Evaluation of immune infiltrating of thyroid cancer based on the intrinsic correlation between pair-wise immune genes

Meng, Jia; Li, Zhuyao; Pan, Mengjiao; Mei, Tao; Lu, Xiaoyan; Yang, Liu

doi:10.1016/j.lfs.2020.118248

Cited by 9 publications

(6 citation statements)

References 45 publications

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“…Indeed, the high-immunity group exhibited more advanced stages, larger tumor sizes, greater lymph node metastases, higher tall cell tumors and more BRAFV600E mutations [ 34 ]. Analogous results were reported by Jia et al [ 35 ]. Bergdorf et al confirmed the presence of certain immune cell types in PTC, particularly dendritic cells and neutrophils, strongly correlated with histological subtypes, mutational status, tumor stage and lymph node metastases [ 36 ].…”

Section: Resultssupporting

confidence: 91%

Transcriptomic landscape of TIMP3 oncosuppressor activity in thyroid carcinoma

et al. 2022

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Background Papillary thyroid cancer (PTC) is the most frequent thyroid tumor. The tissue inhibitor of metalloproteinase-3 (TIMP3) gene encodes a matrix metalloproteinases inhibitor that exerts a tumor suppressor role in several tumor types. TIMP3 is frequently downregulated in PTC by promoter methylation. We have previously functionally demonstrated that TIMP3 exerts an oncosuppressor role in PTC: TIMP3 restoration in the PTC-derived NIM1 cell line affects in vitro migration, invasion and adhesive capability, while reduces tumor growth, angiogenesis and macrophage recruitment in vivo. To get a deeper insight on the mediators of TIMP3 oncosuppressor activity in thyroid tumors, here we focused on the TIMP3 related transcriptome. Methods TCGA database was used for investigating the genes differentially expressed in PTC samples with low and high TIMP3 expression. Genome wide expression analysis of clones NIM1-T23 (expressing a high level of TIMP3 protein) and NIM1-EV (control empty vector) was performed. Gene sets and functional enrichment analysis with clusterProfiler were applied to identify the modulated biological processes and pathways. CIBERSORT was used to evaluate the distribution of different immunological cell types in TCGA-PTC tumor samples with different TIMP3 expression levels. Real time PCR was performed for the validation of selected genes. Results Thyroid tumors with TIMP3-high expression showed a down-modulation of inflammation-related gene sets, along with a reduced protumoral hematopoietic cells fraction; an enrichment of cell adhesion functions was also identified. Similar results were obtained in the TIMP3-overexpessing NIM1 cells in vitro model, where a down-regulation of immune-related function gene sets, some of which also identified in tumor samples, was observed. Interestingly, through enrichment analysis, were also recognized terms related to cell adhesion, extracellular matrix organization, blood vessel maintenance and vascular process functions that have been found modulated in our previous in vitro and in vivo functional studies. Conclusions Our results highlight the correlation of TIMP3 expression levels with the regulation of inflammatory functions and the immune infiltration composition associated with different PTC prognosis, thus providing a broader view on the oncosuppressor role of TIMP3 in PTC.

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Section: Resultssupporting

confidence: 91%

Transcriptomic landscape of TIMP3 oncosuppressor activity in thyroid carcinoma

et al. 2022

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“… 38 39 In addition to helicase activity, ASCC3 was reported to play a role as a scaffold to interact with other proteins 15 29 or recruit other transcriptional or epigenetic regulators to functional sites. 14 31 Moreover, ASCC3 was reported to be involved in the progression of several solid tumors, such as colorectal cancer and thyroid cancer, 8 9 affecting immune cell infiltration in the TME and patient prognosis. In a study by Li J et al , 10 ASCC3 was identified as a negative regulator of the host immune response.…”

Section: Discussionmentioning

confidence: 99%

“…Previous research has demonstrated that activating signal cointegrator 3 (ASCC3) is a protein with helicase activity that is associated with DNA damage repair and immune regulation, and it might be involved in the development of several solid tumors. [8][9][10] However, there has been no investigation into the role of ASCC3 in lung cancer. Single-cell RNA sequencing analysis has revealed differentially expressed genes in non-tumor tissues, primary tumors, and metastases of NSCLC, some of which are associated with NSCLC metastasis, by directly influencing the malignant characteristics of tumor cells or being involved in tumor progression-related signaling pathways.…”

Section: What This Study Addsmentioning

confidence: 99%

“… 14 15 Additionally, ASCC3 has been shown to regulate the expression of immune-related genes, affecting immune cell infiltration and thyroid cancer recurrence. 8 Through Gene Ontology (GO) andKyoto Encyclopedia of Genes and Genomes (KEGG) analysis of our RNA sequencing data, we found that ASCC3 was involved in signal transducer and activator of transcription (STAT) signaling and type I interferon (IFN)-related pathways. Further mechanical exploration revealed that ASCC3 regulated the STAT3 signaling pathway.…”

Section: Introductionmentioning

confidence: 99%

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ASCC3 promotes the immunosuppression and progression of non-small cell lung cancer by impairing the type I interferon response via CAND1-mediated ubiquitination inhibition of STAT3

