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Integration of single-cell sequencing and bulk RNA-seq to identify and develop a prognostic signature related to colorectal cancer stem cells
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Integration of single-cell sequencing and bulk RNA-seq to identify and develop a prognostic signature related to colorectal cancer stem cells
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Journal Article
Integration of single-cell sequencing and bulk RNA-seq to identify and develop a prognostic signature related to colorectal cancer stem cells
2024
Overview
The prognosis for patients with colorectal cancer (CRC) remains worse than expected due to metastasis, recurrence, and resistance to chemotherapy. Colorectal cancer stem cells (CRCSCs) play a vital role in tumor metastasis, recurrence, and chemotherapy resistance. However, there are currently no prognostic markers based on CRCSCs-related genes available for clinical use. In this study, single-cell transcriptome sequencing was employed to distinguish cancer stem cells (CSCs) in the CRC microenvironment and analyze their properties at the single-cell level. Subsequently, data from TCGA and GEO databases were utilized to develop a prognostic risk model for CRCSCs-related genes and validate its diagnostic performance. Additionally, functional enrichment, immune response, and chemotherapeutic drug sensitivity of the relevant genes in the risk model were investigated. Lastly, the key gene RPS17 in the risk model was identified as a potential prognostic marker and therapeutic target for further comprehensive studies. Our findings provide new insights into the prognostic treatment of CRC and offer novel perspectives for a systematic and comprehensive understanding of CRC development.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
/ Biomarkers, Tumor - genetics
/ Cancer
/ Colorectal Neoplasms - genetics
/ Colorectal Neoplasms - mortality
/ Colorectal Neoplasms - pathology
/ Gene Expression Regulation, Neoplastic
/ Humanities and Social Sciences
/ Humans
/ Neoplastic Stem Cells - metabolism
/ Neoplastic Stem Cells - pathology
/ RNA-Seq
/ RPS17
/ Science
/ Sequence Analysis, RNA - methods
/ Single-Cell Analysis - methods
