scholarly journals HTRA3 Is a Prognostic Biomarker and Associated With Immune Infiltrates in Gastric Cancer

2020 ◽  
Vol 10 ◽  
Author(s):  
Ce Ji ◽  
Li-Sha Sun ◽  
Fei Xing ◽  
Nan Niu ◽  
Hong-Li Gao ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Multivariate Analysis ◽  
Confidence Interval ◽  
Enrichment Analysis ◽  
Hazard Ratio ◽  
Gene Set Enrichment Analysis ◽  
Signal Pathways ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
The Impact

HtrA serine peptidase 3 (HTRA3) participates in multiple signal pathways and plays an important regulatory role in various malignancies; however, its role on prognosis and immune infiltrates in gastric cancer (GC) remains unclear. The study investigated HTRA3 expression in tumor tissues and its association with immune infiltrates, and determined its prognostic roles in GC patients. Patients with GC were collected from the cancer genome atlas (TCGA). We compared the expression of HTRA3 in GC and normal gastric mucosa tissues with Wilcoxon rank sum test. And logistic regression was used to evaluate the relationship between HTRA3 and clinicopathological characters. Gene ontology (GO) term analysis, Gene set enrichment analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA) was conducted to explain the enrichmental pathways and functions and quantify the extent of immune cells infiltration for HTRA3. Kaplan-Meier analysis and Cox regression were performed to evaluate the correlation between HTRA3 and survival rates. A nomogram, based on Cox multivariate analysis, was used to predict the impact of HTRA3 on prognosis. High HTRA3 expression was significantly correlated with tumor histological type, histological grade, clinical stage, T stage, and TP53 status (P < 0.05). HTRA3-high GC patients had a lower 10-year progression-free interval [PFI; hazard ratio (HR): 1.46; 95% confidence interval (CI): 1.02–2.08; P = 0.038], disease-specific survival (DSS; HR: 1.65; CI: 1.08–2.52; P = 0.021) and overall survival (OS; HR: 1.59; CI: 1.14–2.22; P = 0.006). Multivariate survival analysis showed that HTRA3 was an independent prognostic marker for PFI (HR: 1.456; CI: 1.021–2.078; P = 0.038), DSS (HR: 1.650; CI: 1.079–2.522; P = 0.021) and OS [hazard ratio (HR): 1.590; 95% confidence interval (CI):1.140–2.219; P = 0.006]. The C-indexes and calibration plots of the nomogram based on multivariate analysis indicated an effective predictive performance for GC patients. GSEA showed that High HTRA3 expression may activate NF-κB pathway, YAP1/WWTR1/TAZ pathway, and TGFβ pathway. There was a negative correlation between the HTRA3 expression and the abundances of adaptive immunocytes (T helper cell 17 cells) and a positive correlation with abundances of innate immunocytes (natural killer cells, macrophages etc.). HTRA3 plays a vital role in GC progression and prognosis and could be a moderate biomarker for prediction for survival after gastrectomy.

scholarly journals Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

2021 ◽  
Vol 12 ◽  
Author(s):  
Michal Marczyk ◽  
Agnieszka Macioszek ◽  
Joanna Tobiasz ◽  
Joanna Polanska ◽  
Joanna Zyla
Keyword(s):  
Association Studies ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Genome Wide Association ◽  
Gene Set Analysis ◽  
Genome Wide Association Studies ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Genome Wide ◽  
The Impact

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar’s test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

scholarly journals Mining the TCGA Database for Microenvironment‐Related Genes with Prognostic Value in Gastric Cancer

2021 ◽  
Author(s):  
Ting Liu ◽  
Zheng Gong ◽  
Hong Zhang ◽  
Yi Wan ◽  
Ming-Han Ren ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Distant Metastasis ◽  
Clinical Stage ◽  
Enrichment Analysis ◽  
Clinicopathological Characteristics ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Protein Protein Interaction

