A clinical scoring system for survival prediction in advanced gastric cancer.

2020 ◽  
Vol 38 (4_suppl) ◽  
pp. 436-436
Author(s):  
Jinchul Kim ◽  
Ja Hyun Yeo ◽  
Jung Yong Hong ◽  
Seung Tae Kim ◽  
Se Hoon Park ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Advanced Gastric Cancer ◽  
Scoring System ◽  
Cox Regression ◽  
Discrimination Performance ◽  
Validation Dataset ◽  
Survival Prediction ◽  
Scoring Model ◽  
Neutrophil Lymphocyte Ratio

436 Background: We established a scoring system using easily approachable clinical characteristics at the timing of initiating palliative chemotherapy to achieve accurate overall survival prediction to first-line treatment consisting of fluoropyrimidines in patients with advanced gastric cancer. Methods: A total of 1,733 patients were included in the study. The dataset was split into a training (n=1156, 67%) and validation set (n=577, 33%). Top-ranked variables were identified using the Random Forest for Survival algorithm and analyzed into a Cox regression model, thereby constructing the scoring system for predicting overall survival of advanced gastric cancer. Results: Five variables were finally included in the scoring system: serum neutrophil-lymphocyte ratio, alkaline phosphatase, albumin level, performance status, and histologic differentiation. The scoring system determined four distinct risk groups in validation dataset with median overall survival of 17.1 month (95% confidence interval [CI] = 14.9 to 20.5 month), 12.9 month (95% CI = 11.4 to 14.6 month), 8.1 month (95% CI = 5.3 to 12.3 month), and 3.9 month (95% CI = 1.5 to 8.2 month), respectively. AUC to estimate discrimination performance of the scoring system was 66.1 for one-year overall survival. Conclusions: We developed a simple and clinically useful predictive scoring model in a relatively homogenous population who initiate fluoropyrimidine-containing chemotherapy in advanced gastric cancer. Generalized application of the scoring model will require additional independent validation. [Table: see text]

scholarly journals Clinical scoring system for the prediction of survival of patients with advanced gastric cancer

ESMO Open ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. e000670 ◽  
Author(s):  
Jinchul Kim ◽  
Jung Yong Hong ◽  
Seung Tae Kim ◽  
Se Hoon Park ◽  
Se Yong Jekal ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Advanced Gastric Cancer ◽  
Scoring System ◽  
Cox Regression ◽  
Laboratory Data ◽  
Discrimination Performance ◽  
Validation Dataset ◽  
Homogeneous Population ◽  
Scoring Model

ObjectiveIn this study, we established a risk scoring system using easily obtained clinical characteristics at the time of initiating palliative chemotherapy to predict accurate overall survival of patients with advanced gastric cancer after first-line treatment with fluoropyrimidine–platinum combination chemotherapy.MethodsA total of 1733 patients treated at the Samsung Medical Center, Korea were included in the study, and clinicopathological and laboratory data were retrospectively analysed. The dataset was split into a training set (n=1156, 67%) and a validation set (n=577, 33%). Top-ranked variables were identified using the random forest survival algorithm and integrated into a Cox regression model, thereby constructing the scoring system for predicting the overall survival of patients with advanced gastric cancer.ResultsThe following five variables were finally included in the scoring system: serum neutrophil–lymphocyte ratio, alkaline phosphatase level, albumin level, performance status and histologic differentiation. The scoring system determined four distinct risk groups in the validation dataset with median overall survival of 17.1 months (95% CI=14.9 to 20.5 months), 12.9 months (95% CI=11.4 to 14.6 months), 8.1 months (95% CI=5.3 to 12.3 months) and 3.9 months (95% CI=1.5 to 8.2 months), respectively. The area under the curve to estimate the discrimination performance of the scoring system was 66.1 considering 1 year overall survival.ConclusionsWe developed a simple and clinically useful predictive scoring model in a homogeneous population with advanced gastric cancer treated with fluoropyrimidine-containing and platinum-containing chemotherapy. However, additional independent validation will be required before the scoring model can be used commonly.

