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Research Article
Degang Ding
Henan Provincial People's Hospital
Corresponding Author
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This work is licensed under a CC BY 4.0 License. Read Full License
Background
Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma. Immunotherapy, especially anti-PD-1, is becoming a pillar of ccRCC treatment. However, precise biomarkers and robust models are needed to select the appropriate patients for immunotherapy.
Methods
A total of 831 ccRCC transcriptomic profiles were obtained from 6 datasets. Unsupervised clustering was performed to identify the immune subtypes among ccRCC samples based on immune cell enrichment scores. Weighted correlation network analysis (WGCNA) was used to identify hub genes distinguishing subtypes and related to prognosis. A machine learning model was established by random forest algorithm, and employed to an open and free online website to predict the immune subtype.
Results
In the identified immune subtypes, subtype2 was enriched in immune cell enrichment scores and immunotherapy biomarkers. WGCNA analysis identified 4 hub genes related to immune subtype, CTLA4, FOXP3, IFNG, and CD19. The random forest model was constructed by mRNA expression of these four hub genes, and the value of areas under the curve of the receiver operating characteristic (AUC) was 0.78. Subtype2 patients in the independent validation cohort had a better drug response and prognosis for immunotherapy treatment. Moreover, an open and free website was developed by the random forest model (https://immunotype.shinyapps.io/ISPRF/).
Conclusions
The current study constructs a model and provides a free online website that could identify suitable ccRCC patients for immunotherapy, and it is an important step forward to personalized treatment.
Keywords
renal cell carcinoma, immune subtypes, machine learning, online website
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No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
- SupportinginformationforISPRFamachinelearningmodeltopredicttheimmunesubtypeofkidneycancersamplesbyfourgenes.docx
Supplementary Figure 1. Overall survival results of immune cells are significantly different between groups (determined by the median value). Supplementary Figure 2. Two-dimensional plots are shown of principal components calculated principal components analysis (PCA). (a) PCA of the expression matrix of six different datasets. (b) PCA of the immune cells enrichment scores of six different datasets. Supplementary Figure 3. (a) Consensus clustering cumulative distribution function (CDF) for k=2 to 6. (b) Delta area curve of consensus clustering, indicating the relative change in area under CDF curve for each category number k compared with k−1. The horizontal axis represents the category number k, and the vertical axis represents the relative change in area under the CDF curve. Supplementary Figure 4. Comparison of the tumor immunotherapy indicators between the two immune subtypes in TCGA dataset. Subtype2 tumors had significantly higher CD8A, PDL1, TIGIT, CTLA4, CYT, IFNG, LAG3, PD1 (PDCD1) and tumor mutational burden (TMB) than subtype1 tumors (p < 0.05). Supplementary Figure 5. Volcano plot showing the gene expression differences between immune subtypes. Blue dots, down-regulated genes in subtype2. Red dots, up-regulated genes in subtype2. Supplementary Figure 6. Overall survival results of four hub genes in turquoise module are significantly different between groups (determined by the median value). Supplementary Figure 7. Overall survival results of ten hub genes in blue module are significantly different between groups (determined by the median value). Supplementary Figure 8. Comparison of the tumor immunotherapy indicators between the two immune subtypes in IMvigor210 dataset. Subtype2 tumors had significantly higher CD8A, PDL1, TIGIT, CTLA4, CYT, IFNG, LAG3, PD1 (PDCD1) and TMB than subtype1 tumors (p < 0.05). Abbreviation: TMB, Tumor Mutational Burden.
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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
Degang Ding
Henan Provincial People's Hospital
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 11 Feb, 2021
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma. Immunotherapy, especially anti-PD-1, is becoming a pillar of ccRCC treatment. However, precise biomarkers and robust models are needed to select the appropriate patients for immunotherapy.
Methods
A total of 831 ccRCC transcriptomic profiles were obtained from 6 datasets. Unsupervised clustering was performed to identify the immune subtypes among ccRCC samples based on immune cell enrichment scores. Weighted correlation network analysis (WGCNA) was used to identify hub genes distinguishing subtypes and related to prognosis. A machine learning model was established by random forest algorithm, and employed to an open and free online website to predict the immune subtype.
Results
In the identified immune subtypes, subtype2 was enriched in immune cell enrichment scores and immunotherapy biomarkers. WGCNA analysis identified 4 hub genes related to immune subtype, CTLA4, FOXP3, IFNG, and CD19. The random forest model was constructed by mRNA expression of these four hub genes, and the value of areas under the curve of the receiver operating characteristic (AUC) was 0.78. Subtype2 patients in the independent validation cohort had a better drug response and prognosis for immunotherapy treatment. Moreover, an open and free website was developed by the random forest model (https://immunotype.shinyapps.io/ISPRF/).
Conclusions
The current study constructs a model and provides a free online website that could identify suitable ccRCC patients for immunotherapy, and it is an important step forward to personalized treatment.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
- SupportinginformationforISPRFamachinelearningmodeltopredicttheimmunesubtypeofkidneycancersamplesbyfourgenes.docx
Supplementary Figure 1. Overall survival results of immune cells are significantly different between groups (determined by the median value). Supplementary Figure 2. Two-dimensional plots are shown of principal components calculated principal components analysis (PCA). (a) PCA of the expression matrix of six different datasets. (b) PCA of the immune cells enrichment scores of six different datasets. Supplementary Figure 3. (a) Consensus clustering cumulative distribution function (CDF) for k=2 to 6. (b) Delta area curve of consensus clustering, indicating the relative change in area under CDF curve for each category number k compared with k−1. The horizontal axis represents the category number k, and the vertical axis represents the relative change in area under the CDF curve. Supplementary Figure 4. Comparison of the tumor immunotherapy indicators between the two immune subtypes in TCGA dataset. Subtype2 tumors had significantly higher CD8A, PDL1, TIGIT, CTLA4, CYT, IFNG, LAG3, PD1 (PDCD1) and tumor mutational burden (TMB) than subtype1 tumors (p < 0.05). Supplementary Figure 5. Volcano plot showing the gene expression differences between immune subtypes. Blue dots, down-regulated genes in subtype2. Red dots, up-regulated genes in subtype2. Supplementary Figure 6. Overall survival results of four hub genes in turquoise module are significantly different between groups (determined by the median value). Supplementary Figure 7. Overall survival results of ten hub genes in blue module are significantly different between groups (determined by the median value). Supplementary Figure 8. Comparison of the tumor immunotherapy indicators between the two immune subtypes in IMvigor210 dataset. Subtype2 tumors had significantly higher CD8A, PDL1, TIGIT, CTLA4, CYT, IFNG, LAG3, PD1 (PDCD1) and TMB than subtype1 tumors (p < 0.05). Abbreviation: TMB, Tumor Mutational Burden.
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