Abstract BackgroundGastric cancer (GC) is the fifth most common cancer worldwide. Previous studies have suggested that the tumor microenvironment (TME) plays an important role in the development and prognosis of GC. In this study, we aimed to identify genes in tumor-infiltrating immune cells (TICs) that influence the progression and prognosis of GC. MethodsWe used the ESTIMATE algorithm to calculate the scores of the stromal and immune components of the TME in 407 GC samples collected from The Cancer Genome Atlas (TCGA) database.The differentially expressed genes (DEGs) were intersected by a protein-protein interaction (PPI) network and analyzed by univariate Cox regression.Further analysis showed the correlation between MCEMP1 and the clinicopathological characteristics of GC patients (clinical stage, distant metastasis) and survival.Then we used Gene set enrichment analysis (GSEA) and CIBERSORT analysis to examine the relationship between MCEMP1 and the TME.ResultsThe analysis revealed that the expression of MCEMP1 was positively correlated with the clinicopathological characteristics of GC patients (clinical stage, distant metastasis) and negatively correlated with survival. Gene set enrichment analysis (GSEA) indicated that gene sets in the MCEMP1 high expression group were concentrated mainly in immune-related pathways. CIBERSORT analysis of the proportion of TICs revealed that neutrophils and M2 macrophages were positively correlated with MCEMP1 expression, suggesting that MCEMP1 is responsible for preservation of the immune-dominant status of the TME. ConclusionHigh MCEMP1 expression might be a biomarker of a poor prognosis in GC patients and provide a clue regarding the different statuses of the TME, offering additional insight into therapy for GC.

scholarly journals Identification METTL18 as a Potential Prognosis Biomarker and Associated With Immune Infiltrates in Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Tian-Hao Li ◽  
Cheng Qin ◽  
Bang-Bo Zhao ◽  
Hong-Tao Cao ◽  
Xiao-Ying Yang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Multivariate Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Wilcoxon Test ◽  
Hepatic Tissue ◽  
Gene Set Enrichment ◽  
Poor Overall Survival ◽  
Gene Set

Methyltransferase-like 18 (METTL18), a METTL family member, is abundant in hepatocellular carcinoma (HCC). Studies have indicated the METTL family could regulate the progress of diverse malignancies while the role of METTL18 in HCC remains unclear. Data of HCC patients were acquired from the cancer genome atlas (TCGA) and gene expression omnibus (GEO). The expression level of METTL18 in HCC patients was compared with normal liver tissues by Wilcoxon test. Then, the logistic analysis was used to estimate the correlation between METTL18 and clinicopathological factors. Besides, Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA) were used to explore relevant functions and quantify the degree of immune infiltration for METTL18. Univariate and Multivariate Cox analyses and Kaplan–Meier analysis were used to estimate the association between METTL18 and prognosis. Besides, by cox multivariate analysis, a nomogram was conducted to forecast the influence of METTL18 on survival rates. METTL18-high was associated with Histologic grade, T stage, Pathologic stage, BMI, Adjacent hepatic tissue inflammation, AFP, Vascular invasion, and TP53 status (P < 0.05). HCC patients with METTL18-high had a poor Overall-Survival [OS; hazard ratio (HR): 1.87, P < 0.001), Disease-Specific Survival (DSS, HR: 1.76, P = 0.015), and Progression-Free Interval (PFI, HR: 1.51, P = 0.006). Multivariate analysis demonstrated that METTL18 was an independent factor for OS (HR: 2.093, P < 0.001), DSS (HR: 2.404, P = 0.015), and PFI (HR: 1.133, P = 0.006). Based on multivariate analysis, the calibration plots and C-indexes of nomograms showed an efficacious predictive effect for HCC patients. GSEA demonstrated that METTL18-high could activate G2M checkpoint, E2F targets, KRAS signaling pathway, and Mitotic Spindle. There was a positive association between the METTL18 and abundance of innate immunocytes (T helper 2 cells) and a negative relation to the abundance of adaptive immunocytes (Dendritic cells, Cytotoxic cells etc.). Finally, we uncovered knockdown of METTL18 significantly suppressed the proliferation, invasion, and migration of HCC cells in vitro. This research indicates that METTL18 could be a novel biomarker to evaluate HCC patients’ prognosis and an important regulator of immune responses in HCC.

scholarly journals Elevated TUBA1A Might Indicate the Clinical Outcomes of Patients with Gastric Cancer, Being Associated with the Infiltration of Macrophages in the Tumor Immune Microenvironment

2020 ◽  
Vol 29 (4) ◽  
pp. 509-522
Author(s):  
Dazhi Wang ◽  
Zheng Jiao ◽  
Yinghui Ji ◽  
Shuyu Zhang
Keyword(s):  
Gastric Cancer ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Single Cells ◽  
Enrichment Analysis ◽  
Clinicopathological Features ◽  
Gene Set Enrichment Analysis ◽  
Regulation Mechanism ◽  
Gene Set Enrichment ◽  
Gene Set

