ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Cancer Manipulates Adjacent Adipose Tissue to Exploit Fatty Acids via HIF-1α/CCL2/PPARα Axis: A Metabolic Circuit to Support Tumor Progression.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- The interplay between lipid metabolism, cancer progression and anti-cancer immunity.Experimental & molecular medicine · 2026Review
- Adipocyte-Derived Palmitic Acid Promotes Breast Cancer Malignancy via ZDHHC15-Mediated S-Palmitoylation of PPARγ.Cancer science · 2026Article
- Potent Anti-adipogenic Activity of Cyclopeptide Mallotumide A on Murine 3T3-L1 Adipocytes.ACS omega · 2026Article
- Deciphering double-negative prostate cancer: from aggressive subtype to novel therapeutic paradigms.Cell communication and signaling : CCS · 2026Review
- Cancer Manipulates Adjacent Adipose Tissue to Exploit Fatty Acids via HIF-1α/CCL2/PPARα Axis: A Metabolic Circuit to Support Tumor Progression.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Metabolic reprogramming in clear cell renal cell carcinoma: core pathways and targeted therapeutic strategies.Frontiers in genetics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.