Abstract
PurposeWith the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), non-small cell lung cancer (NSCLC) patients have achieved remarkable survival benefits in recent years. However, epidermal growth factor receptor (EGFR) wild-type and low expression of programmed death-ligand 1 (PD-L1) NSCLCs remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a metabolic gene signature that could successfully identify high-risk patients and reveal its underlying molecular immunology characteristics.MethodsBy identifying the bottom 50% PD-L1 expression level as PD-L1 low expression and removing EGFR mutant samples, a total of 640 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) tumor samples and 93 adjacent non-tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). We identified differentially expressed metabolic genes (DEMGs) by R package limma and the prognostic genes by Univariate Cox proportional hazards regression analyses. The intersect genes between DEMGs and prognostic genes were put into the least absolute shrinkage and selection operator (LASSO) penalty Cox regression analysis. The metabolic gene signature contained 18 metabolic genes generated and successfully stratified LUAD and LUSC patients into the high-risk and low-risk groups, which was also validated by the Gene Expression Omnibus (GEO) database. Its accuracy was proved by the time-dependent Receiver Operating Characteristic (ROC) curve, Principal Components Analysis (PCA), and nomogram. Furthermore, the Single-sample Gene Set Enrichment Analysis (ssGSEA) and diverse acknowledged methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT-ABS, and CIBERSORT revealed its underlying antitumor immunosuppressive status. Besides, its relationship with somatic copy number alterations (SCNAs) and tumor mutational burden (TMB) was also discussed.ResultsIt is noteworthy that metabolism reprogramming is associated with the survival of the double-negative LUAD and LUSC patients. The SCNAs and TMB of critical metabolic genes can inhibit the antitumor immune process, which might be a promising therapeutic target.
Highlights
Tyrosine kinase inhibitors (TKIs), as a milestone treatment against lung cancer, have demonstrated remarkable therapeutic effects in NSCLC
In 944 metabolism-related genes, 224 Differentially expressed metabolic genes (DEMGs) were identified between tumor tissues and adjacent nontumorous tissues (73 down-regulated and 151 up-regulated, Figures 1A, B), of which 103 metabolic genes related to overall survival (OS) were extracted by univariate Cox regression analysis (Figure 1C)
Based on the optimal value of l (Figures 1F, G), a signature comprised of 18 DEMGs was constructed as follows: risk score = ADCY9*-0.1096 + ACP4*0.4257 + GCLC*0.0124 + ALDOA*0.0196 + UCK2*0.0106 + PLA2G4B*-0.5342 + TXNRD1*0.0865 + PGM2*0.0496 + PTGIS*0.2946 + LDHA*0.0949 + PFKP*0.0838 + PTGES*0.0865 + ALDH1A2*-0.1811 + ISYNA1*-0.1000 + ACP5*-0.1516 + POLR2J3*0.4008 + ACSM5*-0.2192 + ADA*0.0094
Summary
Tyrosine kinase inhibitors (TKIs), as a milestone treatment against lung cancer, have demonstrated remarkable therapeutic effects in NSCLC. TKIs reversibly binds to the intracellular tyrosine kinase domain of the epidermal growth factor receptor (EGFR) by competing with ATP and inhibit activation of downstream signaling [1]. In EGFR mutation-positive patients, erlotinib, gefitinib, and afatinib have achieved better progression-free survival (PFS) and overall survival (OS), most patients inevitably acquired resistance to TKIs within 12 months [2]. They are not always beneficial for EGFR mutation-negative patients. Programmed death-ligand 1 (PD-L1) is expressed on multiple malignant tissues and up-regulated within the tumor microenvironment, resulting in T-cell immunity resistance [3]. Antibodies of PD-L1 can restore T cell function and enhance antitumor immunity [4]
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