COX-2-related tumor immune microenvironment in non-small cell lung cancer: a novel signature to predict hot and cold tumor.

At present, non-small cell lung cancer (NSCLC) remains a great threat to the health of people worldwide. Immune checkpoint inhibitors (ICIs) have shown positive results in the treatment of advanced NSCLC. However, the treatment response of ICIs is not stable and unpredictable. We used a bioinformatics analysis to determine a novel signature to diagnose the hot and cold tumor in NSCLC which may guide the programmed cell death protein 1/programmed cell death 1 ligand 1 (PD-1/PD-L1) therapeutic strategy. The RNA-seq dataset and clinical data of 485 lung adenocarcinoma (LUAD) and 473 lung squamous cell carcinoma (LUSC) samples from The Cancer Genome Atlas (TCGA) database. Tumor infiltrating immune cells was calculated by CIBERSORT algorithm and ConsensusClusterPlus was used to classify the hot and cold tumor. Least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) were performed to determine the diagnostic area under curve (AUC) of novel signature of ICIs treatment. Overall survival (OS) analysis was based on the Kaplan-Meier statistical method. In this study, we found that the expression of PD-1/PD-L1 is associated with COX2 (PTGS2) expression. We identified novel signatures [STMN3, KIRREL1, SH2D3C, VCL, PDCD1, CD274, PTGS2, combined diagnostic (AUC) =0.838], in order to diagnose the hot and cold tumor subtype to indicate the treatment response of PD-1/PD-L1 inhibitor in NSCLC. Furthermore, we found that in hot tumor subtype, high PDCD1 expression group had worse OS than low PDCD1 expression group (P=0.047); high SH2D3C expression group had worse OS than low SH2D3C expression group either (P=0.003). SH2D3C was correlated to PD-1 expression in NSCLC samples (R=0.49, P<0.001). We speculated that SH2D3C likely plays a crucial role in PD-1-related immunotherapy in NSCLC patients. Pathway enrichment showed that the focal adhesion (P=0.005) and actin cytoskeleton (P=0.022) pathways were associated with OS. This study aimed to identify the classification of hot and cold tumors, and develop a novel signature to predict the ICI treatments response for PD-1/PD-L1 high expression NSCLC patients.

Wang T ，Luo Y ，Zhang Q ，Shen Y ，Peng M ，Huang P ，Zhou Z ，Wu X ，Chen K ... -  《-》

Transcriptome Analyses Identify a Metabolic Gene Signature Indicative of Antitumor Immunosuppression of EGFR Wild Type Lung Cancers With Low PD-L1 Expression.

With 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. By 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. It 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.

Wang M ，Zhu J ，Zhao F ，Xiao J ... -  《Frontiers in Oncology》

A 13-gene signature to predict the prognosis and immunotherapy responses of lung squamous cell carcinoma.

Lung squamous cell carcinoma (LUSC) comprises 20-30% of all lung cancers. Immunotherapy has significantly improved the prognosis of LUSC patients; however, only a small subset of patients responds to the treatment. Therefore, we aimed to develop a novel multi-gene signature associated with the immune phenotype of the tumor microenvironment for LUSC prognosis prediction. We stratified the LUSC patients from The Cancer Genome Atlas dataset into hot and cold tumor according to a combination of infiltration status of immune cells and PD-L1 expression level. Kaplan-Meier analysis showed that hot tumors were associated with shorter overall survival (OS). Enrichment analyses of differentially expressed genes (DEGs) between the hot and cold tumors suggested that hot tumors potentially have a higher immune response ratio to immunotherapy than cold tumors. Subsequently, hub genes based on the DEGs were identified and protein-protein interactions were constructed. Finally, we established an immune-related 13-gene signature based on the hub genes using the least absolute shrinkage and selection operator feature selection and multivariate cox regression analysis. This gene signature divided LUSC patients into high-risk and low-risk groups and the former inclined worse OS than the latter. Multivariate cox proportional hazard regression analysis showed that the risk model constructed by the 13 prognostic genes was an independent risk factor for prognosis. Receiver operating characteristic curve analysis showed a moderate predictive accuracy for 1-, 3- and 5-year OS. The 13-gene signature also performed well in four external cohorts (three LUSC and one melanoma cohorts) from Gene Expression Omnibus. Overall, in this study, we established a reliable immune-related 13-gene signature that can stratify and predict the prognosis of LUSC patients, which might serve clinical use of immunotherapy.

Yang Q ，Gong H ，Liu J ，Ye M ，Zou W ，Li H ... -  《Scientific Reports》

PD-L1 and PD-L2 expression correlated genes in non-small-cell lung cancer.

Larsen TV ，Hussmann D ，Nielsen AL 《Cancer Communications》

Association of Survival and Immune-Related Biomarkers With Immunotherapy in Patients With Non-Small Cell Lung Cancer: A Meta-analysis and Individual Patient-Level Analysis.

Yu Y ，Zeng D ，Ou Q ，Liu S ，Li A ，Chen Y ，Lin D ，Gao Q ，Zhou H ，Liao W ，Yao H ... -  《JAMA Network Open》