Trefoil factor-1 upregulation in estrogen-receptor positive breast cancer correlates with an increased risk of bone metastasis

Molecular mechanism of bone metastasis in breast cancer

Bone metastasis is a debilitating complication that frequently occurs in the advanced stages of breast cancer. However, the underlying molecular and cellular mechanisms of the bone metastasis remain unclear. Here, we elucidate how bone metastasis arises from tumor cells that detach from the primary lesions and infiltrate into the surrounding tissue, as well as how these cells disseminate to distant sites. Specifically, we elaborate how tumor cells preferentially grow within the bone micro-environment and interact with bone cells to facilitate bone destruction, characterized as osteoclastic bone metastasis, as well as new bone matrix deposition, characterized as osteoblastic bone metastasis. We also updated the current understanding of the molecular mechanisms underlying bone metastasis and reasons for relapse in breast cancer, and also opportunities of developing novel diagnostic approaches and treatment.

Secretory Trefoil Factor 1 (TFF1) promotes gemcitabine resistance through chemokine receptor CXCR4 in Pancreatic Ductal Adenocarcinoma

Gemcitabine is the first-line treatment option for patients with locally advanced or metastatic pancreatic ductal adenocarcinoma (PDAC). However, the frequent adoption of resistance to gemcitabine by cancer cells poses a significant challenge in treating this aggressive disease. In this study, we focused on analyzing the role of trefoil factor 1 (TFF1) in gemcitabine resistance in PDAC. Analysis of PDAC TCGA and cell line datasets indicated an enrichment of TFF1 in the gemcitabine-resistant classical subtype and suggested an inverse correlation between TFF1 expression and sensitivity to gemcitabine treatment. The genetic ablation of TFF1 in PDAC cells enhanced their sensitivity to gemcitabine treatment in both in vitro and in vivo tumor xenografts. The biochemical studies revealed that TFF1 contributes to gemcitabine resistance through enhanced stemness, increasing migration ability of cancer cells, and induction of anti-apoptotic genes. We further pursued studies to predict possible receptors exerting TFF1-mediated gemcitabine resistance. Protein-protein docking investigations with BioLuminate software revealed that TFF1 binds to the chemokine receptor CXCR4, which was supported by real-time binding analysis of TFF1 and CXCR4 using SPR studies. The exogenous addition of TFF1 increased the proliferation and migration of PDAC cells through the pAkt/pERK axis, which was abrogated by treatment with a CXCR4-specific antagonist AMD3100. Overall, the present study demonstrates the contribution of the TFF1-CXCR4 axis in imparting gemcitabine resistance properties to PDAC cells.

Early short-term hypoxia promotes epidermal cell migration by activating the CCL2-ERK1/2 pathway and epithelial-mesenchymal transition during wound healing

Background

Due to vasculature injury and increased oxygen consumption, the early wound microenvironment is typically in a hypoxic state. We observed enhanced cell migration ability under early short-term hypoxia. CCL2 belongs to the CC chemokine family and was found to be increased in early hypoxic wounds and enriched in the extracellular signal-regulated kinase (ERK)1/2 pathway in our previous study. However, the underlying mechanism through which the CCL2-ERK1/2 pathway regulates wound healing under early short-term hypoxia remains unclear. Activation of epithelial-mesenchymal transition (EMT) is a key process in cancer cell metastasis, during which epithelial cells acquire the characteristics of mesenchymal cells and enhance cell motility and migration ability. However, the relationship between epithelial cell migration and EMT under early short-term hypoxia has yet to be explored.

Methods

HaCaT cells were cultured to verify the effect of early short-term hypoxia on migration through cell scratch assays. Lentiviruses with silenced or overexpressed CCL2 were used to explore the relationship between CCL2 and migration under short-term hypoxia. An acute full-thickness cutaneous wound rat model was established with the application of an ERK inhibitor to reveal the hidden role of the ERK1/2 pathway in the early stage of wound healing. The EMT process was verified in all the above experiments through western blotting.

Results

In our study, we found that short-term hypoxia promoted cell migration. Mechanistically, hypoxia promoted cell migration through mediating CCL2. Overexpression of CCL2 via lentivirus promoted cell migration, while silencing CCL2 via lentivirus inhibited cell migration and the production of related downstream proteins. In addition, we found that CCL2 was enriched in the ERK1/2 pathway, and the application of an ERK inhibitor in vivo and in vitro verified the upstream and downstream relationships between the CCL2 pathway and ERK1/2. Western blot results both in vivo and in vitro demonstrated that early short-term hypoxia promotes epidermal cell migration by activating the CCL2-ERK1/2 pathway and EMT during wound healing.

