Patients with oral tongue squamous cell carcinoma and co‑existing diabetes exhibit lower recurrence rates and improved survival: Implications for treatment

Amir M Salehi; Lixiao Wang; Xiaolian Gu; Philip J Coates; Lena Norberg Spaak; Nicola Sgaramella; Karin Nylander

doi:10.3892/ol.2024.14275

Patients with oral tongue squamous cell carcinoma and co‑existing diabetes exhibit lower recurrence rates and improved survival: Implications for treatment

Oncol Lett. 2024 Feb 6;27(4):142. doi: 10.3892/ol.2024.14275. eCollection 2024 Apr.

Authors

Amir M Salehi¹, Lixiao Wang¹, Xiaolian Gu¹, Philip J Coates², Lena Norberg Spaak¹, Nicola Sgaramella^{1

3}, Karin Nylander¹

Affiliations

¹ Department of Medical Biosciences/Pathology, Umeå University, SE 901 85 Umeå, Sweden.
² Research Centre for Applied Molecular Oncology, Masaryk Memorial Cancer Institute, Brno 656 53, Czech Republic.
³ Department of Oral and Maxillo-Facial Surgery, Mater Dei Hospital, I-70125 Bari, Italy.

Abstract

Locoregional recurrences and distant metastases are major problems for patients with squamous cell carcinoma of the head and neck (SCCHN). Because SCCHN is a heterogeneous group of tumours with varying characteristics, the present study concentrated on the subgroup of squamous cell carcinoma of the oral tongue (SCCOT) to investigate the use of machine learning approaches to predict the risk of recurrence from routine clinical data available at diagnosis. The approach also identified the most important parameters that identify and classify recurrence risk. A total of 66 patients with SCCOT were included. Clinical data available at diagnosis were analysed using statistical analysis and machine learning approaches. Tumour recurrence was associated with T stage (P=0.001), radiological neck metastasis (P=0.010) and diabetes (P=0.003). A machine learning model based on the random forest algorithm and with attendant explainability was used. Whilst patients with diabetes were overrepresented in the SCCOT cohort, diabetics had lower recurrence rates (P=0.015 after adjusting for age and other clinical features) and an improved 2-year survival (P=0.025) compared with non-diabetics. Clinical, radiological and histological data available at diagnosis were used to establish a prognostic model for patients with SCCOT. Using machine learning to predict recurrence produced a classification model with 71.2% accuracy. Notably, one of the findings of the feature importance rankings of the model was that diabetics exhibited less recurrence and improved survival compared with non-diabetics, even after accounting for the independent prognostic variables of tumour size and patient age at diagnosis. These data imply that the therapeutic manipulation of glucose levels used to treat diabetes may be useful for patients with SCCOT regardless of their diabetic status. Further studies are warranted to investigate the impact of diabetes in other SCCHN subtypes.

Keywords: diabetes; random forest; recurrence; squamous cell carcinoma; tongue.

Grants and funding

The present study was supported by Lion's Cancer Research Foundation, Umeå University, The Swedish Cancer Society (contract number 23 2775 Pj 01H), Umeå University, Region Västerbotten, Ministry of Health Czech Republic (MMCI, 00209805) and Czech Science Foundation (GACR 21-13188S).