Novel defined N7-methylguanosine modification-related lncRNAs for predicting the prognosis of laryngeal squamous cell carcinoma

Objective: 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). Methods: 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. Results: 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 (FLG−AS1, RHOA−IT1, AC020913.3, AC027307.2, AC010973.2 and AC010789.1), 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. Conclusion: By creating novel predictive lncRNA signatures for clinical diagnosis & therapy, our findings will contribute to understanding the pathogenetic process of LSCC.