scholarly journals Recognition of DNA Methylation Molecular Features for Diagnosis and Prognosis in Gastric Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Donghui Liu ◽  
Long Li ◽  
Liru Wang ◽  
Chao Wang ◽  
Xiaowei Hu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Correlation Analysis ◽  
Prognostic Model ◽  
Tp53 Mutation ◽  
Risk Group ◽  
Tumor Location ◽  
Diagnostic Model ◽  
Mutation Group ◽  
Gastric Cancer Patients

Background: The management of gastric cancer (GC) still lacks tumor markers with high specificity and sensitivity. The goal of current research is to find effective diagnostic and prognostic markers and to clarify their related mechanisms.Methods: In this study, we integrated GC DNA methylation data from publicly available datasets obtained from TCGA and GEO databases, and applied random forest and LASSO analysis methods to screen reliable differential methylation sites (DMSs) for GC diagnosis. We constructed a diagnostic model of GC by logistic analysis and conducted verification and clinical correlation analysis. We screened credible prognostic DMSs through univariate Cox and LASSO analyses and verified a prognostic model of GC by multivariate Cox analysis. Independent prognostic and biological function analyses were performed for the prognostic risk score. We performed TP53 correlation analysis, mutation and prognosis analysis on eleven-DNA methylation driver gene (DMG), and constructed a multifactor regulatory network of key genes.Results: The five-DMS diagnostic model distinguished GC from normal samples, and diagnostic risk value was significantly correlated with grade and tumor location. The prediction accuracy of the eleven-DMS prognostic model was verified in both the training and validation datasets, indicating its certain potential for GC survival prediction. The survival rate of the high-risk group was significantly lower than that of the low-risk group. The prognostic risk score was an independent risk factor for the prognosis of GC, which was significantly correlated with N stage and tumor location, positively correlated with the VIM gene, and negatively correlated with the CDH1 gene. The expression of CHRNB2 decreased significantly in the TP53 mutation group of gastric cancer patients, and there were significant differences in CCDC69, RASSF2, CHRNB2, ARMC9, and RPN1 between the TP53 mutation group and the TP53 non-mutation group of gastric cancer patients. In addition, CEP290, UBXN8, KDM4A, RPN1 had high frequency mutations and the function of eleven-DMG mutation related genes in GC patients is widely enriched in multiple pathways.Conclusion: Combined, the five-DMS diagnostic and eleven-DMS prognostic GC models are important tools for accurate and individualized treatment. The study provides direction for exploring potential markers of GC.

scholarly journals Prognostic Autophagy-Related Genes of Gastric Cancer Patients on Chemotherapy

2021 ◽  
Author(s):  
xiaolong Liu ◽  
Zhen Ma ◽  
Lei Zhang ◽  
Yang Yu ◽  
Maswikiti Ewetse Paul ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Models ◽  
Risk Group ◽  
Chemotherapy Resistance ◽  
Estimation Method ◽  
High Risk Group ◽  
Chemotherapy Administration ◽  
Clinical Complications ◽  
Product Limit ◽  
Gastric Cancer Patients

Abstract Background Gastric cancer(GC) treated with fluorouracil and cisplatin can cause chemotherapy resistance, which is one of the most common postoperative clinical complications and leads to in poor prognosis. Methods The purpose of this study is to investigate the susceptibility of patients with GC after postoperative chemotherapy based on autophagy-related genes (ATGs). Under the background of TCGA database, for patients with GC undergoing and during chemotherapy,gene expression data was integrated and analyzed. Prognostic genes were screened based on univariate and various analysis regression models. Subjects were divided into two groups: high-risk group and low-risk group. Univariate and various analytical regression models were used to screen for prognostic genes. Median risk score was used for analysis. OS and DFS were evaluated by the product limit estimation method. Subject curve analysis is used to determine the accuracy of the forecast. We also have performed appropriate analysis and conducted some detailed assessments in our work. The differential expression of ATGs was mainly associated with chemotherapy resistance.Results After chemotherapy administration, we have screened 9 ATGs outcomes in the subjects and DFS and OS were precisely predicted by the model of GEO and TCGA databases.Conclusions 9 genes were established as prognostic markers to predict the relationship between ATGs and GC chemotherapy susceptibility, suggesting a better individualized treatment in clinical practice.