Ao,

Gao,

Jin

et al. 2023

J Immunother Cancer

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BackgroundActivating signal cointegrator 3 (ASCC3) has been identified as an oncogenic factor that impairs host immune defense. However, the underlying mechanisms of carcinogenesis and its impact on the antitumor immune response remain unclear. In this study, we aimed to investigate the molecular mechanisms of ASCC3 in the progression of non-small cell lung cancer (NSCLC).MethodsSingle-cell sequencing data from the Gene Expression Omnibus and gene expression profiles from The Cancer Genome Atlas database were analyzed. The expression, clinical relevance and biological functions of ASCC3 in NSCLC were explored. Then, RNA sequencing, immunoprecipitation, mass spectrometry, immunofluorescence, and flow cytometry analyses were conducted to explore the underlying molecular mechanisms. In addition, in vivo experiments in mouse models were conducted to explore the probability of ASCC3 knockdown to improve the efficacy of anti-Programmed Death-1 (PD-1) therapy in NSCLC.ResultsASCC3 was significantly upregulated in NSCLC and correlated with poor pathological characteristics and prognosis in patients with NSCLC. Overexpression of ASCC3 promoted malignant phenotypes of NSCLC cells and induced an immunosuppressive tumor microenvironment, which was characterized by a decrease in CD8+T cells, natural killer cells and dendritic cells but an increase in regulatory T(Treg) cells. Mechanistically, ASCC3 stabilized signal transducer and activator of transcription (STAT)3 signaling by recruiting Cullin-associated and neddylation dissociated 1 (CAND1), which inhibited ubiquitin-mediated degradation of STAT3, thereby impairing the type I interferon response of tumor cells and promoting the immunosuppression and progression of NSCLC. Furthermore, high expression of ASCC3 impaired the efficacy of anti-PD-1 therapy, and an anti-PD-1 antibody combined with ASCC3 knockdown exerted promising synergistic efficacy in a preclinical mouse model.ConclusionASCC3 could stabilize the STAT3 pathway via CAND1, reshaping the tumor microenvironment and inducing resistance to anti-PD-1 therapy, which promotes the progression of NSCLC. It is a reliable prognostic indicator and can be a target in combination therapy for NSCLC.

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“…For three-class classification problem, we adopted the classic "One vs Rest (OVR)" strategy and used the Linear Support Vector Classification (LinearSVC) to develop models. [28][29][30][31] Using LinearSVC, the selected optimal subset features were fit to develop prediction models, and the training and test groups were substituted in the model to obtain prediction scores. Supplement: The "One vs Rest" strategy includes equipping each class with a classifier.…”

Section: Forecast Model Develpmentmentioning

confidence: 99%

Predictive value of radiomics analysis of enhanced CT for three‐tiered microvascular invasion grading in hepatocellular carcinoma

Zheng

Huang

et al. 2023

Medical Physics

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BackgroundMicrovascular invasion (MVI) is a major risk factor, for recurrence and metastasis of hepatocellular carcinoma (HCC) after radical surgery and liver transplantation. However, its diagnosis depends on the pathological examination of the resected specimen after surgery; therefore, predicting MVI before surgery is necessary to provide reference value for clinical treatment. Meanwhile, predicting only the existence of MVI is not enough, as it ignores the degree, quantity, and distribution of MVI and may lead to MVI‐positive patients suffering due to inappropriate treatment. Although some studies have involved M2 (high risk of MVI), majority have adopted the binary classification method or have not included radiomics.PurposeTo develop three‐class classification models for predicting the grade of MVI of HCC by combining enhanced computed tomography radiomics features with clinical risk factors.MethodsThe data of 166 patients with HCC confirmed by surgery and pathology were analyzed retrospectively. The patients were divided into the training (116 cases) and test (50 cases) groups at a ratio of 7:3. Of them, 69 cases were MVI positive in the training group, including 45 cases in the low‐risk group (M1) and 24 cases in the high‐risk group (M2), and 47 cases were MVI negative (M0). In the training group, the optimal subset features were obtained through feature selection, and the arterial phase radiomics model, portal venous phase radiomics model, delayed phase radiomics model, three‐phase radiomics model, clinical imaging model, and combined model were developed using Linear Support Vector Classification. The test group was used for validation, and the efficacy of each model was evaluated through the receiver operating characteristic curve (ROC).ResultsThe clinical imaging features of MVI included alpha‐fetoprotein, tumor size, tumor margin, peritumoral enhancement, intratumoral artery, and low‐density halo. The area under the curve (AUC) of the ROC values of the clinical imaging model for M0, M1, and M2 were 0.831, 0.701, and 0.847, respectively, in the training group and 0.782, 0.534, and 0.785, respectively, in the test group. After combined radiomics analyis, the AUC values for M0, M1, and M2 in the test group were 0.818, 0.688, and 0.867, respectively. The difference between the clinical imaging model and the combined model was statistically significant (p = 0.029).ConclusionThe clinical imaging model and radiomics model developed in this study had a specific predictive value for HCC MVI grading, which can provide precise reference value for preoperative clinical diagnosis and treatment. The combined application of the two models had a high predictive efficacy.

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Evaluation of immune infiltrating of thyroid cancer based on the intrinsic correlation between pair-wise immune genes

Cited by 9 publications

References 45 publications

Transcriptomic landscape of TIMP3 oncosuppressor activity in thyroid carcinoma

Transcriptomic landscape of TIMP3 oncosuppressor activity in thyroid carcinoma

ASCC3 promotes the immunosuppression and progression of non-small cell lung cancer by impairing the type I interferon response via CAND1-mediated ubiquitination inhibition of STAT3

Predictive value of radiomics analysis of enhanced CT for three‐tiered microvascular invasion grading in hepatocellular carcinoma

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