The integrated single-cell analysis developed a lactate metabolism-driven signature to improve outcomes and immunotherapy in lung adenocarcinoma - Zhang, Pengpeng; Pei, Shengbin; Gong, Zeitian; Ren, Qianhe; Xie, Jiaheng; Liu, Hong; Wang, Wei (Frontiers in Endocrinology, 2023)
Abstract Background: It has been suggested that lactate metabolism (LM) is crucial for the development of cancer. A comprehensive investigation of its function in lung adenocarcinoma (LUAD) hasn't been done. Using integrated single-cell RNA sequencing (scRNA-seq) analysis, we built predictive models based on LM-related genes (LMRGs) to propose novel targets for the treatment of LUAD patients. Methods: The most significant genes for LM were identified using the AUCell algorithm and correlation analysis in conjunction with scRNA-seq analysis. Utilize cox- and lasso- regression to build risk models with superior predictive performance and validate them on multiple external independent datasets. Next, we explored the differences in the tumor microenvironment (TME), immunotherapy, mutation landscape, and enriched pathways between different risk groups. Finally, cell experiments were used to verify the impact of AHSA1 in LUAD. Results: A total of 590 genes that regulate LM were identified and prognostic for subsequent analysis. By cox- and lasso-regression, we constructed a 5-gene signature that could predict the prognosis of patients with LUAD. Interestingly, we found that there were differences in the TME, immune cell infiltration level, immune checkpoint level, and mutation landscape between different risk groups, which may be helpful for the clinical treatment of LUAD patients. Conclusion: Based on LMRGs, we constructed a prognostic model that can predict the efficacy of immunotherapy and provide a new direction for treating LUAD.