<i>HMGA2</i> Overexpression-Induced Ovarian Surface Epithelial Transformation Is Mediated Through Regulation of EMT Genes

Wu, Jingjing; Liu, Zhaojian; Shao, Changshun; Gong, Yaoqin; Hernando, Eva; Lee, Peng; Narita, Masashi; Müller, William A.; Liu, Jinsong; Wei, Jian Jun

doi:10.1158/0008-5472.can-10-2550

Cited by 136 publications

(119 citation statements)

References 35 publications

(52 reference statements)

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“…24,25 It has been found that HMGI-C overexpression is associated with enhancing tumor growth and invasion, early metastasis, and a poor prognosis, typically seen in pancreatic cancer, 26 papillary thyroid carcinoma, 27 colorectal cancer, 17 ovarian cancer, 28 lung cancer 14 and HG-PSC. 16 Previous studies have demonstrated that, HMGI-C gene suppression leads to increased apoptosis and simultaneously sensitizes the malignant cells to chemotherapeutic agents.…”

Section: Discussionmentioning

confidence: 99%

Silencing of High Mobility Group Isoform I-C (HMGI-C) Enhances Paclitaxel Chemosensitivity in Breast Adenocarcinoma Cells (MDA-MB-468)

Mansoori¹,

Mohammadi²,

Goldar³

et al. 2016

Adv Pharm Bull

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Section: Discussionmentioning

confidence: 99%

Silencing of High Mobility Group Isoform I-C (HMGI-C) Enhances Paclitaxel Chemosensitivity in Breast Adenocarcinoma Cells (MDA-MB-468)

Mansoori¹,

Mohammadi²,

Goldar³

et al. 2016

Adv Pharm Bull

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“…In addition, pathway analysis also suggested a systematic contribution from deregulated FGF18 to the aberrant signaling pathways responsible for the malignancy. Of the 7 signaling pathways summarized as activated in greater than 50% of ovarian cancers (2) (26,27).…”

Section: Figurementioning

confidence: 99%

FGF18 as a prognostic and therapeutic biomarker in ovarian cancer

Wei

Mok²,

Oliva

et al. 2013

J. Clin. Invest.

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High-throughput genomic technologies have identified biomarkers and potential therapeutic targets for ovarian cancer. Comprehensive functional validation studies of the biological and clinical implications of these biomarkers are needed to advance them toward clinical use. Amplification of chromosomal region 5q31-5q35.3 has been used to predict poor prognosis in patients with advanced stage, high-grade serous ovarian cancer. In this study, we further dissected this large amplicon and identified the overexpression of FGF18 as an independent predictive marker for poor clinical outcome in this patient population. Using cell culture and xenograft models, we show that FGF18 signaling promoted tumor progression by modulating the ovarian tumor aggressiveness and microenvironment. FGF18 controlled migration, invasion, and tumorigenicity of ovarian cancer cells through NF-κB activation, which increased the production of oncogenic cytokines and chemokines. This resulted in a tumor microenvironment characterized by enhanced angiogenesis and augmented tumor-associated macrophage infiltration and M2 polarization. Tumors from ovarian cancer patients had increased FGF18 expression levels with microvessel density and M2 macrophage infiltration, confirming our in vitro results. These findings demonstrate that FGF18 is important for a subset of ovarian cancers and may serve as a therapeutic target.

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“…Both let-7 and miR-29b were demonstrated to play critical roles in cancer proliferation and the ECM pathway in other cancer types (18,19). Furthermore, the main target gene of let-7 in lung cancer (18), HMGA2, was recently shown to induce ovarian surface epithelial transformation through regulation of EMT genes (20). Taken together, these results suggest that let-7, miR-29b, and perhaps some of the other miRNAs in program 3 may play regulatory roles in ovarian cancer through extracellular matrix pathways.…”

Section: Resultsmentioning

confidence: 99%

“…Let C = UDV′, where U and V, whose unnormalized values are shown in Table S5, be three-column orthogonal matrices containing left and right singular vectors, respectively. D is a 3 × 3 diagonal matrix with diagonal entries of (20,10,5). The response matrix Y is generated by Y = XC + E, where E is the matrix with i.i.d.…”

Section: T-svd Model With the Svd Representation Of The Coefficient mentioning

confidence: 99%

Learning regulatory programs by threshold SVD regression

Ma¹,

Xiao

Wong

2014

Proc. Natl. Acad. Sci. U.S.A.

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We formulate a statistical model for the regulation of global gene expression by multiple regulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method for learning such a model from data. Extensive simulations demonstrate that this method offers improved computational speed and higher sensitivity and specificity over competing approaches. The method is used to analyze microRNA (miRNA) and long noncoding RNA (lncRNA) data from The Cancer Genome Atlas (TCGA) consortium. The analysis yields previously unidentified insights into the combinatorial regulation of gene expression by noncoding RNAs, as well as findings that are supported by evidence from the literature.regulatory program | SVD | sparse | multivariate | regression T he development of microarray and next-generation sequencing technologies has enabled rapid quantification of various genome-wide features (DNA sequences, gene expressions, noncoding RNA expressions, methylation, etc.) in a population of samples (1, 2). Large consortia have compiled genetic and molecular profiling data in an enormous number of tumors across hundreds of samples (3,4). A common challenge arising from these large-scale genomic studies is the inference of regulatory relationships between different genome-wide measurements from the complex biological systems where the number of predictors and responses often far exceeds the sample size.To formulate a statistical model for such regulatory relations, consider the situation depicted in Fig. 1 (see Fig. S1 for more detailed illustration of the model schema), where p regulators x = ðx 1 ; . . . ; x p Þ regulate q responses y = ðy 1 ; . . . ; y q Þ through r regulatory programs that are represented by hidden nodes, e.g., h 1 ; . . . ; h r . The activity h j of the jth program depends on the regulators connected to hidden node j, and h j in turn affects the level of the responses that are connected to node j. To express this model mathematically, we denote by u j and v j the unit vectors corresponding respectively to the input weights fa ij , i = 1; . . . ; pg and the output weights fb jk , k = 1; . . . ; qg of the jth program. Then the regulatory relations are represented as h j = σðxu j Þ and y = P r j=1 d j h j v′ j , where x, y are regarded as row vectors, u, v are regarded as column vectors, and σ() is a sigmoidal function. The aforementioned is a standard single-layer neural network model that is widely used in predictive modeling but could be impossible to learn in biological studies where sample size n is much smaller than p or q. Thus, we first simplify the model by taking σ to be the identity function. Then our model becomes h j = xu j , y = P r j=1 d j h j v′ j . We make the biologically plausible assumption that only a small subset of regulators is contributing to any program and that each program regulates only a small subset of responses. Under this assumption, u j and v j are sparse vectors in R p and R q , respectively. The magnitude of the output weight vector (denoted by d j ) repr...

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HMGA2 Overexpression-Induced Ovarian Surface Epithelial Transformation Is Mediated Through Regulation of EMT Genes

Cited by 136 publications

References 35 publications

Silencing of High Mobility Group Isoform I-C (HMGI-C) Enhances Paclitaxel Chemosensitivity in Breast Adenocarcinoma Cells (MDA-MB-468)

Silencing of High Mobility Group Isoform I-C (HMGI-C) Enhances Paclitaxel Chemosensitivity in Breast Adenocarcinoma Cells (MDA-MB-468)

FGF18 as a prognostic and therapeutic biomarker in ovarian cancer

Learning regulatory programs by threshold SVD regression

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