Perspectives of Reprogramming Breast Cancer Metabolism

DCXR promotes cell proliferation by promoting the activity of aerobic glycolysis in breast cancer

The function of human dicarbonyl/L‑xylulose reductase (DCXR) in the pathophysiology of breast cancer is yet to be elucidated. The present study aimed to investigate the function of DCXR in glycolysis and the cell cycle of breast cancer cells with respect to cell proliferation. Differential expressed DCXR was identified in The Cancer Genome Atlas (TCGA) database and verified in clinical breast cancer tissue. DCXR silencing and overexpression were induced by RNA interference and lentiviral vectors, respectively. Cell cycle progression, proliferation and glycolytic activity of breast cancer cells were detected by flow cytometry, Cell Counting Kit‑8 assay and chemical methods, respectively. Tumorigenicity was detected using nude mice xenograft models. The expression of DCXR was increased in TCGA breast cancer database and the function of DCXR was enriched in 'glycolysis' and 'cell cycle'. Further analysis using clinical breast cancer samples confirmed upregulation of DCXR. The silencing of DCXR suppressed proliferation and cell cycle progression of breast cancer cells and significantly decreased the capacity for glycolysis, thereby demonstrating the effect of DCXR on the function of breast cancer cells. Similar conclusions were obtained in DCXR overexpressing cells; notably, DCXR overexpression promoted proliferation, cell cycle progression at S phase and glycolysis. 2‑Deoxy‑D‑glucose inhibited the effect of DCXR on the proliferation and cell cycle progression of breast cancer cells. The present study revealed that DCXR regulated breast cancer cell cycle progression and proliferation by increasing glycolysis activity and thus may serve as an oncogene for breast cancer.

Relationship between metabolic reprogramming and drug resistance in breast cancer

Breast cancer is the leading cause of cancer death in women. At present, chemotherapy is the main method to treat breast cancer in addition to surgery and radiotherapy, but the process of chemotherapy is often accompanied by the development of drug resistance, which leads to a reduction in drug efficacy. Furthermore, mounting evidence indicates that drug resistance is caused by dysregulated cellular metabolism, and metabolic reprogramming, including enhanced glucose metabolism, fatty acid synthesis and glutamine metabolic rates, is one of the hallmarks of cancer. Changes in metabolism have been considered one of the most important causes of resistance to treatment, and knowledge of the mechanisms involved will help in identifying potential treatment deficiencies. To improve women's survival outcomes, it is vital to elucidate the relationship between metabolic reprogramming and drug resistance in breast cancer. This review analyzes and investigates the reprogramming of metabolism and resistance to breast cancer therapy, and the results offer promise for novel targeted and cell-based therapies.

Identification of a novel lipid metabolism-related gene signature within the tumour immune microenvironment for breast cancer

Background

Systemic factors can strongly affect how tumour cells behave, grow, and communicate with other cells in breast cancer. Lipid metabolic reprogramming is a systemic process that tumour cells undergo; however, the formation and dynamics of lipids associated with the tumour immune microenvironment (TIME) remain unclear. The investigation of the sophisticated bidirectional crosstalk of tumour cells with cancer metabolism, gene expression, and TIME could have the potential to identify novel biomarkers for diagnosis, prognosis, and immunotherapy. This study aimed to construct a prognostic signature to detect the bicrosstalk between the lipid metabolic system and the TIME of breast cancer.

Methods

To detect the expression of LRGs and execute GO/KEGG analysis, the R program was chosen. Considering the clinical information and pathological features, a prognostic gene signature was constructed by LASSO Cox regression analysis. TMB, MSI, and immune infiltration analyses were performed, and consensus cluster analysis of LRGs was also performed.

