Blood metabolic biomarkers and the risk of head and neck cancer: An epidemiological study in the Swedish AMORIS Cohort

Huang, Yi; Xiao, Xue; Sadeghi, Fatemeh; Feychting, Maria; Hammar, Niklas; Fang, Fang; Zhang, Zhe; Liu, Qianwei

doi:10.1016/j.canlet.2023.216091

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…A retrospective cohort study based on 4,575,818 individuals in Korea revealed that high TC and high LDL-cholesterol levels are protective factors and could reduce the risk of HNC (164). Additionally, a prospective analysis based on 474,929 participants from the UK biobank demonstrated a significant U-shaped association between HDL-C and HNC risk in males (29). Based on the 561,388 individuals of the Swedish AMORIS cohort, we found a positive association between blood levels of TC, apoA-I and the risk of HNC.…”

Section: Dysregulated Sterol Lipids Profile In Head and Neck Cancermentioning

confidence: 63%

Lipid metabolism reprogramming in head and neck cancer

Liang,

Li,

et al. 2023

Front. Oncol.

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Lipid metabolism reprogramming is one of the most prominent metabolic anomalies in cancer, wherein cancer cells undergo dysregulation of lipid metabolism to acquire adequate energy, cell membrane building blocks, as well as signaling molecules essential for cell proliferation, survival, invasion, and metastasis. These adaptations enable cancer cells to effectively respond to challenges posed by the tumor microenvironment, leading to cancer therapy resistance and poor cancer prognosis. Head and neck cancer, ranking as the seventh most prevalent cancer, exhibits numerous abnormalities in lipid metabolism. Nevertheless, the precise role of lipid metabolic rewiring in head and neck cancer remains unclear. In line with the LIPID MAPS Lipid Classification System and cancer risk factors, the present review delves into the dysregulated molecules and pathways participating in the process of lipid uptake, biosynthesis, transportation, and catabolism. We also present an overview of the latest advancements in understanding alterations in lipid metabolism and how they intersect with the carcinogenesis, development, treatment, and prognosis of head and neck cancer. By shedding light on the significance of metabolic therapy, we aspire to improve the overall prognosis and treatment outcomes of head and neck cancer patients.

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Section: Dysregulated Sterol Lipids Profile In Head and Neck Cancermentioning

confidence: 63%

Lipid metabolism reprogramming in head and neck cancer

Liang,

Li,

et al. 2023

Front. Oncol.

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“…However, molecular analyses are uncommon outside of research institutes and are not performed routinely in the clinical setting, highlighting the importance of analysing the prognostic impact of widely available clinicopathological factors that are easily accessible at the time of diagnosis. Diabetes, characterised by sustained hyperglycemia, represents one such factor, and is known to associate with an increased risk for many cancers including SCCHN, where higher levels of blood glucose and various lipids have been seen up to 30 years before diagnosis (6). Data from The Cancer Genome Atlas (TCGA) or other sources, including genomic, transcriptomic and clinical information have often been used to identify prognostic indicators in patients with SCCHN, often using computational algorithms and artificial intelligence (AI) approaches to produce lists of potential biomarkers, comprising differential expression of specific mRNAs, microRNAs, lncRNAs etc (7)(8)(9)(10)(11).…”

Section: Introductionmentioning

confidence: 99%

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

Salehi,

Wang,

et al. 2024

Oncol Lett

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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.

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