4.6 Article

Gastric Cancer Tumor Microenvironment Characterization Reveals Stromal-Related Gene Signatures Associated With Macrophage Infiltration

Journal

FRONTIERS IN GENETICS
Volume 11, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2020.00663

Keywords

gastric cancer; tumor microenvironment; tumor-associated macrophage; stromal score; bioinformatic; biomarker

Funding

  1. National ST Major Project [2018ZX10301201, 2017ZX10203205]
  2. Innovative Research Groups of National Natural Science Foundation of China [81721091]
  3. Zhejiang International Science and Technology Cooperation Project [2016C04003]
  4. Research Unit Project of Chinese Academy of Medical Sciences [2019-I2M-5-030]
  5. Major program of National Natural Science Foundation of China [91542205]
  6. Natural Science Foundation of Zhejiang Province [LY18H290006]
  7. Key Research Program of Traditional Chinese Medical Science and Technology Plan of Zhejiang Province [2019ZZ010]
  8. Major Traditional Chinese Medical Research of Zhejiang Province [2018ZY006]

Ask authors/readers for more resources

The tumor microenvironment (TME) has attracted attention owing to its essential role in tumor initiation, progression, and metastasis. With the emergence of immunotherapies for various cancers, and their high efficacy, an understanding of the TME in gastric cancer (GC) is critical. The aim of this study was to investigate the effect of various components within the GC TME, and to identify mechanisms that exhibit potential as therapeutic targets. The ESTIMATE algorithm was used to quantify immune and stromal components in GC samples, whose clinicopathological significance and relationship with predicted outcomes were explored. Low tumor mutational burden and high M2 macrophage infiltration, which are considered immune suppressive characteristics and may be responsible for unfavorable prognoses in GC, were observed in the high stromal group (HR = 1.585; 95% CI, 1.112-2.259;P= 0.009). Furthermore, weighted correlation network, differential expression, and univariate Cox analyses were used, along with machine learning methods (LASSO and SVM-RFE), to reveal genome-wide immune phenotypic correlations. Eight stromal-relevant genes cluster (FSTL1, RAB31, FBN1, ANTXR1, LRRC32, CTSK, COL5A2, andENG) were identified as adverse prognostic factors in GC. Finally, using a combination of TIMER database and single-sample gene set enrichment analyses, we found that the identified genes potentially contribute to macrophage recruitment and polarization of tumor-associated macrophages. These findings provide a different perspective into the immune microenvironment and indicate potential prognostic and therapeutic targets for GC immunotherapies.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available