Results

These 16 lipid metabolism-related genes (LRGs) were mainly involved in the process of lipid metabolism and fatty acid binding in breast cancer. Prognosis analysis identified the prognostic value of FABP7(Fatty acid binding protein 7) and NDUFAB1(NADH:ubiquinone oxidoreductase subunit AB1) in breast cancer patients. The prognostic gene signature constructed with FABP7 and NDUFAB1 was significantly related to immune cell infiltration and could predict the overall survival rate with above average correctness of breast cancer patients. FABP7 and NDUFAB1 were proven to have relevance in immune cell infiltration and tumour mutation burden (TMB). Consensus cluster analysis identified that the upregulated mRNAs were mostly related to the oncogenesis process, while the downregulated mRNAs were associated with immune-related signalling pathways.

Conclusion

A comprehensive analysis was performed to evaluate the lipid metabolic system and identified a signature constructed by two prognostic genes for immunotherapies in breast cancer. The results also revealed evidence of vulnerabilities in the interplay between the lipid metabolic system and the TIME in breast cancer. Further data with clinical studies and experiments are warranted.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12944-022-01651-9.

Predicting dynamic response to neoadjuvant chemotherapy in breast cancer: a novel metabolomics approach

Neoadjuvant chemotherapy (NACT) outcomes vary according to breast cancer (BC) subtype. Since pathologic complete response is one of the most important target endpoints of NACT, further investigation of NACT outcomes in BC is crucial. Thus, identifying sensitive and specific predictors of treatment response for each phenotype would enable early detection of chemoresistance and residual disease, decreasing exposures to ineffective therapies and enhancing overall survival rates. We used liquid chromatography−high‐resolution mass spectrometry (LC‐HRMS)‐based untargeted metabolomics to detect molecular changes in plasma of three different BC subtypes following the same NACT regimen, with the aim of searching for potential predictors of response. The metabolomics data set was analyzed by combining univariate and multivariate statistical strategies. By using ANOVA–simultaneous component analysis (ASCA), we were able to determine the prognostic value of potential biomarker candidates of response to NACT in the triple‐negative (TN) subtype. Higher concentrations of docosahexaenoic acid and secondary bile acids were found at basal and presurgery samples, respectively, in the responders group. In addition, the glycohyocholic and glycodeoxycholic acids were able to classify TN patients according to response to treatment and overall survival with an area under the curve model > 0.77. In relation to luminal B (LB) and HER2+ subjects, it should be noted that significant differences were related to time and individual factors. Specifically, tryptophan was identified to be decreased over time in HER2+ patients, whereas LysoPE (22:6) appeared to be increased, but could not be associated with response to NACT. Therefore, the combination of untargeted‐based metabolomics along with longitudinal statistical approaches may represent a very useful tool for the improvement of treatment and in administering a more personalized BC follow‐up in the clinical practice.

Pinpointing Cancer Sub-Type Specific Metabolic Tasks Facilitates Identification of Anti-cancer Targets

Metabolic reprogramming is one of the hallmarks of tumorigenesis. Understanding the metabolic changes in cancer cells may provide attractive therapeutic targets and new strategies for cancer therapy. The metabolic states are not the same in different cancer types or subtypes, even within the same sample of solid tumors. In order to understand the heterogeneity of cancer cells, we used the Pareto tasks inference method to analyze the metabolic tasks of different cancers, including breast cancer, lung cancer, digestive organ cancer, digestive tract cancer, and reproductive cancer. We found that cancer subtypes haves different propensities toward metabolic tasks, and the biological significance of these metabolic tasks also varies greatly. Normal cells treat metabolic tasks uniformly, while different cancer cells focus on different pathways. We then integrated the metabolic tasks into the multi-objective genome-scale metabolic network model, which shows higher accuracy in the in silico prediction of cell states after gene knockout than the conventional biomass maximization model. The predicted potential single drug targets could potentially turn into biomarkers or drug design targets. We further implemented the multi-objective genome-scale metabolic network model to predict synthetic lethal target pairs of the Basal and Luminal B subtypes of breast cancer. By analyzing the predicted synthetic lethal targets, we found that mitochondrial enzymes are potential targets for drug combinations. Our study quantitatively analyzes the metabolic tasks of cancer and establishes cancer type-specific metabolic models, which opens a new window for the development of specific anti-cancer drugs and provides promising treatment plans for specific cancer subtypes.

