scholarly journals Bioinformatics analysis of BUB1 expression and gene regulation network in lung adenocarcinoma

2019 ◽  
Author(s):  
Luyao Wang ◽  
Xue Yang ◽  
Ning An ◽  
Jia Liu
Keyword(s):  
Gene Regulation ◽  
Lung Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Cancer Genomics ◽  
Mitotic Checkpoint ◽  
High Morbidity ◽  
Multiple Cancer ◽  
Gene Regulation Network ◽  
Multiple Tumor ◽  
Normal Tissues

Abstract Lung adenocarcinoma is the most common type of lung cancer with high morbidity and mortality. Potential mechanisms and therapeutic targets of lung adenocarcinoma need further study. BUB1 (BUB1 mitotic checkpoint serine/threonine kinase) encodes a serine/threonine protein kinase which is critical in the mitosis. It is associated with poor prognosis in multiple cancer types. Oncomine database was used to determine the differential expression of BUB1 in normal and lung adenocarcinoma tissues, while UALCAN was used to perform analysis of the relative expression and survival of BUB1 between tumor and normal tissues in different tumor subgroups. We used the cBioPortal for Cancer Genomics to perform GO analysis and KEGG analysis of the top 50 altered neighbor genes of BUB1. The LinkedOmics database was used to determine differential gene expression with BUB1 and to perform functional analysis. The kinase, miRNA and transcription factor target networks correlated with BUB1 were also analysed by LinkedOmics database. The results revealed that BUB1 was highly expressed in lung adenocarcinoma patients. BUB1 involved multiple tumor-related pathways, such as cell cycle, oocyte meiosis and p53 signaling pathway. BUB1 is associated with tumor-associated kinases, microRNAs and transcription factors. Our study reveal BUB1 expression and potential gene regulation networks in lung adenocarcinoma based on bioinformatics analysis, guiding further study on the role and regulation of BUB1 in lung adenocarcinoma.

scholarly journals Enolase 1 Correlated With Cancer Progression and Immune-Infiltrating in Multiple Cancer Types: A Pan-Cancer Analysis

2021 ◽  
Vol 10 ◽  
Author(s):  
Wenhua Xu ◽  
Wenna Yang ◽  
Chunfeng Wu ◽  
Xiaocong Ma ◽  
Haoyu Li ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Stress Protein ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Multiple Cancer ◽  
Clinical Prognosis ◽  
Multiple Tumor ◽  
Normal Tissues

Enolase 1 (ENO1) is an oxidative stress protein expressed in endothelial cells. This study aimed to investigate the correlation of ENO1 with prognosis, tumor stage, and levels of tumor-infiltrating immune cells in multiple cancers. ENO1 expression and its influence on tumor stage and clinical prognosis were analyzed by UCSC Xena browser, Gene Expression Profiling Interactive Analysis (GEPIA), The Cancer Genome Atlas (TCGA), and GTEx Portal. The ENO1 mutation analysis was performed by cBio Portal, and demonstrated ENO1 mutation (1.8%) did not impact on tumor prognosis. The relationship between ENO1 expression and tumor immunity was analyzed by Tumor Immune Estimation Resource (TIMER) and GEPIA. The potential functions of ENO1 in pathways were investigated by Gene Set Enrichment Analysis. ENO1 expression was significantly different in tumor and corresponding normal tissues. ENO1 expression in multiple tumor tissues correlated with prognosis and stage. ENO1 showed correlation with immune infiltrates including B cells, CD8+ and CD4+ T cells, macrophages, neutrophils, and dendritic cells, and tumor purity. ENO1 was proved to be involved in DNA replication, cell cycle, apoptosis, glycolysis process, and other processes. These findings indicate that ENO1 is a potential prognostic biomarker that correlates with cancer progression immune infiltration.

scholarly journals Overexpression of miR-519d in lung adenocarcinoma inhibits cell proliferation and invasion via the association of eIF4H

