scholarly journals Transcriptome Analyses Identify a Metabolic Gene Signature Indicative of Antitumor Immunosuppression of EGFR Wild Type Lung Cancers With Low PD-L1 Expression

2021 ◽  
Vol 11 ◽  
Author(s):  
Min Wang ◽  
Jie Zhu ◽  
Fang Zhao ◽  
Jiani Xiao
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Wild Type ◽  
Cox Regression Analysis ◽  
Metabolic Gene ◽  
Metabolic Genes ◽  
Low Expression

PurposeWith the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), non-small cell lung cancer (NSCLC) patients have achieved remarkable survival benefits in recent years. However, epidermal growth factor receptor (EGFR) wild-type and low expression of programmed death-ligand 1 (PD-L1) NSCLCs remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a metabolic gene signature that could successfully identify high-risk patients and reveal its underlying molecular immunology characteristics.MethodsBy identifying the bottom 50% PD-L1 expression level as PD-L1 low expression and removing EGFR mutant samples, a total of 640 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) tumor samples and 93 adjacent non-tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). We identified differentially expressed metabolic genes (DEMGs) by R package limma and the prognostic genes by Univariate Cox proportional hazards regression analyses. The intersect genes between DEMGs and prognostic genes were put into the least absolute shrinkage and selection operator (LASSO) penalty Cox regression analysis. The metabolic gene signature contained 18 metabolic genes generated and successfully stratified LUAD and LUSC patients into the high-risk and low-risk groups, which was also validated by the Gene Expression Omnibus (GEO) database. Its accuracy was proved by the time-dependent Receiver Operating Characteristic (ROC) curve, Principal Components Analysis (PCA), and nomogram. Furthermore, the Single-sample Gene Set Enrichment Analysis (ssGSEA) and diverse acknowledged methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT-ABS, and CIBERSORT revealed its underlying antitumor immunosuppressive status. Besides, its relationship with somatic copy number alterations (SCNAs) and tumor mutational burden (TMB) was also discussed.ResultsIt is noteworthy that metabolism reprogramming is associated with the survival of the double-negative LUAD and LUSC patients. The SCNAs and TMB of critical metabolic genes can inhibit the antitumor immune process, which might be a promising therapeutic target.

scholarly journals Prognostic Signatures of Metabolic Genes and Metabolism-Related Long Non-coding RNAs Accurately Predict Overall Survival for Osteosarcoma Patients

2021 ◽  
Vol 9 ◽  
Author(s):  
Gong Chao-yang ◽  
Tang Rong ◽  
Shi Yong-qiang ◽  
Liu Tai-cong ◽  
Zhou Kai-sheng ◽  
...  
Keyword(s):  
Overall Survival ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Gene Signature ◽  
Survival Outcomes ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Metabolic Gene ◽  
Metabolic Genes

In this study, we identified eight survival-related metabolic genes in differentially expressed metabolic genes by univariate Cox regression analysis based on the therapeutically applicable research to generate effective treatments (n = 84) data set and genotype tissue expression data set (n = 396). We also constructed a six metabolic gene signature to predict the overall survival of osteosarcoma (OS) patients using least absolute shrinkage and selection operator (Lasso) Cox regression analysis. Our results show that the six metabolic gene signature showed good performance in predicting survival of OS patients and was also an independent prognostic factor. Stratified correlation analysis showed that the metabolic gene signature accurately predicted survival outcomes in high-risk and low-risk OS patients. The six metabolic gene signature was also verified to perform well in predicting survival of OS patients in an independent cohort (GSE21257). Then, using univariate Cox regression and Lasso Cox regression analyses, we identified an eight metabolism-related long noncoding RNA (lncRNA) signature that accurately predicts overall survival of OS patients. Gene set variation analysis showed that the apical surface and bile acid metabolism, epithelial mesenchymal transition, and P53 pathway were activated in the high-risk group based on the eight metabolism-related lncRNA signature. Furthermore, we constructed a competing endogenous RNA (ceRNA) network and conducted immunization score analysis based on the eight metabolism-related lncRNA signature. These results showed that the six metabolic gene signature and eight metabolism-related lncRNA signature have good performance in predicting the survival outcomes of OS patients.

