scholarly journals Exploring Spatial Trends and Influencing Factors for Gastric Cancer Based on Bayesian Statistics: A Case Study of Shanxi, China

2018 ◽  
Vol 15 (9) ◽  
pp. 1824 ◽  
Author(s):  
Gehong Zhang ◽  
Junming Li ◽  
Sijin Li ◽  
Yang Wang
Keyword(s):  
Gastric Cancer ◽  
Vegetation Cover ◽  
High Probability ◽  
Lasso Regression ◽  
Univariable Analysis ◽  
Bayesian Lasso ◽  
Pork Production ◽  
Geographic Factors ◽  
Spatial Trends ◽  
Per Capita

Gastric cancer (GC) is the fourth most common type of cancer and the second leading cause of cancer-related deaths worldwide. To detect the spatial trends of GC risk based on hospital-diagnosed patients, this study presented a selection probability model and integrated it into the Bayesian spatial statistical model. Then, the spatial pattern of GC risk in Shanxi Province in north central China was estimated. In addition, factors influencing GC were investigated mainly using the Bayesian Lasso model. The spatial variability of GC risk in Shanxi has the conspicuous feature of being ‘high in the south and low in the north’. The highest GC relative risk was 1.291 (95% highest posterior density: 0.789–4.002). The univariable analysis and Bayesian Lasso regression results showed that a diverse dietary structure and increased consumption of beef and cow milk were significantly (p ≤ 0.08) and in high probability (greater than 68%) negatively associated with GC risk. Pork production per capita has a positive correlation with GC risk. Moreover, four geographic factors, namely, temperature, terrain, vegetation cover, and precipitation, showed significant (i68%) negatively associated with GC risk. Pork production per capita has a positive correlation with GC risk. Moreover, four geographic factors, namely, temperature, terrain, vegetation cover, and precipitation, showed significant (p < 0.05) associations with GC risk based on univariable analysis, and associated with GC risks in high probability (greater than 60%) inferred from Bayesian Lasso regression model.

scholarly journals Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sheng Zheng ◽  
Zizhen Zhang ◽  
Ning Ding ◽  
Jiawei Sun ◽  
Yifeng Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Efficiency ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

scholarly journals Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hua Ma ◽  
Zhihui He ◽  
Jing Chen ◽  
Xu Zhang ◽  
Pingping Song
Keyword(s):  
Gastric Cancer ◽  
Protein Interactions ◽  
Clustering Algorithm ◽  
Cox Model ◽  
Topological Analysis ◽  
Lasso Regression ◽  
Ppi Network ◽  
Hub Genes ◽  
P53 Signaling ◽  
Analysis Methods

AbstractGastric cancer (GC) is one of the most common types of malignancy. Its potential molecular mechanism has not been clarified. In this study, we aimed to explore potential biomarkers and prognosis-related hub genes associated with GC. The gene chip dataset GSE79973 was downloaded from the GEO datasets and limma package was used to identify the differentially expressed genes (DEGs). A total of 1269 up-regulated and 330 down-regulated genes were identified. The protein-protein interactions (PPI) network of DEGs was constructed by STRING V11 database, and 11 hub genes were selected through intersection of 11 topological analysis methods of CytoHubba in Cytoscape plug-in. All the 11 selected hub genes were found in the module with the highest score from PPI network of all DEGs by the molecular complex detection (MCODE) clustering algorithm. In order to explore the role of the 11 hub genes, we performed GO function and KEGG pathway analysis for them and found that the genes were enriched in a variety of functions and pathways among which cellular senescence, cell cycle, viral carcinogenesis and p53 signaling pathway were the most associated with GC. Kaplan-Meier analysis revealed that 10 out of the 11 hub genes were related to the overall survival of GC patients. Further, seven of the 11 selected hub genes were verified significantly correlated with GC by uni- or multivariable Cox model and LASSO regression analysis including C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1. C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1 may serve as potential prognostic biomarkers and therapeutic targets for GC.

