scholarly journals Identification of genes that influence poor prognosis in osteosarcoma

2021 ◽  
Author(s):  
Yao Lu ◽  
Yanli Li ◽  
Xueliang Zeng ◽  
Bei Li ◽  
Qiongjun Xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Poor Prognosis ◽  
Bone Cancer ◽  
Interaction Network ◽  
Venn Diagram ◽  
Future Research ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Long Term Prognosis

Abstract Objectives Osteosarcoma (OS) is the most common primary bone cancer in children and adolescents. At present, the 5-year overall survival rate of OS patients is about 65%, and the long-term prognosis is still not ideal. The study was designed to screen genes that could contribute to the poor prognosis of OS and explore their potential pathogenic mechanisms. Methods The gene expression profile of the GSE94805 dataset from the GEO database, containing data from 12 U2OS cell samples, including four control, four quiescent, and four senescent samples was obtained. Co-expressed differentially expressed genes (DEGs) in OS U2OS cells were selected using the GEO2R tool and Venn diagram analysis. Next, using the STRING, Cytoscap, and Molecular Complex Detection (MCODE) plug-in, the related protein-protein interaction network among upregulated genes was analyzed. Moreover, Kaplan-Meier plots were used to analyze the relationship between the identified genes and OS prognosis. Genes significantly associated with worse prognosis were evaluated using the Gene Expression Profiling Interactive Analysis. Results Thirteen genes were confirmed to be significantly more expressed in OS than in normal tissues. Five genes (AURKB, EXO1, KIF4A, KIF15, and MCM4) were found to influence OS prognosis. Conclusion We identified five core genes related to the prognosis of OS and constructed a clinical prediction model for OS. Our data may provide a reference for future research on mechanisms, clinical diagnosis, and treatment of OS.

scholarly journals The Expression of LIMK1 is Related to the Poor Prognosis and Immune Function of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Yisheng Peng ◽  
Jun Fan ◽  
Gang Zhu ◽  
Shunde Tan ◽  
Jianfei Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Immune Function ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Tnm Staging ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
The Poor ◽  
Kaplan Meier

Abstract Background: According to reports, LIMK1 may have the effect of promoting tumor progression. However, the effect of the expression of LIMK1 on the healing of patients with hepatocellular carcinoma and its effect on the immune function are still not clear. Therefore, we analyzed the effect of LIMK1 on the healing of patients with hepatocellular carcinoma and its correlation with immunity through bioinformatics analysis.Methods: Download the transcriptional expression profile of LIMK1 in hepatocellular carcinoma tissues and normal tissues in TCGA, and study its expression in hepatocellular carcinoma. Study the expression of LIMK1 in hepatocellular carcinoma through CPTAC and HPA database. The Kaplan-Meier method was used to evaluate the effect of LIMK1 expression on the survival of patients with hepatocellular carcinoma. Use the STRING database to construct a protein-protein interaction (PPI) network. Use the "ClusterProfiler" package for feature-rich analysis. Use TISIDB database and Xiantao platform to study the relationship between LIMK1 mRNA expression and immune infiltration.Results: The expression of LIMK1 in hepatocellular carcinoma tissues was significantly up-regulated. Increased expression of LIMK1 mRNA is related to high TNM staging. In the ROC curve, when the cut-off level is 1.813, the sensitivity and specificity of LIMK1 to distinguish hepatocellular carcinoma from adjacent controls are 80.7% and 86%, respectively.The Kaplan-Meier curve shows that the higher the expression of LIMK1, the worse the survival of patients with hepatocellular carcinoma (42.2 months vs. 70 months, P = 0.001). Correlation analysis studies have shown that the expression of LIMK1 mRNA in hepatocellular carcinoma is related to immune cell infiltration.Conclusion: Up-regulation of LIMK1 may affect the survival rate and immune invasion of hepatocellular carcinoma. Studies have shown that LIMK1 may be related to the poor prognosis of hepatocellular carcinoma, and has a certain relationship with the immune infiltration of hepatocellular carcinoma.

scholarly journals Identification of Biomarkers Related to Prognosis of Glioblastoma and Their Correlation with Immune Infiltration Cells

2021 ◽  
Author(s):  
Wencong Ding ◽  
Guoqiang Jiang ◽  
Songkai Long ◽  
Yongshi Liao ◽  
Jia Liu
Keyword(s):  
Poor Prognosis ◽  
Cox Regression ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Protein Protein Interaction ◽  
Tumor Associated Macrophage ◽  
M1 Macrophage

