The COVID-19 pandemic has led to widespread reports of prolonged neurological symptoms in individuals who have recovered from the acute phase of the infection. These persistent symptoms, collectively ...

Cancer cells drive adjacent adipose tissue to release fatty acids by secreting CCL2, which activates PPARα-dependent lipolysis. The resulting fatty acid influx amplifies HIF-1α/CCL2 signaling, establishing a positive feedback loop that fuels tumor growth. This cancer–adipose crosstalk, validated in 3D coculture, obese mouse models, and breast cancer tissues, underscores CCL2 blockade as a potential therapy for obesity-associated cancers. Abstract Rising obesity rates are closely linked to higher risk of cancer, yet the underlying mechanisms are not fully understood. It is previously reported that fatty acids (FAs) released from cancer-associated adipose tissue enhance hypoxia-inducible factor-1α (HIF-1α) expression in cancer cells, promoting tumor progression. Here, it is elucidated that cancer cells manipulate adjacent adipose tissue by secreting C-C chemokine ligand2 (CCL2) to exploit FAs. Activation of HIF-1α induced by FA influx increases CCL2 expression in cancer cells, which subsequently leads to lipolysis in nearby adipose tissue by activating peroxisome proliferator-activated receptor alpha (PPARα) signaling. This activation in adipose tissue results in the release of FAs into the tumor microenvironment. The increased lipid supply to tumor reactivates the FA/HIF-1α/CCL2 axis in cancer cells, further accelerating tumor growth and CCL2 secretion. This establishes a positive feedback loop between tumor and adjacent adipose tissue, which enhances cancer progression. This crosstalk is validated by using a polydimethylsiloxane-based 3D coculture system and in vivo models. In obese mice, this reciprocal signaling accelerated tumor progression, whereas intra-tumoral injection of CCL2-neutralizing antibody significantly suppressed it. These findings reveal a metabolic circuit for tumor survival and disrupting this interaction may provide promising therapeutic targets, particularly for obese cancer patients.

Background The diagnostic and therapeutic potential of neutrophil extracellular traps (NETs) in liver fibrosis (LF) has not been fully explored. We aim to screen and verify NETs-related liver fibrosis biomarkers through machine learning. Methods In order to obtain NETs-related differentially expressed genes (NETs-DEGs), differential analysis and WGCNA analysis were performed on the GEO dataset (GSE84044, GSE49541) and the NETs dataset. Enrichment analysis and protein interaction analysis were used to reveal the candidate genes and potential mechanisms of NETs-related liver fibrosis. Biomarkers were screened using SVM-RFE and Boruta machine learning algorithms, followed by immune infiltration analysis. A multi-stage model of fibrosis in mice was constructed, and neutrophil infiltration, NETs accumulation and NETs-related biomarkers were characterized by immunohistochemistry, immunofluorescence, flow cytometry and qPCR. Finally, the molecular regulatory network and potential drugs of biomarkers were predicted. Results A total of 166 NETs-DEGs were identified. Through enrichment analysis, these genes were mainly enriched in chemokine signaling pathway and cytokine-cytokine receptor interaction pathway. Machine learning screened CCL2 as a NETs-related liver fibrosis biomarker, involved in ribosome-related processes, cell cycle regulation and allograft rejection pathways. Immune infiltration analysis showed that there were significant differences in 22 immune cell subtypes between fibrotic samples and healthy samples, including neutrophils mainly related to NETs production. The results of in vivo experiments showed that neutrophil infiltration, NETs accumulation and CCL2 level were up-regulated during fibrosis. A total of 5 miRNAs, 2 lncRNAs, 20 function-related genes and 6 potential drugs were identified based on CCL2. Conclusions This study identified CCL2 as a biomarker for nets-related liver fibrosis, providing a new perspective for understanding the mechanisms of nets-mediated fibrosis and promoting therapeutic discovery.

TOPLINE:  Rheumatoid arthritis (RA) was causally linked to the development of idiopathic pulmonary fibrosis, with shared plasma factors CC motif chemokine ligand 2 (CCL2) and CXC motif chemokine ligand 2 (CXCL2) identified as potential diagnostic biomarkers. METHODOLOGY: Idiopathic pulmonary fibrosis shares inflammatory and fibrotic features with RA, but the causal relationship is unknown. Researchers conducted a bidirectional Mendelian randomization analysis using genome-wide association study data from European cohorts to explore causality between the two conditions. They analyzed the data of 14,361 patients with RA and 42,923 control individuals as well as data from 1028 patients with idiopathic pulmonary fibrosis and 196,986 control individuals.

Single-cell RNA sequencing reveals profibrotic macrophage-stromal interactions macrophage-derived CCL2, CCL5, and SPP1, and TGF-β signaling as key drivers of fibrosis and pathogenesis in intrauterine ...

Neuromuscular junctions (NMJs) are denervated in amyotrophic lateral sclerosis (ALS) through unknown mechanisms. Here, the authors show immune cells infiltrating muscle of ALS patients and mouse model...

Neuromuscular junctions (NMJs) are denervated in amyotrophic lateral sclerosis (ALS) through unknown mechanisms. Here, the authors show immune cells infiltrating muscle of ALS patients and mouse model...

In an unprecedented advance in the understanding of amyotrophic lateral sclerosis (ALS), a groundbreaking study published in Nature Communications uncovers a pivotal molecular pathway responsible for