scholarly journals Identification of an Immune-related Signature That Indicates the Dedifferentiation of Thyroid Cells

2020 ◽  
Author(s):  
Xuemin Wang ◽  
Chunyan Li ◽  
Rujia Qin ◽  
Zhaoming Zhong ◽  
Chuan-Zheng Sun
Keyword(s):  
Thyroid Carcinoma ◽  
Risk Score ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Predictive Value ◽  
The Cancer Genome Atlas ◽  
Term Survival ◽  
Thyroid Cells ◽  
Normal Thyroid ◽  
Immune Checkpoint Molecules

Abstract Background: Patients with well-differentiated thyroid carcinoma can achieve long-term survival after reasonable treatments, but there is no standard treatment mode for poorly or undifferentiated thyroid carcinoma and its prognosis is very poor. Immune cells, especially tumor-associated macrophages, account for a large proportion of the tumor microenvironment of anaplastic thyroid carcinomas (ATCs). However, whether immune-related genes can mediate the dedifferentiation of thyroid cells is unclear.Methods: We initially compared the differences of thyroid differentiation score, infitration of immune cells and enriched pathways between ATCs and papillary thyroid carcionma (PTCs) or normal thyroid tissues in Gene Expression Omnibus database. Then, The Cancer Genome Atlas database was used to screen out the prognosis associated IRGs. A risk score was constructed and we next investigated its predictive value for differentiation by applying receiver operating characteristic (ROC) curves and correlation analyses. Kaplan-Meier curves were used to evaluated its prognostic value. We further explored the associations of the risk score with important immune checkpoint molecules, infiltrating immune cells and response to immunotherapy.Results: Compared with PTCs or normal thyroid tissues, ATCs exhibited lower thyroid differentiation scores, higher infiltration of most immune cells and higher activation of inflammatory response. The risk score composed of MMP9 and SDC2 was significantly increased in ATCs and low differentiated PTCs. Moreover, it showed favorable predictive value for differentiation and survival. Higher risk score displayed dedifferentiation status and a worse prognosis. Additionly, the risk score was positively correlated with immune checkpoint molecules PDL1, CTLA4, IDO1, HAVCR2 and infiltration of multiple immune cells. Importantly, we found that samples with higher risk score tend to have a better response to immune checkpoint agents than lower ones.Conclusion: Our findings indicate that the risk score may not only contribute to the judgement of differentiation and prognosis of thyroid cancer, but also help to the prediction of immune cell infiltration and immune checkpoint inhibitor response.

scholarly journals Identification of an immune-related signature indicating the dedifferentiation of thyroid cells

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xuemin Wang ◽  
Wen Peng ◽  
Chunyan Li ◽  
Rujia Qin ◽  
Zhaoming Zhong ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Tumour Microenvironment ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Thyroid Cells ◽  
Normal Thyroid ◽  
Thyroid Carcinomas ◽  
Immune Checkpoint Molecules

Abstract Background Immune cells account for a large proportion of the tumour microenvironment in anaplastic thyroid carcinomas (ATCs). However, the expression pattern of immune-related genes (IRGs) in ATCs is unclear. Our study aimed to identify an immune-related signature indicating the dedifferentiation of thyroid cells. Methods We compared the differences in thyroid differentiation score (TDS), infiltration of immune cells and enriched pathways between ATCs and papillary thyroid carcinomas (PTCs) or normal thyroid tissues in the Gene Expression Omnibus database. Univariate and multivariable Cox analyses were used to screen prognosis-associated IRGs in The Cancer Genome Atlas database. After constructing a risk score, we investigated its predictive value for differentiation and survival by applying receiver operating characteristic and Kaplan–Meier curves. We further explored its associations with important immune checkpoint molecules, infiltrating immune cells and response to immunotherapy. Results Compared with PTCs or normal thyroid tissues, ATCs exhibited lower TDS values and higher enrichment of immune cells and activation of the inflammatory response. The quantitative analyses and immunohistochemical staining validated that most ATC cell lines and ATC tissues had higher expression of MMP9 and lower expression of SDC2 than normal thyroid samples and PTC. Higher risk scores indicates dedifferentiation and a worse prognosis. Additionally, the risk score was positively correlated with the immune checkpoint molecules PDL1, CTLA4, IDO1, and HAVCR2 and infiltration of multiple immune cells. Importantly, we found that the samples with higher risk scores tended to have a better response to immunotherapy than those with lower scores. Conclusion Our findings indicate that the risk score may not only contribute to the determination of differentiation and prognosis of thyroid carcinomas but also help the prediction of immune cells infiltration and immunotherapy response.

