Integrated Single-cell and Bulk RNA Sequencing Analysis Cross Talk between Ferroptosis-related Genes and Prognosis in Oral Cavity Squamous Cell Carcinoma

Author:

Lan Tianjun1,Ren Siqi1,Hu Huijun2,Wang Ruixin1,Chen Qian3,Wu Fan1,Xu Qiuping45,Li Yanyan1,Shao Libin6,Wang Liansheng1,Liu Xin7,Cao Haotian1,Li Jinsong1

Affiliation:

1. Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510010, China

2. Department of Radiology, Sun Yat-Sen Memorial Hospital of Sun Yat-sen University, Guangzhou 510010, China

3. Department of Cardiology, Sun Yat-sen Memorial Hospital of Sun Yat-Sen University, Guangzhou 510010, China

4. Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China

5. Medical Research Center, Sun Yatsen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China

6. Department of Endodontics, Sun Yat-sen Memorial Hospital of Sun Yat-Sen University, Guangzhou, 510010, China

7. Department of Stomatology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, 528308, China

Abstract

Background: Ferroptosis is a new type of programmed apoptosis and plays an important role in tumour inhibition and immunotherapy. Objective: In this study, we aimed to explore the potential role of ferroptosis-related genes (FRGs) and the potential therapeutic targets in oral cavity squamous cell carcinoma (OCSCC). Methods: The transcription data of OCSCC samples were obtained from the Cancer Genome Atlas (TCGA) database as a training dataset. The prognostic FRGs were extracted by univariate Cox regression analysis. Then, we constructed a prognostic model using the least absolute shrinkage and selection operator (LASSO) and Cox analysis to determine the independent prognosis FRGs. Based on this model, risk scores were calculated for the OCSCC samples. The model’s capability was further evaluated by the receiver operating characteristic curve (ROC). Then, we used the GSE41613 dataset as an external validation cohort to confirm the model’s predictive capability. Next, the immune infiltration and somatic mutation analysis were applied. Lastly, single-cell transcriptomic analysis was used to identify the key cells. Results: A total of 12 prognostic FRGs were identified. Eventually, 6 FRGs were screened as independent predictors and a prognostic model was constructed in the training dataset, which significantly stratified OCSCC samples into high-risk and low-risk groups based on overall survival. The external validation of the model using the GSE41613 dataset demonstrated a satisfactory predictive capability for the prognosis of OCSCC. Further analysis revealed that patients in the highrisk group had distinct immune infiltration and somatic mutation patterns from low-risk patients. Mast cell infiltrations were identified as prognostic immune cells and played a role in OCSCC partly through ferroptosis. Conclusion: We successfully constructed a novel 6 FRGs model and identified a prognostic immune cell, which can serve to predict clinical prognoses for OCSCC. Ferroptosis may be a new direction for immunotherapy of OCSCC.

Funder

National Natural Science Foundation of China

Guangdong Science and Technology Development Fund

Science and Technology Program of Guangzhou

Key Laboratory of Malignant Tumor Molecular Mechanism and Translational Medicine of Guangzhou Bureau of Science and Information Technology

Fundamental Research Funds for the Central Universities

Publisher

Bentham Science Publishers Ltd.

Subject

Pharmacology (medical),Cancer Research,Drug Discovery,Oncology,General Medicine

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