Tumor microenvironment related novel signature predict lung adenocarcinoma survival.

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer-related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis. The information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters. A total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year. A series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

Chen J ，Zhou R 《PeerJ》

Identification of prognostic gene signature associated with microenvironment of lung adenocarcinoma.

Lung cancer has the highest morbidity and mortality worldwide, and lung adenocarcinoma (LADC) is the most common pathological subtype. Accumulating evidence suggests the tumor microenvironment (TME) is correlated with the tumor progress and the patient's outcome. As the major components of TME, the tumor-infiltrated immune cells and stromal cells have attracted more and more attention. In this study, differentially expressed immune and stromal signature genes were used to construct a TME-related prognostic model for predicting the outcomes of LADC patients. The expression profiles of LADC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) related to the TME of LADC were identified using TCGA dataset by Wilcoxon rank sum test. The prognostic effects of TME-related DEGs were analyzed using univariate Cox regression. Then, the least absolute shrinkage and selection operator (LASSO) regression was performed to reduce the overfit and the number of genes for further analysis. Next, the prognostic model was constructed by step multivariate Cox regression and risk score of each sample was calculated. Then, survival and Receiver Operating Characteristic (ROC) analyses were conducted to validate the model using TCGA and GEO datasets, respectively. The Kyoto Encyclopedia of Genes and Genomes analysis of gene signature was performed using Gene Set Enrichment Analysis (GSEA). Finally, the overall immune status, tumor purity and the expression profiles of HLA genes of high- and low-risk samples was further analyzed to reveal the potential mechanisms of prognostic effects of the model. A total of 93 TME-related DEGs were identified, of which 23 DEGs were up-regulated and 70 DEGs were down-regulated. The univariate cox analysis indicated that 23 DEGs has the prognostic effects, the hazard ratio ranged from 0.65 to 1.25 (p < 0.05). Then, seven genes were screened out from the 23 DEGs by LASSO regression method and were further analyzed by step multivariate Cox regression. Finally, a three-gene (ADAM12, Bruton Tyrosine Kinase (BTK), ERG) signature was constructed, and ADAM12, BTK can be used as independent prognostic factors. The three-gene signature well stratified the LADC patients in both training (TCGA) and testing (GEO) datasets as high-risk and low-risk groups, the 3-year area under curve (AUC) of ROC curves of three GEO sets were 0.718 (GSE3141), 0.646 (GSE30219) and 0.643 (GSE50081). The GSEA analysis indicated that highly expressed ADAM12, BTK, ERG mainly correlated with the activation of pathways involving in focal adhesion, immune regulation. The immune analysis indicated that the low-risk group has more immune activities and higher expression of HLA genes than that of the high-risk group. In sum, we identified and constructed a three TME-related DEGs signature, which could be used to predict the prognosis of LADC patients.

Yue C ，Ma H ，Zhou Y 《PeerJ》

CCR2 and PTPRC are regulators of tumor microenvironment and potential prognostic biomarkers of lung adenocarcinoma.

Tumor microenvironment (TME) plays an essential role in lung adenocarcinoma (LUAD) development and metastasis. With the development of TME research, it has been proved that differences in tumor-infiltrating immune cells (TICs) and gene expression profile are related to the prognosis of cancer. The aim of our study was to identify key genes affecting immune state in TME of LUAD. The RNA-seq data and clinical characteristics of 594 LUAD patients were downloaded from the TCGA database. ImmuneScore, StromalScore and ESTIMATEScore of each LUAD sample were calculated using ESTIMATE algorithm. Based on the median of different scores, LUAD samples were divided into high and low score groups. Differentially expressed genes (DEGs) between groups were obtained, and univariate Cox regression analysis and protein-protein interaction (PPI) network were used to screen the shared DEGs generating in the intersection analysis. Finally, the CIBORSORT algorithm was performed to calculate the relative contents of TICs for each LUAD sample, and the correlation analysis between TICs and key genes was used to determine the influence of key genes to the TME. In the presented study, we found that three different scores were positively correlated with the prognosis of LUAD patients, and correlation analysis showed the different scores were closely related to tumor progression and metastasis. After performing the intersection analysis, a total of 585 up-regulated and 107 down-regulated DEGs between the high and low score groups were obtained, all of which were enriched in immune-related functions. Having used univariate COX regression analysis and PPI network, the key genes, CCR2 and PTPRC, affecting the immune status of TME and the prognosis of LUAD were acquired. Analysis based on the CIBERSORT algorithm suggested that CCR2 and PTPRC were correlated with a variety of TICs, and closely related to the clinical characteristics of the LUAD patients. Our research showed that CCR2 and PTPRC may be potential prognostic markers in LUAD, which may affect the function of γδT cells and other immune cells by participating in the regulation of TME immune state.

