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 Comprehensive Analysis of circRNA-associated Competing Endogenous RNA Networks and Immune Infiltration in Gastric Cancer

2021 ◽  
Author(s):  
beibei xu ◽  
Endian Zheng ◽  
Yi Huang ◽  
Liang Zheng ◽  
Qiaoli Lan ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Immune Cell ◽  
Clinical Stage ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Cell Infiltration ◽  
Hub Genes ◽  
Qrt Pcr ◽  
Ppi Networks

Abstract BackgroundCircular RNA (circRNA) has been shown to be an important regulator in gastric cancer (GC). However, functions and regulatory mechanisms of circRNA-related competitive endogenous RNA (ceRNA) in GC have not been established.MethodsCircRNA data and clinical data were downloaded from the GEO and TCGA databases. The ceRNA and Protein-Protein Interaction (PPI) networks were constructed through bioinformatics analysis. Function enrichment analysis was performed. Additionally, correlations between expression levels of the top 10 hub genes and immune cell infiltration levels, histopathological grade and clinical stage were determined to establish their clinical values. The differentially expressed circRNA (DEcircRNA) was validated by quantitative real-time PCR (qRT-PCR).ResultsScreening of the GEO and TCGA databases revealed a total of 1627 DEcircRNAs, 6516 DEmRNAs, and 1451 DEmiRNAs. The ceRNA interaction network contained 2 circRNAs, 3 miRNAs and 55 mRNAs. Meanwhile, Gene Ontology (GO) analysis revealed a total of 323 biological processes (BP) terms, 53 cellular components (CC) terms, 51 molecular functions (MF) terms, while the Kyoto Encyclopedia of Genes and Genomes (KEGG) revealed 4 signaling pathways. Gene Set Enrichment Analysis (GSEA) analysis revealed that EPHA4, NCAM1 and NRXN1 were positively correlated with the axon guidan and adhesion molecules pathways. Most of top 10 hub genes were positively correlated with B cells, CD8+ T cells, CD4+ T cells, Neutrophils and Dendritic Cell infiltration. Correlation analysis between hub genes and clinical phenotypes revealed that elevated expressions of EPHA4 and KCNA1 indicated poor tissue differentiation and were associated with clinically advanced stages of GC. The qRT-PCR results revealed that the expression of has_circ_0002504 was significantly down-regulated in 3 GC cell lines which was consistent with the results of our bioinformatics analysis.ConclusionsHas_circ_0001998 and has_circ_0002504 are potential diagnostic biomarkers for GC, and the high expressions of both EPHA4 and KCNA1 may predict poor prognosis.

scholarly journals Bioinformatics analysis identifies COL1A1, THBS2 and SPP1 as potential predictors of patient prognosis and immunotherapy response in gastric cancer

2020 ◽  
Author(s):  
Yali Wang ◽  
Kun Zheng ◽  
Xiuqiong Chen ◽  
Rui Chen ◽  
Yanmei Zou
Keyword(s):  
Gastric Cancer ◽  
Prognostic Significance ◽  
Cancer Prognosis ◽  
Clinicopathological Parameters ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Ppi Networks ◽  
Kaplan Meier ◽  
Predictive Role

Background This study aimed to use bioinformatics tools to explore pivotal genes associated with the occurrence of gastric cancer (GC) and assess their prognostic significance, and link with clinicopathological parameters. We also investigated the predictive role of COL1A1, THBS2, and SPP1 in immunotherapy. Materials and methods We identified differential genes (DEGs) that were up- and down-regulated in the three datasets (GSE26942, GSE13911, and GSE118916) and created protein-protein interaction (PPI) networks from the overlapping DEGs. We then investigated the potential functions of the hub genes in cancer prognosis using PPI networks, and explored the influence of such genes in the immune environment. Results Overall, 268 overlapping DEGs were identified, of which 230 were up-regulated and 38 were down-regulated. CytoHubba selected the top ten hub genes, which included SPP1, TIMP1, SERPINE1, MMP3, COL1A1, BGN, THBS2, CDH2, CXCL8, and THY1. With the exception of SPP1, survival analysis using the Kaplan-Meier database showed that the levels of expression of these genes were associated with overall survival. Genes in the most dominant module explored by MCODE, COL1A1, THBS2, and SPP1, were primarily enriched for two KEGG pathways. Further analysis showed that all three genes could influence clinicopathological parameters and immune microenvironment, and there was a significant correlation between COL1A1, THBS2, SPP1, and PD-L1 expression, thus indicating a potential predictive role for GC response to immunotherapy. Conclusion ECM-receptor interactions and focal adhesion pathways are of great significance in the progression of GC. COL1A1, THBS2 and SPP1 may help predict immunotherapy response in GC patients.

