Dataset Information

High CCR4 expression in the tumor microenvironment is a poor prognostic indicator in lung adenocarcinoma.

ABSTRACT: Background:Clinical trials of anti-CCR4 antibody for solid cancers with or without other immune-modulating agents including immune checkpoint blockade therapy are currently underway. However, little is known about the roles of CCR4+ lymphocytes and their prognostic impact in lung cancer. We hypothesized that high CCR4 expression in the tumor microenvironment would be associated with a poor prognosis and would act as a biomarker in lung adenocarcinoma. Methods:First, the prognostic impact of CCR4 gene expression was explored using pooled data from public transcriptomic databases with online survival analysis software. Second, tissue microarrays (TMAs) were constructed from resected lung adenocarcinoma specimens from tumors up to 3 cm in size. The density of CCR4+ lymphocytes infiltrating the tumor was then assessed by immunohistochemistry and related to survival. Confounding factors were controlled for by multivariate analysis using the Cox proportional hazards model. Results:Higher than median expression of the CCR4 gene was identified as an independent poor prognostic factor for overall survival (OS) by multivariate analysis of 720 lung adenocarcinoma patients in the public databases [HR =1.55 (95% CI: 1.03-2.35), P=0.037]. Consistent with this, high CCR4+ tumor-infiltrating lymphocyte (TIL) density was found to be an independent poor prognostic factor for both OS [HR =2.24 (1.01-5.34), P=0.049] and recurrence-free survival (RFS) [HR =2.20 (1.16-4.39), P=0.017] in the patients from whom TMA were obtained (n=180). Age, male gender, predominantly non-lepidic histological subtype, nodal involvement, and low CD8+ TIL density were also independent poor prognostic factors. However, FOXP3 gene expression and Foxp3+ lymphocyte infiltration did not possess any prognostic value in either study. Conclusions:High CCR4 expression in the tumor microenvironment may be a poor prognostic factor in lung adenocarcinoma. Patients with high CCR4+ lymphocyte infiltration may have a poor prognosis and thus be suitable candidates for clinical trials of anti-CCR4 antibody treatment.

SUBMITTER: Karasaki T

PROVIDER: S-EPMC6129889 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

High CCR4 expression in the tumor microenvironment is a poor prognostic indicator in lung adenocarcinoma.

Karasaki Takahiro T Qiang Guangliang G Anraku Masaki M Sun Yanbin Y Shinozaki-Ushiku Aya A Sato Eiichi E Kashiwabara Kosuke K Nagayama Kazuhiro K Nitadori Jun-Ichi JI Sato Masaaki M Murakawa Tomohiro T Kakimi Kazuhiro K Fukayama Masashi M Nakajima Jun J

Journal of thoracic disease 20180801 8

<h4>Background</h4>Clinical trials of anti-CCR4 antibody for solid cancers with or without other immune-modulating agents including immune checkpoint blockade therapy are currently underway. However, little is known about the roles of CCR4<sup>+</sup> lymphocytes and their prognostic impact in lung cancer. We hypothesized that high CCR4 expression in the tumor microenvironment would be associated with a poor prognosis and would act as a biomarker in lung adenocarcinoma.<h4>Methods</h4>First, the ...[more]

PMID: 30233846

Similar Datasets

Project description:Background:Prognostic genes in the tumor microenvironment play an important role in immune biological processes and the response of cancer to immunotherapy. Thus, we aimed to assess new biomarkers that are associated with immune/stromal cells in lung adenocarcinomas (LUAD) using the ESTIMATE algorithm, which also significantly affects the prognosis of cancer. Methods:The RNA sequencing (RNA-Seq) and clinical data of LUAD were downloaded from the the Cancer Genome Atlas (TCGA ). The immune and stromal scores were calculated for each sample using the ESTIMATE algorithm. The LUAD gene chip expression profile data and the clinical data (GSE37745, GSE11969, and GSE50081) were downloaded from the Gene Expression Omnibus (GEO) for subsequent validation analysis. Differentially expressed genes were calculated between high and low score groups. Univariate Cox regression analysis was performed on differentially expressed genes (DEGs) between the two groups to obtain initial prognosis genes. These were verified by three independent LUAD cohorts from the GEO database. Multivariate Cox regression was used to identify overall survival-related DEGs. UALCAN and the Human Protein Atlas were used to analyze the mRNA /protein expression levels of the target genes. Immune cell infiltration was evaluated using the Tumor Immune Estimation Resource (TIMER) and CIBERSORT methods, and stromal cell infiltration was assessed using xCell. Results:In this study, immune scores and stromal scores are significantly associated with the clinical characteristics of LUAD, including T stage, M stage, pathological stage, and overall survival time. 530 DEGs (18 upregulated and 512 downregulated) were found to coexist in the difference analysis with the immune scores and stromal scores subgroup. Univariate Cox regression analysis showed that 286 of the 530 DEGs were survival-related genes (p < 0.05). Of the 286 genes initially identified, nine prognosis-related genes (CSF2RB, ITK, FLT3, CD79A, CCR4, CCR6, DOK2, AMPD1, and IGJ) were validated from three separate LUAD cohorts. In addition, functional analysis of DEGs also showed that various immunoregulatory molecular pathways, including regulation of immune response and the chemokine signaling pathways, were involved. Five genes (CCR6, ITK, CCR4, DOK2, and AMPD1) were identified as independent prognostic indicators of LUAD in specific data sets. The relationship between the expression levels of these genes and immune genes was assessed. We found that CCR6 mRNA and protein expression levels of LUAD were greater than in normal tissues. We evaluated the infiltration of immune cells and stromal cells in groups with high and low levels of expression of CCR6 in the TCGA LUAD cohort. In summary, we found a series of prognosis-related genes that were associated with the LUAD tumor microenvironment.