scholarly journals Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

2021 ◽  
Vol 7 ◽  
Author(s):  
Xiaoyu Deng ◽  
Qinghua Bi ◽  
Shihan Chen ◽  
Xianhua Chen ◽  
Shuhui Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Prediction Ability ◽  
Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

scholarly journals Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Lei Zhang ◽  
Zhe Zhang ◽  
Zhenglun Yu
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Single Gene ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Test Series ◽  
Cox Regression Analysis ◽  
Incidence And Mortality

Abstract Background Lung cancer (LC) is one of the most lethal and most prevalent malignant tumors, and its incidence and mortality are increasing annually. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. Several biomarkers have been confirmed by data excavation to be related to metastasis, prognosis and survival. However, the moderate predictive effect of a single gene biomarker is not sufficient. Thus, we aimed to identify new gene signatures to better predict the possibility of LUAD. Methods Using an mRNA-mining approach, we performed mRNA expression profiling in large LUAD cohorts (n = 522) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and connections between genes and glycolysis were found in the Cox proportional regression model. Results We confirmed a set of nine genes (HMMR, B4GALT1, SLC16A3, ANGPTL4, EXT1, GPC1, RBCK1, SOD1, and AGRN) that were significantly associated with metastasis and overall survival (OS) in the test series. Based on this nine-gene signature, the patients in the test series could be divided into high-risk and low-risk groups. Additionally, multivariate Cox regression analysis revealed that the prognostic power of the nine-gene signature is independent of clinical factors. Conclusion Our study reveals a connection between the nine-gene signature and glycolysis. This research also provides novel insights into the mechanisms underlying glycolysis and offers a novel biomarker of a poor prognosis and metastasis for LUAD patients.

scholarly journals A Novel Glucose Metabolism-Related Gene Signature for Overall Survival Prediction in Patients with Glioblastoma

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Chaocai Zhang ◽  
Minjie Wang ◽  
Fenghu Ji ◽  
Yizhong Peng ◽  
Bo Wang ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Glucose Metabolism ◽  
Cox Regression ◽  
Gene Signature ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Survival Prediction ◽  
Related Gene ◽  
Cox Regression Analysis

Introduction. Glioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients. Methods. In this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM. Results. Depending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups. Conclusion. The 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.

scholarly journals Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Zi-Hao Wang ◽  
Yun-Zheng Zhang ◽  
Yu-Shan Wang ◽  
Xiao-Xin Ma
Keyword(s):  
Overall Survival ◽  
Endometrial Cancer ◽  
Mrna Expression ◽  
Cox Regression ◽  
Single Gene ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Rank Test ◽  
Cox Regression Analysis

Abstract Background Endometrial cancer (EC) is one of the three major gynecological malignancies. Numerous biomarkers that may be associated with survival and prognosis have been identified through database mining in previous studies. However, the predictive ability of single-gene biomarkers is not sufficiently specific. Genetic signatures may be an improved option for prediction. This study aimed to explore data from The Cancer Genome Atlas (TCGA) to identify a new genetic signature for predicting the prognosis of EC. Methods mRNA expression profiling was performed in a group of patients with EC (n = 548) from TCGA. Gene set enrichment analysis was performed to identify gene sets that were significantly different between EC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Quantitative real-time-PCR was used to verify the reliability of the expression of selected mRNAs. Subsequent multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan–Meier survival estimates and the log‐rank test were used to validate the significance of risk parameters for prognosis prediction. Result Nine genes associated with glycolysis (CLDN9, B4GALT1, GMPPB, B4GALT4, AK4, CHST6, PC, GPC1, and SRD5A3) were found to be significantly related to overall survival. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. Based on the nine-gene signature, the 548 patients with EC were divided into high/low-risk subgroups. The prognostic ability of the nine-gene signature was not affected by other factors. Conclusion A nine-gene signature associated with cellular glycolysis for predicting the survival of patients with EC was developed. The findings provide insight into the mechanisms of cellular glycolysis and identification of patients with poor prognosis in EC.

scholarly journals Identification and Validation of a PPP1R12A-Related Five-Gene Signature Associated With Metabolism to Predict the Prognosis of Patients With Prostate Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhihao Zou ◽  
Ren Liu ◽  
Yingke Liang ◽  
Rui Zhou ◽  
Qishan Dai ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Affinity Purification ◽  
Disease Free Survival ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Cox Regression Analysis