Path to Clonal Theranostics in Luminal Breast Cancers

Integrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations and achieve clonal theranostics. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteomics guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. Most of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString^®) and CDx (Foundation One^®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Many of the protein targets identified and drugs screened were clinically relevant to breast cancer and are in clinical trials. Thus, we described the non-redundant knowledge brought by this clone-tailored approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.

Survival-Associated Metabolic Genes and Risk Scoring System in HER2-Positive Breast Cancer

Human epidermal growth factor receptor 2 (HER2)-positive breast cancer and triple-negative breast cancer have their own genetic, epigenetic, and protein expression profiles. In the present study, based on bioinformatics techniques, we explored the prognostic targets of HER2-positive breast cancer from metabonomics perspective and developed a new risk score system to evaluate the prognosis of patients. By identifying the differences between HER2 positive and normal control tissues, and between triple negative breast cancer and normal control tissues, we found a large number of differentially expressed metabolic genes in patients with HER2-positive breast cancer and triple-negative breast cancer. Importantly, in HER2-positive breast cancer, decreased expression of metabolism-related genes ATIC, HPRT1, ASNS, SULT1A2, and HAL was associated with increased survival. Interestingly, these five metabolism-related genes can be used to construct a risk score system to predict overall survival (OS) in HER2-positive patients. The time-dependent receiver operating characteristic (ROC) curve analysis showed that the predictive sensitivity of the risk scoring system was higher than that of other clinical factors, including age, stage, and tumor node metastasis (TNM) stage. This work shows that specific transcriptional changes in metabolic genes can be used as biomarkers to predict the prognosis of patients, which is helpful in implementing personalized treatment and evaluating patient prognosis.

Sini San Inhibits Chronic Psychological Stress-Induced Breast Cancer Stemness by Suppressing Cortisol-Mediated GRP78 Activation

Chronic psychological stress is closely correlated with breast cancer growth and metastasis. Sini San (SNS) formula is a classical prescription for relieving depression-related symptoms in traditional Chinese medicine (TCM). Current researches have suggested that chronic psychological stress is closely correlated with cancer stem cells (CSCs) and endoplasmic reticulum (ER) stress. This study aimed to investigate the effects of chronic psychological stress on ER stress-mediated breast cancer stemness and the therapeutic implication of SNS. Chronic psychological stress promoted lung metastasis in 4T1 breast tumor-bearing mice and increased the stem cell-like populations and stemness-related gene expression. Meanwhile, GRP78, a marker of ER stress, was significantly increased in the breast tumors and lung metastases under chronic psychological stress. As a biochemical hallmark of chronic psychological stress, cortisol dramatically enhanced the stem cell-like populations and mammospheres formation by activating GRP78 transcriptionally. However, GRP78 inhibitors or shRNA attenuated the stemness enhancement mediated by cortisol. Similarly, SNS inhibited chronic psychological stress-induced lung metastasis and stemness of breast cancer cells, as well as reversed cortisol-induced stem cell-like populations and mammospheres formation by attenuating GRP78 expression. Co-localization and co-immunoprecipitation experiments showed that SNS interrupted the interaction between GRP78 and LRP5 on the cell surface, thus inhibiting the Wnt/β-catenin signaling of breast CSCs. Altogether, this study not only uncovers the biological influence and molecular mechanism of chronic psychological stress on breast CSCs but also highlights SNS as a promising strategy for relieving GRP78-induced breast cancer stemness via inhibiting GRP78 activation.

OXCT1 Enhances Gemcitabine Resistance Through NF-κB Pathway in Pancreatic Ductal Adenocarcinoma

Background

Pancreatic ductal adenocarcinoma (PDAC) is a type of malignant tumor with a five-year survival rate of less than 10%. Gemcitabine (GEM) is the most commonly used drug for PDAC chemotherapy. However, a vast majority of patients with PDAC develop resistance after GEM treatment.