A Prognostic Model to Predict Clinical Outcome in Gastric Cancer Patients with Bone Metastasis

Oncology ◽  
2011 ◽  
Vol 80 (1-2) ◽  
pp. 142-150 ◽  
Author(s):  
Hyung Soon Park ◽  
Sun Young Rha ◽  
Hyo Song Kim ◽  
Woo Jin Hyung ◽  
Ji Soo Park ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Bone Metastasis ◽  
Clinical Outcome ◽  
Cancer Patients ◽  
Prognostic Model ◽  
Gastric Cancer Patients

scholarly journals A Prognostic Nomogram Model Based on mRNA Expression of DNA Methylation-Driven Genes for Gastric Cancer

2020 ◽  
Vol 10 ◽  
Author(s):  
Zuhua Chen ◽  
Bo Liu ◽  
Minxiao Yi ◽  
Hong Qiu ◽  
Xianglin Yuan
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Mrna Expression ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Dna Hypomethylation

PurposeThe exploration and interpretation of DNA methylation-driven genes might contribute to molecular classification, prognostic prediction and therapeutic choice. In this study, we built a prognostic risk model via integrating analysis of the transcriptome and methylation profile for patients with gastric cancer (GC).MethodsThe mRNA expression profiles, DNA methylation profiles and corresponding clinicopathological information of 415 GC patients were downloaded from The Cancer Genome Atlas (TCGA). Differential expression and correlation analysis were performed to identify DNA methylation-driven genes. The candidate genes were selected by univariate Cox regression analyses followed by the least absolute shrinkage and selection operator (LASSO) regression. A prognostic risk nomogram model was then built together with clinicopathological parameters.Results5 DNA methylation-driven genes (CXCL3, F5, GNAI1, GAMT and GHR) were identified by integrated analyses and selected to construct the prognostic risk model with clinicopathological parameters. High expression and low DNA hypermethylation of F5, GNAI1, GAMT and GHR, as well as low expression and high DNA hypomethylation of CXCL3 were significantly associated with poor prognosis rates, respectively. The high-risk group showed a significantly shorter prognosis than the low-risk group in the TCGA dataset (HR = 0.212, 95% CI = 0.139–0.322, P = 2e-15). The final nomogram model showed high predictive efficiency and consistency in the training and validation group.ConclusionWe construct and validate a prognostic nomogram model for GC based on five DNA methylation-driven genes with high performance and stability. This nomogram model might be a powerful tool for prognosis evaluation in the clinic and also provided novel insights into the epigenetics in GC.

VIKTORY trial: Report on AZD1775/paclitaxel in TP53 mutation (+) GC, selumetinib/paclitaxel in ras aberrant GC, AZD5363/paclitaxel in PIK3CA mt and biomarker negative, savolitinib/docetaxel in met (+), and vistusertib/paclitaxel in RICTOR(+) GC.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. 4024-4024 ◽  
Author(s):  
Jeeyun Lee ◽  
Seung Tae Kim ◽  
Peter G. Mortimer ◽  
Simon J Hollingsworth ◽  
Elizabeth A. Harrington ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Tp53 Mutation ◽  
Molecular Targets ◽  
Predictive Biomarker ◽  
Metastatic Gastric Cancer ◽  
Molecular Signatures ◽  
Molecular Screening ◽  
Study Protocols ◽  
Gastric Cancer Patients ◽  
Clinical Trial Information

4024 Background: The VIKTORY trial is a biomarker-based umbrella trial in GC. Methods: See table below. Results: From June 2014 to Jan 2017, 432 metastatic gastric cancer patients were enrolled. 124 (28.7%) were treated on one of the associated study protocols. At January 2017, 25 pts were allocated to selumetinib/paclitaxel arm, 25 to AZD1775/paclitaxel arm, 16 to AZD5363/paclitaxel arm, 16 to vistusertib/paclitaxel arm, 4 to savolitinib monotherapy, 19 to savolitinib/docetaxel arm, 19 to phase I AZD6738/paclitaxel arm. Initial efficacy signals have been seen in several arms (selumetinib/paclitaxel, 6 of 21 evaluable patients in PR). Correlative analyses between molecular signatures and treatment response are ongoing and will be presented at the meeting. For vistusertib/paclitaxel in the biomarker negative arm, we found RICTOR amplification as a promising predictive biomarker for response. Two (of three) GC patients with RICTOR amplification achieved PR to vistusertib/paclitaxel. Conclusions: This is one of the first attempts to undertake a biomarker-driven trial in metastatic GC. 28.7% of the patients were guided to one of the parallel arms based on molecular screening outcomes. We were able to identify potential molecular targets in the biomarker-negative arm, for further assessment in new protocols. Clinical trial information: 02299648. [Table: see text]

scholarly journals DNA methylation of microRNA genes in gastric mucosae of gastric cancer patients: Its possible involvement in the formation of epigenetic field defect

