scholarly journals Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

2020 ◽  
Author(s):  
Jieping Qiu ◽  
Mengyu Sun ◽  
Yaoqun Wang ◽  
Bo Chen
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Kegg Analysis ◽  
Prognosis Model ◽  
Gastric Cancer Patients

Abstract Background: The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer.Methods: GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscpae software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 222 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinical features and prognostic gene signatures, a nomogram was established to predict individual survival probability.Results: GO analysis showed that the 38 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 38 differently expressed autophagy-related genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573-2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients.Conclusions: This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.

scholarly journals Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

2020 ◽  
Author(s):  
Jieping Qiu ◽  
Mengyu Sun ◽  
Yaoqun Wang ◽  
Bo Chen
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cancer Patients ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Kegg Analysis ◽  
Prognosis Model ◽  
Gastric Cancer Patients

Abstract Background : The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer. Methods : GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscape software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 204 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinic-pathological features and prognostic gene signatures, a nomogram was established to predict individual survival probability. Results : GO analysis showed that the 28 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 28 differently expressed autophagy-related genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573-2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients. Conclusions: This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.

scholarly journals Identification and Validation of an Individualized Autophagy-Clinical Prognostic Index in Gastric Cancer Patients

2020 ◽  
Author(s):  
Qiu Jieping ◽  
Jieping Qiu ◽  
Mengyu Sun ◽  
Yaoqun Wang ◽  
Bo Chen
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cell Growth ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Kegg Analysis ◽  
Prognosis Model ◽  
Gastric Cancer Patients

Abstract Background: The purpose of this study is to perform bioinformatics analysis of autophagy-related genes in gastric cancer, and to construct a multi-gene joint signature for predicting the prognosis of gastric cancer.Methods: GO and KEGG analysis were applied for differentially expressed autophagy-related genes in gastric cancer, and PPI network was constructed in Cytoscpae software. In order to optimize the prognosis evaluation system of gastric cancer, we established a prognosis model integrating autophagy-related genes. We used single factor Cox proportional risk regression analysis to screen genes related to prognosis from 222 autophagy-related genes in The Atlas Cancer Genome (TCGA) gastric cancer cohort. Then, the generated genes were applied to the Least Absolute Shrinkage and Selection Operator (LASSO). Finally, the selected genes were further included in the multivariate Cox proportional hazard regression analysis to establish the prognosis model. According to the median risk score, patients were divided into high-risk group and low-risk group, and survival analysis was conducted to evaluate the prognostic value of risk score. Finally, by combining clinical features and prognostic gene signatures, a nomogram was established to predict individual survival probability.Results: GO analysis showed that the 38 differently expressed autophagy-related genes was enriched in cell growth, neuron death, and regulation of cell growth. KEGG analysis showed that the 38 differently expressed autophagy-related genes were related to platinum drug resistance, apoptosis and p53 signaling pathway. The risk score was constructed based on 4 genes (GRID2, ATG4D,GABARAPL2, CXCR4), and gastric cancer patients were significantly divided into high-risk and low-risk groups according to overall survival. In multivariate Cox regression analysis, risk score was still an independent prognostic factor (HR = 1.922, 95% CI = 1.573-2.349, P < 0.001). Cumulative curve showed that the survival time of patients with low-risk score was significantly longer than that of patients with high-risk score (P < 0.001). The external data GSE62254 proved that nomograph had a great ability to evaluate the prognosis of individual gastric cancer patients.Conclusions: This study provides a potential prognostic marker for predicting the prognosis of GC patients and the molecular biology of GC autophagy.

scholarly journals Development a Prognostic Model Integrating lncRNA/ mRNA novel Biomarkers Identified by Bioinformatics Analysis and Experiments in Breast Cancer

2021 ◽  
Author(s):  
Jinrong Wei ◽  
Qianshu Dou ◽  
Futing Ba ◽  
Guo-Qin Jiang
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Risk Score ◽  
Risk Group ◽  
Low Risk ◽  
Breast Cancers ◽  
Rt Pcr ◽  
Cerna Network ◽  
Prognosis Model