BackgroundProstate cancer (PCa) is the most common malignant male neoplasm in the American male population. Our prior studies have demonstrated that protein phosphatase 1 regulatory subunit 12A (PPP1R12A) could be an efficient prognostic factor in patients with PCa, promoting further investigation. The present study attempted to construct a gene signature based on PPP1R12A and metabolism-related genes to predict the prognosis of PCa patients.MethodsThe mRNA expression profiles of 499 tumor and 52 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. We selected differentially expressed PPP1R12A-related genes among these mRNAs. Tandem affinity purification-mass spectrometry was used to identify the proteins that directly interact with PPP1R12A. Gene set enrichment analysis (GSEA) was used to extract metabolism-related genes. Univariate Cox regression analysis and a random survival forest algorithm were used to confirm optimal genes to build a prognostic risk model.ResultsWe identified a five-gene signature (PPP1R12A, PTGS2, GGCT, AOX1, and NT5E) that was associated with PPP1R12A and metabolism in PCa, which effectively predicted disease-free survival (DFS) and biochemical relapse-free survival (BRFS). Moreover, the signature was validated by two internal datasets from TCGA and one external dataset from the Gene Expression Omnibus (GEO).ConclusionThe five-gene signature is an effective potential factor to predict the prognosis of PCa, classifying PCa patients into high- and low-risk groups, which might provide potential novel treatment strategies for these patients.

scholarly journals Prognostic Impact of Autophagy and Immunity Related Biomarker for Uveal Melanoma

2021 ◽  
Author(s):  
Huibin Du ◽  
Yan He ◽  
Wei Lu ◽  
Qi Wan
Keyword(s):  
High Risk ◽  
Uveal Melanoma ◽  
Risk Model ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Impact ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis

Abstract Background: Autophagy and immunity related genes serve crucial roles in carcinogenesis, but little is known about the prognostic impact for uveal melanoma (UM).Methods: Autophagy related and immunity related genes (AIRGs) expression data of 80 UM patients were obtained from the cancer genome atlas project (TCGA) database. Next, univariate cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithms were applied to build a robust AIRGs signature in TCGA and validated in another two independent datasets. Besides, UM patients classified into two subgroups based on the risk model. Differences of clinical outcome, tumor microenvironment and the likelihood of chemotherapeutic response were further explored.Results: In total, a 4-AIRGs signature was constructed and validated in various datasets, which can robustly predict patients’ metastasis-free survival (MFS) and overall survival (OS) and is an independent prognostic factor in UM. The UM patients can be classified into high and low risk subgroups by applied risk score system. The high risk group have poor clinical outcomes, higher CD8+ T cell and macrophage immune-infiltrating and more sensitive to chemotherapies. In addition, Gene Set Enrichment Analysis (GSEA) analysis revealed that hallmark epithelial-mesenchymal transition and KRAS pathways are commonly enriched in high-risk expression phenotype.Conclusion: Thus, our findings provide a new clinical strategy for the accurate diagnosis and identify a novel prognostic autophagy and immunity associated biomarker for the treatment of uveal melanoma.

Development and validation of a six-gene signature for predicting prognosis in prostate cancer patients with lymph node metastasis.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e17524-e17524
Author(s):  
Jinan Guo ◽  
Wenzhuan Xie ◽  
Mengli Huang ◽  
Chan Gao
Keyword(s):  
Lymph Node ◽  
High Risk ◽  
Lymph Node Metastasis ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Wild Type ◽  
Cox Regression Analysis ◽  
Node Metastasis

e17524 Background: Prostate cancer (PCa) patients with lymph node metastasis (LNM) always exhibit poor clinical outcomes. A gene signature that could predict survival in these patients would allow for earlier detection of mortality risk and will also guide individualized therapy. Methods: A prediction model was developed using a public cohort consisting of 623 patients with clinicopathologically confirmed PCa. Data were gathered from cBioPortal and UCSC Xena. Genes expressed differentially in patients with lymph node metastasis versus those without lymph node metastasis were identified. Uni-variate Cox regression analysis and LASSO Cox regression were applied to build a prediction model. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier curves were used to assess the prognostic capacity of the model, followed by external validation using the MSKCC dataset from cBioPortal. Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Results: We identified a six-gene signature (covering GSDMB, SSTR1, MX1, CCBE1, MYBPC1, and FAM3D) that could effectively identify a high-risk subset of PCa patients. ROC analysis indicated that the signature had a good performance (AUC > 0.7) in survival prediction in both the training and the testing/validation cohorts. Cox regression analysis showed that the six-gene signature could independently predict disease-free survival (DFS) as well, although with lower predictive power. Subgroup analyses showed that signature-based risk score may serve as a promising marker to predict DFS in different subgroups, including stage T2 (HR = 0.12, p < 0.001), stage T3 (HR = 0.29, p < 0.001), TP53-wild-type (HR = 0.22, p < 0.001), TP53-mutated (HR = 0.07, p < 0001), AR pathways-wild-type (HR = 0.2, p < 0.001) and AR pathways-mutated(HR = 0.16, p = 0.0419). The performance of the six-gene signature in LNM+ was stable for stratifying the patients according to risk of deatch (HR = 0.23, p = 0.0333). Moreover, GSEA revealed distinct pathway enrichment features in the different risk groups, where pathways related to DNA repair were more prominently enriched in the high-risk group while the low-risk group had higher enrichment of androgen response. Conclusions: We developed a robust six-gene signature that can effectively classify PCa patients into groups with low- and high-risk group, which may help select high-risk patients who require more aggressive adjuvant target therapy or immune therapy.

