scholarly journals Identification of Metabolic Reprogramming Related Gene Signature to Predict the Prognosis of Bladder Cancer Patients

2020 ◽  
Author(s):  
Tinghao Li ◽  
Hang Tong ◽  
Hubin Yin ◽  
Honghao Cao ◽  
Junlong Zhu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Treatment Options ◽  
External Validation ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Delayed Treatment ◽  
Emerging Treatments

Abstract Background: Different kinds of metabolic reprogramming have been widely researched in multifarious cancer types and show up as a guaranteed prognostic predictor, while bladder cancer (BLCA) is most frequent urothelium carcinoma but with poor prognosis despite there are emerging treatments, for lack of reliable predicting biomarkers to early predict the prognosis and delayed treatment options for patients in the terminal stage. Our study aims to explore new prognostic factors related to metabolism in BLCA and make these genes up as novel risk stratification.Methods: We selected a large number of samples downloaded from TCGA (The Cancer Genome Atlas) to find out the possible glycolysis-related genes that correlated with differentiation from cancer sample to normal tissue, aimed to find out a more credible model. To make our signature more believable, we chose the clinical features information from GEO (Gene Expression Omnibus) database as external validation cohort.Results: Finally, we established a three glycolysis-related gene signature based on the expression of AK3, GALK1 and NUP205 to make a prediction on the prognosis of BLCA patients, which were also validated by external cohort and whole mixed cohort. As a result, we built a three glycolysis-related gene signature and found its prognosis value is more valuable in high malignancy patients, which may help physicians to make a more aggressive choice.

scholarly journals A Novel Lactate Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironment in Hepatocellular Carcinoma

2022 ◽  
Vol 9 ◽  
Author(s):  
Yue Li ◽  
Huanye Mo ◽  
Shengli Wu ◽  
Xin Liu ◽  
Kangsheng Tu
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Tumor Microenvironment ◽  
External Validation ◽  
Gene Signature ◽  
Metabolic Reprogramming ◽  
Lactate Metabolism ◽  
Related Gene ◽  
High Group ◽  
Cox Regression Analysis

Hepatocellular carcinoma (HCC) is the main subtype of primary liver cancer with high malignancy and poor prognosis. Metabolic reprogramming is a hallmark of cancer and has great importance on the tumor microenvironment (TME). As an abundant metabolite, lactate plays a crucial role in cancer progression and the immunosuppressive TME. Nonetheless, the potential roles of lactate in HCC remain unclear. In this study, we downloaded transcriptomic data of HCC patients with corresponding clinical information from the TCGA and ICGC portals. The TCGA-HCC dataset used as the training cohort, while the ICGC-LIRI-JP dataset was served as an external validation cohort. Cox regression analysis and the LASSO regression model were combined to construct the lactate metabolism-related gene signature (LMRGS). Then, we assessed the clinical significance of LMRGS in HCC. Besides, enriched molecular functions, tumor mutation burden (TMB), infiltrating immune cells, and immune checkpoint were comprehensively analyzed in different LMRGS subgroups. In total, 66 differentially expressed lactate metabolism-related genes (LMRGs) were screened. The functions of LMRGs were mainly enriched in mitochondrial activity and metabolic processes. The LMRGS comprised of six key LMRGs (FKTN, PDSS1, PET117, PUS1, RARS1, and RNASEH1) had significant clinical value for independently predicting the prognosis of HCC patients. The overall survival and median survival of patients in the LMRGS-high group were significantly shorter than in the LMRGS-low group. In addition, there were differences in TMB between the two LMRGS subgroups. The probability of genetic mutations was higher in the LMRGS-high group. Most importantly, the LMRGS reflected the TME characteristics. In the LMRGS-high group, the immune microenvironment presented a suppressed state, accompanied by more inhibitory immune cell infiltration, including follicular helper T cells and regulatory T cells. Additionally, the expression of inhibitory checkpoint molecules was much higher in the LMRGS-high group. Our study suggested that the LMRGS was a robust biomarker to predict the clinical outcomes and evaluate the TME of patients with HCC.

scholarly journals A glycolysis-based 4-mRNA signature correlates with the prognosis and cell cycle process in patients with bladder cancer

2020 ◽  
Author(s):  
Chen Zhang ◽  
Xin Gou ◽  
Weiyang He ◽  
Huaan Yang ◽  
Hubin Yin
Keyword(s):  
Cell Cycle ◽  
Bladder Cancer ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Cancer Subtypes ◽  
Cancer Tissues