scholarly journals Tumour-Associated Macrophage Polarisation Promotes Progression of Esophageal Carcinoma

2020 ◽  
Author(s):  
Xin Yuan ◽  
Ya Li ◽  
An Zhi Zhang ◽  
Chen Hao Jiang ◽  
Fan Ping Li ◽  
...  
Keyword(s):  
T Cells ◽  
Esophageal Carcinoma ◽  
Tumour Progression ◽  
Tumour Microenvironment ◽  
Hub Genes ◽  
Clinical Prognosis ◽  
Macrophage Polarisation ◽  
Kegg Pathways ◽  
Kaplan Meier

Abstract Background: The immune response mediated by tumour-associated macrophages (TAMs) is vital in tumour progression in many cancers. Fibrinogen-like protein 2 (FGL2) is a critical immunosuppressive factor that regulates the tumour microenvironment. However, no study has yet reported on the relationship between FGL2, tumour-infiltrating lymphocyte recruitment, and prognosis in esophageal carcinoma (ESCA). Methods: Differentially expressed genes (DEGs) in various macrophage phenotypes were analysed using the GEO database. We identified the hub genes involved in affecting ESCA clinical prognosis using Kaplan-Meier plotter. Correlations between hub genes and immune infiltrates were analysed using the Tumour Immune Estimation Resource (TIMER) database, while correlation analysis between FGL2 and cytokine expression was assessed using cBioPortal. In vitro cell co-culture experiments were performed to examine the role of FGL2 in promoting tumorigenesis. Finally, we compared the GO terms and KEGG pathways enriched by the DEGs in M1 and M2 macrophages using DAVID.Results: High FGL2 expression was significantly associated with poorer overall survival and relapse-free survival of esophageal cancer patients. FGL2 expression was positively correlated with immune markers and infiltrating levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. In addition, FGL2 expression was strongly correlated to IL-10, MMP9, CCL5, TIM-3, IL-13, VCAM1, M-CSF and FGF-7 expression. Conclusions: M2-like TAMs may regulate the tumour microenvironment by secreting FGL2, thereby inducing the occurrence and progression of ESCA. Reversing TAM polarization may be an effective strategy that reveals new targets for immunotherapy in treating ESCA.

scholarly journals Screening and Identification of Key Biomarkers in Breast Cancer with Brain Metastasis: Evidence from Bioinformatic Analysis

2020 ◽  
Author(s):  
tao ming Shao ◽  
zhi yang Hu ◽  
wen wei Li ◽  
long yun Pan
Keyword(s):  
Breast Cancer ◽  
Protein Interactions ◽  
Poor Prognosis ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Clinical Prognosis ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Purpose. Breast cancer (BC) has a poor prognosis when brain metastases (BM) occur, and the treatment effect is limited. In this study, we aim to identify representative candidate biomarkers for clinical prognosis of patients with BM and explore the mechanisms underlying the progression of BC.Methods. Herein, we examined the Microarray datasets (GSE125989) obtained from the Gene Expression Omnibus database to find the target genes in BC patients with BM. We employed the GEO2R tool to filter the differentially expressed genes (DEGs) that participate in primary BC and BC with BM. Subsequently, using the DAVID tool, we conducted an enrichment analysis with the screened DEGs based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) functional annotation. The STRING database was employed to analyze the protein-protein interactions of the DEGs and visualized using Cytoscape software. Lastly, the Kaplan-Meier plotter database was employed to determine the prognostic potential of hub genes in BC.Results. We screened out 311 upregulated DEGs and 104 downregulated DEGs. The enrichment analyses revealed that all the DEGs were` enriched in the biological process of extracellular matrix organization, cell adhesion, proteolysis, collagen catabolic process and immune response. The significant enrichment pathways were focal adhesion, protein absorption and digestion, ECM-receptor interaction, PI3K-Akt signalling pathway, and Pathways in cancer. The top ten hub nodes screened out included FN1, VEGFA, COL1A1, MMP2, COL3A1, COL1A2, POSTN, DCN, BGN and LOX. The Kaplan-Meier plotter results showed that the three hub genes (FN1, VEGFA and DCN) are candidate biomarkers for clinical prognosis of patients with BM.Conclusion. we identified seven genes related to poor prognosis in BCBM. FN1, VEGFA and DCN can be considered as potential prognostic markers for BCBM. Meantime, COL1A1, POSTN, BGN and LOX may be linked to the distant transformation of BC.

scholarly journals Bioinformatic Analysis Combined With Experimental Validation Reveals Novel Hub Genes and Pathways Associated With Focal Segmental Glomerulosclerosis

