Progesterone promotes cell migration, invasion and cofilin activation in human astrocytoma cells

Piña-Medina, Ana Gabriela; Hansberg-Pastor, Valeria; González-Arenas, Aliesha; Cerbón, Marco; Camacho‐Arroyo, Ignacio

doi:10.1016/j.steroids.2015.11.008

Cited by 49 publications

(43 citation statements)

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“…Besides, recent studies have demonstrated the role of progesterone in the growth, invasion and migration of astrocytomas, the most common and aggressive tumors in the human brain (Germán-Castelán et al, 2014;González-Agüero et al, 2007;Piña-Medina et al, 2016). Progesterone actions in breast, ovarian and brain cancer have been usually attributed to be mediated by PR…”

Section: Mprs In Breast Gynecological and Brain Cancermentioning

confidence: 99%

“…Importantly, progesterone plays a central role as critical regulator in breast, gynecological and brain cancer (Camacho-Arroyo et al, 2000;Diep et al, 2015;Obr and Edwards, 2012;Piña-Medina et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Besides, it has been reported that progesterone mediated regulation of proliferation, migration, and invasion of human astrocytomas cells is only partially mediated by the intracellular PR, which highlights the importance of studying the participation of mPRs in mediating progesterone actions in brain cancer (Germán-Castelán et al, 2014;Piña-Medina et al, 2016).…”

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confidence: 99%

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Membrane progesterone receptors in reproduction and cancer

Valadez-Cosmes

Vázquez-Martínez

Cerbón

et al. 2016

Molecular and Cellular Endocrinology

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Section: Mprs In Breast Gynecological and Brain Cancermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Membrane progesterone receptors in reproduction and cancer

Valadez-Cosmes

Vázquez-Martínez

Cerbón

et al. 2016

Molecular and Cellular Endocrinology

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“…Grade IV astrocytomas, also known as glioblastoma (GBM), constitute the most common and aggressive brain tumors due to their highly proliferative and infiltrative potential [ 2 ]. Steroid hormones such as progesterone (P4), participate in stimulating astrocytomas’ growth [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Allopregnanolone Alters the Gene Expression Profile of Human Glioblastoma Cells

Zamora-Sánchez

Moral-Morales

Hernández-Vega

et al. 2018

IJMS

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Glioblastomas (GBM) are the most frequent and aggressive brain tumors. In these malignancies, progesterone (P4) promotes proliferation, migration, and invasion. The P4 metabolite allopregnanolone (3α-THP) similarly promotes cell proliferation in the U87 human GBM cell line. Here, we evaluated global changes in gene expression of U87 cells treated with 3α-THP, P4, and the 5α-reductase inhibitor, finasteride (F). 3α-THP modified the expression of 137 genes, while F changed 90. Besides, both steroids regulated the expression of 69 genes. After performing an over-representation analysis of gene ontology terms, we selected 10 genes whose products are cytoskeleton components, transcription factors, and proteins involved in the maintenance of DNA stability and replication to validate their expression changes by RT-qPCR. 3α-THP up-regulated six genes, two of them were also up-regulated by F. Two genes were up-regulated by P4 alone, however, such an effect was blocked by F when cells were treated with both steroids. The remaining genes were regulated by the combined treatments of 3α-THP + F or P4 + F. An in-silico analysis revealed that promoters of the six up-regulated genes by 3α-THP possess cyclic adenosine monophosphate (cAMP) responsive elements along with CCAAT/Enhancer binding protein alpha (CEBPα) binding sites. These findings suggest that P4 and 3α-THP regulate different sets of genes that participate in the growth of GBMs.

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“…MYC is overexpressed in many types of tumors, such as LE, LY, and regulates the expression of two immune checkpoint proteins on the tumor cell surface [32]. CFL1 is highly related to the invasiveness of the malignancy of the central nervous system (CNS) [33]. RABIF transcripts are overexpressed in pancreatic cancer (PA) tissues compared to normal pancreas [34].…”

Section: Resultsmentioning

confidence: 99%

A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression Data

Xia

Han

et al. 2018

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Accurate identification of the cancer types is essential to cancer diagnoses and treatments. Since cancer tissue and normal tissue have different gene expression, gene expression data can be used as an efficient feature source for cancer classification. However, accurate cancer classification directly using original gene expression profiles remains challenging due to the intrinsic high-dimension feature and the small size of the data samples. We proposed a new self-training subspace clustering algorithm under low-rank representation, called SSC-LRR, for cancer classification on gene expression data. Low-rank representation (LRR) is first applied to extract discriminative features from the high-dimensional gene expression data; self-training subspace clustering (SSC) method is then used to generate the cancer classification predictions. The SSC-LRR was tested on two separate benchmark datasets in control with four state of the art classification methods. It generated cancer classification predictions with an overall accuracy 89.7% and a general correlation 0.920, which are 18.9% and 24.4% higher than that of the best control method respectively. In addition, several genes (RNF114, HLA-DRB5, USP9Y and PTPN20) were identified by SSC-LRR as new cancer identifiers that deserve further clinical investigation. Overall, the study demonstrated a new sensitive avenue to recognize cancer classifications from large-scale gene expression data.

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Progesterone promotes cell migration, invasion and cofilin activation in human astrocytoma cells

Cited by 49 publications

References 50 publications

Membrane progesterone receptors in reproduction and cancer

Membrane progesterone receptors in reproduction and cancer

Allopregnanolone Alters the Gene Expression Profile of Human Glioblastoma Cells

A Self-Training Subspace Clustering Algorithm under Low-Rank Representation for Cancer Classification on Gene Expression Data

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