Palbociclib is a cyclin-dependent kinase 4/6 inhibitor and a commonly used antitumor drug. Many cancers are susceptible to palbociclib resistance, however, the underlying metabolism mechanism and extent of resistance to palbociclib are unknown. In this study, LC-MS metabolomics was used to investigate the metabolite changes of colorectal cancer SW620 cells that were resistant to palbociclib. The study indicated that there were 76 metabolite expression differences between SW620 cells with palbociclib resistance and the parental SW620 cells involving amino acids, glutathione, ABC transporters, and so on. MetaboAnalyst 6.0 metabolic pathway analysis showed that arginine synthesis, β-alanine metabolism, and purine metabolism were disrupted. These results may provide potential clues to the metabolism mechanism of drug resistance in cancer cells that are resistant to palbociclib. Our study has the potential to contribute to the study of anti-palbociclib resistance.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent histological subtype of renal cancer, with both incidence and mortality rates on the rise in recent years. This underscores the urgent need for precise diagnostic tools, effective monitoring, and improved management strategies. Advances in tumor sequencing have significantly enhanced our understanding of the molecular biology of ccRCC, leading to the identification of several potential genomic biomarkers. However, no widely accepted tissue or blood biomarker for ccRCC has been incorporated into routine clinical practice. In this study, we integrated transcriptomics and metabolomics analyses using paired cancerous and non-cancerous tissue samples from five patients who underwent radical nephrectomy. Our goal was to systematically investigate the molecular mechanisms underlying ccRCC and identify potential therapeutic targets. We identified ten shared KEGG metabolic pathways between the transcriptome and metabolome, with purine metabolism and glycerophospholipid metabolism emerging as particularly relevant to tumorigenesis and disease progression. Network analysis revealed significant correlations between specific metabolites (e.g., guanosine triphosphate and L-malic acid) and key genes (e.g., MET, SDHC, FH, and SDHB) within the molecular network of ccRCC. Additionally, we identified key enzymes in phospholipid metabolism, such as AK, LPCAT, and LPEAT, as potential therapeutic targets. Inhibiting these pathways could effectively disrupt tumor membrane formation and signal transduction. Our findings not only deepen the understanding of the molecular mechanisms driving ccRCC but also provide valuable insights for early diagnosis, prognosis evaluation, and the development of novel therapeutic strategies. Future work should focus on validating these potential biomarkers and therapeutic targets in larger cohorts and exploring their clinical utility.