Abstract Glioblastoma (GBM) is the most malignant of all known intracranial tumors, meanwhile most patients have a poor prognosis. In order to improve the poor prognosis of GBM patients as much as possible, it is specifically significant to identify biomarkers related to the gene diagnosis and gene therapy. Herein, a total of 343 GBM specimens and 259 non-tumor specimens were collected from four Gene Expression Omnibus (GEO) datasets and TCGA database, and then analyzed the differentially expressed genes (DEGs) from the above data. Through Venn diagram analysis, 54 common upregulated DEGs and 22 common downregulated DEGs were triumphantly recognized. On account of the degree of formation communication in Protein-protein interaction network (PPIN), the 10 upregulated central genes had been ranked, incorporating LOX, IGFBP3, CD44, TIMP1, FN1, VEGFA, POSTN, COL1A1, COL1A2 and COL3A1. By combining the expression levels and the clinical features of GBM, four hub genes (TIMP1, FN1, POSTN and LOX) were significantly up-regulated and related to poor prognosis. Meanwhile, univariate and multivariate Cox regression analysis results suggested that TIMP1 could be one of the independent prognostic factors for GBM patients. Subsequently, TIMP1 was particularly correlated with the immune marker of macrophage M1, macrophage M2, Neutrophils, tumor associated macrophage and Tregs. In conclusion, these findings investigated that TIMP1 might be a new biomarker to determine prognosis and immune infiltration of GBM patients.

scholarly journals MMP1 and MMP9 are potential prognostic biomarkers and targets for uveal melanoma

2021 ◽  
Author(s):  
Tianyu Wang ◽  
Yuanyuan Zhang ◽  
Jianhao Bai ◽  
Yawen Xue ◽  
Qing Peng
Keyword(s):  
Gene Expression ◽  
Uveal Melanoma ◽  
Protein Interaction ◽  
Disease Free Survival ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues

Abstract Background: Uveal melanoma (UVM) is the leading cause of eye-related mortality worldwide. This study aimed to explore the expression and prognostic value of matrix metalloproteinases (MMPs) in UVM.Methods: Gene expression levels were obtained from the Gene Expression Omnibus (GEO) and Oncomine databases. Functional and pathway enrichment analyses were performed using the Metascape database. GeneMANIA was then applied to construct a protein-protein interaction network and identify the hub genes. Moreover, overall (OS) and disease-free survival (DFS) analysis for the hub genes was performed using the UALCAN and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Furthermore, TRRUST was used to predict the targets of the MMPs. Results: Our results revealed that the transcriptional levels of MMP1, MMP9, MMP10, MMP11, MMP13, MMP14, and MMP17 were upregulated in UVM tissues compared to normal tissues. A protein-protein interaction (PPI) network was constructed, and the top 50 hub genes were identified. The functions of MMPs and their neighboring proteins are mainly associated with ECM-receptor interaction, proteoglycans in cancer, the IL-17 signaling pathway, and microRNAs in cancer. Among the MMPs, MMP1/2/9/11/14/15/16/17/24 played significant roles in the progression of UVM from stage 3 to stage 4. We also found that the expression of MMP1, MMP 2, MMP 9, and MMP 16 was positively correlated with OS and DFS in patients with UVM. Additionally, 18 transcription factors associated with nine MMPs were identified.Conclusions: The results of this study may provide potential biomarkers and targets for UVM. However, further studies are required to confirm these results.

scholarly journals MMP1 and MMP9 are potential prognostic biomarkers and targets for uveal melanoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tianyu Wang ◽  
Yuanyuan Zhang ◽  
Jianhao Bai ◽  
Yawen Xue ◽  
Qing Peng
Keyword(s):  
Gene Expression ◽  
Uveal Melanoma ◽  
Protein Interaction ◽  
Disease Free Survival ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues

Abstract Background Uveal melanoma (UVM) is the leading cause of eye-related mortality worldwide. This study aimed to explore the expression and prognostic value of matrix metalloproteinases (MMPs) in UVM. Methods Gene expression levels were obtained from the Gene Expression Omnibus (GEO) and Oncomine databases. Functional and pathway enrichment analyses were performed using the Metascape database. GeneMANIA was then applied to construct a protein-protein interaction network and identify the hub genes. Moreover, overall survival (OS) and disease-free survival (DFS) analysis for the hub genes was performed using the UALCAN and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Furthermore, TRRUST was used to predict the targets of the MMPs. Results Our results revealed that the transcriptional levels of MMP1, MMP9, MMP10, MMP11, MMP13, MMP14, and MMP17 were upregulated in UVM tissues compared to normal tissues. A protein-protein interaction (PPI) network was constructed and the top 50 hub genes were identified. The functions of MMPs and their neighboring proteins are mainly associated with ECM-receptor interaction, proteoglycans in cancer, the IL-17 signaling pathway, and microRNAs in cancer. Among the MMPs, MMP1/2/9/11/14/15/16/17/24 played significant roles in the progression of UVM from stage 3 to stage 4. We also found that the expression of MMP1, MMP2, MMP9, and MMP16 positively correlated with OS and DFS in patients with UVM. Additionally, 18 transcription factors associated with nine MMPs were identified. Conclusions The results of this study may provide potential biomarkers and targets for UVM. However, further studies are required to confirm these results.

