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Research Article
Shengjiao Li
The Affiliated Stomatology Hospital of Tongji University
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Oral squamous cell carcinoma (OSCC) is a life-threatening disease that emerged as a major international health concern, associated with poor prognosis and the absence of specific biomarkers. Studies have shown that the ferroptosis-related genes (FRGs) can be used as tumor prognostic markers. However, FRGs’ prognostic value in OSCC needs further exploration. Our aim was to construct a novel FRG signature for overall survival (OS) prediction in OSCC patients and explore its role in immunotherapy.
Methods: In our study, gene expression profile and clinical data of OSCC patients were collected from a public domain. FRGs were available from the FerrDb database. We performed univariate and multivariate Cox regression analyses to construct a multigene signature. The Kaplan-Meier (K-M) and receiver operating characteristic (ROC) methods were utilized to test the effectiveness of the FRG signature. A differential gene expression analysis was performed by the limma R package, followed by functional enrichment analyses. CIBERSORT was applied to analyze the tumor microenvironment (TME). Finally, the expression of human leukocyte antigen (HLA) and immune checkpoint molecules were analyzed to confirm the sensitivity of immunotherapy.
Results: A total of 103 FRGs, expressed in OSCC (FRGs-OSCC), were identified from the two datasets by the Venn analysis. The Cox regression analysis identified 5 FRGs-OSCC that were associated with overall survival (all P < 0.01). The FRGs-OSCC risk model was established to classify patients into high risk and low risk groups. Compared with the low risk group, the survival time of the high-risk group was significantly reduced (P < 0.001). According to the multivariate Cox regression analyses, the risk score acted as an independent predictor for OS (HR > 1, P < 0.001). The accuracy of the FRGs-OSCC risk predictive model was confirmed by ROC curve analysis. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed significant enrichment of immune-related pathways, and a difference in tumor microenvironment between the two groups. The low risk group had the characteristics of higher expression of HLA and immune checkpoints (IDO1, LAG3, PDCD1 and TIGHT), a lower tumor purity and a higher infiltration of immune cells, indicating a more sensitive response to immunotherapy.
Conclusions: The novel FRGs-OSCC risk score system can be used to predict OSCC prognosis. Ferroptosis targeting may be a therapeutic option for OSCC.
Keywords
Oral squamous cell carcinoma, prognosis, ferroptosis-related genes, tumor immune microenvironment, immunotherapy
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This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.xls
Additional file 1. TCGA datasets of all OSCC samples obtained from the TCGA. The data included 254 oral cancer samples and 19 matched normal samples
- Additionalfile1.xls
Additional file 1. TCGA datasets of all OSCC samples obtained from the TCGA. The data included 254 oral cancer samples and 19 matched normal samples
- Additionalfile2.xls
Additional file 2. Data banks/repositories corresponding to all datasets analyzed in this study.
- Additionalfile2.xls
Additional file 2. Data banks/repositories corresponding to all datasets analyzed in this study.
- Additionalfile3.xls
Additional file 3. 192 ferroptosis-related genes from the FerrDb database.
- Additionalfile3.xls
Additional file 3. 192 ferroptosis-related genes from the FerrDb database.
- Additionalfile4.xls
Additional file 4. The transcriptome data of 103 ferroptosis-related genes in TCGA-OSCC.
- Additionalfile4.xls
Additional file 4. The transcriptome data of 103 ferroptosis-related genes in TCGA-OSCC.
- Additionalfile5.xls
Additional file 5. Univariate Cox Regression of prognostic related genes for OS.
- Additionalfile5.xls
Additional file 5. Univariate Cox Regression of prognostic related genes for OS.
- Additionalfile6.tif
Additional file 6. Relationships between the risk score and clinicopathological parameters in OSCC. (A) Clinical correlation analysis between risk scores and genders. (B) Clinical correlation analysis between risk scores and stages. (C) Clinical correlation analysis between risk scores and metastasis. (D) Clinical correlation analysis between risk scores and the extent of lymph node involvement. (P < 0.05).
- Additionalfile6.tif
Additional file 6. Relationships between the risk score and clinicopathological parameters in OSCC. (A) Clinical correlation analysis between risk scores and genders. (B) Clinical correlation analysis between risk scores and stages. (C) Clinical correlation analysis between risk scores and metastasis. (D) Clinical correlation analysis between risk scores and the extent of lymph node involvement. (P < 0.05).
