Hesperidin PLGA nanoparticles potentiate the efficacy of aPD-1 in treating triple negative breast cancer by regulating CCL2 and ADPN expression in cancer-associated adipocytes.

Triple negative breast cancer (TNBC) represents a heterogeneous subtype of breast cancer characterized by an unfavorable prognosis due to its aggressive biology. Cancer-associated adipocytes (CAAs) play an active role in tumor development, invasion and metastasis, and response to treatment by secreting various cytokines. CAAs secrete CCL2 and ADPN which significantly affect the efficacy of aPD-1 in treating breast cancer. Our recent research has demonstrated that Hesperidin, a natural phenolic compound, significantly inhibits CCL2, elevates ADPN secreted by CAAs in vitro and in vivo, remodels the immune microenvironment, and potentiates the efficacy of aPD-1 in triple-negative breast cancer. We used Oil red staining, Bodipy 493/503 staining and quantitative real-time PCR to verify the formation of CAAs. ELISA was used to detect levels of CCL2, ADPN secreted by CAAs. Changes in the number of immune cells in mouse tumor tissues were detected using flow cytometry and immunofluorescence. Our data suggest that Hesperidin PLGA nanoparticles significantly reduced CCL2 and increased ADPN secreted by CAAs, which concurrently decreased the recruitment of M2 macrophages, Tregs and MDSCs while increased the infiltration of CD8+T cells, M1 macrophages and DCs into tumor, thus significantly potentiated the efficacy of aPD-1 in vivo. This study provides a new combined strategy for the clinical treatment of triple-negative breast cancer by interfering with CCL2, ADPN secreted by CAAs to enhance the efficacy of immunotherapy.

Loss of ACOX1 in clear cell renal cell carcinoma and its correlation with clinical features.

Clear cell renal cell carcinoma (ccRCC) is a major pathological type of kidney cancer with a poor prognosis due to a lack of biomarkers for early diagnosis and prognosis prediction of ccRCC. In this study, we investigated the aberrant expression of Acyl-coenzyme A oxidase 1 (ACOX1) in ccRCC and evaluated its potential in diagnosis and prognosis. ACOX1 is the first rate-limiting enzyme in the peroxidation ß-oxidation pathway and is involved in the regulation of fatty acid oxidative catabolism. The mRNA and protein levels of ACOX1 were significantly downregulated in ccRCC, and its downregulation was closely associated with the tumor-node-metastasis stage of patients. The ROC curves showed that ACOX1 possesses a high diagnostic value for ccRCC. The OS analysis suggested that lower expression of ACOX1 was closely related to the worse outcome of patients. In addition, gene set enrichment analysis suggested that expression of ACOX1 was positively correlated with CDH1, CDH2, CDKL2, and EPCAM, while negatively correlated with MMP9 and VIM, which strongly indicated that ACOX1 may inhibit the invasion and migration of ccRCC by reversing epithelial-mesenchymal transition. Furthermore, we screened out that miR-16-5p is upregulated at the mRNA transcript level in ccRCC and negatively correlated with ACOX1. In conclusion, our results showed that ACOX1 is abnormally low expressed in ccRCC, suggesting that it could serve as a diagnostic and prognostic biomarker for ccRCC. Overexpression of miR-16-5p may be responsible for the inactivation of ACOX1.

DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations.

Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functional regions at an allosteric site. In addition to the known effects of mutations around functional sites, mutations at allosteric sites have been associated with protein structure, dynamics, and energy communication. As a result, identifying driver mutations at allosteric sites will be beneficial for deciphering the mechanisms of cancer and developing allosteric drugs. In this study, we provided a platform called DeepAlloDriver to predict driver mutations using a deep learning method that exhibited >93% accuracy and precision. Using this server, we found that a missense mutation in RRAS2 (Gln72 to Leu) might serve as an allosteric driver of tumorigenesis, revealing the mechanism of the mutation in knock-in mice and cancer patients. Overall, DeepAlloDriver would facilitate the elucidation of the mechanisms underlying cancer progression and help prioritize cancer therapeutic targets. The web server is freely available at: https://mdl.shsmu.edu.cn/DeepAlloDriver.

Essential oil of Acorus tatarinowii Schott inhibits neuroinflammation by suppressing NLRP3 inflammasome activation in 3 × Tg-AD transgenic mice.

