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Related Experiment Video

Updated: Jul 5, 2025

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

Protocol to analyze immune cells in the tumor microenvironment by transcriptome using machine learning.

Yunxi Liao¹, Ziyan Rao¹, Shaodong Huang¹

¹Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China; State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, Beijing 100191, China.

|January 14, 2024

Summary

This summary is machine-generated.

This study introduces a protocol for analyzing tumor microenvironment alterations using single-cell RNA sequencing. It helps identify immune cell infiltration and potential drug targets for cancer immunotherapy.

Keywords:

Bioinformatics Cancer RNA-seq Sequence Analysis Single Cell

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Last Updated: Jul 5, 2025

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

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Published on: August 18, 2023

Predictive Immune Modeling of Solid Tumors

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Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

Published on: April 30, 2021

Area of Science:

Oncology
Immunology
Bioinformatics

Background:

Immunotherapy offers a promising avenue for cancer treatment.
Understanding the tumor microenvironment is crucial for effective cancer therapies.
Phenotypic alterations and immune cell infiltration significantly impact treatment outcomes.

Purpose of the Study:

To present a detailed protocol for analyzing transcriptome-based phenotypic alterations in the tumor microenvironment.
To investigate immune cell infiltration and its association with cancer progression.
To identify potential therapeutic targets for cancer immunotherapy.

Main Methods:

Integration of single-cell RNA sequencing (scRNA-seq) data.
Comparative analysis of mononuclear phagocyte phenotypes and origins.
Inference of cell differentiation trajectories and infiltration processes using machine learning.
Identification of infiltration-associated genes.
Prognostic impact exploration using integrated microarray and bulk RNA-seq data.

Main Results:

The protocol enables comprehensive analysis of tumor immune cell infiltration.
Machine learning identifies key genes associated with immune cell infiltration.
Analysis reveals potential correlations between gene expression and patient prognosis.
Identified genes serve as potential targets for novel drug development.

Conclusions:

The presented protocol facilitates in-depth analysis of the tumor immune microenvironment.
This approach aids in identifying prognostic biomarkers and potential therapeutic targets for cancer immunotherapy.
The study provides a framework for leveraging scRNA-seq data in cancer research.