Abstract Background: Bladder cancer is one of the most prevalent malignancies worldwide. However, traditional indicators have limited predictive effects on the clinical outcomes of bladder cancer. The aim of this study was to develop and validate a glycolysis-related gene signature for predicting the prognosis of patients with bladder cancer that have limited therapeutic options.Methods: mRNA expression profiling was obtained from patients with bladder cancer from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was conducted to identify glycolytic gene sets that were significantly different between bladder cancer tissues and paired normal tissues. A prognosis-related gene signature was constructed by univariate and multivariate Cox analysis. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves were utilized to evaluate the signature. A nomogram combined with the gene signature and clinical parameters was constructed. Correlations between glycolysis-related gene signature and molecular characterization as well as cancer subtypes were analyzed. RT-qPCR was applied to analyze gene expression. Functional experiments were performed to determine the role of PKM2 in the proliferation of bladder cancer cells.Results: Using a Cox proportional regression model, we established that a 4-mRNA signature (NUP205, NUPL2, PFKFB1 and PKM) was significantly associated with prognosis in bladder cancer patients. Based on the signature, patients were split into high and low risk groups, with different prognostic outcomes. The gene signature was an independent prognostic indicator for overall survival. The ability of the 4-mRNA signature to make an accurate prognosis was tested in two other validation datasets. GSEA was performed to explore the 4-mRNA related canonical pathways and biological processes, such as the cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway. A heatmap showing the correlation between risk score and cell cycle signature was generated. RT-qPCR revealed the genes that were differentially expressed between normal and cancer tissues. Experiments showed that PKM2 plays essential roles in cell proliferation and the cell cycle.Conclusion: The established 4‑mRNA signature may act as a promising model for generating accurate prognoses for patients with bladder cancer, but the specific biological mechanism needs further verification.

scholarly journals An immune cell infiltration-related gene signature predicts prognosis for bladder cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hualin Chen ◽  
Yang Pan ◽  
Xiaoxiang Jin ◽  
Gang Chen
Keyword(s):  
Bladder Cancer ◽  
Immune Cells ◽  
Immune Cell ◽  
External Validation ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Prognostic Indicators ◽  
Immune Cell Infiltration ◽  
Related Gene

AbstractTo explore novel therapeutic targets, develop a gene signature and construct a prognostic nomogram of bladder cancer (BCa). Transcriptome data and clinical traits of BCa were downloaded from UCSC Xena database and Gene Expression Omnibus (GEO) database. We then used the method of Single sample Gene Set Enrichment analysis (ssGSEA) to calculate the infiltration abundances of 24 immune cells in eligible BCa samples. By weighted correlation network analysis (WGCNA), we identified turquoise module with strong and significant association with the infiltration abundance of immune cells which were associated with overall survival of BCa patients. Subsequently, we developed an immune cell infiltration-related gene signature based on the module genes (MGs) and immune-related genes (IRGs) from the Immunology Database and Analysis Portal (ImmPort). Then, we tested the prognostic power and performance of the signature in both discovery and external validation datasets. A nomogram integrated with signature and clinical features were ultimately constructed and tested. Five prognostic immune cell infiltration-related module genes (PIRMGs), namely FPR1, CIITA, KLRC1, TNFRSF6B, and WFIKKN1, were identified and used for gene signature development. And the signature showed independent and stable prognosis predictive power. Ultimately, a nomogram consisting of signature, age and tumor stage was constructed, and it showed good and stable predictive ability on prognosis. Our prognostic signature and nomogram provided prognostic indicators and potential immunotherapeutic targets for BCa. Further researches are needed to verify the clinical effectiveness of this nomogram and these biomarkers.

scholarly journals A novel four-gene prognostic signature as a risk biomarker in cervical cancer

2020 ◽  
Author(s):  
Jun Wang ◽  
Hua Zheng ◽  
Yatian Han ◽  
Geng Wang ◽  
Yanbin Li
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Treatment Options ◽  
Treatment Strategies ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Tissue Samples ◽  
Cox Analysis

Abstract Background: Cervical cancer (CC) is a major malignancy affecting women worldwide, with limited treatment options for patients with advanced disease. The aim of this study was to identify novel prognostic biomarkers for CC by a bioinformatics-based analysis using the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA)-CC cohort. Methods: RNA-Seq data from four GEO datasets (GSE5787, GSE6791, GSE26511, and GSE63514) were used to identify differentially expressed genes (DEGs) between CC and normal cervical tissues. Functional and enrichment analyses of the DEGs were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and the Database for Annotation, Visualization and Integrated Discovery (DAVID). The Oncomine database, Cytoscape software, and Kaplan–Meier survival analysis were used for in-depth screening for hub DEGs. Cox regression was then used to develop prognostic signature, which was in turn used to create a nomogram. Results: A total of 207 DEGs were identified in the tissue samples, eight of which were prognostically significant in terms of overall survival (OS). Thereafter, a novel four-gene signature consisting of DSG2, MMP1, SPP1, and MCM2 was developed and validated using stepwise Cox analysis. The area under the receiver operating characteristic (ROC) curve (AUC) values of 0.785, 0.609, and 0.686 in the training, verification, and combination groups, respectively. Moreover, the nomogram analysis showed that a combination of this four-gene signature plus lymph node metastasis (LNM) status effectively predicted the 1- and 3-year OS probabilities of CC patients with accuracies of 69.01% and 83.93%, respectively. Conclusions: We developed a four-gene signature that can accurately predict the prognosis, in terms of OS, of CC patients, and could be a valuable tool for designing treatment strategies.

