Identification of immune-related lncRNAs to improve the prognosis prediction for patients with papillary thyroid cancer

Li, Zhiyang; Lin, Weixun; Zheng, Jiehua; Hong, Weida; Zou, Juan; Zhang, Taofeng; Chen, Yexi; Lü, Hai

doi:10.1042/bsr20204086

Cited by 19 publications

(18 citation statements)

References 19 publications

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“…Of the nine autophagy-related lncRNAs, ve lncRNAs (AL136366.1, AC008063.1, AC092279.1, DOCK9-DT, LINC02471) were protective factors for the prognosis of thyroid cancer, while other four lncRNAs (LINC02454, AC096677.1, AC004918.3, AL162231.2) behaved the opposite. LINC02454 and AL136366.1 have been shown to be pivotal mediators in the pathophysiological process of PTC and are closely associated with the development and prognosis of thyroid cancer [29]. Tan et al reported that LINC02454 is highly expressed in PTC [30].…”

Section: Discussionmentioning

confidence: 99%

An Autophagy-related lncRNA Prognostic Risk Model for Thyroid Cancer

Shan¹,

He²,

Yang³

et al. 2021

Preprint

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Thyroid cancer (TC) is the most common malignancy of the endocrine system and its incidence is gradually rising. Research has demonstrated a close link between autophagy and thyroid cancer. We constructed a prognostic model of autophagy-related long noncoding RNA (lncRNA) in thyroid cancer and explored its prognostic value. A total of 14,142 lncRNAs and 212 autophagy-related genes (ATGs) were obtained from the Cancer Genome Atlas (TCGA) database and the Human Autophagy Database (HADb), respectively. We performed lncRNA-ATGs correlation analysis and finally obtained 1166 autophagy-associated lncRNAs. Subsequently we conducted univariate Cox regression analysis and multivariate Cox regression analysis, a nine-autophagy-related lncRNAs (AC092279.1, AC096677.1, DOCK9-DT, LINC02454, AL136366.1, AC008063.1, AC004918.3, LINC02471, AL162231.2) significantly associated with prognosis was identified. Based on these autophagy-related lncRNAs, a risk model was constructed. The area under the curve (AUC) of the risk score was 0.905, proving that the accuracy of risk signature was superior. In addition, multiple regression analysis showed that risk score was a significant independent prognostic risk factor for thyroid cancer. In this study, a nine autophagy-related lncRNAs in thyroid cancer were established to predict the prognosis of thyroid cancer patients.

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Section: Discussionmentioning

confidence: 99%

An Autophagy-related lncRNA Prognostic Risk Model for Thyroid Cancer

Shan¹,

He²,

Yang³

et al. 2021

Preprint

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“…The lncRNA LINC00460 plays crucial roles in several epithelial cancers [60][61][62] , promoting EMT by facilitating peroxiredoxin-1 into the nucleus in HNSCC 63 . The role of LINC02454 was mainly reported in thyroid cancer [64][65][66] . Abnormal expression of LINC02454 was associated with advanced stage and poor outcome of thyroid cancer 66 .…”

Section: Discussionmentioning

confidence: 99%

“…Abnormal expression of LINC02454 was associated with advanced stage and poor outcome of thyroid cancer 66 . Besides, LINC02454 was deemed as an important immune-related biomarker in auxiliary prognosis prediction for patients with papillary thyroid cancer 65 . High expression of lncRNA SNHG25 promotes cell proliferation, migration and invasion, and decreased apoptosis by up-regulating COMP in epithelial ovarian cancer 67 .…”

Section: Discussionmentioning

confidence: 99%

Identification of Immune-related lncRNA Panel for Predicting Immune Checkpoint Blockade and Prognosis in Head and Neck Squamous Cell Carcinoma

