Research Paper|Volume 15, Issue 19|pp 10010—10030

Identification of an endoplasmic reticulum stress-related prognostic risk model with excellent prognostic and clinical value in oral squamous cell carcinoma

Mingyang Cheng^1,^2,^3,⁴, Xin Fan^1,^2,³, Mu He⁵, Xianglin Dai^1,^2,³, Xiaoli Liu^1,^2,³, Jinming Hong^1,^2,³, Laiyu Zhang^1,^2,³, Lan Liao^1,^2,^3,⁴

¹The Affiliated Stomatological Hospital of Nanchang University, Nanchang, Jiangxi, China
²The Key Laboratory of Oral Biomedicine, Nanchang, Jiangxi, China
³Jiangxi Clinical Research Center for Oral Diseases, Nanchang, Jiangxi, China
⁴Clinical Medical Research Center Affiliated Hospital of Jinggangshan University, Medical Department of Jinggangshan University, Ji'An, Jiangxi, China
⁵The Stomatology College of Nanchang University, Nanchang, Jiangxi, China

* Equal contribution

Received: May 6, 2023Accepted: July 20, 2023Published: August 25, 2023

https://doi.org/10.18632/aging.204983

Copyright: © 2023 Cheng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Background: Recently, endoplasmic reticulum stress related gene (ERS) markers have performed very well in predicting the prognosis of tumor patients.

Methods: The differentially expressed genes in Oral squamous cell carcinoma (OSCC) were obtained from TCGA and GTEx database. Three prognosis-related and differentially expressed ERSs were screened out by Least Absolute Selection and Shrinkage Operator (Lasso) regression to construct a prognostic risk model. Receiver Operating Characteristic Curve (ROC), riskplots and survival curves were used to verify the model’s accuracy in predicting prognosis. Multi-omics analysis of immune infiltration, gene mutation, and stem cell characteristics were performed to explore the possible mechanism of OSCC. Finally, we discussed the model’s clinical application value from the perspective of drug sensitivity.

Results: Three genes used in the model (IBSP, RDM1, RBP4) were identified as prognostic risk factors. Bioinformatics analysis, tissue and cell experiments have fully verified the abnormal expression of these three genes in OSCC. Multiple validation methods and internal and external datasets confirmed the model’s excellent performance in predicting and discriminating prognosis. Cox regression analysis identified risk score as an independent predictor of prognosis. Multi-omics analysis found strong correlations between risk scores and immune cells, cell stemness index, and tumor mutational burden (TMB). It was also observed that the risk score was closely related to the half maximal inhibitory concentration of docetaxel, gefitinib and erlotinib. The excellent performance of the nomogram has been verified by various means.

Conclusion: A prognostic model with high clinical application value was constructed. Immune cells, cellular stemness, and TMB may be involved in the progression of OSCC.