Cytokeratin 19 (KRT19) has a Role in the Reprogramming of Cancer Stem Cell-Like Cells to Less Aggressive and More Drug-Sensitive Cells

Saha, Subbroto Kumar; Kim, Kyeongseok; Yang, Gwang-Mo; Choi, Hye Yeon; Cho, Ssang-Goo

doi:10.3390/ijms19051423

Cited by 51 publications

(42 citation statements)

References 98 publications

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“…Moreover, KRT19 + colon cancer stem cells showed high radio-resistance by raising LGR5 + crypt-based columnar cells in the colon and intestines [22]. Recently, we showed that KRT19 has the ability to reprogram breast cancer [23]. Thus, the molecular mechanism underlying the contradictory roles of KRT19 in various cancers needs to be examined to reveal the function of KRT19 in specific cancers.…”

Section: Introductionmentioning

confidence: 99%

Opposing Regulation of Cancer Properties via KRT19-Mediated Differential Modulation of Wnt/β-Catenin/Notch Signaling in Breast and Colon Cancers

Saha

Yin

Chae

et al. 2019

Cancers

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Although Keratin 19 (KRT19) has been reported as a tumor cell marker and found to interact with other proteins that modulate cancer properties, its role in cancer prognosis remains to be fully elucidated. We found that KRT19 expression was increased in both colon and breast cancer, but that knockdown of KRT19 showed opposing effects on cancer properties. In colon cancer, KRT19 knockdown resulted in suppression of cancer via downregulation of Wnt/Notch signaling without altering NUMB transcription. In breast cancer, KRT19 knockdown led to an increase in cancer properties because of attenuated Wnt and enhanced Notch signaling. In colon cancer, KRT19 interacted with β-catenin but not with RAC1, allowing the LEF/TCF transcription factor to bind primarily to the LEF1 and TCF7 promoter regions, whereas in breast cancer, KRT19 interacted with the β-catenin/RAC1 complex and led to apparent upregulation of NUMB expression and NUMB-mediated suppression of Notch signaling. These results reveal a novel differential role of KRT19 in carcinogenesis, due to differential modulation of Wnt/β-catenin/Notch signaling crosstalk through various interactions of KRT19 with only β-catenin or with the β-catenin/RAC1 complex, which might have implications for clinical cancer research.

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Section: Introductionmentioning

confidence: 99%

Opposing Regulation of Cancer Properties via KRT19-Mediated Differential Modulation of Wnt/β-Catenin/Notch Signaling in Breast and Colon Cancers

Saha

Yin

Chae

et al. 2019

Cancers

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“…Inhibition of cytokeratin-19 in TNBC cells prompts proliferation, migration and drug resistance via CXCR4 upregulation. Moreover, low cytokeratin-19 expression in patient samples correlates with poor prognosis [ 140 ]. Another study evaluated the link between cytokeratin-19 expression in cancer cells during the follow-up period after treatment and reached a different conclusion.…”

Section: The Cxcl12/cxcr4-induced Therapy Resistance In Breast Camentioning

confidence: 99%

The Signaling Duo CXCL12 and CXCR4: Chemokine Fuel for Breast Cancer Tumorigenesis

Zielińska

Katanaev

2020

Cancers

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The CXCL12/CXCR4 signaling pathway has emerged in the recent years as a key player in breast cancer tumorigenesis. This pathway controls many aspects of breast cancer development including cancer cell proliferation, motility and metastasis to all target organs. Moreover, the CXCL12/CXCR4 cascade affects both immune and stromal cells, creating tumor-supporting microenvironment. In this review, we examine state-of-the-art knowledge about detrimental roles of the CXCL12/CXCR4 signaling, discuss its therapeutic potential and suggest further research directions beneficial both for basic research and personalized medicine in breast cancer.

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“…The comparison of the subnetworks of the non-metastatic and the metastatic patients furthermore revealed some patient-specific genes which might give valuable information about specific mechanisms of tumorigenesis and therapeutic vulnerabilities in the respective patient. In general, it seemed that the subnetworks of the non-metastatic patients contained more genes that have been linked to better prognostic outcomes such as JUP, PCBP1 and HMGN2 in GSM615695 (Bailey et al, 2012;Shi et al, 2018;Fan et al, 2018) or RASA1, IL6ST, KRT19 and RPS14 in GSM150990 (Liu et al, 2014;Mathe et al, 2015;Saha et al, 2018;Zhou et al, 2013) while the networks of both metastatic patients harbored genes that are known to be involved in aggressive tumor growth or therapy resistance which might explain the early metastatic spread in these patients. Some examples are CDK1, SFN and XPO1 in GSM519217 (Alexandrou et al, 2019;Neve et al, 2006;Taylor et al, 2019) or CAV1, PTPN11 and FTL in GSM615233 (Qian et al, 2019;Aceto et al, 2012;Chekhun et al, 2013) However, not only the presence of specific genes might be important, but also their overall expression level.…”

Section: Glrp To Deliver Patient-specific Subnetworkmentioning

confidence: 99%

Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

Chereda

Bleckmann

Menck

et al. 2020

Preprint

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MotivationContemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g. distant metastasis in cancer, for each individual patient.ResultsWe extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset, and then applied the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. As a result this method could be potentially highly useful on interpreting classification results on the individual patient level, as for example in precision medicine approaches or a molecular tumor board.Availabilityhttps://gitlab.gwdg.de/UKEBpublic/graph-lrphttps://frankkramer-lab.github.io/MetaRelSubNetVis/Contacttim.beissbarth@bioinf.med.uni-goettingen.de

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Cytokeratin 19 (KRT19) has a Role in the Reprogramming of Cancer Stem Cell-Like Cells to Less Aggressive and More Drug-Sensitive Cells

Cited by 51 publications

References 98 publications

Opposing Regulation of Cancer Properties via KRT19-Mediated Differential Modulation of Wnt/β-Catenin/Notch Signaling in Breast and Colon Cancers

Opposing Regulation of Cancer Properties via KRT19-Mediated Differential Modulation of Wnt/β-Catenin/Notch Signaling in Breast and Colon Cancers

The Signaling Duo CXCL12 and CXCR4: Chemokine Fuel for Breast Cancer Tumorigenesis

Explaining decisions of Graph Convolutional Neural Networks: patient-specific molecular subnetworks responsible for metastasis prediction in breast cancer

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