scholarly journals PREDICTIVE MODEL SELECTION CRITERIA FOR BAYESIAN LASSO REGRESSION

2015 ◽  
Vol 28 (1) ◽  
pp. 67-82 ◽  
Author(s):  
Shuichi Kawano ◽  
Ibuki Hoshina ◽  
Kaito Shimamura ◽  
Sadanori Konishi
Keyword(s):  
Model Selection ◽  
Predictive Model ◽  
Selection Criteria ◽  
Lasso Regression ◽  
Bayesian Lasso ◽  
Model Selection Criteria

scholarly journals Development of a Survival Model Based on Autophagy-Associated Genes for Predicting Prognosis of Gastric Cancer

2020 ◽  
Author(s):  
Wanli Yang ◽  
Lili Duan ◽  
Xinhui Zhao ◽  
Liaoran Niu ◽  
Yiding Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Prediction Model ◽  
Roc Curve ◽  
Survival Model ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Prognosis Prediction ◽  
Cellular Processes ◽  
Model Based

Abstract Background: Gastric cancer (GC) is one of lethal diseases worldwide. Autophagy-associated genes play a crucial role in the cellular processes of GC. Our study aimed to investigate and identify the prognostic potential of autophagy-associated genes signature in GC. Methods: RNA-seq and clinical information of GC and normal controls were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the Wilcoxon signed-rank test was used to pick out the differentially expressed autophagy-associated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the potential roles and mechanisms of autophagy-associated genes in GC. Cox proportional hazard regression analysis and Lasso regression analysis were carried out to identify the overall survival (OS) related autophagy-associated genes, which were then collected to construct a predictive model. Kaplan-Meier method and receiver operating characteristic (ROC) curve were utilized to validate the accuracy of this model. Finally, a clinical nomogram was established by combining the clinical factors and autophagy-associated genes signature. Results: A total of 28 differentially expressed autophagy-associated genes were identified. GO and KEGG analyses revealed that several important cellular processes and signaling pathways were correlated with these genes. Through Cox regression and Lasso regression analyses, we identified 4 OS-related autophagy-associated genes (GRID2, ATG4D, GABARAPL2, and CXCR4) and constructed a prognosis prediction model. GC Patients with high-risk had a worse OS than those in low-risk group (5-year OS, 27.7% vs 38.3%; P=9.524e-07). The area under the ROC curve (AUC) of the prediction model was 0.67. The nomogram was demonstrated to perform better for predicting 3-year and 5-year survival possibility for GC patients with a concordance index (C-index) of 0.70 (95% CI: 0.65-0.72). The calibration curves also presented good concordance between nomogram-predicted survival and actual survival. Conclusions: We constructed and evaluated a survival model based on the autophagy-associated genes for GC patients, which may improve the prognosis prediction in GC.

scholarly journals Integrated Analysis of Stemness-Related LncRNAs Helps Predict the Immunotherapy Responsiveness of Gastric Cancer Patients

2021 ◽  
Vol 9 ◽  
Author(s):  
Quan Jiang ◽  
Lingli Chen ◽  
Hao Chen ◽  
Zhaoqing Tang ◽  
Fenglin Liu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Independent Risk Factor ◽  
Critical Role ◽  
Tumor Biology ◽  
Integrated Analysis ◽  
Lasso Regression ◽  
Immune Microenvironment ◽  
Critical Feature ◽  
Non Coding Rnas ◽  
Gastric Cancer Patients

