scholarly journals Tissue-based metabolomics profiling reveals metabolic signatures and major metabolic pathways of gastric cancer

2021 ◽  
Author(s):  
Yaqin Wang ◽  
Wenchao Chen ◽  
Kun Li ◽  
Gang Wu ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Predictive Ability ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Pentose Phosphate ◽  
P Value ◽  
Pathway Enrichment Analysis ◽  
Dodecanoic Acid ◽  
Orthogonal Projections ◽  
Latent Structures

Abstract Purpose This study was aimed to screen differential metabolites between gastric cancer (GC) and paracancerous (PC) tissues and find new biomarkers of GC. Methods GC (n = 28) and matched PC (n = 28) tissues were collected and LC-MS/MS analyses were performed to detect metabolites of GC and PC tissues in positive and negative models. Principal component analysis (PCA) and orthogonal projections to latent structures-discriminate analysis (OPLS-DA) were conducted to describe distribution of origin data and general separation and estimate the robustness and the predictive ability of our mode. Differential metabolites were screened based on criterion of variables with p value < 0.05 and VIP (variable importance in the projection) > 1.0. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic power of differential metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed to search for metabolite pathways and MetaboAnalyst was used for pathway enrichment analysis. Results Several metabolites were significantly changed in GC group compared with PC group. Thirteen metabolites with high VIP were chose and among which 1-methylnicotinamide, dodecanoic acid and sphinganine possessed high AUC values (AUC > 0.8) indicating an excellent discriminatory ability on GC. Pathways such as pentose phosphate pathway and histidine metabolism were focused based on differential metabolites demonstrating their effects on progress of GC. Conclusions In conclusion, we investigated the tissue-based metabolomics profile of GC and several differential metabolites and signaling pathways were focused. Further study is needed to verify those results.

scholarly journals 1H NMR-Based Analysis of Serum Metabolites in Monocrotaline-Induced Pulmonary Arterial Hypertensive Rats

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Taijie Lin ◽  
Jinping Gu ◽  
Caihua Huang ◽  
Suli Zheng ◽  
Xu Lin ◽  
...  
Keyword(s):  
Intraperitoneal Injection ◽  
Ventricular Hypertrophy ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Metabolic Profiles ◽  
Metabolic Dysfunction ◽  
Sprague Dawley ◽  
Mean Pulmonary Arterial Pressure ◽  
Pulmonary Arterial

Aims. To study the changes of the metabolic profile during the pathogenesis in monocrotaline (MCT) induced pulmonary arterial hypertension (PAH).Methods. Forty male Sprague-Dawley (SD) rats were randomly divided into 5 groups (n=8, each). PAH rats were induced by a single dose intraperitoneal injection of 60 mg/kg MCT, while 8 rats given intraperitoneal injection of 1 ml normal saline and scarified in the same day (W0) served as control. Mean pulmonary arterial pressure (mPAP) was measured through catherization. The degree of right ventricular hypertrophy and pulmonary hyperplasia were determined at the end of first to fourth weeks; nuclear magnetic resonance (NMR) spectra of sera were then acquired for the analysis of metabolites. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to discriminate different metabolic profiles.Results. The prominent changes of metabolic profiles were seen during these four weeks. Twenty specific metabolites were identified, which were mainly involved in lipid metabolism, glycolysis, energy metabolism, ketogenesis, and methionine metabolism. Profiles of correlation between these metabolites in each stage changed markedly, especially in the fourth week. Highly activated methionine and betaine metabolism pathways were selected by the pathway enrichment analysis.Conclusions. Metabolic dysfunction is involved in the development and progression of PAH.

scholarly journals Metabonomic-Transcriptome Integration Analysis on Osteoarthritis and Rheumatoid Arthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Ningyang Gao ◽  
Li Ding ◽  
Jian Pang ◽  
Yuxin Zheng ◽  
Yuelong Cao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gap Junction ◽  
Molecular Mechanisms ◽  
Linear Models ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Nf Kappa B ◽  
Linear Module ◽  
Hedgehog Acyltransferase

