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.

scholarly journals Construction and Analysis of a Prognostic Model of Osteosarcoma Based on m6A-Related lncRNA

2021 ◽  
Author(s):  
Zhian Ling ◽  
Yuting Liang ◽  
Suping Wei ◽  
Yuanming Chen ◽  
Jinmin Zhao
Keyword(s):  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis

Abstract Background N6-methylandenosine (m6A) methylation is one of the most common methylation modifications in RNA. At present, a large number of studies have found that m6A methylation can regulate the occurrence and development of tumors by modifying mRNA. However, it is still unclear how m6A modifies Long non-coding RNA (lncRNA) that regulates mRNA expression by interacting with miRNA to affect the occurrence and development of osteosarcoma(OS). Therefore, exploring the lncRNAs related to m6A methylation and identifying lncRNAs that have both prognostic effects and immune functions are things that need to be solved urgently. Methods The published gene expression data of OS and complete clinical annotation files were obtained from the TARGET database. LncRNAs with P <0.001 from the results of Pearson correlation coefficient analysis as m6A-related lncRNAs were screened. Single-factor Cox regression analysis was used to screening prognostic- related lncRNA combined with the clinical information of patients and constructed a prognostic model based on lasso regression analysis. Then we explored the differences in survival and immune function of different subtypes that be obtained using the Consensus Cluster. The enrichment of differential genes between high and low risk groups in the KEGG pathway is achieved through Gene set enrichment analysis(GSEA). Results We obtained 706 lncRNAs in the TARGET database. Consensus clustering method were used to divide patients with OS into subgroups based on the expression of 26 prognostic-related lncRNAs. Through Kaplan-Meier survival analysis, there are significant differences between the two subgroups. The average immune score (P = 0.02), stromal score(P =0.027), and estimate score༈P = 0.015༉were higher in cluster 1 than in cluster 2. We found that compared with cluster 2, SIGLEC15, HAVCR2, LAG3, and PDCD1 were highly expressed in cluster 1.We obtain a prognostic model by lasso regression analysis. In the training group and the text group, the OS curve showed that patients in the high-risk group had a poorer prognosis than those in the low-risk group. In the training set, univariate Cox regression analysis and multivariate Cox regression analysis showed that the risk score was correlated with the prognosis of OS patients. In the high-risk group, the Linoleic acid metabolism and the Glycine, serine and threonine metabolism pathway were mainly involved by Gene Set Enrichment analysis. The abundance of Mast cells activated (P ≦0.024) and T cells CD4 (P ≦0.0044) naive were positively association the risk score. Conclusions This study clarified the important role of m6A-related lncRNAs in the prognosis and immune microenvironment of patients with OS, and indicate that m6A-related prognostic lncRNA signals may provide new targets for the diagnosis and treatment of OS.

scholarly journals Hypoxia Constructing the Prognostic Model of Colorectal Adenocarcinoma and Related to the Immune Microenvironment

2021 ◽  
Vol 9 ◽  
Author(s):  
Yuanyuan Zhang ◽  
Feng Yang ◽  
Xiaohong Peng ◽  
Xiaoyu Li ◽  
Na Luo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Clinical Application ◽  
Prognostic Model ◽  
Colorectal Adenocarcinoma ◽  
External Validation ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Common Target

Background: Hypoxia is a common phenomenon in solid tumors, which plays an important role in tumor proliferation, apoptosis, angiogenesis, invasion and metastasis, energy metabolism and chemoradiotherapy resistance. However, comprehensive analysis of hypoxia markers in colorectal adenocarcinoma (COAD) is still lacking. And there is a need for mechanism exploration and clinical application.Methods: The gene expression, mutation and clinical data of COAD were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. Tumor samples from TCGA were randomly divided into the training and internal validation groups, while tumor samples from GEO were used as the external validation group. Univariate COX—LASSO—multivariate COX method was applied to construct the prognostic model. We clustered all TCGA tumor samples into high, medium and low hypoxia groups, evaluated the correlation between hypoxia degree and immunoactivity, and explored the combined effect of mutation for common target genes and model riskscore on survival in COAD patients. Finally, we developed a dynamic nomograph App online for direct clinical application and carried out multiple validations of the prognostic model.Results: Our hypoxia-related prognostic model for COAD patients is accurate and has been successfully validated internally and externally. Single Sample Gene Set Enrichment Analysis (ssGSEA) and Gene Set Enrichment Analysis (GSEA) results suggest that for COAD patients with higher hypoxia, the stronger the associated immunosuppressive activity, providing a possible mechanism for the lower survival rate. Finally, the dynamic nomograph App online enhances the clinical translational significance of the study.Conclusion: In this study, an accurate prognostic model for COAD patients was established and validated. In addition, our innovative findings include correlations between hypoxia levels and immune activity, as well as an in-depth exploration of common target gene mutations.

