Phosphatidylethanolamine (18:2e/18:2) may inhibit adipose tissue wasting in patients with cancer cachexia by increasing lysophosphatidic acid receptor 6.

BACKGROUND: Cancer associated cachexia is characterized by the significant loss of adipose tissue, leading to devastating weight loss and muscle wasting in the majority of cancer patients. The effects and underlying mechanisms of degradation metabolites on adipocytes in cachectic patients remain poorly understood. To address this knowledge gap, we conducted a comprehensive study combining lipidomic analysis of subcutaneous and visceral adipose tissue with transcriptomics data from the database to investigate the mechanisms of lipid regulation in adipocytes. METHODS: We collected subcutaneous and visceral adipose tissue samples from cachectic and noncachectic cancer patients. Lipidomic analysis was performed to identify differentially expressed lipids in both types of adipose tissue. Additionally, transcriptomics data from the GEO database were analyzed to explore gene expression patterns in adipocytes. Bioinformatics analysis was employed to determine the enrichment of differentially expressed genes in specific pathways. Furthermore, molecular docking studies were conducted to predict potential protein targets of specific lipids, with a focus on the PI3K-Akt signaling pathway. Western blot analysis was used to validate protein levels of the identified target gene, lysophosphatidic acid receptor 6 (LPAR6), in subcutaneous and visceral adipose tissue from cachectic and noncachectic patients. RESULTS: Significant lipid differences in subcutaneous and visceral adipose tissue between cachectic and noncachectic patients were identified by multivariate statistical analysis. Cachectic patients exhibited elevated Ceramides levels and reduced CerG2GNAc1 levels (P < 0.05). A total of 10 shared lipids correlated with weight loss and IL-6 levels, enriched in Sphingolipid metabolism, GPI-anchor biosynthesis, and Glyceropholipid metabolism pathways. LPAR6 expression was significantly elevated in both adipose tissues of cachectic patients (P < 0.05). Molecular docking analysis indicated strong binding of Phosphatidylethanolamine (PE) (18:2e/18:2) to LPAR6. CONCLUSIONS: Our findings suggest that specific lipids, including PE(18:2e/18:2), may mitigate adipose tissue wasting in cachexia by modulating the expression of LPAR6 through the PI3K-Akt signaling pathway. The identification of these potential targets and mechanisms provides a foundation for future investigations and therapeutic strategies to combat cachexia. By understanding the underlying lipid regulation in adipocytes, we aim to develop targeted interventions to ameliorate the devastating impact of cachexia on patient outcomes and quality of life. Nevertheless, further studies and validation are warranted to fully elucidate the intricate mechanisms involved and translate these findings into effective clinical interventions.

Elevated 18F-FDG uptake in subcutaneous adipose tissue correlates negatively with nutritional status and prognostic survival in cachexia patients with gastric cancer.

BACKGROUND: Browning of white adipose tissue is a crucial factor contributing to adipose loss in cachexia patients, detectable via 18F-Fluorodeoxyglucose (18F-FDG) uptake. The present study elucidates the clinical relevance of 18F-FDG uptake in the subcutaneous adipose tissue of gastric cancer patients, specifically focusing on adipose browning and its implications on patient clinical parameters and prognosis. METHODS: This investigation encompassed 770 gastric cancer patients, with PET-CT imaging and clinical data meticulously combined. The 18F-FDG uptake in subcutaneous adipose tissue at the third lumbar layer was quantified, and its correlation with clinical parameters, particularly those related to nutritional status and fat metabolism, was examined. Kaplan-Meier curves were subsequently employed to probe the relationship between 18F-FDG uptake and overall survival. RESULTS: Of the 770 gastric cancer patients, 252 exhibited cancer-associated cachexia, while 518 did not. Cachectic patients demonstrated elevated 18F-FDG uptake in subcutaneous adipose tissue relative to non-cachectic patients (P < 0.001). Increased 18F-FDG uptake was also correlated with reduced plasma concentrations of albumin, prealbumin, hemoglobin, platelets, cholesterol, apolipoprotein A, low-density lipoprotein, and elevated IL-6 concentrations (all P < 0.05). A significant inverse correlation was observed between 18F-FDG uptake and BMI, albumin, low-density lipoprotein, cholesterol, and apolipoprotein A (all P < 0.05). Patients with higher 18F-FDG uptake exhibited diminished overall survival rates compared to those with lower 18F-FDG uptake (P = 0.0065). Furthermore, 18F-FDG uptake in subcutaneous adipose tissue was an independent prognostic indicator in gastric cancer patients (P = 0.028). CONCLUSIONS: Browning of subcutaneous adipose tissue was markedly elevated in cachectic gastric cancer patients compared to non-cachectic counterparts. Increased 18F-FDG uptake in subcutaneous adipose tissue in cachectic gastric cancer patients was inversely correlated with nutritional status and survival prognosis.

