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Profiling Cell-state Fingerprints Based on Deep Learning Model with Meta-programs of Pan-cancer.

Zebin Wen^1,2, Yulong Zhang², Guanchuan Lin²

¹Precision Regenerative Medicine Research Centre, Medical Science Division, and State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau Special Administrative Region 999078, China.

Genomics, Proteomics & Bioinformatics

|December 2, 2025

Summary

This summary is machine-generated.

This study used single-cell RNA sequencing to identify shared cancer cell states and developed a deep learning model, StateNet, for cancer profiling. StateNet reveals prognosis-related programs and aids in distinguishing patient survival outcomes.

Keywords:

Deep learning Meta-programs Pan-cancer Single-cell RNA sequencing

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Area of Science:

Cancer Biology
Genomics
Computational Biology

Background:

Understanding shared cell states in cancer is crucial for developing effective treatments.
Mechanisms of malignant cell dominance in pan-cancer commonalities are not well understood.

Purpose of the Study:

To analyze cell states across diverse cancer types using single-cell RNA sequencing.
To develop a deep learning model (StateNet) for cancer cell profiling and identifying shared characteristics.
To uncover prognosis-related programs and risk-associated genes in cancer.

Main Methods:

Label-free multiplexed single-cell RNA sequencing (scRNA-seq) on 159,372 cells from 245 cell lines across 14 tissue types.
Integration of public and proprietary scRNA-seq datasets.
Development and application of the deep learning model StateNet for cell-state fingerprinting and analysis.
Perturbation experiments to validate the role of epithelial-mesenchymal transition programs.
Construction of Cox models using prognosis-related programs on 3210 cancer samples.

Main Results:

Identification of 21 meta-programs (MPs) representing shared characteristics and 16 biological processes across pan-cancer landscapes.
StateNet generated cell-state fingerprints, delineating cell line individuality.
Pinpointed ACAT2 as a potential mediator between hypoxia and lipid metabolism.
Epithelial-mesenchymal transition programs were found vital for cell line classification.
Unveiled prognosis-related programs and identified risk-associated genes linked to patient survival outcomes.

Conclusions:

StateNet is a novel tool for cancer profiling, elucidating shared commonalities and individualities of pan-cancer cells.
The model effectively analyzes scRNA-seq data, reveals prognosis-related programs, and distinguishes patient survival.
Findings provide insights into cancer mechanisms and potential therapeutic targets.