scholarly journals Screening and Validation of the Hypoxia-Related Signature of Evaluating Tumor Immune Microenvironment and Predicting Prognosis in Gastric Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Jun-Peng Pei ◽  
Chun-Dong Zhang ◽  
Maimaititusun Yusupu ◽  
Cheng Zhang ◽  
Dong-Qiu Dai
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Poor Prognosis ◽  
Immune Escape ◽  
The Cancer Genome Atlas ◽  
Signal Pathways ◽  
Immune Microenvironment ◽  
Good Ability ◽  
Tumor Immune Microenvironment ◽  
Score Model

BackgroundHypoxia is one driving factor of gastric cancer. It causes a series of immunosuppressive processes and malignant cell responses, leading to a poor prognosis. It is clinically important to identify the molecular markers related to hypoxia.MethodsWe screened the prognostic markers related to hypoxia in The Cancer Genome Atlas database, and a risk score model was developed based on these markers. The relationships between the risk score and tumor immune microenvironment were investigated. An independent validation cohort from Gene Expression Omnibus was applied to validate the results. A nomogram of risk score model and clinicopathological factor was developed to individually predict the prognosis.ResultsWe developed a hypoxia risk score model based on SERPINE1 and EFNA3. Quantified real-time PCR was further applied to verified gene expressions of SERPINE1 and EFNA3 in gastric cancer patients and cell lines. A high-risk score is associated with a poor prognosis through the immunosuppressive microenvironment and immune escape mechanisms, including infiltration of immunosuppressive cells, expression of immune checkpoint molecules, and enrichment of signal pathways related to cancer and immunosuppression. The nomogram basing on the hypoxia-related risk score model showed a good ability to predict prognosis and high clinical net benefits.ConclusionsThe hypoxia risk score model revealed a close relationship between hypoxia and tumor immune microenvironment. The current study potentially provides new insights of how hypoxia affects the prognosis, and may provide a new therapeutic target for patients with gastric cancer.

scholarly journals Progressive and Prognostic Performance of an Extracellular Matrix-Receptor Interaction Signature in Gastric Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Xiangchou Yang ◽  
Liping Chen ◽  
Yuting Mao ◽  
Zijing Hu ◽  
Muqing He
Keyword(s):  
Gastric Cancer ◽  
Extracellular Matrix ◽  
Risk Score ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Clinicopathologic Features ◽  
Normal Tissues ◽  
Prognostic Application ◽  
Score Model

The role of an extracellular matrix- (ECM-) receptor interaction signature has not been fully clarified in gastric cancer. This study performed comprehensive analyses on the differentially expressed ECM-related genes, clinicopathologic features, and prognostic application in gastric cancer. The differentially expressed genes between tumorous and matched normal tissues in The Cancer Genome Atlas (TCGA) and validation cohorts were identified by a paired t -test. Consensus clusters were built to find the correlation between clinicopathologic features and subclusters. Then, the least absolute shrinkage and selection operator (lasso) method was used to construct a risk score model. Correlation analyses were made to reveal the relation between risk score-stratified subgroups and clinicopathologic features or significant signatures. In TCGA (26 pairs) and validation cohort (134 pairs), 25 ECM-related genes were significantly highly expressed and 11 genes were downexpressed in gastric cancer. ECM-based subclusters were slightly related to clinicopathologic features. We constructed a risk score model = 0.081 ∗ log 2   CD 36 + 0.043 ∗ log 2   COL 5 A 2 + 0.001 ∗ log 2   ITGB 5 + 0.039 ∗ log 2   SDC 2 + 0.135 ∗ log 2   SV 2 B + 0.012 ∗ log 2   THBS 1 + 0.068 ∗ log 2   VTN + 0.023 ∗ log 2   VWF . The risk score model could well predict the outcome of patients with gastric cancer in both training ( n = 351 , HR: 1.807, 95% CI: 1.292-2.528, P = 0.00046 ) and validation ( n = 300 , HR: 1.866, 95% CI: 1.347-2.584, P = 0.00014 ) cohorts. Besides, risk score-based subgroups were associated with angiogenesis, cell adhesion molecules, complement and coagulation cascades, TGF-beta signaling, and mismatch repair-relevant signatures ( P < 0.0001 ). By univariate (1.845, 95% CI: 1.382-2.462, P < 0.001 ) and multivariate (1.756, 95% CI: 1.284-2.402, P < 0.001 ) analyses, we regarded the risk score as an independent risk factor in gastric cancer. Our findings revealed that ECM compositions became accomplices in the tumorigenesis, progression, and poor survival of gastric cancer.

