Anterior Gradient 3 Promotes Breast Cancer Development and Chemotherapy Response

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification

The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes and targeted treatment. Furthermore, numerous studies have highlighted the capability of graph neural networks (GNN) to model complex biological systems and capture non-linear interactions in high-throughput data. GNN has proven to be useful in leveraging multiple types of omics data, including prior biological knowledge from various sources, such as transcriptomics, genomics, proteomics, and metabolomics, to improve cancer classification. However, current works do not fully utilize the non-linear learning potential of GNN and lack of the integration ability to analyse high-throughput multi-omics data simultaneously with prior biological knowledge. Nevertheless, relying on limited prior knowledge in generating gene graphs might lead to less accurate classification due to undiscovered significant gene-gene interactions, which may require expert intervention and can be time-consuming. Hence, this study proposes a graph classification model called associative multi-omics graph embedding learning (AMOGEL) to effectively integrate multi-omics datasets and prior knowledge through GNN coupled with association rule mining (ARM). AMOGEL employs an early fusion technique using ARM to mine intra-omics and inter-omics relationships, forming a multi-omics synthetic information graph before the model training. Moreover, AMOGEL introduces multi-dimensional edges, with multi-omics gene associations or edges as the main contributors and prior knowledge edges as auxiliary contributors. Additionally, it uses a gene ranking technique based on attention scores, considering the relationships between neighbouring genes. Several experiments were performed on BRCA and KIPAN cancer subtypes to demonstrate the integration of multi-omics datasets (miRNA, mRNA, and DNA methylation) with prior biological knowledge of protein-protein interactions, KEGG pathways and Gene Ontology. The experimental results showed that the AMOGEL outperformed the current state-of-the-art models in terms of classification accuracy, F1 score and AUC score. The findings of this study represent a crucial step forward in advancing the effective integration of multi-omics data and prior knowledge to improve cancer subtype classification.

Modulation of Protein Disulfide Isomerase Functions by Localization: The Example of the Anterior Gradient Family

Significance: Oxidative folding within the endoplasmic reticulum (ER) introduces disulfide bonds into nascent polypeptides, ensuring proteins' stability and proper functioning. Consequently, this process is critical for maintaining proteome integrity and overall health. The productive folding of thousands of secretory proteins requires stringent quality control measures, such as the unfolded protein response (UPR) and ER-Associated Degradation (ERAD), which contribute significantly to maintaining ER homeostasis. ER-localized protein disulfide isomerases (PDIs) play an essential role in each of these processes, thereby contributing to various aspects of ER homeostasis, including maintaining redox balance, proper protein folding, and signaling from the ER to the nucleus. Recent Advances: Over the years, there have been increasing reports of the (re)localization of PDI family members and other ER-localized proteins to various compartments. A prime example is the anterior gradient (AGR) family of PDI proteins, which have been reported to relocate to the cytosol or the extracellular environment, acquiring gain of functions that intersect with various cellular signaling pathways. Critical Issues: Here, we summarize the functions of PDIs and their gain or loss of functions in non-ER locations. We will focus on the activity, localization, and function of the AGR proteins: AGR1, AGR2, and AGR3. Future Directions: Targeting PDIs in general and AGRs in particular is a promising strategy in different human diseases. Thus, there is a need for innovative strategies and tools aimed at targeting PDIs; those strategies should integrate the specific localization and newly acquired functions of these PDIs rather than solely focusing on their canonical roles.

AVBAE-MODFR: A novel deep learning framework of embedding and feature selection on multi-omics data for pan-cancer classification

Integration analysis of cancer multi-omics data for pan-cancer classification has the potential for clinical applications in various aspects such as tumor diagnosis, analyzing clinically significant features, and providing precision medicine. In these applications, the embedding and feature selection on high-dimensional multi-omics data is clinically necessary. Recently, deep learning algorithms become the most promising cancer multi-omic integration analysis methods, due to the powerful capability of capturing nonlinear relationships. Developing effective deep learning architectures for cancer multi-omics embedding and feature selection remains a challenge for researchers in view of high dimensionality and heterogeneity. In this paper, we propose a novel two-phase deep learning model named AVBAE-MODFR for pan-cancer classification. AVBAE-MODFR achieves embedding by a multi2multi autoencoder based on the adversarial variational Bayes method and further performs feature selection utilizing a dual-net-based feature ranking method. AVBAE-MODFR utilizes AVBAE to pre-train the network parameters, which improves the classification performance and enhances feature ranking stability in MODFR. Firstly, AVBAE learns high-quality representation among multiple omics features for unsupervised pan-cancer classification. We design an efficient discriminator architecture to distinguish the latent distributions for updating forward variational parameters. Secondly, we propose MODFR to simultaneously evaluate multi-omics feature importance for feature selection by training a designed multi2one selector network, where the efficient evaluation approach based on the average gradient of random mask subsets can avoid bias caused by input feature drift. We conduct experiments on the TCGA pan-cancer dataset and compare it with four state-of-the-art methods for each phase. The results show the superiority of AVBAE-MODFR over SOTA methods.