Methods

We screened for GEM resistance genes through bioinformatics analysis. We used immunohistochemistry to analyze 3-oxoacid CoA-transferase 1 (OXCT1) expression in PDAC tissues. The survival data were analyzed using the Kaplan–Meier curve. The expression levels of the genes related to OXCT1 and the NF-κB signaling pathway were quantified using real−time quantitative PCR and western blot analyses. We performed flow cytometry to detect the apoptosis rate. Colony formation assay was performed to measure the cell proliferation levels. The cytotoxicity assays of cells were conducted using RTCA. The downstream pathway of OXCT1 was identified via the Gene Set Enrichment Analysis. Tumor growth response to GEM in vivo was also determined in mouse models.

Results

Bioinformatics analysis revealed that OXCT1 is the key gene leading to GEM resistance. Patients with high OXCT1 expression exhibited short relapse-free survival under GEM treatment. OXCT1 overexpression in PDAC cell lines exerted inhibitory effect on apoptosis after GEM treatment. However, the down-regulation of OXCT1 showed the opposite effect. Blocking the NF-κB signaling pathway also reduced GEM resistance of PDAC cells. Tumor growth inhibition induced by GEM in vivo reduced after OXCT1 overexpression. Moreover, the effect of OXCT1 on GEM refractoriness in PDAC cell lines was reversed through using an NF-κB inhibitor.

Conclusion

OXCT1 promoted GEM resistance in PDAC via the NF-κB signaling pathway both in vivo and in vitro. Our results suggest that OXCT1 could be used as a potential therapeutic target for patients with PDAC.

Sodium cantharidate induces Apoptosis in breast cancer cells by regulating energy metabolism via the protein phosphatase 5-p53 axis

Breast cancer is the leading cause of cancer-related death in women worldwide, and despite multiple chemotherapeutic approaches, effective treatment strategies for advanced metastatic breast cancer are still lacking. Metabolic reprogramming is essential for tumor cell growth and propagation, and most cancers, including breast cancer, are accompanied by abnormalities in energy metabolism. Here, we confirmed that sodium cantharidate inhibited cell viability using the Cell Counting Kit-8, clonogenic assay, and Transwell assay. The cell cycle and apoptosis assays indicated that sodium cantharidate induced apoptosis and cell cycle arrest in breast cancer cells. Additionally, proteomic assays, western blots, and metabolic assays revealed that sodium cantharidate converted the metabolic phenotype of breast cancer cells from glycolysis to oxidative phosphorylation. Furthermore, bioinformatics analysis identified possible roles for p53 with respect to the effects of sodium cantharidate on breast cancer cells. Western blot, docking, and phosphatase assays revealed that the regulation of p53 activity by sodium cantharidate was related to its inhibition of protein phosphatase 5 activity. Moreover, sodium cantharidate significantly inhibited tumor growth in tumor-bearing nude mice. In summary, our study provides evidence for the use of sodium cantharidate as an effective and new therapeutic candidate for the treatment of human breast cancer in clinical trials.

