Forkhead box C1 boosts triple‐negative breast cancer metastasis through activating the transcription of chemokine receptor‐4

Pan, Hongchao; Peng, Zhilan; Lin, Jiediao; Ren, Xiaosha; Zhang, Guojun; Cui, Yukun

doi:10.1111/cas.13823

Cited by 35 publications

(35 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two key genes of TNBC, ER and PR, were among the genes selected by enetLTS. Other genes selected by enetLTS, FOXA1 [ 25 ], GATA3 [ 25 ], SPDEF [ 26 ], FOXC1 [ 27 ], EN1 [ 28 ], HORMAD1 [ 29 ], KRT16 [ 30 ], and CT83 [ 31 ], have been reported to be related to TNBC.…”

Section: Resultsmentioning

confidence: 99%

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

et al. 2020

View full text Add to dashboard Cite

Background Previous studies have reported that labeling errors are not uncommon in omics data. Potential outliers may severely undermine the correct classification of patients and the identification of reliable biomarkers for a particular disease. Three methods have been proposed to address the problem: sparse label-noise-robust logistic regression (Rlogreg), robust elastic net based on the least trimmed square (enetLTS), and Ensemble. Ensemble is an ensembled classification based on distinct feature selection and modeling strategies. The accuracy of biomarker selection and outlier detection of these methods needs to be evaluated and compared so that the appropriate method can be chosen. Results The accuracy of variable selection, outlier identification, and prediction of three methods (Ensemble, enetLTS, Rlogreg) were compared for simulated and an RNA-seq dataset. On simulated datasets, Ensemble had the highest variable selection accuracy, as measured by a comprehensive index, and lowest false discovery rate among the three methods. When the sample size was large and the proportion of outliers was ≤5%, the positive selection rate of Ensemble was similar to that of enetLTS. However, when the proportion of outliers was 10% or 15%, Ensemble missed some variables that affected the response variables. Overall, enetLTS had the best outlier detection accuracy with false positive rates < 0.05 and high sensitivity, and enetLTS still performed well when the proportion of outliers was relatively large. With 1% or 2% outliers, Ensemble showed high outlier detection accuracy, but with higher proportions of outliers Ensemble missed many mislabeled samples. Rlogreg and Ensemble were less accurate in identifying outliers than enetLTS. The prediction accuracy of enetLTS was better than that of Rlogreg. Running Ensemble on a subset of data after removing the outliers identified by enetLTS improved the variable selection accuracy of Ensemble. Conclusions When the proportion of outliers is ≤5%, Ensemble can be used for variable selection. When the proportion of outliers is > 5%, Ensemble can be used for variable selection on a subset after removing outliers identified by enetLTS. For outlier identification, enetLTS is the recommended method. In practice, the proportion of outliers can be estimated according to the inaccuracy of the diagnostic methods used.

show abstract

Section: Resultsmentioning

confidence: 99%

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Moreover, patient data analysis shows that overexpression of KLF8 and CXCR4 correlates with metastatic potential [ 38 ]. Likewise, transcription factor forkhead box C1 (FOXC1) induces the expression of CXCR4 and enhances migration of MDA-MD-231 cells and lung metastasis in vivo [ 39 ]. In addition, POU domain class 2 transcription factor 1 (known as POU1F1 (Pit-1)) induces the expression of both CXCR4 and CXCL12 in breast cancer cells [ 40 ].…”

Section: Transcriptional Regulation Of Cxcr4 and Cxcl12 In Breast mentioning

confidence: 99%

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

Zielińska

Katanaev

2020

Cancers

View full text Add to dashboard Cite

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.

show abstract

“…The metastatic potential of triple-negative breast cancer (TNBC) to lungs has been found to be regulated by another transcription factor, forkhead box C1 (FOXC1). This protein, by increasing the expression of chemokine receptor-4 (CXCR4), also promotes the Lung metastatic capability of Breast cancer cells in vivo [ 92 ]. In conclusion, Snail 1 and forkhead box C1 that influence the immune microenvironment in Breast cancer, if targeted therapeutically, the desired results to overcome breast cancer lung metastasis in the future, can, therefore, be accomplished.…”

Section: Role Of Secretory Factors In Breast Cancer Lung Metastasismentioning

confidence: 99%

Cancer Cell-derived Secretory Factors in Breast Cancer-associated Lung Metastasis: Their Mechanism and Future Prospects

Urooj

Wasim

Mushtaq

et al. 2020

CCDT

View full text Add to dashboard Cite

: In Breast cancer, Lung is the second most common site of metastasis after the bone. Various factors are responsible for Lung metastasis occurring secondary to Breast cancer. Cancer cellderived secretory factors are commonly known as ‘Cancer Secretomes’. They exhibit a prompt role in the mechanism of Breast cancer lung metastasis. They are also major constituents of hostassociated tumor microenvironment. Through cross-talk between cancer cells and the extracellular matrix components, cancer cell-derived extracellular matrix components (CCECs) such as hyaluronan, collagens, laminin and fibronectin cause ECM remodeling at the primary site (breast) of cancer. However, at the secondary site (lung), tenascin C, periostin and lysyl oxidase, along with pro-metastatic molecules Coco and GALNT14, contribute to the formation of pre-metastatic niche (PMN) by promoting ECM remodeling and lung metastatic cells colonization. Cancer cell-derived secretory factors by inducing cancer cell proliferation at the primary site, their invasion through the tissues and vessels and early colonization of metastatic cells in the PMN, potentiate the mechanism of Lung metastasis in Breast cancer. : On the basis of biochemical structure, these secretory factors are broadly classified into proteins and non-proteins. This is the first review that has highlighted the role of cancer cell-derived secretory factors in Breast cancer Lung metastasis (BCLM). It also enumerates various researches that have been conducted to date in breast cancer cell lines and animal models that depict the prompt role of various types of cancer cell-derived secretory factors involved in the process of Breast cancer lung metastasis. In the future, by therapeutically targeting these cancer driven molecules, this specific type of organ-tropic metastasis in breast cancer can be successfully treated.

show abstract

Forkhead box C1 boosts triple‐negative breast cancer metastasis through activating the transcription of chemokine receptor‐4

Cited by 35 publications

References 42 publications

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

Comparison of methods for the detection of outliers and associated biomarkers in mislabeled omics data

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

Cancer Cell-derived Secretory Factors in Breast Cancer-associated Lung Metastasis: Their Mechanism and Future Prospects

Contact Info

Product

Resources

About