脂质代谢相关的结肠癌四基因预后模型
Lipid Metabolism-Related Colon Cancer Four-Gene Prognostic Model
DOI: 10.12677/hjbm.2026.162032, PDF,   
作者: 王柱浩:大理大学临床医学院,云南 大理;王剑华*:大理大学第一附属医院,云南 大理
关键词: 脂肪酸代谢结肠癌预后模型Fatty Acid Metabolism Colon Cancer Prognostic Model
摘要: 目的:通过生物信息学方法构建脂质代谢相关基因异构模型,探讨其在结肠癌患者中的应用价值。方法:从TCGA (The Cancer Genome Atlas, TCGA)和GEO (Gene Expression Omnibus, GEO)数据库中收集临床信息和RNA测序数据。对筛选出的脂质代谢相关差异表达基因进行Lasso-Cox回归分析,建立结肠癌预后模型。并构建了预后模型的诺模图,以分析其在评估结肠癌患者生存期和临床分期中的价值。结果:分析了722个FAM相关基因在结肠癌和正常组织中的表达差异,并通过Lasso-Cox回归分析构建了基于4个FAM基因的结肠癌预后预测模型,并在验证集中验证了该模型的实用性。此外,基于预后模型计算的风险分数被验证为结肠癌患者的独立预后因素。我们进一步构建了由风险评分特征、年龄和美国癌症联合委员会分期组成的诺模图,供临床应用。结论:本研究通过生物信息学方法,构建了一个结肠癌四基因的预后模型。并系统揭示了该模型在结肠癌中的临床预测作用。本研究的发现,进一步增强了FAM相关基因在结肠癌中的预后价值。
Abstract: Objective: To construct a model of lipid metabolism related gene isomerism by bioinformatics method, and to explore its application value in patients with colon cancer. Methods: Clinical information and RNA sequencing data were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Lasso-Cox regression analysis was performed on the screened differentially expressed genes related to lipid metabolism to establish a prognostic model of colon cancer. The nomogram of the prognostic model was constructed to analyze its value in evaluating the survival time and clinical stage of colon cancer patients. Results: The expression differences of 722 FAM-related genes in colon cancer and normal tissues were analyzed. The prognostic prediction model of colon cancer based on four FAM genes was constructed by Lasso-Cox regression analysis, and the practicability of the model was verified in the validation set. In addition, the risk score calculated based on the prognostic model was validated as an independent prognostic factor for colon cancer patients. We further constructed a nomogram consisting of risk score characteristics, age, and American Joint Committee on Cancer staging for clinical application. Conclusion: In this study, a prognostic model of four genes in colon cancer was constructed by bioinformatics methods. The clinical predictive effect of the model in colon cancer was systematically revealed. The findings of this study further enhanced the prognostic value of FAM-related genes in colon cancer.
文章引用:王柱浩, 王剑华. 脂质代谢相关的结肠癌四基因预后模型[J]. 生物医学, 2026, 16(2): 307-322. https://doi.org/10.12677/hjbm.2026.162032

参考文献

[1] Siegel, R.L., Miller, K.D., Fuchs, H.E. and Jemal, A. (2022) Cancer Statistics, 2022. CA: A Cancer Journal for Clinicians, 72, 7-33. [Google Scholar] [CrossRef] [PubMed]
[2] The Cancer Genome Atlas Network (2012) Comprehensive Molecular Characterization of Human Colon and Rectal Cancer. Nature, 487, 330-337. [Google Scholar] [CrossRef] [PubMed]
[3] Miller, K.D., Nogueira, L., Mariotto, A.B., Rowland, J.H., Yabroff, K.R., Alfano, C.M., et al. (2019) Cancer Treatment and Survivorship Statistics, 2019. CA: A Cancer Journal for Clinicians, 69, 363-385. [Google Scholar] [CrossRef] [PubMed]
[4] Xie, Y., Chen, Y. and Fang, J. (2020) Comprehensive Review of Targeted Therapy for Colorectal Cancer. Signal Transduction and Targeted Therapy, 5, Article No. 22. [Google Scholar] [CrossRef] [PubMed]
[5] Hanahan, D. and Weinberg, R.A. (2011) Hallmarks of Cancer: The Next Generation. Cell, 144, 646-674. [Google Scholar] [CrossRef] [PubMed]
[6] Liu, H., Wang, S., Wang, J., Guo, X., Song, Y., Fu, K., et al. (2025) Energy Metabolism in Health and Diseases. Signal Transduction and Targeted Therapy, 10, Article No. 69. [Google Scholar] [CrossRef] [PubMed]
[7] Currie, E., Schulze, A., Zechner, R., Walther, T.C. and Farese, R.V. (2013) Cellular Fatty Acid Metabolism and Cancer. Cell Metabolism, 18, 153-161. [Google Scholar] [CrossRef] [PubMed]
[8] Hoy, A.J., Nagarajan, S.R. and Butler, L.M. (2021) Tumour Fatty Acid Metabolism in the Context of Therapy Resistance and Obesity. Nature Reviews Cancer, 21, 753-766. [Google Scholar] [CrossRef] [PubMed]
[9] Zhu, J. and Thompson, C.B. (2019) Metabolic Regulation of Cell Growth and Proliferation. Nature Reviews Molecular Cell Biology, 20, 436-450. [Google Scholar] [CrossRef] [PubMed]