2009 ◽  
Vol 124 (10) ◽  
pp. 2367-2374 ◽  
Author(s):  
Takayuki Ando ◽  
Takeichi Yoshida ◽  
Shotaro Enomoto ◽  
Kiyoshi Asada ◽  
Masae Tatematsu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Dna Methylation ◽  
Cancer Patients ◽  
Gastric Cancer Patients ◽  
Microrna Genes ◽  
Field Defect

scholarly journals Cyclin D1 and Clinicopathological Characteristics of Gastric Cancer

2021 ◽  
Author(s):  
Rahmat Cahyanur ◽  
Amanda Pitarini Utari ◽  
Nur Rahadiani
Keyword(s):  
Gastric Cancer ◽  
Cyclin D1 ◽  
Demographic Data ◽  
Tumor Location ◽  
Lymph Node Involvement ◽  
Cross Sectional Study ◽  
Clinicopathological Characteristics ◽  
Cancer Cell Growth ◽  
Cross Sectional ◽  
Gastric Cancer Patients

Abstract Gastric cancer is found at a rate of 2.4 to 3.5 percent in Indonesia, with the majority of cases discovered at an advanced stage. Cyclin D1 is a protein that promotes cancer cell growth. It has been shown to be expressed in a variety of cancers, including stomach cancer. A recent study of cyclin D1 in gastric cancer has been associated with lymph node involvement, metastasis, poor prognosis, and lack of response to platinum chemotherapy. This study aims to determine the relationship between cyclin D1 expression and clinicopathological features in gastric cancer. This cross-sectional study used medical records and paraffin blocks of gastric cancer patients at Cipto Mangunkusumo General Hospital, Jakarta, in 2015–2020. Demographic data, clinical characteristics, radiological findings, histopathological features, and cyclin D1 expression were collected and examined. Data was collected from 39 subjects. Most of the subjects experienced eating disorders (69.23%), weight loss (76.92%), melena (53.85%), and anemia (51.28%). Tumor locations were mostly found in the cardia and corpus of the gaster. Overexpression of cyclin D1 was found in 30.77% of cases. Cyclin D1 expression was greater in subjects with liver metastases (50% vs. 14.8%, p 0.04). Cyclin D1 expression was not associated with tumor location, TNM stage, and histopathological findings.

scholarly journals Construction on of a Ferroptosis-Related lncRNA-Based Model to Improve the Prognostic Evaluation of Gastric Cancer Patients Based on Bioinformatics

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiahui Pan ◽  
Xinyue Zhang ◽  
Xuedong Fang ◽  
Zhuoyuan Xin
Keyword(s):  
Gastric Cancer ◽  
Cancer Patients ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Survival Curve ◽  
Curve Analysis ◽  
Roc Curve Analysis ◽  
Cox Regression Analysis ◽  
Gastric Cancer Patients

BackgroundGastric cancer is one of the most serious gastrointestinal malignancies with bad prognosis. Ferroptosis is an iron-dependent form of programmed cell death, which may affect the prognosis of gastric cancer patients. Long non-coding RNAs (lncRNAs) can affect the prognosis of cancer through regulating the ferroptosis process, which could be potential overall survival (OS) prediction factors for gastric cancer.MethodsFerroptosis-related lncRNA expression profiles and the clinicopathological and OS information were collected from The Cancer Genome Atlas (TCGA) and the FerrDb database. The differentially expressed ferroptosis-related lncRNAs were screened with the DESeq2 method. Through co-expression analysis and functional annotation, we then identified the associations between ferroptosis-related lncRNAs and the OS rates for gastric cancer patients. Using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a prognostic model based on 17 ferroptosis-related lncRNAs. We also evaluated the prognostic power of this model using Kaplan–Meier (K-M) survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA).ResultsA ferroptosis-related “lncRNA–mRNA” co-expression network was constructed. Functional annotation revealed that the FOXO and HIF-1 signaling pathways were dysregulated, which might control the prognosis of gastric cancer patients. Then, a ferroptosis-related gastric cancer prognostic signature model including 17 lncRNAs was constructed. Based on the RiskScore calculated using this model, the patients were divided into a High-Risk group and a low-risk group. The K-M survival curve analysis revealed that the higher the RiskScore, the worse is the obtained prognosis. The ROC curve analysis showed that the area under the ROC curve (AUC) of our model is 0.751, which was better than those of other published models. The multivariate Cox regression analysis results showed that the lncRNA signature is an independent risk factor for the OS rates. Finally, using nomogram and DCA, we also observed a preferable clinical practicality potential for prognosis prediction of gastric cancer patients.ConclusionOur prognostic signature model based on 17 ferroptosis-related lncRNAs may improve the overall survival prediction in gastric cancer.