scholarly journals Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

2020 ◽  
Vol 48 (7) ◽  
pp. 030006052091001
Author(s):  
Ziqi Meng ◽  
Jiarui Wu ◽  
Xinkui Liu ◽  
Wei Zhou ◽  
Mengwei Ni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Molecular Therapy ◽  
Integrated Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Interactive Analysis ◽  
Kaplan Meier

Objective The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC). Methods Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes. Results We identified 11 hub genes ( CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway. Conclusions In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

scholarly journals Identification of Core Prognosis-Related Candidate Genes in Cervical Cancer via Integrated Bioinformatical Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Jianxia Wei ◽  
Yang Wang ◽  
Kejian Shi ◽  
Ying Wang
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Protein Interactions ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Protein Protein Interactions ◽  
Normal Tissues ◽  
Interactive Analysis ◽  
Kaplan Meier ◽  
Search Tool

Purposes. Cervical cancer (CC) is one of the highest frequently occurred malignant gynecological tumors with high rates of morbidity and mortality. Here, we aimed to identify significant genes associated with poor outcome. Materials and methods. Differentially expressed genes (DEGs) between CC tissues and normal cervical tissues were picked out by GEO2R tool and Venn diagram software. Database for Annotation, Visualization and Integrated Discovery (DAVID) was performed to analyze gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. The protein-protein interactions (PPIs) of these DEGs were visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, Kaplan-Meier analysis was applied to analyze the overall survival among these genes. The Gene Expression Profiling Interactive Analysis (GEPIA) was applied for further validation of the expression level of these genes. Results. The mRNA expression profile datasets of GSE63514, GSE27678, and GSE6791 were downloaded from the Gene Expression Omnibus database (GEO). In total, 76 CC tissues and 35 normal tissues were collected in the three profile datasets. There were totally 73 consistently expressed genes in the three datasets, including 65 up-regulated genes and 8 down-regulated genes. Of PPI network analyzed by Molecular Complex Detection (MCODE) plug-in, all 65 up-regulated genes and 4 down-regulated genes were selected. The results of the Kaplan-Meier survival analysis showed that 3 of the 65 up-regulated genes had a significantly worse prognosis, while 3 of the 4 down-regulated genes had a significantly better outcome. For validation in GEPIA, 4 of 6 genes (PLOD2, ANLN, AURKA, and AR) were confirmed to be significantly deregulated in CC tissues compared to normal tissues. Conclusion. We have identified three up-regulated (PLOD2, ANLN, and AURKA) and a down-regulated DEGs (AR) with poor prognosis in CC on the basis of integrated bioinformatical methods, which could be regarded as potential therapeutic targets for CC patients.

scholarly journals TTK, CDC25A, and ESPL1 as Prognostic Biomarkers for Endometrial Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Qiannan Yang ◽  
Bojun Yu ◽  
Jing Sun
Keyword(s):  
Gene Expression ◽  
Endometrial Cancer ◽  
Protein Interactions ◽  
Poor Prognosis ◽  
Molecular Mechanisms ◽  
Gene Expression Omnibus ◽  
Venn Diagram ◽  
Kegg Pathways ◽  
Kaplan Meier ◽  
Pathway Analyses

Objective. Endometrial cancer (EC) is one of the most common malignant gynaecological tumours worldwide. This study was aimed at identifying EC prognostic genes and investigating the molecular mechanisms of these genes in EC. Methods. Two mRNA datasets of EC were downloaded from the Gene Expression Omnibus (GEO). The GEO2R tool and Draw Venn Diagram were used to identify differentially expressed genes (DEGs) between normal endometrial tissues and EC tissues. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Next, the protein-protein interactions (PPIs) of these DEGs were determined by the Search Tool for the Retrieval of Interacting Genes (STRING) tool and Cytoscape with Molecular Complex Detection (MCODE). Furthermore, Kaplan-Meier survival analysis was performed by UALCAN to verify genes associated with significantly poor prognosis. Next, Gene Expression Profiling Interactive Analysis (GEPIA) was used to verify the expression levels of these selected genes. Additionally, a reanalysis of the KEGG pathways was performed to understand the potential biological functions of selected genes. Finally, the associations between these genes and clinical features were analysed based on TCGA cancer genomic datasets for EC. Results. In EC tissues, compared with normal endometrial tissues, 147 of 249 DEGs were upregulated and 102 were downregulated. A total of 64 upregulated genes were assembled into a PPI network. Next, 14 genes were found to be both associated with significantly poor prognosis and highly expressed in EC tissues. Reanalysis of the KEGG pathways found that three of these genes were enriched in the cell cycle pathway. TTK, CDC25A, and ESPL1 showed higher expression in cancers with late stage and higher tumour grade. Conclusion. In summary, through integrated bioinformatics approaches, we found three significant prognostic genes of EC, which might be potential therapeutic targets for EC patients.