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.

A Six-Gene Prognostic Risk Prediction Model In Hepatitis B Virus-Associated Hepatocellular Carcinoma

2021 ◽  
Vol 44 (3) ◽  
pp. E32-44
Author(s):  
Jia Shen ◽  
Ming Shu ◽  
Shujie Xie ◽  
Jia Yan ◽  
Kaile Pan ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Prognostic Score ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Training Set ◽  
Gene Set Enrichment ◽  
B Virus ◽  
Score Model ◽  
Normal Controls

Purpose: This study aimed to screen hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC)-related feature ribonucleic acids (RNAs) and to establish a prognostic model. Methods: The transcriptome expression data of HBV-associated HCC were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus database. Differential RNAs between HBV-associated HCC and normal controls were identified by a meta-analysis of TCGA, GSE55092 and GSE121248. Weighted gene co-expression network analysis was performed to identify key RNAs and modules. A prognostic score model was established using TCGA as a training set by Cox regression analysis and was validated in E-TABM-36 dataset. Additionally, independent prognostic clinical factors were screened, and the function of lncRNAs waspredicted through Gene Set Enrichment Analysis. Results: A total of 710 consistent differential RNAs between HBV-associated HCC and normal controls were obtained, including five lncRNAs and 705 mRNAs. An optimized combination of six differential RNAs (DSCR4, DBH, ECM1, GDAP1, MATR3 and RFC4) was selected and a prognostic score model was constructed. Kaplan-Meier analysis demonstrated that the prognosis of the high-risk and low-risk groups separated by this model was significantly different in the training set and the validation set. Gene Set Enrichment Analysis showed that the co-expression genes of DSCR4 were significantly correlated with neuroactive ligand receptor interactionpathway. Conclusion: A prognostic model based on DSCR4, DBH, ECM1, GDAP1, MATR3 and RFC4 was developed that can accurately predict the prognosis of patients with HBV-associated HCC. These genes, as well as histologic grade, may serve as independent prognostic factors in HBV-associated HCC.

A Hypoxia-related LncRNA Signature Predicts Survival of Primary Lower-grade glioma

2021 ◽  
Author(s):  
Yanjia Hu ◽  
Jing Zhang ◽  
Jing Chen
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
Gene Set Enrichment Analysis ◽  
Lower Grade ◽  
Prognostic Signature ◽  
Gene Set Enrichment

Abstract Background Hypoxia-related long non-coding RNAs (lncRNAs) have been proven to play a role in multiple cancers and can serve as prognostic markers. Lower-grade gliomas (LGGs) are characterized by large heterogeneity. Methods This study aimed to construct a hypoxia-related lncRNA signature for predicting the prognosis of LGG patients. Transcriptome and clinical data of LGG patients were obtained from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). LGG cohort in TCGA was chosen as training set and LGG cohorts in CGGA served as validation sets. A prognostic signature consisting of fourteen hypoxia-related lncRNAs was constructed using univariate and LASSO Cox regression. A risk score formula involving the fourteen lncRNAs was developed to calculate the risk score and patients were classified into high- and low-risk groups based on cutoff. Kaplan-Meier survival analysis was used to compare the survival between two groups. Cox regression analysis was used to determine whether risk score was an independent prognostic factor. A nomogram was then constructed based on independent prognostic factors and assessed by C-index and calibration plot. Gene set enrichment analysis and immune cell infiltration analysis were performed to uncover further mechanisms of this lncRNA signature. Results LGG patients with high risk had poorer prognosis than those with low risk in both training and validation sets. Recipient operating characteristic curves showed good performance of the prognostic signature. Univariate and multivariate Cox regression confirmed that the established lncRNA signature was an independent prognostic factor. C-index and calibration plots showed good predictive performance of nomogram. Gene set enrichment analysis showed that genes in the high-risk group were enriched in apoptosis, cell adhesion, pathways in cancer, hypoxia etc. Immune cells were higher in high-risk group. Conclusion The present study showed the value of the 14-lncRNA signature in predicting survival of LGGs and these 14 lncRNAs could be further investigated to reveal more mechanisms involved in gliomas.

