Dataset Information

Identification of Distinct Immune Subtypes in Colorectal Cancer Based on the Stromal Compartment.

ABSTRACT: The tumor environment is of vital importance for the incidence and development of colorectal cancer. Increasing evidence in recent years has elaborated the vital role of the tumor environment in cancer subtype classification and patient prognosis, but a comprehensive understanding of the colorectal tumor environment that is purely dependent on the stromal compartment is lacking. To decipher the tumor environment in colorectal cancer and explore the role of its immune context in cancer classification, we performed a gene expression microarray on the stromal compartment of colorectal cancer and adjacent normal tissues. Through the integrated analysis of our data with public gene expression microarray data of stromal and epithelial colorectal cancer tissues processed through laser capture microdissection, we identified four highly connected gene modules representing the biological features of four tissue compartments by applying a weighted gene coexpression network analysis algorithm and classified colorectal cancers into three immune subtypes by adopting a nearest template prediction algorithm. A systematic analysis of the four identified modules mainly reflected the close interplay between the biological changes of intrinsic and extrinsic characteristics at the initiation of colorectal cancer. Colorectal cancers were stratified into three immune subtypes based on gene templates identified from representative gene modules of the stromal compartment: active immune, active stroma, and mixed type. These immune subtypes differed by the immune cell infiltration pattern, expression of immune checkpoint inhibitors, mutation landscape, extent of mutation burden, extent of copy number burden, prognosis and chemotherapeutic sensitivity. Further analysis indicated that activation of the NF-kB signaling pathway was the major mechanism causing the no immune infiltration milieu in the active stroma subtype and that inhibitors of the NF-kB signaling pathway could be candidate drugs for treating patients with an active stroma. Overall, these results suggest that characterizing colorectal cancer by the tumor environment is of vital importance in predicting patients' clinical outcomes and helping guide precision and personalized treatment.

SUBMITTER: Shen R

PROVIDER: S-EPMC6965328 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Identification of Distinct Immune Subtypes in Colorectal Cancer Based on the Stromal Compartment.

Shen Rongfang R Li Ping P Li Bing B Zhang Botao B Feng Lin L Cheng Shujun S

Frontiers in oncology 20200110

The tumor environment is of vital importance for the incidence and development of colorectal cancer. Increasing evidence in recent years has elaborated the vital role of the tumor environment in cancer subtype classification and patient prognosis, but a comprehensive understanding of the colorectal tumor environment that is purely dependent on the stromal compartment is lacking. To decipher the tumor environment in colorectal cancer and explore the role of its immune context in cancer classifica ...[more]

PMID: 31998649

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Project description:BackgroundColorectal cancer (CRC) is one of the most common malignancies.An early diagnosis and an accurate prognosis are major focuses of CRC research. Tumor microenvironment cells and the extent of infiltrating immune and stromal cells contribute significantly to the tumor prognosis.MethodsImmune and stromal scores were calculated based on the ESTIMATE algorithm using the sample expression profile of the The Cancer Genome Atlas (TCGA) database. GSE102479 was used as the validation database. Differentially expressed genes whose expression was significantly associated with the prognosis of CRC patients were identified based on the immune matrix score. Survival analysis was conducted on the union of the differentially expressed genes. A protein-protein interaction (PPI) network was constructed using the STRING database to identify the closely connected modules. To conduct functional enrichment analysis of the relevant genes, GO and KEGG pathway analyses were performed with Cluster Profiler. Pivot analysis of the ncRNAs and TFs was performed by using the RAID2.0 database and TRRUST v2 database. TF-mRNA regulatory relationships were analyzed in the TRRUST V2 database. Hubgene targeting relationships were screened in the TargetScan, miRTarBase and miRDB databases. The SNV data of the hub genes were analyzed by using the R maftools package. A ROC curve was drawn based on the TCGA database. The proportion of immune cells was estimated using CIBERSORT and the LM22 feature matrix.ResultsThe results showed that the matrix score was significantly correlated with colorectal cancer stage T. A total of 789 differentially expressed genes and 121 survival-related prognostic genes were identified. The PPI network showed that 22 core genes were related to the CRC prognosis. Furthermore, four ncRNAs that regulated the core prognosis genes, 11 TFs with regulatory effects on the core prognosis genes, and two drugs, quercetin and pseudoephedrine, that have regulatory effects on colorectal cancer were also identified.ConclusionsWe obtained a list of tumor microenvironment-related genes for CRC patients. These genes could be useful for determining the prognosis of CRC patients. To confirm the function of these genes, additional experiments are necessary.

Project description:Immune checkpoint blockade (ICB) has become a promising therapy for multiple cancers. However, only a small proportion of patients display a limited antitumor response. The present study aimed to classify distinct immune subtypes and investigate the tumor microenvironment (TME) of urothelial carcinoma, which may help to understand treatment failure and improve the immunotherapy response. RNA-seq data and clinical parameters were obtained from TCGA-BLCA, E-MTAB-4321, and IMVigor210 datasets. A consensus cluster method was used to distinguish different immune subtypes of patients. Infiltrating immune cells, TME signatures, immune checkpoints, and immunogenic cell death modulators were evaluated in distinct immune subtypes. Dimension reduction analysis was performed to visualize the immune status of urothelial carcinoma based on graph learning. Weighted gene co-expression network analysis (WGCNA) was performed to obtain hub genes to predict responses after immunotherapy. Patients with urothelial carcinoma were classified into four distinct immune subtypes (C1, C2, C3 and C4) with various types of molecular expression, immune cell infiltration, and clinical characteristics. Patients with the C3 immune subtype displayed abundant immune cell infiltrations in the tumor microenvironment and were typically identified as "hot" tumor phenotypes, whereas those with the C4 immune subtype with few immune cell infiltrations were identified as "cold" tumor phenotypes. The immune-related and metastasis-related signaling pathways were enriched in the C3 subtype compared to the C4 subtype. In addition, tumor mutation burden, inhibitory immune checkpoints, and immunogenic cell death modulators were highly expressed in the C3 subtype. Furthermore, patients with the C4 subtype had a better probability of overall survival than patients with the C3 subtype in TCGA-BLCA and E-MTAB-4321 cohorts. Patients with the C1 subtype had the best prognosis when undergoing anti-PD-L1 antibody treatment. Finally, the immune landscape of urothelial carcinoma showed the immune status in each patient, and TGFB3 was identified as a potential biomarker for the prediction of immunotherapy resistance after anti-PD-L1 monoclonal antibody treatment. The present study provided a bioinformatics basis for understanding the immune landscape of the tumor microenvironment of urothelial carcinoma.

Dataset Information

Identification of Distinct Immune Subtypes in Colorectal Cancer Based on the Stromal Compartment.

Publications

Identification of Distinct Immune Subtypes in Colorectal Cancer Based on the Stromal Compartment.

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