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.

scholarly journals Evaluation of SYK Gene as a Prognostic Biomarker and Suggested Aromatic Potential Phytochemicals to Halt the Colorectal Cancer

2021 ◽  
Author(s):  
Partha Biswas ◽  
Dipta Dey ◽  
Atikur Rahman ◽  
Md. Aminul Islam ◽  
Tasmina Ferdous Susmi ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Molecular Docking ◽  
Drug Repositioning ◽  
Treatment Strategies ◽  
Interaction Network ◽  
Genetic Alterations ◽  
Gene Encoding ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Potential Biomarker

Background: Colorectal cancer is considered the third most fetal among all type of cancer. Spleen tyrosine kinase (SYK) is a non-receptor type tyrosine-protein that plays crucial role in signaling mediated via immune receptor. We adopted an onco-informatics analysis to evaluate the SYK expression and prognostic value of SYK in colorectal cancer, and identification of potential phytochemicals which may inhibit overexpression of SYK protein as well as minimized colorectal cancer. Materials & Methods: Differential expression of SYK gene was analyzed using the several transcriptomic databases including Oncomine, UALCAN, GENT2 and GEPIA2. The server, cBioPortal was used to analyze mutation and copy number alterations whereas GENT2, GEPIA, OncoLnc and PrognoScan were employed to examine the survival rate. A protein-protein interaction network of SYK and co-expressed genes of SYK was conducted via GeneMANIA. Considering SYK gene encoding protein as drug target, selected phytochemicals were assessed by molecular docking using PyRx 0.8 packages. YASARA molecular dynamics simulators were applied for the post validation of the molecular docking data. Results: We have observed significant overexpression of mRNA expression levels of SYK gene colorectal adenocarcinoma (COAD) samples compared with normal tissues. Significant methylation level and various genetic alterations are assembled in SYK gene which can lead to the development of colorectal cancer. As a result, lower level of SYK expression was related to the more chances of patients’ survival by which all the outcomes from the multiple bioinformatics platforms and web resources have demonstrated the significant evidences that the SYK kinsase can possess as a potential biomarker for the treatment of colorectal cancer. Here, aromatic phytochemicals namely, Kaempferol and Glabridin targeting SYK showed more stability compared to controls and may be useful for the treatment of colorectal cancer. Conclusion: Our study showed dysregulated expression of SYK in colorectal cancer and potentiality to act as a biomarker for the prognosis of CRC. Moreover, we have shown phytochemicals (Kaempferol and Glabridin) target SYK as potential treatment strategies and drug repositioning potentiality in colorectal cancer.

scholarly journals Lipid metabolic gene-wide profile and survival signature of lung adenocarcinoma 

2020 ◽  
Author(s):  
Jinyou Li ◽  
Qiang Li ◽  
Zhenyu Su ◽  
Qi Sun ◽  
Yong Zhao ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lipid Metabolism ◽  
Lung Adenocarcinoma ◽  
Interaction Network ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
High Morbidity ◽  
Hub Genes ◽  
Histologic Subtype ◽  
Normal Tissues

Abstract Background: Lung cancer is the cancer with high morbidity and mortality across the globe, and lung adenocarcinoma (LUAD) is the most common histologic subtype. The disorder of lipid metabolism is related to the development of cancer. Analysis of lipid-related transcriptome helps shed light on diagnosis and prognostic biomarkers of LUAD. Methods: In this study, we performed an expression analysis of 1045 lipid metabolism-related genes between LUAD tumors and normal tissues from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort. The interaction network of differential expression genes (DEGs) was constructed to identify. The association between hub genes and overall survival was evaluated and formed a model to predict the prognosis of LUAD using a nomogram, and the model was validated by another cohort (GSE13213). Results: Finally, a total of 217 lipid metabolism-related DEGs were detected in LUAD. They were significantly enriched in Glycerophospholipid metabolism, fatty acid metabolic process, and Eicosanoid Signaling. Then we identified 6 hub genes through network and cytoHubba, including INS, LPL, HPGDS, DGAT1, UGT1A6, and CYP2C9. The high expression of CYP2C9, UGT1A6, and INS, whereas low expressions of DGAT1, HPGDS, and LPL, were associated with worse overall survival (OS) for 1925 LUAD patients. Our model found that the high-risk score group had a worse OS, and the validated cohort had the same result.Conclusion: This study constructed a signature of six lipid metabolic genes, which was significantly associated with the diagnosis and prognosis of LUAD patients. The gene signature can be used as a biomarker for LUAD in the term of lipid metabolic.