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 Kalirin-RAC controls nucleokinetic migration in ADRN-type neuroblastoma

2021 ◽  
Vol 4 (5) ◽  
pp. e201900332
Author(s):  
Elena A Afanasyeva ◽  
Moritz Gartlgruber ◽  
Tatsiana Ryl ◽  
Bieke Decaesteker ◽  
Geertrui Denecker ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Time Lapse ◽  
Dna Methyltransferases ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Risk Characteristics ◽  
Weak Points ◽  
Treatment Research ◽  
Time Lapse Imaging

The migrational propensity of neuroblastoma is affected by cell identity, but the mechanisms behind the divergence remain unknown. Using RNAi and time-lapse imaging, we show that ADRN-type NB cells exhibit RAC1- and kalirin-dependent nucleokinetic (NUC) migration that relies on several integral components of neuronal migration. Inhibition of NUC migration by RAC1 and kalirin-GEF1 inhibitors occurs without hampering cell proliferation and ADRN identity. Using three clinically relevant expression dichotomies, we reveal that most of up-regulated mRNAs in RAC1- and kalirin–GEF1–suppressed ADRN-type NB cells are associated with low-risk characteristics. The computational analysis shows that, in a context of overall gene set poverty, the upregulomes in RAC1- and kalirin–GEF1–suppressed ADRN-type cells are a batch of AU-rich element–containing mRNAs, which suggests a link between NUC migration and mRNA stability. Gene set enrichment analysis–based search for vulnerabilities reveals prospective weak points in RAC1- and kalirin–GEF1–suppressed ADRN-type NB cells, including activities of H3K27- and DNA methyltransferases. Altogether, these data support the introduction of NUC inhibitors into cancer treatment research.

scholarly journals DPYSL2 as potential diagnostic and prognostic biomarker linked to immune infiltration in lung adenocarcinoma

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yang-Jie Wu ◽  
Ai-Tao Nai ◽  
Gui-Cheng He ◽  
Fei Xiao ◽  
Zhi-Min Li ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Roc Curve ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
List Type ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Normal Tissues ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Background Dihydropyrimidinase like 2 (DPYSL2) has been linked to tumor metastasis. However, the function of DPSY2L in lung adenocarcinoma (LUAD) is yet to be explored. Methods Herein, we assessed DPYSL2 expression in various tumor types via online databases such as Oncomine and Tumor Immune Estimation Resource (TIMER). Further, we verified the low protein and mRNA expressions of DPYSL2 in LUAD via the ULCAN, The TCGA and GEPIA databases. We applied the ROC curve to examine the role of DPYSL2 in diagnosis. The prognostic significance of DPYSL2 was established through the Kaplan–Meier plotter and the Cox analyses (univariate and multivariate). TIMER was used to explore DPYSL2 expression and its connection to immune infiltrated cells. Through Gene Set Enrichment Analysis, the possible mechanism of DPYSL2 in LUAD was investigated. Results In this study, database analysis revealed lower DPYSL2 expression in LUAD than in normal tissues. The ROC curve suggested that expression of DPYSL2 had high diagnostic efficiency in LUAD. The DPYSL2 expression had an association with the survival time of LUAD patients in the Kaplan–Meier plotter and the Cox analyses. The results from TIMER depicted a markedly positive correlation of DPYSL2 expression with immune cells infiltrated in LUAD, such as macrophages, dendritic cells, CD4+ T cells, and neutrophils. Additionally, many gene markers for the immune system had similar positive correlations in the TIMER analysis. In Gene Set Enrichment Analysis, six immune-related signaling pathways were associated with DPYSL2. Conclusions In summary, DPYSL2 is a novel biomarker with diagnostic and prognostic potential for LUAD as well as an immunotherapy target. Highlights Expression of DPYSL2 was considerably lower in LUAD than in normal tissues. Investigation of multiple databases showed a high diagnostic value of DPYSL2 in LUAD. DPYSL2 can independently predict the LUAD outcomes. Immune-related mechanisms may be potential ways for DPYSL2 to play a role in LUAD.

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