scholarly journals Identification of a novel autophagy-related gene signature for predicting metastasis and survival in patients with osteosarcoma

2020 ◽  
Author(s):  
Guangzhi Zhang ◽  
Yajun Deng ◽  
Zuolong Wu ◽  
Enhui Ren ◽  
Wenhua Yuan ◽  
...  
Keyword(s):  
Muscle Tissue ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Tissue Expression ◽  
Gene Signature ◽  
Related Gene ◽  
Normal Muscle ◽  
Tissue Samples ◽  
Protein Protein Interaction ◽  
Gene Expression Profile Data

Abstract Background: Osteosarcoma (OS) is a bone malignant tumor that occurs in children and adolescents. Due to a lack of reliable prognostic biomarkers, the prognosis of OS patients is often uncertain. This study aimed to construct an autophagy-related gene signature to predict the prognosis of OS patients.Methods: The gene expression profile data of OS and normal muscle tissue samples were downloaded separately from the Therapeutically Applied Research To Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases . The differentially expressed autophagy-related genes (DEARGs) in OS and normal muscle tissue samples were screened using R software, before being subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A protein-protein interaction (PPI) network was constructed and hub autophagy-related genes were screened. Finally, the screened autophagy-related genes were subjected to univariate Cox regression, Lasso Cox regression, survival analysis, and clinical correlation analysis.Results: A total of 120 DEARGs and 10 hub autophagy-related genes were obtained. A prognostic autophagy-related gene signature consisting of 9 genes ( BNIP3 , MYC , BAG1 , CALCOCO2 , ATF4 , AMBRA1 , EGFR , MAPK1 , and PEX ) was constructed. This signature was significantly correlated to the prognosis ( P <0.0001) and distant metastasis of OS patients ( P = 0.013).Conclusion: This signature based on 9 autophagy-related genes could predict metastasis and survival in patients with OS.

scholarly journals Establishment and Validation of a Novel Metabolism-related Gene Signature for Predicting Overall Survival of Patients with Breast Cancer

2021 ◽  
Author(s):  
Jian Li ◽  
Yang Liu ◽  
Fei Liu ◽  
Qiang Tian ◽  
Baojiang Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Treatment Strategies ◽  
Enrichment Analysis ◽  
Risk Groups ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Current Status ◽  
Lasso Regression ◽  
Related Gene

Abstract It is well known that Breast cancer is a heterogeneous disease.Although the current recurrence and mortality rate have been greatly improved, many people still suffer relapse and metastasis.Metabolic reprograming is currently considered to be a new hallmark of cancer.Therefore,in this study, we comprehensively analyzed the prognostic effect of metabolic-related gene signatures in breast cancer and its relationship with the immune microenvironment.We constructed a novel metabolic-related gene signature containing 6 genes to distinguish between high and low risk groups by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression, and validated its robustness and accuracy through multiple databases.The metabolic gene signature may be an independent risk factor for BC both in the training and the testing set,the nomogram has a moderately accurate performance,and the C index was 0.757 and 0.728 respectively.The signature can reveal metabolic characteristics based on gene set enrichment analysis and at the same time monitor the status of TME.This gene signature can be used as a promising independent prognostic marker for BC patients, and can indicate the current status of TME, providing more clues for exploring new diagnostic and treatment strategies.

scholarly journals Genomics and prognosis analysis of epithelial-mesenchymal transition in colorectal cancer patients

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Zizhen Zhang ◽  
Sheng Zheng ◽  
Yifeng Lin ◽  
Jiawei Sun ◽  
Ning Ding ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Cox Regression ◽  
Epithelial Mesenchymal Transition ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Related Gene ◽  
Mesenchymal Transition ◽  
Cox Regression Analysis

Abstract Background The epithelial-mesenchymal transition (EMT) plays a pivotal role in various physiological processes, such as embryonic development, tissue morphogenesis, and wound healing. EMT also plays an important role in cancer invasion, metastasis, and chemoresistance. Additionally, EMT is partially responsible for chemoresistance in colorectal cancer (CRC). The aim of this research is to develop an EMT-based prognostic signature in CRC. Methods RNA-seq and microarray data, together with clinical information, were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. A total of 244 differentially expressed EMT-related genes (ERGs) were obtained by comparing the expression between normal and tumor tissues. An EMT-related signature of 11 genes was identified as crucially related to the overall survival (OS) of patients through univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and Cox regression analysis. Finally, we established a clinical nomogram to predict the survival possibility of CRC patients by integrating clinical characteristics and the EMT-related gene signature. Results Two hundred and forty-four differentially expressed ERGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that EMT-related signaling pathway genes were highly related to CRC. Kaplan-Meier analysis revealed that the 11-EMT signature could significantly distinguish high- and low-risk patients in both TCGA and GEO CRC cohorts. In addition, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusion We developed a novel EMT-related gene signature for the prognosis prediction of CRC patients, which could improve the individualized outcome prediction in CRC.