The immune microenvironment plays a critical role in tumor biology. As a critical feature of cancers, stemness is acknowledged as a contributor to the development of drug resistance in gastric cancers (GCs). Long non-coding RNAs (lncRNAs) have been revealed to participate in this process. In this study, we aimed to develop a stemness-related lncRNA signature (SRLncSig) with guiding significance for immunotherapy. Three cohorts (TCGA, Zhongshan, and IMvigor210) were enrolled for analysis. A list of stemness-related lncRNAs (SRlncRNAs) was collected by co-expression strategy under the threshold of coefficient value &gt;0.35 and p-value &lt; 0.05. Cox and Lasso regression analysis was further applied to find out the SRlncRNAs with prognosis-predictive value to establish the SRLncSig in the TCGA cohort. IPS and TIDE algorithms were further applied to predict the efficacy of SRLncSig in TCGA and Zhongshan cohorts. IMvigor210 was composed of patients with clinical outcomes of immunotherapy. The results indicated that SRLncSig not only was confirmed as an independent risk factor for GCs but also identified as a robust indicator for immunotherapy. The patient with a lower SRLncSig score was more likely to benefit from immunotherapy, and the results were highly consistent in three cohorts. In conclusion, our study not only could clarify the correlations between stemness and immunotherapy in GC patients but also provided a model to guide the applications of immunotherapy in clinical practice.

scholarly journals NF-κB-Related Metabolic Gene Signature Predicts the Prognosis and Immunotherapy Response in Gastric Cancer

2022 ◽  
Vol 2022 ◽  
pp. 1-30
Author(s):  
Qiuxiang Chen ◽  
Xiaojing Du ◽  
Sunkuan Hu ◽  
Qingke Huang
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Cancer Metabolism ◽  
Gene Signature ◽  
Sufficient Evidence ◽  
Consensus Clustering ◽  
Lasso Regression ◽  
Immune Infiltration ◽  
Related Risk ◽  
Cluster 2

Background. Sufficient evidence indicated the crucial role of NF-κB family played in gastric cancer (GC). The novel discovery that NF-κB could regulate cancer metabolism and immune evasion greatly increased its attraction in cancer research. However, the correlation among NF-κB, metabolism, and cancer immunity in GC still requires further improvement. Methods. TCGA, hTFtarget, and MSigDB databases were employed to identify NF-κB-related metabolic genes (NFMGs). Based on NFMGs, we used consensus clustering to divide GC patients into two subtypes. GSVA was employed to analyze the enriched pathway. ESTIMATE, CIBERSORT, ssGSEA, and MCPcounter algorithms were applied to evaluate immune infiltration in GC. The tumor immune dysfunction and exclusion (TIDE) algorithm was used to predict patients’ response to immunotherapy. We also established a NFMG-related risk score by using the LASSO regression model and assessed its efficacy in TCGA and GSE62254 datasets. Results. We used 27 NFMGs to conduct an unsupervised clustering on GC samples and classified them into two clusters. Cluster 1 was characterized by high active metabolism, tumor mutant burden, and microsatellite instability, while cluster 2 was featured with high immune infiltration. Compared to cluster 2, cluster 1 had a better prognosis and higher response to immunotherapy. In addition, we constructed a 12-NFMG (ADCY3, AHCY, CHDH, GUCY1A2, ITPA, MTHFD2, NRP1, POLA1, POLR1A, POLR3A, POLR3K, and SRM) risk score. Followed analysis indicated that this risk score acted as an effectively prognostic factor in GC. Conclusion. Our data suggested that GC subtypes classified by NFMGs may effectively guide prognosis and immunotherapy. Further study of these NFMGs will deepen our understanding of NF-κB-mediated cancer metabolism and immunity.

scholarly journals A Genomic-Clinicopathologic Nomogram for the Preoperative Prediction of Lymph Node Metastasis in Gastric Cancer

2020 ◽  
Author(s):  
Xin Zhong ◽  
Feichao Xuan ◽  
Yun Qian ◽  
Junhai Pan ◽  
Suihan Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Clinical Performance ◽  
Preoperative Evaluation ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Genomic Signature ◽  
Lasso Regression ◽  
Clinical Value ◽  
Therapy Strategy