scholarly journals CPXM1: a novel biomarker for gastric cancer prognosis

2021 ◽  
Author(s):  
Jun Du ◽  
Mengxiang Zhu ◽  
Wenwu Yan ◽  
Changsheng Yao ◽  
Qingyi Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Poor Prognosis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Signal Pathways ◽  
Data Set ◽  
Kaplan Meier ◽  
Cancer Genome Atlas ◽  
Cox Proportional Hazard Regression ◽  
The Relationship

Abstract Background The molecular role of carboxypeptidase X, M14 family member (CPXM1) in oncogenesis or tumor progression remains unclear. The aim of this study was to determine whether CPXM1 can be used as a potential prognostic biomarker for gastric cancer (GC). Methods We first demonstrated the relationship between CPXM1 expression and GC in various public databases. Secondly, the expression of CPXM1 in GC tissues was further verified by immunohistochemical staining using tissue microarray containing 96 cases of GC patients. Kaplan–Meier analysis and a Cox proportional hazard regression model were performed to evaluate the relationship between the expression of CPXM1 and the survival of GC patients. Finally, we used the expression data of CPXM1 in The Cancer Genome Atlas database to predict CPXM1-related signaling pathways through bioinformatics analysis. Results The expression level of CPXM1 in GC tissues was significantly correlated with tumor size (p = 0.041) and lymph node metastasis (p = 0.014). In addition, Kaplan–Meier analysis showed that the expression of CPXM1 in GC tissues was significantly associated with poor prognosis (p = 0.011). Multivariate analysis indicated that CPXM1 is a potential predictor of poor prognosis in GC patients (p = 0.026). The results of biosynthesis analysis demonstrated that the data set of CPXM1 high expression was mainly enriched in cancer-related signal pathways. Conclusion CPXM1 is an effective biomarker for the prognosis of GC patients and may play a key role in the occurrence and progression of GC.

scholarly journals A risk score model with five long non-coding RNAs for predicting prognosis in gastric cancer: an integrated analysis combining TCGA and GEO datasets

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10556
Author(s):  
Yiguo Wu ◽  
Junping Deng ◽  
Shuhui Lai ◽  
Yujuan You ◽  
Jing Wu
Keyword(s):  
Gastric Cancer ◽  
Risk Score ◽  
Prognostic Value ◽  
Therapeutic Targets ◽  
The Cancer Genome Atlas ◽  
Stage Ii ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Non Coding Rnas ◽  
Score Model

Background Gastric cancer (GC) is one of the most common carcinomas of the digestive tract, and the prognosis for these patients may be poor. There is evidence that some long non-coding RNAs(lncRNAs) can predict the prognosis of patients with GC. However, few lncRNA signatures have been used to predict prognosis. Herein, we aimed to construct a risk score model based on the expression of five lncRNAs to predict the prognosis of patients with GC and provide new potential therapeutic targets. Methods We performed differentially expressed and survival analyses to identify differentially expressed survival-ralated lncRNAs by using GC patient expression profile data from The Cancer Genome Atlas (TCGA) database. We then established a formula including five lncRNAs to predict the prognosis of patients with GC. In addition, to verify the prognostic value of this risk score model, two independent Gene Expression Omnibus (GEO) datasets, GSE62254 (N = 300) and GSE15459 (N = 200), were employed as validation groups. Results Based on the characteristics of five lncRNAs, patients with GC were divided into high or low risk subgroups. The prognostic value of the risk score model with five lncRNAs was confirmed in both TCGA and the two independent GEO datasets. Furthermore, stratification analysis results showed that this model had an independent prognostic value in patients with stage II–IV GC. We constructed a nomogram model combining clinical factors and the five lncRNAs to increase the accuracy of prognostic prediction. Enrichment analysis based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the five lncRNAs are associated with multiple cancer occurrence and progression-related pathways. Conclusion The risk score model including five lncRNAs can predict the prognosis of patients with GC, especially those with stage II-IV, and may provide potential therapeutic targets in future.

