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

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.

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Application value of nomogram and prognostic factors of gastric cancer patients who underwent D2 radical lymphadenectomy

BMC Gastroenterology ◽

10.1186/s12876-019-1098-6 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 2

Author(s):

Guang-Chuan Mu ◽

Yuan Huang ◽

Zhi-Ming Liu ◽

Xiang-Hua Wu ◽

Xin-Gan Qin ◽

...

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Prognostic Factors ◽

Cancer Patients ◽

Cox Regression ◽

Term Survival ◽

Long Term Survival ◽

Cox Regression Analysis ◽

Gastric Cancer Patients

Abstract Background The aim of this study was to explore the prognostic factors and establish a nomogram to predict the long-term survival of gastric cancer patients. Methods The clinicopathological data of 421 gastric cancer patients, who were treated with radical D2 lymphadenectomy by the same surgical team between January 2009 and March 2017, were collected. The analysis of long-term survival was performed using Cox regression analysis. Based on the multivariate analysis results, a prognostic nomogram was formulated to predict the 5-year survival rate probability. Results In the present study, the total overall 3-year and 5-year survival rates were 58.7 and 45.8%, respectively. The results of the univariate Cox regression analysis revealed that tumor staging, tumor location, Borrmann type, the number of lymph nodes dissected, the number of lymph node metastases, positive lymph nodes ratio, lymphocyte count, serum albumin, CEA, CA153, CA199, BMI, tumor size, nerve invasion, and vascular invasion were prognostic factors for gastric cancer (all, P < 0.05). However, merely tumor staging, tumor location, positive lymph node ratio, CA199, BMI, tumor size, nerve invasion, and vascular invasion were independent risk factors, based on the results of the multivariate Cox regression analysis (all, P < 0.05). The nomogram based on eight independent prognostic factors revealed a well-degree of differentiation with a concordance index of 0.76 (95% CI: 0.72–0.79, P < 0.001), which was better than the AJCC-7 staging system (concordance index = 0.68). Conclusion The present study established a nomogram based on eight independent prognostic factors to predict long-term survival in gastric cancer patients. The nomogram would be beneficial for more accurately predicting the prognosis of gastric cancer, and provide important basis for making individualized treatment plans following surgery.

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Identification and Validation of the Immune Regulator CXCR4 as a Novel Promising Target for Gastric Cancer

Frontiers in Immunology ◽

10.3389/fimmu.2021.702615 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shuai Xue ◽

Ming Ma ◽

Songhua Bei ◽

Fan Li ◽

Chenqu Wu ◽

...

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Cox Regression ◽

Malignant Tumors ◽

Univariate Analysis ◽

Prognostic Indicator ◽

Migration And Invasion ◽

Cox Regression Analysis ◽

Gastric Cancer Patients

Immune checkpoint blockade has attracted a lot of attention in the treatment of human malignant tumors. We are trying to establish a prognostic model of gastric cancer (GC) based on the expression profile of immunoregulatory factor-related genes. Based on the TCGA database, we identified 234 differentially expressed immunoregulatory factors. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) conducted enrichment analysis to clarify the biological functions of differential expression of immunoregulatory factors. STRING database predicted the interaction network between 234 differently expressed immune regulatory factors. The expression of 11 immunoregulatory factors was significantly related to the overall survival of gastric cancer patients. Univariate Cox regression analysis, Kaplan–Meier analysis and multivariate Cox regression analysis found that immunomodulatory factors were involved in the progression of gastric cancer and promising biomarkers for predicting prognosis. Among them, CXCR4 was related to the low survival of GC patients and a key immunomodulatory factor in GC. Based on TCGA data, the high expression of CXCR4 in GC was positively correlated with the advanced stage and grade of gastric cancer and related to poor prognosis. Univariate analysis and multivariate analysis indicated that CXCR4 was an independent prognostic indicator for TCGA gastric cancer patients. In vitro functional studies had shown that CXCR4 promoted the proliferation, migration, and invasion of gastric cancer cells. In summary, this study has determined the prognostic value of 11 immunomodulatory factors in gastric cancer. CXCR4 is an independent prognostic indicator for gastric cancer patients, which may help to improve the individualized prognostic prediction of GC and provide candidates for the diagnosis and treatment of GC.

