Comprehensive Characterization of Immune Landscape Based on Epithelial-Mesenchymal Transition Signature in OSCC: Implication for Prognosis and Immunotherapy.
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ABSTRACT:
SUBMITTER: Zhang SY
PROVIDER: S-EPMC8281347 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIncreasing evidence has revealed the effect of epithelial-mesenchymal transition (EMT) on tumor microenvironment and cancer treatment. However, an EMT-based signature to predict the prognosis and therapeutic effect in gastric cancer (GC) has rarely been established.MethodsDifferentially expressed genes (DEGs) between paired primary gastric and ovarian metastatic tumors were identified through comparative RNA-seq analysis, followed by the construction of metastasis-related EMT signature (MEMTS) based on DEGs and EMT gene set. Then, both The Cancer Genome Atlas (TCGA) cohort and the Asian Cancer Research Group (ACRG) cohort were analyzed to explore the potential association between MEMTS and prognosis in GC. Samsung Medical Center (SMC) cohort and two individual immunotherapy treatment cohorts, including Kim cohort and Hugo cohort, were utilized to evaluate the predictive value of MEMTS on the response to adjuvant therapy and immunotherapy, respectively. Finally, the potential association of MEMTS with tumor environment and immune escape mechanisms was investigated.ResultsHigh MEMTS predicted a poor prognosis in patients with GC. Patients with low MEMTS potentially gained more benefits from adjuvant chemoradiotherapy than those with high MEMTS. MEMTS reliably predicted the response to immunotherapy in GC (area under the curve = 0.896). MEMTS was significantly associated with cancer-associated fibroblasts and stromal score in the aspect of the tumor microenvironment.ConclusionMEMTS serves as a potential biomarker to predict the prognosis and response to adjuvant therapy and immunotherapy in GC. MEMTS-based evaluation of individual tumors enables personalized treatment for GC patients in the future.
| S-EPMC9234207 | biostudies-literature
Project description:An emerging body of evidence has recently recognized the coexistence of epithelial-mesenchymal transition (EMT) and immune response. However, a systems-level view and survey of the interplay between EMT and immune escape program, and their impact on tumor behavior and clinical outcome across various types of cancer is lacking. Here, we performed comprehensive multi-omics analyses to characterize the landscape of crosstalk between EMT and immune evasion and their clinical relevance across 17 types of solid cancer. Our study showed the presence of complex and dynamic immunomodulatory crosstalk between EMT and immune evasion shared by pan-cancer, and the crosstalk was significantly associated with cancer prognosis and immunotherapy response. Integrative quantitative analyses of genomics and immunogenomics revealed that cellular composition of immune infiltrates, non-synonymous mutation burden, chromosomal instability and oncogenic gene alterations are associated with the balance between EMT and immune evasion. Finally, we proposed a scoring model termed EMT-CYT Index (ECI) to quantify the EMT-immunity axis, which was a superior predictor of prognosis and immunotherapy response across different malignancies. By providing a systematic overview of crosstalk between EMT and immune evasion, our study highlights the potential of pan-cancer EMT-immunity crosstalk as a paradigm for dissecting molecular mechanisms underlying cancer progression and guiding more effective and generalized immunotherapy strategies.
| S-EPMC8219790 | biostudies-literature
Project description:Epithelial-mesenchymal transition (EMT) is associated with tumor invasion and progression, and is regulated by DNA methylation. A prognostic signature of lung squamous cell carcinoma (LUSC) with EMT-related gene data has not yet been established. In our study, we constructed a co-expression network using differentially expressed genes (DEGs) obtained from The Cancer Genome Atlas (TCGA) to identify hub genes. We conducted a correlation analysis between the differentially methylated hub genes and differentially expressed EMT-related genes to screen EMT-related differentially methylated genes (ERDMGs). Functional enrichment was performed to annotate the ERDMGs. The least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses were performed to build a survival prognosis prediction model. Additionally, druggability analysis was performed to predict the potential drug targets of ERDMGs. We screened 11 ERDMGs that were enriched in cell adhesion molecules and other signaling pathways. Finally, we constructed a 4-ERDMG model, which showed good ability to predict survival prognosis in the training and validation sets. The model could serve as an independent predictive factor for patients with LUSC. Additionally, our druggability analysis predicted that CC chemokine ligand 23 (CCL23) and Hepatocyte nuclear factor 1b (HNF1B) may be the underlying drug targets of LUSC. We established a new risk score (RS) system as a prognostic indicator to predict the outcome of patients with LUSC, which will help in the improvement of treatment strategies.