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 Lipid metabolism gene-wide profile and survival signature of lung adenocarcinoma

2020 ◽  
Author(s):  
Jinyou Li ◽  
Qiang Li ◽  
Zhenyu Su ◽  
Qi Sun ◽  
Yong Zhao ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lipid Metabolism ◽  
Lung Adenocarcinoma ◽  
Interaction Network ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
High Morbidity ◽  
Hub Genes ◽  
Histologic Subtype ◽  
Normal Tissues

Abstract Background: Lung cancer has high morbidity and mortality across the globe, and lung adenocarcinoma (LUAD) is the most common histologic subtype. Disordered lipid metabolism is related to the development of cancer. Analysis of lipid-related transcriptome helps shed light on the diagnosis and prognostic biomarkers of LUAD. Methods: In this study, expression analysis of 1045 lipid metabolism-related genes was performed between LUAD tumors and normal tissues derived from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort. The interaction network of differentially expressed genes (DEGs) was constructed to identify the hub genes. The association between hub genes and overall survival (OS) was evaluated and formed a model to predict the prognosis of LUAD using a nomogram. The model was validated by another cohort, GSE13213. Results: A total of 217 lipid metabolism-related DEGs were detected in LUAD. Genes were significantly enriched in glycerophospholipid metabolism, fatty acid metabolic process, and eicosanoid signaling. Through network analysis and cytoHubba, 6 hub genes were identified, including INS, LPL, HPGDS, DGAT1, UGT1A6, and CYP2C9. High expression of CYP2C9, UGT1A6, and INS, and low expressions of DGAT1, HPGDS, and LPL, were associated with worse overall survival for 1925 LUAD patients. The model showed that the high-risk score group had a worse OS, and the validated cohort showed the same result.Conclusions: In this study, a signature of 6 lipid metabolism genes was constructed, which was significantly associated with the diagnosis and prognosis of LUAD patients. Thus, the gene signature can be used as a biomarker for LUAD.

scholarly journals Identifying hub genes of papillary thyroid carcinoma in the TCGA and GEO database using bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9120
Author(s):  
Ying Wan ◽  
Xiaolian Zhang ◽  
Huilin Leng ◽  
Weihua Yin ◽  
Wenxing Zeng ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
Candidate Genes ◽  
Interaction Network ◽  
Good Prognosis ◽  
The Cancer Genome Atlas ◽  
Genome Database ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Degree Of Malignancy ◽  
Geo Database

Background Thyroid carcinoma (THCA) is a common endocrine malignant tumor. Papillary carcinoma with low degree of malignancy and good prognosis is the most common. It can occur at any age, but it is more common in young adults. Although the mortality rate is decreased due to early diagnosis, the survival rate varies depending on the type of tumor. Therefore, the purpose of this study is to identify hub biomarkers and novel therapeutic targets for THCA. Methods The GSE3467, GSE3678, GSE33630 and GSE53157 were obtained from the GEO database, including 100 thyroid tumors and 64 normal tissues to obtain the intersection of differentially expressed genes, and a protein-protein interaction network was constructed to obtain the HUB gene. The corresponding overall survival information from The Cancer Genome Atlas Project-THCA was then included in this research. The signature mechanism was studied by analyzing the gene ontology and the Kyoto Encyclopedia of Genes and Genome database. Results In this research, we identified eight candidate genes (FN1, CCND1, CDH2, CXCL12, MET, IRS1, DCN and FMOD) from the network. Also, expression verification and survival analysis of these candidate genes based on the TCGA database indicate the robustness of the above results. Finally, our hospital samples validated the expression levels of these genes. Conclusion The research identified eight mRNA (four up–regulated and four down–regulated) which serve as signatures and could be a potential prognostic marker of THCA.

