Abstract

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, and ENG) 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.

Highlights

  • Gastric cancer (GC) is the fifth most frequently diagnosed cancer and the third leading cause of cancer-related deaths worldwide (Bray et al, 2018)

  • Based on the median value of TMB, we found that the high-TMB group was significantly associated with better survival prognosis than the low-TMB group (HR = 1.648, P = 0.031) in GC (Figure 1D)

  • Biological behaviors of cancers are determined by genetic instability, cancer cell epigenetic abnormalities, and the surrounding milieu that the cancer cells interact with for growth, survival, proliferation, and metastasis (Hanahan and Weinberg Robert, 2011; Mlecnik et al, 2011)

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Summary

Introduction

Gastric cancer (GC) is the fifth most frequently diagnosed cancer and the third leading cause of cancer-related deaths worldwide (Bray et al, 2018). With the understanding of the diversity and complexity of TME in GC deepening, mounting evidence suggests its crucial role in tumor initiation, progression, immune evasion, and its effect on tumor response to immunotherapies (Lee et al, 2014). Nowadays, emerging computational methods are supporting these analyses and rapidly revealing a broader intra-tumoral immune landscape. Such methods are based on gene expression profiles and immunological features, which include Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and Celltype Identification By Estimating Relative Subsets Of RNA Transcripts (CIOBSORT) algorithms (Yoshihara et al, 2013; Newman et al, 2015)

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