A signature of Ten Chromodomain Helicase DNA (CHD) Genes Related LncRNAs Predicts Overall Survival in Gastric Cancer Patients

2021 ◽  
Author(s):  
xiaodong Wang ◽  
yaxian Li ◽  
yida Lu ◽  
Yongxiang Li
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Mutations ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Limma Package ◽  
Prognosis Model ◽  
Gastric Cancer Patients

Abstract Background: Gastric cancer (GC) is one of the most common tumors in the world and is often found at an advanced stage. Gene mutations and changes in lncRNA expression often led to the occurrence of GC. In this study, we explored the function of Chromodomain Helicase DNA (CHD) genes and a multi-gene prognostic model was established for its co-expressed lncRNA.Methods: We used the "Limma" package of R software to analyze the expression differences of CHDs genes. Use Kaplan-Meier curve to plot the relationship between risk score and patient survival time. Receiver operating characteristic (ROC) curve was used to describe the reliability of the model. The data used is obtained from The Cancer Genome Atlas (TCGA) database and CellMiner database. Results: We found that CHDs gene mutations had significant statistical differences with tumor mutation load and were involved in biological functions such as DNA winding and transcription. 10 CHDs gene-related lncRNAs were used to establish a prognosis model for GC patients. Univariate and multivariate Cox regression analysis proved that risk score could be an independent prognostic factor. Conclusion: In our study, we developed a novel prognostic model using 10 CHDs-related lncRNAs to predict individualized survival in patients with GC.

scholarly journals Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden

2021 ◽  
Vol 27 ◽  
Author(s):  
Weijun Ma ◽  
Weidong Li ◽  
Lei Xu ◽  
Lu Liu ◽  
Yu Xia ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Survival Outcomes ◽  
Activated T Cells ◽  
Single Nucleotide ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Surgical Methods

Introduction: Gastric cancer is one of the most common cancers. Although some progress has been made in the treatment of gastric cancer with the improvement of surgical methods and the application of immunotherapy, the prognosis of gastric cancer patients is still unsatisfactory. In recent years, there has been increasing evidence that tumor mutational load (TMB) is strongly associated with survival outcomes and response to immunotherapy. Given the variable response of patients to immunotherapy, it is important to investigate clinical significance of TMB and explore appropriate biomarkers of prognosis in patients with gastric cancer (GC).Material and Methods: All data of patients with gastric cancer were obtained from the database of The Cancer Genome Atlas (TCGA). Samples were divided into two groups based on median TMB. Differently expressed genes (DEGs) between the high- and low-TMB groups were identified and further analyzed. We identified TMB-related genes using Lasso, univariate and multivariate Cox regression analysis and validated the survival result of 11 hub genes using Kaplan-Meier Plotter. In addition, “CIBERSORT” package was utilized to estimate the immune infiltration.Results: Single nucleotide polymorphism (SNP), C > T transition were the most common variant type and single nucleotide variant (SNV), respectively. Patients in the high-TMB group had better survival outcomes than those in the low-TMB group. Besides, eleven TMB-related DEGs were utilized to construct a prognostic model that could be an independent risk factor to predict the prognosis of patients with GC. What’s more, the infiltration levels of CD4+ memory-activated T cells, M0 and M1 macrophages were significantly increased in the high-TMB group compared with the low-TMB group.Conclusions: Herein, we found that patients with high TMB had better survival outcomes in GC. In addition, higher TMB might promote immune infiltration, which could provide new ideas for immunotherapy.

scholarly journals The Novel Ferroptosis-related Gene Markers That Can Predict the Survival in Gastric Cancer Patients

2020 ◽  
Author(s):  
Bang Chen ◽  
Xin Xu ◽  
ShaoFu Zhu ◽  
ShiYi Yang ◽  
Kang Yang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Cancer Biology ◽  
Cox Regression ◽  
Tumor Development ◽  
Enrichment Analysis ◽  
Gene Markers ◽  
Basic Part ◽  
Cox Regression Analysis ◽  
Gastric Cancer Patients

