RARRES2 regulates lipid metabolic reprogramming to mediate the development of brain metastasis in triple negative breast cancer

YTHDF2 favors protumoral macrophage polarization and implies poor survival outcomes in triple negative breast cancer

Patients with triple-negative breast cancer (TNBC) frequently experience resistance to chemotherapy, leading to recurrence. The approach of optimizing anti-tumoral immunological effect is promising in overcoming such resistance, given the heterogeneity and lack of biomarkers in TNBC. In this study, we focused on YTHDF2, an N6-methyladenosine (m6A) RNA-reader protein, in macrophages, one of the most abundant intra-tumoral immune cells. Using single-cell sequencing and ex vivo experiments, we discovered that YTHDF2 significantly promotes pro-tumoral phenotype polarization of macrophages and is closely associated with down-regulated antigen-presentation signaling to other immune cells in TNBC. The in vitro deprivation of YTHDF2 favors anti-tumoral effect. Expressions of multiple transcription factors, especially SPI1, were consistently observed in YTHDF2-high macrophages, providing potential therapeutic targets for new strategies. In conclusion, YTHDF2 in macrophages appears to promote pro-tumoral effects while suppressing immune activity, indicating the treatment targeting YTHDF2 or its transcription factors could be a promising strategy for chemoresistant TNBC.

Targeting fatty acid synthase in preclinical models of TNBC brain metastases synergizes with SN-38 and impairs invasion

Fatty acid synthesis (FAS) has been shown to play a key role in the survival of brain-metastatic (BM) breast cancer. We demonstrate that the fatty acid synthase inhibitor TVB-2640 synergizes with the topoisomerase inhibitor SN-38 in triple-negative breast cancer (TNBC) BM cell lines, upregulates FAS and downregulates cell cycle progression gene expression, and slows the motility of TNBC BM cell lines. The combination of SN-38 and TVB-2640 warrants further consideration as a potential therapeutic option in TNBC BMs.

Fbxo45 facilitates the malignant progression of breast cancer by targeting Bim for ubiquitination and degradation

BACKGROUND

Breast cancer is one of the common malignancies in women. Evidence has demonstrated that FBXO45 plays a pivotal role in oncogenesis and progression. However, the role of FBXO45 in breast tumorigenesis remains elusive. Exploration of the regulatory mechanisms of FBXO45 in breast cancer development is pivotal for potential therapeutic interventions in patients with breast cancer.

METHODS

Hence, we used numerous approaches to explore the functions of FBXO45 and its underlaying mechanisms in breast cancer pathogenesis, including CCK-8 assay, EdU assay, colony formation analysis, apoptosis assay, RT-PCR, Western blotting, immunoprecipitation, ubiquitination assay, and cycloheximide chase assay.

RESULTS

We found that downregulation of FBXO45 inhibited cell proliferation, while upregulation of FBXO45 elevated cell proliferation in breast cancer. Silencing of FBXO45 induced cell apoptosis, whereas overexpression of FBXO45 inhibited cell apoptosis in breast cancer. Moreover, FBXO45 interacted with BIM and regulated its ubiquitination and degradation. Furthermore, knockdown of FBXO45 inhibited cell proliferation via regulation of BIM pathway. Notably, overexpression of FBXO45 facilitated tumor growth in mice. Strikingly, FBXO45 expression was associated with poor survival of breast cancer patients.

CONCLUSION

Our study could provide the rational for targeting FBXO45 to obtain benefit for breast cancer patients. Altogether, modulating FBXO45/Bim axis could be a promising strategy for breast cancer therapy.

The impact of lipidome on breast cancer: a Mendelian randomization study

OBJECTIVE

This study aims to investigate the association between specific lipidomes and the risk of breast cancer (BC) using the Two-Sample Mendelian Randomization (TSMR) approach and Bayesian Model Averaging Mendelian Randomization (BMA-MR) method.

METHOD

The study analyzed data from large-scale GWAS datasets of 179 lipidomes to assess the relationship between lipidomes and BC risk across different molecular subtypes. TSMR was employed to explore causal relationships, while the BMA-MR method was carried out to validate the results. The study assessed heterogeneity and horizontal pleiotropy through Cochran's Q, MR-Egger intercept tests, and MR-PRESSO. Moreover, a leave-one-out sensitivity analysis was performed to evaluate the impact of individual single nucleotide polymorphisms on the MR study.

