Sijie Yuan, Ziyu Zhai, Yixu Wang, Jilin Peng, Yinghui Ding, Kun Zhao, Xiaodan Zhu, Yuan Zhang, Ling Li, Fanglei Ye, Le Wang
{"title":"基于气味相关的长链非编码rna的头颈部鳞状细胞癌预后模型的构建与验证。","authors":"Sijie Yuan, Ziyu Zhai, Yixu Wang, Jilin Peng, Yinghui Ding, Kun Zhao, Xiaodan Zhu, Yuan Zhang, Ling Li, Fanglei Ye, Le Wang","doi":"10.21037/tcr-2024-2520","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>As a unique form of apoptosis, anoikis significantly influences tumor biology. Studies have revealed the diverse roles of long non-coding RNAs (lncRNAs) in cancer signaling pathways; however, the prognostic significance of anoikis-related long non-coding RNAs (ARLncs) in head and neck squamous cell carcinoma (HNSCC) remains unexplored. Therefore, this research was undertaken to establish a risk model and assess its predictive ability for prognosis and immune landscape in individuals with HNSCC.</p><p><strong>Methods: </strong>Data on HNSCC were retrieved from The Cancer Genome Atlas (TCGA). Anoikis-associated genes were acquired from GeneCards, followed by identification of ARLxncs using Pearson correlation analysis. A total of 268 ARLncs from HNSCC samples were extracted from TCGA, and highly relevant ARLncs were identified using Pearson analysis. These ARLncs were subjected to comprehensive bioinformatics analyses, including univariate Cox regression and least absolute shrinkage and selection operator analyses, and an overall survival (OS)-score and OS-signature were generated.</p><p><strong>Results: </strong>Based on the risk score, patients with HNSCC were stratified into high- and low-risk subgroups to assess the differences in pathway enrichment, prognosis, immune infiltration level, tumor mutation burden, and drug susceptibility. TCGA-HNSCC samples were divided into two subtypes (clusters 1 and 2), with patients in cluster 2 exhibiting worse prognosis and higher levels of tumor-infiltrating lymphocytes (TILs) than patients in cluster 1. Subsequently, we constructed a valid prognostic risk model comprising 12 ARLncs in HNSCC that demonstrated efficacy in predicting prognosis. Patients with high-risk scores exhibited significantly worse OS, lower numbers of TILs, and lower sensitivity to chemotherapy drugs than patients with low-risk scores.</p><p><strong>Conclusions: </strong>Overall, we successfully established a novel prognostic model based on ARLncs, which holds significant promise for predicting prognosis and personalized therapy for patients with HNSCC.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 7","pages":"4160-4178"},"PeriodicalIF":1.7000,"publicationDate":"2025-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335720/pdf/","citationCount":"0","resultStr":"{\"title\":\"Construction and validation of an anoikis-related long non-coding RNA-based prognostic model for head and neck squamous cell carcinoma.\",\"authors\":\"Sijie Yuan, Ziyu Zhai, Yixu Wang, Jilin Peng, Yinghui Ding, Kun Zhao, Xiaodan Zhu, Yuan Zhang, Ling Li, Fanglei Ye, Le Wang\",\"doi\":\"10.21037/tcr-2024-2520\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>As a unique form of apoptosis, anoikis significantly influences tumor biology. Studies have revealed the diverse roles of long non-coding RNAs (lncRNAs) in cancer signaling pathways; however, the prognostic significance of anoikis-related long non-coding RNAs (ARLncs) in head and neck squamous cell carcinoma (HNSCC) remains unexplored. Therefore, this research was undertaken to establish a risk model and assess its predictive ability for prognosis and immune landscape in individuals with HNSCC.</p><p><strong>Methods: </strong>Data on HNSCC were retrieved from The Cancer Genome Atlas (TCGA). Anoikis-associated genes were acquired from GeneCards, followed by identification of ARLxncs using Pearson correlation analysis. A total of 268 ARLncs from HNSCC samples were extracted from TCGA, and highly relevant ARLncs were identified using Pearson analysis. These ARLncs were subjected to comprehensive bioinformatics analyses, including univariate Cox regression and least absolute shrinkage and selection operator analyses, and an overall survival (OS)-score and OS-signature were generated.</p><p><strong>Results: </strong>Based on the risk score, patients with HNSCC were stratified into high- and low-risk subgroups to assess the differences in pathway enrichment, prognosis, immune infiltration level, tumor mutation burden, and drug susceptibility. TCGA-HNSCC samples were divided into two subtypes (clusters 1 and 2), with patients in cluster 2 exhibiting worse prognosis and higher levels of tumor-infiltrating lymphocytes (TILs) than patients in cluster 1. Subsequently, we constructed a valid prognostic risk model comprising 12 ARLncs in HNSCC that demonstrated efficacy in predicting prognosis. Patients with high-risk scores exhibited significantly worse OS, lower numbers of TILs, and lower sensitivity to chemotherapy drugs than patients with low-risk scores.</p><p><strong>Conclusions: </strong>Overall, we successfully established a novel prognostic model based on ARLncs, which holds significant promise for predicting prognosis and personalized therapy for patients with HNSCC.</p>\",\"PeriodicalId\":23216,\"journal\":{\"name\":\"Translational cancer research\",\"volume\":\"14 7\",\"pages\":\"4160-4178\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2025-07-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12335720/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Translational cancer research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.21037/tcr-2024-2520\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/7/25 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/tcr-2024-2520","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/7/25 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
Construction and validation of an anoikis-related long non-coding RNA-based prognostic model for head and neck squamous cell carcinoma.
Background: As a unique form of apoptosis, anoikis significantly influences tumor biology. Studies have revealed the diverse roles of long non-coding RNAs (lncRNAs) in cancer signaling pathways; however, the prognostic significance of anoikis-related long non-coding RNAs (ARLncs) in head and neck squamous cell carcinoma (HNSCC) remains unexplored. Therefore, this research was undertaken to establish a risk model and assess its predictive ability for prognosis and immune landscape in individuals with HNSCC.
Methods: Data on HNSCC were retrieved from The Cancer Genome Atlas (TCGA). Anoikis-associated genes were acquired from GeneCards, followed by identification of ARLxncs using Pearson correlation analysis. A total of 268 ARLncs from HNSCC samples were extracted from TCGA, and highly relevant ARLncs were identified using Pearson analysis. These ARLncs were subjected to comprehensive bioinformatics analyses, including univariate Cox regression and least absolute shrinkage and selection operator analyses, and an overall survival (OS)-score and OS-signature were generated.
Results: Based on the risk score, patients with HNSCC were stratified into high- and low-risk subgroups to assess the differences in pathway enrichment, prognosis, immune infiltration level, tumor mutation burden, and drug susceptibility. TCGA-HNSCC samples were divided into two subtypes (clusters 1 and 2), with patients in cluster 2 exhibiting worse prognosis and higher levels of tumor-infiltrating lymphocytes (TILs) than patients in cluster 1. Subsequently, we constructed a valid prognostic risk model comprising 12 ARLncs in HNSCC that demonstrated efficacy in predicting prognosis. Patients with high-risk scores exhibited significantly worse OS, lower numbers of TILs, and lower sensitivity to chemotherapy drugs than patients with low-risk scores.
Conclusions: Overall, we successfully established a novel prognostic model based on ARLncs, which holds significant promise for predicting prognosis and personalized therapy for patients with HNSCC.
期刊介绍：
Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
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