Conclusions

Our work demonstrated that hypoxia in the early stage serves as a stimulus for triggering wound healing through activating the CCL2-ERK1/2 pathway and EMT, which promote epidermal cell migration and accelerate wound closure. These findings provide additional detailed insights into the mechanism of wound healing and new targets for clinical treatment.

A semi-supervised approach for the integration of multi-omics data based on transformer multi-head self-attention mechanism and graph convolutional networks

BACKGROUND AND OBJECTIVES

Comprehensive analysis of multi-omics data is crucial for accurately formulating effective treatment plans for complex diseases. Supervised ensemble methods have gained popularity in recent years for multi-omics data analysis. However, existing research based on supervised learning algorithms often fails to fully harness the information from unlabeled nodes and overlooks the latent features within and among different omics, as well as the various associations among features. Here, we present a novel multi-omics integrative method MOSEGCN, based on the Transformer multi-head self-attention mechanism and Graph Convolutional Networks(GCN), with the aim of enhancing the accuracy of complex disease classification. MOSEGCN first employs the Transformer multi-head self-attention mechanism and Similarity Network Fusion (SNF) to separately learn the inherent correlations of latent features within and among different omics, constructing a comprehensive view of diseases. Subsequently, it feeds the learned crucial information into a self-ensembling Graph Convolutional Network (SEGCN) built upon semi-supervised learning methods for training and testing, facilitating a better analysis and utilization of information from multi-omics data to achieve precise classification of disease subtypes.

RESULTS

The experimental results show that MOSEGCN outperforms several state-of-the-art multi-omics integrative analysis approaches on three types of omics data: mRNA expression data, microRNA expression data, and DNA methylation data, with accuracy rates of 83.0% for Alzheimer's disease and 86.7% for breast cancer subtyping. Furthermore, MOSEGCN exhibits strong generalizability on the GBM dataset, enabling the identification of important biomarkers for related diseases.

CONCLUSION

MOSEGCN explores the significant relationship information among different omics and within each omics' latent features, effectively leveraging labeled and unlabeled information to further enhance the accuracy of complex disease classification. It also provides a promising approach for identifying reliable biomarkers, paving the way for personalized medicine.

Organ-Specificity of Breast Cancer Metastasis

Breast cancer (BC) remains one of the most common malignancies among women worldwide. Breast cancer shows metastatic heterogeneity with priority to different organs, which leads to differences in prognosis and response to therapy among patients. The main targets for metastasis in BC are the bone, lung, liver and brain. The molecular mechanism of BC organ-specificity is still under investigation. In recent years, the appearance of new genomic approaches has led to unprecedented changes in the understanding of breast cancer metastasis organ-specificity and has provided a new platform for the development of more effective therapeutic agents. This review summarises recent data on molecular organ-specific markers of metastasis as the basis of a possible therapeutic approach in order to improve the diagnosis and prognosis of patients with metastatically heterogeneous breast cancer.

Animal models of cancer metastasis to the bone

Cancer metastasis is a major cause of mortality from several tumors, including those of the breast, prostate, and the thyroid gland. Since bone tissue is one of the most common sites of metastasis, the treatment of bone metastases is crucial for the cure of cancer. Hence, disease models must be developed to understand the process of bone metastasis in order to devise therapies for it. Several translational models of different bone metastatic tumors have been developed, including animal models, cell line injection models, bone implant models, and patient-derived xenograft models. However, a compendium on different bone metastatic cancers is currently not available. Here, we have compiled several animal models derived from current experiments on bone metastasis, mostly involving breast and prostate cancer, to improve the development of preclinical models and promote the treatment of bone metastasis.

Anterior Gradient 2 is a Significant Prognostic Biomarker in Bone Metastasis of Breast Cancer

Background: The study aimed to detect DEGs associated with BRCA bone metastasis, filter prognosis biomarkers, and explore possible pathways.

Methods: GSE175692 dataset was used to detect DEGs between BRCA bone metastatic cases and non-bone metastatic cases, followed by the construction of a PPI network among DEGs. The main module among the PPI network was then determined and pathway analysis on genes within the module was performed. Through performing Cox regression, Kaplan-Meier, nomogram, and ROC curve analyses using GSE175692 and GSE124647 datasets at the same time, the most significant prognostic biomarker was gradually filtered. Finally, important pathways associated with prognostic biomarkers were explored by GSEA analysis.

