BackgroundGlutamine (Gln) metabolism has been reported to play an essential role in cancer. However, a comprehensive analysis of its role in lung adenocarcinoma is still unavailable. This study established a novel system of quantification of Gln metabolism to predict the prognosis and immunotherapy efficacy in lung cancer. Further, the Gln metabolism in tumor microenvironment (TME) was characterized and the Gln metabolism-related genes were identified for targeted therapy.MethodsWe comprehensively evaluated the patterns of Gln metabolism in 513 patients diagnosed with lung adenocarcinoma (LUAD) based on 73 Gln metabolism-related genes. Based on differentially expressed genes (DEGs), a risk model was constructed using Cox regression and Lasso regression analysis. The prognostic efficacy of the model was validated using an individual LUAD cohort form Shandong Provincial Hospital, an integrated LUAD cohort from GEO and pan-cancer cohorts from TCGA databases. Five independent immunotherapy cohorts were used to validate the model performance in predicting immunotherapy efficacy. Next, a series of single-cell sequencing analyses were used to characterize Gln metabolism in TME. Finally, single-cell sequencing analysis, transcriptome sequencing, and a series of in vitro experiments were used to explore the role of EPHB2 in LUAD.ResultsPatients with LUAD were eventually divided into low- and high-risk groups. Patients in low-risk group were characterized by low levels of Gln metabolism, survival advantage, “hot” immune phenotype and benefit from immunotherapy. Compared with other cells, tumor cells in TME exhibited the most active Gln metabolism. Among immune cells, tumor-infiltrating T cells exhibited the most active levels of Gln metabolism, especially CD8 T cell exhaustion and Treg suppression. EPHB2, a key gene in the model, was shown to promote LUAD cell proliferation, invasion and migration, and regulated the Gln metabolic pathway. Finally, we found that EPHB2 was highly expressed in macrophages, especially M2 macrophages. It may be involved in the M2 polarization of macrophages and mediate the negative regulation of M2 macrophages in NK cells.ConclusionThis study revealed that the Gln metabolism-based model played a significant role in predicting prognosis and immunotherapy efficacy in lung cancer. We further characterized the Gln metabolism of TME and investigated the Gln metabolism-related gene EPHB2 to provide a theoretical framework for anti-tumor strategy targeting Gln metabolism.

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Background: The peculiarity and the lack of clinical studies of dual primary lung cancer (DPLC) led to limited knowledge about its clinical characteristics and prognosis. The current study performed a retrospective analysis and established a prognostic nomogram to assess the prognostic factors and clinical characteristics of DPLC.Methods: A total of 1419 DPLC patients with pathological con rmation from SEER were selected and analyzed by univariate and multivariable Cox regression analyses. The independent prognostic factors were included to establish a nomogram. The accuracy and reliability of prognostic model were evaluated by C-indexes, calibration plots, receiver operating characteristic (ROC) curves, decision curve analyses (DCA) and integrated discrimination improvement (IDI) scores. Chi-square test was used to assess the differences between DPLC and single primary lung cancer (SPLC) or synchronous DPLC (sDPLC) and metachronous DPLC (mDPLC).Results: Cox regression analysis showed that age, sex, histological type, stage, LN metastasis, surgery, chemotherapy were independent prognostic factors, we included these factors to establish a prognostic model. In the training cohort, the C-index was 0.690, and the area under curves (AUC) of 3-and 5-year survival time were 0.720 and 0.723. The calibration plots in training cohort and validation cohort were in excellent agreement. DCA and IDI showed that the predictive effect of the novel prognostic model was better than the model based on 8th AJCC TNM system. Chi-square test indicated that DPLC and SPLC had statistical differences on pathological and clinical features. Conclusions: The clinical and pathological characteristics of DPLC were different from the SPLC. The nomogram based on signi cant factors could provide accurate and individualized survival predictions for DPLC.