Purpose. This study is aimed at exploring the potential metabolite/gene biomarkers, as well as the differences between the molecular mechanisms, of osteoarthritis (OA) and rheumatoid arthritis (RA). Methods. Transcriptome dataset GSE100786 was downloaded to explore the differentially expressed genes (DEGs) between OA samples and RA samples. Meanwhile, metabolomic dataset MTBLS564 was downloaded and preprocessed to obtain metabolites. Then, the principal component analysis (PCA) and linear models were used to reveal DEG-metabolite relations. Finally, metabolic pathway enrichment analysis was performed to investigate the differences between the molecular mechanisms of OA and RA. Results. A total of 976 DEGs and 171 metabolites were explored between OA samples and RA samples. The PCA and linear module analysis investigated 186 DEG-metabolite interactions including Glycogenin 1- (GYG1-) asparagine_54, hedgehog acyltransferase- (HHAT-) glucose_70, and TNF receptor-associated factor 3- (TRAF3-) acetoacetate_35. Finally, the KEGG pathway analysis showed that these metabolites were mainly enriched in pathways like gap junction, phagosome, NF-kappa B, and IL-17 pathway. Conclusions. Genes such as HHAT, GYG1, and TRAF3, as well as metabolites including glucose, asparagine, and acetoacetate, might be implicated in the pathogenesis of OA and RA. Metabolites like ethanol and tyrosine might participate differentially in OA and RA progression via the gap junction pathway and phagosome pathway, respectively. TRAF3-acetoacetate interaction may be involved in regulating inflammation in OA and RA by the NF-kappa B and IL-17 pathway.

scholarly journals A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

2021 ◽  
Author(s):  
Boxuan Liu ◽  
Yun Zhao ◽  
Shuanying Yang
Keyword(s):  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Risk Model ◽  
Cox Regression ◽  
Predictive Ability ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.

scholarly journals Tissue-based metabolomics reveals metabolic signatures and major metabolic pathways of gastric cancer with help of transcriptomic data from TCGA

2021 ◽  
Author(s):  
Ya qin Wang ◽  
Wen chao Chen ◽  
Kun Li ◽  
Gang Wu ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Metabolic Pathways ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Overlapping Genes ◽  
Expression Of Genes ◽  
Human Protein Atlas ◽  
Transcriptomics Data

Purpose: The aim of this study was to screen differential metabolites of gastric cancer (GC) and identify the key metabolic pathways of GC. Methods: GC (n=28) and matched paracancerous (PC) tissues were collected, and LC-MS/MS analysis were performed to detect metabolites of GC and PC tissues. Metabolite pathways based on differential metabolites were enriched by MetaboAnalyst, and genes related to metabolite pathways were identified using the KEGGREST function of the R software package. Transcriptomics data from The Cancer Genome Atlas (TCGA) was analyzed to obtain differentially expressed genes (DEGs) of GC. Overlapping genes were acquired from metabonimics and transcriptomics data. Pathway enrichment analysis was performed using String. The protein expression of genes was validated by the Human Protein Atlas (HPA) database. Results: A total of 325 key metabolites were identified, 111 of which were differentially expressed between the GC and PC groups. Seven metabolite pathways enriched by MetaboAnalyst were chosen, and 361 genes were identified by KEGGREST. A total of 2831 DEGs were identified from the TCGA cohort. Of these, 1317 were down-regulated, and 1636 were up-regulated. Twenty-two overlapping genes were identified between genes related to metabolism and DEGs. Glycerophospholipid (GPL) metabolism is likely associated with GC, of which AGPAT9 and ETNPPL showed lower expressed in GC tissues. Conclusions: We investigated the tissue-based metabolomics profile of GC, and several differential metabolites were identified. GPL metabolism may affect on progression of GC.

scholarly journals Significant Changes in Metabolic Profiles after Intervention with Selenium and Coenzyme Q10 in an Elderly Population

