AGR2 and AGR3 play an important role in the clinical characterization and prognosis of basal like breast cancer

Gene expression profiling for the diagnosis of male breast cancer

BACKGROUND

Male breast cancer (MBC) is a rare malignancy, but its global incidence has shown a notable increase in recent decades. Factors such as limited health literacy, inadequate health education, and reluctance to seek medical attention contribute to the late-stage diagnosis of most MBC patients. Consequently, there is an urgent need for a highly specific and sensitive diagnostic approach to MBC.

METHODS

This retrospective study enrolled 20 patients with 30 surgical or biopsy MBC specimens from August 2020 to August 2023. The 90-gene expression assay was performed to determine the tissue of origin. Predicted tumor types were then compared to the reference diagnosis for accuracy calculation. The differentially expressed genes were identified between male and female breast cancer.

RESULT

The 90-gene expression assay demonstrated an overall accuracy of 96.7% (29/30) when compared with the pathological diagnosis. For primary, lymph node metastatic, and distant metastatic tumors, the accuracies were 100% (15/15), 90.9% (10/11), and 100% (4/4), respectively. Five genes (RPS4Y1, PI15, AZGP1, PRRX1, and AGR2) were up-regulated, and six (XIST, PIGR, SFRP1, PLA2G2A, S100A2, and CHI3L1) were down-regulated in MBC.

CONCLUSION

Our findings highlight the promising performance of the 90-gene expression assay in accurately identifying the tumor origin in MBC. Incorporating this assay into pathological diagnoses has the potential to empower oncologists with precision treatment options, ultimately enhancing the care and outcomes for patients with MBC.

Oncoinformatic screening of the gene clusters involved in the HER2-positive breast cancer formation along with the in silico pharmacodynamic profiling of selective long-chain omega-3 fatty acids as the metastatic antagonists

The HER2-positive patients occupy ~ 30% of the total breast cancer patients globally where no prevalent drugs are available to mitigate the frequent metastasis clinically except lapatinib and neratinib. This scarcity reinforced researchers' quest for new medications where natural substances are significantly considered. Valuing the aforementioned issues, this research aimed to study the ERBB2-mediated string networks that work behind the HER2-positive breast cancer formation regarding co-expression, gene regulation, GAMA-receptor-signaling pathway, cellular polarization, and signal inhibition. Following the overexpression, promotor methylation, and survivability profiles of ERBB2, the super docking position of HER2 was identified using the quantum tunneling algorithm. Supramolecular docking was conducted to study the target specificity of EPA and DHA fatty acids followed by a comprehensive molecular dynamic simulation (100 ns) to reveal the RMSD, RMSF, Rg, SASA, H-bonds, and MM/GBSA values. Finally, potential drug targets for EPA and DHA in breast cancer were constructed to determine the drug-protein interactions (DPI) at metabolic stages. Considering the values resulting from the combinational models of the oncoinformatic, pharmacodynamic, and metabolic parameters, long-chain omega-3 fatty acids like EPA and DHA can be considered as potential-targeted therapeutics for HER2-positive breast cancer treatment.

AGR2 and FOXA1 as prognostic markers in ER-positive breast cancer

BACKGROUND

The prognostic role of either forkhead box A1 (FOXA1) or anterior gradient 2 (AGR2) in breast cancer has been found separately. Considering that there were interplays between them depending on ER status, we aimed to assess the statistical interaction between AGR2 and FOXA1 on breast cancer prognosis and examine the prognostic role of the combination of them by ER status.

METHODS

AGR2 and FOXA1 expression in tumor tissues were evaluated with tissue microarrays by immunohistochemistry in 915 breast cancer patients with follow up data. The expression levels of these two markers were treated as binary variables, and many different cutoff values were tried for each marker. Survival and Cox proportional hazard analyses were used to evaluate the relationship between AGR2, FOXA1 and prognosis, and the statistical interaction between them on the prognosis was assessed on multiplicative scale.

RESULTS

Statistical interaction between AGR2 and FOXA1 on the PFS was significant with all the cutoff points in ER-positive breast cancer patients but not ER-negative ones. Among ER-positive patients, the poor prognostic role of the high level of FOXA1 was significant only in patients with the low level of AGR2, and vice versa. When AGR2 and FOXA1 were considered together, patients with low levels of both markers had significantly longer PFS compared with all other groups.

CONCLUSIONS

There was a statistical interaction between AGR2 and FOXA1 on the prognosis of ER-positive breast cancer. The combination of AGR2 and FOXA1 was a more useful marker for the prognosis of ER-positive breast cancer patients.

Potential Role of AGR2 for Mammalian Skin Wound Healing

The limited ability of mammals to regenerate has garnered significant attention, particularly in regard to skin wound healing (WH), which is a critical step for regeneration. In human adults, skin WH results in the formation of scars following injury or trauma, regardless of severity. This differs significantly from the scarless WH observed in the fetal skin of mammals or anamniotes. This review investigates the role of molecular players involved in scarless WH, which are lost or repressed in adult mammalian WH systems. Specifically, we analyze the physiological role of Anterior Gradient (AGR) family proteins at different stages of the WH regulatory network. AGR is activated in the regeneration of lower vertebrates at the stage of wound closure and, accordingly, is important for WH. Mammalian AGR2 is expressed during scarless WH in embryonic skin, while in adults, the activity of this gene is normally inhibited and is observed only in the mucous epithelium of the digestive tract, which is capable of full regeneration. The combination of AGR2 unique potencies in postnatal mammals makes it possible to consider it as a promising candidate for enhancing WH processes.

