Identification of <i>ECI2</i> as Potential Prognostic Biomarkers Based on a Fatty Acid Metabolism‐Related Gene Model in Clear Cell Renal Cell Carcinoma

<b>Background:</b> Clear cell renal cell carcinoma (ccRCC) is the most common and highly malignant subtype of renal cancer, characterized by significant lipid deposition. Research has indicated that its growth and metastasis are closely associated with fatty acid metabolism. <b>Methods:</b> In this study, we integrated TCGA transcriptome data, CPTAC proteomics data, and the single-cell dataset GSE152938 to identify differentially expressed genes related to fatty acid metabolism in ccRCC. Using the LASSO algorithm, we constructed a prognostic model based on these genes. Western blot and PCR analyses confirmed the expression levels of the <i>ECI2</i> in ccRCC, while lentiviral transduction was used to investigate the effects of <i>ECI2</i> expression on tumor biological behaviors. <b>Results:</b> Our findings demonstrated that <i>ECI2</i> expression is downregulated in ccRCC, and lower <i>ECI2</i> levels correlate with better patient prognosis. Functional assays showed that overexpression of <i>ECI2</i> significantly inhibited the proliferation and migration of ccRCC cells and increased their sensitivity to the chemotherapeutic drug oxaliplatin. <b>Conclusion:</b> This study highlights the potential tumor-suppressive role of <i>ECI2</i> in ccRCC and suggests its viability as a diagnostic and therapeutic target.