The tumor-adipose microenvironment (TAME) is a universal microecosystem, that is characterized by the dysfunction of lipid metabolism, such as excessive free fatty acids (FFAs). Macrophages are the most abundant immune cell type within TAME, although their diversity in the TAME is not clear. We first reveal that infiltration of M2-like macrophages in the TAME is associated with poor survival in breast cancer. To explore lipid-associated alterations in the TAME, we also detected the levels of FFAs transporters including fatty acid binding proteins (FABPs) and fatty acid transport protein 1 (FATP1). The results indicated that expression of fatty acid transporters in the TAME is tightly linked to the function of macrophages and predicts survival in breast cancer. To explore the impact of FFAs transporters on the function of macrophages, we performed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics. Consequently, we identified a special subpopulation of macrophages defined as lipid-associated macrophages (LAMs), highly expressed macrophage markers (CD163, SPP1 and C1QC), genes involved in lipid metabolism (FABP3, FABP4, FABP5, LPL and LIPA) and some lipid receptors (LGALS3 and TREM2). Functionally, LAMs were characterized by a canonical functional signature of M2-like macrophages, lipid accumulation and enhancing phagocytosis, and they were mostly distributed in tumor-adipose junctional regions. Finally, the allograft cancer mouse models confirmed that LAMs depletion in the TAME synergizes the antitumorigenic effects of anti-PD1 therapy. In summary, we defined a novel subtype of macrophages in the TAME, that has unique features and clinical outcomes.
Abdominal aortic aneurysm (AAA) is a progressive chronic dilatation of the abdominal aorta without effective medical treatment. This study aims to clarify the potential of long non-coding RNA SENCR as a treatment target in AAA. Angiotensin II (Ang-II) was used to establish AAA mouse model as well as a cell model based on the mouse aortic vascular smooth muscle cells (VSMCs). Reverse transcription quantitative PCR and western blot were performed to measure the expression of SENCR and proteins, respectively. Apoptotic rate in VSMCs was determined using Annexin V-FITC/PI double staining, and cell apoptosis in aortic tissues was determined by TUNEL staining. Hematoxylin and eosin and Elastica van Gieson staining were used for histological analysis of aortic tissues. SENCR was downregulated in AAA tissues and Ang-II-stimulated VSMCs. Overexpression of SENCR inhibited Ang-II-induced VSMC apoptosis, while inhibition of SENCR facilitated VSMC apoptosis. Moreover, overexpression of SENCR suppressed matrix metalloproteinase (MMP)-2 and MMP-9 expression and promoted tissue inhibitor of metalloproteinases 1 (TIMP-1) expression in Ang-II-induced VSMCs, while inhibition of SENCR expression led to the opposite results. In vivo, overexpressed SENCR improved the pathological change in aortic tissues and the damage in arterial wall elastic fibres induced by Ang-II, as well as it suppressed Ang-II-induced cell apoptosis and extracellular matrix degradation in aortic tissues. Overall, overexpression of SENCR inhibited AAA formation via suppressing VSMC apoptosis and extracellular matrix degradation. We provided a reliable evidence for SENCR acting as a potential target for AAA treatment.
Background: This study aimed to develop and validate a nomogram for predicting mortality in patients with thoracic fractures without neurological compromise and hospitalized in the intensive care unit.Methods: A total of 298 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included in the study, and 35 clinical indicators were collected within 24 h of patient admission. Risk factors were identified using the least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was established, and a nomogram was constructed. Internal validation was performed by the 1,000 bootstrap samples; a receiver operating curve (ROC) was plotted, and the area under the curve (AUC), sensitivity, and specificity were calculated. In addition, the calibration of our model was evaluated by the calibration curve and Hosmer-Lemeshow goodness-of-fit test (HL test). A decision curve analysis (DCA) was performed, and the nomogram was compared with scoring systems commonly used during clinical practice to assess the net clinical benefit.Results: Indicators included in the nomogram were age, OASIS score, SAPS II score, respiratory rate, partial thromboplastin time (PTT), cardiac arrhythmias, and fluid-electrolyte disorders. The results showed that our model yielded satisfied diagnostic performance with an AUC value of 0.902 and 0.883 using the training set and on internal validation. The calibration curve and the Hosmer-Lemeshow goodness-of-fit (HL). The HL tests exhibited satisfactory concordance between predicted and actual outcomes (P = 0.648). The DCA showed a superior net clinical benefit of our model over previously reported scoring systems.Conclusion: In summary, we explored the incidence of mortality during the ICU stay of thoracic fracture patients without neurological compromise and developed a prediction model that facilitates clinical decision making. However, external validation will be needed in the future.
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