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Identification of a Prognostic Immune Signature for Esophageal Squamous Cell Carcinoma to Predict Survival and Inflammatory Landscapes

ABSTRACT: Immunotherapy has achieved success in the treatment of esophageal squamous cell carcinoma (ESCC). However, studies concerning immune phenotypes within the ESCC microenvironment and their relationship with prognostic outcomes are limited. We constructed and validated an individual immune-related risk signature for patients with ESCC. We collected 196 ESCC cases, including 119 samples from our previous public data (GSE53624) to use as a training set and an independent cohort with 77 quantitative real-time polymerase chain reaction (qRT-PCR) data, which we used for validation. Head and neck squamous cell carcinoma (HNSCC) and lung squamous cell carcinoma (LUSC) cohorts were also collected for validation. A least absolute shrinkage and selection operator (LASSO) model and a stepwise Cox proportional hazards regression model were used to construct the immune-specific signature. The potential mechanism and inflammatory landscapes of the signature were explored using bioinformatics and immunofluorescence assay methods. This signature predicted different prognoses in clinical subgroups and the independent cohort, as well as in patients with HNSCC and LUSC. Further exploration revealed that the signature was associated with specific inflammatory activities (activation of macrophages and T-cell signaling transduction). Additionally, high-risk patients exhibited distinctive immune checkpoints panel and higher regulatory T cell and fibroblast infiltration. This signature served as an independent prognostic factor in ESCC. This was the first applicable immune-related risk signature for ESCC. Our results furnished new hints of immune profiling of ESCC, which may provide some clues to further optimize associated cancer immunotherapies.

SUBMITTER: Zhang C

PROVIDER: S-EPMC7773787 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Project description:Esophageal carcinoma (EC) is a common malignant cancer worldwide. Esophageal squamous cell carcinoma (ESCC), the main type of EC, is difficult to treat because of the widespread morbidity, high fatality rates, and low quality of life caused by postoperative complications and no specific molecular target. In this study, we screened genes to establish a prognostic model for ESCC. The transcriptome expression profiles of 81 ESCC tissues and 340 normal esophageal mucosal epithelium tissues were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cohorts. The transcriptome expression datasets of 19 esophageal squamous carcinoma cell lines were downloaded from Cancer Cell Line Encyclopedia (CCLE). The R software Limma package was used to identify 6,231 differentially expressed genes and 647 differentially expressed immune-related genes between normal and ESCC tissues. Gene functional analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was used to screen out 18 immune-related prognostic genes. We then established the prognostic and risk signature using these genes, and the patients were divided into low-risk and high-risk groups. Compared with high-risk group patients, the low-risk group patients had longer overall survival. M1 macrophages and resting dendritic cells were differentially distributed between the low-risk and high-risk groups and were related to patient survival. We also examined the functional immune cell and immune molecule levels in low-risk and high-risk group patients, with significant differences in the tumor microenvironment between the two groups. To further verify the accuracy of the prognostic risk model, we performed area under the ROC curve (AUC) analysis. The AUC value was 0.931 for the prognostic risk, which was better than the microsatellite instability (MSI) and Tumor Immune Dysfunction and Exclusion (TIDE) scores. In conclusion, we found 18 immune-related prognostic genes related to the occurrence of ESCC and established a prognostic model for predicting disease severity.

Project description:Esophageal squamous cell carcinoma (ESCC) is one of the six most commonly diagnosed tumor types in the Chinese population. Gene expression profiles help to predict the prognosis of patients with ESCC. Disease recurrence as the survival endpoint has been analyzed in the majority of previous studies; therefore, the aim of the present study was to construct a robust gene signature in order to determine the overall survival (OS) of patients with ESCC. The gene expression and clinical data of patients with ESCC were downloaded from The Cancer Genome Atlas (TCGA) database. Of the selected data (172 samples from surviving patients), 72 samples were randomly selected as modeling data, and verification was conducted using the entire dataset. Data from the Gene Expression Omnibus was analyzed simultaneously, and a venn diagram was constructed to determine the intersection between these two sets of results; a total of 97 genes were found to be associated with OS. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these genes were primarily associated with specific pathways (Homo sapiens), including DNA replication, protein processing in endoplasmic reticulum and influenza A. A five-gene signature was identified with a robust likelihood-based survival modeling approach. Using regression coefficient modeling, a prognostic model consisting of the C-X-C motif chemokine ligand 8, DNA damage inducible transcript 3, RAB27A, member RAS oncogene family, replication factor C subunit 2 and elongation factor for RNA polymerase II 2 genes was constructed and validated. Based on these results, patients were subdivided into high and low-risk groups. Compared with the high-risk group, the OS time of patients in the low-risk group was significantly increased. Furthermore, it was determined that the five genes were all differentially expressed in ESCC tissues compared with normal tissues, indicating the potential role of these genes in ESCC initiation and progression. In another independent cohort, this five-gene signature was further confirmed and was considered as an independent prognostic biomarker for OS prediction in patients with ESCC. In conclusion, the OS of patients with ESCC may be predicted using this five-gene signature, which may be useful in identifying patients with high-risk ESCC.

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Identification of a Prognostic Immune Signature for Esophageal Squamous Cell Carcinoma to Predict Survival and Inflammatory Landscapes

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