BACKGROUND: Shi chang pu (Acorus tatarinowii Schott) is a herbal used in the treatment of Alzheimer's disease (AD) in China. The essential oil of Shi chang pu (SCP-oil) is the main active component. However, its effects on the neuroinflammation of AD have not been well studied. PURPOSE: Neuroinflammation mediated by the NLRP3 inflammasome plays a crucial role in AD. This study was designed to evaluate the effect of SCP-oil on cognitive impairment of AppSwe/PSEN1M146V/MAPTP301L triple transgenic (3 × Tg-AD) mice model and investigate the mechanism underlying its anti-inflammation effects. METHODS: Thirty-two 3 × Tg-AD mice at 12 months and 8 wild-type B6 mice were used for this experiment. The 3 × Tg-AD mice were administered with SCP-oil or donepezil hydrochloride for 8 weeks. Morris water maze test and step-down test were used to evaluate the cognitive ability of mice. The pathological changes, neuroinflammation, and the NLRP3 inflammasome related-protein of AD mice were detected by histomorphological examination, TUNEL staining, immunofluorescence, immunohistochemistry, qRT-PCR, Elisa, and western blot assays. RESULTS: SCP-oil treatment attenuated cognitive dysfunction of 3 × Tg-AD mice. Moreover, SCP-oil also ameliorated characteristics pathological of AD, such as pathological changes damage, deposition of Aß, phosphorylation of Tau, and neuronal loss. Additionally, SCP-oil treatment alleviated the neuroinflammation and inhibited phosphorylation of IKKß, NF-κB, and NLRP3 inflammasome related-protein NLRP3, ASC, Caspase-1, cleaved-Caspase-1, and GSDMD-N in the hippocampus of 3 × Tg-AD mice. CONCLUSION: Overall, SCP-oil contributed to neuroprotection in 3 × Tg-AD mice by reduced activation of NLRP3 inflammasome by inhibiting the NF-κB signaling pathway.

An Autophagy-Associated Prognostic Gene Signature for Breast Cancer.

Autophagy is closely related to breast cancer and has the dual role of promoting and inhibiting the progression of breast cancer. In this study, we aimed to establish an autophagy-related gene signature for the prognosis of breast cancer. A gene signature composed of the eight most survival-relevant autophagy-associated genes was identified by least absolute shrinkage and selection operator (LASSO) regression analysis. A risk score was calculated based on the gene signature, which divided breast cancer patients into low- or high-risk groups and showed good and poor prognosis, respectively. The risk score displayed good prognostic performance in both the training cohort (TCGA, 1-10-year AUC > 0.63) and the validation cohort (GEO, 1-10-year AUC > 0.66). The multivariate Cox regression and stratified analysis revealed that the risk score was an independent prognostic factor for breast cancer patients. Moreover, the high-risk score was associated with higher infiltration of neutrophils and M2-polarized macrophages, and lower infiltration of resting memory CD4+ T cells, CD8+ T cells, and NK cells. Finally, the high-risk score was associated with myc target, glycolysis, and mTORC1 signaling. The risk score developed based on the autophagy-associated gene signature was an independent prognostic biomarker for breast cancer.

A Cluster of Metabolic-Related Genes Serve as Potential Prognostic Biomarkers for Renal Cell Carcinoma.

Renal cell carcinoma (RCC) is the most common type of renal cancer, characterized by the dysregulation of metabolic pathways. RCC is the second highest cause of death among patients with urologic cancers and those with cancer cell metastases have a 5-years survival rate of only 10-15%. Thus, reliable prognostic biomarkers are essential tools to predict RCC patient outcomes. This study identified differentially expressed genes (DEGs) in the gene expression omnibus (GEO) database that are associated with pre-and post-metastases in clear cell renal cell carcinoma (ccRCC) patients and intersected these with metabolism-related genes in the Kyoto encyclopedia of genes and genomes (KEGG) database to identify metabolism-related DEGs (DEMGs). GOplot and ggplot packages for gene ontology (GO) and KEGG pathway enrichment analysis of DEMGs with log (foldchange) (logFC) were used to identify metabolic pathways associated with DEMG. Upregulated risk genes and downregulated protective genes among the DEMGs and seven independent metabolic genes, RRM2, MTHFD2, AGXT2, ALDH6A1, GLDC, HOGA1, and ETNK2, were found using univariate and multivariate Cox regression analysis, intersection, and Lasso-Cox regression analysis to establish a metabolic risk score signature (MRSS). Kaplan-Meier survival curve of Overall Survival (OS) showed that the low-risk group had a significantly better prognosis than the high-risk group in both the training cohort (p < 0.001; HR = 2.73, 95% CI = 1.97-3.79) and the validation cohort (p = 0.001; HR = 2.84, 95% CI = 1.50-5.38). The nomogram combined with multiple clinical information and MRSS was more effective at predicting patient outcomes than a single independent prognostic factor. The impact of metabolism on ccRCC was also assessed, and seven metabolism-related genes were established and validated as biomarkers to predict patient outcomes effectively.