Multi-omics comprehensive analysis of renal clear cell carcinoma to distinguish subtypes with different molecular characterizations and therapeutic strategies
Abstract Purpose Kidney renal clear cell carcinoma (KIRC) is the most prevalent heterogeneous subtype of malignant renal cell carcinoma and is well known as a common genitourinary cancer. Stratifying tumors based on heterogeneity is essential for better treatment options. Methods In this study, consensus clusters were constructed based on gene expression, DNA methylation, and gene mutation data, which were combined with multiple clustering algorithms. We further analyzed the gene differences, pathway enrichment, prognosis, genetic alterations, immunotherapy response and drug sensitivity of each subtype. In addition, we also performed integrated analysis of bulk data and scRNA-Seq data. Results Among the two identified subtypes, CS1 (consensus subtype) was enriched in more inflammation-related and oncogenic pathways than CS2, showing a worse prognosis. We found more copy number variations and BAP1 mutations in CS1. Although CS1 had a high immune infiltration score, it exhibited high expression of suppressive immune features. Based on the prediction of immunotherapy and drug sensitivity, we inferred that CS1 may respond poorly to immunotherapy and be less sensitive to targeted drugs. The analysis of bulk data combined with single-cell data further verified that the suppressive immune features were highly expressed in CS1 and the JAK STAT signaling pathway was enriched in CS1. Finally, the robustness of the new subtyping was successfully validated in four external datasets. Conclusion In conclusion, we conducted a comprehensive analysis of multi-omics data with 10 clustering algorithms to reveal the molecular characteristics of KIRC patients and validated the relevant conclusions by single-cell analysis and external data. Our findings discovered new KIRC subtypes and may further guide personalized and precision treatments..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
ResearchSquare.com - (2023) vom: 11. Sept. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Ruan, Xinjia [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
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| doi: |
10.21203/rs.3.rs-3182826/v1 |
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| PPN (Katalog-ID): |
XRA04030860X |
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| 520 | |a Abstract Purpose Kidney renal clear cell carcinoma (KIRC) is the most prevalent heterogeneous subtype of malignant renal cell carcinoma and is well known as a common genitourinary cancer. Stratifying tumors based on heterogeneity is essential for better treatment options. Methods In this study, consensus clusters were constructed based on gene expression, DNA methylation, and gene mutation data, which were combined with multiple clustering algorithms. We further analyzed the gene differences, pathway enrichment, prognosis, genetic alterations, immunotherapy response and drug sensitivity of each subtype. In addition, we also performed integrated analysis of bulk data and scRNA-Seq data. Results Among the two identified subtypes, CS1 (consensus subtype) was enriched in more inflammation-related and oncogenic pathways than CS2, showing a worse prognosis. We found more copy number variations and BAP1 mutations in CS1. Although CS1 had a high immune infiltration score, it exhibited high expression of suppressive immune features. Based on the prediction of immunotherapy and drug sensitivity, we inferred that CS1 may respond poorly to immunotherapy and be less sensitive to targeted drugs. The analysis of bulk data combined with single-cell data further verified that the suppressive immune features were highly expressed in CS1 and the JAK STAT signaling pathway was enriched in CS1. Finally, the robustness of the new subtyping was successfully validated in four external datasets. Conclusion In conclusion, we conducted a comprehensive analysis of multi-omics data with 10 clustering algorithms to reveal the molecular characteristics of KIRC patients and validated the relevant conclusions by single-cell analysis and external data. Our findings discovered new KIRC subtypes and may further guide personalized and precision treatments. | ||
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| 700 | 1 | |a Lai, Chong |4 aut | |
| 700 | 1 | |a Lu, Xiaofan |4 aut | |
| 700 | 1 | |a Zhang, Dandan |4 aut | |
| 700 | 1 | |a Lai, Maode |4 aut | |
| 700 | 1 | |a Yan, Fangrong |4 aut | |
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