Tumor Microenvironment in Gene Signatures: Critical Biology or Confounding Noise?
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ABSTRACT:
SUBMITTER: Morris JS
PROVIDER: S-EPMC4987158 | biostudies-other | 2016 Aug
REPOSITORIES: biostudies-other
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Project description:ObjectiveThe aim of this study is to systematically analyze the transcriptional sequencing data of cervical cancer (CC) to find an Tumor microenvironment (TME) prognostic marker to predict the survival of CC patients.MethodsThe expression profiles and clinical follow-up information of CC were downloaded from the TCGA and GEO. The RNA-seq data of TCGA-CESC samples were used for CIBERSORT analysis to evaluate the penetration pattern of TME in 285 patients, and construct TMEscore. Other data sets were used to validate and evaluate TMEscore model. Further, survival analysis of TMEscore related DEGs was done to select prognosis genes. Functional enrichment and PPI networks analysis were performed on prognosis genes.ResultsThe TMEscore model has relatively good results in TCGA-CESC (HR = 2.47,95% CI = 1.49-4.11), TCGA-CESC HPV infection samples (HR = 2.13,95% CI = 1-4.51), GSE52903 (HR = 2.65, 95% CI = 1.06-6.6), GSE44001 (HR = 2.1, 95% CI = 0.99-4.43). Patients with high/low TMEscore have significant difference in prognosis (log-rank test, P = 0.00025), and the main difference between high TMEscore subtypes and low TMEscore subtypes is immune function-related pathways. Moreover, Kaplan-Meier survival curves found out a list of identified prognosis genes (n = 86) which interestingly show significant enrichment in immune-related functions. Finally, PPI network analysis shows that highly related nodes such as CD3D, CD3E, CD8A, CD27 in the module may become new targets of CC immunotherapy.ConclusionsTMEscore may become a new prognostic indicator predicting the survival of CC patients. The prognostic genes (n = 86) may help provide new strategies for tumor immunotherapy.
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Project description:BackgroundThe tumor microenvironment (TME) involves infiltration of multiple immune cell subsets, which could influence the prognosis and clinical characteristics. The increasing evidence on the role of tumor-infiltrating lymphocytes (TILs) in primary and metastatic melanomas supports that the immune system is involved in the progression and outcomes of melanoma. However, the immune infiltration landscape in melanoma has not been systematically elucidated.MethodsIn this study, we used CIBERSORT and ESTIMATE algorithms to analyze immune infiltration pattern of 993 melanoma samples. Then we screened differential expression genes (DEGs) related to immune subtypes and survival. The immune cell infiltration (ICI) score was constructed by using principal-component analysis (PCA) based on immune signature genes from DGEs. Gene set enrichment analysis (GSEA) was applied to explore high and low ICI score related pathways. Finally, the predictive ability of ICI score was evaluated in survival prognosis and immunotherapy benefit.ResultWe identified three ICI clusters and three gene clusters associated with different immune subtypes and survival outcomes. Then the ICI score was constructed, and we found that high ICI score exhibited activated immune characteristics and better prognosis. High ICI score was significantly enriched in immune pathways and highly expressed immune signature genes. More importantly, we confirmed that melanoma patients with high ICI score had longer overall survival and rate of response to immunotherapy.ConclusionWe presented a comprehensive immune infiltration landscape in melanoma. Our results will facilitate understanding of the melanoma tumor microenvironment and provide a new immune therapy strategy.
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Project description:The osteosarcoma (OS) microenvironment is composed of tumor cells, immune cells, and stromal tissue and is emerging as a pivotal player in OS development and progression. Thus, microenvironment-targeted strategies are urgently needed to improve OS treatment outcomes. Using principal component analysis (PCA), we systematically examined the tumor microenvironment (TME) and immune cell infiltration of 88 OS cases and constructed a TME scoring system based on the TMEscore high and TMEscore low phenotypes. Our analysis revealed that TMEscore high correlates with longer survival in OS patients, elevated immune cell infiltration, increased immune checkpoints, and increased sensitivity to chemotherapy. TMEscore low strongly correlated with immune exclusion. These observations were externally validated using a GEO dataset (GSE21257) from 53 OS patients. Our laboratory data also proved our findings. This finding enhances our understanding of the immunological landscape in OS and may uncover novel targeted therapeutic strategies.