Biomolecules ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 553
Author(s):  
Alehagen ◽  
Johansson ◽  
Aaseth ◽  
Alexander ◽  
Surowiec ◽  
...  
Keyword(s):  
Cardiovascular Mortality ◽  
Coenzyme Q10 ◽  
Metabolic Profiling ◽  
Demographic Data ◽  
Principal Component ◽  
Elderly Persons ◽  
Double Blind ◽  
Orthogonal Projections ◽  
Healthy Elderly ◽  
Latent Structures

Selenium and coenzyme Q10 (SeQ10) are important for normal cellular function. Low selenium intake leads to increased cardiovascular mortality. Intervention with these substances with healthy elderly persons over a period of four years in a double-blind, randomised placebo-controlled prospective study showed reduced cardiovascular mortality, increased cardiac function, and a lower level of NT-proBNP. Therefore, we wanted to evaluate changes in biochemical pathways as a result of the intervention with SeQ10 using metabolic profiling. From a population of 443 healthy elderly individuals that were given 200 µg selenium and 200 mg coenzyme Q10, or placebo daily for four years, we selected nine males on active intervention and nine males on placebo for metabolic profiling in the main study. To confirm the results, two validation studies (study 1 n = 60 males, study 2 n = 37 males) were conducted. Principal component analyses were used on clinical and demographic data to select representative sets of samples for analysis and to divide the samples into batches for analysis. Gas chromatography time-of-flight mass spectrometry-based metabolomics was applied. The metabolite data were evaluated using univariate and multivariate approaches, mainly T-tests and orthogonal projections to latent structures (OPLS) analyses. Out of 95 identified metabolites, 19 were significantly decreased due to the intervention after 18 months of intervention. Significant changes could be seen in the pentose phosphate, the mevalonate, the beta-oxidation and the xanthine oxidase pathways. The intervention also resulted in changes in the urea cycle, and increases in the levels of the precursors to neurotransmitters of the brain. This adds information to previous published results reporting decreased oxidative stress and inflammation. This is the first-time metabolic profiling has been applied to elucidate the mechanisms behind an intervention with SeQ10. The study is small and should be regarded as hypothesis-generating; however, the results are interesting and, therefore, further research in the area is needed. This study was registered at Clinicaltrials.gov, with the identifier NCT01443780

Transcriptome Analysis Reveals Dynamic Changes of Inflammation and Stress Responses during Different Infected Stages with Burkholderia pseudomallei

2020 ◽  
Author(s):  
Chenglong Rao(Former Corresponding Author) ◽  
Chan Mao ◽  
Yupei Xia ◽  
Meijuan Zhang ◽  
Zhiqiang Hu ◽  
...  
Keyword(s):  
Stress Responses ◽  
Burkholderia Pseudomallei ◽  
Cellular Response ◽  
Cell Model ◽  
Mrna Level ◽  
A549 Cells ◽  
Enrichment Analysis ◽  
Host Cells ◽  
P Value ◽  
Pathway Enrichment Analysis

Abstract Background: Burkholderia pseudomallei causes melioidosis and usually affects patients’ lungs, its persistent infection promotes the fusion of host cells, leading to the formation of multinucleated giant cells (MGCs) at the late infected stage. In this study, the global transcriptomic responses of B. pseudomallei infection of a human lung epithelial A549 cell model with different infected stages were investigated by means of microarray analysis to further elucidate the host cellular factors involved in the occurrence and development of the event. Results: A set of 35 common differential expression genes (DEGs) in EI and LI on the mRNA level applying a cut-off level of 1.5-fold change and a p-value < 0.05 were observed. Microarray data were further verified by Real-Time quantitative PCR (RT-qPCR). GO classification and pathway enrichment analysis revealed these DEGs mainly involved in inflammatory response related processes, such as cellular response to tumor necrosis factor, cellular response to lipopolysaccharide, positive regulation of NF-κB transcription factor activity. p-eIF2α, ATF4,NF-κB2(p52) and IL-1β were next selected to be validated by western bloting, which indicated B. pseudomallei could activate the eIF2α-ATF4 axis and NF-κB2 pathway in A549 cells. Conclusion: Our data shed light on the transcriptome dynamics of A549 cells which persistently infected with B. pseudomallei and suggested that the formation of MGCs may be a means for B. pseudomallei to manipulate the host's inflammation and stress response to adapt to intracellular life.

