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Downloaded Using the R Package “TCGA Biolinks” (12) 2872 Original Article Systemic immune microenvironment and regulatory network analysis in patients with lung adenocarcinoma Libao Liu1#, Shilei Xu2#, Lei Huang3, Jinyuan He1, Gang Liu4, Shaohong Ma1, Yimin Weng1, Shaohong Huang1 1Department of Cardiothoracic Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; 2Department of General Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; 3Department of Nursing, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China; 4Department of Cardiovascular Medicine, First Affiliated Hospital of Sun at-senY University, Guangzhou, China Contributions: (I) Conception and design: L Liu, S Xu, Y Weng, S Huang; (II) Administrative support: L Huang, J He; (III) Provision of study materials or patients: L Huang, G Liu; (IV) Collection and assembly of data: S Ma, Y Weng; (V) Data analysis and interpretation: L Liu, S Xu; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. #These authors contributed equally to this work. Correspondence to: Shaohong Huang. Department of Cardiothoracic Surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China. Email: [email protected]; Yimin Weng. Department of Cardiothoracic surgery, Third Affiliated Hospital of Sun Yat-sen University, Guangzhou 510630, China. Email: [email protected]. Background: This study applied a complex bioinformatics analysis to explore the hub regulators and immune network to further elucidate the molecular mechanisms of lung adenocarcinoma (LUAD) immune regulation. Methods: LUAD immunological microenvironment features and microenvironment-related differential expression genes (DEGs) were identified by ESTIMATE algorithm and linear models for microarray analyses (LIMMA), respectively. CIBERSORT and Igraph algorithms were applied to construct the LUAD- related immunocyte infiltration and regulatory network. Kaplan-Meier survival analysis, and univariate and multivariate Cox analysis were used to predict independent risk factors and screen for the hub genes. In addition, hub genes-correlated gene set enrichment analysis (GSEA), tumor mutation burden (TMB), and clinic pathological relation analyses were also performed. Results: Stromal, immune, and microenvironment comprehensive features (ESTIMATE score) were associated with overall survival (OS) in LUAD patients (all, P<0.05). T-cell activation, chemokine activity, and immune effect or dysfunction gene ontology maps were associated with the LUAD immune microenvironment. The immune infiltration cell subtypes mast cells (masT-cells) resting [The Cancer Genome Atlas (TCGA): P=0.01; Gene Expression Omnibus (GEO): P=1.79e−05] and activated T-cells (CD4 memory) (TCGA: P<0.01; GEO: P=8.52e−05) were found to have an important role in the immune cell regulatory network. Finally, ITGAL [univariate hazard ratio (HR) =0.80, 95% confidence interval (CI): 0.69–0.93, P<0.01; multivariate HR =0.59, 95% CI: 0.40–0.86, P=0.01] and KLRB1 (univariate HR =0.78, 95% CI: 0.69–0.89, P<0.01; multivariate HR =0.72, 95% CI: 0.58–0.90, P<0.01) were correlated with the T-cell receptor signaling pathway and anaplastic lymphoma kinase (ALK) fusion (ITGAL: P=0.034; KLRB1: P=0.050), and were considered as candidate biomarkers. A significant relation between KLRB1 expression level and TMB (P=3.6e−05) was identified, while no relation was detected for ITGAL (P=0.11). Conclusions: The T-cell activation and activated T-cell (CD4 memory) pathways were predominantly involved in LUAD immune microenvironment regulation. The expression levels of ITGAL and KLRB1 were significantly correlated with the T-cell receptor signaling pathway and LUAD TMB, and were independent risk factors for OS. Keywords: Lung adenocarcinoma (LUAD); immunological microenvironment features; T-cell activation; tumor mutation burden (TMB); immunoregulation Submitted Jun 07, 2020. Accepted for publication Apr 16, 2021. doi: 10.21037/tcr-20-2275 View this article at: https://dx.doi.org/10.21037/tcr-20-2275 © Translational Cancer Research. All rights reserved. Transl Cancer Res 2021;10(6):2859-2872 | http://dx.doi.org/10.21037/tcr-20-2275 2860 Liu et al. Systemic immune microenvironment analysis for LUAD Introduction neoantigens can help to elucidate the immune adaptation, cancer cells escape, and thus is important to develop the Lung cancer is a relatively common malignancy, and global individualized therapy. Especially, LUAD can be considered cancer statistics indicate that the incidence is increasing as a treatable cancer if targeting to the driver biomarkers (3). yearly (1,2). In the United States in 2019 there were Thus, this study applied a complex bioinformatics approximately 116,000 newly diagnosed lung cancers in analysis to explore the hub