Abstract Background: Gastric cancer(GC) refers to malignant tumor that derived from gastric epithelial cells. Ferroptosis is another programmed cell demise mode that is Fe-dependent, unique concerning apoptosis, cell necrosis, and autophagy. Current research demonstrates that ferroptosis assumes a basic part of cancer biology. Extracellular matrix(ECM) has been confirmed to play an essential role in the proliferation, apoptosis, metabolism and differentiation of tumor cells. As an important component of the tumor microenvironment, ECM interacts with the immune microenvironment and affects tumor development and progression. Methods: GC data were downloaded from the TCGA database. Furthermore, 259 ferroptosis-related genes were acquired with the FerrDb database. COX regression analysis was used to screen ferroptosis-related genes related to GC's prognosis, and these genes constructed the prediction model. The risk score of the model and clinical data of GC were further analyzed to get the correlation between the model and the overall survival(OS) rate and clinicopathological features. Finally, GO and KEGG enrichment analysis was carried out on the genes of the model. To further analyze the correlation between the genes in the model and tumor immunity, ssGSEA was used to score immune cells and calculate immune-related pathways' activity quantitatively. Results: A prognosis model was constructed according to the 11 ferroptosis-related genes related to prognosis to predict the prognosis of GC patients better. According to univariate and multivariate, risk score can be regarded as an independent predictor.Conclusions: we identified 11 ferroptosis-related genes (NOX4, NOX5, SLC1A5, GLS2, MYB, TGFBR1, NF2, ZFP36, DUSP1, SLC1A4, SP1), which affected the prognosis of GC patients.

Eight-gene prognostic signature associated with hypoxia and ferroptosis for gastric cancer with general applicability

Epigenomics ◽  
2021 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Cancer Genome Atlas ◽  
Genome Atlas

Aims: To investigate the prognostic significance of hypoxia- and ferroptosis-related genes for gastric cancer (GC). Materials & methods: We extracted data on 259 hypoxia- and ferroptosis-related genes from The Cancer Genome Atlas and identified the differentially expressed genes between normal (n = 32) and tumor (n = 375) tissues. A risk score was established by univariate Cox regression analysis and LASSO penalized Cox regression analysis. Results: The risk score contained eight genes showed good performance in predicting overall survival and relapse-free survival in GC patients in both the training cohort (The Cancer Genome Atlas, n = 350) and the testing cohorts (GSE84437, n = 431; GSE62254, n = 300; GSE15459, n = 191; GSE26253, n = 432). Conclusion: The eight-gene signature may help to the improve the prognostic risk classification of GC.

scholarly journals Development and Validation of a Scoring System Based on 9 Glycolysis-Related Genes for Prognosis Prediction in Gastric Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303382097167
Author(s):  
Tianqi Luo ◽  
Yufei Du ◽  
Jinling Duan ◽  
Chengcai Liang ◽  
Guoming Chen ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Regression Analysis ◽  
Adjuvant Chemotherapy ◽  
Cancer Patients ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Groups ◽  
Tumor Stage ◽  
Cox Regression Analysis ◽  
Gastric Cancer Patients

Gastric cancer is a malignant tumor with high morbidity and mortality worldwide. However, increasing evidences have revealed the correlation between the glycolysis process and tumorigenesis. This study is aim to develop a list of glycolysis-related genes for risk stratification in gastric cancer patients. We included 500 patients’ sample data from GSE62254 and GSE26942 datasets, and classified patients into training (n = 350) and testing sets (n = 150) at a ratio of 7: 3. Univariate and multivariate Cox regression analysis were performed to screen genes having prognostic value. Based on HALLMARK gene sets, we identified 9 glycolysis-related genes (BPNT1, DCN, FUT8, GMPPA, GPC3, LDHC, ME2, PLOD2, and UGP2). On the basis of risk score developed by the 9 genes, patients were classified into high- and low-risk groups. The survival analysis showed that the high-risk patients had a worse prognosis ( p < 0.001). Similar finding was observed in the testing cohort and 2 independent cohorts (GSE13861 and TCGA-STAD, all p < 0.001). The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for overall survival ( p < 0.001). Furthermore, we constructed a nomogram that integrated the risk score and tumor stage, age, and adjuvant chemotherapy. Through comparing the results of the receiver operating characteristic curves and decision curve analysis, we found that the nomogram had a superior predictive accuracy than conventional TNM staging system, suggesting that the risk score combined with other clinical factors (age, tumor stage, and adjuvant chemotherapy) can develop a robust prediction for survival and improve the individualized clinical decision making of the patient. In conclusion, we identified 9 glycolysis-related genes from hallmark glycolysis pathway. Based on the 9 genes, gastric cancer patients were separated into different risk groups related to survival.

scholarly journals Construction of a Novel Prognostic Immune-Related LncRNA Risk Model for Gastric Cancer

2021 ◽  
Author(s):  
Gen-hua Yang
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Cox Regression ◽  
Vital Role ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Cancer Genome Atlas ◽  
Score Model

