CKMT2 regulates glucose metabolism via amplifying the Warburg effect through interaction with LDHB, which promotes the growth and progression of CRC. These insights unveil a novel regulatory mechanism by which CKMT2 influences CRC and provide promising targets for future CRC therapeutic interventions.

The results proved that the expression levels of the 9 biomarkers could be used to predict the prognosis of CRC patients.

Finally, four drugs were screened with a higher sensitivity to the high-risk set. Taken together, a novel model according to five CRLs was set up to forecast the prognosis of pRCC patients, which provides a potential strategy to treat pRCC by a combination of cuproptosis and immunotherapy.

Similar results were validated in an independent GEO cohort, confirming the stability and reliability of the prognostic signature. The hypoxia-immune-based prognostic signature might contribute to the optimization of risk stratification for survival and personalized management of osteosarcoma patients.

The phenotype associated with GGH was also validated in a xenograft nude mouse model. Our results provide insight into the connection between metabolism and the tumor microenvironment in colon cancer and provides preliminary evidence for the role of GGH, providing a basis for subsequent studies.

This study revealed VEGFA expression and potential gene regulatory network in patients with renal cell carcinoma, thereby laying a foundation for further research on the role of VEGFA in renal cell carcinoma occurrence. Moreover, the study provides new renal cell carcinoma therapeutic targets and prognostic biomarkers as a reference for fundamental and clinical research.

This study provided a metabolism-related gene signature for prognostic prediction of STAD and explored the association between metabolism and the immune microenvironment for future research, thereby furthering the understanding of the crosstalk between different molecular mechanisms in human STAD. Some prognosis-related metabolic pathways have been revealed, and the survival of STAD patients could be predicted by a risk model based on these pathways, which could serve as prognostic markers in clinical practice.