scholarly journals Identification of an immune-related signature indicating the dedifferentiation of thyroid cells

2021 ◽  
Author(s):  
Xuemin Wang ◽  
Wen Peng ◽  
Chunyan Li ◽  
Rujia Qin ◽  
Zhaoming Zhong ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Tumour Microenvironment ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Thyroid Cells ◽  
Normal Thyroid ◽  
Thyroid Carcinomas ◽  
Immune Checkpoint Molecules

Abstract Background: Immune cells account for a large proportion of the tumour microenvironment in anaplastic thyroid carcinomas (ATCs). However, the expression pattern of immune-related genes (IRGs) in ATCs is unclear. Our study aimed to identify an immune-related signature indicating the dedifferentiation of thyroid cells.Methods: We compared the differences in thyroid differentiation score (TDS), infiltration of immune cells and enriched pathways between ATCs and papillary thyroid carcinomas (PTCs) or normal thyroid tissues in the Gene Expression Omnibus database. Univariate and multivariable Cox analyses were used to screen prognosis-associated IRGs in The Cancer Genome Atlas database. After constructing a risk score, we investigated its predictive value for differentiation and survival by applying receiver operating characteristic and Kaplan-Meier curves. We further explored its associations with important immune checkpoint molecules, infiltrating immune cells and response to immunotherapy. Results: Compared with PTCs or normal thyroid tissues, ATCs exhibited lower TDS values and higher enrichment of immune cells and activation of the inflammatory response. The quantitative analyses and immunohistochemical staining validated that most ATC cell lines and ATC tissues had higher expression of MMP9 and lower expression of SDC2 than normal thyroid samples and PTC. Higher risk scores indicates dedifferentiation and a worse prognosis. Additionally, the risk score was positively correlated with the immune checkpoint molecules PDL1, CTLA4, IDO1, and HAVCR2 and infiltration of multiple immune cells. Importantly, we found that the samples with higher risk scores tended to have a better response to immunotherapy than those with lower scores. Conclusion: Our findings indicate that the risk score may not only contribute to the determination of differentiation and prognosis of thyroid carcinomas but also help the prediction of immune cells infiltration and immunotherapy response.

scholarly journals Classification of Lung Adenocarcinoma Based on Immune Checkpoint and Screening of Related Genes

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ting Zhou ◽  
Ping Yang ◽  
Sanyuan Tang ◽  
Zhongshan Zhu ◽  
Xiaobing Li ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Signaling Pathway ◽  
Risk Score ◽  
Immune Cells ◽  
Immune Checkpoint ◽  
Software Package ◽  
Immune Escape ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
R Software

Aims. Lung adenocarcinoma (LUAD) cells could escape from the monitoring of immune cells and metastasize rapidly through immune escape. Therefore, we aimed to develop a method to predict the prognosis of LUAD patients based on immune checkpoints and their associated genes, thus providing guidance for LUAD treatment. Methods. Gene sequencing data were downloaded from the Cancer Genome Atlas (TCGA) and analyzed by R software and R Bioconductor software package. Based on immune checkpoint genes, kmdist clustering in ConsensusClusterPlus R software package was utilized to classify LUAD. CIBERSORT was used to quantify the abundance of immune cells in LUAD samples. LM22 signature was performed to distinguish 22 phenotypes of human infiltrating immune cells. Gene set variation analysis (GSVA) was performed on immune checkpoint cluster and immune checkpoint score using GSVA R software package. The risk score was calculated by LASSO regression coefficient. Gene Ontology (GO), Hallmark, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed. PROC was performed to generate the ROC curve and calculate the area under the curve (AUC). Results. According to the immune checkpoint, LUAD was classified into clusters 1 and 2. Survival rate, immune infiltration patterns, TMB, and immune score were significantly different between the two clusters. Functional prediction showed that the functions of cluster 1 focused on apoptosis, JAK/STAT signaling pathway, TNF-α/NFκB signaling pathway, and STAT5 signaling pathway. The risk score model was constructed based on nine genes associated with immune checkpoints. Survival analysis and ROC analysis showed that patients with high-risk score had poor prognosis. The risk score was significantly correlated with cancer status (with tumor), male proportion, status, tobacco intake, and cancer stage. With the increase of the risk score, the enrichment of 22 biological functions increased, such as p53 signaling pathway. The signature was verified in IMvigor immunotherapy dataset with excellent diagnostic accuracy. Conclusion. We established a nine-gene signature based on immune checkpoints, which may contribute to the diagnosis, prognosis, and clinical treatment of LUAD.