Early Breast Cancer Detection Using Untargeted and Targeted Metabolomics

Breast cancer (BC) is a common cause of morbidity and mortality, particularly in women. Moreover, the discovery of diagnostic biomarkers for early BC remains a challenging task. Previously, we [Jasbi et al. J. Chromatogr. B. 2019, 1105, 26-37] demonstrated a targeted metabolic profiling approach capable of identifying metabolite marker candidates that could enable highly sensitive and specific detection of BC. However, the coverage of this targeted method was limited and exhibited suboptimal classification of early BC (EBC). To expand the metabolome coverage and articulate a better panel of metabolites or mass spectral features for classification of EBC, we evaluated untargeted liquid chromatography quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) data, both individually as well as in conjunction with previously published targeted LC-triple quadruple (QQQ)-MS data. Variable importance in projection scores were used to refine the biomarker panel, whereas orthogonal partial least squares-discriminant analysis was used to operationalize the enhanced biomarker panel for early diagnosis. In this approach, 33 altered metabolites/features were detected by LC-QTOF-MS from 124 BC patients and 86 healthy controls. For EBC diagnosis, significance testing and analysis of the area under receiver operating characteristic (AUROC) curve identified six metabolites/features [ethyl (R)-3-hydroxyhexanoate; caprylic acid; hypoxanthine; and m/z 358.0018, 354.0053, and 356.0037] with p < 0.05 and AUROC > 0.7. These metabolites informed the construction of EBC diagnostic models; evaluation of model performance for the prediction of EBC showed an AUROC = 0.938 (95% CI: 0.895-0.975), with sensitivity = 0.90 when specificity = 0.90. Using the combined untargeted and targeted data set, eight metabolic pathways of potential biological relevance were indicated to be significantly altered as a result of EBC. Metabolic pathway analysis showed fatty acid and aminoacyl-tRNA biosynthesis as well as inositol phosphate metabolism to be most impacted in response to the disease. The combination of untargeted and targeted metabolomics platforms has provided a highly predictive and accurate method for BC and EBC diagnosis from plasma samples. Furthermore, such a complementary approach yielded critical information regarding potential pathogenic mechanisms underlying EBC that, although critical to improved prognosis and enhanced survival, are understudied in the current literature. All mass spectrometry data and deidentified subject metadata analyzed in this study have been deposited to Mendeley Data and are publicly available (DOI: 10.17632/kcjg8ybk45.1).

Baohuoside i suppresses breast cancer metastasis by downregulating the tumor-associated macrophages/C-X-C motif chemokine ligand 1 pathway

BACKGROUND

Breast cancer is the most common malignancy in women and metastasis is the leading cause of breast cancer-related deaths. Our previous studies have shown that XIAOPI formula, a newly approved drug by the State Food and Drug Administration of China (SFDA), can dramatically inhibit breast cancer metastasis by modulating the tumor-associated macrophages/C-X-C motif chemokine ligand 1 (TAMs/CXCL1) pathway. However, the bioactive compound accounting for the anti-metastatic effect of XIAOPI formula remains unclear.

PURPOSE

This study was designed to separate the anti-metastatic bioactive compound from XIAOPI formula and to elucidate its action mechanisms.

STUDY DESIGN/METHODS

TAMs/CXCL1 promoter activity-guided fractionation and multiple chemical structure identification approaches were conducted to screen the bioactive compound from XIAOPI formula. Breast cancer cells and TAMs were co-cultured in vitro or co-injected in vivo to simulate their coexistence. Multiple molecular biology experiments, zebrafish breast cancer xenotransplantation model and mouse breast cancer xenografts were applied to validate the anti-metastatic activity of the screened compound.

RESULTS

Bioactivity-guided fractionation identified baohuoside I (BHS) as the key bioactive compound of XIAOPI formula in inhibiting TAMs/CXCL1 promoter activity. Functional studies revealed that BHS could significantly inhibit the migration and invasion as well as the expression of metastasis-related proteins in both human and mouse breast cancer cells, along with decreasing the proportion of breast cancer stem cells (CSCs). Furthermore, BHS could suppress the M2 phenotype polarization of TAMs and therefore attenuate their CXCL1 expression and secretion. Notably, mechanistic investigations validated TAMs/CXCL1 as the crucial target of BHS in suppressing breast cancer metastasis as exogenous addition of CXCL1 significantly abrogated the anti-metastatic effect of BHS on breast cancer cells. Moreover, BHS was highly safe in vivo as it exhibited no observable embryotoxicity or teratogenic effect on zebrafish embryos. More importantly, BHS remarkably suppressed breast cancer metastasis and TAMs/CXCL1 activity in both zebrafish breast cancer xenotransplantation model and mouse breast cancer xenografts.

CONCLUSION

This study not only provides novel insights into TAMs/CXCL1 as a reliable screening target for anti-metastatic drug discovery, but also suggests BHS as a promising candidate drug for metastatic breast cancer treatment.