RESULTS

By examining 179 lipidome traits as exposures and BC as the outcome, the study revealed significant causal effects of glycerophospholipids, sphingolipids, and glycerolipids on BC risk. Specifically, for estrogen receptor-positive BC (ER+ BC), phosphatidylcholine (P < 0.05) and phosphatidylinositol (OR: 0.916-0.966, P < 0.05) within glycerophospholipids play significant roles, along with the importance of glycerolipids (diacylglycerol (OR = 0.923, P < 0.001) and triacylglycerol, OR: 0.894-0.960, P < 0.05)). However, the study did not observe a noteworthy impact of sphingolipids on ER+BC. In the case of estrogen receptor-negative BC (ER- BC), not only glycerophospholipids, sphingolipids (OR = 1.085, P = 0.008), and glycerolipids (OR = 0.909, P = 0.002) exerted an influence, but the protective effect of sterols (OR: 1.034-1.056, P < 0.05) was also discovered. The prominence of glycerolipids was minimal in ER-BC. Phosphatidylethanolamine (OR: 1.091-1.119, P < 0.05) was an important causal effect in ER-BC.

CONCLUSIONS

The findings reveal that phosphatidylinositol and triglycerides levels decreased the risk of BC, indicating a potential protective role of these lipid molecules. Moreover, the study elucidates BC's intricate lipid metabolic pathways, highlighting diverse lipidome structural variations that may have varying effects in different molecular subtypes.

Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma

BACKGROUND

Fatty acids synthesis and metabolism (FASM)-driven lipid mobilization is essential for energy production during nutrient shortages. However, the molecular characteristics, physiological function and clinical prognosis value of FASM-associated gene signatures in hepatocellular carcinoma (HCC) remain elusive.

METHODS

The Gene Expression Omnibus database (GEO), the Cancer Genome Atlas (TCGA), and International Cancer Genome Consortium (ICGC) database were utilized to acquire transcriptome data and clinical information of HCC patients. The ConsensusClusterPlus was employed for unsupervised clustering. Subsequently, immune cell infiltration, stemness index and therapeutic response among distinct clusters were decoded. The tumor immune dysfunction and exclusion (TIDE) algorithm was utilized to anticipate the response of patients towards immunotherapy, and the genomics of drug sensitivity in cancer (GDSC) tool was employed to predict their response to antineoplastic medications. Least absolute shrinkage and selection operator (LASSO) regression analysis and protein-protein interaction (PPI) network were employed to construct prognostic model and identity hub gene. Single cell RNA sequencing (scRNA-seq) and CellChat were used to analyze cellular interactions. The hub gene of FASM effect on promoting tumor progression was confirmed through a series of functional experiments.

RESULTS

Twenty-six FASM-related genes showed differential expression in HCC. Based on these FASM-related differential genes, two molecular subtypes were established, including Cluster1 and Cluster2 subtype. Compared with cluster2, Cluster1 subtype exhibited a worse prognosis, higher risk, higher immunosuppressive cells infiltrations, higher immune escape, higher cancer stemness and enhanced treatment-resistant. PPI network identified Acetyl-CoA carboxylase1 (ACACA) as central gene of FASM and predicted a poor prognosis. A strong interaction between cancer stem cells (CSCs) with high expression of ACACA and macrophages through CD74 molecule (CD74) and integrin subunit beta 1 (ITGB1) signaling was identified. Finally, increased ACACA expression was observed in HCC cells and patients, whereas depleted ACACA inhibited the stemness straits and drug resistance of HCC cells.

CONCLUSIONS

This study provides a resource for understanding FASM heterogeneity in HCC. Evaluating the FASM patterns can help predict the prognosis and provide new insights into treatment response in HCC patients.

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

Therapeutic protein PAK restrains the progression of triple negative breast cancer through degrading SREBP-1 mRNA

Triple-negative breast cancer (TNBC) represents the most challenging subtype of breast cancer. Studies have implicated an upregulation of lipid synthesis pathways in the initiation and progression of TNBC. Targeting lipid synthesis pathways may be a promising therapeutic strategy for TNBC. Our previous study developed a therapeutic protein PAK with passive targeting and inhibiting tumor proliferation. In this study, we further substantiate the efficacy of PAK in TNBC. Transcriptome sequencing analysis revealed PAK-mediated downregulation of genes involved in fatty acid synthesis, including key genes like SREBP-1, FASN, and SCD1. RNA immunoprecipitation experiments demonstrated a significant binding affinity of PAK to SREBP-1 mRNA, facilitating its degradation process. Both in vitro and in vivo models, PAK hampered TNBC progression by downregulating lipid synthesis pathways. In conclusion, this study emphasizes that PAK inhibits the progression of TNBC by binding to and degrading SREBP-1 mRNA, revealing a new strategy for regulating lipid synthesis in the intervention of TNBC and its therapeutic significance.