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. 2021 Jun 22;12(17):5153-5163.
doi: 10.7150/jca.54475. eCollection 2021.

Development and Validation of a Nomogram based on cell growth-related Biomarkers for Oral Squamous Cell Carcinoma

Yanjie Shuai  1 , Yuansheng Duan  1 , Mengqian Zhou  1 , Kai Yue  1 , Dandan Liu  1 , Yan Fang  1 , Yuxuan Wang  1 , Yansheng Wu  1 , Ze Zhang  1 , Xudong Wang  1
Affiliations

Development and Validation of a Nomogram based on cell growth-related Biomarkers for Oral Squamous Cell Carcinoma

Yanjie Shuai et al. J Cancer. .

Abstract

Purpose: We aimed to develop a prognostic nomogram based on immunohistochemistry (IHC) biomarkers of patients with oral squamous cell carcinoma (OSCC). Methods: A total of 294 patients were enrolled in the study. The least absolute shrinkage and selection operator (LASSO) Cox regression model was performed to develop a combined IHC score (IHCs) classifier. Results: Five biomarkers, specifically c-Met, Vimentin, HIF-2α, VEGF-c, and Bcl-2 were extracted. Then, an IHCs classifier was developed, and patients were stratified into high- and low-IHCs groups. In the training cohort, the 5-year overall survival (OS) was 62.1% in low-IHCs group and 28.2% in high-IHCs group (P<0.001). The 5-year OS was 68.6% for the low-IHCs group and 28.4% for the high-IHCs group in the validation cohort (P<0.001). The area under the ROC curve (AUROC) of the combination of the IHCs classifier and TNM stage was 0.746 (95% CI: 0.658-0.833) in the training cohort and 0.735 (95% CI: 0.651-0.818) in the validation cohort, respectively. Conclusions: The nomogram could effectively predict the prognosis for patients with OSCC and may be employed as a potential tool to guide the individual decision-making process.

Keywords: biomarkers; immunohistochemistry; nomogram; oral squamous cell carcinoma; prognosis.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
Representative IHC images of the 16 biomarkers expression. Bar, 100 um.
Figure 2
Figure 2
Feature selection using LASSO Cox regression model. (A) LASSO coefficient profiles the 16 biomarkers associated with OSCC. (B) Tuning parameter selection in the LASSO model. We selected λ via 1-SE (standard error) criteria. A value λ = 0.009 with log (λ) = -4.673 was chosen by 10-fold cross-validation via the 1-SE criteria.
Figure 3
Figure 3
Clinicopathological risk factors for patients with OS. (C) Univariate cox regression analysis of patients with OS in the validation cohort. (D) Multivariate cox regression analysis of patients with OS in the validation cohort.
Figure 3
Figure 3
Clinicopathological risk factors for patients with OS. (C) Univariate cox regression analysis of patients with OS in the validation cohort. (D) Multivariate cox regression analysis of patients with OS in the validation cohort.
Figure 4
Figure 4
The specificity and significance of the clinical use of the IHCs classier. (A) Comparison of the prognostic value of IHCs with TNM staging in the training cohort. (B) Kaplan-Meier survival analysis of overall survival according to the IHCs classier in the training cohort. (C) Comparison of the prognostic value of IHCs with TNM staging in the validation cohort. (D) Kaplan-Meier survival analysis of overall survival according to the IHCs classier in the validation cohort.
Figure 5
Figure 5
The nomogram to predict 1-year, 3-year and 5-year survival probability for OSCC. (A) A nomogram established by the combination of the clinical factors and IHCs. (B)(D) The calibration curve for predicting patients 5-year OS in the training cohort and validation cohort. (C)(E) Evaluation of nomogram using decision curve analysis for the clinical utility of the nomogram in the training cohort and validation cohort.
See this image and copyright information in PMC

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