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Project description:BACKGROUND Telomeres are strongly associated with cancer, as their shortening over time is associated with an increased risk of tumor growth and progression. However, the prognostic value of telomere-related genes (TRGs) in breast cancer has not been systematically elucidated. MATERIAL AND METHODS The transcriptome and clinical data of breast cancer were downloaded from TCGA and GEO databases, and prognostic TRGs were identified by differential expression analysis and univariate and multivariate Cox regression analyses. Gene set enrichment analysis (GSEA) of different risk groups was performed. Molecular subtypes of breast cancer were constructed by consensus clustering analysis, and the differences in immune infiltration and chemotherapy sensitivity between subtypes were analyzed. RESULTS Differential expression analysis revealed 86 significantly differentially expressed TRGs in breast cancer, of which 43 were significantly associated with breast cancer prognosis. A predictive risk signature consisting of 6 tumor-related genes (TRGs) was developed, which can accurately stratify patients with breast cancer into 2 distinct groups with significantly different prognoses. Significantly different risk scores were found among different racial groups, treatment groups, and pathological features groups. GSEA results showed that patients in the low-risk group had activated immune responses and repressed cilium-related biological processes. Consistent clustering analysis based on these 6 TRGs obtained 2 molecular models with significant prognosis differences, which revealed distinct immune infiltration and chemotherapy sensitivity. CONCLUSIONS This study conducted a systematic investigation of the expression pattern of TRGs in breast cancer and its prognostic and clustering implications, thereby offering a reference for utilizing it to predict prognosis and evaluate treatment response.
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Project description:This study characterizes the transcriptome and the cellular tumor microenvironment (TME) of conjunctival melanoma (CM) and identifies prognostically relevant biomarkers. 12 formalin-fixed and paraffin-embedded CM were analyzed by MACE RNA sequencing, including six cases each with good or poor clinical outcome, the latter being defined by local recurrence and/or systemic metastases. Eight healthy conjunctival specimens served as controls. The TME of CM, as determined by bioinformatic cell type enrichment analysis, was characterized by the enrichment of melanocytes, pericytes and especially various immune cell types, such as plasmacytoid dendritic cells, natural killer T cells, B cells and mast cells. Differentially expressed genes between CM and control were mainly involved in inhibition of apoptosis, proteolysis and response to growth factors. POU3F3, BIRC5 and 7 were among the top expressed genes associated with inhibition of apoptosis. 20 genes, among them CENPK, INHA, USP33, CASP3, SNORA73B, AAR2, SNRNP48 and GPN1, were identified as prognostically relevant factors reaching high classification accuracy (area under the curve: 1.0). The present study provides new insights into the TME and the transcriptional profile of CM and additionally identifies new prognostic biomarkers. These results add new diagnostic tools and may lead to new options of targeted therapy for CM.
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Project description:Background: Pancreatic cancer (PC) is one of the most lethal types of cancer with extremely poor diagnosis and prognosis, and the tumor microenvironment plays a pivotal role during PC progression. Poor prognosis is closely associated with the unsatisfactory results of currently available treatments, which are largely due to the unique pancreatic tumor microenvironment (TME). Methods: In this study, a total of 177 patients with PC from The Cancer Genome Atlas (TCGA) cohort and 65 patients with PC from the GSE62452 cohort in Gene Expression Omnibus (GEO) were included. Based on the proportions of 22 types of infiltrated immune cell subpopulations calculated by cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the TME was classified by K-means clustering and differentially expressed genes (DEGs) were determined. A combination of the elbow method and the gap statistic was used to explore the likely number of distinct clusters in the data. The ConsensusClusterPlus package was utilized to identify radiomics clusters, and the samples were divided into two subtypes. Result: Survival analysis showed that the patients with TMEscore-high phenotype had better prognosis. In addition, the TMEscore-high had better inhibitory effect on the immune checkpoint. A total of 10 miRNAs, 311 DEGs, and 68 methylation sites related to survival were obtained, which could be biomarkers to evaluate the prognosis of patients with pancreatic cancer. Conclusions: Therefore, a comprehensive description of TME characteristics of pancreatic cancer can help explain the response of pancreatic cancer to immunotherapy and provide a new strategy for cancer treatment.
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Project description: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.
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