Traumatic inflammatory response: pathophysiological role and clinical value of cytokines.

Severe trauma is an intractable problem in healthcare. Patients have a widespread immune system response that is complex and vital to survival. Excessive inflammatory response is the main cause of poor prognosis and poor therapeutic effect of medications in trauma patients. Cytokines are signaling proteins that play critical roles in the body's response to injuries, which could amplify or suppress immune responses. Studies have demonstrated that cytokines are closely related to the severity of injuries and prognosis of trauma patients and help present cytokine-based diagnosis and treatment plans for trauma patients. In this review, we introduce the pathophysiological mechanisms of a traumatic inflammatory response and the role of cytokines in trauma patients. Furthermore, we discuss the potential of cytokine-based diagnosis and therapy for post-traumatic inflammatory response, although further clarification to elucidate the underlying mechanisms of cytokines following trauma is warranted.

Mitochondrial antioxidant SkQ1 decreases inflammation following hemorrhagic shock by protecting myocardial mitochondria.

Background: Hemorrhagic shock (HS) is a type of hypovolemic shock characterized by hemodynamic instability, tissue hypoperfusion and cellular hypoxia. In pathophysiology, the gradual accumulation of reactive oxygen species (ROS) damages the mitochondria, leading to irreversible cell damage and the release of endogenous damage-associated molecular patterns (DAMPs) including mitochondrial DAMPs (MTDs), eventually triggering the inflammatory response. The novel mitochondria-targeted antioxidant SkQ1 (Visomitin) effectively eliminate excessive intracellular ROS and exhibits anti-inflammatory effects; however, the specific role of SkQ1 in HS has not yet been explicated. Methods and results: A 40% fixed-blood-loss HS rat model was established in this study. Transmission electron microscopy showed that after HS, the myocardial mitochondrial ultrastructure was damaged and the mtDNA release in circulation was increased and the differentially expressed genes were significantly enriched in mitochondrial and ROS-related pathways. Mitochondria-targeted antioxidant SkQ1 attenuated the increased ROS induced by HS in myocardial tissues and by oxygen-glucose deprivation (OGD) in cardiomyocytes. Ultrastructurally, SkQ1 protected the myocardial mitochondrial structure and reduced the release of the peripheral blood mtDNA after HS. RNA-seq transcriptome analysis showed that 56.5% of the inflammation-related genes, which altered after HS, could be significantly reversed after SkQ1 treatment. Moreover, ELISA indicated that SkQ1 significantly reversed the HS-induced increases in the TNF-α, IL-6, and MCP-1 protein levels in rat peripheral blood. Conclusion: HS causes damage to the rat myocardial mitochondrial structure, increases mtDNA release and ROS contents, activates the mitochondrial and ROS-related pathways, and induces systemic inflammatory response. The mitochondrial antioxidant SkQ1 can improve rat myocardial mitochondria ultrastructure, reduce mtDNA and ROS contents, and decrease inflammation by protecting myocardial mitochondria, thereby playing a novel protective role in HS.

Single-cell sequencing unveils key contributions of immune cell populations in cancer-associated adipose wasting.

Adipose tissue loss seen with cancer-associated cachexia (CAC) may functionally drive cachexia development. Using single-cell transcriptomics, we unveil a large-scale comprehensive cellular census of the stromal vascular fraction of white adipose tissues from patients with or without CAC. We report depot- and disease-specific clusters and developmental trajectories of adipose progenitors and immune cells. In adipose tissues with CAC, clear pro-inflammatory transitions were discovered in adipose progenitors, macrophages and CD8+ T cells, with dramatically remodeled cell interactome among these cells, implicating a synergistic effect in promoting tissue inflammation. Remarkably, activated CD8+ T cells contributed specifically to increased IFNG expression in adipose tissues from cachexia patients, and displayed a significant pro-catabolic effect on adipocytes in vitro; whereas macrophage depletion resulted in significantly rescued adipose catabolism and alleviated cachexia in a CAC animal model. Taken together, these results unveil causative mechanisms underlying the chronical inflammation and adipose wasting in CAC.

Dense correspondence of deformable volumetric images via deep spectral embedding and descriptor learning.