Prognostic Impact of AGR3 Protein Expression in Breast Cancer: A Systematic Review and Meta-analysis

OBJECTIVE

 To investigate the clinicopathological significance and prognosis of the expression of the anterior gradient 3 (AGR3) protein in women with breast cancer.

DATA SOURCES

 The PubMed, CINAHL, EMBASE, Scopus, and Web of Science databases were searched for studies published in English and without restrictions regarding the year of publication. The search terms were: breast cancer AND anterior gradient 3 OR AGR3 expression.

STUDY SELECTION

 We included observational or interventional studies, studies on AGR3 protein expression by immunohistochemistry, and studies on invasive breast cancer. Case reports, studies with animals, and reviews were excluded. In total, 4 studies were included, containing 713 cases of breast cancer.

DATA COLLECTION

 Data were extracted on clinicopathological characteristics and survival. A meta-analysis of the prevalence of AGR3 expression was performed according to the clinicopathological characteristics, hazard ratios (HRs), and overall survival and disease-free survival.

DATA SYNTHESIS

 The expression of AGR3 was found in 62% of the cases, and it was associated with histological grade II, positivity of estrogen and progesterone receptors, low expression of ki67, recurrence or distant metastasis, and lumen subtypes. In patients with low and intermediate histological grades, AGR3 expression was associated with worse overall survival (HR: 2.39; 95% confidence interval [95%CI]: 0.628-4.159; p = 0.008) and worse disease-free survival (HR: 3.856; 95%CI: 1.026-6.686; p = 0.008).

CONCLUSION

 The AGR3 protein may be a biomarker for the early detection of breast cancer and predict prognosis in luminal subtypes. In addition, in patients with low and intermediate histological grades, AGR3 protein expression may indicate an unfavorable prognosis in relation to survival.

Mechanisms of drug resistance in breast cancer liver metastases: Dilemmas and opportunities

Breast cancer is the leading cause of cancer-related deaths in females worldwide, and the liver is one of the most common sites of distant metastases in breast cancer patients. Patients with breast cancer liver metastases face limited treatment options, and drug resistance is highly prevalent, leading to a poor prognosis and a short survival. Liver metastases respond extremely poorly to immunotherapy and have shown resistance to treatments such as chemotherapy and targeted therapies. Therefore, to develop and to optimize treatment strategies as well as to explore potential therapeutic approaches, it is crucial to understand the mechanisms of drug resistance in breast cancer liver metastases patients. In this review, we summarize recent advances in the research of drug resistance mechanisms in breast cancer liver metastases and discuss their therapeutic potential for improving patient prognoses and outcomes.

Over-expression of Anterior Gradient 3 Is Associated With Tumor Progression and Poor Survival in Gastric Cancer

BACKGROUND/AIM

Anterior gradient (AGR) proteins, including AGR1, AGR2, and AGR3, which are members of the protein disulfide isomerase family, have been reported as biomarkers for various carcinogenesis processes. Although AGR2 and AGR1 have been demonstrated to be associated with gastric cancer (GC) progression and poor survival, the effect of AGR3 on the progression and prognosis of GC remains unknown. Therefore, our study aimed to examine the expression and prognostic significance of AGR3 in patients with GC.

PATIENTS AND METHODS

We investigated 271 GC patients receiving curative surgery. Formalin-fixed and paraffin-embedded tissue blocks were obtained, and long-term survival analysis was performed. The expression of AGR3 in GC tissues was investigated by quantitative reverse transcription-polymerase chain reaction, western blotting, and immunohistochemistry.

RESULTS

AGR3 was over-expressed in GC tissue compared with paired normal tissue at the mRNA and protein levels. AGR3 over-expression was significantly associated with larger tumor size, deeper tumor invasion, lymph node metastasis, and advanced tumor stage. The overall survival of patients with positive AGR3 expression was significantly lower than that of patients without positive AGR3 expression. Multivariate analysis demonstrated that AGR3 and age were independent prognostic factors associated with overall survival.

CONCLUSION

Over-expression of AGR3 was significantly associated with tumor progression and poor survival of GC patients. Therefore, AGR3 may be a novel biomarker and prognostic factor for GC.

Development and validation of a prognostic model and gene co-expression networks for breast carcinoma based on scRNA-seq and bulk-seq data

Background

Breast carcinoma is the most common malignancy among women worldwide. It is characterized by a complex tumor microenvironment (TME), in which there is an intricate combination of different types of cells, which can cause confusion when screening tumor-cell-related signatures or constructing a gene co-expression network. The recent emergence of single-cell RNA sequencing (scRNA-seq) is an effective method for studying the changing omics of cells in complex TMEs.

