Clinical Significance and Immunometabolism Landscapes of a Novel Recurrence-Associated Lipid Metabolism Signature In Early-Stage Lung Adenocarcinoma: A Comprehensive Analysis

Abstract

BackgroundThe early-stage lung adenocarcinoma (LUAD) incidence has increased with heightened public awareness and lung cancer screening implementation. Lipid metabolism abnormalities are associated with lung cancer initiation and progression. However, the comprehensive features and clinical significance of the immunometabolism landscape and lipid metabolism-related genes (LMRGs) in cancer recurrence for early-stage LUAD remain obscure.MethodsLMRGs were extracted from Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Samples from The Cancer Genome Atlas (TCGA) were used as training cohort, and samples from four Gene Expression Omnibus (GEO) datasets were used as validation cohorts. The LUAD recurrence-associated LMRG molecular pattern and signature was constructed through unsupervised consensus clustering, time-dependent receiver operating characteristic (ROC), and least absolute shrinkage and selection operator (LASSO) analyses. Kaplan-Meier, ROC, and multivariate Cox regression analyses and prognostic meta-analysis were used to test the suitability and stability of the signature. We used Gene Ontology (GO), KEGG pathway, immune cell infiltration, chemotherapy response analyses, gene set variation analysis (GSVA), and GSEA to explore molecular mechanisms and immune landscapes related to the signature and the potential of the signature to predict immunotherapy or chemotherapy response.ResultsFirst, two LMRG molecular patterns were established, which showed diverse prognoses and immune infiltration statuses. Then, a 12-gene signature was identified, and a risk model was built. The signature remained an independent prognostic parameter in multivariate Cox regression and prognostic meta-analysis. In addition, this signature stratified patients into high- and low-risk groups with significantly different recurrence rates and was well validated in different clinical subgroups and several independent validation cohorts. The results of GO and KEGG analyses and GSEA showed that there were differences in multiple lipid metabolism, immune response, and drug metabolism pathways between the high- and low-risk groups. Further analyses revealed that the signature-based risk model was related to distinct immune cell proportions, immune checkpoint parameters, and immunotherapy and chemotherapy response, consistent with the GO, KEGG, and GSEA results.ConclusionsThis is the first lipid metabolism-based signature for predicting recurrence, and it could provide vital guidance to achieve optimized antitumor for immunotherapy or chemotherapy for early-stage LUAD.

Comprehensive analysis of a novel signature incorporating lipid metabolism and immune-related genes for assessing prognosis and immune landscape in lung adenocarcinoma

“…The time-dependent ROC curve analysis indicated high accuracy of the prognostic signature in predicting the survival of LUAD patients. Moreover, the results of external validation based on five GEO datasets and prognostic meta-analysis all confirmed the relatively better robustness of the prognostic signature compared to previous studies ( 9 , 37 ).…”

“…The time-dependent ROC curve analysis indicated high accuracy of the prognostic signature in predicting the survival of LUAD patients. Moreover, the results of external validation based on five GEO datasets and prognostic meta-analysis all confirmed the relatively better robustness of the prognostic signature compared to previous studies (9,37). F2RL1, also known as protease-activated receptor 2 (PAR2), has been reported to be associated with the occurrence and development of lung adenocarcinoma (44) and intestinal tumors (45,46).…”

“…Therefore, it is suggested that there is a close link between lipid metabolism patterns and antitumor immunity. However, most previous similar studies ( 9 , 14 , 43 ) have constructed prognostic models based on single lipid metabolism features, which often have the limitation of poor robustness and extrapolation. To overcome the shortcomings of previous studies, both immune-related and lipid metabolism-related genes were included in the present study to further improve the accuracy and robustness of prognostic signatures by providing multiscale clinical features.…”

“…Moreover, several studies have tried to construct prognostic models for lung cancer patients based on lipid metabolism-related genes. For example, Zhu’s study focused on early-stage LUAD patients using lipid metabolism-related genes to establish prognostic models and validated them according to external Gene Expression Omnibus (GEO) databases ( 9 ). Another study developed a signature based on serum lipid profiles to distinguish patients with early-stage lung cancer from healthy individuals, thereby achieving early diagnosis and treatment ( 10 ).…”

Comprehensive Analysis of a Cancer-Immunity Cycle–Based Signature for Predicting Prognosis and Immunotherapy Response in Patients With Colorectal Cancer

“…It is more helpful to objectively evaluate the clinical significance of the risk model according to the median risk score. Thus, other signature-based studies have also divided patients using the median value as the risk score cutoff for predicting survival and immunotherapy efficacy in cancer ( 47 , 48 ). The prognostic significance of the cancer-immunity cycle–based signature was successfully established in several independent datasets, thereby promoting the exploration of biological mechanisms.…”