scholarly journals Hyaluronan-mediated motility receptor expression functions as a prognostic biomarker in uterine carcinosarcoma based on bioinformatics analysis

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110210
Author(s):  
Hui Sun ◽  
Li Ma ◽  
Jie Chen
Keyword(s):  
Interaction Network ◽  
Maximal Clique ◽  
Receptor Expression ◽  
The Cancer Genome Atlas ◽  
Uterine Carcinosarcoma ◽  
Sequencing Data ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Potential Biomarker

Objective Uterine carcinosarcoma (UCS) is a rare, aggressive tumour with a high metastasis rate and poor prognosis. This study aimed to explore potential key genes associated with the prognosis of UCS. Methods Transcriptional expression data were downloaded from the Gene Expression Profiling Interactive Analysis database and differentially expressed genes (DEGs) were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses using Metascape. A protein–protein interaction network was constructed using the STRING website and Cytoscape software, and the top 30 genes obtained through the Maximal Clique Centrality algorithm were selected as hub genes. These hub genes were validated by clinicopathological and sequencing data for 56 patients with UCS from The Cancer Genome Atlas database. Results A total of 1894 DEGs were identified, and the top 30 genes were considered as hub genes. Hyaluronan-mediated motility receptor (HMMR) expression was significantly higher in UCS tissues compared with normal tissues, and elevated expression of HMMR was identified as an independent prognostic factor for shorter survival in patients with UCS. Conclusions These results suggest that HMMR may be a potential biomarker for predicting the prognosis of patients with UCS.

Low Expression of SCN4B Correlated with DNA Hypermethylation and Poor Prognosis in Non-small Cell Lung Cancer

2021 ◽  
Author(s):  
Meixiang Yu ◽  
Zi Wang ◽  
Qianzhou Lv ◽  
Wanhua Yang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Poor Prognosis ◽  
Small Cell ◽  
Clinical Grade ◽  
Small Cell Lung ◽  
Immunohistochemistry Staining ◽  
Kaplan Meier ◽  
Nsclc Patients ◽  
Low Expression

Abstract Background:Voltage-gated sodium channels β subunits 4 (SCN4B), a tumor suppressor, was previously reported to be associated with DNA methylation and poor prognosis in multiple cancers except lung cancer. This study aimed to explore whether the low expression of SCN4B was correlated with DNA methylation and clinical prognosis in non-small cell lung cancer (NSCLC) . Methods:The gene expression profiles (GDS3837and GSE50081) were extracted from Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) analysis was performed to explore the expression of SCN4B in NSCLC tissue compared with normal tissue, with the cut-off value p < 0.05 and the absolute value of the log2 fold change ≥ 1.5. Immunohistochemistry staining was used to validated its expression using The Human Protein Atlas database. And MPRESS was used to analysis the relations of SCN4B expression between DNA methylation. Then, the Fisher exact and Wilcoxon rank-sum tests were used to calculate the associations of SCN4B expression with NSCLC clinicopathological features such as clinical grade and tumor node metastasis (TNM) stage, while Kaplan–Meier survival analysis and cox regression analysis were performed to estimate the prognostic value of SCN4B expression in NSCLC. Results: Our DEGS analysis results showed a significantly decreased expression of SCN4B (p=6.5e-22) in NSCLC, which were validated by immunohistochemistry staining. Besides, this decreasing trend continued as the clinical grade and T stage advanced (p<0.05). There was a negative correlation between the SCN4B expression and DNA promoter methylation (p<0.01). Kaplan–Meier survival analysis indicated that NSCLC patients with low expression of SCN4B had a worse prognosis than those with high expression (p < 0.004). Meanwhile, univariate and multivariate analysis indicated SCN4B expression was an independent unfavorable prognostic factor for OS in NSCLC (Hazard Ratio= 0.236, p = 0.009; Hazard Ratio=0.219, p = 0.003, respectively).Conclusions: SCN4B expression was significantly downregulated in NSCLC, which might be attributed to DNA promoter hypermethylation. The low expression of SCN4B indicated a potential unfavorable prognostic factor for NSCLC patients.

scholarly journals Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Wei Han ◽  
Biao Huang ◽  
Xiao-Yu Zhao ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Metastatic Melanoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Subtype Identification ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

Sign in / Sign up

Export Citation Format

Share Document