regulators and immune network, females and 112,000 in males (1). However, with advances and to further elucidate the molecular mechanisms of of medical technology, delay-adjusted incidence rates in LUAD immune regulation. males decreased 2.9% from 2008 to 2015, and decreased 1.5% in females from 2006 to 2015 (3). Mortality rates also decreased for lung cancer patients: from 2012 to 2016 Methods the mortality rate decreased 4.3% for males and 3.1% for Analysis of LUAD immunological microenvironment females (1). In countries with more advanced healthcare and features education systems, there has been a decline in the incidence of new lung cancer cases of about 3% annually from 2008 In order to determine tumor purity and the degree of through 2016. In addition, data has suggested that lung immune infiltration, the ESTIMATE algorithm (https:// cancer-specific survival has improved in large part due to bioinformatics.mdanderson.org/estimate/) and gene advances in immune-targeted treatments (4). expression signatures was used (11). The algorithm The use of immunotherapies, including those targeted at calculates a stromal and immune score of each tissue sample anti-cytotoxic T-lymphocyte associated protein 4 (CTLA-4) which predicts tumor stromal and immune infiltration, and and programmed death-1 (PD-1) and its ligands then produces the ESTIMATE score, a comprehensive (PD-L1 and PD-L2), has become an important treatment score reflecting the tumor microenvironment, which reflects for many solid tumors, including lung cancer. Nishio the tumor purity (11). The RNA-seq fragments per kilobase et al. demonstrated that treatment with pembrolizumab, of exon per million fragments mapped reads (FPKM) from a humanized monoclonal antibody against PD-1, was The Cancer Genome Atlas (TCGA)-LUAD project were well-tolerated and improved progression-free survival downloaded using the R Package “TCGA biolinks” (12). (PFS) and overall survival (OS) in Japanese patients (5). Raw Affymetrix microarray data of GSE31210 was Quoix et al. reported that treatment with TG4010, an downloaded from Gene Expression Omnibus (GEO; http:// immunosuppressive agent targeting CD16, CD56, and www.ncbi.nlm.nih.gov/geo/) (13). The download included CD69 triple-positive activated lymphocytes (TrPAL), data of 246 tumor samples, including 11 ALK fusion tumor combined with chemotherapy, improved PFS of patients samples, 20 KRAS mutation samples, 127 epidermal growth with advanced non-small cell lung cancer (NSCLC) (6). factor receptor (EGFR) mutation samples, and 68 EGFR/ Hui et al. also reported that pembrolizumab improved long- KRAS/ALK- tumor samples. In addition, the GSE31210 term OS in patients with advanced NSCLC (7). dataset matching the GPL570-Affymetrix Human Genome Emerging evidence has revealed a close relation between U133 plus 2.0 Array platform (Affymetrix, Santa Clara, CA, immune system dysfunction and NSCLC, and immune USA) was also downloaded. For each sample, the stromal checkpoint inhibitors are considered as a second-line treatment score, immune score, and TCGA-LUAD and GSE31210 option for patients who have failed chemotherapy (8). estimated scores were calculated using the ESTIMATE However, the significant heterogeneity had revealed algorithm in the “ESTIMATE” R package. Kaplan-Meier between the lung adenocarcinoma (LUAD) and lung survival analysis using the stromal, immune, and ESTIMATE squamous cell carcinoma (LUSC) in term of their genomic scores was performed using the “survminer” package, and an and clinical attributes. For example, the subtype and optimal cutoff point for each variable was calculated using the prediction model constructed based on gene expression can “maxstat” algorithm. The study was conducted in accordance accurately assess the response to the checkpoint of CD274 with the Declaration of Helsinki (as revised in 2013). (programmed death ligand 1) in patients with LUSC and LUAD (9). Studies of the effectiveness of immunotherapies Data processing and identification of co-differentially have shown the importance of interactions between the expressed genes tumor microenvironment and cancer cell heterogeneity (10). Identifying the truly cancer-specific biomarkers or Based on the cutoff point of ESTIMATE score, TCGA- © Translational Cancer Research. All rights reserved. Transl Cancer Res 2021;10(6):2859-2872 | https://dx.doi.org/10.21037/tcr-20-2275 Translational Cancer Research, Vol 10, No 6 June 2021 2861 LUAD and GSE31210 tumor samples were divided html#/main/step1) and Kyoto Encyclopedia of Genes and into a higher risk group and a lower risk group. In order Genomes (KEGG) pathway networks were constructed to facilitate comparative analysis, FPKM data were using cutoff criteria of P<0.05 and
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