Background and Aims: TUBA1A belongs to the tubulin superfamily, and its role in gastric cancer (GC) remains unclear. This study assessed the expression and effect of TUBA1A in GC, as well as its association with survival and clinicopathological features. Gene set enrichment analysis (GSEA) results revealed that high TUBA1A expression was associated with multiple pathways, including those that contributed to the infiltration of macrophages in the tumor microenvironment. Since increased infiltration of macrophages can lead to oxaliplatin resistance, we analyzed the association between TUBA1A, the infiltration of macrophages to the tumor microenvironment, and the inhibitory concentration 50% (IC50) of oxaliplatin. In addition, we analyzed the possible epigenetic regulation mechanism. Methods: A total of 1,881 samples, including 1,618 patients with GC and 263 normal samples, were examined. The associations between clinicopathological features and TUBA1A were assessed by chi-square test, survival was assessed by Kaplan-Meier analysis, and gene set enrichment analysis (GSEA) was performed to explore the potential mechanisms. The associations between TUBA1A and immune infiltration of M0-, M1-, and M2- polarized macrophages were examined by applying deconvolution’s quantification and Pearson’s correlation analysis. The association of TUBA1A with the IC50 of oxaliplatin was analyzed by Pearson correlation test. The mechanisms of TUBA1A dysregulation were studied by analyzing methylation data. A single-cell TUBA1A mRNA expression map of the stomach was drawn from the analysis of stomach single-cell RNA sequencing data that included more than 13,000 single cells of 17 stomach cell types. Results: TUBA1A expression was elevated in GC (p<0.01) and indicated poorer overall survival (p<0.001), first progression survival (p<0.001), and post-progression survival (p<0.01). High TUBA1A expression was significantly correlated with more aggressive clinicopathological features of GC patients (p<0.001). Elevated TUBA1A contributes to the infiltration of macrophages to the tumor microenvironment (p<0.001) and increased the IC50 of oxaliplatin in vitro (p<0.05), while hypomethylation was shown to contribute to the upregulation of TUBA1A (p<0.05). Conclusions: TUBA1A might be a potential prognostic marker and therapeutic target in GC. TUBA1A is significantly associated with the infiltration of M2-polarized macrophages in GC, and the IC50 of oxaliplatin. Hypomethylation contributes to the upregulation of TUBA1A in GC.

scholarly journals Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Scrub Typhus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Acid Base ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

scholarly journals Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mike Fang ◽  
Brian Richardson ◽  
Cheryl M. Cameron ◽  
Jean-Eudes Dazard ◽  
Mark J. Cameron
Keyword(s):  
Drug Targets ◽  
T Regulatory Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Regulatory Cells ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Gastroenteropancreatic Neuroendocrine Tumor ◽  
Public Datasets

Abstract Background In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Results We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting. Conclusions dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

scholarly journals Key pathways involved in prostate cancer based on gene set enrichment analysis and meta analysis

2011 ◽  
Vol 10 (4) ◽  
pp. 3856-3887 ◽  
Author(s):  
Q.Y. Ning ◽  
J.Z. Wu ◽  
N. Zang ◽  
J. Liang ◽  
Y.L. Hu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Key Pathways

scholarly journals A Novel Prognostic Model Based On Cytosolic DNA Sensing-Related Gene Signature for Pancreatic Cancer

2021 ◽  
Author(s):  
Chuan-Qi Xu ◽  
Kui-Sheng Yang ◽  
Shu-Xian Zhao ◽  
Jian Lv
Keyword(s):  
Pancreatic Cancer ◽  
Prognostic Factor ◽  
Prognostic Model ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Independent Prognostic Factor ◽  
Gene Set Enrichment Analysis ◽  
Dna Sensing ◽  
Gene Set Enrichment ◽  
Gene Set

Abstract Objective: Pancreatic cancer (PC) is one of the most malignant tumors. Cytosolic DNA sensing have been found to play an essential role in tumor. In this study, a cytosolic DNA sensing-related genes (CDSRGs) signature was constructed and the potential mechanisms also been discussed.Methods: The RNA expression and clinical data of PC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Subsequently, univariate (UCR) and multivariate Cox regression (MCR) analyses were conducted to establish a prognostic model in the TCGA patients, which was verified by GEO patients. Cancer immune infiltrates were investigated via single sample gene set enrichment analysis (ssGSEA) and Tumor Immune Estimation Resource (TIMER). Finally, Gene Set Enrichment Analysis (GSEA) was used to investigate the related signaling pathways.Results: A prognostic model comprising four genes (POLR2E,IL18, MAVS, and FADD) was established. The survival rate of patients in the low-risk group was significantly higher than that of patients in the high-risk group. In addition, CDSRGs-risk score was proved as an independent prognostic factor in PC. Immune infiltrates and drug sensitivity are associated with POLR2E,IL18, MAVS, and FADD expression.Conclusions: In summary, we present and validated a CDSRGs risk model that is an independent prognostic factor and indicates the immune characteristics of PC. This prognostic model may facilitate the personalized treatment and monitoring.

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