scholarly journals Integrated Analysis of lncRNA-Associated ceRNA Network Identifies Two lncRNA Signatures as a Prognostic Biomarker in Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shuyan Zhang ◽  
Shanshan Li ◽  
Jian-Lin Guo ◽  
Ningyi Li ◽  
Cai-Ning Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Proportional Hazards ◽  
Cox Regression ◽  
Cox Proportional Hazards ◽  
Integrated Analysis ◽  
Survival Prediction ◽  
Survival Prognosis ◽  
Cox Regression Analysis ◽  
Cerna Network

Background. Gastric cancer (GC) is a malignant tumour that originates in the gastric mucosal epithelium and is associated with high mortality rates worldwide. Long noncoding RNAs (lncRNAs) have been identified to play an important role in the development of various tumours, including GC. Yet, lncRNA biomarkers in a competing endogenous RNA network (ceRNA network) that are used to predict survival prognosis remain lacking. The aim of this study was to construct a ceRNA network and identify the lncRNA signature as prognostic factors for survival prediction. Methods. The lncRNAs with overall survival significance were used to construct the ceRNA network. Function enrichment, protein-protein interaction, and cluster analysis were performed for dysregulated mRNAs. Multivariate Cox proportional hazards regression was performed to screen the potential prognostic lncRNAs. RT-qPCR was used to measure the relative expression levels of lncRNAs in cell lines. CCK8 assay was used to assess the proliferation of GC cells transfected with sh-lncRNAs. Results. Differentially expressed genes were identified including 585 lncRNAs, 144 miRNAs, and 2794 mRNAs. The ceRNA network was constructed using 35 DElncRNAs associated with overall survival of GC patients. Functional analysis revealed that these dysregulated mRNAs were enriched in cancer-related pathways, including TGF-beta, Rap 1, calcium, and the cGMP-PKG signalling pathway. A multivariate Cox regression analysis and cumulative risk score suggested that two of those lncRNAs (LINC01644 and LINC01697) had significant prognostic value. Furthermore, the results indicate that LINC01644 and LINC01697 were upregulated in GC cells. Knockdown of LINC01644 or LINC01697 suppressed the proliferation of GC cells. Conclusions. The authors identified 2-lncRNA signature in ceRNA regulatory network as prognostic biomarkers for the prediction of GC patient survival and revealed that silencing LINC01644 or LINC01697 inhibited the proliferation of GC cells.

scholarly journals A signature of seven immune‐related genes predicts overall survival in male gastric cancer patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xin Xu ◽  
Yida Lu ◽  
Youliang Wu ◽  
Mingliang Wang ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Immune Related Genes

Abstract Background Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC. Methods RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT. Results A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment. Conclusions Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

scholarly journals Combined Neutrophil-to-Lymphocyte and Platelet-Volume-to-Platelet Ratio (NLR and PVPR Score) Represents a Novel Prognostic Factor in Advanced Gastric Cancer Patients

2021 ◽  
Vol 10 (17) ◽  
pp. 3902
Author(s):  
Kamil Konopka ◽  
Agnieszka Micek ◽  
Sebastian Ochenduszko ◽  
Joanna Streb ◽  
Paweł Potocki ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Advanced Gastric Cancer ◽  
Cancer Patients ◽  
Inflammatory Markers ◽  
Cox Regression ◽  
Risk Of Death ◽  
Immune System Activation ◽  
Increased Risk ◽  
Gastric Cancer Patients

