Cochrane’s risk of bias scores for the QUADAS-C tool.

Cochrane’s risk of bias scores for the QUADAS-C tool.</p

Forest plot for Area Under the Curves (AUCs) of included studies using random effect model with inverse method.

Forest plot for Area Under the Curves (AUCs) of included studies using random effect model with inverse method.</p

Summary Receiver Operating Characteristic (SROC) curves of subgroups and overall analysis for bivariate model.

Summary Receiver Operating Characteristic (SROC) curves of subgroups and overall analysis for bivariate model.</p

Subgroup analysis of the AUS meta-analysis of included studies.

Subgroup analysis of the AUS meta-analysis of included studies.</p

Flow chart of systematic review according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline.

Flow chart of systematic review according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guideline.</p

Basic characteristics of the included studies.

BackgroundHead and neck squamous cell carcinoma (HNSCC) is a group of malignancies arising from the epithelium of the head and neck. Despite efforts in treatment, results have remained unsatisfactory, and the death rate is high. Early diagnosis of HNSCC has clinical importance due to its high rates of invasion and metastasis. This systematic review and meta-analysis evaluated the diagnostic accuracy of lncRNAs in HNSCC patients.MethodsPubMed, ISI, SCOPUS, and EMBASE were searched for original publications published till April 2023 using MeSH terms and free keywords “long non-coding RNA” and “head and neck squamous cell carcinoma” and their expansions. The Reitsma bivariate random effect model pooled diagnostic test performance for studies that reported specificity and sensitivity; diagnostic AUC values from all trials were meta-analyzed using the random effects model with the inverse variance method.ResultsThe initial database search yielded 3209 articles, and 25 studies met our criteria. The cumulative sensitivity and specificity for lncRNAs in the diagnosis of HNSCC were 0.74 (95%CI: 0.68–0.7 (and 0.79 (95%CI: 0.74–0.83), respectively. The pooled AUC value for all specimen types was found to be 0.83. Using the inverse variance method, 71 individual lncRNAs yielded a pooled AUC of 0.77 (95%CI: 0.74–0.79). Five studies reported on the diagnostic accuracy of the MALAT1 lncRNA with a pooled AUC value of 0.83 (95%CI: 0.73–0.94).ConclusionsLncRNAs could be used as diagnostic biomarkers for HNSCC, but further investigation is needed to validate clinical efficacy and elucidate mechanisms. High-throughput sequencing and bioinformatics should be used to ascertain expression profiles.</div

Funnel plot of included studies.

BackgroundHead and neck squamous cell carcinoma (HNSCC) is a group of malignancies arising from the epithelium of the head and neck. Despite efforts in treatment, results have remained unsatisfactory, and the death rate is high. Early diagnosis of HNSCC has clinical importance due to its high rates of invasion and metastasis. This systematic review and meta-analysis evaluated the diagnostic accuracy of lncRNAs in HNSCC patients.MethodsPubMed, ISI, SCOPUS, and EMBASE were searched for original publications published till April 2023 using MeSH terms and free keywords “long non-coding RNA” and “head and neck squamous cell carcinoma” and their expansions. The Reitsma bivariate random effect model pooled diagnostic test performance for studies that reported specificity and sensitivity; diagnostic AUC values from all trials were meta-analyzed using the random effects model with the inverse variance method.ResultsThe initial database search yielded 3209 articles, and 25 studies met our criteria. The cumulative sensitivity and specificity for lncRNAs in the diagnosis of HNSCC were 0.74 (95%CI: 0.68–0.7 (and 0.79 (95%CI: 0.74–0.83), respectively. The pooled AUC value for all specimen types was found to be 0.83. Using the inverse variance method, 71 individual lncRNAs yielded a pooled AUC of 0.77 (95%CI: 0.74–0.79). Five studies reported on the diagnostic accuracy of the MALAT1 lncRNA with a pooled AUC value of 0.83 (95%CI: 0.73–0.94).ConclusionsLncRNAs could be used as diagnostic biomarkers for HNSCC, but further investigation is needed to validate clinical efficacy and elucidate mechanisms. High-throughput sequencing and bioinformatics should be used to ascertain expression profiles.</div

Forest plot for the bivariate model of diagnostic meta-analysis of subgroups.

Forest plot for the bivariate model of diagnostic meta-analysis of subgroups.</p

Cochrane’s risk of bias graph for the QUADAS-C tool.

Cochrane’s risk of bias graph for the QUADAS-C tool.</p

Bivariate model for diagnostic meta-analysis, estimation method: REML.

Bivariate model for diagnostic meta-analysis, estimation method: REML.</p