scholarly journals Identification of ferroptosis genes in immune infiltration and prognosis in thyroid papillary carcinoma using network analysis

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ruoting Lin ◽  
Conor E. Fogarty ◽  
Bowei Ma ◽  
Hejie Li ◽  
Guoying Ni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Thyroid Carcinoma ◽  
Risk Score ◽  
Immune Cells ◽  
Survival Curve ◽  
Enrichment Analysis ◽  
Papillary Thyroid ◽  
Hub Genes ◽  
Cox Analysis ◽  
Cluster 2

Abstract Background Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analyse the gene expression profiles of the disease and established a new model for the correlation. Methods The thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 types of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated. Results We identify two subtypes in PTC based on the expression of ferroptosis related genes, with the proportion of cluster 1 significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. The mutations of Braf and Nras are detected as the major mutations of cluster 1 and 2, respectively. Subsequent analyses of TME immune cells infiltration indicated cluster 1 is remarkably richer than cluster 2. The risk score of THCA is in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data shows that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer. Conclusions Our study finds seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we construct the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.

scholarly journals The Impact of Immune Microenvironment on the Prognosis of Pancreatic Ductal Adenocarcinoma Based on Multi-Omics Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Bing Yang ◽  
Mingyao Zhou ◽  
Yunzi Wu ◽  
Yuanyuan Ma ◽  
Qin Tan ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Immune Cells ◽  
Cox Model ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Rapid Progression ◽  
Sequencing Analysis ◽  
Prognostic Evaluation ◽  
The Impact

Pancreatic ductal adenocarcinoma (PDAC) is a malignant tumor characterized by rapid progression, early metastasis, high recurrence, and limited responsiveness to conventional therapies. The 5-year survival rate of PDAC is extremely low (<8%), which lacks effective prognostic evaluation indicators. In this study, we used xCell to analyze infiltrating immune cells in a tumor and through the univariate and multivariate Cox analyses screened out two prognosis-related immune cells, CD4+TN and common lymphoid progenitor (CLP), which were used to construct a Cox model and figure out the risk-score. It was found that the constructed model could greatly improve the sensitivity of prognostic evaluation, that the higher the risk-score, the worse the prognosis. In addition, the risk-score could also identify molecular subtypes with poor prognosis and immunotherapy sensitivity. Through transcriptome and whole-exome sequencing analysis of PDAC dataset from The Cancer Genome Atlas (TCGA), it was found that copy number deletion and low expression of CCL19 might be crucial factors to affect the risk-score. Lastly, validation of the above findings was confirmed not only in Gene Expression Omnibus (GEO) datasets but also in our PDAC patient samples, Peking2020 cohort.

scholarly journals Prognostic and Predictive Value of Tumor-infiltrating Leukocytes and of Immune Checkpoint Molecules PD1 and PDL1 in Clear Cell Renal Cell Carcinoma

2020 ◽  
Vol 13 (2) ◽  
pp. 336-345 ◽  
Author(s):  
Philipp J. Stenzel ◽  
Mario Schindeldecker ◽  
Katrin E. Tagscherer ◽  
Sebastian Foersch ◽  
Esther Herpel ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Immune Checkpoint ◽  
Predictive Value ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Immune Checkpoint Molecules

scholarly journals Weightlessness Induced Apoptosis in Normal Thyroid Cells and Papillary Thyroid Carcinoma Cells via Extrinsic and Intrinsic Pathways