Abstract Purpose: The purpose of this study is to established a prognosis model based on the expression profiles of lncRNAs and mRNAs for breast cancers.Methods: Single Variable Cox Proportional Risk Regression analysis and difference analysis were applied to screen survival-related and differently expressed lncRNAs and mRNAs between tumor and normal tissues from TCGA data. GO and KEGG analysis were applied for top 30 survival-related genes. LncRNA/mRNA co-expressed network was constructed based on correlation analysis. LASSO analysis and Multivariate Stepwise Cox Regression analysis were applied to establish the prognosis model. RT-PCR experiments were applied to verify the correctness of the analysis results. Relative components of the TME in breast cancers with high and low risk groups were analysed by xCell and Cox proportional risk regression analysis. The ceRNA network was constructed by calculating the Pearson correlation coefficient (PCC) for miRNA-mRNA and miRNA-lncRNA using paired miRNA, mRNA, and lncRNA expression profile data.Results:Venn diagrams showed that there were 60 genes and 54 lncRNAs that were differently expressed and related with survival. Through lncRNA/mRNA co-expressed network construction, 19 lncRNA and 16 mRNA hub genes were gained. The genes were then included in LASSO and multivariate Cox proportional hazard regression analysis, and finally, 3 lncRNAs (LINC01497, LINC02766, LINC02528) and 2 mRNAs (C20orf85, CST1) were selected as prognosis predictive genes. According to the median risk score of the 5 candidates, patients were divided into high-risk group and low-risk group. The results of RT-PCR were consistent with the analysis results. The proportions of Adipocytes, Endothelial cells, HSCs, Fibroblasts were significantly lower in low risk score tissues compared with the high risk score tissues, while the proportions of M1 macrophages, MSCs, Th2 cells were significantly higher. A lncRNA-miRNA-mRNA ceRNA network containing 3 lncRNAs, 2 mRNAs, and 158 miRNAs was finally constructed, preliminarily revealed a proper mechanism of the 5 molecules playing important roles in breast cancer progression and prognosis prediction.Conclusion: We found that LINC01497, LINC02766, LINC02528 and C20orf85, CST1 may serve as a powerful prognostic tool to optimize the prognosis evaluation system of breast cancer.

scholarly journals A Novel Ferroptosis-Related Genes Model for Prognosis Prediction of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Fei Li ◽  
Dongcen Ge ◽  
Shu-lan Sun
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Prediction Model ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background. Ferroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. The aim of this study is to investigate the relationship between ferroptosis and the prognosis of lung adenocarcinoma (LUAD).Methods. RNA-seq data was collected from the LUAD dataset of The Cancer Genome Altas (TCGA) database. We used ferroptosis-related genes as the basis, and identify the differential expression genes (DEGs) between cancer and paracancer. The univariate Cox regression analysis were used to screen the prognostic-related genes. We divided the patients into training and validation sets. Then, we screened out key genes and built a 5 genes prognostic prediction model by the applications of the least absolute shrinkage and selection operator (LASSO) 10-fold cross-validation and the multi-variate Cox regression analysis. We divided the cases by the median value of risk score and validated this model in the validation set. Meanwhile, we analyzed the somatic mutations, and estimated the score of immune infiltration in the high- and low-risk groups, as well as performed functional enrichment analysis of DEGs.Results. The result revealed that the high-risk score triggered the worse prognosis. The maximum area under curve (AUC) of the training set and the validation set of in this study was 0.7 and 0.69. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of cases with survival time of 1, 3 and 5 years are 0.698, 0.71 and 0.73. In addition, the mutation frequency of patients in the high-risk group was higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results.Conclusion. This study constructed a novel LUAD prognosis prediction model base on 5 ferroptosis-related genes, which can provide a prognostic evaluation tool for the clinical therapeutic decision.

Palliative prognostic tools for the terminally ill patients with gastric cancer.

2014 ◽  
Vol 32 (31_suppl) ◽  
pp. 120-120 ◽  
Author(s):  
Beodeul Kang ◽  
Hye Jin Choi ◽  
Sun Young Rha
Keyword(s):  
Gastric Cancer ◽  
Survival Time ◽  
Cancer Patients ◽  
Risk Group ◽  
Terminally Ill ◽  
Risk Groups ◽  
Low Risk ◽  
Intermediate Risk ◽  
Poor Risk ◽  
Gastric Cancer Patients