A new metabolic signature contributes to disease progression and predicts worse survival in melanoma

Metabolic reprogramming is a common hallmark of tumor cells and is a crucial mediator of resistance toward anticancer therapies. The pattern of a metabolism-related signature in melanoma remains unknown. Here, we explored the role of a multi-metabolism-related gene signature in melanoma.We used the training and validation sets to develop a multi-metabolism-related gene signature. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) method were used for constructing a model. The predictive role of the metabolic signature with clinicopathological features of melanoma was also analyzed. Functional analysis of this metabolic signature was also investigated.A ten metabolism-related gene signature was identified and can stratify melanoma into high- and low- risk groups. The signature was correlated with progressive T stage, Breslow thickness, Clark level, and worse survival (all Ps< 0.01). This metabolic signature was shown as an independent prognostic factor and was also a predictive indicator for worse survival in various clinical and molecular features of melanoma. Furthermore, the metabolic signature was implicated in immune responses such as the regulation of T cell activation and cytokine activity. The metabolic signaturewas associated with the progression and worse survival of melanoma. Our study offered a valuable metabolism-targeted therapy approach for melanoma.

Bioinformatics Analysis of Metabolomics Data Unveils Association of Metabolic Signatures with Methylation in Breast Cancer

Breast cancer (BC) contributes the highest global cancer mortality in women. BC tumors are highly heterogeneous, so subtyping by cell-surface markers is inadequate. Omics-driven tumor stratification is urgently needed to better understand BC and tailor therapies for personalized medicine. We used unsupervised k-means and partition around medoids (pam) to cluster metabolomics data from two data sets. The first comprised 271 BC tumors (data set 1) that were estrogen receptor (ER) positive (ER+, n = 204) or negative (ER-, n = 67) with 162 identified and validated metabolites. The second data set contained 67 BC samples (data set 2; ER+, n = 33; ER-, n = 34) and 352 known metabolites. Significance Analysis of Microarrays (SAM) was used to identify the most significant metabolites among these clusters, which were then reassigned into new clusters using prediction analysis of microarrays (PAM). Generally, metabolome-defined BC subtypes identified from either data set 1 or data set 2 were different from the well-known receptor- or transcriptome-defined subtypes. Metabolomics-directed clustering of data set 2 identified distinctive BC tumors characterized by metabolome profiles that associated with DNA methylation (p-value = 0.000 048, χ² test). Pathway analysis of cluster metabolites revealed that nitrogen metabolism and aminoacyl-tRNA biosynthesis were highly related to BC subtyping. The pipeline may be run from GitHub: https://github.com/FADHLyemen/Metabolomics_signature. Our proposed bioinformatics pipeline analyzed metabolomics data from BC tumors, revealing clusters characterized by unique metabolic signatures that may potentially stratify BC patients and tailor precision treatment.

CCL5 derived from tumor-associated macrophages promotes prostate cancer stem cells and metastasis via activating β-catenin/STAT3 signaling

Prostate cancer stem cells (PCSCs) play a critical role in prostate cancer progression and metastasis, which remains an obstacle for successful prostate cancer treatment. Tumor-associated macrophages (TAMs) are the most abundant immune cell population within the tumor microenvironment (TME). Systematic investigation of the interaction and network signaling between PCSCs and TAMs may help in searching for the critical target to suppress PCSCs and metastasis. Herein, we demonstrated that TAMs-secreted CCL5 could significantly promote the migration, invasion, epithelial–mesenchymal transition (EMT) of prostate cancer cells as well as the self-renewal of PCSCs in vitro. QPCR screening validated STAT3 as the most significant response gene in prostate cancer cells following CCL5 treatment. RNA-sequencing and mechanistic explorations further revealed that CCL5 could promote PCSCs self-renewal and prostate cancer metastasis via activating the β-catenin/STAT3 signaling. Notably, CCL5 knockdown in TAMs not only significantly suppressed prostate cancer xenografts growth and bone metastasis but also inhibited the self-renewal and tumorigenicity of PCSCs in vivo. Finally, clinical investigations and bioinformatic analysis suggested that high CCL5 expression was significantly correlated with high Gleason grade, poor prognosis, metastasis as well as increased PCSCs activity in prostate cancer patients. Taken together, TAMs/CCL5 could promote PCSCs self-renewal and prostate cancer metastasis via activating β-catenin/STAT3 signaling. This study provides a novel rationale for developing TAMs/CCL5 as a potential molecular target for PCSCs elimination and metastatic prostate cancer prevention.

