Dataset Information

Prognostic implications of preoperative systemic inflammatory markers in oral squamous cell carcinoma, and correlations with the local immune tumor microenvironment.

ABSTRACT:

Purpose

The aim of this study was to investigate the prognostic significance of preoperative inflammatory markers in peripheral blood of patients with oral squamous cell carcinoma (OSCC), and to establish correlations with the infiltrate of macrophages and lymphocytes in the local immune tumor microenvironment (TME).

Materials and methods

Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and systemic immune-inflammation index (SII) were retrospectively evaluated in a cohort of 348 OSCC patients, and correlated with overall (OS) and disease-specific survival (DSS). Immunohistochemical analysis of tumoral and stromal infiltration of CD8+, CD4+, FOXP3+ and CD20+ lymphocytes and CD68+ and CD163+ macrophages was performed in a subset of 119 OSCC patient samples, and correlations further assessed.

Results

NLR, SII, and LMR were significantly associated with a poorer OS in univariate analysis; however, only NLR remained a significant independent predictor in the multivariate analysis (HR = 1.626, p = 0.04). NLR and SII were inversely and significantly correlated with stromal infiltration of CD8+, CD4+, and CD20+ lymphocytes. Moreover, a significant correlation between LMR was also found to significantly associate with stromal infiltration of CD8+, CD4+, and CD20+ lymphocytes, stromal CD68+ and CD163+ macrophages, and also tumoral infiltration of CD4+ and CD20+ lymphocytes.

Conclusions

Preoperative NLR, SII, and LMR may serve as valuable systemic markers to predict OSCC patient survival, with NLR emerging as an independent predictor of poor OS. Moreover, strong significant correlations were exclusively observed between systemic inflammatory markers and the local stromal infiltration of lymphocytes in the TME.

SUBMITTER: Ruiz-Ranz M

PROVIDER: S-EPMC9360320 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Prognostic implications of preoperative systemic inflammatory markers in oral squamous cell carcinoma, and correlations with the local immune tumor microenvironment.

Ruiz-Ranz Marta M Lequerica-Fernández Paloma P Rodríguez-Santamarta Tania T Suárez-Sánchez Faustino J FJ López-Pintor Rosa M RM García-Pedrero Juana M JM de Vicente Juan C JC

Frontiers in immunology 20220726

<h4>Purpose</h4>The aim of this study was to investigate the prognostic significance of preoperative inflammatory markers in peripheral blood of patients with oral squamous cell carcinoma (OSCC), and to establish correlations with the infiltrate of macrophages and lymphocytes in the local immune tumor microenvironment (TME).<h4>Materials and methods</h4>Neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), and systemic immune-inflammation index (SII ...[more]

PMID: 35958590

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Project description:BackgroundWe aimed to develop novel diagnostic and prognostic signatures based on preoperative inflammatory, immunological, and nutritional parameters in blood (PIINPBs) by machine learning algorithms for patients with oral squamous cell carcinoma (OSCC).MethodsA total of 486 OSCC patients and 200 age and gender-matched non-OSCC patients who were diagnosed and treated at our institution for noninfectious, nontumor diseases were retrospectively enrolled and divided into training and validation cohorts. Based on PIINPB, 6 machine learning classifiers including random forest, support vector machine, extreme gradient boosting, naive Bayes, neural network, and logistic regression were used to derive diagnostic models, while least absolute shrinkage and selection operator (LASSO) analyses were employed to construct prognostic signatures. A novel prognostic nomogram integrating a PIINPB-derived prognostic signature and selected clinicopathological parameters was further developed. Performances of these signatures were assessed by receiver operating characteristic (ROC) curves, calibrating curves, and decision tree.ResultsDiagnostic models developed by machine learning algorithms from 13 PIINPBs, which included counts of white blood cells (WBC), neutrophils (N), monocytes (M), lymphocytes (L), platelets (P), albumin (ALB), and hemoglobin (Hb), along with albumin-globulin ratio (A/G), neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), lymphocyte-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI), displayed satisfactory discriminating capabilities in patients with or without OSCC, and among OSCC patients with diverse pathological grades and clinical stages. A prognostic signature based on 6 survival-associated PIINPBs (L, P, PNI, LMR, SII, A/G) served as an independent factor to predict patient survival. Moreover, a novel nomogram integrating prognostic signature and tumor size, pathological grade, cervical node metastasis, and clinical stage significantly enhanced prognostic power [3-year area under the curve (AUC) =0.825; 5-year AUC =0.845].ConclusionsOur results generated novel and robust diagnostic and prognostic signatures derived from PIINPBs by machine learning for OSCC. Performance of these signatures suggest the potential for PIINPBs to supplement current regimens and provide better patient stratification and prognostic prediction.

Dataset Information

Prognostic implications of preoperative systemic inflammatory markers in oral squamous cell carcinoma, and correlations with the local immune tumor microenvironment.

Purpose

Materials and methods

Results

Conclusions

Publications

Prognostic implications of preoperative systemic inflammatory markers in oral squamous cell carcinoma, and correlations with the local immune tumor microenvironment.

Similar Datasets

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