- Additionalfile7.tif
Additional file 7. Kaplan-Meier curves of overall survival (OS) between high-risk and low-risk groups. (A) The K-M analysis of OS based on female patients. (B) The K-M analysis of OS based on male patients. (C) The K-M analysis of OS based on Grade1/2 samples. (D) The K-M analysis of OS based on Grade 3/4 samples.
- Additionalfile7.tif
Additional file 7. Kaplan-Meier curves of overall survival (OS) between high-risk and low-risk groups. (A) The K-M analysis of OS based on female patients. (B) The K-M analysis of OS based on male patients. (C) The K-M analysis of OS based on Grade1/2 samples. (D) The K-M analysis of OS based on Grade 3/4 samples.
- Additionalfile8.tif
Additional file 8. Kaplan-Meier curves of overall survival (OS) between high-risk and low-risk groups. (A) The K-M analysis of OS based on N + samples. (B) The K-M analysis of OS based on N0 samples. (C) The K-M analysis of OS based on M0 samples.
- Additionalfile8.tif
Additional file 8. Kaplan-Meier curves of overall survival (OS) between high-risk and low-risk groups. (A) The K-M analysis of OS based on N + samples. (B) The K-M analysis of OS based on N0 samples. (C) The K-M analysis of OS based on M0 samples.
- Additionalfile9.xls
Additional file 9. The list of differentially expressed genes.
- Additionalfile9.xls
Additional file 9. The list of differentially expressed genes.
- Additionalfile10.xls
Additional file 10. The enriched Gene ontology (GO) terms of differentially expressed genes between high-risk and low-risk groups.
- Additionalfile10.xls
Additional file 10. The enriched Gene ontology (GO) terms of differentially expressed genes between high-risk and low-risk groups.
- Additionalfile11.xls
Additional file 11. The enriched pathways of differentially expressed genes between high-risk and low-risk groups.
- Additionalfile11.xls
Additional file 11. The enriched pathways of differentially expressed genes between high-risk and low-risk groups.
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A new preprint design is coming!
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research Article
Shengjiao Li
The Affiliated Stomatology Hospital of Tongji University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 15 Dec, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Epigenetics & Genomics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Oral squamous cell carcinoma (OSCC) is a life-threatening disease that emerged as a major international health concern, associated with poor prognosis and the absence of specific biomarkers. Studies have shown that the ferroptosis-related genes (FRGs) can be used as tumor prognostic markers. However, FRGs’ prognostic value in OSCC needs further exploration. Our aim was to construct a novel FRG signature for overall survival (OS) prediction in OSCC patients and explore its role in immunotherapy.
Methods: In our study, gene expression profile and clinical data of OSCC patients were collected from a public domain. FRGs were available from the FerrDb database. We performed univariate and multivariate Cox regression analyses to construct a multigene signature. The Kaplan-Meier (K-M) and receiver operating characteristic (ROC) methods were utilized to test the effectiveness of the FRG signature. A differential gene expression analysis was performed by the limma R package, followed by functional enrichment analyses. CIBERSORT was applied to analyze the tumor microenvironment (TME). Finally, the expression of human leukocyte antigen (HLA) and immune checkpoint molecules were analyzed to confirm the sensitivity of immunotherapy.
Results: A total of 103 FRGs, expressed in OSCC (FRGs-OSCC), were identified from the two datasets by the Venn analysis. The Cox regression analysis identified 5 FRGs-OSCC that were associated with overall survival (all P < 0.01). The FRGs-OSCC risk model was established to classify patients into high risk and low risk groups. Compared with the low risk group, the survival time of the high-risk group was significantly reduced (P < 0.001). According to the multivariate Cox regression analyses, the risk score acted as an independent predictor for OS (HR > 1, P < 0.001). The accuracy of the FRGs-OSCC risk predictive model was confirmed by ROC curve analysis. The results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed significant enrichment of immune-related pathways, and a difference in tumor microenvironment between the two groups. The low risk group had the characteristics of higher expression of HLA and immune checkpoints (IDO1, LAG3, PDCD1 and TIGHT), a lower tumor purity and a higher infiltration of immune cells, indicating a more sensitive response to immunotherapy.