Esculetin Inhibits Cancer Cell Glycolysis by Binding Tumor PGK2, GPD2, and GPI

Glycolysis can improve the tolerance of tissue cells to hypoxia, and its intermediates provide raw materials for the synthesis and metabolism of the tumor cells. If it can inhibit the activity of glycolysis-related enzymes and control the energy metabolism of tumor, it can be targeted for the treatment of malignant tumor. The target proteins phosphoglycerate kinase 2 (PGK2), glycerol-3-phosphate dehydrogenase (GPD2), and glucose-6-phosphate isomerase (GPI) were screened by combining transcriptome, proteomics, and reverse docking. We detected the binding constant of the active compound using microscale thermophoresis (MST). It was found that esculetin bound well with three potential target proteins. Esculetin significantly inhibited the rate of glycolysis, manifested by differences of cellular lactate production and glucose consumption in HepG2 cells with or without esculetin. It was found that GPD2 bound strongly to GPI, revealing the direct interaction between the two glycolysis-related proteins. Animal tests have further demonstrated that esculetin may have anticancer effects by affecting the activity of PGK2, GPD2, and GPI. The results of this study demonstrated that esculetin can affect the glucose metabolism by binding to glycolytic proteins, thus playing an anti-tumor role, and these proteins which have direct interactions are potential novel targets for tumor treatment by esculetin.

Evaluation of long-chain acyl-coenzyme A synthetase 4 (ACSL4) expression in human breast cancer

Background and purpose:

Breast cancer (BC) is one of the major causes of female cancer-related death. It has recently been demonstrated that metabolic reprogramming including alteration in lipid metabolism is indicated in various types of cancer. The enzymes of the acyl-coenzyme A synthetase long-chain family (ACSLs) are responsible for converting fatty acids to their corresponding fatty acyl-coenzyme A esters which are essential for some lipid metabolism pathways. ACSL4 is one of the isoforms of ACSLs and has a marked preference for arachidonic and eicosapentaenoic acids. The objective of this study was to evaluate ACSL4 expression, its prognostic significance, and its correlation with p53 tumor suppressor in BC patients.

Experimental approach:

In this study 55 pairs of fresh samples of BC and adjacent non-cancerous tissue were used to analyze ACSL4 expression, using real-time polymerase chain reaction and immunohistochemistry (IHC) staining. The expression of other studied variables was also examined using the IHC technique.

Findings / Results:

ACSL4 expression was significantly higher in BC tissues compared to the adjacent normal tissue. This upregulation was negatively correlated with Ki-67 and age, and positively correlated with p53 status. The correlation between ACSL4 and p53 may indicate the role of p53 in the regulation of lipid metabolism in cancer cells, in addition to its role in the regulation of ferroptosis cell death.

Conclusion and implications:

Our results indicated that the expression of ACSL4 may be considered as a prognostic indicator and potential therapeutic target in BC. However, further studies are needed to confirm the significance of these findings.

XIAOPI Formula Inhibits Breast Cancer Stem Cells via Suppressing Tumor-Associated Macrophages/C-X-C Motif Chemokine Ligand 1 Pathway

