Dataset Information

Comprehensive Characterization of Immune Landscape Based on Epithelial-Mesenchymal Transition Signature in OSCC: Implication for Prognosis and Immunotherapy.

ABSTRACT: Current anatomic TNM stage classification fails to capture the immune heterogeneity of oral squamous cell carcinoma (OSCC). Increasing evidence indicates the strong association between epithelial-mesenchymal transition (EMT) and tumor immune response. In this study, we employed an EMT signature to classify OSCC patients into epithelial- (E-) and mesenchymal- (M-) phenotypes using TCGA and GSE41613 transcriptome data. The ESTIMATE and CIRBERSORT analyses implied that the EMT signature genes originated from the stroma of the bulk tissue. The M-subtype tumors were characterized as "immune-hot" with more immune cell infiltration than the E-subtype ones. The low infiltration of active immune cells, the high infiltration of inactive immune cells, and the high expressions of immune checkpoints demonstrated an immunosuppressive characteristic of the M-subtype tumors. Moreover, we developed and validated a novel prognostic classifier based on the EMT score, the expressions of seven immune checkpoints, and the TNM stages, which could improve the prediction efficiency of the current clinical parameter. Together, our findings provide a better understanding of the tumor immune heterogeneity and may aid guiding immunotherapy in OSCC.

SUBMITTER: Zhang SY

PROVIDER: S-EPMC8281347 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Comprehensive Characterization of Immune Landscape Based on Epithelial-Mesenchymal Transition Signature in OSCC: Implication for Prognosis and Immunotherapy.

Zhang Si-Yuan SY Ren Xian-Yue XY Wang Chun-Yang CY Chen Xi-Juan XJ Cao Ruo-Yan RY Liu Qin Q Pan Xue X Zhou Jia-Ying JY Zhang Wei-Lin WL Tang Xin-Ran XR Cheng Bin B Wu Tong T

Frontiers in oncology 20210701

Current anatomic TNM stage classification fails to capture the immune heterogeneity of oral squamous cell carcinoma (OSCC). Increasing evidence indicates the strong association between epithelial-mesenchymal transition (EMT) and tumor immune response. In this study, we employed an EMT signature to classify OSCC patients into epithelial- (E-) and mesenchymal- (M-) phenotypes using TCGA and GSE41613 transcriptome data. The ESTIMATE and CIRBERSORT analyses implied that the EMT signature genes origi ...[more]

PMID: 34277389

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Project description:Immunotherapy has yielded encouraging results in the treatment of advanced hepatocellular carcinoma (HCC). However, the relationship between epithelial-mesenchymal transition (EMT) and immunotherapy for HCC has not been adequately explained. In this study, we comprehensively analyzed a bulk RNA sequence dataset of 365 HCC patients in The Cancer Genome Atlas (TCGA) dataset. Subsequently, we constructed a prognostic signature based on 6 EMT-related genes and divided 365 HCC patients into high- and low-risk groups. The predictive efficacy of the signature was well validated in different clinical subgroups and in two independent external datasets. We further explored the relationship between prognostic signature and immunotherapy response in terms of immune cell infiltration, somatic mutations, tumor mutation burden (TMB), microsatellite instability (MSI), immune checkpoint-associated gene expression, single-nucleotide variants (SNV) neoantigens, cancer testicular antigens (CTA) scores, and tumor immune dysfunction and exclusion (TIDE) scores. We validated the predictive efficacy of prognostic signature for immunotherapy response using external independent immunotherapy data. Real-time quantitative polymerase chain reaction (qRT-PCR) was used to validate EMT-related gene overexpression in HCC tissue samples. Prognostic signature was an independent risk factor affecting the prognosis of HCC patients and has shown superiority in predicting patient survival compared to other clinical factors. Compared with the low-risk group, the proportion of Activated_CD4_T_cell, Type_2_T_helper_cel, and macrophages were higher in the tumor microenvironment of HCC patients in the high-risk group, while the Activated_CD8_T_cell and CD56bright_natural_killer_cell proportions were lower. The prognostic signature was positively correlated with TMB scores, MSI scores, SNV neoantigens scores, expression levels of immune checkpoint-related genes, and TIDE scores, and patients in the high-risk group were more suitable for immunotherapy. qRT-PCR confirms overexpression of 6 EMT-related genes in HCC tissues for the construction of prognostic signature. Our novel prognostic signature can effectively predict the prognosis and immunotherapy response of HCC patients. In the future, it will be an effective tool for physicians to screen suitable immunotherapy populations and improve response rates and overall survival (OS).

Project description:BackgroundTumor metastasis is one of the leading reasons of the dismal prognosis of hepatocellular carcinoma (HCC). Epithelial-mesenchymal transition (EMT) is closely associated with tumor metastasis including HCC. The purpose of this study is to construct and validate an EMT-related gene signature for predicting the prognosis of HCC patients.MethodsGene expression data of HCC patients was downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was performed to found the EMT-related gene sets which were obviously distinct between normal samples and paired HCC samples. Cox regression analysis was used to develop an EMT-related prognostic signature, and the performance of the signature was evaluated by Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves. A nomogram incorporating the independent predictors was established. Quantitative real-time polymerase chain reaction (qRT-PCR) was used to detect the expression levels of the hub genes in HCC cell lines, and the role of PDCD6 in the metastasis of HCC was determined by functional experiments.ResultsAn EMT-related 5-gene signature (PDCD6, TCOF1, TRIM28, EZH2 and FAM83D) was constructed using univariate and multivariate Cox regression analysis. Based on the signature, the HCC patients were classified into high- and low-risk groups, and patients in high-risk group had a poor prognosis. Time-dependent ROC and Cox regression analyses suggested that the signature could predict HCC prognosis exactly and independently. The predictive capacity of the signature was also validated in two external cohorts. GSEA results showed that many cancer-related signaling pathways such as PI3K/Akt/mTOR pathway and TGF-β/SMAD pathway were enriched in high-risk group. The result of qRT-PCR revealed that PDCD6, TCOF1 and FAM83D were highly expressed in HCC cancer cells. Among them, PDCD6 were found to promote cell migration and invasion.ConclusionThe EMT-related 5-gene signature can serve as a promising prognostic biomarker for HCC patients and may provide a novel mechanism of HCC metastasis.

Dataset Information

Comprehensive Characterization of Immune Landscape Based on Epithelial-Mesenchymal Transition Signature in OSCC: Implication for Prognosis and Immunotherapy.

Publications

Comprehensive Characterization of Immune Landscape Based on Epithelial-Mesenchymal Transition Signature in OSCC: Implication for Prognosis and Immunotherapy.

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