scholarly journals Establishment of A Nomogram for Predicting the Prognosis of Soft Tissue Sarcoma Based on Seven Glycolysis-Related Gene Risk Score

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuhang Liu ◽  
Changjiang Liu ◽  
Hao Zhang ◽  
Xinzeyu Yi ◽  
Aixi Yu
Keyword(s):  
Soft Tissue ◽  
Soft Tissue Sarcoma ◽  
Cox Regression ◽  
Sequence Data ◽  
Tissue Expression ◽  
Gene Signature ◽  
Prognostic Indicators ◽  
Related Gene ◽  
Training Set ◽  
Predictive Values

Background: Soft tissue sarcoma (STS) is a group of tumors with a low incidence and a complex type. Therefore, it is an arduous task to accurately diagnose and treat them. Glycolysis-related genes are closely related to tumor progression and metastasis. Hence, our study is dedicated to the development of risk characteristics and nomograms based on glycolysis-related genes to assess the survival possibility of patients with STS.Methods: All data sets used in our research include gene expression data and clinical medical characteristics in the Genomic Data Commons Data Portal (National Cancer Institute) Soft Tissue Sarcoma (TCGA SARC) and GEO database, gene sequence data of corresponding non-diseased human tissues in the Genotype Tissue Expression (GTEx).Next, transcriptome data in TCGA SARC was analyzed as the training set to construct a glycolysis-related gene risk signature and nomogram, which were confirmed in external test set.Results: We identified and verified the 7 glycolysis-related gene signature that is highly correlated with the overall survival (OS) of STS patients, which performed excellently in the evaluation of the size of AUC, and calibration curve. As well as, the results of the analysis of univariate and multivariate Cox regression demonstrated that this 7 glycolysis-related gene characteristic acts independently as an influence predictor for STS patients. Therefore, a prognostic-related nomogram combing 7 gene signature with clinical influencing features was constructed to predict OS of patients with STS in the training set that demonstrated strong predictive values for survival.Conclusion: These results demonstrate that both glycolysis-related gene risk signature and nomogram were efficient prognostic indicators for patients with STS. These findings may contribute to make individualize clinical decisions on prognosis and treatment.

scholarly journals Identification of an EMT-Related Gene Signature for Predicting Overall Survival in Gastric Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Weiyu Dai ◽  
Yizhi Xiao ◽  
Weimei Tang ◽  
Jiaying Li ◽  
Linjie Hong ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Cox Regression ◽  
External Validation ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Time Dependent ◽  
Pathway Enrichment Analysis ◽  
Related Gene ◽  
Pathway Enrichment ◽  
Qrt Pcr

BackgroundIt has been widely reported that epithelial-mesenchymal transition (EMT) is associated with malignant progression in gastric cancer (GC). Integration of the molecules related to EMT for predicting overall survival (OS) is meaningful for understanding the role of EMT in GC. Here, we aimed to establish an EMT-related gene signature in GC.MethodsTranscriptional profiles and clinical data of GC were downloaded from The Cancer Genome Atlas (TCGA). We constructed EMT-related gene signature for predicting OS by using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier analysis were performed to assess its predictive value. A nomogram combining the prognostic signature with clinical characteristics for OS prediction was established. And its predictive power was estimated by concordance index (C-index), time-dependent ROC curve, calibration curve and decision curve analysis (DCA). GSE62254 dataset from Gene Expression Omnibus (GEO) was used for external validation. Quantitative real-time PCR (qRT-PCR) was used to detected the mRNA expression of the five EMT-related genes in human normal gastric mucosal and GC cell lines. To further understand the potential mechanisms of the signature, Gene Set Enrichment Analysis (GSEA), pathway enrichment analysis, predictions of transcription factors (TFs)/miRNAs were performed.ResultsA novel EMT-related gene signature (including ITGAV, DAB2, SERPINE1, MATN3, PLOD2) was constructed for OS prediction of GC. With external validation, ROC curves indicated the signature’s good performance. Patients stratified into high- and low-risk groups based on the signature yielded significantly different prognosis. Univariate and multivariate Cox regression suggested that the signature was an independent prognostic variable. Nomogram for prognostication including the signature presented better predictive accuracy and clinical usefulness than the similar model without risk score to some extent with external validation. The qRT-PCR assays suggested that high expression of the five EMT-related genes could be found in human GC cell lines compared with normal gastric mucosal cell line. GSEA and pathway enrichment analysis revealed that focal adhesion and ECM-receptor interaction might be the two important pathways to the signature.ConclusionOur EMT-related gene signature may have practical application as an independent prognostic factor in GC.