scholarly journals Nanopore Sequencing Reveals Global Transcriptome Signatures of Mitochondrial and Ribosomal Gene Expressions in Various Human Cancer Stem-like Cell Populations

Cancers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 1136
Author(s):  
Kaya E. Witte ◽  
Oliver Hertel ◽  
Beatrice A. Windmöller ◽  
Laureen P. Helweg ◽  
Anna L. Höving ◽  
...  
Keyword(s):  
Human Cancer ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Therapy Resistance ◽  
Ribosomal Gene ◽  
Pathway Enrichment Analysis ◽  
Nanopore Sequencing ◽  
Gene Expressions ◽  
Metastasis Therapy ◽  
Novel Therapeutic Strategies

Cancer stem cells (CSCs) are crucial mediators of tumor growth, metastasis, therapy resistance, and recurrence in a broad variety of human cancers. Although their biology is increasingly investigated within the distinct types of cancer, direct comparisons of CSCs from different tumor types allowing comprehensive mechanistic insights are rarely assessed. In the present study, we isolated CSCs from endometrioid carcinomas, glioblastoma multiforme as well as adenocarcinomas of lung and prostate and assessed their global transcriptomes using full-length cDNA nanopore sequencing. Despite the expression of common CSC markers, principal component analysis showed a distinct separation of the CSC populations into three clusters independent of the specific type of tumor. However, GO-term and KEGG pathway enrichment analysis revealed upregulated genes related to ribosomal biosynthesis, the mitochondrion, oxidative phosphorylation, and glycolytic pathways, as well as the proteasome, suggesting a great extent of metabolic flexibility in CSCs. Interestingly, the GO term “NF-kB binding” was likewise found to be elevated in all investigated CSC populations. In summary, we here provide evidence for high global transcriptional similarities between CSCs from various tumors, which particularly share upregulated gene expression associated with mitochondrial and ribosomal activity. Our findings may build the basis for identifying novel therapeutic strategies targeting CSCs.

scholarly journals Weighted gene co-expression network analysis identifies potential candidate biomarkers for adenocarcinoma of the esophagogastric junction

2020 ◽  
Author(s):  
Zhiyong Lai ◽  
Wenhui Yang ◽  
Weibin Li ◽  
Tiantian Zhang ◽  
Kai Jia ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Network Analysis ◽  
Esophagogastric Junction ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Potential Candidate ◽  
Hub Genes ◽  
Analysis Strategy ◽  
Gene Modules

Abstract Background: Gastric cancer (GC) is the fifth most common kind of malignant tumor, and commonly leads to death. As a subtype of gastric cancer, adenocarcinoma of the esophagogastric junction (AEG), accounts for about 50% of all gastric cancer cases. So far, the systematic co-expression analysis of this tumor has not fully explained its pathogenesis. The purpose of this study was to construct RNAs co-expression networks to predict candidate hub genes associated with the tumorigenesis of AEG. Methods: The RNA-seq data of 22 AEG patients was processed with weighted gene co-expression network analysis strategy. Differentiate the modules with clinical tumor markers and preservation, and carry out gene ontology and pathway enrichment analysis. We identified the co-expression modules and used GO and KEGG terms to investigated the functional enrichment of co-expression genes, suggesting that blue and brown modules are related to the biological processes of tumorigenesis. Results: Twenty-five distinct co-expression gene modules were identified, and as the top hub genes of tumorigenic gene modules, CD93, TRIM28, SLC3A2, CBX4, PATL1, and ZNF473 with high intramodular connectivity were assumed as intramodular hub genes in AEG. Conclusion: The weighted gene co-expression network analysis conducted in this study screened out CD93, TRIM28, SLC3A2, CBX4, PATL1, and ZNF473 may act as candidate biomarker in GC and AEG.