| S-EPMC10344514 | biostudies-literature
Project description:(1) Background: Endometrial cancer is the most prevalent cause of gynecological malignant tumor worldwide. The prognosis of endometrial carcinoma patients with distant metastasis is poor. (2) Method: The RNA-Seq expression profile and corresponding clinical data were downloaded from the Cancer Genome Atlas database and the Gene Expression Omnibus databases. To predict patients' overall survival, a 9 EMT-related genes prognosis risk model was built by machine learning algorithm and multivariate Cox regression. Expressions of nine genes were verified by RT-qPCR. Responses to immune checkpoint blockades therapy and drug sensitivity were separately evaluated in different group of patients with the risk model. (3) Endometrial carcinoma patients were assigned to the high- and low-risk groups according to the signature, and poorer overall survival and disease-free survival were showed in the high-risk group. This EMT-related gene signature was also significantly correlated with tumor purity and immune cell infiltration. In addition, eight chemical compounds, which may benefit the high-risk group, were screened out. (4) Conclusions: We identified a novel EMT-related gene signature for predicting the prognosis of EC patients. Our findings provide potential therapeutic targets and compounds for personalized treatment. This may facilitate decision making during endometrial carcinoma treatment.
| S-EPMC8872195 | biostudies-literature
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).
| S-EPMC9763151 | biostudies-literature
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.
| S-EPMC7953549 | biostudies-literature
Project description:Epithelial-mesenchymal transition (EMT), a switch of polarized epithelial cells to a migratory, fibroblastoid phenotype, is considered a key process driving tumor cell invasiveness and metastasis. Using breast cancer cell lines as a model system, we sought to discover gene-expression signatures of EMT with clinical and mechanistic relevance. A supervised comparison of epithelial and mesenchymal breast cancer lines defined a 200-gene EMT signature that was prognostic across multiple breast cancer cohorts. Immunostaining of LYN, a top-ranked EMT signature gene and Src-family tyrosine kinase, was associated with significantly shorter overall survival (P=0.02), and correlated with the basal-like (“triple-negative”) phenotype. In mesenchymal breast cancer lines, RNAi-mediated knockdown of LYN inhibited cell migration and invasion, but not proliferation. Dasatinib, a dual-specificity tyrosine kinase inhibitor, also blocked invasion (but not proliferation) at nanomolar concentrations that inhibit LYN kinase activity, suggesting that LYN is a likely target and invasion a relevant endpoint for dasatinib therapy. Our findings define a prognostically-relevant EMT signature in breast cancer, and identify LYN as a mediator of invasion and possible new therapeutic target (and theranostic marker for dasatinib response), with particular relevance to clinically-aggressive basal-like breast cancer. Cell Line: cell line(epithelial-like/fibroblast-like/normal breast fibroblasts) Keywords: Logical Set Set of arrays organized by shared biological context, such as organism, tumors types, processes, etc. HEEBO oligonucleotide microarrays from the Stanford Functional Genomics Facility were used to perform gene expression profiling of 20 human breast cell lines, in comparison to a universal RNA reference. Expression data were analyzed by Significance Analysis of Microarrays to identify a 200-gene signature characteristic of EMT.