Deformable image correspondence plays an essential role in a variety of medical image analysis tasks. Most existing deep learning-based registration and correspondence techniques exploit metric space alignments in the spatial domain and learn a nonlinear voxel-wise mapping function between volumetric images and displacement fields, agnostic to intrinsic structure correspondence. When confronted with high-frequency perturbations of patients' poses and anatomical structural variations, they relied on prior rigid and affine transformations, as well as additional segmentation masks and landmark annotations for reliable registration. This paper presents a data-driven spectral mapping-based correspondence framework to handle the intrinsic correspondence of anatomical structures. At the core of our approach lies a deep convolutional framework that approximates spectral bases and optimizes volumetric descriptors. The multi-path graph convolutional network-based spectral embedding approximation module relieves the computationally expensive eigendecomposition-based embedding of volumetric images. The deep descriptor learning module surpasses the prior hand-crafted descriptors and the descriptor selection. We showcase the efficacy of the core modules, i.e., the spectral embedding approximation and descriptor learning, for volumetric image correspondence and the atlas-based registration on two volumetric image datasets. The proposed method achieves comparable correspondence accuracy with the state-of-the-art deep registration models, resilient to pose and shape perturbations.

Deep Volumetric Descriptor Learning for Dense Correspondence of Cone-Beam Computed Tomography via Spectral Maps.

The deep neural network has achieved great success in 3D volumetric correspondence. These methods infer the dense displacement or velocity fields directly from the extracted volumetric features without addressing the intrinsic structure correspondence, being prone to shape and pose variations. On the other hand, the spectral maps address the intrinsic structure matching in the low dimensional embedding space, remain less involved in volumetric image correspondence. This paper presents an unsupervised deep volumetric descriptor learning neural network via the low dimensional spectral maps to address the dense volumetric correspondence. The neural network is optimized by a novel criterion on descriptor alignments in the spectral domain regarding the supervoxel graph. Aside from the deep convolved multi-scale features, we explicitly address the supervoxel-wise spatial and cross-channel dependencies to enrich deep descriptors. The dense volumetric correspondence is formulated as the low-dimensional spectral mapping. The proposed approach has been applied to both synthetic and clinically obtained cone-beam computed tomography images to establish dense supervoxel-wise and up-scaled voxel-wise correspondences. Extensive series of experimental results demonstrate the contribution of the proposed approach in volumetric descriptor extraction and consistent correspondence, facilitating attribute transfer for segmentation and landmark location. The proposed approach performs favorably against the state-of-the-art volumetric descriptors and the deep registration models, being resilient to pose or shape variations and independent of the prior transformations.

Unsupervised random forest for affinity estimation.

This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster compactness. The binary forest-based metric is extended to continuous metrics by exploiting both the common traversal path and the smallest shared parent node. The proposed forest-based metric efficiently estimates affinity by passing down data pairs in the forest using a limited number of decision trees. A pseudo-leaf-splitting (PLS) algorithm is introduced to account for spatial relationships, which regularizes affinity measures and overcomes inconsistent leaf assign-ments. The random-forest-based metric with PLS facilitates the establishment of consistent and point-wise correspondences. The proposed method has been applied to automatic phrase recognition using color and depth videos and point-wise correspondence. Extensive experiments demonstrate the effectiveness of the proposed method in affinity estimation in a comparison with the state-of-the-art.

Identification of an Immune-Related LncRNA Signature in Gastric Cancer to Predict Survival and Response to Immune Checkpoint Inhibitors.

Immune microenvironment in gastric cancer is closely associated with patient's prognosis. Long non-coding RNAs (lncRNAs) are emerging as key regulators of immune responses. In this study, we aimed to construct a prognostic model based on immune-related lncRNAs (IRLs) to predict the overall survival and response to immune checkpoint inhibitors (ICIs) of gastric cancer (GC) patients. The IRL signature was constructed through a bioinformatics method, and its predictive capability was validated. A stratification analysis indicates that the IRL signature can distinguish different risk patients. A nomogram based on the IRL and other clinical variables efficiently predicted the overall survival of GC patients. The landscape of tumor microenvironment and mutation status partially explain this signature's predictive capability. We found the level of cancer-associated fibroblasts, endothelial cells, M2 macrophages, and stroma cells was high in the high-risk group, while the number of CD8+ T cells and T follicular helper cells was high in the low-risk group. Immunophenoscore (IPS) is validated for ICI response, and the IRL signature low-risk group received higher IPS, representing a more immunogenic phenotype that was more inclined to respond to ICIs. In addition, we found RNF144A-AS1 was highly expressed in GC patients and promoted the proliferation, migration, and invasive capacity of GC cells. We concluded that the IRL signature represents a novel useful model for evaluating GC survival outcomes and could be implemented to optimize the selection of patients to receive ICI treatment.

miR-410-3P inhibits adipocyte differentiation by targeting IRS-1 in cancer-associated cachexia patients.

