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利用机器学习构建结肠癌分子亚型模型。

Constructing a molecular subtype model of colon cancer using machine learning.

作者信息

Zhou Bo, Yu Jiazi, Cai Xingchen, Wu Shugeng

机构信息

Department of General Surgery, Ningbo Medical Center Lihuili Hospital, Ningbo University, Ningbo, China.

Medical School, Ningbo University, Ningbo, China.

出版信息

Front Pharmacol. 2022 Sep 16;13:1008207. doi: 10.3389/fphar.2022.1008207. eCollection 2022.

DOI:10.3389/fphar.2022.1008207
PMID:36188575
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9523145/
Abstract

Colon cancer (CRC) is one of the malignant tumors with a high incidence in the world. Many previous studies on CRC have focused on clinical research. With the in-depth study of CRC, the role of molecular mechanisms in CRC has become increasingly important. Currently, machine learning is widely used in medicine. By combining machine learning with molecular mechanisms, we can better understand CRC's pathogenesis and develop new treatments for it. We used the R language to construct molecular subtypes of colon cancer and subsequently explored prognostic genes with GEPIA2. Enrichment analysis is used by WebGestalt to obtain differential genes. Protein-protein interaction networks of differential genes were constructed using the STRING database and the Cytoscape tool. TIMER2.0 and TISIDB databases were used to investigate the correlation of these genes with immune-infiltrating cells and immune targets. The cBioportal database was used to explore genomic alterations. In our study, the molecular prognostic model of CRC was constructed to study the prognostic factors of CRC, and finally, it was found that Charcot-Leyden crystal galectin (CLC), zymogen granule protein 16 (ZG16), leucine-rich repeat-containing protein 26 (LRRC26), intelectin 1 (ITLN1), UDP-GlcNAc: betaGal beta-1,3-N-acetylglucosaminyltransferase 6 (B3GNT6), chloride channel accessory 1 (CLCA1), growth factor independent 1 transcriptional repressor (GFI1), aquaporin 8 (AQP8), HEPACAM family member 2 (HEPACAM2), and UDP glucuronosyltransferase family 2 member B15 (UGT2B15) were correlated with the subtype model of CRC prognosis. Enrichment analysis shows that differential genes were mainly associated with immune-inflammatory pathways. GFI1 and CLC were associated with immune cells, immunoinhibitors, and immunostimulator. Genomic analysis shows that there were no significant changes in differential genes. By constructing molecular subtypes of colon cancer, we discovered new colon cancer prognostic markers, which can provide direction for new treatments in the future.

摘要

结肠癌（CRC）是世界上发病率较高的恶性肿瘤之一。此前许多关于CRC的研究都集中在临床研究上。随着对CRC研究的深入，分子机制在CRC中的作用变得越来越重要。目前，机器学习在医学中被广泛应用。通过将机器学习与分子机制相结合，我们可以更好地理解CRC的发病机制，并开发新的治疗方法。我们使用R语言构建结肠癌的分子亚型，随后用GEPIA2探索预后基因。利用WebGestalt进行富集分析以获得差异基因。使用STRING数据库和Cytoscape工具构建差异基因的蛋白质-蛋白质相互作用网络。利用TIMER2.0和TISIDB数据库研究这些基因与免疫浸润细胞和免疫靶点的相关性。使用cBioportal数据库探索基因组改变。在我们的研究中，构建了CRC的分子预后模型以研究CRC的预后因素，最终发现查科-莱登结晶半乳糖凝集素（CLC）、酶原颗粒蛋白16（ZG16）、富含亮氨酸重复序列蛋白26（LRRC26）、肝免疫凝集素1（ITLN1）、UDP-N-乙酰葡糖胺：β-半乳糖β-1,3-N-乙酰葡糖胺基转移酶6（B3GNT6）、氯通道辅助蛋白1（CLCA1）、生长因子独立1转录抑制因子（GFI1）、水通道蛋白8（AQP8）、肝细胞膜相关分子家族成员2（HEPACAM2）和尿苷二磷酸葡萄糖醛酸基转移酶家族2成员B15（UGT2B15）与CRC预后的亚型模型相关。富集分析表明差异基因主要与免疫炎症途径相关。GFI1和CLC与免疫细胞、免疫抑制剂和免疫刺激剂相关。基因组分析表明差异基因无显著变化。通过构建结肠癌的分子亚型，我们发现了新的结肠癌预后标志物，可为未来的新治疗提供方向。

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