Comprehensive site-specific whole genome profiling of stromal and epithelial colonic gene signatures in human sigmoid colon and rectal tissue

Knight, Jason; Kim, Eunji; Ivanov, Ivan; Davidson, Laurie A.; Goldsby, Jennifer S.; Hullar, Meredith A.J.; Randolph, Timothy W.; Kaz, Andrew M.; Levy, Lisa; Lampe, Johanna W.; Chapkin, Robert S.

doi:10.1152/physiolgenomics.00023.2016

Cited by 15 publications

(15 citation statements)

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“…Lately, the function of homeobox genes in tumor growth and proliferation has been backed by several studies that have reported that many homeobox genes influence the tumors especially those of the digestive system . Knight et al demonstrated by RNA‐seq analysis that expression of HOXD9 in normal‐appearing mucosa is significantly higher in the sigmoid colon compared to the rectum . But, the expression of HOXD9 in CRC is yet to be studied.…”

Section: Discussionmentioning

confidence: 99%

HOXD9 promote epithelial‐mesenchymal transition and metastasis in colorectal carcinoma

et al. 2020

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Background: HOXD9, a Hox family member, is involved in cancer growth and metastasis. But, its regulation mechanism at the molecular level particularly in colorectal cancer (CRC), is mostly unknown. Methods: The HOXD9 protein expression levels were analyzed using immunofluorescence, immunohistochemistry (IHC) assays, and western blot. The in vivo and in vitro roles of HOXD9 in CRC were determined using colony formation and EdU incorporation, CCK-8, wound scratch and transwell invasion assay, and animal models. Results: Expression of HOXD9 was higher in CRC than in matched healthy tissues.High expression of HOXD9 has significantly associated with the American Joint Committee on Cancer (AJCC) stages, tumor differentiation, lymph node metastasis, and other serious invasions, and it had a poor prognosis. In vitro, HOXD9 encouraged proliferation, movement and EMT processes in cells of CRC. Also, TGF-β1 promoted the expression of HOXD9 and this effect was dependent on the dose and downregulation of HOXD9 repressed TGF-β1 -induced EMT. In vivo, HOXD9 promoted the invasive and metastasis of CRC cells via orthotopic implantation. Conclusions: The ectopic expression of HOXD9 promoted the invasion metastasis in cells of the colorectal tumor by induction of EMT in vitro and vivo. | 3933 LIU et aL How to cite this article: Liu M, Xiao Y, Tang W, et al. HOXD9 promote epithelial-mesenchymal transition and metastasis in colorectal carcinoma.

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Section: Discussionmentioning

confidence: 99%

HOXD9 promote epithelial‐mesenchymal transition and metastasis in colorectal carcinoma

et al. 2020

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“…The data provide insight into the gene expression landscape of the normal epithelium and stroma prior to the onset of intestinal tumorigenesis. This is noteworthy because the development of cancer is intimately linked to cross talk between cancer cells and the surrounding stromal cells 34. The data set consists of 29 epithelium and 30 stroma biopsies from the sigmoid colon.…”

Section: Resultsmentioning

confidence: 99%

The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data

et al. 2017

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Ranking feature sets for phenotype classification based on gene expression is a challenging issue in cancer bioinformatics. When the number of samples is small, all feature selection algorithms are known to be unreliable, producing significant error, and error estimators suffer from different degrees of imprecision. The problem is compounded by the fact that the accuracy of classification depends on the manner in which the phenomena are transformed into data by the measurement technology. Because next-generation sequencing technologies amount to a nonlinear transformation of the actual gene or RNA concentrations, they can potentially produce less discriminative data relative to the actual gene expression levels. In this study, we compare the performance of ranking feature sets derived from a model of RNA-Seq data with that of a multivariate normal model of gene concentrations using 3 measures: (1) ranking power, (2) length of extensions, and (3) Bayes features. This is the model-based study to examine the effectiveness of reporting lists of small feature sets using RNA-Seq data and the effects of different model parameters and error estimators. The results demonstrate that the general trends of the parameter effects on the ranking power of the underlying gene concentrations are preserved in the RNA-Seq data, whereas the power of finding a good feature set becomes weaker when gene concentrations are transformed by the sequencing machine.

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“…Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that MMP2 is involved in bladder cancer and other cancer pathways. Although investigations have focused on their roles in colon cancer (Knight, et al, 2016;Yde, et al, 2016) and in vivo status in animal studies (Jiang, et al, 2019), the relationship between these two proteins and prostate cancer is not reported yet. Our analysis suggested a potential role for these two proteins in prostate cancer; although experimental studies are necessary to confirm this relationship.…”

Section: Prostate Cancer Associated Network Analysismentioning

confidence: 99%

Disease associated protein-protein interaction network reconstruction based on comprehensive influence analysis

Zhu

Ling

et al. 2019

Preprint

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Proteins and their interactions are fundamental to biological system. With the scientific paradigm shifting to systems biology, functional study of proteins from a network viewpoint to get a deep understanding of their roles in human life and diseases being increasingly essential. Although several methods already existed for protein-protein interaction (PPI) network building, the precise reconstruction of disease associated PPI network remains a challenge. In this paper we introduce a novel concept of comprehensive influence of proteins in network, in which direct and indirect connections are adopted for the calculation of influential effects of a protein with different weights. With the optimized weights, we calculate and select the important proteins and their interactions to reconstruct the PPI network for further validation and confirmation. To evaluate the performance of the method, we compared our model with the six existed ones by using five standard data sets. The results indicated that our method outperforms the existed ones. We then applied our model to prostate cancer and Parkinson's disease to predict novel disease associated proteins for the future experimental validation. Author SummaryThe diverse protein-protein interaction networks have dramatic effects on biological system. The disease associated PPI networks are generally reconstructed from experimental data with computational models but with limited accuracy. We developed a novel concept of comprehensive influence of proteins in network for reconstructing the PPI network. Our model outperforms the state-of-the-art ones and we then applied our model to identify novel interactions for further validation. / 21experimental methods and the complexity of biomedical systems, the experimental results often present high false-positive and false-negative rates (Cheng, et al., 2016). Moreover, experimental verification of the PPI network is time consuming and expensive. Therefore, rapid and accurate computational methods represent useful alternative for predicting protein interactions, with such results providing guidance for experiments, including determination of unknown relationships between disease-causing genes and protein interactions (Zhu, et al., 2015). Such knowledge can be useful to the understanding of protein structure and evolution, as well as the overall function of the network and its associated dynamic processes (Cowen, et al., 2017;Hoeng, et al., 2014).The unknown interaction between proteins can be predicted by the theory of complex network. PPI network consists of nodes and edges, with the nodes representing proteins and the edges representing associations or interactions between the two proteins (Gani, et al., 2015). Libennowell et al. considered the influence of common neighbor nodes in the network and introduced similarity indices to include common neighbors (Libennowell and Kleinberg, 2007). Pujari et al. suggested that each attribute of a pair of connected proteins represents different information (Pujari and Kanawati, 2012), and L...

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Comprehensive site-specific whole genome profiling of stromal and epithelial colonic gene signatures in human sigmoid colon and rectal tissue

Cited by 15 publications

References 63 publications

HOXD9 promote epithelial‐mesenchymal transition and metastasis in colorectal carcinoma

HOXD9 promote epithelial‐mesenchymal transition and metastasis in colorectal carcinoma

The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data

Disease associated protein-protein interaction network reconstruction based on comprehensive influence analysis

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