Abstract Background and AimStudies have recently shown that immune-related lncRNAs play a vital role in the occurrence and development of human malignancies. However, the study in gastric cancer (GC) remains unclear. Here, we aimed to identify immune-related lncRNAs and construct a risk score model to predict the prognosis of GC patients.Methods:RNA expression data and clinical characteristics of GC were download from The Cancer Genome Atlas (TCGA) database. Immune genes were obtained from the Molecular Signatures Database (MSigDB). Immune-related lncRNAs were acquired by correlation coefficient between the immune genes and lncRNAs using “limma R” package and Cytoscape 3.6.1. The risk score model was constructed by univariate and multivariate Cox regression, and its prognostic value was verified in TCGA cohort. Results:A total of 146 immune-related lncRNAs were obtained compared 375 GC samples with 32 normal samples. A five immune-related lncRNA (AP001528.2, LINC02542, LINC02526, PVT1 and LINC01094) risk score model was constructed to predict prognosis of GC patients by Cox regression analysis. Moreover, GC patients with higher risk score had a poorer overall survival than that with lower risk score (P<0.001). Furthermore, ROC analysis revealed that the risk score model had the best predictive effect compared with clinicopathological features during 5 years followed-up (AUC = 0.679). Indeed, PCA analysis showed that the patients in the low- and high- group were significantly distinguished in different directions based on the risk score model. Conclusion:This study indicated that a five immune-related lncRNA risk score model possessed a satisfactory predictive prognosis, which might be potential prognostic biomarkers and immunotherapy targets for GC patients in future.

scholarly journals Integrated analysis of iron metabolic-related genes in hepatocellular carcinoma

2021 ◽  
Author(s):  
Li Wang ◽  
Jialin Qu ◽  
Man Jiang ◽  
Na Zhou ◽  
Zhixuan Ren ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters ◽  
Integrated Analysis ◽  
Consensus Clustering ◽  
Cox Regression Analysis

Abstract Background Iron is a nutrient essential for hemoglobin synthesis, DNA synthesis, and energy metabolism in all mammals. Iron metabolic involved in numerous types of cancers including hepatocellular cancer. In this study, we aim to identify prognostic model that based on iron metabolic-related genes that could effectively predict the prognosis for HCC patients. Methods The RNA microarray and clinical data of HCC patients that obtained from The Cancer Genome Atlas (TCGA) database. We identify the clusters of HCC patients with different clinical outcome performed by consensus clustering analysis. Four iron metabolic-related genes (FLVCR1, FTL, HIF1A, HMOX1) were screen for prognostic model by performed the Cox regression analysis. The efficacy of prognostic model was validated by the International Cancer Genome Consortium (ICGC) database. Meantime, the expressions value of FLVCR1, FTL, HIF1A, HMOX1 was performed using Oncomine database, the Human Protein Atlas and Kaplan Meier-plotter. Result The patients with low-risk score have better prognosis than high risk score both in TCGA cohort and ICGC cohort. The prognostic model showed well performance for predicting the prognosis of HCC patients than other clinicopathological parameters by OS-related ROC curves. Conclusion Our survival models that based on Iron metabolic can be independent risk factors for hepatocellular carcinoma patients.

scholarly journals Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dongsheng He ◽  
Shengyin Liao ◽  
Lifang Cai ◽  
Weiming Huang ◽  
Xuehua Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Factors ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Calibration Plot ◽  
Clinical Parameters ◽  
T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

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.

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

Expression and Clinical Significance of Serum Exosomal miR-375 in Patients with Gastric Cancer

2021 ◽  
Vol 7 (5) ◽  
pp. 3896-3904
Author(s):  
Daoting Deng ◽  
Hong Zhang ◽  
Junxi Liu ◽  
Lina Ma ◽  
Xinrui Lei ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cancer Patients ◽  
Cox Regression ◽  
Prognostic Significance ◽  
Cox Regression Analysis ◽  
Healthy Group ◽  
Cancer Group ◽  
Kaplan Meier ◽  
Gastric Cancer Patients ◽  
Gastric Cancer Group

To explore exosomal miR-375 expression in gastric cancer patients and its relationship with patient prognosis. A total of 53 patients diagnosed with gastric cancer in our hospital from May 2014 to May 2016 were included as the gastric cancer group, and 46 healthy women who came to our hospital for physical examination during the same period were enrolled as the healthy group. Exosomal miR-375 expression level was detected using qRT-PCR, and the diagnostic performance and prognostic significance of exosomal miR-375 in gastric cancer were explored. The gastric cancer group showed increased exosomal miR-375 expression than the healthy group (P< 0.05); Kaplan-Meier survival analysis exhibited that serum exosomal miR-375 has an AUC of 0.778, sensitivity of 69.57%, and specificity of 75.47%, whereas Cox regression analysis showed that the miR-375 expression in exosomes was an independent risk factor affecting the prognosis of gastric cancer patients (P< 0.05). Patient with gastric cancer showed upregulated miR-375 expression in serum exosomes. Serum exosomal miR-375 was found to has positive sensitivity and specificity in the diagnosis of gastric cancer, which may be associated with poor prognosis of gastric cancer patients.

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