Macrophages are the most abundant stromal cells associated with the host immune system in multiple malignancies including breast cancer. With proven clinical efficacy and no noticeable adverse effects, XIAOPI formula (XPS) has been approved for breast hyperplasia treatment by the State Food and Drug Administration of China (SFDA) in 2018. The existing knowledge about the anti-breast cancer activities and mechanisms of XPS has been very limited. The present study aimed to investigate whether XPS could exert an anti-breast cancer effect by regulating tumor-associated macrophages (TAMs) in tumor microenvironment. Herein, breast cancer cells and TAMs were co-cultured using the transwell co-culture system to simulate the coexistence of them. XPS could significantly inhibit the proliferation, colony formation, breast cancer stem cells (CSCs) subpopulation, mammosphere formation abilities as well as stemness-related genes expression in both human and mouse breast cancer cells in the co-culture system. Additionally, XPS could suppress M2 phenotype polarization as well as C-X-C motif chemokine ligand 1 (CXCL1) expression and secretion of TAMs. Notably, further mechanistic explorations verified TAMs/CXCL1 as the critical target of XPS in inhibiting breast CSCs self-renewal in the co-culture system as the exogenous CXCL1 administration could abrogate the inhibitory effect of XPS on breast CSCs self-renewal. More importantly, XPS significantly inhibited mammary tumor growth, breast CSCs subpopulation, and TAMs/CXCL1 activity in mouse 4T1-Luc xenografts in vivo without any detectable side effects. Taken together, this study not only uncovers the immunomodulatory mechanism of XPS in treating breast cancer but also sheds novel insights into TAMs/CXCL1 as a potential molecular target for breast CSCs elimination.

Metabolite sensing and signaling in cell metabolism

Metabolite sensing is one of the most fundamental biological processes. During evolution, multilayered mechanisms developed to sense fluctuations in a wide spectrum of metabolites, including nutrients, to coordinate cellular metabolism and biological networks. To date, AMPK and mTOR signaling are among the best-understood metabolite-sensing and signaling pathways. Here, we propose a sensor-transducer-effector model to describe known mechanisms of metabolite sensing and signaling. We define a metabolite sensor by its specificity, dynamicity, and functionality. We group the actions of metabolite sensing into three different modes: metabolite sensor-mediated signaling, metabolite-sensing module, and sensing by conjugating. With these modes of action, we provide a systematic view of how cells sense sugars, lipids, amino acids, and metabolic intermediates. In the future perspective, we suggest a systematic screen of metabolite-sensing macromolecules, high-throughput discovery of biomacromolecule-metabolite interactomes, and functional metabolomics to advance our knowledge of metabolite sensing and signaling. Most importantly, targeting metabolite sensing holds great promise in therapeutic intervention of metabolic diseases and in improving healthy aging.

A simple, three-part model provides a systematic view of how cells sense sugars, lipids, amino acids and metabolic intermediates. Cells quickly and accurately perceive changes in intra- and extracellular molecules such as nutrients to respond to changing environments. Drawing on existing knowledge about AMPK and MTORC1 signaling, Yi-Ping Wang and Qun-Ying Lei at Fudan University in Shanghai propose a model in which three components: a sensor, transducer and effector enable metabolic sensing and signaling to proceed. The sensor detects the metabolite, and, through conjugation, conformational changes or protein–protein interactions, transmits this information to the transducer, which decides the appropriate response. The transducer then issues orders to effector proteins which coordinate the action. The future identification of novel metabolic sensors through systematic screening could lead to new therapeutic interventions for metabolic and age-related diseases.

Metabolic Reprogramming of Cancer Associated Fibroblasts: The Slavery of Stromal Fibroblasts

Cancer associated fibroblasts (CAFs) are the main stromal cell type of solid tumour microenvironment and undergo an activation process associated with secretion of growth factors, cytokines, and paracrine interactions. One of the important features of solid tumours is the metabolic reprogramming that leads to changes of bioenergetics and biosynthesis in both tumour cells and CAFs. In particular, CAFs follow the evolution of tumour disease and acquire a catabolic phenotype: in tumour tissues, cancer cells and tumour microenvironment form a network where the crosstalk between cancer cells and CAFs is associated with cell metabolic reprogramming that contributes to CAFs activation, cancer growth, and progression and evasion from cancer therapies. In this regard, the study of CAFs metabolic reprogramming could contribute to better understand their activation process, the interaction between stroma, and cancer cells and could offer innovative tools for the development of new therapeutic strategies able to eradicate the protumorigenic activity of CAFs. Therefore, this review focuses on CAFs metabolic reprogramming associated with both differentiation process and cancer and stromal cells crosstalk. Finally, therapeutic responses and potential anticancer strategies targeting CAFs metabolic reprogramming are reviewed.