Identification of Hub Genes and Key Modules in Stomach Adenocarcinoma Using nsNMF-Based Data Integration Technique

2019 ◽  
Author(s):  
Sk Md Mosaddek Hossain ◽  
Sumanta Ray ◽  
Anirban Mukhopadhyay
Keyword(s):  
Data Integration ◽  
Integration Technique ◽  
Hub Genes ◽  
Stomach Adenocarcinoma

Semantic Interation, Text Mining, Tools and Technologies

2015 ◽  
pp. 47-63
Author(s):  
Chandrakant Ekkirala
Keyword(s):  
Text Mining ◽  
Data Integration ◽  
Semantic Integration ◽  
Semantic Technologies ◽  
Integration Technique ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Periodic Data ◽  
Integration Techniques ◽  
Mining Algorithms

Semantic technologies have gained prominence over the last several years. Semantic technologies are explored in detail and semantic integration of data will be outlined. The various data integration techniques and approaches will also be touched upon. Text Mining, different associated algorithms and the various tools and technologies used in text mining will be enumerated in detail. The chapter will have the following sections – 1. Data Integration Techniques • Data Integration Technique – Extraction, Transformation and Loading (ETL) • Data Integration Technique – Data Federation 2. Data Integration Approaches • Need Based Data Integration • Periodic Data Integration • Continuous Data Integration 3. Semantic Integration 4. Semantic Technologies 5. Semantic Web Technologies 6. Text Mining 7. Text Mining Algorithms 8. Tools and Technologies for Text Mining

A geomatics data integration technique for coastal change monitoring

2005 ◽  
Vol 30 (6) ◽  
pp. 651-664 ◽  
Author(s):  
J. P. Mills ◽  
S. J. Buckley ◽  
H. L. Mitchell ◽  
P. J. Clarke ◽  
S. J. Edwards
Keyword(s):  
Data Integration ◽  
Coastal Change ◽  
Integration Technique ◽  
Change Monitoring

Watershed response analysis using digital data integration technique

1993 ◽  
Vol 13 (5) ◽  
pp. 177-180 ◽  
Author(s):  
P. Manavalan ◽  
J. Krishnamurthy ◽  
B. Manikiam ◽  
S. Adiga ◽  
K. Radhakrishnan ◽  
...  
Keyword(s):  
Data Integration ◽  
Digital Data ◽  
Response Analysis ◽  
Integration Technique ◽  
Watershed Response

scholarly journals Prognostic Value and Immune Infiltrates of ABCA8 and FABP4 in Stomach Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Ya Guo ◽  
Zhong Wei Wang ◽  
Wang Hui Su ◽  
Jing Chen ◽  
Ya Li Wang
Keyword(s):  
Poor Prognosis ◽  
Immune Cell ◽  
Genetic Alterations ◽  
Prognostic Biomarkers ◽  
Venn Diagram ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Interactive Analysis ◽  
Human Protein Atlas ◽  
Stomach Adenocarcinoma

Background. Stomach adenocarcinoma (STAD) is a common malignancy worldwide with poor prognosis. Therefore, it is important to identify a valuable prognostic biomarker for STAD. The aim of present study was to identify novel prognostic biomarkers for STAD and evaluate the potential role of hub genes in STAD. Methods. Gene Expression Profiling Interactive Analysis (GEPIA) and Cancer RNA-Seq Nexus were performed to identify differentially expressed genes (DEGs). Subsequently, hub genes were selected by a Venn diagram, and the expression of key genes was confirmed by UALCAN database. Furthermore, survival analysis of these hub genes was performed using Oncolnc and Human Protein Atlas (HPA) database. Gene alteration status of hub genes was assessed by cBioPortal. Finally, we investigated the association between hub genes and immune cell infiltration in STAD through the Tumor Immune Estimation Resource (TIMER) and GEPIA database. Results. Three common hub genes were obtained, including 2 downregulated DEGs (ABCA8 and FABP4) and one upregulated DEG (SLC52A3). Furthermore, increased expression of ABCA8 and FABP4 and decreased expression of SLC52A3 were correlated with poor prognosis. Meanwhile, three hub genes showed genetic alterations in various datasets of STAD. Finally, our results showed that ABCA8 and FABP4 displayed a positive correlation with immune infiltration, especially in M2 macrophages. Conclusions. The results of this study suggest that ABCA8 and FABP4 may be used as prognostic biomarkers and correlated with immune infiltration in STAD.

