关键词: 肺鳞癌, 预后模型, 免疫微环境, 乳酸代谢, 生物信息学

Abstract: Objective To screen out differentially expressed lactic acid metabolism genes inlung squamous cell carcinoma (LUSC), and to investigate their influence on LUSC prognosis andimmune microenvironment through bioinformatics analysis. Methods RNA sequencing and clinicaldata were obtained from public databases. Differentially expressed lactic acid metabolism genes in LUSC and normal tissues were identified by integrated bioinformatics analysis. Univariate Cox, LASSO and multivariate Cox regression analyses were applied to screen out survival-associated lactic acid metabolism genes. CIBERSORT was used to evaluate the relationship between gene expression atlas and immune cell infiltration in patients of different risk groups. Kaplan-Meier (KM) survival analysis and receiver operating characteristics ( ROC) were used to verify the predictive power of the model in an internal training cohort and an external validation cohort, respectively. Risk groups and clinical information were combined to produce a nomogram to predict the survival time of LUSC patients. Results A total of 34 survival-associated lactic acid metabolism genes were successfullyscreened out, and prognosis models were constructed based on these genes. According to this model, LUSC patients were divided into a high-risk group and a low-risk group. Resting CD4 + memoryT cells ( P = 0. 001), activated CD4 + memory T cells ( P < 0. 001), resting NK cells ( P =0. 03), monocytes (P = 0. 01), M0 macrophages (P = 0. 009), activated dendritic cells (P <0. 001), active mast cells (P = 0. 04) and neutrophils (P< 0. 001) in LUSC immune microenvironment were associated with risk groups. KM survival analysis showed that the total survival time ofthe low-risk group was significantly longer than that of the high-risk group (P< 0. 001). The areasunder the ROC curve (AUC) of LUSC patients at 1, 3 and 5 years were all larger than 0. 7. Aftercombining risk groups and clinical information, a nomogram was obtained that could predict the survival rate of LUSC patients at 1, 3 and 5 years, and the correction curve showed a good predictionaccuracy. Conclusion LUSC prognosis model and its risk groups constructed by 34 lactic acid metabolism genes can be used to evaluate the immune microenvironment and the survival rate of LUSCpatients at 1, 3 and 5 years.

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