Tumor Biology ◽  
2017 ◽  
Vol 39 (3) ◽  
pp. 101042831769456 ◽  
Author(s):  
Yong Bai ◽  
Chunya Lu ◽  
Guojun Zhang ◽  
Yu Hou ◽  
Yanjie Guo ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Lung Adenocarcinoma ◽  
Bioinformatics Analysis ◽  
Cell Counting ◽  
Lung Tumorigenesis ◽  
Lung Cancers ◽  
Normal Tissues ◽  
Polymerase Chain ◽  
Proliferation And Invasion ◽  
Inhibit Cell Proliferation

Lung cancer is one of the deadliest types of cancer worldwide due to its high mortality rate. Adenocarcinoma constitutes 20%–30% of all lung cancers. In recent years, studies on the mechanisms of lung tumorigenesis and development have in part focused on the microRNAs for their crucial role in the progress of different cancers. As for our study, we demonstrated that miR-519d was differently downregulated and eIF4H was significantly overexpressed in lung adenocarcinoma via the detection of quantitative real-time polymerase chain reaction compared with the adjacent normal tissues. Furthermore, Cell Counting Kit-8 assay, colony formation assay, xenograft tumor experiment, Ki67 immunohistochemistry assay and transwell assay were performed to explain that the upregulated miR-519d could inhibit the proliferation and invasion of A549 and H1299 cells. To further advance our understanding of the mechanisms of miR-519d, we performed the bioinformatics analysis and the luciferase report assay. The results from these procedures revealed eIF4H to be one of the targets of miR-519d. Downregulated eIF4H was analogous to the overexpressed miR-519d obtained from miR-519d agomir and si-eIF4H transfection. In summary, it can be concluded that miR-519d targets eIF4H in lung adenocarcinoma to inhibit cell proliferation and invasion. This mechanism may offer new insights into the tumorigenesis and development of lung adenocarcinoma.

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 miR-182-5p Serves as an Oncogene in Lung Adenocarcinoma through Binding to STARD13

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Xuhui Wu ◽  
Wei Wang ◽  
Gongzhi Wu ◽  
CongXiong Peng ◽  
Jichun Liu
Keyword(s):  
Cell Proliferation ◽  
Lung Adenocarcinoma ◽  
Untranslated Region ◽  
Target Gene ◽  
Bioinformatics Analysis ◽  
Inverse Correlation ◽  
Inhibitory Effect ◽  
Migration And Invasion ◽  
High Morbidity ◽  
Prevalent Subtype

Lung cancer as one of the commonest invasive malignancies is featured by high morbidity and mortality, wherein lung adenocarcinoma (LUAD) is the most prevalent subtype. Accumulating evidence exhibited that microRNAs are involved in LUAD occurrence and progression. In this study, miR-182-5p was observed to increase in both LUAD tissue and cell lines. Overexpression of miR-182-5p could prominently facilitate cell proliferation, migration, and invasion in LUAD. Through bioinformatics analysis, STARD13 was theorized as the target gene of miR-182-5p, which was lowly expressed in LUAD. Further molecular experiments manifested that miR-182-5p bound to the 3 ′ -untranslated region of STARD13, and there was an inverse correlation between STARD13 and miR-182-5p in LUAD. Rescue experiments demonstrated that silencing STARD13 conspicuously restored the inhibitory effect of decreased miR-182-5p on cell proliferation, migration, and invasion in LUAD. Together, our findings revealed novel roles of the miR-182-5p/STARD13 axis in LUAD progression.

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

2020 ◽  
Author(s):  
Jinyou Li ◽  
Qiang Li ◽  
Zhenyu Su ◽  
Qi Sun ◽  
Yong Zhao ◽  
...  
Keyword(s):  
Lipid Metabolism ◽  
Lung Adenocarcinoma ◽  
Interaction Network ◽  
Gene Signature ◽  
High Morbidity ◽  
Hub Genes ◽  
Histologic Subtype ◽  
Normal Tissues ◽  
Diagnosis And Prognosis ◽  
Common Histologic Subtype

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 TCGA-LUAD cohort. The interaction network of differential expression genes (DEGs) was constructed to identify. The association between hub genes and overall survival (OS) 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 and steroid metabolism . 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 OS for 719 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 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 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.

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