Dataset Information

Distribution pattern of tumor associated macrophages predicts the prognosis of gastric cancer.

ABSTRACT:

Purpose

As mayor biomarkers in tumor microenvironment (TME), tumor associated macrophages (TAMs) of gastric cancer (GC) still needs further studies in terms of the number and distribution pattern.

Methods

Herein, tissue microarrays (TMA) incorporating 494 GC surgical samples in duplicate were stained for TAMs infiltration analysis. TAMs number was counted according to the locations, including infiltrating macrophages in cancer nest (MC), in invasive front (MF) and in stroma (MS). Correlations between TAMs number, distribution pattern and clinic-pathological features and survival analyses were performed.

Results

Infiltrating macrophages number in GC tissues was much higher than that in peritumoral tissues. TAMs number was not significantly correlated with the overall survival (OS). TAMs distribution pattern could be categorized into MC or MF/MS dominant pattern, and correlated with histological grade (P =0.001). The median OS of MF/MS dominant pattern (22.1, 95%CI: 23.5-28.9) was significantly shorter than that of MC dominant pattern (25.6, 95%CI: 28.5-35.6) (P =0.002). By receiver operating characteristic curve (ROC) analysis, the predictive value of TAMs distribution pattern was superior to histological grade and pM stage, but inferior to pN and TNM stage.

Conclusions

TAMs distribution pattern could be an independent prognostic factor for the OS of GC patients, and patients with MF/MS dominant pattern had worse outcomes.

SUBMITTER: Liu JY

PROVIDER: S-EPMC5696219 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Publications

Distribution pattern of tumor associated macrophages predicts the prognosis of gastric cancer.

Liu Jiu-Yang JY Peng Chun-Wei CW Yang Gui-Fang GF Hu Wen-Qing WQ Yang Xiao-Jun XJ Huang Chao-Qun CQ Xiong Bin B Li Yan Y

Oncotarget 20171006 54

<h4>Purpose</h4>As mayor biomarkers in tumor microenvironment (TME), tumor associated macrophages (TAMs) of gastric cancer (GC) still needs further studies in terms of the number and distribution pattern.<h4>Methods</h4>Herein, tissue microarrays (TMA) incorporating 494 GC surgical samples in duplicate were stained for TAMs infiltration analysis. TAMs number was counted according to the locations, including infiltrating macrophages in cancer nest (MC), in invasive front (MF) and in stroma (MS). ...[more]

PMID: 29190953

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Project description:BackgroundSUMOylation is an important component of post-translational protein modifications (PTMs), and bladder cancer (BCa) is the ninth most common cancer around the world. But the comprehensive role of SUMOylation in shaping tumor microenvironment (TME) and influencing tumor clinicopathological features and also the prognosis of patients remains unclear.MethodsUsing the data downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), we comprehensively evaluated the SUMOylation patterns of 570 bladder cancer samples, and systematically correlated these SUMOylation patterns with TME immune cell infiltrating characteristics. The SUMO score was constructed to quantify SUMOylation patterns of individuals using principal component analysis (PCA) algorithms.ResultsTwo distinct SUMOylation patterns and gene clusters were finally determined. Significant differences in the prognosis of patients were found among two different SUMOylation patterns and gene clusters, so were in the mRNA transcriptome and the landscape of TME immune cell infiltration. We also established a set of scoring system named SUMO score to quantify the SUMOylation pattern of individuals with BCa, which was discovered to be tightly connected with tumor clinicopathological characteristics and could predict the prognosis of patients with BCa. Moreover, SUMO score was a considerable predictive indicator for the survival outcome independent of tumor mutation burden (TMB) and low SUMO score was related to better response to immunotherapy using PD-1 blockade. We also found that there existed a significant relationship between sensitivity to commonly used chemotherapy drugs and SUMO score. Finally, a nomograph based on five features, namely, SUMO score, age, gender, T category, and M category was constructed to predict the survival probability of patients with BCa in 1, 3, and 5 years, respectively.ConclusionsOur work demonstrated and overviewed the complicated regulation mechanisms of SUMOylation in bladder cancer, and better understanding and evaluating SUMOylation patterns could be helpful in guiding clinical therapeutic strategy and improving the prognosis of patients with BCa.

Project description:Background: Gastric carcinoma (GC) is a carcinoma with a high incidence rate, and it is a deadly carcinoma globally. An effective tool, that is, able to predict different survival outcomes for GC patients receiving individualized treatments is deeply needed. Methods: In total, data from 975 GC patients were collected from TCGA-STAD, GSE15459, and GSE84437. Then, we performed a comprehensive unsupervised clustering analysis based on 54 TGFβ-pathway-related genes and correlated these patterns with tumor microenvironment (TME) cell-infiltrating characteristics. WGCNA was then applied to find the module that had the closest relation with these patterns. The least absolute shrinkage and selection operator (LASSO) algorithm was combined with cross validation to narrow down variables and random survival forest (RSF) was used to create a risk score. Results: We identified two different TGFβ regulation patterns and named them as TGFβ Cluster 1 and Cluster 2. TGFβ Cluster 1 was linked to significantly poorer survival outcomes and represented an inflamed TME subtype of GC. Using WGCNA, a module (magenta) with the closest association with the TGFβ clusters was identified. After narrowing down the gene list by univariate Cox regression analysis, the LASSO algorithm and cross validation, four of the 243 genes in the magenta module were applied to build a risk score. The group with a higher risk score exhibited a considerably poorer survival outcome with high predictive accuracy. The risk score remained an independent risk factor in multivariate Cox analysis. Moreover, we validated this risk score using GSE15459 and GSE84437. Furthermore, we found that the group with a higher risk score represented an inflamed TME according to the evidence that the risk score was remarkably correlated with several steps of cancer immunity cycles and a majority of the infiltrating immune cells. Consistently, the risk score was significantly related to immune checkpoint genes and T cell-inflamed gene expression profiles (GEPs), indicating the value of predicting immunotherapy. Conclusions: We have developed and validated a TGFβ-associated signature, that is, capable of predicting the survival outcome as well as depicting the TME immune characteristics of GC. In summary, this signature may contribute to precision medicine for GC.

Dataset Information

Distribution pattern of tumor associated macrophages predicts the prognosis of gastric cancer.

Purpose

Methods

Results

Conclusions

Publications

Distribution pattern of tumor associated macrophages predicts the prognosis of gastric cancer.

Similar Datasets

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