scholarly journals A glycolysis-based 4-mRNA signature correlates with the prognosis and cell cycle process in patients with bladder cancer

2020 ◽  
Author(s):  
Chen Zhang ◽  
Xin Gou ◽  
Weiyang He ◽  
Huaan Yang ◽  
Hubin Yin
Keyword(s):  
Cell Cycle ◽  
Bladder Cancer ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Related Gene ◽  
Cancer Subtypes ◽  
Cancer Tissues

Abstract Background: Bladder cancer is one of the most prevalent malignancies worldwide. However, traditional indicators have limited predictive effects on the clinical outcomes of bladder cancer. The aim of this study was to develop and validate a glycolysis-related gene signature for predicting the prognosis of patients with bladder cancer that have limited therapeutic options. Methods: mRNA expression profiling was obtained from patients with bladder cancer from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was conducted to identify glycolytic gene sets that were significantly different between bladder cancer tissues and paired normal tissues . A prognosis-related gene signature was constructed by univariate and multivariate Cox analysis. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves were utilized to evaluate the signature. A nomogram combined with the gene signature and clinical parameters was constructed. Correlations between glycolysis-related gene signature and molecular characterization as well as cancer subtypes were analyzed. RT-qPCR was applied to analyze gene expression. Functional experiments were performed to determine the role of PKM2 in the proliferation of bladder cancer cells. Results: Using a Cox proportional regression model, we established that a 4-mRNA signature (NUP205, NUPL2, PFKFB1 and PKM) was significantly associated with prognosis in bladder cancer patients. Based on the signature, patients were split into high and low risk groups, with different prognostic outcomes. The gene signature was an independent prognostic indicator for overall survival. The ability of the 4-mRNA signature to make an accurate prognosis was tested in two other validation datasets. GSEA was performed to explore the 4-mRNA related canonical pathways and biological processes, such as the cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway. A heatmap showing the correlation between risk score and cell cycle signature was generated. RT-qPCR revealed the genes that were differentially expressed between normal and cancer tissues. Experiments showed that PKM2 plays essential roles in cell proliferation and the cell cycle. Conclusion: The established 4‑mRNA signature may act as a promising model for generating accurate prognoses for patients with bladder cancer, but the specific biological mechanism needs further verification.

A ferroptosis-related gene signature predicts overall survival in patients with lung adenocarcinoma

2021 ◽  
Author(s):  
Xinliang Gao ◽  
Mingbo Tang ◽  
Suyan Tian ◽  
Jialin Li ◽  
Wei Liu
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
Therapeutic Option ◽  
Risk Groups ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Related Gene ◽  
Underlying Mechanisms

Aims: To elucidate the association between ferroptosis-related genes and prognosis in patients with lung adenocarcinoma (LUAD). Materials & methods: A ferroptosis-related gene signature was made by lasso regression analysis through the LUAD datasets of the Cancer Genome Atlas. The prognostic value of the multigene signature was externally validated in the GSE72094 dataset from the Gene Expression Omnibus database. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were used to explore underlying mechanisms. Results and conclusion: We established a novel ferroptosis-related gene signature for overall survival in LUAD that was predictive in both the training and validation cohorts. Immune-related pathways were significantly enriched, and immune status differed between the high- and low-risk groups. Targeting ferroptosis is a potential therapeutic option in LUAD. These results still need to be confirmed by more studies.

scholarly journals Novel Epigenetic Eight-Gene Signature Predictive of Poor Prognosis and MSI-Like Phenotype in Human Metastatic Colorectal Carcinomas

Cancers ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 158
Author(s):  
Valentina Condelli ◽  
Giovanni Calice ◽  
Alessandra Cassano ◽  
Michele Basso ◽  
Maria Grazia Rodriquenz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Poor Prognosis ◽  
Cpg Island ◽  
Colon Adenocarcinoma ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Cpg Island Methylator Phenotype ◽  
Prognostic Information ◽  
Global Methylation

