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Abstract：
The impact of efferocytosis-related genes (ERGs) on the diagnosis of colorectal cancer (CRC) remains unclear. In this study, efferocytosis-associated biomarkers for the diagnosis of CRC were identified by integrating data from transcriptome sequencing and public databases. Finally, the expression of biomarkers was validated by real-time quantitative polymerase chain reaction (RT-qPCR). Our study may provide a reference for CRC diagnosis.
BACKGROUND: It has been shown that some efferocytosis related genes (ERGs) are associated with the development of cancer. However, it is still uncertain how ERGs may influence the diagnosis of colorectal cancer (CRC).
METHODS: In our study, the CRC cohorts were gained from transcriptome sequencing and the gene expression omnibus (GEO) database (GSE71187). Efferocytosis related biomarkers with diagnostic utility for CRC were identified through combining differentially expressed analysis, machine learning algorithms, and receiver operating characteristic (ROC) analysis. Then, infiltration abundance of immune cells between CRC and control was evaluated. The regulatory networks (including mRNA-miRNA-lncRNA and miRNA/transcription factors (TF)-mRNA networks) were created. Finally, the expression of biomarkers was validated via real-time quantitative polymerase chain reaction (RT-qPCR).
RESULTS: There were 3 biomarkers (ELMO3, P2RY12, and PDK4) related diagnosis for CRC patients gained. ELMO3 was highly expressed in CRC group, while P2RY12 and PDK4 was lowly expressed. Besides, the infiltrating abundance of 3 immune cells between CRC and control groups was significantly differential, namely activated CD4 memory T cells, macrophages M0, and resting mast cells. We then constructed a mRNA-miRNA-lncRNA network containing 3 mRNAs, 33 miRNAs, and 22 lncRNAs, and a miRNA/TF-mRNA network including 3 mRNAs, 33 miRNAs, and 7 TFs. Additionally, RT-qPCR results revealed that the expression trends of all biomarkers were consistent with the transcriptome sequencing data and GSE71187.
CONCLUSIONS: Taken together, this study provides three efferocytosis related biomarkers (ELMO3, P2RY12, and PDK4) for diagnosis of CRC, providing a scientific reference for further studies of CRC.
BACKGROUND: It has been shown that some efferocytosis related genes (ERGs) are associated with the development of cancer. However, it is still uncertain how ERGs may influence the diagnosis of colorectal cancer (CRC).
METHODS: In our study, the CRC cohorts were gained from transcriptome sequencing and the gene expression omnibus (GEO) database (GSE71187). Efferocytosis related biomarkers with diagnostic utility for CRC were identified through combining differentially expressed analysis, machine learning algorithms, and receiver operating characteristic (ROC) analysis. Then, infiltration abundance of immune cells between CRC and control was evaluated. The regulatory networks (including mRNA-miRNA-lncRNA and miRNA/transcription factors (TF)-mRNA networks) were created. Finally, the expression of biomarkers was validated via real-time quantitative polymerase chain reaction (RT-qPCR).
RESULTS: There were 3 biomarkers (ELMO3, P2RY12, and PDK4) related diagnosis for CRC patients gained. ELMO3 was highly expressed in CRC group, while P2RY12 and PDK4 was lowly expressed. Besides, the infiltrating abundance of 3 immune cells between CRC and control groups was significantly differential, namely activated CD4 memory T cells, macrophages M0, and resting mast cells. We then constructed a mRNA-miRNA-lncRNA network containing 3 mRNAs, 33 miRNAs, and 22 lncRNAs, and a miRNA/TF-mRNA network including 3 mRNAs, 33 miRNAs, and 7 TFs. Additionally, RT-qPCR results revealed that the expression trends of all biomarkers were consistent with the transcriptome sequencing data and GSE71187.
CONCLUSIONS: Taken together, this study provides three efferocytosis related biomarkers (ELMO3, P2RY12, and PDK4) for diagnosis of CRC, providing a scientific reference for further studies of CRC.
摘要：
Efferocytosis相关基因（ERGs）对结直肠癌（CRC）诊断的影响尚不清楚。在这项研究中,通过整合来自转录组测序和公共数据库的数据,鉴定了用于CRC诊断的细胞增殖相关生物标志物.最后,通过实时定量聚合酶链反应(RT-qPCR)验证生物标志物的表达.本研究可为CRC的诊断提供参考。
背景：已经表明，一些有效细胞增多相关基因（ERGs）与癌症的发展有关。然而,ERGs如何影响结直肠癌(CRC)的诊断仍不确定.
方法：在我们的研究中，CRC队列来自转录组测序和基因表达综合(GEO)数据库(GSE71187).通过结合差异表达分析，鉴定了具有CRC诊断效用的Efferocytosis相关生物标志物。机器学习算法，和接收机工作特性(ROC)分析。然后，评估CRC和对照之间免疫细胞的浸润丰度。建立调控网络(包括mRNA-miRNA-lncRNA和miRNA/转录因子(TF)-mRNA网络)。最后,通过实时定量聚合酶链反应(RT-qPCR)验证生物标志物的表达.
结果：有3种生物标志物（ELMO3、P2RY12和PDK4）与CRC患者的诊断相关。ELMO3在CRC组中高表达，而P2RY12和PDK4低表达。此外,3个免疫细胞的浸润丰度在CRC组和对照组之间有显著差异,即激活的CD4记忆T细胞，巨噬细胞M0和静息肥大细胞。然后我们构建了一个包含3个mRNA的mRNA-miRNA-lncRNA网络，33个miRNAs,和22个lncRNAs，和包含3个mRNA的miRNA/TF-mRNA网络，33个miRNAs,和7个TFs。此外,RT-qPCR结果表明，所有生物标志物的表达趋势与转录组测序数据和GSE71187一致。
结论：综合来看，这项研究提供了三种与红细胞增多相关的生物标志物（ELMO3，P2RY12和PDK4）用于诊断CRC，为进一步研究CRC提供科学参考。
背景：已经表明，一些有效细胞增多相关基因（ERGs）与癌症的发展有关。然而,ERGs如何影响结直肠癌(CRC)的诊断仍不确定.
方法：在我们的研究中，CRC队列来自转录组测序和基因表达综合(GEO)数据库(GSE71187).通过结合差异表达分析，鉴定了具有CRC诊断效用的Efferocytosis相关生物标志物。机器学习算法，和接收机工作特性(ROC)分析。然后，评估CRC和对照之间免疫细胞的浸润丰度。建立调控网络(包括mRNA-miRNA-lncRNA和miRNA/转录因子(TF)-mRNA网络)。最后,通过实时定量聚合酶链反应(RT-qPCR)验证生物标志物的表达.
结果：有3种生物标志物（ELMO3、P2RY12和PDK4）与CRC患者的诊断相关。ELMO3在CRC组中高表达，而P2RY12和PDK4低表达。此外,3个免疫细胞的浸润丰度在CRC组和对照组之间有显著差异,即激活的CD4记忆T细胞，巨噬细胞M0和静息肥大细胞。然后我们构建了一个包含3个mRNA的mRNA-miRNA-lncRNA网络，33个miRNAs,和22个lncRNAs，和包含3个mRNA的miRNA/TF-mRNA网络，33个miRNAs,和7个TFs。此外,RT-qPCR结果表明，所有生物标志物的表达趋势与转录组测序数据和GSE71187一致。
结论：综合来看，这项研究提供了三种与红细胞增多相关的生物标志物（ELMO3，P2RY12和PDK4）用于诊断CRC，为进一步研究CRC提供科学参考。
