Dataset Information

A novel necroptosis-related LncRNA signature for prediction of prognosis and therapeutic responses of head and neck squamous cell carcinoma.

ABSTRACT: Background: Long non-coding RNAs (lncRNAs) play an essential role in the occurrence and prognosis of tumors, and it has great potential as biomarkers of tumors. However, the roles of Necroptosis-related lncRNA (NRLs) in Head and neck squamous cell carcinoma (HNSCC) remain elusive. Methods: We comprehensively analyzed the gene expression and clinical information of 964 HNSCC in four cohorts. LASSO regression was utilized to construct a necroptosis-related lncRNA prognosis signature (NLPS). We used univariate and multivariate regression to assess the independent prognostic value of NLPS. Based on the optimal cut-off, patients were divided into high- and low-risk groups. In addition, the immune profile, multi-omics alteration, and pharmacological landscape of NLPS were further revealed. Results: A total of 21 NRLs associated with survival were identified by univariate regression in four cohorts. We constructed and validated a best prognostic model (NLPS). Compared to the low-risk group, patients in the high group demonstrated a more dismal prognosis. After adjusting for clinical features by multivariate analysis, NLPS still displayed independent prognostic value. Additionally, further analysis found that patients in the low-risk group showed more abundant immune cell infiltration and immunotherapy response. In contrast, patients in the high-risk group were more sensitive to multiple chemotherapeutic agents. Conclusion: As a promising tool, the establishment of NLPS provides guidance and assistance in the clinical management and personalized treatment of HNSCC.

SUBMITTER: Meng Z

PROVIDER: S-EPMC9395581 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A novel necroptosis-related LncRNA signature for prediction of prognosis and therapeutic responses of head and neck squamous cell carcinoma.

Meng Zilu Z Yang Wenhan W Zhu Lei L Liu Wanyu W Wang Yudong Y

Frontiers in pharmacology 20220809

<b>Background:</b> Long non-coding RNAs (lncRNAs) play an essential role in the occurrence and prognosis of tumors, and it has great potential as biomarkers of tumors. However, the roles of Necroptosis-related lncRNA (NRLs) in Head and neck squamous cell carcinoma (HNSCC) remain elusive. <b>Methods:</b> We comprehensively analyzed the gene expression and clinical information of 964 HNSCC in four cohorts. LASSO regression was utilized to construct a necroptosis-related lncRNA prognosis signature ...[more]

PMID: 36016575

Similar Datasets

Project description:BackgroundThe mechanistic aspects of the involvement of long noncoding RNAs (lncRNAs) in NETosis, the process of neutrophil extracellular trap (NET) formation in head and neck squamous cell carcinoma (HNSCC), lack comprehensive elucidation. The involvement of these molecules in the immune microenvironment and plausible HNSCC prognosis remain to see the light of the day. The plausible functioning of NETosis-related lncRNAs with their plausible prognostic impact in HNSCC was probed in this work.MethodsThe scrutiny of lncRNAs linked to NETosis entailed the probing of twenty-four genes associated with the process employing Pearson's correlation analysis on HNSCC patients' RNA sequencing data from The Cancer Genome Atlas (TCGA) database. The application of univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses yielded a NETosis-related lncRNA signature that was subjected to probing for its suitability in prognosis employing survival and nomogram analyses.ResultsThe NETosis-related lncRNA signature inclusive of five lncRNAs facilitated patients to be segregated as high-risk and low-risk groups with the former documenting a poor prognosis. Regression unearthed that the risk score was an independent factor for prognosis. The receiver operating characteristic (ROC) or receiver operating characteristic curve analysis documented a one-year area under time-dependent ROC curve (AUC) value of 0.711 that is corroborative of the accuracy of this signature. Additional probing documented an evident enriching of immune-linked pathways in the low-risk patients, while the high-risk patients documented an immunologically "cold" profile as per the infiltration of immune cells. We verified lncRNA expression from our NETosis-related lncRNA signature in vitro, which reflects the reliability of our model to a certain extent. Moreover, we also verified the function of the lncRNA. We found that LINC00426 contributes to the innate immune cGAS-STING signaling pathway, which explain to some extent the role of our prognostic model in predicting "hot" and "cold" tumors.ConclusionsThe plausible prognostic relevance of the NETosis-related lncRNA signature (with five lncRNAs) emerges that is suggestive of its promise in targeting HNSCC.

Project description:BackgroundEsophageal cancer (EC), one highly malignant gastrointestinal cancer, is the 6th leading cause of cancer-related deaths worldwide. Necroptosis and long non-coding RNA (lncRNA) play important roles in the occurrence and development of EC, but the research on the role of necroptosis-related lncRNA in EC is not conclusive. This study aims to use bioinformatics to investigate the prognostic value of necroptosis-related lncRNA in EC.MethodsTranscriptome data containing EC and normal samples, and clinical information were obtained from the Cancer Genome Atlas database. 102 necroptosis-related genes were obtained from Kanehisa Laboratories. Necroptosis-related lncRNAs were screened out via univariate, multivariate Cox and the least absolute shrinkage and selection operator regression analyses to construct the risk predictive model. The reliability of the risk model was evaluated mainly through quantitative real-time PCR (qRT-PCR), the receiver operating characteristic (ROC) curves and the constructed nomogram. KEGG pathways were explored in the high- and low-risk groups of EC patients via gene set enrichment analyses (GSEA) software. Immune microenvironment and potential therapeutic agents in risk groups were also analyzed.ResultsA 6 necroptosis-related lncRNAs risk model composed of AC022211.2, Z94721.1, AC007991.2, SAMD12-AS1, AL035461.2 and AC051619.4 was established to predict the prognosis level of EC patients. qRT-PCR analysis showed upregulated Z94721.1 and AL035461.2 mRNA levels and downregulated AC051619.4 mRNA level in EC tissues compared with normal tissues. According to clinical characteristics, the patients in the high-risk group had a shorter overall survival than the low-risk group. The ROC curve and nomogram confirmed this model as one independent and predominant predictor. GSEA analysis showed metabolic and immune-related pathways enriched in the risk model. Most of the immune cells and immune checkpoints were positively correlated with the risk model, mainly active in the high-risk group. For the prediction of potential therapeutic drugs, 16 compounds in the high-risk group and 2 compounds in the low-risk group exhibited higher sensitivity.ConclusionsOur results supported the necroptosis-related lncRNA signature could independently predict prognosis of EC patients, and provided theoretical basis for improving the clinical treatment of EC.

Dataset Information

A novel necroptosis-related LncRNA signature for prediction of prognosis and therapeutic responses of head and neck squamous cell carcinoma.

Publications

A novel necroptosis-related LncRNA signature for prediction of prognosis and therapeutic responses of head and neck squamous cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets