{"title":"Deciphering the prognostic potential of a necroptosis-related gene signature in head and neck squamous cell carcinoma: a bioinformatic analysis.","authors":"Shizhe Wang, Junjian Jiang, Min Xing, Hongru Su","doi":"10.21037/tcr-24-743","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Necroptosis, an alternative mode of programmed cell death (PCD) that overcomes apoptosis resistance, has been implicated in the progression and drug resistance of cancer. The aim of this study is to find the biological and prognostic significance of necroptosis in patients with head and neck squamous cell carcinoma (HNSCC).</p><p><strong>Methods: </strong>Integrated clinical datasets from The Cancer Genome Atlas (TCGA) HNSCC cohort underwent analysis. R package \"DESeq2\" was used to conduct differential gene expression analysis between normal and tumor tissues in the cohort, resulting in the identification of 2,172 differentially expressed genes (DEGs). A total of 159 necroptosis-related genes (NRGs) were extracted and performed a Venn analysis to identify the optimal necroptosis-related DEGs, resulting in the selection of 25 genes specifically associated with necroptosis in HNSCC. Then prognostic analyze, Cox regression analysis and prognostic model were demonstrated the ability to predict the extent of immunological infiltration in HNSCC.</p><p><strong>Results: </strong>Among these DEGs, five genes (<i>FADD, H2AZ1, PYGL, JAK3</i>, and <i>ZBP1</i>) were found to have prognostic value (P<0.05). Then, bioinformatic analyses were conducted, and the biological and clinical significance of these five genes were demonstrated. Furthermore, Cox regression analysis was performed to develop a prognostic gene model based on these genes, which effectively classified HNSCC patients into low- or high-risk groups. The prognostic model also demonstrated the ability to predict the extent of immunological infiltration in HNSCC. Additionally, a predictive nomogram based on the clinicopathological features of these five prognostic DEGs was constructed.</p><p><strong>Conclusions: </strong>We performed a systematic bioinformatic analysis to identify necroptosis-related prognostic genes in HNSCC patients. These genes' prognostic value was synthesized into a predictive nomogram for forecasting HNSCC progression.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 1","pages":"340-353"},"PeriodicalIF":1.5000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11833364/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/tcr-24-743","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/17 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
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Abstract
Background: Necroptosis, an alternative mode of programmed cell death (PCD) that overcomes apoptosis resistance, has been implicated in the progression and drug resistance of cancer. The aim of this study is to find the biological and prognostic significance of necroptosis in patients with head and neck squamous cell carcinoma (HNSCC).
Methods: Integrated clinical datasets from The Cancer Genome Atlas (TCGA) HNSCC cohort underwent analysis. R package "DESeq2" was used to conduct differential gene expression analysis between normal and tumor tissues in the cohort, resulting in the identification of 2,172 differentially expressed genes (DEGs). A total of 159 necroptosis-related genes (NRGs) were extracted and performed a Venn analysis to identify the optimal necroptosis-related DEGs, resulting in the selection of 25 genes specifically associated with necroptosis in HNSCC. Then prognostic analyze, Cox regression analysis and prognostic model were demonstrated the ability to predict the extent of immunological infiltration in HNSCC.
Results: Among these DEGs, five genes (FADD, H2AZ1, PYGL, JAK3, and ZBP1) were found to have prognostic value (P<0.05). Then, bioinformatic analyses were conducted, and the biological and clinical significance of these five genes were demonstrated. Furthermore, Cox regression analysis was performed to develop a prognostic gene model based on these genes, which effectively classified HNSCC patients into low- or high-risk groups. The prognostic model also demonstrated the ability to predict the extent of immunological infiltration in HNSCC. Additionally, a predictive nomogram based on the clinicopathological features of these five prognostic DEGs was constructed.
Conclusions: We performed a systematic bioinformatic analysis to identify necroptosis-related prognostic genes in HNSCC patients. These genes' prognostic value was synthesized into a predictive nomogram for forecasting HNSCC progression.
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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