# Disulfidptosis ‒ related lncRNAs are biomarkers of prognosis and immune response in Head and Neck Squamous Cell Carcinoma

## TL;DR

This study identifies six long non-coding RNAs linked to disulfidptosis that predict survival and immune response in head and neck cancer patients.

## Contribution

A novel six-lncRNA model is proposed for HNSCC prognosis and immune infiltration prediction based on disulfidptosis-related biomarkers.

## Key findings

- A six DRlncRNA model predicts HNSCC patient prognosis effectively.
- DRlncRNAs correlate with tumor mutation burden and immune cell infiltration in HNSCC.
- The model shows potential as a clinical therapeutic target for HNSCC.

## Abstract

•Disulfidptosis-related lncRNAs can predict the prognosis of HNSCC patients.•Disulfidptosis-related lncRNAs are associated with tumor mutational burden in HNSCC.•Disulfidptosis-related lncRNAs are closely related to immune responses in HNSCC.

Disulfidptosis-related lncRNAs can predict the prognosis of HNSCC patients.

Disulfidptosis-related lncRNAs are associated with tumor mutational burden in HNSCC.

Disulfidptosis-related lncRNAs are closely related to immune responses in HNSCC.

This study aims to explore the role of Disulfidptosis-Related long Non-Coding RNAs (DRlncRNAs) in the prognosis and immune infiltration of Head and Neck Squamous Cell Carcinoma (HNSCC).

Using bioinformatics approaches, this study investigates the prognostic significance of DRlncRNAs in HNSCC patients and their potential association with the immune microenvironment. RNA sequencing data and clinical information for HNSCC were obtained from The Cancer Genome Atlas (TCGA) database. DRlncRNAs were identified through Pearson correlation analysis, and a prognostic model consisting of six DRlncRNAs was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression, along with univariate and multivariate Cox analyses.

The predictive performance of the model was assessed using Receiver Operating Characteristic (ROC) curves and Principal Component Analysis (PCA), and further validated using calibration curves, a nomogram, and univariate/multivariate Cox analyses. In addition to functional enrichment analysis, the associations between the model and Tumor Mutation Burden (TMB), immune cell infiltration, and drug sensitivity were also examined.

We developed a novel predictive model composed of six DRlncRNAs to predict the prognosis of HNSCC patients and proposed potential clinical therapeutic targets from the perspective of disulfidptosis.

Level 5.