Differential gene expression profiles and identification of the genes relevant to clinicopathologic factors in colorectal cancer selected by cDNA array method in combination with principal component analysis

2003 ◽  
Author(s):  
Takuya Tsunoda ◽  
Yasuhiro Koh ◽  
Fumiaki Koizumi ◽  
Shoji Tsukiyama ◽  
Hiroshi Ueda ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Principal Component Analysis ◽  
Differential Gene Expression ◽  
Expression Profiles ◽  
Cdna Array ◽  
Gene Expression Profiles ◽  
Principal Component ◽  
Clinicopathologic Factors ◽  
Differential Gene

The relationship between tumor budding, tumor microenvironment, and survival in patients with primary operable colorectal cancer.

2019 ◽  
Vol 37 (4_suppl) ◽  
pp. 581-581
Author(s):  
Hester Catharina Van Wyk ◽  
Antonia K. Roseweir ◽  
Ditte Anderson ◽  
Elizabeth Sutton ◽  
Paul G. Horgan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Differential Gene Expression ◽  
Evaluation Method ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Venous Invasion ◽  
Tumour Microenvironment ◽  
Differential Gene ◽  
Tumour Budding

581 Background: Tumour budding is an independent prognostic factor in colorectal cancer and has recently been defined by the International Consensus Conference on Tumour Budding. The aim was to use the ITBCC budding evaluation method to examine relationships between tumour budding, tumour factors, tumour microenvironment, gene expression profiles and survival in patients with primary operable CRC. Methods: Hematoxylin and Eosin (H&E) stained slides of 953 CRC patients, diagnosed between 1997 and 2007 were evaluated for tumour budding according to the ITBCC-criteria. The tumour microenvironment was evaluated using tumour stroma percentage (TSP) and Klintrup–Makinen (KM) grade to assess the tumour inflammatory cell infiltrate. Differential gene expression was assessed using TempO-Seq gene expression profiling (BioSpyder Technologies Inc., CA, USA) using the Surrogate+Tox targeted panel (2,733 genes selected for biological diversity, maximal information content, and widespread pathway coverage). Results: High budding (n = 269/ 28%) was significantly associated with TNM stage (P < 0.001), venous invasion (P < 0.001), weak KM grade (P < 0.001), high TSP (P < 0.001) and reduced cancer specific survival (CSS) (HR = 5.04; 95% confidence interval [CI], 3.50-9.51; P < 0.001) and was independent of venous invasion, KM grade, and Ki67 proliferation index. RNA expression analysis was employed using TempO-Seq to determine differential gene expression between tumours with (n = 8) and without budding (n = 18). Three genes were identified as significantly differentially expressed: S100A2 (S100 calcium binding protein A2) was upregulated by 2.9 fold (padj < 0.00001); REG1A (regenerating family member 1 alpha) was downregulated by 4.7 fold (padj < 0.01) and LCN2 (lipocalin 2) was downregulated by 2.2 fold (padj < 0.01). Conclusions: Tumour budding stratifies patients’ survival in primary operable colorectal cancer and associates with differing gene expression profiles and factors of the tumour. Therefore, the ITBCC budding evaluation method should be used to assess tumour budding as supplement the TNM staging system and can help to further subdivide colorectal cancer into new prognostic groups.

scholarly journals Differential Gene Expression Profiles of Cells from Normal, Traumatic and Idiopathic Scoliotic Discs Identify Molecular Dysregulation in Scoliosis

2016 ◽  
Vol 6 (1_suppl) ◽  
pp. s-0036-1582635-s-0036-1582635 ◽  
Author(s):  
Sibylle Grad ◽  
Ying Zhang ◽  
Olga Rozhnova ◽  
Elena Schelkunova ◽  
Mikhail Mikhailovsky ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Differential Gene

scholarly journals RankerGUI: A Computational Framework to Compare Differential Gene Expression Profiles Using Rank Based Statistics

2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Differential Gene Expression ◽  
Web Application ◽  
Cellular Response ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Experimental Conditions ◽  
Differential Gene

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.

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