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Integrating single-cell and bulk expression data to identify and analyze cancer prognosis-related genes

  • 0Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.
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February 21, 2024

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February 21, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a novel polygenic risk score (PRS) using single-cell sequencing to predict cancer prognosis, demonstrating its effectiveness in breast and lung cancer patients for survival prediction.

Area Of Science

  • Genomics
  • Oncology
  • Bioinformatics

Background

  • Traditional cancer prognosis relies on tissue samples, limiting insights into cell heterogeneity.
  • Single-cell sequencing offers a more detailed approach to understanding cancer biology and predicting outcomes.

Purpose Of The Study

  • To develop a reliable prognosis model for breast cancer using bulk and single-cell expression profiles.
  • To identify critical genes and construct a polygenic risk score (PRS) for cancer survival prediction.
  • To validate the workflow's applicability to other cancers, such as lung cancer.

Main Methods

  • Comprehensive analysis of bulk and single-cell expression profiles from breast cancer and normal tissues.
  • Screening of differentially expressed genes and inference of malignancy-related genes.
  • Cox regression analysis and polygenic risk score (PRS) calculation for eight critical genes.

Main Results

  • A polygenic risk score (PRS) was developed, capable of stratifying breast cancer patients into high-risk and low-risk groups.
  • The PRS demonstrated significant correlation with overall survival time and relapse-free interval.
  • The workflow successfully constructed a prognosis model for lung cancer, indicating broad applicability.

Conclusions

  • The developed PRS is an independent prognostic factor for breast cancer, outperforming traditional methods.
  • The study provides a robust workflow for cancer biomarker discovery and personalized treatment strategies.
  • The findings offer new insights into cancer survival prediction and therapeutic target identification.

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