2022 ◽  
Vol 8 ◽  
Author(s):  
Yan-Pei Hou ◽  
Tian-Tian Diao ◽  
Zhi-Hui Xu ◽  
Xin-Yue Mao ◽  
Chang Wang ◽  
...  
Keyword(s):  
Focal Segmental Glomerulosclerosis ◽  
Experimental Validation ◽  
Therapeutic Targets ◽  
Bioinformatic Analysis ◽  
Pcr Analysis ◽  
Diagnostic Efficiency ◽  
Hub Genes ◽  
Roc Curve Analysis ◽  
Qrt Pcr ◽  
Segmental Glomerulosclerosis

Background: Focal segmental glomerulosclerosis (FSGS) is a type of nephrotic syndrome leading to end-stage renal disease, and this study aimed to explore the hub genes and pathways associated with FSGS to identify potential diagnostic and therapeutic targets.Methods: We downloaded the microarray datasets GSE121233 and GSE129973 from the Gene Expression Omnibus (GEO) database. The datasets comprise 25 FSGS samples and 25 normal samples. The differential expression genes (DEGs) were identified using the R package “limma”. Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the database for Annotation, Visualization and Integrated Discovery (DAVID) to identify the pathways and functional annotation of the DEGs. The protein–protein interaction (PPI) was constructed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape software. The hub genes of the DEGs were then evaluated using the cytoHubba plugin of Cytoscape. The expression of the hub genes was validated by quantitative real-time polymerase chain reaction (qRT-PCR) using the FSGS rat model, and receiver operating characteristic (ROC) curve analysis was performed to validate the accuracy of these hub genes.Results: A total of 45 DEGs including 18 upregulated and 27 downregulated DEGs, were identified in the two GSE datasets (GSE121233 and GSE129973). Among them, five hub genes with a high degree of connectivity were selected. From the PPI network, of the top five hub genes, FN1 was upregulated, while ALB, EGF, TTR, and KNG1 were downregulated. The qRT-PCR analysis of FSGS rats confirmed that the expression of FN1 was upregulated and that of EGF and TTR was downregulated. The ROC analysis indicated that FN1, EGF, and TTR showed considerable diagnostic efficiency for FSGS.Conclusion: Three novel FSGS-specific genes were identified through bioinformatic analysis combined with experimental validation, which may promote our understanding of the molecular underpinning of FSGS and provide potential therapeutic targets for the clinical management.

scholarly journals Integration of Gene Expression Profile Data of Human Epicardial Adipose Tissue from Coronary Artery Disease to Verification of Hub Genes and Pathways

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Weitie Wang ◽  
Qing Liu ◽  
Yong Wang ◽  
Hulin Piao ◽  
Bo Li ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Adipose Tissue ◽  
Coronary Artery ◽  
Focal Adhesion ◽  
Molecular Mechanisms ◽  
Epicardial Adipose Tissue ◽  
Hub Genes ◽  
Qrt Pcr ◽  
Ppi Networks ◽  
Artery Disease

Background. This study aim to identify the core pathogenic genes and explore the potential molecular mechanisms of human coronary artery disease (CAD). Methodology. Two gene profiles of epicardial adipose tissue from CAD patients including GSE 18612 and GSE 64554 were downloaded and integrated by R software packages. All the coexpression of deferentially expressed genes (DEGs) were picked out and analyzed by DAVID online bioinformatic tools. In addition, the DEGs were totally typed into protein-protein interaction (PPI) networks to get the interaction data among all coexpression genes. Pictures were drawn by cytoscape software with the PPI networks data. CytoHubba were used to predict the hub genes by degree analysis. Finally all the top 10 hub genes and prediction genes in Molecular complex detection were analyzed by Gene ontology and Kyoto encyclopedia of genes and genomes pathway analysis. qRT-PCR were used to identified all the 10 hub genes. Results. The top 10 hub genes calculated by the degree method were AKT1, MYC, EGFR, ACTB, CDC42, IGF1, FGF2, CXCR4, MMP2 and LYN, which relevant with the focal adhesion pathway. Module analysis revealed that the focal adhesion was also acted an important role in CAD, which was consistence with cytoHubba. All the top 10 hub genes were verified by qRT-PCR which presented that AKT1, EGFR, CDC42, FGF2, and MMP2 were significantly decreased in epicardial adipose tissue of CAD samples (p<0.05) and MYC, ACTB, IGF1, CXCR4, and LYN were significantly increased (p<0.05). Conclusions. These candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of CAD.

scholarly journals Identification of Novel Hub Genes Associated With Gastric Cancer Using Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Xiao-Qing Lu ◽  
Jia-qian Zhang ◽  
Jun Qiao ◽  
Sheng-Xiao Zhang ◽  
Meng-Ting Qiu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Omnibus ◽  
High Recurrence Rate ◽  
Hub Genes ◽  
Tissue Samples ◽  
Qrt Pcr

