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》

Hypoxia-related lncRNAs to build prognostic classifier and reveal the immune characteristics of EGFR wild type and low expression of PD-L1 squamous and adenocarcinoma NSCLC.

Recently, the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) have achieved remarkable survival benefits in non-small cell lung cancer (NSCLC) treatment. However, epidermal growth factor receptor (EGFR) wild type and low expression of programmed death-ligand 1 (PD-L1) NSCLC remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a hypoxia-related lncRNAs (hypolncRNAs) classifier that could successfully identify the high-risk patients and reveal its underlying molecular immunology characteristics. By identifying the bottom 25% PD-L1 expression level as low expression of PD-L1 and removing EGFR mutant samples, a total of 222 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) samples and 93 adjacent non-tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). A 0 or 1 matrix was constructed by cyclically pairing hypoxia-related long non-coding RNAs (hypolncRNAs) and divided into the train set and test set. The univariate Cox regression analysis determined the prognostic hypolncRNAs pairs. Then, the prognostic classifier contained nine hypolncRNAs pairs which were generated by Lasso regression and multivariate Cox analysis. It successfully stratified EGFR wild type and low expression of PD-L1 squamous and adenocarcinoma NSCLC (double-negative LUAD and LUSC) patients into the high- and low-risk groups, whose accuracy was proved by the time-dependent receiver operating characteristic (ROC) curve. Furthermore, diverse acknowledged immunology methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT-ABS, CIBERSORT, and the single-sample gene set enrichment analysis (ssGSEA) revealed its underlying antitumor immunosuppressive status in the high-risk patients. It is noteworthy that hypolncRNAs are associated with the survival of double-negative LUAD and LUSC patients, for which the possible mechanism is inhibiting the antitumor immune process.

Zhao F ，Wang M ，Zhu J 《Cancer Medicine》

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 ... -  《-》

Development and validation of a novel lipid metabolism-related gene prognostic signature and candidate drugs for patients with bladder cancer.

Bladder cancer (BLCA) is a common cancer associated with an unfavorable prognosis. Increasing numbers of studies have demonstrated that lipid metabolism affects the progression and treatment of tumors. Therefore, this study aimed to explore the function and prognostic value of lipid metabolism-related genes in patients with bladder cancer. Lipid metabolism-related genes (LRGs) were acquired from the Molecular Signature Database (MSigDB). LRG mRNA expression and patient clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a signature for predicting overall survival of patients with BLCA. Kaplan-Meier analysis was performed to assess prognosis. The connectivity Map (CMAP) database was used to identify small molecule drugs for treatment. A nomogram was constructed and assessed by combining the signature and other clinical factors. The CIBERSORT, MCPcounter, QUANTISEQ, XCELL, CIBERSORT-ABS, TIMER and EPIC algorithms were used to analyze the immunological characteristics. An 11-LRG signature was successfully constructed and validated to predict the prognosis of BLCA patients. Furthermore, we also found that the 11-gene signature was an independent hazardous factor. Functional analysis suggested that the LRGs were closely related to the PPAR signaling pathway, fatty acid metabolism and AMPK signaling pathway. The prognostic model was closely related to immune cell infiltration. Moreover, the expression of key immune checkpoint genes (PD1, CTLA4, PD-L1, LAG3, and HAVCR2) was higher in patients in the high-risk group than in those in the low-risk group. The prognostic signature based on 11-LRGs exhibited better performance in predicting overall survival than conventional clinical characteristics. Five small molecule drugs could be candidate drug treatments for BLCA patients based on the CMAP dataset. In conclusion, the current study identified a reliable signature based on 11-LRGs for predicting the prognosis and response to immunotherapy in patients with BLCA. Five small molecule drugs were identified for the treatments of BLCA patients.

Zhu K ，Xiaoqiang L ，Deng W ，Wang G ，Fu B ... -  《Lipids in Health and Disease》

Predictive values of genomic variation, tumor mutational burden, and PD-L1 expression in advanced lung squamous cell carcinoma treated with immunotherapy.

Immune checkpoint inhibitors (ICIs) prolong overall survival (OS) in patients with advanced lung squamous cell carcinoma (LUSC). However, predictive and prognostic factors related to ICIs in LUSC remain elusive. This study aimed to identify predictors that are related to better clinical benefit and outcomes in LUSC patients treated with immunotherapy. Capture-based targeted sequencing was performed in 64 patients with advanced LUSC who underwent immunotherapy. Tumor mutational burden (TMB) was defined as the sum of nonsynonymous single nucleotide and indel variants. Programmed cell death ligand-1 (PD-L1) expression was evaluated by immunohistochemical analysis. Clinicopathological characteristics including age, sex, performance status, smoking history, body mass index (BMI), blood fat, brain metastases, liver metastases, previous thoracic radiotherapy, and treatment lines were analyzed. The most commonly mutated genes included TP53, CDKN2A, KEAP1, CREBBP, KRAS, BIM, AMER1, and APC. Copy number variations most frequently occurred in AR, SOX2, PIK3CA, EGFR, RICTOR, FGFR1, and ZNF703. The median and mean TMB was 9.35 and 10.62 mutations per megabase, respectively. Positive PD-L1 expression was detected in 29.7% patients. Patients with a history of heavy smoking (≥ 40 pack-years) were more likely to have positive PD-L1 expression (35% vs. 16.7%, P=0.04) and higher TMB (11.1 vs. 9.8 mut/Mb, P=0.04). Gene alterations had no impact on PD-L1 expression or TMB level. The median progression-free survival (PFS) was 6.7 months and median OS was 13.7 months. Higher TMB was independently associated with longer PFS (P=0.01) and OS (P=0.02), and this correlation was more pronounced in patients treated with ICIs as a single agent (P=0.0001). Higher TMB was also associated with better disease control rate (DCR) (P=0.02). Compared with wild-type, patients with KRAS mutation and EGFR amplification had higher objective response rates (ORR, P=0.01). The predictive value of TMB is more significant in LUSC patients receiving ICI as a single agent than as a combination therapy. The combination of Eastern Cooperative Oncology Group performance status (ECOG-PS), smoking status, TMB, PD-L1, and genomic variation might be helpful for personalized immunotherapy decisions in clinical practice for advanced LUSC.

Xu Y ，Li H ，Huang Z ，Chen K ，Yu X ，Sheng J ，Zhang HH ，Fan Y ... -  《-》