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

Sep 21, 2026

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Compared with traditional cancer prognosis evaluation based on tissue samples, single-cell sequencing captures cell-type heterogeneity that can improve biomarker discovery. Bulk and single-cell expression profiles of breast cancer and normal cells were jointly analyzed to identify malignant and non-malignant markers and to build a prognosis model. Highly reliable differentially expressed genes were screened from bulk datasets, and malignancy-related genes were inferred from single-cell data. Eight key genes were used for Cox regression, polygenic risk score calculation, and validation in two cohorts. Polygenic risk score stratified patients into high- and low-risk groups, correlated with overall survival and relapse-free interval, and acted as an independent prognostic factor beyond clinicopathological features. A comparative workflow test in lung cancer showed it can also generate a reasonable prognosis model. The study provides practical code and insight for investigating biomarkers, drug targets, survival prediction, treatment response, and personalized treatment.

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Introduction

Cancer prognosis modeling using multi-source biomarkers

Role of single-cell transcriptomics in identifying cancer-related genes

Heliyon 10 (2024) e25640 Contents lists available at ScienceDirect Heliyon journal [homepage:](homepage: www.cell.com/heliyon)[ www.cell.com/heliyon](homepage: www.cell.com/heliyon) | Research article
Integrating single-cell and bulk expression data to identify and analyze cancer prognosis-related genes
Shengbao Bao , Yaxin Fan , Yichao Mei , Junxiang Gao *
Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China | | | | --- | --- | --- | | A R T I C L E I N F O | | A B S T R A C T | | Keywords:
Single-cell Biomarker Prognosis model Polygenic risk score | | Compared with traditional evaluation methods of cancer prognosis based on tissue samples, single-cell sequencing technology can provide information on cell type heterogeneity for predicting biomarkers related to cancer prognosis. Therefore, the bulk and single-cell expression profiles of breast cancer and normal cells were comprehensively analyzed to identify malignant and non-malignant markers and construct a reliable prognosis model. We first screened highly reliable differentially expressed genes from bulk expression profiles of multiple breast cancer tissues and normal tissues, and inferred genes related to cell malignancy from single-cell data. Then we identified eight critical genes related to breast cancer to conduct Cox regression analysis, calculate polygenic risk score (PRS), and verify the predictive ability of PRS in two data groups. The results show that PRS can divide breast cancer patients into high-risk group and low-risk group. PRS is related to the overall survival time and relapse-free interval and is a prognosis factor independent of conventional clinicopathological characteristics. Breast cancer is usually regarded as a cancer with a relatively good prognosis. In order to further explore whether this workflow can be applied to cancer with poor prognosis, we selected lung cancer for a comparative study. The results show that this workflow can also build a reasonable prognosis model for lung cancer. This study provides new insight and practical source code for further research on cancer biomarkers and drug targets. It also provides basis for survival prediction, treatment response prediction, and personalized treatment. | 1. Introduction Cancer prognosis models can predict the development trend and prognosis results based on patient biomarkers, clinical features, and other information to help doctors better assess the probability of future recurrence, death, disability, or complications of patients, thereby formulating more personalized treatment plans, improving treatment effectiveness and life quality of patients. Constructing a cancer prognosis model requires collecting and integrating multiple data types. In addition to clinical data such as age, gender, and medical history collected during diagnosis and treatment, molecular-level information such as DNA, RNA, or protein extracted from patient tissue samples should also be used. Many studies have identified cancer-related genes using bulk RNA-seq [1,2] and detected differentially expressed genes (DEGs) by comparing their expression profiles in normal and cancer tissues. These DEGs may be closely related to the occurrence, development, and prognosis of cancer and can, therefore, be used to construct cancer prognosis models. Compared to bulk transcriptome sequencing of a tissue, the recently developed single-cell transcriptome can sequence individual * Corresponding author. E-mail address: [gao200@mail.hzau.edu.cn](gao200@mail.hzau.edu.cn) (J. Gao). [https://doi.org/10.1016/j.heliyon.2024.e25640](https://doi.org/10.1016/j.heliyon.2024.e25640) Received 27 September 2023; Received in revised form 3 January 2024; Accepted 31 January 2024 Available online 10 February 2024 2405-8440/© 2024 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ([http://creativec

FAQs

How does the study differ from traditional cancer prognosis evaluation methods?

Traditional approaches rely mainly on tissue-sample based evaluations. This study integrates bulk and single-cell expression profiles to capture cell-type heterogeneity relevant to prognosis biomarkers.

What modeling steps were used to build the prognosis model?

The workflow screened reliable differentially expressed genes from bulk breast cancer datasets, inferred malignancy-related genes from single-cell data, then selected eight critical genes for Cox regression and polygenic risk score calculation and validation.

What does the polygenic risk score (PRS) indicate in the results?

PRS separates breast cancer patients into high- and low-risk groups and is associated with overall survival and relapse-free interval. It also serves as an independent prognosis factor beyond conventional clinicopathological characteristics.

Sep 21, 2026

Uploaded by Patrick