Methods

The Dysregulated genes of malignant epithelial cells was screened by performing a comprehensive analysis of the public scRNA-seq data of 58 samples. Co-expression and Gene Set Enrichment Analysis (GSEA) analysis were performed based on scRNA-seq data of malignant cells to illustrate the potential function of these dysregulated genes. Iterative LASSO-Cox was used to perform a second-round screening among these dysregulated genes for constructing risk group. Finally, a breast cancer prognosis prediction model was constructed based on risk grouping and other clinical characteristics.

Results

Our results indicated a transcriptional signature of 1,262 genes for malignant breast cancer epithelial cells. To estimate the function of these genes in breast cancer, we also constructed a co-expression network of these dysregulated genes at single-cell resolution, and further validated the results using more than 300 published transcriptomics datasets and 31 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening datasets. Moreover, we developed a reliable predictive model based on the scRNA-seq and bulk-seq datasets.

Conclusions

Our findings provide insights into the transcriptomics and gene co-expression networks during breast carcinoma progression and suggest potential candidate biomarkers and therapeutic targets for the treatment of breast carcinoma. Our results are available via a web app (https://prognosticpredictor.shinyapps.io/GCNBC/).

High-throughput proteomics of breast cancer interstitial fluid: identification of tumor subtype-specific serologically relevant biomarkers

Despite significant advancements in breast cancer (BC) research, clinicians lack robust serological protein markers for accurate diagnostics and tumor stratification. Tumor interstitial fluid (TIF) accumulates aberrantly externalized proteins within the local tumor space, which can potentially gain access to the circulatory system. As such, TIF may represent a valuable starting point for identifying relevant tumor-specific serological biomarkers. The aim of the study was to perform comprehensive proteomic profiling of TIF to identify proteins associated with BC tumor status and subtype. A liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of 35 TIFs of three main subtypes: luminal (19), Her2 (4), and triple-negative (TNBC) (12) resulted in the identification of > 8800 proteins. Unsupervised hierarchical clustering segregated the TIF proteome into two major clusters, luminal and TNBC/Her2 subgroups. High-grade tumors enriched with tumor infiltrating lymphocytes (TILs) were also stratified from low-grade tumors. A consensus analysis approach, including differential abundance analysis, selection operator regression, and random forest returned a minimal set of 24 proteins associated with BC subtypes, receptor status, and TIL scoring. Among them, a panel of 10 proteins, AGR3, BCAM, CELSR1, MIEN1, NAT1, PIP4K2B, SEC23B, THTPA, TMEM51, and ULBP2, was found to stratify the tumor subtype-specific TIFs. In particular, upregulation of BCAM and CELSR1 differentiates luminal subtypes, while upregulation of MIEN1 differentiates Her2 subtypes. Immunohistochemistry analysis showed a direct correlation between protein abundance in TIFs and intratumor expression levels for all 10 proteins. Sensitivity and specificity were estimated for this protein panel by using an independent, comprehensive breast tumor proteome dataset. The results of this analysis strongly support our data, with eight of the proteins potentially representing biomarkers for stratification of BC subtypes. Five of the most representative proteomics databases currently available were also used to estimate the potential for these selected proteins to serve as putative serological markers.

AGR3 promotes estrogen receptor-positive breast cancer cell proliferation in an estrogen-dependent manner

Breast cancer is one of the most common malignancies and the leading cause of cancer-associated death among women. Anterior gradient 3 (AGR3) is a cancer-associated gene and is similar to its homologous oncogene AGR2. However, whether AGR3 participates in breast cancer progression remains unclear. The present study aimed to investigate the function of AGR3 in ER-positive breast cancer. In the present study, reverse transcription-quantitative PCR was used to detect AGR3 mRNA expression in breast cancer tissues and cell lines; linear correlation analysis was used to investigate the correlation between AGR3 and estrogen receptor 1 (ESR1) expression in breast cancer via GEO dataset analysis; western blotting was used to assess the levels of AGR3, ER and GAPDH; small interfering (si)RNA transfection was used to knock down AGR3 and ESR1 expression; and finally the Cell Counting Kit-8 assay was used to evaluate cell viability. In the present study, AGR3 expression was markedly increased in estrogen receptor (ER)-positive breast cancer tissues and cell lines compared with that in ER-negative breast cancer. AGR3 expression was upregulated in estrogen-treated T47D cells, whereas 4-hydroxytamoxifen, an inhibitor of estrogen-ER activity in breast cancer cells, downregulated AGR3 expression in T47D cells. Functional assays demonstrated that knockdown of AGR3 using siRNAs inhibited T47D cell proliferation compared with that of the negative control group. Additionally, AGR3 expression was decreased after knocking down ESR1. The present results suggested that AGR3 may serve an important role in estrogen-mediated cell proliferation in breast cancer and that AGR3 knockdown may be a potential therapeutic strategy for ER-positive breast cancer.