Endocrinology ◽  
2003 ◽  
Vol 144 (9) ◽  
pp. 4172-4179 ◽  
Author(s):  
Peter Kossmehl ◽  
Mehdi Shakibaei ◽  
Augusto Cogoli ◽  
Manfred Infanger ◽  
Francesco Curcio ◽  
...  
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Carcinoma Cells ◽  
Papillary Thyroid ◽  
Thyroid Cells ◽  
Normal Thyroid ◽  
Induced Apoptosis

scholarly journals Identification of Ferroptosis Genes In Immune Infiltration and Prognosis In Thyroid Papillary Carcinoma Using Network Analysis

2021 ◽  
Author(s):  
Ruoting Lin ◽  
Conor E. Fogarty ◽  
Bowei Ma ◽  
Hejie Li ◽  
Guoying Ni ◽  
...  
Keyword(s):  
Thyroid Carcinoma ◽  
Risk Score ◽  
Immune Cells ◽  
Survival Curve ◽  
Enrichment Analysis ◽  
Papillary Thyroid ◽  
Hub Genes ◽  
Major Mutation ◽  
Cox Analysis ◽  
Cluster 2

Abstract Background: Papillary thyroid carcinoma (PTC) is the most common thyroid cancer. While many patients survive, a portion of PTC cases display high aggressiveness and even develop into refractory differentiated thyroid carcinoma. This may be alleviated by developing a novel model to predict the risk of recurrence. Ferroptosis is an iron-dependent form of regulated cell death (RCD) driven by lethal accumulation of lipid peroxides, is regulated by a set of genes and shows a variety of metabolic changes. To elucidate whether ferroptosis occurs in PTC, we analysed the gene expression profiles of the disease and established a new model for the correlation. Methods: The thyroid carcinoma (THCA) datasets were downloaded from The Cancer Genome Atlas (TCGA), UCSC Xena and MisgDB, and included 502 tumour samples and 56 normal samples. A total of 60 ferroptosis related genes were summarised from MisgDB database. Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA) were used to analyse pathways potentially involving PTC subtypes. Single sample GSEA (ssGSEA) algorithm was used to analyse the proportion of 28 kinds of immune cells in the tumour immune infiltration microenvironment in THCA and the hclust algorithm was used to conduct immune typing according to the proportion of immune cells. Spearman correlation analysis was performed on the ferroptosis gene expression and the correlation between immune infiltrating cells proportion. We established the WGCNA to identify genes modules that are highly correlated with the microenvironment of immune invasion. DEseq2 algorithm was further used for differential analysis of sequencing data to analyse the functions and pathways potentially involving hub genes. GO and KEGG enrichment analysis was performed using Clusterprofiler to explore the clinical efficacy of hub genes. Univariate Cox analysis was performed for hub genes combined with clinical prognostic data, and the results was included for lasso regression and constructed the risk regression model. ROC curve and survival curve were used for evaluating the model. Univariate Cox analysis and multivariate Cox analysis were performed in combination with the clinical data of THCA and the risk score value, the clinical efficacy of the model was further evaluated.Results: We identified two subtypes in PTC, using Braf as the major mutation of subtype C1 and NRAS as the major mutation of subtype C2. The proportion of cluster 1 was significantly higher than cluster 2 in ferroptosis signature genes that are positively associated. Subsequent analyses of TME immune cells infiltration indicated cluster 1 was remarkably richer than cluster 2. The risk score of THCA in good performance evaluated by ROC curve and survival curve, in conjunction with univariate Cox analysis and multivariate Cox analysis results based on the clinical data showed that the risk score of the proposed model could be used as an independent prognostic indicator to predict the prognosis of patients with papillary thyroid cancer.Conclusions: Our study found seven crucial genes, including Ac008063.2, Apoe, Bcl3, Acap3, Alox5ap, Atxn2l and B2m, and regulation of apoptosis by parathyroid hormone-related proteins significantly associated with ferroptosis and immune cells in PTC, and we constructed the risk score model which can be used as an independent prognostic index to predict the prognosis of patients with PTC.

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