SADLN: Self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition

Integrating multi-omics data for cancer subtype recognition is an important task in bioinformatics. Recently, deep learning has been applied to recognize the subtype of cancers. However, existing studies almost integrate the multi-omics data simply by concatenation as the single data and then learn a latent low-dimensional representation through a deep learning model, which did not consider the distribution differently of omics data. Moreover, these methods ignore the relationship of samples. To tackle these problems, we proposed SADLN: A self-attention based deep learning network of integrating multi-omics data for cancer subtype recognition. SADLN combined encoder, self-attention, decoder, and discriminator into a unified framework, which can not only integrate multi-omics data but also adaptively model the sample's relationship for learning an accurately latent low-dimensional representation. With the integrated representation learned from the network, SADLN used Gaussian Mixture Model to identify cancer subtypes. Experiments on ten cancer datasets of TCGA demonstrated the advantages of SADLN compared to ten methods. The Self-Attention Based Deep Learning Network (SADLN) is an effective method of integrating multi-omics data for cancer subtype recognition.

AGR2: a secreted protein worthy of attention in diagnosis and treatment of breast cancer

AGR2 is a secreted protein widely existing in breast. In precancerous lesions, primary tumors and metastatic tumors, the expression of AGR2 is increased, which has aroused our interest. This review introduces the gene and protein structure of AGR2. Its endoplasmic reticulum retention sequence, protein disulfide isomerase active site and multiple protein binding sequences endow AGR2 with diverse functions inside and outside breast cancer cells. This review also enumerates the role of AGR2 in the progress and prognosis of breast cancer, and emphasizes that AGR2 can be a promising biomarker and a target for immunotherapy of breast cancer, providing new ideas for early diagnosis and treatment of breast cancer.

Identifying gene expression-based biomarkers in online learning environments

Motivation

Gene expression-based classifiers are often developed using historical data by training a model on a small set of patients and a large set of features. Models trained in such a way can be afterwards applied for predicting the output for new unseen patient data. However, very often the accuracy of these models starts to decrease as soon as new data is fed into the trained model. This problem, known as concept drift, complicates the task of learning efficient biomarkers from data and requires special approaches, different from commonly used data mining techniques.

Results

Here, we propose an online ensemble learning method to continually validate and adjust gene expression-based biomarker panels over increasing volume of data. We also propose a computational solution to the problem of feature drift where gene expression signatures used to train the classifier become less relevant over time. A benchmark study was conducted to classify the breast tumors into known subtypes by using a large-scale transcriptomic dataset (∼3500 patients), which was obtained by combining two datasets: SCAN-B and TCGA-BRCA. Remarkably, the proposed strategy improves the classification performances of gold-standard biomarker panels (e.g. PAM50, OncotypeDX and Endopredict) by adding features that are clinically relevant. Moreover, test results show that newly discovered biomarker models can retain a high classification accuracy rate when changing the source generating the gene expression profiles.

Availability and implementation

github.com/UEFBiomedicalInformaticsLab/OnlineLearningBD.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Biglycan Promotes Cancer Stem Cell Properties, NFκB Signaling and Metastatic Potential in Breast Cancer Cells

Simple Summary

Breast cancer stem cells (BCSCs) are a small sub-population of cells within tumors with high metastatic potential. We identified biglycan (BGN) as a prospective molecular target in BCSCs that regulates the aggressive phenotypes of these cells. These findings establish a foundation for the development of therapeutics against BGN to eliminate BCSCs and prevent metastatic breast cancer.

Abstract

It is a major challenge to treat metastasis due to the presence of heterogenous BCSCs. Therefore, it is important to identify new molecular targets and their underlying molecular mechanisms in various BCSCs to improve treatment of breast cancer metastasis. Here, we performed RNA sequencing on two distinct co-existing BCSC populations, ALDH⁺ and CD29^hi CD61⁺ from PyMT mammary tumor cells and detected upregulation of biglycan (BGN) in these BCSCs. Genetic depletion of BGN reduced BCSC proportions and tumorsphere formation. Furthermore, BCSC associated aggressive traits such as migration and invasion were significantly reduced by depletion of BGN. Glycolytic and mitochondrial metabolic assays also revealed that BCSCs exhibited decreased metabolism upon loss of BGN. BCSCs showed decreased activation of the NFκB transcription factor, p65, and phospho-IκB levels upon BGN ablation, indicating regulation of NFκB pathway by BGN. To further support our data, we also characterized CD24⁻/CD44⁺ BCSCs from human luminal MCF-7 breast cancer cells. These CD24⁻/CD44⁺ BCSCs similarly exhibited reduced tumorigenic phenotypes, metabolism and attenuation of NFκB pathway after knockdown of BGN. Finally, loss of BGN in ALDH⁺ and CD29^hi CD61⁺ BCSCs showed decreased metastatic potential, suggesting BGN serves as an important therapeutic target in BCSCs for treating metastasis of breast cancer.