BACKGROUNDS: Cancer-associated cachexia (CAC) is a metabolic syndrome characterized by progressive depletion of adipose and muscle tissue that cannot be corrected by conventional nutritional therapy. Adipose tissue, an important form of energy storage, exhibits marked loss in the early stages of CAC, which affects quality of life and efficacy of chemotherapy. MicroRNAs (miRNAs) are a class of noncoding RNAs that widely exist in all kinds of eukaryotic cells and play regulatory roles in various biological processes. However, the role of miRNAs in adipose metabolism in CAC has rarely been reported. This study attempted to identify important miRNAs in adipose metabolism in CAC and explore their mechanism to identify a new predictive marker or therapeutic target for CAC-related adipose tissue loss (CAL). METHODS: In this study, miRNA sequencing was firstly used to identify differentially expressed miRNAs related to CAL and the reliability of the conclusions was verified in large population samples. Furthermore, functional experiments were performed by up and down regulating miR-410-3p in adipocytes. The binding of miR-410-3p to Insulin Receptor Substrate 1 (IRS-1) was verified by Luciferase reporter assay and functional experiments of IRS-1 were performed in adipocytes. Finally, the expression of miR-410-3p in serum exosomes was detected. RESULTS: miR-410-3p was selected as differentially expressed miRNA through screening and validation. Adipogenesis was suppressed in miR-410-3p upregulation experiment and increased in downregulation experiment. Luciferase reporter assay showed that miR-410-3p binds to 3' non-coding region of IRS-1 and represses its expression and ultimately inhibits adipogenesis. miR-410-3p was highly expressed in serum exosomes of CAC patients, which was consistent with results in adipose tissue. CONCLUSIONS: The expression of miR-410-3p was higher in subcutaneous adipose tissues and serum exosomes of CAC patients, which significantly inhibits adipogenesis and lipid accumulation. The study shows that miR-410-3p could downregulate IRS-1 and downstream adipose differentiation factors including C/EBP-a and PPAR-γ by targeting 3' noncoding region.

Novel noncoding RNA CircPTK2 regulates lipolysis and adipogenesis in cachexia.

OBJECTIVE: Cancer-associated cachexia is a devastating pathological disorder characterized by skeletal muscle wasting and fat storage depletion. Circular RNA, a newly discovered class of noncoding RNAs with important roles in regulating lipid metabolism, has not been fully understood in the pathology of cachexia. We aimed to identify circular RNAs that are upregulated in adipose tissues from cachectic patients and explore their function and mechanism in lipid metabolism. METHODS: Whole transcriptome RNA sequencing was used to screen for differentially expressed circRNAs. Quantitative reverse transcription PCR was applied to detect the expression level of circPTK2 in adipose tissues. The diagnostic value of circPTK2 was evaluated in adipose tissues from patients with and without cachexia. Then, function experiments in vitro and in vivo were performed to evaluate the effects of circPTK2 on lipolysis and adipogenesis. Mechanistically, luciferase reporter assay, RNA immunoprecipitation, and fluorescent in situ hybridization were performed to confirm the interaction between circPTK2 and miR-182-5p in adipocytes. RESULTS: We detected 66 differentially expressed circular RNA candidates and proved that circPTK2 was upregulated in adipose tissues from cachectic patients. Then we identified that circPTK2 was closely related to the pathological process of cachexia and could be used as a diagnostic marker. Mechanistically, circPTK2 bound competitively to miR-182-5p and abrogated the suppression on its target gene JAZF1, which finally led to promotion of lipolysis and inhibition of adipogenesis. In vivo experiments demonstrated that overexpression of circPTK2 inhibited adipogenesis and enhanced lipolysis. CONCLUSIONS: Our findings reveal the novel role of circPTK2 in promoting lipolysis and reducing adipogenesis via a ceRNA mechanism and provide a potential diagnostic biomarker and therapeutic target for cancer-associated cachexia.

Multiple Antigenic Peptide System Coupled with Amyloid Beta Protein Epitopes As An Immunization Approach to Treat Alzheimer's Disease.

Latest studies suggest that Alzheimer's disease (AD) is one of the "four big killers" that threaten the health of the elderly. AD affects about 46 million people across the world, and there is a critical need for research on new therapies for treating AD. The purpose of the present study was to develop and evaluate immunogens to elicit antibodies against the formation of amyloid beta protein (Aß), a pathological hallmark of AD, as a therapeutic strategy in AD. In this study, serial potential immunogenic epitopes were screened according to the Aß sequence. The screened linear epitopes on the Aß C-terminal fragment were coupled with either the carrier protein keyhole limpet hemocyanin (KLH) or the synthesized 4-branch peptide (MAP4). MAP4 immunogens could effectively elicit immunogenicity against Aß1-42 monomer and fiber in Balb/C mice. Furthermore, MAP4 sera could also effectively inhibit the formation of the Aß1-42 fiber. Interestingly, one of the MAP4 sera was able to depolymerize the Aß1-42 fibers that had aggregated. The monoclonal antibody, 1H7, was shown to inhibit the formation of Aß1-42 fiber. MAP4 carrier may provide benefits over current immunization strategies, as it does not induce inflammation. In conclusion, the MAP4-Aß conjugates offer a promising approach for the development of a safe and effective AD vaccine.