ArticleActa biochimica et biophysica Sinica2022
ACACB is a novel metabolism-related biomarker in the prediction of response to cetuximab therapy inmetastatic colorectal cancer.
Article in Acta biochimica et biophysica Sinica, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 10 citations in OpenAlex.
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- Identification of prognostic and therapeutic biomarkers associated with macrophage and lipid metabolism in pancreatic cancer.Scientific reports · 2025Article
- A novel mitochondrial-related risk model for predicting prognosis and immune checkpoint blockade therapy response in uterine corpus endometrial carcinoma.Scientific reports · 2025Article
- Negative correlation between acetyl-CoA acyltransferase 2 and cetuximab resistance in colorectal cancer.Acta biochimica et biophysica Sinica · 2023Article
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14 authors at 2 institutions in 1 country.
Funding
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Abstract
Cetuximab is one of the most valuable targeted therapy monoclonal antibodies in the treatment of metastatic colorectal cancer (CRC). However, the mechanisms affecting cetuximab resistance in CRC treatment remain unclear. Metabolism, especially fatty acid metabolism, has been reported to play an important role in tumor treatment. The correlation between cetuximab resistance and metabolism and whether it can be a new biomarker to evaluate the sensitivity of cetuximab in CRC treatment still need to be further explored. In this study, we perform a comprehensive analysis to confirm the relationship between fatty acid metabolism and cetuximab resistance, and the differentially expressed genes (DEGs) related to cetuximab drug resistance in CRC are screened by bioinformatics technology. We find that acetyl-CoA carboxylase beta (ACACB), ADH1C, CES1, MGLL, FMO5, and GPT are the hub DEGs, and ACACB is the most important biomarker among them. In addition, we systematically analyze the role of ACACB in the tumorigenesis of CRC, including tissue expression, CRC cell growth, cetuximab sensitivity, and potential downstream pathways, by using bioinformatics techniques,
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