120 Background: Terminally ill patients with gastric cancer have specific gastrointestional symptoms and signs related with cancer progression. To estimate accurate survival expectancy of gastric cancer patients is important for timely decision making of their end of life issues. Methods: We reviewed the 276 patients with terminally ill gastric cancer who were treated at Yonsei Cancer Center between January 2007 and December 2011 and eventually were died. Retrospectively, we conducted the data of clinical signs, symptoms, and laboratory results at the time of cessation of the active treatment. Then, we established the palliative survival estimation model by stratification of risk group. Results: Median palliative survival time from the decision to stop further treatment to death was 42days. In the multivariate Cox regression analysis, 5 parameters were identified as prognostically significant factors: anorexia, dyspnea, hypoalbuminemia, elevated blood urea nitrogen, and elevated serum alkaline phosphatase. We scored each variables as 1-3 for symptom(1:asymptomatic, 2:symptomatic, 3:symptomatic requiring intervention) and 1-2 for lab results(1:normal, 2:abnormal) and summed up each scores. Using the total score, patients were divided into 3 risk groups: low-risk(5-7points), intermediate-risk(8-11points), and poor-risk patients(12point). As a result, median palliative survival for low-risk group(n=110) was 87.0±7.4days, intermediate-risk group(n=158) and poor-risk group(n=8) were 31.0±2.1days and 6.0±2.1days, respectively (p<0.0001). Conclusions: Using multivariate analysis and summation of each prognostic factor score, 3 risk groups were determined. After validation by prospective multicenter trial, this palliative survival time estimation tool will be helpful to inform the accurate survival for terminally ill gastric cancer patients.

Different Pathological Features and Prognosis in Gastric Cancer Patients Coming From High-Risk and Low-Risk Areas of Italy

2009 ◽  
Vol 250 (1) ◽  
pp. 43-50 ◽  
Author(s):  
Daniele Marrelli ◽  
Corrado Pedrazzani ◽  
Giovanni Corso ◽  
Alessandro Neri ◽  
Marianna Di Martino ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Cancer Patients ◽  
Low Risk ◽  
Pathological Features ◽  
Risk Areas ◽  
Gastric Cancer Patients

scholarly journals Development and validation of Pyroptosis‑related lncRNAs prediction model for bladder cancer

2022 ◽  
Author(s):  
Thongher Lia ◽  
Yanxiang Shao ◽  
Parbatraj Regmi ◽  
Xiang Li
Keyword(s):  
Bladder Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis

Bladder cancer is one of the highly heterogeneous disorders accompanied by a poor prognosis. This study aimed to construct a model based on pyroptosis‑related lncRNA to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis‑related lncRNAs were identified by utilizing a co-expression network of Pyroptosis‑related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, 8 pyroptosis-related lncRNA markers were established using Lasso regression and multivariate Cox regression analysis. Patients were separated into high and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (p &lt; 0.001), and In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS ( HR&gt;1, P&lt;0.01). The area under the curve (AUC) of the 3- and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739 respectively. In conclusion, these 8 pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.

scholarly journals Development and Validation of a Robust Immune-Related Prognostic Signature for Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
M2 Macrophages ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Training Cohort

Background. An increasing number of reports have found that immune-related genes (IRGs) have a significant impact on the prognosis of a variety of cancers, but the prognostic value of IRGs in gastric cancer (GC) has not been fully elucidated. Methods. Univariate Cox regression analysis was adopted for the identification of prognostic IRGs in three independent cohorts (GSE62254, n = 300 ; GSE15459, n = 191 ; and GSE26901, n = 109 ). After obtaining the intersecting prognostic genes, the three independent cohorts were merged into a training cohort ( n = 600 ) to establish a prognostic model. The risk score was determined using multivariate Cox and LASSO regression analyses. Patients were classified into low-risk and high-risk groups according to the median risk score. The risk score performance was validated externally in the three independent cohorts (GSE26253, n = 432 ; GSE84437, n = 431 ; and TCGA, n = 336 ). Immune cell infiltration (ICI) was quantified by the CIBERSORT method. Results. A risk score comprising nine genes showed high accuracy for the prediction of the overall survival (OS) of patients with GC in the training cohort ( AUC > 0.7 ). The risk of death was found to have a positive correlation with the risk score. The univariate and multivariate Cox regression analyses revealed that the risk score was an independent indicator of the prognosis of patients with GC ( p < 0.001 ). External validation confirmed the universal applicability of the risk score. The low-risk group presented a lower infiltration level of M2 macrophages than the high-risk group ( p < 0.001 ), and the prognosis of patients with GC with a higher infiltration level of M2 macrophages was poor ( p = 0.011 ). According to clinical correlation analysis, compared with patients with the diffuse and mixed type of GC, those with the Lauren classification intestinal GC type had a significantly lower risk score ( p = 0.00085 ). The patients’ risk score increased with the progression of the clinicopathological stage. Conclusion. In this study, we constructed and validated a robust prognostic signature for GC, which may help improve the prognostic assessment system and treatment strategy for GC.

