@Article{biocell.2023.030796,
AUTHOR = {ZHAOXU YAO, HAIBIN MA, LIN LIU, QIAN ZHAO, LONGCHAO QIN, XUEYAN REN, CHUANJUN WU, KAILI SUN},
TITLE = {Novel defined N7-methylguanosine modification-related lncRNAs for predicting the prognosis of laryngeal squamous cell carcinoma},
JOURNAL = {BIOCELL},
VOLUME = {47},
YEAR = {2023},
NUMBER = {9},
PAGES = {1965--1975},
URL = {http://www.techscience.com/biocell/v47n9/54298},
ISSN = {1667-5746},
ABSTRACT = { <b>Objective:</b> Through integrated bioinformatics analysis, the goal of this work was to find new, characterised N7-methylguanosine modification-related long non-coding RNAs (m7G-lncRNAs) that might be used to predict the prognosis of laryngeal squamous cell carcinoma (LSCC). <b>Methods:</b> The clinical data and LSCC gene expression data for the current investigation were initially retrieved from the TCGA database & sanitised. Then, using co-expression analysis of m7G-associated mRNAs & lncRNAs & differential expression analysis (DEA) among LSCC & normal sample categories, we discovered lncRNAs that were connected to m7G. The prognosis prediction model was built for the training category using univariate & multivariate COX regression & LASSO regression analyses, & the model’s efficacy was checked against the test category data. In addition, we conducted DEA of prognostic m7G-lncRNAs among LSCC & normal sample categories & compiled a list of co-expression networks & the structure of prognosis m7G-lncRNAs. To compare the prognoses for individuals with LSCC in the high- & low-risk categories in the prognosis prediction model, survival and risk assessments were also carried out. Finally, we created a nomogram to accurately forecast the outcomes of LSCC patients & created receiver operating characteristic (ROC) curves to assess the prognosis prediction model’s predictive capability. <b>Results:</b> Using co-expression network analysis & differential expression analysis, we discovered 774 m7G-lncRNAs and 551 DEm7G-lncRNAs, respectively. We then constructed a prognosis prediction model for six m7G-lncRNAs (<i>FLG−AS1</i>, <i>RHOA−IT1</i>, <i>AC020913.3</i>, <i>AC027307.2</i>, <i>AC010973.2</i> and <i>AC010789.1</i>), identified 32 DEPm7G-lncRNAs, analyzed the correlation between 32 DEPm7G-lncRNAs and 13 DEPm7G-mRNAs, and performed survival analyses and risk analyses of the prognosis prediction model to assess the prognostic performance of LSCC patients. By displaying ROC curves and a nomogram, we finally checked the prognosis prediction model's accuracy. <b>Conclusion:</b> By creating novel predictive lncRNA signatures for clinical diagnosis & therapy, our findings will contribute to understanding the pathogenetic process of LSCC.},
DOI = {10.32604/biocell.2023.030796}
}