Conclusions: The novel FRGs-OSCC risk score system can be used to predict OSCC prognosis. Ferroptosis targeting may be a therapeutic option for OSCC.
Figure 1
Figure 1
Figure 2
Figure 2
Figure 3
Figure 3
Figure 4
Figure 4
Figure 5
Figure 5
Figure 6
Figure 6
Figure 7
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
- Additionalfile1.xls
Additional file 1. TCGA datasets of all OSCC samples obtained from the TCGA. The data included 254 oral cancer samples and 19 matched normal samples
- Additionalfile1.xls
Additional file 1. TCGA datasets of all OSCC samples obtained from the TCGA. The data included 254 oral cancer samples and 19 matched normal samples
- Additionalfile2.xls
Additional file 2. Data banks/repositories corresponding to all datasets analyzed in this study.
- Additionalfile2.xls
Additional file 2. Data banks/repositories corresponding to all datasets analyzed in this study.
- Additionalfile3.xls
Additional file 3. 192 ferroptosis-related genes from the FerrDb database.
- Additionalfile3.xls
Additional file 3. 192 ferroptosis-related genes from the FerrDb database.
- Additionalfile4.xls
Additional file 4. The transcriptome data of 103 ferroptosis-related genes in TCGA-OSCC.
- Additionalfile4.xls
Additional file 4. The transcriptome data of 103 ferroptosis-related genes in TCGA-OSCC.
- Additionalfile5.xls
Additional file 5. Univariate Cox Regression of prognostic related genes for OS.
- Additionalfile5.xls
Additional file 5. Univariate Cox Regression of prognostic related genes for OS.
- Additionalfile6.tif
Additional file 6. Relationships between the risk score and clinicopathological parameters in OSCC. (A) Clinical correlation analysis between risk scores and genders. (B) Clinical correlation analysis between risk scores and stages. (C) Clinical correlation analysis between risk scores and metastasis. (D) Clinical correlation analysis between risk scores and the extent of lymph node involvement. (P < 0.05).
- Additionalfile6.tif
Additional file 6. Relationships between the risk score and clinicopathological parameters in OSCC. (A) Clinical correlation analysis between risk scores and genders. (B) Clinical correlation analysis between risk scores and stages. (C) Clinical correlation analysis between risk scores and metastasis. (D) Clinical correlation analysis between risk scores and the extent of lymph node involvement. (P < 0.05).
- Additionalfile7.tif
Additional file 7. Kaplan-Meier curves of overall survival (OS) between high-risk and low-risk groups. (A) The K-M analysis of OS based on female patients. (B) The K-M analysis of OS based on male patients. (C) The K-M analysis of OS based on Grade1/2 samples. (D) The K-M analysis of OS based on Grade 3/4 samples.
- Additionalfile7.tif
Additional file 7. Kaplan-Meier curves of overall survival (OS) between high-risk and low-risk groups. (A) The K-M analysis of OS based on female patients. (B) The K-M analysis of OS based on male patients. (C) The K-M analysis of OS based on Grade1/2 samples. (D) The K-M analysis of OS based on Grade 3/4 samples.
- Additionalfile8.tif
Additional file 8. Kaplan-Meier curves of overall survival (OS) between high-risk and low-risk groups. (A) The K-M analysis of OS based on N + samples. (B) The K-M analysis of OS based on N0 samples. (C) The K-M analysis of OS based on M0 samples.
- Additionalfile8.tif
Additional file 8. Kaplan-Meier curves of overall survival (OS) between high-risk and low-risk groups. (A) The K-M analysis of OS based on N + samples. (B) The K-M analysis of OS based on N0 samples. (C) The K-M analysis of OS based on M0 samples.
- Additionalfile9.xls
Additional file 9. The list of differentially expressed genes.
- Additionalfile9.xls
Additional file 9. The list of differentially expressed genes.
- Additionalfile10.xls
Additional file 10. The enriched Gene ontology (GO) terms of differentially expressed genes between high-risk and low-risk groups.
- Additionalfile10.xls
Additional file 10. The enriched Gene ontology (GO) terms of differentially expressed genes between high-risk and low-risk groups.
- Additionalfile11.xls
Additional file 11. The enriched pathways of differentially expressed genes between high-risk and low-risk groups.
- Additionalfile11.xls
Additional file 11. The enriched pathways of differentially expressed genes between high-risk and low-risk groups.
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