Prognostic Analysis of Lactic Acid Metabolism Genes in Oral Squamous Cell Carcinoma

Shiying Shen; Hongrong Zhang; Yemei Qian; Xue Zhou; Jingyi Li; Liqin Zhang; Zheyi Sun; Weihong Wang

doi:10.1016/j.identj.2024.04.005

Prognostic Analysis of Lactic Acid Metabolism Genes in Oral Squamous Cell Carcinoma

Int Dent J. 2024 Apr 26:S0020-6539(24)00112-6. doi: 10.1016/j.identj.2024.04.005. Online ahead of print.

Authors

Shiying Shen¹, Hongrong Zhang¹, Yemei Qian¹, Xue Zhou¹, Jingyi Li¹, Liqin Zhang¹, Zheyi Sun², Weihong Wang³

Affiliations

¹ Department of Oral and Maxillofacial Surgery, Affiliated Stomatology Hospital of Kunming Medical University, Kunming, China; Yunnan Key Laboratory of Stomatology, Kunming, China.
² Yunnan Key Laboratory of Stomatology, Kunming, China; Department of Operative Dentistry, Preventive Dentistry and Endodontics, School of Stomatology, The Affiliated Stomatology Hospital, Kunming Medical University, Kunming, China. Electronic address: sunzheyi@kmmu.edu.cn.
³ Department of Oral and Maxillofacial Surgery, Affiliated Stomatology Hospital of Kunming Medical University, Kunming, China; Yunnan Key Laboratory of Stomatology, Kunming, China. Electronic address: wangweihong@kmmu.edu.cn.

PMID: 38677972
DOI: 10.1016/j.identj.2024.04.005

Abstract

Objectives: Oral squamous cell carcinoma (OSCC) is the most common malignant tumour in the oral and maxillofacial region. Lactic acid accumulation in the tumour microenvironment (TME) has gained attention for its dual role as an energy source for cancer cells and an activator of signalling pathways crucial to tumour progression. This study aims to reveal the impact of lactate-related genes (LRGs) on the prognosis, TME, and immune characteristics of OSCC, with the ultimate goal of developing a novel prognostic model.

Methods: Unsupervised clustering analysis of LRGs in OSCC patients from The Cancer Genome Atlas database was conducted to evaluate and compare TME, immune features, and clinical characteristics across various lactate subtypes. A refined prognostic model was developed through the application of Cox and Least absolute shrinkage and selection operator (LASSO) regression techniques. External validation sets were then utilised to improve model accuracy, along with a detailed correlation analysis of drug sensitivity.

Results: The Cancer Genome Atlas-OSCC patients were categorised into 4 distinct lactate subtypes based on LRGs. Notably, patients in subtype 1 and subtype 2 exhibited the least and most favourable prognoses, respectively. Subtype 1 patients showed elevated expression levels of immune checkpoint genes. Further analysis identified 1086 genes with significant expression differences between cancer and noncancer tissues, as well as between subtype 1 and subtype 2 patients. Selected genes for the prognostic model included ZNF662, CGNL1, VWCE, and ZFP42. The high-risk group defined by this model had a significantly poorer prognosis (P < .0001) and functioned as an independent prognostic factor (P < .001), accurately predicting 1-, 3-, and 5-year survival rates. Additionally, individuals in the high-risk category exhibited heightened sensitivity to chemotherapy drugs such as AZ6102 and Venetoclax.

Conclusions: The predictive model based on the genes ZNF662, CGNL1, VWCE, and ZFP42 can serve as a reliable biomarker, providing accurate prognostic predictions for OSCC patients and potential opportunities for pharmaceutical interventions.

Keywords: Lactate-related genes (LRG); Oral squamous cell carcinoma (OSCC); Risk score.