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Cancer-Critical Genes II: Tumor Suppressor Genes

Cancer-Critical Genes II: Tumor Suppressor Genes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...

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Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

Published on: March 12, 2021

CPRCSdb: A comprehensive phenotype-associated single-cell transcriptomic database for human cancer.

Yizhen Gong^1,2, Yuxiang Yan^1,3, Zhaomeng Liu^1,2

¹The First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan 421001, China.

Computational and Structural Biotechnology Journal

|December 3, 2025

Summary

This summary is machine-generated.

A new database, CPRCSdb, links single-cell RNA sequencing data to clinical cancer phenotypes. This resource aids in understanding tumor heterogeneity and its impact on patient outcomes and therapy responses.

Keywords:

Cell heterogeneity Disease phenotype association Single cell database

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Cancer-Associated Fibroblasts from Mouse Mammary Tumors as Tools for Molecular and Computational Studies

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Published on: July 3, 2025

Area of Science:

Oncology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) offers high-resolution insights into tumor microenvironment cellular heterogeneity.
Cellular heterogeneity significantly influences clinical outcomes such as survival, metastasis, and treatment response.
A gap exists in databases correlating scRNA-seq data with clinical phenotypes.

Purpose of the Study:

To develop CPRCSdb, a comprehensive database integrating clinical phenotypes with single-cell transcriptomic data.
To provide a resource linking cellular heterogeneity to clinically relevant cancer phenotypes.
To facilitate research into the mechanisms of tumorigenesis and cancer progression.

Main Methods:

Integrated 1053 manually curated scRNA-seq samples (over 4.05 million cells) and 101 datasets.
Included 11,032 bulk RNA-seq samples across 29 cancer types.
Analyzed 6197 associations between bulk phenotypes and single-cell heterogeneity, covering 5 disease and 30 drug response categories.

Main Results:

CPRCSdb contains extensive data on cellular heterogeneity across numerous cancer types and clinical phenotypes.
Identified over 1.13 million clinically relevant cells through rigorous quality control.
The database supports downstream analyses like differential expression and cell-cell communication.

Conclusions:

CPRCSdb is a valuable resource for studying the role of cellular heterogeneity in cancer.
Facilitates research linking molecular data to clinical outcomes and therapeutic responses.
Aims to advance understanding of cancer biology and inform treatment strategies.