Wei J ，Fang D ，Zhou W 《-》

A Robust Seven-Gene Signature Associated With Tumor Microenvironment to Predict Survival Outcomes of Patients With Stage III-IV Lung Adenocarcinoma.

Due to the relatively insidious early symptoms of lung adenocarcinoma (LUAD), most LUAD patients are at an advanced stage at the time of diagnosis and lose the best chance of surgical resection. Mounting evidence suggested that the tumor microenvironment (TME) was highly correlated with tumor occurrence, progress, and prognosis. However, TME in advanced LUAD remained to be studied and reliable prognostic signatures based on TME in advanced LUAD also had not been well-established. This study aimed to understand the cell composition and function of TME and construct a gene signature associated with TME in advanced LUAD. The immune, stromal, and ESTIMATE scores of each sample from The Cancer Genome Atlas (TCGA) database were, respectively, calculated using an ESTIMATE algorithm. The LASSO and Cox regression model were applied to select prognostic genes and to construct a gene signature associated with TME. Two independent datasets from the Gene Expression Omnibus (GEO) were used for external validation. Twenty-two subsets of tumor-infiltrating immune cells (Tiics) were analyzed using the CIBERSORT algorithm. Favorable overall survival (OS) and progression-free survival (PFS) were found in patients with high immune score (p = 0.048 and p = 0.028; respectively) and stromal score (p = 0.024 and p = 0.025; respectively). Based on the immune and stromal scores, 453 differentially expressed genes (DEGs) were identified. Using the LASSO and Cox regression model, a seven-gene signature containing AFAP1L2, CAMK1D, LOXL2, PIK3CG, PLEKHG1, RARRES2, and SPP1 was identified to construct a risk stratification model. The OS and PFS of the high-risk group were significantly worse than that of the low-risk group (p < 0.001 and p < 0.001; respectively). The receiver operating characteristic (ROC) curve analysis confirmed the good potency of the seven-gene signature. Similar findings were validated in two independent cohorts. In addition, the proportion of macrophages M2 and Tregs was higher in high-risk patients (p = 0.041 and p = 0.022, respectively). Our study established and validated a seven-gene signature associated with TME, which might serve as a prognosis stratification tool to predict survival outcomes of advanced LUAD patients. In addition, macrophages M2 polarization may lead to worse prognosis in patients with advanced LUAD.

Zhao H ，Zhang X ，Guo L ，Shi S ，Lu C ... -  《Frontiers in Genetics》

Identification of a three-gene expression signature and construction of a prognostic nomogram predicting overall survival in lung adenocarcinoma based on TCGA and GEO databases.

Lung adenocarcinoma (LUAD) is the major cause of cancer mortality. Traditional prognostic factors have limited importance after including other parameters. Thus, developing a more credible prognostic model combined with genes and clinical parameters is necessary. The messenger RNA (mRNA) expression and clinical information from The Cancer Genome Atlas (TCGA)-LUAD datasets and microarray data from three Gene Expression Omnibus (GEO) databases were obtained. We identified differentially-expressed genes (DEGs) between lung tumor and normal tissues through integrated analysis of the three GEO datasets. Univariate and multivariate Cox regression analyses were conducted to select survival-associated DEGs and to establish a prognostic gene signature which was associated with overall survival (OS). The expression of gene proteins was assessed in 180 LUAD tissue microarrays (TMAs) by immunohistochemistry (IHC). We verified its predictive performance with a Kaplan-Meier (KM) curve, receiver operating characteristic (ROC) curve, and Harrell's concordance index (C-index) and validated it in external GEO databases. Multivariate Cox regression analysis was performed to identify the significant prognostic indicators in LUAD. Furthermore, we established a prognostic nomogram based on TCGA-LUAD dataset. A three-gene signature was constructed to predict the OS of LUAD patients. The KM analysis, ROC curve, and C-index present a good predictive ability of the gene signature in TCGA dataset [P<0.0001; C-index 0.6375; 95% confidence interval (CI): 0.5632-0.7118; area under the ROC curve (AUC) 0.674] and the external GEO datasets (P=0.05, 0.004, and 0.04, respectively). Univariate and multivariate Cox regression analyses also verified that LUAD patients with low-risk scores had a decreased risk of death compared to those with a high-risk score in TCGA database [hazard ratio (HR) =0.3898; 95% CI: 0.1938-0.7842; P<0.05]. Finally, we constructed a nomogram integrating the gene signature and clinicopathological parameters (P<0.0001; C-index 0.762; 95% CI: 0.714-0.845; AUC 0.8136). Compared with conventional staging, a nomogram can effectively improve prognosis prediction. The nomogram is closely associated to the OS of LUAD patients. This consequence may be beneficial to individualized treatment and clinical decision-making.

Zhou Y ，Gao S ，Yang R ，Du C ，Wang Y ，Wu Y ... -  《-》