Investigating the Cell Origin and Liver Metastasis Factors of Colorectal Cancer by Single-Cell Transcriptome Analysis

被引:0
|
作者
Sha, Zhilin [1 ]
Gao, Qingxiang [1 ]
Wang, Lei [2 ]
An, Ni [3 ]
Wu, Yingjun [1 ]
Wei, Dong [4 ]
Wang, Tong [5 ]
Liu, Chen [1 ,6 ]
Shen, Yang [1 ,6 ]
机构
[1] Naval Med Univ, Eastern Hepatobiliary Surg Hosp, Dept Biliary Tract Surg 1, Shanghai, Peoples R China
[2] Yancheng Hosp Tradit Chinese Med, Dept Gen Surg, Yancheng, Jiangsu, Peoples R China
[3] Chinese Peoples Liberat Army Gen Hosp, Med Ctr 8, Dept Anesthesiol, Beijing, Peoples R China
[4] 1 Peoples Hosp Pinghu, Dept Gen Surg, Ward 2, Pinghu, Zhejiang, Peoples R China
[5] 32295 Troop Chinese PLA, Dept Anesthesiol, Liaoyang, Peoples R China
[6] Naval Med Univ, Eastern Hepatobiliary Surg Hosp, 700 North Moyu Rd, Shanghai 201823, Peoples R China
来源
ONCOTARGETS AND THERAPY | 2024年 / 17卷
关键词
colorectal cancer; liver metastasis; single-cell sequencing; SOX4; prognostic; PROGRESSION; FEATURES;
D O I
10.2147/OTT.S454295
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Background: Colorectal cancer (CRC) is one of the deadliest causes of death by cancer worldwide. Liver metastasis (LM) is the main cause of death in patients with CRC. Therefore, identification of patients with the greatest risk of liver metastasis is critical for early treatment and reduces the mortality of patients with colorectal cancer liver metastases. Methods: Initially, we characterized cell composition through single-cell transcriptome analysis. Subsequently, we employed copy number variation (CNV) and pseudotime analysis to delineate the cellular origins of LM and identify LM-related epithelial cells (LMECs). The LM-index was constructed using machine learning algorithms to forecast the relative abundance of LMECs, reflecting the risk of LM. Furthermore, we analyzed drug sensitivity and drug targeted gene expression in LMECs and patients with a high risk of LM. Finally, functional experiments were conducted to determine the biological roles of metastasis-related gene in vitro. Results: Single-cell RNA sequencing analysis revealed different immune landscapes between primary CRC and LM tumor. LM originated from chromosomal variants with copy number loss of chr1 and chr6p and copy number gain of chr7 and chr20q. We identified the LMECs cluster and found LM-associated pathways such as Wnt/beta-catenin signaling and KRAS signaling. Subsequently, we identified ten metastasis-associated genes, including SOX4, and established the LM-index, which correlates with poorer prognosis, higher stage, and advanced age. Furthermore, we screened two drugs as potential candidates for treating LM, including Linsitinib_1510, Lapatinib_1558. Immunohistochemistry results demonstrated significantly elevated SOX4 expression in tumor samples compared to normal samples. Finally, in vitro experiments verified that silencing SOX4 significantly inhibited tumor cell migration and invasion. Conclusion: This study reveals the possible cellular origin and driving factors of LM in CRC at the single cell level, and provides a reference for early detection of CRC patients with a high risk of LM.
引用
收藏
页码:345 / 358
页数:14
相关论文
共 50 条
  • [21] Single-cell transcriptome analysis of human oocyte ageing
    Yuan, Lihua
    Yin, Ping
    Yan, Hua
    Zhong, Xiufang
    Ren, Chunxia
    Li, Kai
    Chin Heng, Boon
    Zhang, Wuwen
    Tong, Guoqing
    JOURNAL OF CELLULAR AND MOLECULAR MEDICINE, 2021, 25 (13) : 6289 - 6303
  • [22] Integrating single-cell with transcriptome-proteome Mendelian randomization reveals colorectal cancer targets
    Song Wang
    Xin Yao
    Shenshen Li
    Shanshan Wang
    Xuyu Huang
    Jing Zhou
    Xiao Li
    Jieying Wen
    Weixuan Lan
    Yunsi Huang
    Hao Li
    Yunlong Sun
    Xiaoqian Zhao
    Qiaoling Chen
    Xuedong Han
    Ziming Zhu
    Xinyue Zhang
    Tao Zhang
    Discover Oncology, 16 (1)
  • [23] Single-cell transcriptomics reveals intratumor heterogeneity and the potential roles of cancer stem cells and myCAFs in colorectal cancer liver metastasis and recurrence
    Zhan, Yao
    Sun, Dong
    Gao, Jie
    Gao, Qinglun
    Lv, Yanfeng
    Du, Tiantian
    Dong, Yaqi
    Wang, Yunshan
    Zhan, Hanxiang
    Li, Juan
    Li, Peilong
    Du, Lutao
    Wang, Chuanxin
    CANCER LETTERS, 2025, 612
  • [24] Phenotype molding of T cells in colorectal cancer by single-cell analysis
    Di, Jiabo
    Liu, Maoxing
    Fan, Yingcong
    Gao, Pin
    Wang, Zaozao
    Jiang, Beihai
    Su, Xiangqian
    INTERNATIONAL JOURNAL OF CANCER, 2020, 146 (08) : 2281 - 2295
  • [25] Comparative analysis of single-cell transcriptome reveals heterogeneity and commonality in the immune microenvironment of colorectal cancer and inflammatory bowel disease
    Lv, Hongchao
    Mu, Yu
    Zhang, Chen
    Zhao, Meiqi
    Jiang, Ping
    Xiao, Shan
    Sun, Haiming
    Wu, Nan
    Sun, Donglin
    Jin, Yan
    FRONTIERS IN IMMUNOLOGY, 2024, 15
  • [26] Single-cell transcriptome analysis reveals heterogeneity of neutrophils in non-small cell lung cancer
    Wang, Yunzhen
    Zhu, Ziyi
    Luo, Raojun
    Chen, Wenwen
    JOURNAL OF GENE MEDICINE, 2024, 26 (05)
  • [27] Investigating the role of senescence biomarkers in colorectal cancer heterogeneity by bulk and single-cell RNA sequencing
    Ding, Chengsheng
    Xu, Ximo
    Zhang, Xian
    Zhang, Enkui
    Li, Shuchun
    Fan, Xiaodong
    Ma, Junjun
    Yang, Xiao
    Zang, Lu
    SCIENTIFIC REPORTS, 2024, 14 (01):
  • [28] Investigating intratumour heterogeneity by single-cell sequencing
    Ren, Shan-Cheng
    Qu, Min
    Sun, Ying-Hao
    ASIAN JOURNAL OF ANDROLOGY, 2013, 15 (06) : 729 - 734
  • [29] Single-cell data revealed the regulatory mechanism of TNK cell heterogeneity in liver metastasis from gastric cancer
    Gao, Jun
    Liu, Yujuan
    Tao, Lu
    Zeng, Peng
    Ye, Guiying
    Zheng, Ying
    Zhang, Nai
    DISCOVER ONCOLOGY, 2024, 15 (01)
  • [30] Single-cell profiling of the copy-number heterogeneity in colorectal cancer
    Song, Shiyu
    Feng, Lin
    Xi, Kexing
    Sun, Zhigang
    Kong, Deyang
    Luo, Zhenkai
    Pei, Wei
    Zhang, Haizeng
    CHINESE MEDICAL JOURNAL, 2023, 136 (06) : 707 - 718