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Identification and validation of molecular subtypes and prognostic models in patients with kidney cancer based on
Jiaao Sun1, Shiyan Song1, Qiancheng Ma1
1First Affiliated Hospital of Dalian Medical University, Dalian, China.
Background:
B cells play a variety of complex roles in cancer, both promoting cancer progression and enhancing anti-tumor immune responses, but their mechanism of action in kidney cancer has not been elucidated.
Results:
We collected kidney cancer sample data from the GEO database and TCGA database, mapped the single-cell landscape inside kidney cancer tissue, identified 25 B-cell-related genes, and based on this, identified related molecular subtypes of kidney cancer patients, and explored their internal microenvironment characteristics. Finally, we constructed a 6-gene biological prognostic model that can be used to predict survival in patients with renal cancer, and we further validated the predictive performance of the model based on imaging omics. It is worth mentioning that the structural patterns and functional sites of 6 model gene transcription proteins were also mined.
Conclusions:
Overall, we explored for the first time the profound role of B cells in kidney cancer and developed a bio-predictive model based on B cell-related genes, providing scientific guidance for personalized treatment of kidney cancer patients.
Insights
This study reveals the role of B cells in kidney cancer, identifying key genes to create a prognostic model for predicting patient survival and guiding personalized treatment strategies.
Area of Science:
- Immunology
- Oncology
- Bioinformatics
Background:
- B cells have complex roles in cancer, influencing both tumor growth and immune response.
- The specific mechanisms of B cells in kidney cancer remain unclear.
Purpose of the Study:
- To elucidate the role of B cells in kidney cancer.
- To identify B cell-related genes and molecular subtypes.
- To develop a prognostic model for kidney cancer survival prediction.
Main Methods:
- Analysis of kidney cancer data from GEO and TCGA databases.
- Single-cell landscape mapping within kidney cancer tissue.
- Identification of B cell-related genes and molecular subtypes.
- Construction and validation of a 6-gene prognostic model using imaging omics.
Main Results:
- Identified 25 B-cell-related genes and associated molecular subtypes of kidney cancer.
- Developed a 6-gene prognostic model with validated predictive performance for renal cancer survival.
- Characterized the microenvironment of identified subtypes and analyzed protein structures of model genes.
Conclusions:
- This study provides the first comprehensive exploration of B cell involvement in kidney cancer.
- A novel bio-predictive model based on B cell-related genes offers guidance for personalized kidney cancer treatment.
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