Abstract
Background: Lipid metabolism disorder, a new hallmark of cancer initiation, has been involved in lung adenocarcinoma (LUAD). However, few biomarkers about lipid metabolism-related genes (LMRGs) have been developed for prognosis prediction and clinical treatment of LUAD patients. Methods: In this study, we constructed and validated an effective prognostic prediction model for LUAD patients depending on LMRGs. Subsequently, we investigated the prediction model from immune microenvironment, genomic changes, and immunotherapy. Results: Then, eleven LMRGs were identified and applied to LUAD subtyping. In comparison with the high-risk group, the low-risk group exhibited a remarkably favorable prognosis, along with a higher immune score and lower tumor purity. Moreover, the low-risk group presented higher levels of immune checkpoint molecules, lower tumor immune dysfunction and exclusion (TIDE) score and tumor mutation burden (TMB), and higher likelihood of benefiting from immunotherapy. Furthermore, the genomic changes of six LMRGs (CD79A, HACD1, CYP17A1, SLCO1B3, ANGPTL4, and LDHA) were responsible for the difference in susceptibility to LUAD by greatly influencing B-cell activation. Conclusion: Generally speaking, the LMRG model is a reliable independent biomarker for predicting adverse outcomes in LUAD patients and has the potential to facilitate risk-stratified immunotherapy.
Highlights
Lung cancer, composed of approximately 85% non-small-cell lung cancer (NSCLC) and 15% small cell lung cancer (SCLC), is one of the most prevalent malignant cancers worldwide, with over 1.4 million deaths each year (Zarogoulidis et al, 2013)
A total of 217 (32.7%) lipid metabolism-related genes (LMRGs) (Figure 2A) were differentially expressed between tumor tissues (n = 500) and adjacent tissues (n = 59), 61 of which were remarkably associated with overall survival (OS) with the univariate Cox regression analysis (Figure 2B)
The risk score of each lung adenocarcinoma (LUAD) patient was calculated with the coefficients (Figure 2D) achieved from the multivariate stepwise regression analysis
Summary
Lung cancer, composed of approximately 85% non-small-cell lung cancer (NSCLC) and 15% small cell lung cancer (SCLC), is one of the most prevalent malignant cancers worldwide, with over 1.4 million deaths each year (Zarogoulidis et al, 2013). Lung adenocarcinoma (LUAD) has exceeded lung squamous cell carcinoma (LUSC) in the morbidity and become the most common pathological type of lung cancer (Bray et al, 2018). Traditional prognostic prediction still relies on histopathologic diagnosis and tumor stage. These models fail to identify high-risk population and predict LUAD patients who are more likely to benefit from immunotherapies. It is imperative to explore accurate and effective prognostic biomarkers and models to assist clinical individualized treatment. A new hallmark of cancer initiation, has been involved in lung adenocarcinoma (LUAD). Few biomarkers about lipid metabolism-related genes (LMRGs) have been developed for prognosis prediction and clinical treatment of LUAD patients
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