Project description:Lung adenocarcinoma (LUAD) has been the major cause of tumor-associated mortality in recent years and exhibits a poor outcome. New data revealed that peroxisomes have a function in the regulation of the development and progression of several tumors. However, the prognostic values of peroxisome-related genes (PRGs) were rarely reported. Genomic sequence, mutation, and clinical data of 535 LUAD tissues were obtained from TCGA data sets. Within the TCGA cohort, a multigene signature was constructed with the assistance of the LASSO Cox regression model. Three GEO data sets, including GSE3141, GSE31210, and GSE72094, were obtained as validation cohorts. ROC assays, Kaplan-Meier methods, and multivariate assays were applied to examine the prognostic capacities of the novel signature. Gene Set Enrichment Analysis (GSEA) was performed to further understand the underlying molecular mechanisms. In this study, we identified 47 differentially expressed peroxisome-related genes (PRGs), including 25 increased and 22 decreased PRGs. A prognostic model of six PRGs was established. The univariate and multivariate Cox analyses both showed that the p value of risk score was less than 0.05. In LUAD patients, the strong connection between the risk score and overall survival was further verified in three other GEO data sets. TMB and cancer stem cell infiltration were shown to be significantly higher in the high-risk group in comparison to the low-risk group. The TIDE score of the group with the low risk was considerably greater than that of the group with the high risk. Several drugs, targeting PRG-related genes, were available for the treatments of LUAD. Overall, we developed a novel peroxisome-related prognostic signature for LUAD patients. This signature could successfully indicate LUAD patients' chances of survival as well as their immune system's responsiveness to treatments. In addition, it has the potential to serve as immunotherapeutic targets for LUAD patients.

Project description:Background: Immune and stromal cells in the tumor microenvironment (TME) significantly contribute to the prognosis of lung adenocarcinoma; however, the TME-related immune prognostic signature is unknown. The aim of this study was to develop a novel immune prognostic model of the TME in lung adenocarcinoma. Methods: First, the immune and stromal scores among lung adenocarcinoma patients were determined using the ESTIMATE algorithm in accordance with The Cancer Genome Atlas (TCGA) database. Differentially expressed immune-related genes (IRGs) between high and low immune/stromal score groups were analyzed, and a univariate Cox regression analysis was performed to identify IRGs significantly correlated with overall survival (OS) among patients with lung adenocarcinoma. Furthermore, a least absolute shrinkage and selection operator (LASSO) regression analysis was performed to generate TME-related immune prognostic signatures. Gene set enrichment analysis was performed to analyze the mechanisms underlying these immune prognostic signatures. Finally, the functions of hub IRGs were further analyzed to delineate the potential prognostic mechanisms in comprehensive TCGA datasets. Results: In total, 702 intersecting differentially expressed IRGs (589 upregulated and 113 downregulated) were screened. Univariate Cox regression analysis revealed that 58 significant differentially expressed IRGs were correlated with patient prognosis in the training cohort, of which three IRGs (CLEC17A, INHA, and XIRP1) were identified through LASSO regression analysis. A robust prognostic model was generated on the basis of this three-IRG signature. Furthermore, functional enrichment analysis of the high-risk-score group was performed primarily on the basis of metabolic pathways, whereas analysis of the low-risk-score group was performed primarily on the basis of immunoregulation and immune cell activation. Finally, hub IRGs CLEC17A, INHA, and XIRP1 were considered novel prognostic biomarkers for lung adenocarcinoma. These hub genes had different mutation frequencies and forms in lung adenocarcinoma and participated in different signaling pathways. More importantly, these hub genes were significantly correlated with the infiltration of CD4+ T cells, CD8+ T cells, macrophages, B cells, and neutrophils. Conclusions: The robust novel TME-related immune prognostic signature effectively predicted the prognosis of patients with lung adenocarcinoma. Further studies are required to further elucidate the regulatory mechanisms of these hub IRGs in the TME and to develop new treatment strategies.

Dataset Information

High CCR4 expression in the tumor microenvironment is a poor prognostic indicator in lung adenocarcinoma.

Publications

High CCR4 expression in the tumor microenvironment is a poor prognostic indicator in lung adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets