Clinical and functional significance of tumor/stromal ATR expression in breast cancer patients

Proteomics Investigation of the Impact of the Enterococcus faecalis Secretome on MCF-7 Tumor Cells

Breast cancer is the most prevalent form of cancer among women. The microenvironment of a cancer tumor is surrounded by various cells, including the microbiota. An imbalance between microbes and their host may contribute to the development and spread of breast cancer. Therefore, the objective of this study is to investigate the influence of Enterococcus faecalis on a breast cancer cell line (MCF-7) to mimic the luminal A subtype of breast cancer, using an untargeted proteomics approach to analyze the proteomic profiles of breast cancer cells after their treatment with E. faecalis in order to understand the microbiome and its role in the development of cancer. The breast cancer cell line MCF-7 was cultured and then treated with a 10% bacterial supernatant at two time points (24 h and 48 h) at 37 °C in a humidified incubator with 5% CO2. Proteins were then extracted and separated using two-dimensional difference (2D-DIGE) gel electrophoresis, and the statistically significant proteins (p-value < 0.05, fold change > 1.5) were identified via matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF-MS). The protein fingerprints showed a differential protein expression pattern in the cells treated with E. faecalis for 24 and 48 h compared with the control. We found 58 statistically significant proteins changes in the MCF-7 breast cancer cells affected by E. faecalis. Kilin and transgelin were upregulated after 24 h of treatment and could be used as diagnostic and prognostic markers for breast cancer. In addition, another protein involved in the inhibition of cell proliferation was coiled-coil domain-containing protein 154. The protein markers identified in this study may serve as possible biomarkers for breast cancer progression. This promotes their future uses as important therapeutic goals in the treatment and diagnosis of cancer and increases our understanding of the breast microbiome and its role in the development of cancer.

Define cancer-associated fibroblasts (CAFs) in the tumor microenvironment: new opportunities in cancer immunotherapy and advances in clinical trials

Despite centuries since the discovery and study of cancer, cancer is still a lethal and intractable health issue worldwide. Cancer-associated fibroblasts (CAFs) have gained much attention as a pivotal component of the tumor microenvironment. The versatility and sophisticated mechanisms of CAFs in facilitating cancer progression have been elucidated extensively, including promoting cancer angiogenesis and metastasis, inducing drug resistance, reshaping the extracellular matrix, and developing an immunosuppressive microenvironment. Owing to their robust tumor-promoting function, CAFs are considered a promising target for oncotherapy. However, CAFs are a highly heterogeneous group of cells. Some subpopulations exert an inhibitory role in tumor growth, which implies that CAF-targeting approaches must be more precise and individualized. This review comprehensively summarize the origin, phenotypical, and functional heterogeneity of CAFs. More importantly, we underscore advances in strategies and clinical trials to target CAF in various cancers, and we also summarize progressions of CAF in cancer immunotherapy.

E. coli Secretome Metabolically Modulates MDA-MB-231 Breast Cancer Cells' Energy Metabolism

Breast cancer (BC) is commonly diagnosed in women. BC cells are associated with altered metabolism, which is essential to support their energetic requirements, cellular proliferation, and continuous survival. The altered metabolism of BC cells is a result of the genetic abnormalities of BC cells. Risk factors can also enhance it, including age, lifestyle, hormone disturbances, etc. Other unknown BC-promoting risk factors are under scientific investigation. One of these investigated factors is the microbiome. However, whether the breast microbiome found in the BC tissue microenvironment can impact BC cells has not been studied. We hypothesized that E. coli, part of a normal breast microbiome with more presence in BC tissue, secretes metabolic molecules that could alter BC cells' metabolism to maintain their survival. Thus, we directly examined the impact of the E. coli secretome on the metabolism of BC cells in vitro. MDA-MB-231 cells, an in vitro model of aggressive triple-negative BC cells, were treated with the E. coli secretome at different time points, followed by untargeted metabolomics analyses via liquid chromatography-mass spectrometry to identify metabolic alterations in the treated BC cell lines. MDA-MB-231 cells that were not treated were used as controls. Moreover, metabolomic analyses were performed on the E. coli secretome to profile the most significant bacterial metabolites affecting the metabolism of the treated BC cell lines. The metabolomics results revealed about 15 metabolites that potentially have indirect roles in cancer metabolism that were secreted from E. coli in the culture media of MDA-MB-231 cells. The cells treated with the E. coli secretome showed 105 dysregulated cellular metabolites compared to controls. The dysregulated cellular metabolites were involved in the metabolism of fructose and mannose, sphingolipids, amino acids, fatty acids, amino sugar, nucleotide sugar, and pyrimidine, which are vital pathways required for the pathogenesis of BC. Our findings are the first to show that the E. coli secretome modulates the BC cells' energy metabolism, highlighting insights into the possibility of altered metabolic events in BC tissue in the actual BC tissue microenvironment that are potentially induced by the local bacteria. Our study provides metabolic data that could be as a basis for future studies searching for the underlying mechanisms mediated by bacteria and their secretome to alter the metabolism of BC cells.