Results: The 74 DEGs were detected between bone metastasis and non-bone metastasis groups. A total of 15 nodes were included in the main module among the whole PPI network and they mainly correlated with the IL-17 signaling pathway. We then performed Cox analysis on 15 genes using two datasets and only enrolled the genes with p < 0.05 in Cox analysis into the further analyses. Kaplan-Meier analyses using two datasets showed that the common biomarker AGR2 expression was related to the survival time of BRCA metastatic cases. Further, the nomogram determined the greatest contribution of AGR2 on the survival probability and the ROC curve revealed its optimal prognostic performance. More importantly, high expression of AGR2 prolonged the survival time of BRCA bone metastatic patients. These results all suggested the importance of AGR2 in metastatic BRCA. Finally, we performed the GSEA analysis and found that AGR2 was negatively related to IL-17 and NF-kβ signaling pathways.

Conclusion: AGR2 was finally determined as the most important prognostic biomarker in BRCA bone metastasis, and it may play a vital role in cancer progression by regulating IL-17 and NF-kB signaling pathways.

Prominin 1 Significantly Correlated with Bone Metastasis of Breast Cancer and Influenced the Patient’s Prognosis

Background

This study is aimed at identifying the important biomarkers associated with bone metastasis (BM) in breast cancer (BRCA).

Methods

The GSE175692 dataset was used to detect significant differential expressed genes (DEGs) between BRCA samples with or without BM, and DEG-related pathways were then explored. Further, we constructed the protein-protein interaction (PPI) network on GEGs and filtered 5 vital nodes. We then performed the Cox regression, Kaplan-Meier analysis, nomogram, and ROC curve to filter the most significant prognosis genes. The GSE14020 and GSE124647 datasets were used to verify the expression and prognostic value of hub genes, respectively. Finally, the gene set enrichment analysis (GSEA) was performed to reveal the potential mechanism.

Results

Totally, 74 DEGs were detected, which mainly correlated with infectious disease, signaling molecules, and interaction. The 5 important DEGs were then filtered, and the Cox regression further showed that 2 genes, including prominin 1 (PROM1) and C-C motif chemokine ligand 2 (CCL2), were related to the prognosis of BRCA metastasis patients. Especially, PROM1 presented a better prognostic performance on the survival probability of patients than CCL2. Verification analysis further confirmed the abnormal expression and significant prognostic influence of PROM1. Finally, GSEA revealed that PROM1 was negatively related to IGF1 and mTOR pathways in BRCA metastasis.

Conclusion

PROM1 was an important biomarker associated with BRCA bone metastasis and affected the prognosis of metastatic BRCA patients. It may play a vital role in metastatic BRCA by negatively regulating IGF1 and mTOR pathways.

Breast Cancer Genomics: Primary and Most Common Metastases

Specific genomic alterations have been found in primary breast cancer involving driver mutations that result in tumorigenesis. Metastatic breast cancer, which is uncommon at the time of disease onset, variably impacts patients throughout the course of their disease. Both the molecular profiles and diverse genomic pathways vary in the development and progression of metastatic breast cancer. From the most common metastatic site (bone), to the rare sites such as orbital, gynecologic, or pancreatic metastases, different levels of gene expression indicate the potential involvement of numerous genes in the development and spread of breast cancer. Knowledge of these alterations can, not only help predict future disease, but also lead to advancement in breast cancer treatments. This review discusses the somatic landscape of breast primary and metastatic tumors.

Bone-Specific Enhancement of Antibody Therapy for Breast Cancer Metastasis to Bone

Despite the rapid evolution of therapeutic antibodies, their clinical efficacy in the treatment of bone tumors is hampered due to the inadequate pharmacokinetics and poor bone tissue accessibility of these large macromolecules. Here, we show that engineering therapeutic antibodies with bone-homing peptide sequences dramatically enhances their concentrations in the bone metastatic niche, resulting in significantly reduced survival and progression of breast cancer bone metastases. To enhance the bone tumor-targeting ability of engineered antibodies, we introduced varying numbers of bone-homing peptides into permissive sites of the anti-HER2 antibody, trastuzumab. Compared to the unmodified antibody, the engineered antibodies have similar pharmacokinetics and in vitro cytotoxic activity, but exhibit improved bone tumor distribution in vivo. Accordingly, in xenograft models of breast cancer metastasis to bone sites, engineered antibodies with enhanced bone specificity exhibit increased inhibition of both initial bone metastases and secondary multiorgan metastases. Furthermore, this engineering strategy is also applied to prepare bone-targeting antibody-drug conjugates with enhanced therapeutic efficacy. These results demonstrate that adding bone-specific targeting to antibody therapy results in robust bone tumor delivery efficacy. This provides a powerful strategy to overcome the poor accessibility of antibodies to the bone tumors and the consequential resistance to the therapy.