Disulfidptosis-related immune patterns predict prognosis and characterize the tumor microenvironment in oral squamous cell carcinoma

Abstract

Background: Establishing a prognostic risk model based on immunological and disulfidptosis signatures enables precise prognosis prediction of oral squamous cell carcinoma (OSCC).

Methods: Differentially expressed immune and disulfidptosis genes were identified in OSCC and normal tissues. We examined the model's clinical applicability and its relationship to immune cell infiltration. Additionally, the risk score, ssGSEA, ESTIMATE, and CIBERSORT were used to evaluate the intrinsic molecular subtypes, immunological checkpoints, abundances of tumor-infiltrating immune cell types and proportions between the two risk groups. GO-KEGG and GSVA analyses were performed to identify enriched pathways.

Results: We analyzed the correlation immune genes based on the 14 disulfidptosis-related genes, and found 379 disulfidptosis-related immune genes (DRIGs). After univariate Cox regression we obtained 30 DRIGs and least absolute shrinkage and selection operator (LASSO) regression to reduce the number of genes to 16. Finally we created a nine-DRIGs risk model, of which four were upregulated and five were downregulated. The analysis results showed that disulfidptosis was tightly related to immune cells, immunological-related pathways, the tumor microenvironment (TME), immune checkpoints, human leukocyte antigen (HLA), and tumor mutational burden (TMB). The nomogram, integrating the risk score and clinical factors, accurately predicted overall survival.

Conclusions: This novel risk model highlights the role of disulfidptosis-related immune genes in OSCC prognosis. With this model, we can more accurately predict the prognosis of patients with OSCC, as well as assess the potential effects of their TME and immunotherapy.

Keywords: Disulfidptosis; Immune gene; Oral squamous cell carcinoma; Prognosis; Risk model; TCGA.

Fig. 1

Identification of disulfidptosis-related genes in OSCC. A Comparison of disulfidptosis-related genes between normal and tumor tissues. B The connection among disulfidptosis-related genes. C The mutational status of disulfidptosis-related gene and the TMB of samples. D The interaction of disulfidptosis-related genes and immune genes

Fig. 2

Disulfidptosis-related genes relative RNA expression level in HIOEC, HN30, SCC9, CAL27 cell lines

Fig. 3

DRIGs from the TCGA OSCC cohort were used to build the prognostic model. A Univariate Cox analysis of 30 selected DRIGs B, C LASSO coefficient profiles in the tenfold cross validation. D Multivariate Cox analysis of the nine DRIGs

Fig. 4

Construction of the prognostic model in the training set. A The risk score distributions, B survival times, C overall survival of OSCCs, D time-dependent ROC curve in the OSCC GEO cohort

Fig. 5

Kaplan–Meier survival analysis of different subgroups. Patients with male (A), female (B), age ≤ 65 (C), age > 65 (D), G1-G2 (E), G3-G4 (F), stage I-II (G), stage III-IV (H), stage T1-T2 (I), stage T3-T4 (J), N0 (K), and N1-N3 (L)

Fig. 6

DRIGs for nomogram construction. A Nomogram that combined the risk score and clinical data to facilitate prognosis prediction. B The ROC curves demonstrated the nomogram’s predictive effectiveness. C The calibration curve demonstrated the agreement between the actual observed prognostic value and the value projected by the nomogram