Background: The aim of this study was to investigate the potential role of lipid metabolism-associated genes (LMAGs) in neoadjuvant chemoradiotherapy (nCRT) and immunotherapy for rectal cancer.
Methods: Differential LMAGs were characterized and functional enrichment analysis was performed. Multiple machine learning algorithms were combined to explore candidate LMAGs. ROC analysis was performed to evaluate the predicting accuracy of candidate LMAGs. The expression patterns, prognostic value, genetic alterations, and immune cell infiltration of the top-ranked LMAGs were investigated.
Results: We identified 45 LMAGs that were differentially expressed in tumor samples of nCRT responders and non-responders. These LMAGs were closely associated with lipid metabolism-related biological processes and pathways. ROC analysis revealed that the SREBF2 gene, an important transcription factor in regulating lipid metabolism, was the highest predictor of nCRT in rectal cancer. SREBF2 was highly expressed in rectal cancer tissues and high expression of SREBF2 was associated with favorable prognosis. Multivariate analysis showed that SREBF2 was an independent prognostic factor, and we integrated it with other clinical factors to establish an effective prognostic nomogram. SREBF2 also played a synergistic role with its co-expressed genes in the prognostic process of rectal cancer. Furthermore, SREBF2 was demonstrated to be closely associated with multiple immune infiltrating cells, and immunotherapy-related genes and may be used to predict the response to immunotherapy.
Conclusion: Our study suggests that LMAGs may serve as promising biomarkers in nCRT combined with immunotherapy for rectal cancer. However, large-scale clinical trials and biological experiments are necessary to demonstrate the efficacy and underlying mechanisms.
Keywords: Immunotherapy; Lipid metabolism; Machine learning; Neoadjuvant chemoradiotherapy; Rectal cancer.
Copyright © 2023. Published by Elsevier Inc.