scholarly journals Lipid metabolism gene-wide profile and survival signature of lung adenocarcinoma

2020 ◽  
Author(s):  
Jinyou Li ◽  
Qiang Li ◽  
Zhenyu Su ◽  
Qi Sun ◽  
Yong Zhao ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lipid Metabolism ◽  
Lung Adenocarcinoma ◽  
Interaction Network ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
High Morbidity ◽  
Hub Genes ◽  
Histologic Subtype ◽  
Normal Tissues

Abstract Background: Lung cancer has high morbidity and mortality across the globe, and lung adenocarcinoma (LUAD) is the most common histologic subtype. A disordered lipid metabolism is related to the development of cancer. Analysis of lipid-related transcriptome helps shed light on the diagnosis and prognostic biomarkers of LUAD. Methods: In this study, expression analysis of 1045 lipid metabolism-related genes was performed between LUAD tumors and normal tissues derived from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort. The interaction network of differentially-expressed genes (DEGs) was constructed to identify the hub genes. The association between hub genes and overall survival (OS) was evaluated and formed a model to predict the prognosis of LUAD using a nomogram. The model was validated by another cohort, GSE13213. Results: A total of 217 lipid metabolism-related DEGs were detected in LUAD. Genes were significantly enriched in glycerophospholipid metabolism, fatty acid metabolic process, and eicosanoid signaling. Through network analysis and cytoHubba, 6 hub genes were identified, including INS, LPL, HPGDS, DGAT1, UGT1A6, and CYP2C9. High expression of CYP2C9, UGT1A6, and INS, and low expressions of DGAT1, HPGDS, and LPL, were associated with a worse overall survival for 1925 LUAD patients. The model showed that the high-risk score group had a worse OS, and the validated cohort showed the same result. Conclusions: In this study, a signature of 6 lipid metabolism genes was constructed, which was significantly associated with the diagnosis and prognosis of LUAD patients. Thus, the gene signature can be used as a biomarker for LUAD.

scholarly journals Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Chengzhang Li ◽  
Jiucheng Xu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Random Forest ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Maximal Clique ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Fisher Score ◽  
Protein Protein Interaction ◽  
Kaplan Meier

AbstractThis study aimed to select the feature genes of hepatocellular carcinoma (HCC) with the Fisher score algorithm and to identify hub genes with the Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to examine the enrichment of terms. Gene set enrichment analysis (GSEA) was used to identify the classes of genes that are overrepresented. Following the construction of a protein-protein interaction network with the feature genes, hub genes were identified with the MCC algorithm. The Kaplan–Meier plotter was utilized to assess the prognosis of patients based on expression of the hub genes. The feature genes were closely associated with cancer and the cell cycle, as revealed by GO, KEGG and GSEA enrichment analyses. Survival analysis showed that the overexpression of the Fisher score–selected hub genes was associated with decreased survival time (P < 0.05). Weighted gene co-expression network analysis (WGCNA), Lasso, ReliefF and random forest were used for comparison with the Fisher score algorithm. The comparison among these approaches showed that the Fisher score algorithm is superior to the Lasso and ReliefF algorithms in terms of hub gene identification and has similar performance to the WGCNA and random forest algorithms. Our results demonstrated that the Fisher score followed by the application of the MCC algorithm can accurately identify hub genes in HCC.

scholarly journals Lipid metabolism gene-wide profile and survival signature of lung adenocarcinoma