scholarly journals nPTD Classification: An Updated Classification of Gastric Cancer Location for Function Preserving Gastrectomy Based on Physiological Lymphatic Flow

2021 ◽  
Author(s):  
Shinichi Kinami ◽  
Naohiko Nakamura ◽  
Tomoharu Miyashita ◽  
Hidekazu Kitakata ◽  
Sachio Fushida ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Proximal Gastrectomy ◽  
Flow Distribution ◽  
Muscle Layer ◽  
Tumor Location ◽  
Lymphatic Flow ◽  
Greater Curvature ◽  
Gastric Cancer Patients ◽  
The Right ◽  
Lesser Curvature

Abstract Background: The correlation between tumor location and lymphatic flow distribution in gastric cancer has been previously reported, and PTD (Proximal – Transitional – Distal) classification, proposed. We updated and developed the nPTD classification.Method: We retrospectively studied gastric cancer patients who underwent the dye method sentinel node biopsy from 1993 to 2020. The inclusion criteria were a single lesion type 0 cancer of ≤5 cm in the long axis, clinically node-negative, and invasion within the proper muscle layer pathologically. In this study, the distribution of dyed lymphatic flow was evaluated for each occupied area of the tumor.Results: We selected 416. The tumors located watershed of the right and left gastroepiploic artery near greater curvature had extensive lymphatic flow; therefore, a newly circular region with a diameter of 5 cm is set on the watershed on greater curvature between P and T zone as the ‘n’ zone. In addition, for cancers located in the lesser P curvature, lymphatic flow to the greater curvature was not observed. Therefore, the P zone is divided into two: the lesser curvature side (PL) and the greater curvature side (PG).Conclusions: The advantage of the nPTD classification is that it provides not only proper nodal dissection, but also adequate function-preserving gastrectomy. If the tumor is localized within the PL, the proximal gastrectomy resection area can be further reduced. In contrast, for cancers located in the ‘n’ zone, near-total gastrectomy is required because of the extensive lymphatic flow.

scholarly journals Novel Prognostic Biomarkers in Gastric Cancer: CGB5, MKNK2, and PAPPA2

2021 ◽  
Vol 11 ◽  
Author(s):  
Min Qin ◽  
Zhihai Liang ◽  
Heping Qin ◽  
Yifang Huo ◽  
Qing Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Regression Analysis ◽  
Immune Cells ◽  
Prognostic Model ◽  
Prognostic Markers ◽  
Immune Cell ◽  
Risk Group ◽  
P Value ◽  
Expression Data

IntroductionGastric cancer is one of the most common malignant tumors of the digestive tract. However, there are no adequate prognostic markers available for this disease. The present study used bioinformatics to identify prognostic markers for gastric cancer that would guide the clinical diagnosis and treatment of this disease.Materials and MethodsGene expression data and clinical information of gastric cancer patients along with the gene expression data of 30 healthy samples were downloaded from the TCGA database. The initial screening was performed using the WGCNA method combined with the analysis of differentially expressed genes, which was followed by univariate analysis, multivariate COX regression analysis, and Lasso regression analysis for screening the candidate genes and constructing a prognostic model for gastric cancer. Subsequently, immune cell typing was performed using CIBERSORT to analyze the expression of immune cells in each sample. Finally, we performed laboratory validation of the results of our analyses using immunohistochemical analysis.ResultsAfter five screenings, it was revealed that only three genes fulfilled all the screening requirements. The survival curves generated by the prognostic model revealed that the survival rate of the patients in the high-risk group was significantly lower compared to the patients in the low-risk group (P-value < 0.001). The immune cell component analysis revealed that the three genes were differentially associated with the corresponding immune cells (P-value < 0.05). The results of immunohistochemistry also support our analysis.ConclusionCGB5, MKNK2, and PAPPA2 may be used as novel prognostic biomarkers for gastric cancer.

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.

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