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.

Background: Evidence suggests that metastasis is chiefly responsible for the poor prognosis of colon adenocarcinoma (COAD). The tumor microenvironment plays a vital role in regulating this biological process. However, the mechanisms involved remain unclear. The aim of this study was to identify crucial metastasis-related biomarkers in the tumor microenvironment and investigate its association with tumor-infiltrating immune cells. Methods: We obtained gene expression profiles and clinical information from The Cancer Genome Atlas database. According to the “Estimation of STromal and Immune cells in MAlignant Tumor tissue using Expression data” algorithm, each sample generated the immune and stromal scores. Following correlation analysis, the metastasis-related gene was identified in The Cancer Genome Atlas database and validated in the GSE40967 dataset from Gene Expression Omnibus. The correlation between metastasis-related gene and infiltrating immune cells was assessed using the Tumor IMmune Estimation Resource database. Results: The analysis included 332 patients; the metastatic COAD samples showed a low immune score. Correlation analysis results showed that interferon regulatory factor 1 (IRF1) was associated with tumor stage, lymph node metastasis, and distant metastasis. Furthermore, significant associations between IRF1 and CD8+ T cells, T cell (general), dendritic cells, T-helper 1 cells, and T cell exhaustion were demonstrated by Spearmans correlation coefficients and P values. Conclusions: The present findings suggest that IRF1 is associated with metastasis and the degree of immune infiltration of CD8+ T cells (general), dendritic cells, T-helper 1 cells, and T cell exhaustion in COAD. These results may provide information for immunotherapy in colon cancer.

Background RNA N6-methyladenosine (m 6 A) methylation, the most abundant and prominent form of epigenetic modification, is involved in hepatocellular carcinoma (HCC) initiation and progression. However, the role of m 6 A methylation in HCC tumor microenvironment (TME) formation is unexplored. This study aimed to reveal the TME features of HCC patients with distinct m 6 A expression patterns and establish a prognostic model based on m6A signatures for HCC cohorts. Material/Methods We classified the m 6 A methylation patterns in 365 HCC samples based on 21 m 6 A modulators using a consensus clustering algorithm. Single-sample gene set enrichment analysis algorithm was used to quantify the abundance of immune cell infiltration. Gene set variation analysis revealed the biological characteristics between the m6A modification patterns. The m 6 A-based prognostic model was constructed using a training set with least absolute shrinkage and selection operator regression and validated in internal and external datasets. Results Two distinct m 6 A modification patterns exhibiting different TME immune-infiltrating characteristics, heterogeneity, and prognostic variations were identified in the HCC cohort. After depicting the immune landscape of TME in HCC, we found patients with high LRPPRC m 6 A modulator expression had depletion of T cells, cytotoxic cells, dendritic cells, and cytolytic activity response. A high m 6 A score, characterized by suppression of immunity, indicated an immune-excluded TME phenotype, with poor survival. A nomogram was developed to facilitate HCC clinical decision making. Conclusions Our results highlight the nonnegligible role of m 6 A methylation in TME formation and reveal a potential clinical application of the m 6 A-associated prognostic model for patients with HCC.

Background: Hepatocellular carcinoma is one of the most aggressive gastrointestinal tumor, with a high recurrence and mortality rate. Novel immunotherapy targeting the tumor microenvironment are innovative and promising therapeutic approaches for some tumor, including hepatocellular carcinoma. However, patr of patients with HCC remain a diminished response to immunotherapy on account of the insufficient understanding on the tumor microenvironment. Methods: The correlation between infiltrated immune cells and hepatocellular carcinoma patients prognosis were confirmed via MCP-counter algorithms. The single-cell RNA sequencing dataset (GSE146115) was performed to identify T cells marker genes in hepatocellular carcinoma. GSE10141, GSE14520 dataset and TCGA-liver hepatocellular carcinoma were used to construct and validate the T cell marker genes signature (TCMS).Results: The survival analysis revealed the correlation between infiltrating T cells and the overall survival of patients with hepatocellular carcinoma. Four T cell marker genes included in the TCMS model were: HELZ, GZMA, SLC2A2, JAK3. The TCMS risk score could stratify patients with hepatocellular carcinoma into high- and low-risk score subgroups. TCMS risk score remained a influential feature of overall survival and one of the independent prognostic risk factors in multivariate analysis in TCGC validation cohort. Besides, correlation analysis indicated increased expression of HELZ, GZMA and JAK3 were significantly correlated with high degree of CD8+ T cell, CD4+ T cell infiltration.Conclusion: We successfully constructed the T cell marker genes signature with powerful predictive function and provided a new understanding of T cell infiltration in tumor microenvironment which might offer practices instructions for HCC immunotherapy.