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

2020 ◽  
Author(s):  
Jinyou Li ◽  
Qiang Li ◽  
Zhenyu Su ◽  
Qi Sun ◽  
Yong Zhao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lipid Metabolism ◽  
Bioinformatic Analysis ◽  
Gene Signature ◽  
High Morbidity ◽  
Hub Genes ◽  
Normal Tissues ◽  
Diagnosis And Prognosis ◽  
Ppi Networks ◽  
Kaplan Meier

Abstract Background Lung cancer is a worldwide cancer with high morbidity and mortality. More and more evidence shows that the disorder of lipid metabolism is the key to the development of cancer, and analysis of lipid-related genes may lead to diagnosis and prognostic biomarkers related to lung cancer. Methods In this study, we performed the differentially expressed analysis of 1045 lipid metabolism-related genes between LUAD tumors and normal tissues in the TCGA-LUAD cohort. Then the bioinformatic analysis of DEGs was showed. PPI networks and cytoHubba APP determine hub genes. The association between hub genes and overall survival was evaluated by Kaplan-Meier Plotter. To predict the prognosis of LUAD patients, a nomogram was built, the nomogram 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 PPI 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 1925 LUAD patients. Based on the nomogram, we found that the high-risk score group had a worse OS, and the validated cohort had the same result. Conclusion In conclusion, we generated a lipid metabolic transcriptome-wide profile of LUAD patients and found that significant lipid metabolic pathways were correlated with the LUAD. Furthermore, we constructed a signature of six lipid metabolic genes, which significantly associated with diagnosis and prognosis of LUAD patients. The gene signature can be used as a biomarker for LUAD.

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 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 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.

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 NaPi- II b as a potential diagnostic and prognostic biomarker in ovarian cancers

2018 ◽  
Author(s):  
Shoufeng Zhao ◽  
Zhipeng Wang
Keyword(s):  
Ovarian Cancer ◽  
Cancer Cell Line ◽  
Normal Tissues ◽  
Diagnosis And Prognosis ◽  
Kaplan Meier ◽  
Potential Biomarker ◽  
Tumor Types ◽  
First Time ◽  
Kaplan Meier Plotter ◽  
Normal Controls

ABSTRACTOvarian cancer (OC) is commonly diagnosed at an advanced stage due to a lack of effective biomarkers and specificity required for accurate clinical diagnosis. The purpose of this study was to estimate the diagnosis and prognosis of the NaPi- II b in ovarian cancer. Herein, by performing data mining using the databases of Oncomine and Cancer Cell Line Encyclopedia (CCLE), we are for the first time to report that the expression level of NaPi- II b transcripts in a variety of tumor types compared with the normal controls. Based on Kaplan-Meier plotter, we investigated the prognostic values of NaPi- II b specifically high expressed in OC patients. The results of the Oncomine analysis showed that relative expression of NaPi- II b was distinctly high in OC tissues vs. normal tissues. CCLE analysis indicated that the expression of NaPi- II b in OC cell lines expressed the highest level in all cancer lines. In overall survival (OR) analysis, NaPi- II b mRNA high expressions were correlated to worse OR in OC patients. These results indicate that NaPi- II b may be a novel potential biomarker for determining the diagnosis and predicting the prognosis of OC.

scholarly journals Nomogram Models Based on Gene Expression in Prediction of Breast Cancer Bone Metastasis

2021 ◽  
Author(s):  
Teng-di Fan ◽  
Di-kai Bei ◽  
Song-wei Li
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Bone Metastasis ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Training Set ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Patient Prognosis ◽  
Kaplan Meier Plotter

Abstract Objective: To design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods: Dataset GSE124647 was used as a training set, and GSE14020 was taken as a validation set. In the training cohort, limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC non-bone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan-Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the prognostic value of hub genes in BC was explored. Results: A total of 1858 DEGs were obtained. WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival. While GJA1, IGFBP6, MDFI, ITGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival. Kaplan-Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion: Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

scholarly journals Differentially Expressed miRNAs in Tumor, Adjacent, and Normal Tissues of Lung Adenocarcinoma

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Tian ◽  
Rui Li ◽  
Zhenzhu Chen ◽  
Yanting Shen ◽  
Jiafeng Lu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Target Genes ◽  
Expression Profiles ◽  
Major Type ◽  
Regulatory Pathways ◽  
Normal Tissues ◽  
Qrt Pcr ◽  
Diagnosis And Prognosis ◽  
Tumor Tissues