Background: Chemotherapy is a cornerstone of treatment in advanced gastric cancer (GC) with a proven impact on overall survival, however, reliable predictive markers are missing. The role of various inflammatory markers has been tested in gastric cancer patients, but there is still no general consensus on their true clinical applicability. High neutrophil-to-lymphocyte (NLR) and low (medium)-platelets-volume-to-platelet ratio (PVPR) are known markers of unspecific immune system activation, correlating significantly with outcomes in advanced GC patients. Methods: Metastatic GC patients (N:155) treated with chemotherapy +/− trastuzumab were enrolled in this retrospective study. Pre-treatment NLR and PVPR, as well as other inflammatory markers were measured in peripheral blood. Univariate Cox regression was conducted to find markers with a significant impact on overall survival (OS) and progression-free survival (PFS). Spearman correlation and Cohen’s kappa was used to analyze multicollinearity. Multiple multivariable Cox regression models were built to study the combined impact of NLR and PVPR, as well as other known prognostic factors on OS. Results: Elevated NLR was significantly associated with increased risk of death (HR = 1.95; 95% CI: 1.17–3.24), and lower PVPR was significantly associated with improved outcomes (HR = 0.53; 95% CI: 0.32–0.90). A novel inflammatory marker, based on a combination of NLR and PVPR, allows for the classification of GC patients into three prognostic groups, characterized by median OS of 8.4 months (95% CI 5.8–11.1), 10.5 months (95% CI 8.8–12.1), and 15.9 months (95% CI 13.5–18.3). Conclusion: The NLR and PVPR score (elevated NLR and decreased PVPR) is a marker of detrimental outcome of advanced GC patients treated with chemotherapy.

scholarly journals A signature of seven immune-related genes predicts overall survival in male gastric cancer patients

2021 ◽  
Author(s):  
Xin Xu ◽  
Yida Lu ◽  
Youliang Wu ◽  
Mingliang Wang ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Immune Related Genes

Abstract Background: Gastric cancer (GC) has a high mortality rate and is one of the most fatal malignant tumours. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) associated with the prognosis of male GC.Method: RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analyses were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from the Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT.Results: A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified to be significantly associated with the overall survival (OS) of male GC patients. Survival analysis indicated that patients in the high-risk group exhibited a poor clinical outcome. The results of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had excellent predictive performance in both TCGA and validated cohorts. Besides, the results of tumour-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumour immune microenvironment.Conclusions: Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

A Simple Scoring Model Predicting the Outcome of COVID-19 Patients: Tanta COVID score

2021 ◽  
Vol 21 ◽  
Author(s):  
Mohammed Elhendawy ◽  
Ferial El-Kalla ◽  
Sherief Abd-Elsalam ◽  
Dalia ElSharawy ◽  
Shaimaa S Soliman ◽  
...  
Keyword(s):  
Scoring System ◽  
Model Building ◽  
Cox Regression ◽  
Laboratory Data ◽  
Discrimination Performance ◽  
University Hospital ◽  
Severe Illness ◽  
Health Providers ◽  
Scoring Model ◽  
Increased Risk

Background & Aim: COVID-19 is a worldwide pandemic with high rates of morbidity and mortality, and an uncertain prognosis leading to an increased risk of infection in health providers and limited hospital care capacities. In this study, we have proposed a predictive, interpretable prognosis scoring system with the use of readily obtained clinical, radiological and laboratory characteristics to accurately predict worsening of the condition and overall survival of patients with COVID -19. Methods: This is a single-center, observational, prospective, cohort study. A total of 347 patients infected with COVID-19 presenting to the Tanta university hospital, Egypt, were enrolled in the study, and clinical, radiological and laboratory data were analyzed. Top-ranked variables were identified and selected to be integrated into a Cox regression model, building the scoring system for accurate prediction of the prognosis of patients with COVID-19. Results: The six variables that were finally selected in the scoring system were lymphopenia, serum CRP, ferritin, D-Dimer, radiological CT lung findings and associated chronic debilitating disease. The scoring system discriminated risk groups with either mild disease or severe illness characterized by respiratory distress (and also those with hypoxia and in need for oxygen therapy or mechanical ventilation) or death. The area under the curve to estimate the discrimination performance of the scoring system was more than 90%. Conclusion: We proposed a simple and clinically useful predictive scoring model for COVID-19 patients. However, additional independent validation will be required before the scoring model can be used commonly.