scholarly journals Plasma Metabolic Profiling of Pediatric Sepsis in a Chinese Cohort

2021 ◽  
Vol 9 ◽  
Author(s):  
Guo-Bang Li ◽  
Hong-Rong Hu ◽  
Wen-Feng Pan ◽  
Bo Li ◽  
Zhi-Ying Ou ◽  
...  
Keyword(s):  
Fatty Acids ◽  
Pathogenic Bacteria ◽  
Bacterial Species ◽  
Disease Onset ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Control Subjects ◽  
Sepsis Survivors ◽  
Pediatric Sepsis

Sepsis represents one of the most pressing problems in pediatrics, characterized by pathogenic bacteria invading the blood, growing and multiplying in the blood circulation, and ultimately causing severe infections. Most children with sepsis have a rapid disease onset and frequently exhibit sudden high fever or first chills. Here we performed comprehensive metabolomic profiling of plasma samples collected from pediatric sepsis patients to identify specific metabolic alterations associated with these patients (n = 84, designated as case subjects) as compared to healthy cohorts (n = 59, designated as control subjects). Diagnostic models were constructed using MetaboAnalyst, R packages, and multiple statistical methods, such as orthogonal partial least squares-discriminant analysis, principal component analysis, volcano plotting, and one-way ANOVA. Our study revealed a panel of metabolites responsible for the discrimination between case and control subjects with a high predictive value of prognosis. Moreover, significantly altered metabolites in sepsis survivors versus deceased patients (non-survivors) were identified as those involved in amino acids, fatty acids, and carbohydrates metabolism. Nine metabolites including organic acids and fatty acids were also identified with significantly higher abundance in sepsis patients with related microbes, implicating greater potentials to distinguish bacterial species using metabolomic analysis than blood culture. Pathway enrichment analysis further revealed that fatty acid metabolism might play an important role in the pathogenesis of sepsis.

scholarly journals Multi-omics analysis of m6A modification-related patterns based on m6A regulators and tumor microenvironment infiltration in lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xincheng Wu ◽  
Zhengping Bai
Keyword(s):  
Tumor Microenvironment ◽  
Lung Adenocarcinoma ◽  
Cytotoxic T Lymphocyte ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Comprehensive Analysis ◽  
Survival Advantage ◽  
Pathway Enrichment Analysis ◽  
Mutation Burden

AbstractEpigenetic modifications, especially N6-methyladenosine (m6A) modification, play a key role in tumor microenvironment (TME) infiltration. However, the regulatory role of m6A modification in the TME of lung adenocarcinoma (LUAD) remains unclear. A total of 2506 patients with LUAD were included in the analysis and divided into different groups according to distinct m6A modification-related patterns based on 23 m6A regulators. A comprehensive analysis was performed to explore TME infiltration in different m6A modification-related patterns. Principal component analysis was performed to obtain the m6Ascore and to quantify m6A modification-related patterns in different individuals. Three distinct m6A modification-related patterns were identified by 23 m6A regulators. The pathway enrichment analysis showed that m6Acluster-A was associated with immune activation; m6Acluster-B was associated with carcinogenic activation; m6Acluster-C was prominently related to substance metabolism. M6Acluster-A was remarkably rich in TME-infiltrating immune cells and patients with this pattern showed a survival advantage. The m6Ascore could predict TME infiltration, tumor mutation burden (TMB), the effect of tumor immunotherapy, and the prognosis of patients in LUAD. High m6Ascore was characterized by increased TME infiltration, reduced TMB, and survival advantage. Patients with a high m6Ascore exhibited significantly improved clinical response to anti-cytotoxic T lymphocyte antigen-4 (anti-CTLA4) immunotherapy. This study explored the regulatory mechanisms of TME infiltration in LUAD. The comprehensive analysis of m6A modification-related patterns may contribute to the development of individualized immunotherapy and the improvement of the overall effectiveness of immunotherapy for LUAD patients.

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