Lung cancer is the leading cause of cancer deaths. Non-small-cell lung cancer (NSCLC) is the major type of lung cancer. The aim of this study was to characterize the expression profiles of miRNAs in adenocarcinoma (AC), one major subtype of NSCLC. In this study, the miRNAs were detected in normal, adjacent, and tumor tissues by next-generation sequencing. Then the expression levels of differential miRNAs were quantified by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). In the results, 259, 401, and 389 miRNAs were detected in tumor, adjacent, and normal tissues of pooled AC samples, respectively. In addition, for the first time we have found that miR-21-5p and miR-196a-5p were gradually upregulated from normal to adjacent to tumor tissues; miR-218-5p was gradually downregulated with 2-fold or greater change in AC tissues. These 3 miRNAs were validated by qRT-PCR. Lastly, we predicted target genes of these 3 miRNAs and enriched the potential functions and regulatory pathways. The aberrant miR-21-5p, miR-196a-5p, and miR-218-5p may become biomarkers for diagnosis and prognosis of lung adenocarcinoma. This research may be useful for lung adenocarcinoma diagnosis and the study of pathology in lung cancer.

scholarly journals Bioinformatics Analysis Reveals Biomarkers With Prognostic Benefits in Diffuse Type Gastric Cancer

2020 ◽  
Author(s):  
Sheng Li ◽  
Chao Yu ◽  
Yuanguang Cheng ◽  
Fangchao Du ◽  
Gang Wen
Keyword(s):  
Gastric Cancer ◽  
Pcr Analysis ◽  
Diffuse Type ◽  
Hub Genes ◽  
Screening Criteria ◽  
Clinical Prognosis ◽  
Qrt Pcr ◽  
Ppi Networks ◽  
Immunohistochemistry Analysis ◽  
Kaplan Meier

Abstract BackgroundGastric cancer (GC) is one of the most common malignancies in digestive system, among which the differentiation of diffuse type GC is relatively poor, the probability of distant metastasis and lymph node metastasis is relatively high, and the clinical prognosis is relatively poor. The purpose of this study is to explore potential signaling pathways and key biomarkers that drive the development of diffuse type GC. Methods Using the “limma” package in R to screen Differentially expressed genes. Screening hub genes by PPI analysis. Immunohistochemistry analysis and qRT-PCR analysis was carried out to detect genes expression. Using Kaplan-Meier Plotter database analyzed the prognostic roles of hub genes.ResultsA total of 355 DEGs consisting of 293 diffuse type DEGs and 62 intestinal type DEGs were selected according to screening criteria, 3 hub genes were chosen from diffuse type DEGs according to the degree of connectivity by using protein-protein interaction (PPI) networks and Cytoscape software including AGT, CXCL12 and ADRB2. Immunohistochemistry analysis and qRT-PCR results showed that the expression of three genes was related to the different GC lauren types. The Kaplan Meier analysis showed that the expression values of these three genes were related to prognosis of diffuse type GC. ConclusionsAGT, CXCL12 and ADRB2 might contribute to the progression of diffuse type GC, which could have potential as biomarkers or therapeutic targets for diffuse type GC.

scholarly journals Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

2021 ◽  
Author(s):  
Zimeng Wei ◽  
Min Zhao ◽  
Linnan Zang
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Analysis Tool ◽  
Hub Genes ◽  
Kegg Analysis ◽  
Ppi Networks

Abstract Background Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Methods Gene expression profiles were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, we detected the expression of hub genes in LUAD and other cancers via GEPIA, Oncomine and HPA databases. Finally, Kaplan-Meier plotter were performed to analyze the prognosis efficacy of hub genes. Results 74 up-regulated and 238 down-regulated DEGs were identified. As for the up-regulated DEGs, KEGG analysis results revealed they were mainly enrolled in protein digestion and absorption. However, the down-regulated DEGs were primarily enriched in cell adhesion molecules. Subsequently, 9 hub genes: KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2, were identified and showed higher expression in both LUAD and other cancers. Finally, all these hub genes were found significantly related to the prognosis of LUAD (p < 0.05). Conclusions Our results screened out the hub genes and pathways that were related to the development and prognosis of LUAD, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of LUAD.

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