Epigenetics is involved in tumor progression and drug resistance in human colorectal carcinoma (CRC). This study addressed the hypothesis that the DNA methylation profiling may predict the clinical behavior of metastatic CRCs (mCRCs). The global methylation profile of two human mCRC subgroups with significantly different outcome was analyzed and compared with gene expression and methylation data from The Cancer Genome Atlas COlon ADenocarcinoma (TCGA COAD) and the NCBI GENE expression Omnibus repository (GEO) GSE48684 mCRCs datasets to identify a prognostic signature of functionally methylated genes. A novel epigenetic signature of eight hypermethylated genes was characterized that was able to identify mCRCs with poor prognosis, which had a CpG-island methylator phenotype (CIMP)-high and microsatellite instability (MSI)-like phenotype. Interestingly, methylation events were enriched in genes located on the q-arm of chromosomes 13 and 20, two chromosomal regions with gain/loss alterations associated with adenoma-to-carcinoma progression. Finally, the expression of the eight-genes signature and MSI-enriching genes was confirmed in oxaliplatin- and irinotecan-resistant CRC cell lines. These data reveal that the hypermethylation of specific genes may provide prognostic information that is able to identify a subgroup of mCRCs with poor prognosis.

scholarly journals Identification and development of a novel invasion-related gene signature for prognosis prediction in colon adenocarcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jiahua Liu ◽  
Chunhui Jiang ◽  
Chunjie Xu ◽  
Dongyang Wang ◽  
Yuguang Shen ◽  
...  
Keyword(s):  
T Cells ◽  
External Validation ◽  
Colon Adenocarcinoma ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Immune Infiltration ◽  
Prognosis Prediction ◽  
Protein Levels ◽  
Limma Package ◽  
Cox Analysis

AbstractThe overall survival of metastatic colon adenocarcinoma (COAD) remains poor, so it is important to explore the mechanisms of metastasis and invasion. This study aimed to identify invasion-related genetic markers for prognosis prediction in patients with COAD. Three molecular subtypes (C1, C2, and C3) were obtained based on 97 metastasis-related genes in 365 COAD samples from The Cancer Genome Atlas (TCGA). A total of 983 differentially expressed genes (DEGs) were identified among the different subtypes by using the limma package. A 6-gene signature (ITLN1, HOXD9, TSPAN11, GPRC5B, TIMP1, and CXCL13) was constructed via Lasso-Cox analysis. The signature showed strong robustness and could be used in the training, testing, and external validation (GSE17537) cohorts with stable predictive efficiency. Compared with other published signatures, our model showed better performance in predicting outcomes. Pan-cancer expression analysis results showed that ITLN1, TSPAN11, CXCL13, and GPRC5B were downregulated and TIMP1 was upregulated in most tumor samples, including COAD, which was consistent with the results of the TCGA and GEO cohorts. Western blot analysis and immunohistochemistry were performed to validate protein expression. Tumor immune infiltration analysis results showed that TSPAN11, GPRC5B, TIMP1, and CXCL13 protein levels were significantly positively correlated with CD4+ T cells, macrophages, neutrophils, and dendritic cells. Further, the TIMP1 and CXCL13 proteins were significantly related to the tumor immune infiltration of CD8+ T cells. We recommend using our signature as a molecular prognostic classifier to assess the prognostic risk of patients with COAD.

scholarly journals A lipid metabolism-related genes prognosis biomarker associated with the tumor immune microenvironment in colorectal carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chao Yang ◽  
Shuoyang Huang ◽  
Fengyu Cao ◽  
Yongbin Zheng
Keyword(s):  
Lipid Metabolism ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Risk Groups ◽  
Clinical Parameter ◽  
Gene Expression Omnibus ◽  
Metabolic Reprogramming ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment

Abstract Background and aim Lipid metabolic reprogramming is considered to be a new hallmark of malignant tumors. The purpose of this study was to explore the expression profiles of lipid metabolism-related genes (LMRG) in colorectal cancer (CRC). Methods The lipid metabolism statuses of 500 CRC patients from the Cancer Genome Atlas (TCGA) and 523 from the Gene Expression Omnibus (GEO GSE39582) database were analyzed. The risk signature was constructed by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression. Results A novel four-LMRG signature (PROCA1, CCKBR, CPT2, and FDFT1) was constructed to predict clinical outcomes in CRC patients. The risk signature was shown to be an independent prognostic factor for CRC and was associated with tumour malignancy. Principal components analysis demonstrated that the risk signature could distinguish between low- and high-risk patients. There were significantly differences in abundances of tumor-infiltrating immune cells and mutational landscape between the two risk groups. Patients in the low-risk group were more likely to have higher tumor mutational burden, stem cell characteristics, and higher PD-L1 expression levels. Furthermore, a genomic-clinicopathologic nomogram was established and shown to be a more effective risk stratification tool than any clinical parameter alone. Conclusions This study demonstrated the prognostic value of LMRG and showed that they may be partially involved in the suppressive immune microenvironment formation.

Sign in / Sign up

Export Citation Format

Share Document