| S-ECPF-GEOD-13915 | biostudies-other
Project description:BackgroundAcute myeloid leukemia (AML) is a highly heterogenous disease with varying clinical outcomes among patients. Epithelial-mesenchymal transition (EMT) is an important mechanism underlying cancer metastasis and chemotherapy resistance. However, few EMT-based signatures have been established to predict AML prognosis and treatment efficacy.MethodsBy conducting comparative RNA-seq analysis, we discovered the differential expression of EMT genes between AML patients with relapse and those without relapse. Based on the prognostic analysis of the differentially expressed EMT genes, a metastasis-related EMT signature (MEMTs) was constructed. An analysis was conducted on both TARGET and TCGA cohorts to explore the possible association between MEMTs and prognosis in AML. Three separate chemotherapy treatment cohorts were utilized to assess the predictive efficacy of MEMTs for chemotherapy response. In addition, the potential correlation between MEMTs and the tumor microenvironment was also investigated. Finally, random forest analysis and functional experiments were conducted to verify the key MEMTs gene associated with AML metastasis.ResultsBased on expression and prognostic analysis, we constructed MEMTs that include three EMT genes (CDH2, LOX, and COL3A1). Our findings suggested that the MEMTs could act as a prognostic factor for AML patients, and furthermore, it proved to be a predictor of their response to chemotherapy. Specifically, high MEMTs was associated with worse prognosis and poor response to chemotherapy, while low MEMTs was linked to better prognosis and higher response rates. Random forest and functional experiments demonstrate that CDH2 is a key gene promoting leukemia cell metastasis among the three MEMTs genes.ConclusionThe identification of MEMTs could potentially act as a predictor for the prognosis and the response to chemotherapy in AML patients. Individual tumor evaluation based on MEMTs could provide personalized treatment options for AML patients in the future.
| S-EPMC10257403 | biostudies-literature
Project description:Background: Although many genes related to epithelial-mesenchymal transition (EMT) have been explored in hepatocellular carcinoma (HCC), their prognostic significance still needs further analysis. Methods: Differentially expressed EMT-related genes were obtained through the integrated analysis of 4 Gene expression omnibus (GEO) datasets. The univariate Cox regression and Lasso Cox regression models are utilized to determine the EMT-related gene signature. Based on the results of multivariate Cox regression, a predictive nomogram is established. Time-dependent ROC curve and calibration curve are used to show the distinguishing ability and consistency of the nomogram. Finally, we explored the correlation between EMT risk score and immune immunity. Results: We identified a nine EMT-related gene signature to predict the survival outcome of HCC patients. Based on the EMT risk score's median, HCC patients in each dataset were divided into high and low-risk groups. The survival outcomes of HCC patients in the high-risk group were significantly worse than those in the low-risk group. The prediction nomogram based on the EMT risk score has better distinguishing ability and consistency. High EMT risk score was related to immune infiltration. Conclusion: The nomogram based on the EMT risk score can reliably predict the survival outcome of HCC patients, thereby providing benefits for medical decisions.
| S-EPMC9065556 | biostudies-literature
Project description:Traditional pathological diagnoses and clinical methods are insufficient to accurately predict the prognosis of lung adenocarcinoma (LUAD). Epithelial-mesenchymal transition (EMT) process is closely related to tumor cell migration. However, the prognostic value of EMT-related genes in LUAD is still unclear. In this study, we collected bulk RNA-sequencing (RNA-seq) and microarray data of LUAD patients from public databases and identified different expressed EMT-related genes in tumor and normal tissues. Then, we used the least absolute shrinkage and selection operator Cox regression model to develop a multigene signature in the cancer genome atlas (TCGA) cohort and validated the model in the OncoSG (Singapore Oncology Data Portal) cohort as well as other datasets. Finally, we constructed a 12-gene signature to divide LUAD patients into high-risk and low-risk groups of overall survival (OS), which has a better stability and accuracy in predicating the OS of patients compared with some other published signatures of LUAD. In addition, evaluation of the risk model using the time-related receiver operating characteristic (ROC) curve confirmed the predictive ability of the model. Functional analysis showed that these genes are related to immunity. CD8 T cell and CD4 T cell types were significantly negatively correlated with the risk score in the analysis of immune infiltration. In general, our model provides useful information that may help clinicians better predict the prognosis of LUAD patients and provides potential targets for immunotherapy of LUAD.
| S-EPMC8753132 | biostudies-literature