scholarly journals A signature of seven immune-related genes predicts overall survival in male gastric cancer patients

2020 ◽  
Author(s):  
Xin Xu ◽  
Youliang Wu ◽  
Mingliang Wang ◽  
Yida Lu ◽  
Xiaodong Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
External Validation ◽  
Differentially Expressed ◽  
Survival Prediction ◽  
Cox Regression Analysis ◽  
Immune Related Genes

Abstract Background: Gastric cancer (GC) is one of the most fatal malignant tumors with a high mortality rate. Male sex has been proven as an independent risk factor for GC. This study aimed to identify immune-related genes (IRGs) for the prognosis of male GC.Method: RNA sequencing and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed IRGs between male GC and normal tissues were identified by integrated bioinformatics analysis. Univariate and multivariate Cox regression analysis were applied to screen survival-associated IRGs. Then, GC patients were separated into high- and low-risk groups based on the median risk score. Furthermore, a nomogram was constructed based on the TCGA dataset. The prognostic value of the risk signature model was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell’s concordance index and calibration curves. In addition, the gene expression dataset from Gene Expression Omnibus (GEO) was also downloaded for external validation. The relative proportions of 22 types of infiltrating immune cells in each male GC sample were evaluated using CIBERSORT.Results: A total of 276 differentially expressed IRGs were screened, including 189 up-regulated and 87 down-regulated genes. Subsequently, a seven-IRGs signature (LCN12, CCL21, RNASE2, CGB5, NRG4, AGTR1 and NPR3) was identified and showed a significant association with the overall survival (OS) of male GC patients. Survival analysis indicated that patients with high-risk group exhibited a poor clinical outcome. The result of multivariate analysis revealed that the risk score was an independent prognostic factor. The established nomogram could be used to evaluate the prognosis of individual male GC patients. Further analysis showed that the prognostic model had an excellent predictive performance in both TCGA and validated cohorts. Besides, the result of tumor-infiltrating immune cell analysis indicated that the seven-IRGs signature could reflect the status of the tumor immune microenvironment.Conclusions: Our study developed a novel seven-IRGs risk signature for individualized survival prediction of male GC patients.

A Novel Scoring System Based on Fibrinogen and the Neutrophil-Lymphocyte Ratio as a Predictor of Chemotherapy Response and Prognosis in Patients with Advanced Gastric Cancer

Oncology ◽  
2016 ◽  
Vol 90 (4) ◽  
pp. 186-192 ◽  
Author(s):  
Takaaki Arigami ◽  
Yoshikazu Uenosono ◽  
Sumiya Ishigami ◽  
Keishi Okubo ◽  
Takashi Kijima ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Advanced Gastric Cancer ◽  
Scoring System ◽  
Chemotherapy Response ◽  
Neutrophil Lymphocyte Ratio ◽  
Lymphocyte Ratio

scholarly journals A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jaeseung Shin ◽  
Joon Seok Lim ◽  
Yong-Min Huh ◽  
Jie-Hyun Kim ◽  
Woo Jin Hyung ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Advanced Gastric Cancer ◽  
Cox Regression ◽  
Characteristic Curve ◽  
External Validation ◽  
Locally Advanced ◽  
Clinical Model ◽  
Locally Advanced Gastric Cancer ◽  
Contrast Enhanced Ct ◽  
Preoperative Ct

AbstractThis study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P < 0.001 [internal validation]; 0.652, P = 0.010 [external validation]) and the merged model (0.719, P < 0.001; 0.651, P = 0.014) were significantly higher than those of the clinical model (0.616; 0.594). The radiomics-based model on preoperative CT images may improve RFS prediction and high-risk stratification in the preoperative setting of LAGC.

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