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

Tobacco Regulatory Science ◽

10.18001/trs.7.5.1.162 ◽

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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Development of prognosis model for colon cancer based on autophagy-related genes

World Journal of Surgical Oncology ◽

10.1186/s12957-020-02061-w ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Xu Wang ◽

Yuanmin Xu ◽

Ting Li ◽

Bo Chen ◽

Wenqi Yang

Keyword(s):

Colon Cancer ◽

Regression Analysis ◽

High Risk ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Patient Survival ◽

Expression Profiles ◽

Cox Regression Analysis ◽

Risk Patients

Abstract Background Autophagy is an orderly catabolic process for degrading and removing unnecessary or dysfunctional cellular components such as proteins and organelles. Although autophagy is known to play an important role in various types of cancer, the effects of autophagy-related genes (ARGs) on colon cancer have not been well studied. Methods Expression profiles from ARGs in 457 colon cancer patients were retrieved from the TCGA database (https://portal.gdc.cancer.gov). Differentially expressed ARGs and ARGs related to overall patient survival were identified. Cox proportional-hazard models were used to investigate the association between ARG expression profiles and patient prognosis. Results Twenty ARGs were significantly associated with the overall survival of colon cancer patients. Five of these ARGs had a mutation rate ≥ 3%. Patients were divided into high-risk and low-risk groups based on Cox regression analysis of 8 ARGs. Low-risk patients had a significantly longer survival time than high-risk patients (p < 0.001). Univariate and multivariate Cox regression analysis showed that the resulting risk score, which was associated with infiltration depth and metastasis, could be an independent predictor of patient survival. A nomogram was established to predict 1-, 3-, and 5-year survival of colon cancer patients based on 5 independent prognosis factors, including the risk score. The prognostic nomogram with online webserver was more effective and convenient to provide information for researchers and clinicians. Conclusion The 8 ARGs can be used to predict the prognosis of patients and provide information for their individualized treatment.

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Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

2021 ◽

Vol 16 ◽

Author(s):

Dongqing Su ◽

Qianzi Lu ◽

Yi Pan ◽

Yao Yu ◽

Shiyuan Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

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

Frontiers in Genetics ◽

10.3389/fgene.2021.739470 ◽

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.

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Integrated analysis of 1804 samples of six centers to construct and validate a robust immune-related prognostic signature associated with stromal cell abundance in tumor microenvironment for gastric cancer

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02485-y ◽

2022 ◽

Vol 20 (1) ◽

Author(s):

Junyu Huo ◽

Ge Guan ◽

Jinzhen Cai ◽

Liqun Wu

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Risk Score ◽

Stromal Cell ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Integrated Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis

Abstract Background Stromal cells in tumor microenvironment could promote immune escape through a variety of mechanisms, but there are lacking research in the field of gastric cancer (GC). Methods We identified differential expressed immune-related genes (DEIRGs) between the high- and low-stromal cell abundance GC samples in The Cancer Genome Atlas and GSE84437 datasets. A risk score was constructed basing on univariate cox regression analysis, LASSO regression analysis, and multivariate cox regression analysis in the training cohort (n=772). The median value of the risk score was used to classify patients into groups with high and low risk. We conducted external validation of the prognostic signature in four independent cohorts (GSE26253, n=432; GSE62254, n=300; GSE15459, n=191; GSE26901, n=109) from the Gene Expression Omnibus (GEO) database. The immune cell infiltration was quantified by the CIBERSORT method. Results The risk score contained 6 genes (AKT3, APOD, FAM19A5, LTBP3, NOV, and NOX4) showed good performance in predicting 5-year overall survival (OS) rate and 5-year recurrence-free survival (RFS) rate of GC patients. The risk death and recurrence of GC patients growing with the increasing risk score. The patients were clustered into three subtypes according to the infiltration of 22 kinds of immune cells quantified by the CIBERSORT method. The proportion of cluster A with the worst prognosis in the high-risk group was significantly higher than that in the low-risk group; the risk score of cluster C subtype with the best prognosis was significantly lower than that of the other two subtypes. Conclusion This study established and validated a robust prognostic model for gastric cancer by integrated analysis 1804 samples of six centers, and its mechanism was explored in combination with immune cell infiltration characterization.

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Construction and Validation of Prognostic Markers of Liver Cancer Based on Autophagy Genes

Anti-Cancer Agents in Medicinal Chemistry ◽

10.2174/1871520621666210329100052 ◽

2021 ◽

Vol 21 ◽

Author(s):

Dawei Zhou ◽

Junchen Wan ◽

Jiang Luo ◽

Yuhao Tao

Keyword(s):

