Identification and Verification of a Novel Disulfidptosis-Related lncRNAs Prognostic Signature to Predict the Prognosis and  Immune Activity of Head and Neck Squamous Carcinoma

Zi  Yin; Jue  Wang; Changqing  Zhu; Chenli  Xu; Juan  Fang; Qiaoqin  Li

doi:10.18502/ijph.v53i10.16720

Zi Yin Department of Pathology, School of Basic Medicine, Hubei University of Medicine, Shiyan, China
Jue Wang Clinical Pathology Test and Consultation Center, Hubei University of Medicine, Shiyan, China
Changqing Zhu Department of Pathology, School of Basic Medicine, Hubei University of Medicine, Shiyan, China
Chenli Xu Department of Pathology, School of Basic Medicine, Hubei University of Medicine, Shiyan, China
Juan Fang Department of Pathology, School of Basic Medicine, Hubei University of Medicine, Shiyan, China
Qiaoqin Li Department of Pathology, School of Basic Medicine, Hubei University of Medicine, Shiyan, China

DOI: https://doi.org/10.18502/ijph.v53i10.16720

Keywords: Head and neck squamous carcinoma; Disulfidptosis; Long non-coding RNAs; Immune activity

Abstract

Background: We aimed to explore the prediction value of disulfidptosis-related long noncoding RNAs (lncRNAs) on the prognosis and immunotherapy efficiency of patients with head and neck squamous carcinoma (HNSCC).

Methods: Clinical and RNA-seq information were collected from The Cancer Genome Atlas (TCGA) and Genome Data Sharing (GDC) portal. The Pearson correlation analysis, univariate COX regression analysis, the least absolute shrinkage and selection operator (LASSO) COX regression were employed to construct the disulfidptosis-related lncRNAs (DRLs) prognostic model. The Kaplan-Meier survival curve, principal component analysis (PCA), receiver operating characteristic (ROC) curves and areas under the curves (AUCs) were used to examine the accuracy of the prognostic model. ssGSEA, mutation and functional and gene set enrichment analysis was performed to quantify the immune cell infiltration, immune function and functional enrichments. Finally, the mRNA expression of the DRLs was verified by real‑time PCR (RT-PCR) in HNSCC cells.

Results: A new DRLs prognostic model (AC083967.1, AC106820.5, AC245041.2, AL590617.2, AP002478.1, and VPS9D1-AS1) with an independent prognostic value of HNSCC patients was successfully identified. In addition, the DRLs prognostic model was related with immune signature and drug therapy response. Meanwhile, the mRNA expression level of the 6 DRLs detected by RT-PCR was consistent with the results of bioinformatic analysis.

Conclusion: We developed a new DRLs prognostic model of HNSCC, which could effectively predicate the prognosis and therapy response of HNSCC patients and provide insights into personalized therapeutics.