scholarly journals Immune-Related LncRNAs Affect the Prognosis of Osteosarcoma, Which Are Related to the Tumor Immune Microenvironment

2021 ◽  
Vol 9 ◽  
Author(s):  
Qingshan Huang ◽  
Yilin Lin ◽  
Chenglong Chen ◽  
Jingbing Lou ◽  
Tingting Ren ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Immune Escape ◽  
Risk Group ◽  
High Risk Group ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Background: Abnormal expression of lncRNA is closely related to the occurrence and metastasis of osteosarcoma. The tumor immune microenvironment (TIM) is considered to be an important factor affecting the prognosis and treatment of osteosarcoma. This study aims to explore the effect of immune-related lncRNAs (IRLs) on the prognosis of osteosarcoma and its relationship with the TIM.Methods: Ninety-five osteosarcoma samples from the TARGET database were included. Iterative LASSO regression and multivariate Cox regression analysis were used to screen the IRLs signature with the optimal AUC. The predict function was used to calculate the risk score and divide osteosarcoma into a high-risk group and low-risk group based on the optimal cut-off value of the risk score. The lncRNAs in IRLs signature that affect metastasis were screened for in vitro validation. Single sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithms were used to evaluate the role of TIM in the influence of IRLs on osteosarcoma prognosis.Results: Ten IRLs constituted the IRLs signature, with an AUC of 0.96. The recurrence and metastasis rates of osteosarcoma in the high-risk group were higher than those in the low-risk group. In vitro experiments showed that knockdown of lncRNA (AC006033.2) could increase the proliferation, migration, and invasion of osteosarcoma. ssGSEA and ESTIMATE results showed that the immune cell content and immune score in the low-risk group were generally higher than those in the high-risk group. In addition, the expression levels of immune escape-related genes were higher in the high-risk group.Conclusion: The IRLs signature is a reliable biomarker for the prognosis of osteosarcoma, and they alter the prognosis of osteosarcoma. In addition, IRLs signature and patient prognosis may be related to TIM in osteosarcoma. The higher the content of immune cells in the TIM of osteosarcoma, the lower the risk score of patients and the better the prognosis. The higher the expression of immune escape-related genes, the lower the risk score of patients and the better the prognosis.

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 Tumor Immune Microenvironment Related Gene-Based Model to Predict Prognosis and Response to Compounds in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jiang Yang ◽  
Shasha Hong ◽  
Xiaoyi Zhang ◽  
Jingchun Liu ◽  
Ying Wang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Killer Cell ◽  
Figo Stage ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Natural Killer Cell Cytotoxicity ◽  
Tumor Immune Microenvironment

BackgroundThe tumor immune microenvironment (TIME) has been recognized to be an imperative factor facilitating the acquisition of many cancer-related hallmarks and is a critical target for targeted biological therapy. This research intended to construct a risk score model premised on TIME-associated genes for prediction of survival and identification of potential drugs for ovarian cancer (OC) patients.Methods and ResultsThe stromal and immune scores were computed utilizing the ESTIMATE algorithm in OC patient samples from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network and differentially expressed genes analyses were utilized to detect stromal-and immune-related genes. The Least Absolute Shrinkage and Selection Operator (LASSO)-Cox regression was utilized for additional gene selection. The genes that were selected were utilized as the input for a stepwise regression to construct a TIME-related risk score (TIMErisk), which was then validated in Gene Expression Omnibus (GEO) database. For the evaluation of the protein expression levels of TIME regulators, the Human Protein Atlas (HPA) dataset was utilized, and for their biological functions, the TIMER and CIBERSORT algorithm, immunoreactivity, and Immune Cell Abundance Identifier (ImmuCellAI) were used. Possible OC medications were forecasted utilizing the Genomics of Drug Sensitivity in Cancer (GDSC) database and connectivity map (CMap). TIMErisk was developed based on ALPK2, CPA3, PTGER3, CTHRC1, PLA2G2D, CXCL11, and ZNF683. High TIMErisk was recognized as a poor factor for survival in the GEO and TCGA databases; subgroup analysis with FIGO stage, grade, lymphatic and venous invasion, debulking, and tumor site also indicated similar results. Functional immune cells corresponded to more incisive immune reactions, including secretion of chemokines and interleukins, natural killer cell cytotoxicity, TNF signaling pathway, and infiltration of activated NK cells, eosinophils, and neutrophils in patients with low TIMErisk. Several small molecular medications which may enhance the prognosis of patients in the TIMErisk subgroup were identified. Lastly, an enhanced predictive performance nomogram was constructed by compounding TIMErisk with the FIGO stage and debulking.ConclusionThese findings may offer a valuable indicator for clinical stratification management and personalized therapeutic options for OC patients and may be a foundation for future mechanistic research of their association.