Signaling pathways in cancer-associated fibroblasts: recent advances and future perspectives

As a critical component of the tumor microenvironment (TME), cancer-associated fibroblasts (CAFs) play important roles in cancer initiation and progression. Well-known signaling pathways, including the transforming growth factor-β (TGF-β), Hedgehog (Hh), Notch, Wnt, Hippo, nuclear factor kappa-B (NF-κB), Janus kinase (JAK)/signal transducer and activator of transcription (STAT), mitogen-activated protein kinase (MAPK), and phosphoinositide 3-kinase (PI3K)/AKT pathways, as well as transcription factors, including hypoxia-inducible factor (HIF), heat shock transcription factor 1 (HSF1), P53, Snail, and Twist, constitute complex regulatory networks in the TME to modulate the formation, activation, heterogeneity, metabolic characteristics and malignant phenotype of CAFs. Activated CAFs remodel the TME and influence the malignant biological processes of cancer cells by altering the transcriptional and secretory characteristics, and this modulation partially depends on the regulation of signaling cascades. The results of preclinical and clinical trials indicated that therapies targeting signaling pathways in CAFs demonstrated promising efficacy but were also accompanied by some failures (e.g., NCT01130142 and NCT01064622). Hence, a comprehensive understanding of the signaling cascades in CAFs might help us better understand the roles of CAFs and the TME in cancer progression and may facilitate the development of more efficient and safer stroma-targeted cancer therapies. Here, we review recent advances in studies of signaling pathways in CAFs and briefly discuss some future perspectives on CAF research.

High AUF1 level in stromal fibroblasts promotes carcinogenesis and chemoresistance and predicts unfavorable prognosis among locally advanced breast cancer patients

Background

Locally advanced breast cancer (LABC), the most aggressive form of the disease, is a serious threat for women's health worldwide. The AU-rich RNA-binding factor 1 (AUF1) promotes the formation of chemo-resistant breast cancer stem cells. Thereby, we investigated the power of AUF1 expression, in both cancer cells and their stromal fibroblasts, as predictive biomarker for LABC patients’ clinical outcome following neoadjuvant treatment.

Methods

We have used immunohistochemistry to assess the level of AUF1 on formalin-fixed paraffin-embedded tissues. Immunoblotting was utilized to show the effect of AUF1 ectopic expression in breast stromal fibroblasts on the expression of various genes both in vitro and in orthotopic tumor xenografts. Cytotoxicity was evaluated using the WST1 assay, while a label-free real-time setting using the xCELLigence RTCA technology was utilized to assess the proliferative, migratory and invasive abilities of cells.

Results

We have shown that high AUF1 immunostaining (≥ 10%) in both cancer cells and their adjacent cancer-associated fibroblasts (CAFs) was significantly associated with higher tumor grade. Kaplan–Meier univariate analysis revealed a strong correlation between high AUF1 level in CAFs and poor patient’s survival. This correlation was highly significant in patients with triple negative breast cancer, who showed poor disease-free survival (DFS) and overall survival (OS). High expression of AUF1 in CAFs was also associated with poor OS of ER+/Her2− patients. Similarly, AUF1-positive malignant cells tended to be associated with shorter DFS and OS of ER+/Her2+ patients. Interestingly, neoadjuvant therapy downregulated AUF1 to a level lower than 10% in malignant cells in a significant number of patients, which improved both DFS and OS. In addition, ectopic expression of AUF1 in breast fibroblasts activated these cells and enhanced their capacity to promote, in an IL-6-dependent manner, the epithelial-to-mesenchymal transition and stemness processes. Furthermore, these AUF1-expressing cells enhanced the chemoresistance of breast cancer cells and their growth in orthotopic tumor xenografts.

Conclusions

The present findings show that the CAF-activating factor AUF1 has prognostic/predictive value for breast cancer patients and could represent a great therapeutic target in order to improve the precision of cancer treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01543-x.

[Value of prediction models for prognosis prediction of colorectal cancer: an analysis based on TCPA database]

OBJECTIVE

To assess the value of the combination of multiple proteins in predicting the prognosis of colorectal cancer (CRC) through bioinformatics analysis.

OBJECTIVE

The protein expression and clinical data were downloaded from TCPA database. Perl and R were used to screen the prognostic-related proteins, and through Cox analysis, the proteins that served as independent prognostic factors of CRC were identified to build the prediction model. Survival analyses were conducted for each of the proteins included in the prediction model and the risk score of the model, and risk curves was drawn for the risk score and the patients' survival status to verify the performance of the model. Independent prognosis analysis and ROC analysis were used to assess the value and advantages of the model in prognosis prediction. The interactions between the proteins included in the model and the differential expressions of the key genes related with the proteins were analyzed.

OBJECTIVE

Six proteins were screened for model construction. Compared with a single gene, the model showed much greater prognostic value for CRC. Independent prognostic analysis showed that the risk score of the prediction model was significantly related with the prognosis (P < 0.001), and the model could be used as an independent risk factor for prognostic assessment of the patients. ROC analysis showed that the model had good specificity and sensitivity for prognostic prediction (AUC=0.734). Protein interactions showed that BID, SLC1A5 and SRC_pY527 were significantly correlated with other proteins (P < 0.001), and SLC1A5 and SRC_pY527 had the most significant interactions with other proteins (P < 0.001). Except for those of INPP4B, the key genes related with the proteins in the prediction model had significant differential expressions at the mRNA level in CRC (P < 0.05).

OBJECTIVE

The prediction model constructed based on 6 proteins has good prognostic value for CRC. The proteins SLC1A5 and SRC_pY527 play key roles in the prognosis of CRC, and SRC_pY527 may regulate the occurrence and progression of CRC through the SRC/AKT/MAPK signal axis and thus may serve as a new therapeutic target of CRC.