Regression Analysis ◽

High Risk ◽

Liver Cancer ◽

Cancer Patients ◽

Prognostic Model ◽

Cox Regression ◽

Risk Group ◽

Primary Liver Cancer ◽

Risk Groups ◽

Cox Regression Analysis

Background: Liver cancer is one of the most common diseases in the world. At present, the mechanism of autophagy genes in liver cancer is not very clear. Therefore, it is meaningful to study the role and prognostic value of autophagy genes in liver cancer. Objective: The purpose of this study is to conduct a bioinformatics analysis of autophagy genes related to primary liver cancer to establish a prognostic model of primary liver cancer based on autophagy genes. Results: Through difference analysis, 31 differential autophagy genes were screened out and then analyzed by GO and KEGG analysis. At the same time, we built a PPI network. To optimize the evaluation of the prognosis of liver cancer patients, we integrated multiple autophagy genes to establish a prognostic model. By using univariate cox regression analysis, 15 autophagy genes related to prognosis were screened out. Then we included these 15 genes into the Least Absolute Shrinkage and Selection Operator (LASSO), and performed multi-factor cox regression analysis on the 9 selected genes to construct a prognostic model. The risk score of each patient was calculated based on 4 genes(BIRC5, HSP8, SQSTM1, and TMEM74) which participated in the establishing of the model, then the patients were divided into high-risk groups and low-risk groups. In the multivariate cox regression analysis, the risk score was the independent prognostic factors (HR=1.872, 95%CI=1.544-2.196, P<0.001). Survival analysis showed that the survival time of the low-risk group was significantly longer than that of the high-risk group. Combining clinical characteristics and autophagy genes, we constructed a nomogram for predicting prognosis. The external dataset GSE14520 proved that the nomogram has a good prediction for individual patients with primary liver cancer. Conclusion: This study provided potential autophagy-related markers for liver cancer patients to predict their prognosis and revealed part of the molecular mechanism of liver cancer autophagy. At the same time, the certain gene pathways and protein pathways related to autophagy may provide some inspiration for the development of anticancer drugs.

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Outcomes of patients with nonmetastatic gastric adenocarcinoma according to perioperative treatment strategy: a real-world, population-based study

Journal of Comparative Effectiveness Research ◽

10.2217/cer-2021-0113 ◽

2021 ◽

Vol 10 (15) ◽

pp. 1143-1151

Author(s):

Omar Abdel-Rahman

Keyword(s):

Gastric Cancer ◽

Regression Analysis ◽

Adjuvant Chemotherapy ◽

Treatment Strategy ◽

Cox Regression ◽

Population Based ◽

Survival Outcomes ◽

Perioperative Chemotherapy ◽

Cox Regression Analysis ◽

Perioperative Treatment

Aim: To assess the survival outcomes of patients with nonmetastatic gastric cancer according to the type of perioperative treatment strategy used (surgery-only, adjuvant chemo-radiotherapy, adjuvant chemotherapy, perioperative chemotherapy) in a population-based setting. Materials & methods: Surveillance, Epidemiology and End Results research-plus database was explored, and patients with nonmetastatic gastric cancer who were treated with an oncologic surgery were reviewed. Multivariable Cox regression analysis was used to examine the impact of treatment strategy on overall and cancer-specific survival. Results: A total of 11,526 patients were found to be eligible and they were included in the current analysis. Looking at the percentages of different treatment strategies throughout the study years (2006–2017), the use of the following strategies increased: adjuvant chemotherapy (20.1 vs 10.6%), and perioperative chemotherapy (21.3 vs 0.5%); while the use of the following strategies decreased: surgery only (36.2 vs 58.2%), and adjuvant chemo-radiotherapy (22.4 vs 30.6%). Using multivariable Cox regression analysis, the following factors were associated with worse overall survival: older age (hazard [HR]: 1.021; 95% CI: 1.018–1.023), males (HR: 1.09; 95% CI: 1.04–1.14), Black race (HR: 1.11; 95% CI: 1.04–1.19), cardia subsite (HR: 1.09; 95% CI: 1.02–1.17), grade 3–4 (HR:1.32; 95% CI: 1.25–1.40), diffuse histology (HR: 1.46; 95% CI: 1.35–1.58), clinically node positive (HR:1.43; 95% CI: 1.34–1.53), total gastrectomy (HR: 1.20; 95% CI: 1.13–1.28), and surgery-only approach (HR: 1.65; 95% CI: 1.55–1.75). Conclusion: Among patients with localized gastric cancer, patients who were treated with surgery-only, and to a less extent, patients who were treated with surgery followed by adjuvant chemotherapy have worse survival outcomes; while those treated with perioperative chemotherapy have the best survival outcomes.

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Development and validation of Pyroptosis‑related lncRNAs prediction model for bladder cancer

Bioscience Reports ◽

10.1042/bsr20212253 ◽

2022 ◽

Author(s):

Thongher Lia ◽

Yanxiang Shao ◽

Parbatraj Regmi ◽

Xiang Li

Keyword(s):

Bladder Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Risk Group ◽

Expression Profiles ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Cox Regression Analysis

Bladder cancer is one of the highly heterogeneous disorders accompanied by a poor prognosis. This study aimed to construct a model based on pyroptosis‑related lncRNA to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis‑related lncRNAs were identified by utilizing a co-expression network of Pyroptosis‑related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, 8 pyroptosis-related lncRNA markers were established using Lasso regression and multivariate Cox regression analysis. Patients were separated into high and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (p < 0.001), and In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS ( HR>1, P<0.01). The area under the curve (AUC) of the 3- and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739 respectively. In conclusion, these 8 pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.

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