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

IF 2.7 4区医学 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

OncoTargets and therapy Pub Date : 2024-04-17 DOI:10.2147/ott.s454295

Zhilin Sha, Qingxiang Gao, Lei Wang, Ni An, Yingjun Wu, Dong Wei, Tong Wang, Chen Liu, Yang Shen

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引用次数: 0

Abstract

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.

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通过单细胞转录组分析研究结直肠癌的细胞起源和肝转移因素

背景：结肠直肠癌（CRC）是全球最致命的癌症死因之一。肝转移（LM）是导致 CRC 患者死亡的主要原因。因此，识别肝转移风险最大的患者对于早期治疗和降低结直肠癌肝转移患者的死亡率至关重要：最初，我们通过单细胞转录组分析确定了细胞组成的特征。随后，我们利用拷贝数变异（CNV）和伪时间分析来确定肝转移瘤的细胞起源，并识别与肝转移瘤相关的上皮细胞（LMECs）。我们利用机器学习算法构建了 LM 指数，以预测 LMEC 的相对丰度，从而反映出 LM 的风险。此外，我们还分析了 LMEC 和 LM 高危患者的药物敏感性和药物靶向基因表达。最后，我们进行了功能实验，以确定转移相关基因在体外的生物学作用：结果：单细胞RNA测序分析表明，原发性CRC和LM肿瘤的免疫景观不同。LM起源于染色体变异，chr1和chr6p拷贝数丢失，chr7和chr20q拷贝数增大。我们确定了 LMECs 群，并发现了与 LM 相关的通路，如 Wnt/beta-catenin 信号转导和 KRAS 信号转导。随后，我们确定了包括 SOX4 在内的十个转移相关基因，并建立了 LM 指数，该指数与较差的预后、较高的分期和高龄相关。此外，我们还筛选出两种治疗 LM 的潜在候选药物，包括 Linsitinib_1510、Lapatinib_1558。免疫组化结果显示，与正常样本相比，肿瘤样本中SOX4的表达明显升高。最后，体外实验验证了沉默SOX4能显著抑制肿瘤细胞的迁移和侵袭：结论：这项研究从单细胞水平揭示了乳腺癌可能的细胞起源和驱动因素，为早期发现乳腺癌高危人群提供了参考。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

OncoTargets and therapy BIOTECHNOLOGY & APPLIED MICROBIOLOGY-ONCOLOGY

CiteScore

9.70

自引率

0.00%

发文量

221

审稿时长

1 months

期刊介绍： OncoTargets and Therapy is an international, peer-reviewed journal focusing on molecular aspects of cancer research, that is, the molecular diagnosis of and targeted molecular or precision therapy for all types of cancer. The journal is characterized by the rapid reporting of high-quality original research, basic science, reviews and evaluations, expert opinion and commentary that shed novel insight on a cancer or cancer subtype. Specific topics covered by the journal include: -Novel therapeutic targets and innovative agents -Novel therapeutic regimens for improved benefit and/or decreased side effects -Early stage clinical trials Further considerations when submitting to OncoTargets and Therapy: -Studies containing in vivo animal model data will be considered favorably. -Tissue microarray analyses will not be considered except in cases where they are supported by comprehensive biological studies involving multiple cell lines. -Biomarker association studies will be considered only when validated by comprehensive in vitro data and analysis of human tissue samples. -Studies utilizing publicly available data (e.g. GWAS/TCGA/GEO etc.) should add to the body of knowledge about a specific disease or relevant phenotype and must be validated using the authors’ own data through replication in an independent sample set and functional follow-up. -Bioinformatics studies must be validated using the authors’ own data through replication in an independent sample set and functional follow-up. -Single nucleotide polymorphism (SNP) studies will not be considered.