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Jinyou Li ◽  
Qiang Li ◽  
Zhenyu Su ◽  
Qi Sun ◽  
Yong Zhao ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lipid Metabolism ◽  
Lung Adenocarcinoma ◽  
Interaction Network ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
High Morbidity ◽  
Hub Genes ◽  
Histologic Subtype ◽  
Normal Tissues

Abstract Background Lung cancer has high morbidity and mortality across the globe, and lung adenocarcinoma (LUAD) is the most common histologic subtype. Disordered lipid metabolism is related to the development of cancer. Analysis of lipid-related transcriptome helps shed light on the diagnosis and prognostic biomarkers of LUAD. Methods In this study, expression analysis of 1045 lipid metabolism-related genes was performed between LUAD tumors and normal tissues derived from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) cohort. The interaction network of differentially expressed genes (DEGs) was constructed to identify the hub genes. The association between hub genes and overall survival (OS) was evaluated and formed a model to predict the prognosis of LUAD using a nomogram. The model was validated by another cohort, GSE13213. Results A total of 217 lipid metabolism-related DEGs were detected in LUAD. Genes were significantly enriched in glycerophospholipid metabolism, fatty acid metabolic process, and eicosanoid signaling. Through network analysis and cytoHubba, 6 hub genes were identified, including INS, LPL, HPGDS, DGAT1, UGT1A6, and CYP2C9. High expression of CYP2C9, UGT1A6, and INS, and low expressions of DGAT1, HPGDS, and LPL, were associated with worse overall survival for 1925 LUAD patients. The model showed that the high-risk score group had a worse OS, and the validated cohort showed the same result. Conclusions In this study, a signature of 6 lipid metabolism genes was constructed, which was significantly associated with the diagnosis and prognosis of LUAD patients. Thus, the gene signature can be used as a biomarker for LUAD.

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 Nine Genes Mediate the Therapeutic Effects of Iodine-131 Radiotherapy in Thyroid Carcinoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Han Shuwen ◽  
Yang Xi ◽  
Da Miao ◽  
Xu Jiamin ◽  
Zhuang Jing ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
Vascular Function ◽  
Endocrine System ◽  
The Cancer Genome Atlas ◽  
Therapeutic Effects ◽  
Ppi Network ◽  
Sequencing Data ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Iodine 131

Background. Thyroid carcinoma (THCA) is one of the most common malignancies of the endocrine system, which is usually treated by surgery combined with iodine-131 (I131) radiotherapy. Aims. This study is aimed at exploring the potential targets of I131 radiotherapy in THCA. Methods. The RNA-sequencing data of THCA in The Cancer Genome Atlas database (including 568 THCA samples) was downloaded. The differentially expressed genes (DEGs) between the tumour samples whether or not subjected to I131 radiotherapy were identified using edgeR package. Using the WGCNA package, the module that was relevant with I131 radiotherapy was selected. The intersection genes of the hub module nodes and the DEGs were obtained as hub genes, followed by the function and pathway enrichment analyses using the clusterProfiler package. Moreover, the protein-protein interaction (PPI) network for the hub genes was constructed using Cytoscape software. In addition, more important hub genes were analysed with function mining using the GenCLiP2 online tool. The qPCR analysis was used to verify the mRNA expression of more important hub genes in THCA tissues. Results. There were 500 DEGs (167 upregulated and 333 downregulated) between the two groups. WGCNA analysis showed that the green module (428 nodes) exhibited the most significant correlation with I131 radiotherapy. A PPI network was built after the identification of 53 hub genes. In the PPI network, CDH5, KDR, CD34, FLT4, EMCN, FLT1, ROBO4, PTPRB, and CD93 exhibited higher degrees, which were mainly implicated in the vascular function. The relative expression of nine mRNAs in the THCA tissues treated with I131 was lower. Conclusion. I131 radiotherapy might exert therapeutic effects by targeting CDH5, KDR, CD34, FLT4, EMCN, FLT1, ROBO4, PTPRB, and CD93 in THCA patients.

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