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 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 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 A Novel TGF-β Risk Score Predicts the Clinical Outcomes and Tumour Microenvironment Phenotypes in Bladder Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhi Liu ◽  
Tiezheng Qi ◽  
Xiaowen Li ◽  
Yiyan Yao ◽  
Belaydi Othmane ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Score ◽  
Tumour Microenvironment ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Normal Tissues ◽  
Cox Analysis

BackgroundThe TGF-β pathway plays critical roles in numerous malignancies. Nevertheless, its potential role in prognosis prediction and regulating tumour microenvironment (TME) characteristics require further elucidation in bladder cancer (BLCA).MethodsTGF-β-related genes were comprehensively summarized from several databases. The TCGA-BLCA cohort (training cohort) was downloaded from the Cancer Genome Atlas, and the independent validation cohorts were gathered from Xiangya Hospital (Xinagya cohort) and Gene Expression Omnibus. Initially, we identified differentially expressed TGF-β genes (DEGs) between cancer and normal tissues. Subsequently, univariate Cox analysis was applied to identify prognostic DEGs, which were further used to develop the TGF-β risk score by performing LASSO and multivariate Cox analyses. Then, we studied the role of the TGF-β risk score in predicting prognosis and the TME phenotypes. In addition, the role of the TGF-β risk score in guiding precision treatments for BLCA has also been assessed.ResultsWe successfully constructed a TGF-β risk score with an independent prognostic prediction value. A high TGF-β risk score indicated an inflamed TME, which was supported by the positive relationships between the risk score, enrichment scores of anticancer immunity steps, and the infiltration levels of tumour-infiltrating immune cells. In addition, the risk score positively correlated with the expression of several immune checkpoints and the T cell inflamed score. Consistently, the risk score was positively related to the enrichment scores of most immunotherapy-positive pathways. In addition, the sensitivities of six common chemotherapeutic drugs were positively associated with the risk score. Furthermore, higher risk score indicated higher sensitivity to radiotherapy and EGFR-targeted therapy. On the contrary, patients with low-risk scores were more sensitive to targeted therapies, including the blockade of FGFR3 and WNT-β-catenin networks.ConclusionsWe first constructed and validated a TGF-β signature that could predict the prognosis and TME phenotypes for BLCA. More importantly, the TGF-β risk score could aid in individual precision treatment for BLCA.

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 N6-methyladenosine (m6A) RNA methylation signature as a predictor of stomach adenocarcinoma outcomes and its association with immune checkpoint molecules

2020 ◽  
Vol 48 (9) ◽  
pp. 030006052095140
Author(s):  
Pingfan Mo ◽  
Siyuan Xie ◽  
Wen Cai ◽  
Jingjing Ruan ◽  
Qin Du ◽  
...  
Keyword(s):  
Overall Survival ◽  
Immune Checkpoint ◽  
Cox Regression ◽  
Rna Degradation ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Clinicopathological Feature ◽  
Rna Methylation ◽  
Immune Checkpoint Molecules ◽  
Programmed Death

Objective Although N6-methyladenosine (m6A) RNA methylation is the most common mRNA modification process, few studies have examined the role of m6A in stomach adenocarcinomas (STADs). Methods In this retrospective study, we analyzed 293 STAD samples from The Cancer Genome Atlas with complete clinicopathological feature profiles. The m6A methylation risk signature was derived from LASSO–Cox regression analyses with 15 m6A regulators. Statistical analysis was performed and figures were prepared using R software ( https://www.R-project.org/ ). Results The m6A signature was established as follows: risk score = FTO × 0.127 + YTHDF1 × 0.004 + KIAA1429 × 0.044 + YTHDC2 × 0.112 − RBM15 × 0.135 − ALKBH5 × 0.019 − YTHDF2 × 0.028, which was confirmed as an independent prognostic indicator to predict overall survival of patients with STAD. Risk scores and tumor grades were closely associated. Cell cycle, p53 signaling pathways, DNA mismatch repair, and RNA degradation were enriched in the low-risk subgroup. This subgroup showed significantly higher expression of immune checkpoint molecules including PD-1 (programmed death 1), PD-L1 (programmed death-ligand 1), and CTLA-4 (cytotoxic T-lymphocyte–associated antigen 4), suggesting that the signature may be a useful immunotherapy predictor. Conclusions We established an m6A methylation signature as an independent prognostic tool to predict overall survival, which may also be useful as an immunotherapy predictor.

scholarly journals NOL12 Acts as an Oncogenic Biomarker and Predicts the Efficacy of Immune Checkpoint Inhibitors in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Jianfeng Huang ◽  
Weibiao Kang ◽  
Shubo Pan ◽  
Changjun Yu ◽  
Zhigang Jie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
High Risk ◽  
Risk Score ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Clinicopathological Parameters ◽  
Migration And Invasion