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.

scholarly journals Cyclin B1 acts as a tumor microenvironment-related cancer promoter and prognostic biomarker in hepatocellular carcinoma

2021 ◽  
Vol 49 (5) ◽  
pp. 030006052110162
Author(s):  
Yangming Hou ◽  
Xin Wang ◽  
Junwei Wang ◽  
Xuemei Sun ◽  
Xinbo Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Proportional Hazards ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Cyclin B1 ◽  
Cox Proportional Hazards ◽  
Gene Set Enrichment Analysis ◽  
Tumor Promoter ◽  
Protein Protein Interaction

Objectives The present study aimed to develop a gene signature based on the ESTIMATE algorithm in hepatocellular carcinoma (HCC) and explore possible cancer promoters. Methods The ESTIMATE and CIBERSORT algorithms were applied to calculate the immune/stromal scores and the proportion of tumor-infiltrating immune cells (TICs) in a cohort of HCC patients. The differentially expressed genes (DEGs) were screened by Cox proportional hazards regression analysis and protein–protein interaction (PPI) network construction. Cyclin B1 (CCNB1) function was verified using experiments. Results The stromal and immune scores were associated with clinicopathological factors and recurrence-free survival (RFS) in HCC patients. In total, 546 DEGs were up-regulated in low score groups, 127 of which were associated with RFS. CCNB1 was regarded as the most predictive factor closely related to prognosis of HCC and could be a cancer promoter. Gene Set Enrichment Analysis (GSEA) and CIBERSORT analyses indicated that CCNB1 levels influenced HCC tumor microenvironment (TME) immune activity. Conclusions The ESTIMATE signature can be used as a prognosis tool in HCC. CCNB1 is a tumor promoter and contributes to TME status conversion.

scholarly journals Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

2021 ◽  
Vol 27 ◽  
Author(s):  
Aoshuang Qi ◽  
Mingyi Ju ◽  
Yinfeng Liu ◽  
Jia Bi ◽  
Qian Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Antigen Processing ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Gene Set ◽  
Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) &lt; 1), and HLA-F was risky (HR &gt; 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

scholarly journals Identification of a five-gene signature in association with overall survival for hepatocellular carcinoma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11273
Author(s):  
Lei Yang ◽  
Weilong Yin ◽  
Xuechen Liu ◽  
Fangcun Li ◽  
Li Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cell Cycle ◽  
Overall Survival ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Enrichment Score ◽  
Hub Genes ◽  
Cox Regression Analysis

Background Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC. Methods The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort. Results We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.

scholarly journals Combination the Gene Expression of Adjacent Normal and Tumor Makes more Robust Gene Signature as Cancer Prognosis Biomarker

2020 ◽  
Author(s):  
Wei Ma ◽  
Dandan Li ◽  
Changjian Zhang ◽  
Ming Xiong ◽  
Yuanyuan Qiao
Keyword(s):  
Expression Profile ◽  
Normal Tissue ◽  
Cox Regression ◽  
Expression Profiles ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Adjacent Normal Tissue

Abstract Purpose: We tried to explore new gene signature via the combination of tumor-derived expression profile and the adjacent normal-derived expression profile to find more robust cancer biomarker. Methods: Log2 transformed ratio of tumor tissue and the adjacent normal tissue (Log2TN) expression, tumor-derived expression, and normal-derived expression were used to do univariate Cox regression in The Cancer Genome Atlas (TCGA) lung squamous cell carcinoma (LUSC) respectively. Then, we used factor analysis and least absolute shrinkage and selection operator Cox (LASSO-Cox) to select gene signature in TCGA LUSC for Log2TN, tumor, and adjacent normal respectively.Results: By comparing Log2TN with tumor and adjacent normal in LUSC, we found that genes derived from Log2TN show more robust (p = 0.006 and p = 0.001) and have lower p-values (p < 0.001). Gene signature selected from Log2TN shows the best generalization in the three GEO datasets even though only tumor-derived expression profiles were available in the three datasets. Enrichment analysis showed that the tumor cells mainly focus on proliferation with losing functional of metabolism.Conclusions: These results indicate that (1) Log2TN could get more robust genes and gene signature than tumor-derived expression profiles used traditionally; (2) the adjacent-normal tissue may also play an important role in the progress and outcome of the tumor.Implications for Cancer Survivors: By combined of tumor-derived expression profile and the adjacent normal-derived expression profile, we could find more robust gene signature than traditionally method. Using these robust gene signatures, robust cancer biomarkers could be constructed and will do great help to improve cancer prognosis.

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

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