Abstract Background: Preoperative evaluation of the lymph node (LN) state is of pivotal significance for therapy strategy decisions in gastric cancer (GC) patients. However, there is lack of noninvasive method that can identify such status preoperatively. We aimed at developing a genomic biosignature based model to forecast the possibility of LN metastasis in GC patients.Methods: We employed the RNA profile retrieving strategy and conducted RNA expression profiling in a large GC cohort (GSE62254, n = 300) from Gene Expression Ominus (GEO). In the exploratory stage, 300 GC patients from GSE62254 were involved and the differentially expressed RNAs (DERs) for LN-status were determined using R software. The GC samples in GSE62254 were randomly divided into a learning set (n = 210) and a verification set (n = 90). By performing the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signature was established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic effectiveness of this model was assessed, as well as the clinical performance subsequently assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERsResults: Based on this genomic signature, we established a nomogram which robustly distinguished LN status in the learning (AUC = 0.916, 95% CI 0.833–0.999) and verification sets (AUC = 0.775, 95% CI 0.647–0.903). DCA demonstrated the clinical value of this nomogram. Functional enrichment analysis of the DERs was conducted using bioinformatics methods which posited that these DERs were involved with several lymphangiogenesis-correlated cascades.Conclusions: Here, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be readily utilized to estimate the possibility of LN metastasis with good performance in GC. The uncovering of the DERs reveals the prospective biogenesis of LN metastasis in GC.

A note on accuracy of Bayesian LASSO regression in GWS

2011 ◽  
Vol 142 (1-3) ◽  
pp. 310-314 ◽  
Author(s):  
Fabyano Fonseca Silva ◽  
Luis Varona ◽  
Marcos Deon V. de Resende ◽  
Júlio Sílvio S. Bueno Filho ◽  
Guilherme J.M. Rosa ◽  
...  
Keyword(s):  
Lasso Regression ◽  
Bayesian Lasso

scholarly journals A genomic-clinicopathologic Nomogram for the preoperative prediction of lymph node metastasis in gastric cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xin Zhong ◽  
Feichao Xuan ◽  
Yun Qian ◽  
Junhai Pan ◽  
Suihan Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Lymph Node ◽  
Clinical Performance ◽  
Preoperative Evaluation ◽  
Functional Enrichment ◽  
Genomic Signature ◽  
Diagnostic Efficiency ◽  
Lasso Regression ◽  
Clinical Value ◽  
Therapeutic Decisions

Abstract Background Preoperative evaluation of lymph node (LN) state is of pivotal significance for informing therapeutic decisions in gastric cancer (GC) patients. However, there are no non-invasive methods that can be used to preoperatively identify such status. We aimed at developing a genomic biosignature based model to predict the possibility of LN metastasis in GC patients. Methods We used the RNA profile retrieving strategy and performed RNA expression profiling in a large GC cohort (GSE62254, n = 300) from Gene Expression Ominus (GEO). In the exploratory stage, 300 GC patients from GSE62254 were involved and the differentially expressed RNAs (DERs) for LN-status were determined using the R software. GC samples in GSE62254 were randomly allocated into a learning set (n = 210) and a verification set (n = 90). By using the Least absolute shrinkage and selection operator (LASSO) regression approach, a set of 23-RNA signatures were established and the signature based nomogram was subsequently built for distinguishing LN condition. The diagnostic efficiency, as well as the clinical performance of this model were assessed using the decision curve analysis (DCA). Metascape was used for bioinformatic analysis of the DERs. Results Based on the genomic signature, we established a nomogram that robustly distinguished LN status in the learning (AUC = 0.916, 95% CI 0.833–0.999) and verification sets (AUC = 0.775, 95% CI 0.647–0.903). DCA demonstrated the clinical value of this nomogram. Functional enrichment analysis of the DERs was performed using bioinformatics methods which revealed that these DERs were involved in several lymphangiogenesis-correlated cascades. Conclusions In this study, we present a genomic signature based nomogram that integrates the 23-RNA biosignature based scores and Lauren classification. This model can be utilized to estimate the probability of LN metastasis with good performance in GC. The functional analysis of the DERs reveals the prospective biogenesis of LN metastasis in GC.

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