA19-9. Importantly, our nomogram displayed higher C-index for OS than previous reported models, indicating a better predictive value of our model. Conclusions: A simple and practical nomogram for patient prognosis in PDAC of pancreatic head following pancreaticoduodenectomy was established, which shows satisfactory predictive efficacy and deserves further evaluation in the future.

scholarly journals Construction and Validation of a Metabolic Reprogramming Related Prognostic Model for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Xiaohong - Liu ◽  
Qian - Xu ◽  
Zi-Jing - Li ◽  
Bin - Xiong
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Metabolic Reprogramming ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract BackgroundMetabolic reprogramming is an important hallmark in the development of malignancies. Numerous metabolic genes have been demonstrated to participate in the progression of hepatocellular carcinoma (HCC). However, the prognostic significance of the metabolic genes in HCC remains elusive. MethodsWe downloaded the gene expression profiles and clinical information from the GEO, TCGA and ICGC databases. The differently expressed metabolic genes were identified by using Limma R package. Univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) Cox regression analysis were utilized to uncover the prognostic significance of metabolic genes. A metabolism-related prognostic model was constructed in TCGA cohort and validated in ICGC cohort. Furthermore, we constructed a nomogram to improve the accuracy of the prognostic model by using the multivariate Cox regression analysis.ResultsThe high-risk score predicted poor prognosis for HCC patients in the TCGA cohort, as confirmed in the ICGC cohort (P < 0.001). And in the multivariate Cox regression analysis, we observed that risk score could act as an independent prognostic factor for the TCGA cohort (HR (hazard ratio) 3.635, 95% CI (confidence interval)2.382-5.549) and the ICGC cohort (HR1.905, 95%CI 1.328-2.731). In addition, we constructed a nomogram for clinical use, which suggested a better prognostic model than risk score.ConclusionsOur study identified several metabolic genes with important prognostic value for HCC. These metabolic genes can influence the progression of HCC by regulating tumor biology and can also provide metabolic targets for the precise treatment of HCC.

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 The association between plasma furin and cardiovascular events after acute myocardial infarction

2020 ◽  
Author(s):  
Zhi-wei Liu ◽  
Qiang Ma ◽  
Jie Liu ◽  
Jing-Wei Li ◽  
Yun-Dai Chen
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Regression Analysis ◽  
Cox Regression ◽  
Critical Role ◽  
Cardiac Events ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Atheromatous Plaques ◽  
Key Enzyme

Abstract Background: Furin is the key enzyme to cleave pro-BNP and plays a critical role in the cardiovascular system through its involvement in the lipid metabolism, blood pressure and formation of atheromatous plaques. NT-proBNP and recently corin, which is also a key enzyme to cleave pro-BNP, have been approved as predictors of prognosis after acute myocardial infarction (AMI). We here conducted this cohort study to investigate the relationship between plasma furin and the prognosis outcome in patients after AMI. Methods: We enrolled 1100 AMI patients and measured their plasma furin concentration. The primary endpoint was the major adverse cardiac events (MACE), a composite of cardiovascular (CV) death, non-fatal myocardial infarction or non-fatal stroke. The association of plasma furin concentration with AMI outcomes was explored by using Kaplan–Meier curve and multivariate Cox regression analysis. Results: Our results showed that slight increase of mean cTNT in patients with higher furin concentration (P=0.016). Over a median follow-up of 31 months, multivariate Cox regression analysis suggested that plasma furin was not associated with MACE (HR: 1.01; 95% CI: 0.93-1.06; P=0.807) after adjustment for potential conventional risk factors. However, plasma furin was associated with non-fatal MI (HR: 1.09; 95% CI: 1.01-1.17; P=0.022) after fully adjustment. Subgroup analysis indicated no relationship between plasma furin and MACE in different subgroup populations.Conclusions: Our study demonstrated that plasma furin was not associated with risk of MACE and may not be used as a predictor of poor prognosis after AMI. But higher levels of plasma furin may be associated with higher risk of non-fatal MI.

scholarly journals Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

2020 ◽  
Author(s):  
Chengjian Ji ◽  
Yichun Wang ◽  
Liangyu Yao ◽  
Jiaochen Luan ◽  
Rong Cong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract Background Renal cell carcinoma (RCC) is one of the major malignant tumors of the urinary system, with a high mortality rate and a poor prognosis. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC. Although the diagnosis and treatment methods have been significantly improved, the incidence and mortality of ccRCC are high and still increasing. The occurrence and development of ccRCC are closely related to the changes of classic metabolic pathways. This article aims to explore the relationship between metabolic genes and the prognosis of patients with ccRCC. Patients and methods: Gene expression profiles of 63 normal kidney tissues and 446 ccRCC tissues from TCGA database and gene expression profiles of 39 ccRCC tissues from GEO database were used to obtain differentially expressed genes (DEGs) in ccRCC. Through the the KEGG gene sets of GSEA database, we obtained metabolic genes (MGs). Univariate Cox regression analysis was used to identify prognostic MGs. Lasso regression analysis was used to eliminate false positives because of over-fitting. Multivariate Cox regression analysis was used to established a prognostic model. Gene expression data and related survival data of 101 ccRCC patients from ArrayExpress database were used for external validation. Survival analysis, ROC curve analysis, independent prognostic analysis and clinical correlation analysis were performed to evaluate this model. Results We found that there were 479 abnormally expressed MGs in ccRCC tissues. Through univariate Cox regression analysis, Lasso regression analysis and multivariate Cox regression analysis, we identified 4 prognostic MGs (P4HA3, ETNK2, PAFAH2 and ALAD) and established a prognostic model (riskScore). Whether in the training cohort, the testing cohort or the entire cohort, this model could accurately stratify patients with different survival outcomes. The prognostic value of riskScore and 4 MGs was also confirmed in the ArrayExpress database. Results of GSEA analysis show that DEGs in patients with better prognosis were enriched in metabolic pathways. Then, a new Nomogram with higher prognostic value was constructed to better predict the 1-year OS, 3-year OS and 5-year OS of ccRCC patients. In addition, we successfully established a ceRNA network to further explain the differences in the expression of these MGs between high-risk patients and low-risk patients Conclusion We have successfully established a risk model (riskScore) based on 4 MGs, which could accurately predict the prognosis of patients with ccRCC. Our research may shed new light on ccRCC patients' prognosis and treatment management.

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