scholarly journals A 4-gene-based hypoxia signature is associated with tumor immune microenvironment and predicts the prognosis of pancreatic cancer patients

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jianfeng Ding ◽  
Xiaobo He ◽  
Xiao Cheng ◽  
Guodong Cao ◽  
Bo Chen ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Risk Score ◽  
Risk Model ◽  
Expression Profiles ◽  
Risk Groups ◽  
Prognostic Indicator ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment ◽  
Immunosuppressive Microenvironment

Abstract Background Pancreatic cancer (PAC) is one of the most devastating cancer types with an extremely poor prognosis, characterized by a hypoxic microenvironment and resistance to most therapeutic drugs. Hypoxia has been found to be one of the factors contributing to chemoresistance in PAC, but also a major driver of the formation of the tumor immunosuppressive microenvironment. However, the method to identify the degree of hypoxia in the tumor microenvironment (TME) is incompletely understood. Methods The mRNA expression profiles and corresponding clinicopathological information of PAC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, respectively. To further explore the effect of hypoxia on the prognosis of patients with PAC as well as the tumor immune microenvironment, we established a hypoxia risk model and divided it into high- and low-risk groups in line with the hypoxia risk score. Results We established a hypoxia risk model according to four hypoxia-related genes, which could be used to demonstrate the immune microenvironment in PAC and predict prognosis. Moreover, the hypoxia risk score can act as an independent prognostic factor in PAC, and a higher hypoxia risk score was correlated with poorer prognosis in patients as well as the immunosuppressive microenvironment of the tumor. Conclusions In summary, we established and validated a hypoxia risk model that can be considered as an independent prognostic indicator and reflected the immune microenvironment of PAC, suggesting the feasibility of hypoxia-targeted therapy for PAC patients.

scholarly journals Differentiated tumor immune microenvironment of Epstein-Barr virus-associated and negative gastric cancer: implication in prognosis and immunotherapy

Oncotarget ◽  
2017 ◽  
Vol 8 (40) ◽  
pp. 67094-67103 ◽  
Author(s):  
Jing Ma ◽  
Jianhui Li ◽  
Yiming Hao ◽  
Yongzhan Nie ◽  
Zengshan Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Epstein Barr Virus ◽  
Immune Microenvironment ◽  
Barr Virus ◽  
Tumor Immune Microenvironment ◽  
Epstein Barr

scholarly journals RNA N6-Methyladenosine Regulators Contribute to Tumor Immune Microenvironment and Have Clinical Prognostic Impact in Breast Cancer

2022 ◽  
Vol 12 ◽  
Author(s):  
Lan-Xin Mu ◽  
You-Cheng Shao ◽  
Lei Wei ◽  
Fang-Fang Chen ◽  
Jing-Wei Zhang
Keyword(s):  
Breast Cancer ◽  
Immune Cell ◽  
Risk Model ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Impact ◽  
Immune Microenvironment ◽  
Model Based ◽  
Tumor Immune Microenvironment

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p &lt; 0.001) and m6aRiskscore (p &lt; 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

scholarly journals Identifying Novel Cell Glycolysis-Related Gene Signature Predictive of Overall Survival in Gastric Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xin Zhao ◽  
Jiaxuan Zou ◽  
Ziwei Wang ◽  
Ge Li ◽  
Yi Lei
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Risk Score ◽  
Malignant Tumors ◽  
Single Gene ◽  
Univariate Analysis ◽  
The Cancer Genome Atlas ◽  
Clinical Value ◽  
Specificity And Sensitivity

Background. Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients’ prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC. Materials and Methods. In our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups. Results. We identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients’ OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients. Conclusion. The signature consisting of four glycolysis-related genes could be used to forecast the GC patients’ prognosis.

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