2. Academic Validation
  3. Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer

Machine learning-based screening and validation of liver metastasis-specific genes in colorectal cancer

  • Sci Rep. 2024 Jul 30;14(1):17679. doi: 10.1038/s41598-024-68706-y.
Shiyao Zheng # 1,  Hongxin He # 1,  Jianfeng Zheng # 2,  Xingshu Zhu 3,  Nan Lin 3 4,  Qing Wu 5,  Enhao Wei 1,  Caiming Weng 6,  Shuqian Chen 7,  Xinxiang Huang 1,  Chenxing Jian 8 9,  Shen Guan 10,  Chunkang Yang 11 12
Affiliations

Affiliations

  • 1 Department of Gastrointestinal Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China.
  • 2 Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China.
  • 3 Department of General Surgery, 900TH Hospital of Joint Logistics Support Force, Fuzhou, 350025, People's Republic of China.
  • 4 Fuzong Clinical Medical College of Fujian Medical University, Department of General Surgery, 900th Hospital of Joint Logistics Support Force, PLA, Fuzhou, 350025, People's Republic of China.
  • 5 Department of Oncology, Molecular Oncology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou, 350005, People's Republic of China.
  • 6 Department of Gastric Surgery, Fujian Medical University Union Hospital, Fuzhou, 350002, People's Republic of China.
  • 7 Shengli Clinical Medical College of Fujian Medical University, Fuzhou, 350001, People's Republic of China.
  • 8 School of Clinical Medicine, Fujian Medical University, Fuzhou, 350108, People's Republic of China. ptyyjcx@126.com.
  • 9 Department of Anorectal Surgery, Afliated Hospital of Putian University, Putian, 351106, People's Republic of China. ptyyjcx@126.com.
  • 10 Department of Gastrointestinal Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China. goldson13@outlook.com.
  • 11 Department of Gastrointestinal Surgical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, People's Republic of China. chunkang129@fjmu.edu.cn.
  • 12 Fujian Key Laboratory of Translational Cancer Medicine, Fuzhou, 350014, People's Republic of China. chunkang129@fjmu.edu.cn.
  • # Contributed equally.
PMID: 39085446 DOI: 10.1038/s41598-024-68706-y
Abstract

Colorectal liver metastasis (CRLM) is challenging in the clinical treatment of colorectal Cancer. Limited research has been conducted on how CRLM develops. RNA Sequencing data were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Four machine learning algorithms were used to screen the hub CRLM-specific genes, including Least Absolute Shrinkage and Selection Operator (Lasso), Random forest, SVM-RFE, and XGboost. The model for identifying CRLM was developed using stepwise logistic regression and was validated using internal and independent datasets. The prognostic value of hub CRLM-specific genes was assessed using the Lasso-Cox method. The in vitro experiments were performed using SW620 cells. The CRLM identification model was developed based on four CRLM-specific genes (SPP1, ZG16, P2RY14, and PRKAR2B), and the model efficacy was validated using GSE41258 and three external cohorts. Five CRLM-specific prognostic hub genes, SPP1, ZG16, P2RY14, CYP2E1, and C5, were identified using the Lasso-Cox algorithm, and a risk score was constructed. The risk score was validated using the GSE39582 cohort. Three genes have both efficacy in identifying CRLM and prognostic value: ZG16, P2RY14, and SPP1. Immune infiltration and enrichment analyses demonstrated that SPP1 was associated with M2 macrophage polarization and extracellular matrix remodeling. In vitro experiments indicated that SPP1 may act as a cancer-promoting factor. The hub CRLM-specific gene SPP1 can help determine the diagnosis, prognosis, and immune infiltration of patients with CRLM.

Keywords

Bioinformatics; Colorectal liver metastasis; Machine learning; Prognosis; Tumor immune microenvironment.

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