scholarly journals A Ferroptosis-Related lncRNAs Signature Predicts Prognosis and Therapeutic Response of Gastric Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Shilang Xiao ◽  
Xiaoming Liu ◽  
Lingzhi Yuan ◽  
Fen Wang
Keyword(s):  
Gastric Cancer ◽  
High Risk ◽  
Correlation Analysis ◽  
Risk Score ◽  
Therapeutic Response ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Biological Processes ◽  
Spearman Correlation

Background: Accumulating literature demonstrates that long noncoding RNAs (lncRNAs) are involved in ferroptosis and gastric cancer progression. However, the predictive value of ferroptosis-related lncRNAs for prognosis and therapeutic response is yet to be elucidated in gastric cancer (GC).Method: The transcriptomic data and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. The association between ferroptosis-related lncRNAs and ferroptosis regulators was analyzed by Spearman correlation analysis. Then, we established a risk predictive model based on the ferroptosis-related lncRNAs using multivariate Cox regression analysis. Furthermore, we performed correlation analysis for the risk score and characteristics of biological processes, immune landscape, stromal activity, genomic integrity, drug response, and immunotherapy efficacy.Results: We constructed a 17-ferroptosis-related-lncRNA signature via multivariate Cox analysis to divide patients into two groups: low- and high-risk groups. The low-risk group was linked to prolonged overall survival and relapse-free survival. The risk score had good predictive ability to predict the prognosis of GC patients compared with other clinical biomarkers. We found that the high-risk group was associated with activation of carcinogenetic signaling pathways, including stromal activation, epithelial-mesenchymal-transition (EMT) activation, and immune escape through integrated bioinformatics analysis. In contrast, the low-risk group was associated with DNA replication, immune-flamed state, and genomic instability. Additionally, through Spearman correlation analysis, we found that patients in the high-risk group may respond well to drugs targeting cytoskeleton, WNT signaling, and PI3K/mTOR signaling, and drugs targeting chromatin histone acetylation, cell cycle, and apoptosis regulation could bring more benefits for the low-risk group. The high-risk group was associated with poor immunotherapy efficacy.Conclusion: Our study systematically evaluated the role of ferroptosis-related lncRNAs in t tumor microenvironment, therapeutic response, and prognosis of GC. Risk score–based stratification could reflect the characteristic of biological processes, immune landscape, stromal activity, genomic stability, and pharmaceutical profile in GC patients. The ferroptosis-related lncRNA signature could serve as a reliable biomarker to predict prognosis and therapeutic response of patients with GC.

scholarly journals A signature of 24 aging‑related gene pairs predict overall survival in gastric cancer

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Yankai Zhang ◽  
Yichao Yan ◽  
Ning Ning ◽  
Zhanlong Shen ◽  
Yingjiang Ye
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Operating Characteristic ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Related Gene ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Aging is the major risk factor for most human cancers. We aim to develop and validate a reliable aging-related gene pair signature (ARGPs) to predict the prognosis of gastric cancer (GC) patients. Methods The mRNA expression data and clinical information were obtained from two public databases, The Cancer Genome Atlas (TCGA) dataset, and Gene Expression Omnibus (GEO) dataset, respectively. The best prognostic signature was established using Cox regression analysis (univariate and least absolute shrinkage and selection operator). The optimal cut-off value to distinguish between high- and low-risk patients was found by time-dependent receiver operating characteristic (ROC). The prognostic ability of the ARGPS was evaluated by a log‐rank test and a Cox proportional hazards regression model. Results The 24 ARGPs were constructed for GC prognosis. Using the optimal cut-off value − 0.270, all patients were stratified into high risk and low risk. In both TCGA and GEO cohorts, the results of Kaplan–Meier analysis showed that the high-risk group has a poor prognosis (P < 0.001, P = 0.002, respectively). Then, we conducted a subgroup analysis of age, gender, grade and stage, and reached the same conclusion. After adjusting for a variety of clinical and pathological factors, the results of multivariate COX regression analysis showed that the ARGPs is still an independent prognostic factor of OS (HR, 4.919; 95% CI 3.345–7.235; P < 0.001). In comparing with previous signature, the novel signature was superior, with an area under the receiver operating characteristic curve (AUC) value of 0.845 vs. 0.684 vs. 0.695. The results of immune infiltration analysis showed that the abundance of T cells follicular helper was significantly higher in the low-risk group, while the abundance of monocytes was the opposite. Finally, we identified and incorporated independent prognostic factors and developed a superior nomogram to predict the prognosis of GC patients. Conclusion Our study has developed a robust prognostic signature that can accurately predict the prognostic outcome of GC patients.

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