Zhou¹,

Zhan²,

Shen³

et al. 2021

Preprint

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Immunotherapy is changing head and neck squamous cell carcinoma (HNSCC) treatment pattern. According to the Chinese Society of Clinical Oncology (CSCO) guidelines, immunotherapy has been deemed as first-line recommendation for recurrent/metastatic HNSCC, marking that advanced HNSCC has officially entered the era of immunotherapy. Long non-coding RNAs impact every step of cancer immunity. Therefore, reliable immune-lncRNA able to accurately predict the immune landscape and survival of HNSCC are crucial to clinical management. In the current study, we downloaded the transcriptomic and clinical data of HNSCC from The Cancer Genome Altas and identified differentially expressed immune-related lncRNAs (DEir-lncRNAs). Further then, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify proper DEir-lncRNAs to construct optimal risk model. Low-risk and high-risk groups were classified based on the optimal cut-off value generated by the areas under curve for receiver operating characteristic curves (AUC), and Kaplan-Meier survival curves were utilized to validate the prediction model. We then evaluated the model based on the clinical factors, immune cell infiltration, chemotherapeutic and immunotherapeutic efficacy between two groups. Our results constructed a risk model consisted of 18 DEir-lncRNA pairs showing significantly association with survival of patients with HNSCC. Besides, HNSCC patients with low risk score significantly enriched of CD8+ T cell, and corelated with high chemosensitivity and immunotherapeutic sensitivity. In summary, our risk model could be served as a promising clinical prediction indicator, effective discoursing of the immune cell infiltration of HNSCC patients, and distinguishing patients who could benefit from chemotherapy and immunotherapy.

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“…Several previous studies have constructed prognostic signatures of TC patients. Most research has focused on immune-related genes, like Gan et al 77 Li et al 78 Peng et al, 79 and so on. They established different immune-related signatures to predict the prognosis of patients with TC.…”

Section: Dovepressmentioning

confidence: 99%

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

Li¹,

Jiang²,

Feng³

et al. 2021

OTT

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Background:The detection rate of thyroid cancer (TC) has been continuously improved due to the development of detection technology. Epithelial-mesenchymal transition (EMT) is thought to be closely related to the malignant progression of tumors. However, the relationship between EMT-related genes (ERGs) characteristics and the diagnosis and prognosis of TC patients has not been studied. Methods: Four datasets from Gene Expression Omnibus (GEO) were used to perform transcriptomic profile analysis. The overlapping differentially expressed ERGs (DEERGs) were analyzed using the R package "limma". Then, the hub genes, which had a higher degree, were identified by the protein-protein interaction (PPI) network. Gene expression analysis between the TC and normal data, the disease-free survival (DFS) analysis of TC patients from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) cohort, function analysis, and immunohistochemistry (IHC) were performed to verify the importance of the hub genes. Finally, a prognostic risk scoring was constructed to predict DFS in patients with the selected genes. Results: A total of 43 DEERGs were identified and 10 DEERGs were considered hub ERGs, which had a high degree of connectivity in the PPI network. Then, the differential expressions of FN1, ITGA2, and KIT between TC and normal tissues were verified in the TCGA-THCA cohort and their protein expressions were also verified by IHC. DFS analysis indicated upregulations of FN1 expression (P<0.01) and ITGA2 expression (P<0.01) and downregulation of KIT expression (P=0.01) increased risks of decreased DFS for TCGA-THCA patients. Besides, by building a prognostic risk scoring model, we found that the DFS of TCGA-THCA patients was significantly worse in high-risk groups. Conclusion: In summary, these hub ERGs were potential biomarkers for diagnosis and prognosis of TC, which can provide a basis for further exploring the efficacy of EMT in patients with TC.

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Identification of immune-related lncRNAs to improve the prognosis prediction for patients with papillary thyroid cancer

Cited by 19 publications

References 19 publications

An Autophagy-related lncRNA Prognostic Risk Model for Thyroid Cancer

An Autophagy-related lncRNA Prognostic Risk Model for Thyroid Cancer

Identification of Immune-related lncRNA Panel for Predicting Immune Checkpoint Blockade and Prognosis in Head and Neck Squamous Cell Carcinoma

Identification of the EMT-Related Genes Signature for Predicting Occurrence and Progression in Thyroid Cancer

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