Abstract Background: Gastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy.Methods: Gene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytoHubba plugin. Their prognostic values were assessed by Kaplan–Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs. Results: Total of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients.Discussion: We have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.

scholarly journals KK-LC-1 may be an effective prognostic biomarker for gastric cancer

2021 ◽  
Author(s):  
Jun Ji ◽  
Jiahui Chen ◽  
Anqiang Wang ◽  
Wei Zhang ◽  
Hongge Ju ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Staining Intensity ◽  
Intestinal Type ◽  
Clinicopathological Parameters ◽  
Diffuse Type ◽  
Clinical Prognosis ◽  
Cox Regression Analysis ◽  
Normal Tissues

Abstract Background: To detect the expression of Kita-Kyushu lung cancer antigen-1 (KK-LC-1) in gastric cancer (GC) specimens and analyze the associations between KK-LC-1 expression and clinicopathological parameters and clinical prognosis. Methods : A total of 94 patients with GC who underwent surgical resection were enrolled in this study. The expression of KK-LC-1 in GC tissues was detected by immunohistochemistry. The assessment of KK-LC-1 expression was conducted using the H-scoring system. H-score was calculated by the multiplication of the overall staining intensity with the percentage of positive cells. The expression of KK-LC-1 in the cytoplasm and was scored to achieve respective H-score values. The correlations between KK-LC-1 expression and clinicopathological parameters and clinical prognosis were analyzed using Chi-square test, Kaplan-Meier method and Cox regression. Results: In the cytoplasm, the expression of KK-LC-1 in tumor tissues was significantly higher than that in normal tissues (P < 0.001, respectively). Using the median H-score as the cutoff value, it was discovered that, GC patients with higher levels of KK-LC-1expression in the cytoplasm, had favorable overall survival (P =0.016), and it was still statistically meaningful in Cox regression analysis. At the same time, the study found that there was a negative correlation between KK-LC-1’s protein expression and the pathological grade of the tumor (P = 0.036); KK-LC-1 protein is more highly expressed in the intestinal type than the diffuse type, and it is statistically significant. The high expression of KK-LC-1 protein in the intestinal type is more than that in the diffuse type (P =0.008). Conclusions: Our research data shows that KK-LC-1’s expression in GC is higher than that of normal tissues, which is associated with a longer overall survival in GC. KK-LC-1 can be used as a biomarker for GC patients with good prognosis.

scholarly journals PCOLCE Is Potent Prognostic Biomarker and Associates With Immune Infiltration in Gastric Cancer

2020 ◽  
Vol 7 ◽  
Author(s):  
Aizhai Xiang ◽  
Xia Lin ◽  
Lvping Xu ◽  
Honggang Chen ◽  
Jufeng Guo ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
T Cells ◽  
Cancer Patients ◽  
Prognostic Biomarker ◽  
M2 Macrophage ◽  
Th1 Cell ◽  
Clinical Prognosis ◽  
Immune Infiltration ◽  
Kaplan Meier ◽  
Gastric Cancer Patients

BackgroundThe exact biological role of PCOLCE was not yet clear and there were few reports study the correlation of PCOLCE gene expression level with the occurrence and development of gastric cancer.MethodsThe expression of PCOLCE was analyzed by performing the Oncomine and Ualcan database. We evaluated the function of PCOLCE on clinical prognosis with the use of Kaplan–Meier plotter database. The relationship between PCOLCE and cancer immune in filtrates was researched by Tumor Immune Estimation Resource (TIMER) site database.ResultsPCOLCE significantly upregulated in gastric cancer patients compared to normal gastric samples. And the increased expression of PCOLCE mRNA was closely linked to shorter overall survival (OS), progress-free survival (PFS) in all gastric cancers. Besides, PCOLCE expression displayed a tight correlation with infiltrating levels of macrophages and dendritic cells (DCs) in gastric cancer. Moreover, PCOLCE expression was positively correlated with diverse immune marker sets in gastric cancer.ConclusionAll the results above suggested that overexpression of PCOLCE indicated unfavorable prognosis in patients with gastric cancer. PCOLCE was correlated with immune infiltrating levels including those of B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils, and DCs in gastric cancer patients. All the findings suggested that PCOLCE could be used as a prognostic biomarker for determining prognosis and immune infiltration in gastric cancer. Additionally, PCOLCE expression potentially contributed to the regulation of monocyte, M2 macrophage, Tfh, CD8 + T cell, TAM, Th1 cell Thus PCOLCE is a potential target for gastric cancer therapy and these preliminary findings require further study to determine whether PCOLCE-targeting reagents might be developed for clinical application in gastric cancer.

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.

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