Dataset Information

Identification of a 6-cytokine prognostic signature in patients with primary glioblastoma harboring M2 microglia/macrophage phenotype relevance.

ABSTRACT:

Background

Glioblastomas (GBM) are comprised of a heterogeneous population of tumor cells, immune cells, and extracellular matrix. Interactions among these different cell types and pro-/anti-inflammatory cytokines may promote tumor development and progression.

Aims

The objective of this study was to develop a cytokine-related gene signature to improve outcome prediction for patients with primary GBM.

Methods

Here, we used Cox regression and risk-score analysis to develop a cytokine-related gene signature in primary GBMs from the whole transcriptome sequencing profile of the Chinese Glioma Genome Atlas (CGGA) database (n=105). We also examined differences in immune cell phenotype and immune factor expression between the high-risk and low-risk groups.

Results

Cytokine-related genes were ranked based on their ability to predict survival in the CGGA database. The six genes showing the strongest predictive value were CXCL10, IL17R, CCR2, IL17B, IL10RB, and CCL2. Patients with a high-risk score had poor overall survival and progression-free survival. Additionally, the high-risk group was characterized by increased mRNA expression of M2 microglia/macrophage markers and elevated levels of IL10 and TGF?1.

Conclusion

The six cytokine-related gene signature is sufficient to predict survival and to identify a subgroup of primary GBM exhibiting the M2 cell phenotype.

SUBMITTER: Cai J

PROVIDER: S-EPMC4433225 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Identification of a 6-cytokine prognostic signature in patients with primary glioblastoma harboring M2 microglia/macrophage phenotype relevance.

Cai Jinquan J Zhang Wei W Yang Pei P Wang Yinyan Y Li Mingyang M Zhang Chuanbao C Wang Zheng Z Hu Huimin H Liu Yanwei Y Li Qingbin Q Wen Jinchong J Sun Bo B Wang Xiaofeng X Jiang Tao T Jiang Chuanlu C

PloS one 20150515 5

<h4>Background</h4>Glioblastomas (GBM) are comprised of a heterogeneous population of tumor cells, immune cells, and extracellular matrix. Interactions among these different cell types and pro-/anti-inflammatory cytokines may promote tumor development and progression.<h4>Aims</h4>The objective of this study was to develop a cytokine-related gene signature to improve outcome prediction for patients with primary GBM.<h4>Methods</h4>Here, we used Cox regression and risk-score analysis to develop a ...[more]

PMID: 25978454

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Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is the most common type of esophageal cancer and the seventh most prevalent cause of cancer-related death worldwide. Tumor microenvironment (TME) has been confirmed to play an crucial role in ESCC progression, prognosis, and the response to immunotherapy. There is a need for predictive biomarkers of TME-related processes to better prognosticate ESCC outcomes.AimTo identify a novel gene signature linked with the TME to predict the prognosis of ESCC.MethodsWe calculated the immune/stromal scores of 95 ESCC samples from The Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm, and identified differentially expressed genes (DEGs) between high and low immune/stromal score patients. The key prognostic genes were further analyzed by the intersection of protein-protein interaction (PPI) networks and univariate Cox regression analysis. Finally, a risk score model was constructed using multivariate Cox regression analysis. We evaluated the associations between the risk score model and immune infiltration via the CIBERSORT algorithm. Moreover, we validated the signature using the Gene Expression Omnibus (GEO) database. Within the ten gene signature, five rarely reported genes were further validated with quantitative real time polymerase chain reaction (qRT-PCR) using an ESCC tissue cDNA microarray.ResultsA total of 133 up-regulated genes were identified as DEGs. Ten prognostic genes were selected based on intersection analysis of univariate COX regression analysis and PPI, and consisted of C1QA, C1QB, C1QC, CD86, C3AR1, CSF1R, ITGB2, LCP2, SPI1, and TYROBP (HR>1, p<0.05). The expression of 9 of these genes in the tumor samples were significantly higher compared to matched adjacent normal tissue based on the GEO database (p<0.05). Next, we assessed the ability of the ten-gene signature to predict the overall survival of ESCC patients, and found that the high-risk group had significantly poorer outcomes compared to the low-risk group using univariate and multivariate analyses in the TCGA and GEO cohorts (HR=2.104, 95% confidence interval:1.343-3.295, p=0.001; HR=1.6915, 95% confidence interval:1.053-2.717, p=0.0297). Additionally, receiver operating characteristic (ROC) curve analysis demonstrated a relatively sensitive and specific profile for the signature (1-, 2-, 3-year AUC=0.672, 0.854, 0.81). To identify the basis for these differences in the TME, we performed correlation analyses and found a significant positive correlation with M1 and M2 macrophages and CD8+ T cells, as well as a strong correlation to M2 macrophage surface markers. A nomogram based on the risk score and select clinicopathologic characteristics was constructed to predict overall survival of ESCC patients. For validation, qRT-PCR of an ESCC patient cDNA microarray was performed, and demonstrated that C1QA, C3AR1, LCP2, SPI1, and TYROBP were up-regulated in tumor samples and predict poor prognosis.ConclusionThis study established and validated a novel 10-gene signature linked with M2 macrophages and poor prognosis in ESCC patients. Importantly, we identified C1QA, C3AR1, LCP2, SPI1, and TYROBP as novel M2 macrophage-correlated survival biomarkers. These findings may identify potential targets for therapy in ESCC patients.

Dataset Information

Identification of a 6-cytokine prognostic signature in patients with primary glioblastoma harboring M2 microglia/macrophage phenotype relevance.

Background

Aims

Methods

Results

Conclusion

Publications

Identification of a 6-cytokine prognostic signature in patients with primary glioblastoma harboring M2 microglia/macrophage phenotype relevance.

Similar Datasets

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