scholarly journals Five‑long non‑coding RNA risk score system for the effective prediction of gastric cancer patient survival

2019 ◽  
Author(s):  
Zunqi Hu ◽  
Dejun Yang ◽  
Yuan Tang ◽  
Xin Zhang ◽  
Ziran Wei ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Patient ◽  
Risk Score ◽  
Gastric Cancer Patient ◽  
Patient Survival ◽  
Score System ◽  
Non Coding Rna ◽  
Risk Score System ◽  
Long Non Coding Rna

PTK7 as a novel marker for favorable gastric cancer patient survival

2012 ◽  
Vol 106 (7) ◽  
pp. 880-886 ◽  
Author(s):  
Yi Lin ◽  
Lian-Hai Zhang ◽  
Xiao-Hong Wang ◽  
Xiao-Fang Xing ◽  
Xiao-Jing Cheng ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Patient ◽  
Gastric Cancer Patient ◽  
Patient Survival

scholarly journals A novel risk score system for prognostic evaluation in adenocarcinoma of the oesophagogastric junction: a large population study from the SEER database and our center

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jun Wang ◽  
Le Shi ◽  
Jing Chen ◽  
Beidi Wang ◽  
Jia Qi ◽  
...  
Keyword(s):  
Risk Score ◽  
Patient Survival ◽  
Risk Scores ◽  
Rank Test ◽  
Seer Database ◽  
Score System ◽  
Prognostic Evaluation ◽  
Log Rank Test ◽  
Oesophagogastric Junction ◽  
Risk Score System

Abstract Background The incidence rate of adenocarcinoma of the oesophagogastric junction (AEG) has significantly increased over the past decades, with a steady increase in morbidity. The aim of this study was to explore a variety of clinical factors to judge the survival outcomes of AEG patients. Methods We first obtained the clinical data of AEG patients from the Surveillance, Epidemiology, and End Results Program (SEER) database. Univariate and least absolute shrinkage and selection operator (LASSO) regression models were used to build a risk score system. Patient survival was analysed using the Kaplan-Meier method and the log-rank test. The specificity and sensitivity of the risk score were determined by receiver operating characteristic (ROC) curves. Finally, the internal validation set from the SEER database and external validation sets from our center were used to validate the prognostic power of this model. Results We identified a risk score system consisting of six clinical features that can be a good predictor of AEG patient survival. Patients with high risk scores had a significantly worse prognosis than those with low risk scores (log-rank test, P-value < 0.0001). Furthermore, the areas under ROC for 3-year and 5-year survival were 0.74 and 0.75, respectively. We also found that the benefits of chemotherapy and radiotherapy were limited to stage III/IV AEG patients in the high-risk group. Using the validation sets, our novel risk score system was proven to have strong prognostic value for AEG patients. Conclusions Our results may provide new insights into the prognostic evaluation of AEG.

scholarly journals A four prognosis-associated lncRNAs (PALnc) based risk score system reflects immune cell infiltration and predicts patient survival in pancreatic cancer

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hongkai Zhuang ◽  
Shanzhou Huang ◽  
Zixuan Zhou ◽  
Zuyi Ma ◽  
Zedan Zhang ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Multivariate Analysis ◽  
Signaling Pathway ◽  
Risk Score ◽  
Roc Analysis ◽  
Immune Cell ◽  
Patient Survival ◽  
Immune Cell Infiltration ◽  
Score System ◽  
Risk Score System

Abstract Background Pancreatic cancer (PC) is one of the most common cancers and the leading cause of cancer-related death worldwide. Exploring novel predictive biomarkers for PC patients’ prognosis is in urgent need. Methods In the present study, we conducted Cox proportional hazards regression to identify critical prognosis-associated lncRNAs (PALncs) in TCGA PC dataset. Based on the results of multivariate analysis, a PALnc-based risk score system was established, and validated in GSE62452 dataset. The validity and reliability of the risk score system for prognosis of PC were evaluated through ROC analysis. And function enrichment analyses for the PALncs were also performed. Result In the multivariate analysis, four PALncs (LINC00476, C9orf163, LINC00346 and DSCR9) were screened out to develop a risk score system, which showed a high AUC at 3 and 5 years overall survival (0.785 at 3 year OS, 0.863 at 5 year OS) in TCGA datasets. And the ROC analysis of the risk score system for RFS in TCGA dataset revealed that AUC for RFS was 0.799 at 3 years and 0.909 at 5 years. Further, the AUC for OS in the validation cohort was 0.705 at 3 years and 0.959 at 5 years. Furthermore, the functional enrichment analysis revealed that these PALncs may be involved in various pathways related to cancer, including Ras family activation, autophagy in cancer, MAPK signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. And correlation analysis of these tumor infiltrating immune cells and risk score system revealed that the infiltration level of B cell naïve, plasma cells, and CD8+ T cells are negatively correlated to the risk score system, while macrophages M2 positively correlated to the risk score system. Conclusion Our study established a four PALncs based risk score system, which reflects immune cell infiltration and predicts patient survival for PC.

scholarly journals Gross genomic damage measured by DNA image cytometry independently predicts gastric cancer patient survival

2009 ◽  
Vol 101 (6) ◽  
pp. 1011-1018 ◽  
Author(s):  
J A M Belien ◽  
T E Buffart ◽  
A J Gill ◽  
M A M Broeckaert ◽  
P Quirke ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Patient ◽  
Gastric Cancer Patient ◽  
Patient Survival ◽  
Image Cytometry ◽  
Genomic Damage ◽  
Dna Image Cytometry

scholarly journals Cholesterol import and steroidogenesis are biosignatures for gastric cancer patient survival

Oncotarget ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 692-704 ◽  
Author(s):  
Wei-Chun Chang ◽  
Shang-Fen Huang ◽  
Yang-Ming Lee ◽  
Hsueh-Chou Lai ◽  
Bi-Hua Cheng ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Patient ◽  
Gastric Cancer Patient ◽  
Patient Survival
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