scholarly journals The Prognostic Value of Metabolic Genes in Gastric Adenocarcinoma

2021 ◽  
Author(s):  
Sanling Huang ◽  
Zefu Liu ◽  
Fangzhou Xu ◽  
Chongxiang Chen ◽  
Hongyu Zhang
Keyword(s):  
Gastric Adenocarcinoma ◽  
Cox Regression ◽  
Risk Group ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract Background: A great number of metabolic genes have been discovered in gastric cancer (GC); however, their prognostic roles remain incompletely clear so far. Methods: The Cancer Genome Atlas-stomach adenocarcinoma, and GSE84437 dataset collected via the Gene Expression Omnibus (GEO) database were utilized for retrieving those clinicopathological data and RNA expression patterns. Besides, the signature was obtained through the lasso Cox regression model and univariate Cox regression analysis. A novel 13-gene metabolic signature (including GSTA2, POLD3, GLA, GGT5, DCK, CKMT2, ASAH1, OPLAH, ME1, ACYP1, NNMT, POLR1A, and RDH12) was constructed for predicting the prognosis for gastric adenocarcinoma. Results: The results suggested that, the survival in high risk group was remarkably dismal compared with that in low risk group. In addition, that as-constructed signature had been identified as a factor to independently predict the prognosis for gastric adenocarcinoma patients. Moreover, the signature-based nomogram showed certain benefits in predicting the overall survival (OS). Besides, results of gene set enrichment analysis (GSEA) suggested that some pathways were markedly enriched, which help to illustrate the possible mechanisms. Conclusions: The new 13-gene metabolic model is constructed in this study to predict the prognosis for GC. It probably reflects dysregulation in the metabolic microenvironment, in the meantime of providing metabolic treatment biomarkers, and predicting the treatment response to gastric adenocarcinoma.

scholarly journals A glycolysis-based three-gene signature predicts survival in patients with lung squamous cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guichuan Huang ◽  
Jing Zhang ◽  
Ling Gong ◽  
Yi Huang ◽  
Daishun Liu
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Cox Regression ◽  
Lung Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background Lung cancer is one of the most lethal and most prevalent malignant tumors worldwide, and lung squamous cell carcinoma (LUSC) is one of the major histological subtypes. Although numerous biomarkers have been found to be associated with prognosis in LUSC, the prediction effect of a single gene biomarker is insufficient, especially for glycolysis-related genes. Therefore, we aimed to develop a novel glycolysis-related gene signature to predict survival in patients with LUSC. Methods The mRNA expression files and LUSC clinical information were obtained from The Cancer Genome Atlas (TCGA) dataset. Results Based on Gene Set Enrichment Analysis (GSEA), we found 5 glycolysis-related gene sets that were significantly enriched in LUSC tissues. Univariate and multivariate Cox proportional regression models were performed to choose prognostic-related gene signatures. Based on a Cox proportional regression model, a risk score for a three-gene signature (HKDC1, ALDH7A1, and MDH1) was established to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis indicated that the risk score for this three-gene signature can be used as an independent prognostic indicator in LUSC. Additionally, based on the cBioPortal database, the rate of genomic alterations in the HKDC1, ALDH7A1, and MDH1 genes were 1.9, 1.1, and 5% in LUSC patients, respectively. Conclusion A glycolysis-based three-gene signature could serve as a novel biomarker in predicting the prognosis of patients with LUSC and it also provides additional gene targets that can be used to cure LUSC patients.

Sign in / Sign up

Export Citation Format

Share Document