AGR3 in Breast Cancer: Prognostic Impact and Suitable Serum-Based Biomarker for Early Cancer Detection

Garczyk, Stefan; Stillfried, Saskia von; Antonopoulos, Wiebke; Hartmann, Arndt; Schrauder, Michael G.; Fasching, Peter A.; Anzeneder, Tobias; Tannapfel, Andrea; Ergönenc, Y.; Knüchel, Ruth; Rose, Michael; Dahl, Edgar

doi:10.1371/journal.pone.0122106

Cited by 45 publications

(70 citation statements)

References 58 publications

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“…The anterior grade homology 2 protein ( AGR2 ) gene was reported in a regulatory pathway with ERα and FOXA1 and has been associated with metastatic breast cancer (Liu et al, 2005; Wright et al, 2014). AGR3 may serve as a biomarker in luminal breast cancer (Garczyk et al, 2015). The guanidine nucleotide exchange factor gene VAV3 was recently found to be involved in resistance to endocrine therapy in breast cancer (Aguilar et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Enhancer-Mediated Oncogenic Function of the Menin Tumor Suppressor in Breast Cancer

et al. 2017

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SUMMARY While the multiple endocrine neoplasia type 1 (MEN1) gene functions as a tumor suppressor in a variety of cancer types, we explored its oncogenic role in breast tumorigenesis. The MEN1 gene product menin is involved in H3K4 trimethylation and co-activates transcription. We integrated ChIP-seq and RNA-seq data to identify menin target genes. Our analysis revealed that menin-dependent target gene promoters display looping to distal enhancers that are bound by menin, FOXA1 and GATA3. In this fashion, MEN1 co-regulates a proliferative breast cancer-specific gene expression program in ER+ cells. In primary mammary cells MEN1 exerts an anti-proliferative function by regulating a distinct expression signature. Our findings clarify the cell type-specific functions of MEN1 and inform the development of menin-directed treatments for breast cancer.

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Section: Discussionmentioning

confidence: 99%

Enhancer-Mediated Oncogenic Function of the Menin Tumor Suppressor in Breast Cancer

et al. 2017

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“…AGR3 is expressed in the ciliated cells of the airway epithelium and oviduct as well as in the stomach, prostate and liver [Bonser et al., ; Obacz et al., ]. Unlike many other PDI‐family members, AGR3 is not induced by the unfolded protein response [Bonser et al., ], but it is up‐regulated by androgens and oestrogens [Vaarala et al., ; Bu et al., ], and has been suggested to be a potential therapeutic target and/or a molecular marker for hormone‐responsive breast cancer and prostate cancer [Fletcher et al., ; King et al., ; Gray et al., ; Bu et al., ; Garczyk et al., ; Obacz et al., ]. A knockout mouse model implicated AGR3 in mucociliary clearance and in calcium‐modulated ciliary beat frequency [Bonser et al., ].…”

Section: Other Human Agr Proteinsmentioning

confidence: 99%

The role of protein disulphide isomerase AGR2 in the tumour niche

Delom

Nazaraliyev

Fessart

2018

Biology of the Cell

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In recent years, the discovery of 'tumour niche', a microenvironment that favours tumour development has changed our perspective of cancer. This microenvironment generated by the tumour cells itself and surrounding cells is capable of providing essential elements for its growth. Consequently, the homoeostasis of the secretory pathway (SP) has become an essential player in cancer development. The SP not only promotes cellular adaptation to protein misfolding due to oncogenic transformation or challenging tumour niche but also allows tumour cells to produce specific secretomes. This impacts tumour cells in cis-or trans-as well as stromal cells in the tumour niche. In this context, the Anterior GRadient 2 (AGR2) protein has been identified as a key player. AGR2 is a protein disulphide isomerase that resides in the endoplasmic reticulum (ER) and mediates the formation of disulphide bonds, catalyses the cysteine-based redox reactions and assists the quality control of proteins. AGR2 not only plays an essential role in the homoeostasis of the SP but also exerts pro-oncogenic gain-of-function due to its reported mislocalisation in the tumour niche microenvironment. In this review, we summarise the dual role of AGR2, inside and outside the ER, on the tumour niche and its microenvironment.

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“…Even though a systematic biological interpretation of the signatures identified is beyond the scope of this work, to ascertain the reliability of our results we compared them with published data. The top features in the BRCA-ER rSNFi signature include multiple genes known to be associated with breast carcinoma progression and outcome such as AGR3, B3GNT and MLPH (29,30,31). In addition we find the estrogen receptor gene (ESR1 from the gene and ER-alpha from the prot layer) and the transcription factor GATA3 (from both gene and prot layers) (32).…”

Section: Resultsmentioning

confidence: 94%

Integrative Network Fusion: a multi-omics approach in molecular profiling

Chierici

Bussola

Marcolini

et al. 2020

Preprint

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Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold crossvalidation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein abundances and copy number variants are used to predict estrogen receptor status (BRCA-ER, N=381) and breast invasive carcinoma subtypes (BRCA-subtypes, N=305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N=157; KIRC-OS, N=181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs 0.80; FS: 56 vs 1801) and BRCA-subtypes 1 Chierici et al. INF(0.84 vs 0.80; 302 vs 1801), improving KIRC-OS performance (0.38 vs 0.31; 111 vs 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes 1 .

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AGR3 in Breast Cancer: Prognostic Impact and Suitable Serum-Based Biomarker for Early Cancer Detection

Cited by 45 publications

References 58 publications

Enhancer-Mediated Oncogenic Function of the Menin Tumor Suppressor in Breast Cancer

Enhancer-Mediated Oncogenic Function of the Menin Tumor Suppressor in Breast Cancer

The role of protein disulphide isomerase AGR2 in the tumour niche

Integrative Network Fusion: a multi-omics approach in molecular profiling

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