# Unveiling disulfidptosis-linked lncRNA signatures: insights into the immune microenvironment and drug responsiveness in oral squamous cell carcinoma

## TL;DR

This study identifies 9 lncRNAs linked to disulfidptosis in oral cancer, which could help predict patient outcomes and immune responses.

## Contribution

The study introduces a novel disulfidptosis-related lncRNA risk model for oral squamous cell carcinoma prognosis and treatment prediction.

## Key findings

- Nine disulfidptosis-linked lncRNAs were identified as correlated with oral squamous cell carcinoma.
- A risk model and nomogram were developed for clinical prognosis assessment in OSCC patients.
- 12 potential drugs, including BMS-754807_2171 and Foretinib_2040, were predicted for OSCC treatment.

## Abstract

Oral squamous cell carcinoma (OSCC) has a highly incidence rate and mortality rate all over the world. Hitherto, there are limited studies on survival significance between disulfidptosis-related lncRNAs (DRLs) and OSCC. Therefore, this study was conducted to investigate the potential role of these DRLs and provide some theoretical support in the clinical treatment of OSCC.

OSCC-related lncRNAs and disulfidptosis-related genes (DRGs) were retrieved from public databases. Using Pearson correlation, machine learning, and expression profiling, we identified differentially expressed DRLs (DE-DRLs), developed a DE-DRLs-based risk model and independent prognostic nomogram, performed immunological and tumor microenvironment analyses to explore DE-DRLs regulatory mechanisms, predicted potential drugs for OSCC, and validated bioinformatics findings.

In this study, 9 DE-DRLs were identified that correlated with OSCC. The risk model and nomogram showed good clinical utility for assessing the likelihood of OSCC occurrence. Patients exhibiting elevated levels of eosinophils, activated natural killer (NK) cells, or naïve CD4+ T cells experienced significantly poorer overall survival (OS), and patients with high tumor mutational burden (TMB) had worse prognosis. 12 drugs were identified for OSCC treatment, such as BMS-754807_2171 and Foretinib_2040.

Our study identified 9 DE-DRLs correlated with OSCC, which will be a personalized prediction tool for prognosis and immune responses in OSCC patients.

Flowchart detailing a study on tumor samples from the TCGA-HNSC cohort. Samples are narrowed down, excluding certain origins. Processes include separating datasets for analysis, exploring tumor microenvironment, and gene expression analysis. Statistical methods like Pearson correlation, Cox analysis, LASSO, and multivariate analysis are applied. Prognostic validation and statistical analyses such as ROC plotting and survival analysis are conducted. Ends with the creation of a disulfidptosis-related lncRNA prognosis model.