A Data integration technique for pore pressure evaluation and drilling prognosis

1996 ◽  
Author(s):  
E. F. Doyle ◽  
S. V. Wagin ◽  
A. G. Madatov ◽  
H. B. Helle
Keyword(s):  
Data Integration ◽  
Pore Pressure ◽  
Integration Technique

scholarly journals Transcriptome Based System Biology Exploration Reveals Homogeneous Tumorigenicity of Alimentary Tract Malignancy

2021 ◽  
Vol 10 ◽  
Author(s):  
Yu-Chen Lu ◽  
Jing-Qi Shi ◽  
Zi-Xin Zhang ◽  
Jia-Yi Zhou ◽  
Hai-Kun Zhou ◽  
...  
Keyword(s):  
Digestive Tract ◽  
Alimentary Tract ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Competitive Endogenous Rnas ◽  
Cancer Genome Atlas ◽  
Stomach Adenocarcinoma ◽  
Gene Modules ◽  
Single Tumor

Malignancies of alimentary tract include esophageal carcinoma (ESCA), stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), and rectum adenocarcinoma (READ). Despite of their similarities in cancer development and progression, there are numerous researches concentrating on single tumor but relatively little on their common mechanisms. Our study explored the transcriptomic data of digestive tract cancers from The Cancer Genome Atlas database, yielding their common differentially expressed genes including 1,700 mRNAs, 29 miRNAs, and 362 long non-coding RNAs (lncRNAs). There were 12 mRNAs, 5 miRNAs, and 16 lncRNAs in the core competitive endogenous RNAs network by RNA-RNA interactions, highlighting the prognostic nodes of SERPINE1, hsa-mir-145, and SNHG1. In addition, the weighted gene co-expression network analysis (WGCNA) illustrated 20 gene modules associated with clinical traits. By taking intersections of modules related to the same trait, we got 67 common genes shared by ESCA and READ and screened 5 hub genes, including ADCY6, CXCL3, NPBWR1, TAS2R38, and PTGDR2. In conclusion, the present study found that SERPINE1/has-mir-145/SNHG1 axis acted as promising targets and the hub genes reasoned the similarity between ESCA and READ, which revealed the homogeneous tumorigenicity of digestive tract cancers at the transcriptome level and led to further comprehension and therapeutics for digestive tract cancers.

scholarly journals An insight to HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) pathogenesis; evidence from high-throughput data integration and meta-analysis

Retrovirology ◽  
2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Sayed-Hamidreza Mozhgani ◽  
Mehran Piran ◽  
Mohadeseh Zarei-Ghobadi ◽  
Mohieddin Jafari ◽  
Seyed-Mohammad Jazayeri ◽  
...  
Keyword(s):  
Data Integration ◽  
High Throughput ◽  
Meta Analysis ◽  
Spastic Paraparesis ◽  
Tropical Spastic Paraparesis ◽  
Hub Genes ◽  
Down Regulation ◽  
Major Genes ◽  
High Throughput Data ◽  
Network Analyses

Abstract Background Human T-lymphotropic virus 1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a progressive disease of the central nervous system that significantly affected spinal cord, nevertheless, the pathogenesis pathway and reliable biomarkers have not been well determined. This study aimed to employ high throughput meta-analysis to find major genes that are possibly involved in the pathogenesis of HAM/TSP. Results High-throughput statistical analyses identified 832, 49, and 22 differentially expressed genes for normal vs. ACs, normal vs. HAM/TSP, and ACs vs. HAM/TSP groups, respectively. The protein–protein interactions between DEGs were identified in STRING and further network analyses highlighted 24 and 6 hub genes for normal vs. HAM/TSP and ACs vs. HAM/TSP groups, respectively. Moreover, four biologically meaningful modules including 251 genes were identified for normal vs. ACs. Biological network analyses indicated the involvement of hub genes in many vital pathways like JAK-STAT signaling pathway, interferon, Interleukins, and immune pathways in the normal vs. HAM/TSP group and Metabolism of RNA, Viral mRNA Translation, Human T cell leukemia virus 1 infection, and Cell cycle in the normal vs. ACs group. Moreover, three major genes including STAT1, TAP1, and PSMB8 were identified by network analysis. Real-time PCR revealed the meaningful down-regulation of STAT1 in HAM/TSP samples than AC and normal samples (P = 0.01 and P = 0.02, respectively), up-regulation of PSMB8 in HAM/TSP samples than AC and normal samples (P = 0.04 and P = 0.01, respectively), and down-regulation of TAP1 in HAM/TSP samples than those in AC and normal samples (P = 0.008 and P = 0.02, respectively). No significant difference was found among three groups in terms of the percentage of T helper and cytotoxic T lymphocytes (P = 0.55 and P = 0.12). Conclusions High-throughput data integration disclosed novel hub genes involved in important pathways in virus infection and immune systems. The comprehensive studies are needed to improve our knowledge about the pathogenesis pathways and also biomarkers of complex diseases.

Sign in / Sign up

Export Citation Format

Share Document