Abstract Background: Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis worldwide. However, the pathogenesis of HCC remains poorly understood. Methods: Through data mining and analyses of The Cancer Genome Atlas (TCGA) datasets, the NOL12 expression in HCC was determined and the associations between its expression and patient survival and clinicopathological parameters were evaluated. The pro-tumorigenic roles of NOL12 on HCC in vitro were further verified by loss-of-function assay. The correlation between NOL12 expression and tumor-infiltrating immune cells (TICs) was analyzed by CIBERSORTx method. In addition, the risk signature based on 8 NOL12-related genes was established to accurately evaluate the prognosis of patients with HCC and to further predict the efficacy of immune checkpoint inhibitors (ICIs) in HCCResults: We found that NOL12 was significantly overexpressed in independent HCC datasets from TCGA database. High expression of NOL12 is associated with worse reduced overall survival (OS), high pathological grade, node metastasis and advanced clinical stage in patients with HCC. Moreover, NOL12 knockdown significantly inhibited cell proliferation, migration and invasion. CIBERSORTx analysis revealed that twelve types of TICs are correlated with NOL12 expression. The risk signature based on 8 NOL12-related genes is an independent prognostic factor for patients with HCC. The OS rate of patients in the low-risk score group was better than that in the high-risk score group. In addition, the total tumor mutation burden (TMB) in the high-risk score group increased significantly, and the risk scores could be used as an alternative indicator of ICI response. Conclusions: Our findings indicated that NOL12 might be involved in the progression of HCC and can be used as a potential therapeutic target. Moreover, the NOL12-related risk signature may have predictive relevance with regard to ICI therapy.

scholarly journals Cell-free DNA copy number variations predict efficacy of immune checkpoint inhibitor-based therapy in hepatobiliary cancers

2021 ◽  
Vol 9 (5) ◽  
pp. e001942
Author(s):  
Xu Yang ◽  
Ying Hu ◽  
Keyan Yang ◽  
Dongxu Wang ◽  
Jianzhen Lin ◽  
...  
Keyword(s):  
Combination Therapy ◽  
Clinical Outcome ◽  
Risk Score ◽  
Immune Checkpoint ◽  
Copy Number ◽  
Immune Checkpoint Inhibitor ◽  
Checkpoint Inhibitor ◽  
Copy Number Variations ◽  
Risk Scores ◽  
Free Dna

BackgroundThis study was designed to screen potential biomarkers in plasma cell-free DNA (cfDNA) for predicting the clinical outcome of immune checkpoint inhibitor (ICI)-based therapy in advanced hepatobiliary cancers.MethodsThree cohorts including 187 patients with hepatobiliary cancers were recruited from clinical trials at the Peking Union Medical College Hospital. Forty-three patients received combination therapy of programmed cell death protein 1 (PD-1) inhibitor with lenvatinib (ICI cohort 1), 108 patients received ICI-based therapy (ICI cohort 2) and 36 patients received non-ICI therapy (non-ICI cohort). The plasma cfDNA and blood cell DNA mutation profiles were assessed to identify efficacy biomarkers by a cancer gene-targeted next-generation sequencing panel.ResultsBased on the copy number variations (CNVs) in plasma cfDNA, the CNV risk score model was constructed to predict survival by using the least absolute shrinkage and selection operator Cox regression methods. The results of the two independent ICI-based therapy cohorts showed that patients with lower CNV risk scores had longer overall survival (OS) and progression-free survival (PFS) than those with high CNV risk scores (log-rank p<0.01). In the non-ICI cohort, the CNV risk score was not associated with PFS or OS. Furthermore, the results indicated that 53% of patients with low CNV risk scores achieved durable clinical benefit; in contrast, 88% of patients with high CNV risk scores could not benefit from combination therapy (p<0.05).ConclusionsThe CNVs in plasma cfDNA could predict the clinical outcome of the combination therapy of PD-1 inhibitor with lenvatinib and other ICI-based therapies in hepatobiliary cancers.

scholarly journals Immunogenomic Analyses of the Prognostic Predictive Model for Patients With Renal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Feng ◽  
Jiahui Zhao ◽  
Dechao Wei ◽  
Pengju Guo ◽  
Xiaobing Yang ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Predictive Model ◽  
Risk Score ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Survival Outcomes ◽  
Prognostic Signature ◽  
Therapeutic Benefits

BackgroundRenal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients. Here, we aim to construct an immune prognostic predictive model to assist clinician to predict RCC prognosis.MethodsHerein, an immune prognostic signature was developed, and its predictive ability was confirmed in the kidney renal clear cell carcinoma (KIRC) cohorts based on The Cancer Genome Atlas (TCGA) dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy.ResultsThe immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response.ConclusionsThe immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

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