[10] Butler, L.M., Perone, Y., Dehairs, J., Lupien, L.E., de Laat, V., Talebi, A., et al. (2020) Lipids and Cancer: Emerging Roles in Pathogenesis, Diagnosis and Therapeutic Intervention. Advanced Drug Delivery Reviews, 159, 245-293. [Google Scholar] [CrossRef] [PubMed]
[11] Luo, X., Cheng, C., Tan, Z., Li, N., Tang, M., Yang, L., et al. (2017) Emerging Roles of Lipid Metabolism in Cancer Metastasis. Molecular Cancer, 16, Article No. 76. [Google Scholar] [CrossRef] [PubMed]
[12] Ritchie, M.E., Phipson, B., Wu, D., Hu, Y., Law, C.W., Shi, W., et al. (2015) Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies. Nucleic Acids Research, 43, e47-e47. [Google Scholar] [CrossRef] [PubMed]
[13] Zhu, Y., Zhang, H., Yao, X., Zhang, S., Dai, B., Shen, Y., et al. (2010) Development and Evaluation of a Nomogram to Predict Inguinal Lymph Node Metastasis in Patients with Penile Cancer and Clinically Negative Lymph Nodes. Journal of Urology, 184, 539-545. [Google Scholar] [CrossRef] [PubMed]
[14] Yu, G., Wang, L., Han, Y. and He, Q. (2012) Clusterprofiler: An R Package for Comparing Biological Themes among Gene Clusters. OMICS: A Journal of Integrative Biology, 16, 284-287. [Google Scholar] [CrossRef] [PubMed]
[15] Subramanian, A., Tamayo, P., Mootha, V.K., Mukherjee, S., Ebert, B.L., Gillette, M.A., et al. (2005) Gene Set Enrichment Analysis: A Knowledge-Based Approach for Interpreting Genome-Wide Expression Profiles. Proceedings of the National Academy of Sciences, 102, 15545-15550. [Google Scholar] [CrossRef] [PubMed]
[16] Hänzelmann, S., Castelo, R. and Guinney, J. (2013) GSVA: Gene Set Variation Analysis for Microarray and RNA-Seq Data. BMC Bioinformatics, 14, Article No. 7. [Google Scholar] [CrossRef] [PubMed]
[17] Simon, N., Friedman, J., Hastie, T. and Tibshirani, R. (2011) Regularization Paths for Cox’s Proportional Hazards Model via Coordinate Descent. Journal of Statistical Software, 39, 1-13. [Google Scholar] [CrossRef] [PubMed]
[18] Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians, 71, 209-249. [Google Scholar] [CrossRef] [PubMed]
[19] Grady, W.M. and Carethers, J.M. (2008) Genomic and Epigenetic Instability in Colorectal Cancer Pathogenesis. Gastroenterology, 135, 1079-1099. [Google Scholar] [CrossRef] [PubMed]
[20] Kuo, C. and Ann, D.K. (2018) When Fats Commit Crimes: Fatty Acid Metabolism, Cancer Stemness and Therapeutic Resistance. Cancer Communications, 38, Article No. 47. [Google Scholar] [CrossRef] [PubMed]
[21] Zaidi, N., Lupien, L., Kuemmerle, N.B., Kinlaw, W.B., Swinnen, J.V. and Smans, K. (2013) Lipogenesis and Lipolysis: The Pathways Exploited by the Cancer Cells to Acquire Fatty Acids. Progress in Lipid Research, 52, 585-589. [Google Scholar] [CrossRef] [PubMed]
[22] Munir, R., Lisec, J., Swinnen, J.V. and Zaidi, N. (2019) Lipid Metabolism in Cancer Cells under Metabolic Stress. British Journal of Cancer, 120, 1090-1098. [Google Scholar] [CrossRef] [PubMed]
[23] Munir, R., Lisec, J., Jaeger, C. and Zaidi, N. (2021) Abundance, Fatty Acid Composition and Saturation Index of Neutral Lipids in Colorectal Cancer Cell Lines. Acta Biochimica Polonica, 68, 115-118. [Google Scholar] [CrossRef] [PubMed]
[24] Noda, K., Nakao, S., Zandi, S., Engelstädter, V., Mashima, Y. and Hafezi-Moghadam, A. (2009) Vascular Adhesion Protein-1 Regulates Leukocyte Transmigration Rate in the Retina during Diabetes. Experimental Eye Research, 89, 774-781. [Google Scholar] [CrossRef] [PubMed]
[25] Li, Y., Hung, J., Yu, T., Liou, J., Wei, J., Kao, H., et al. (2014) Serum Vascular Adhesion Protein-1 Predicts All-Cause Mortality and Cancer-Related Mortality in Subjects with Colorectal Cancer. Clinica Chimica Acta, 428, 51-56. [Google Scholar] [CrossRef] [PubMed]
[26] Zhang, L., Yao, D., Xia, Y., Zhou, F., Zhang, Q., Wang, Q., et al. (2021) The Structural Basis for Glycerol Permeation by Human Aqp7. Science Bulletin, 66, 1550-1558. [Google Scholar] [CrossRef] [PubMed]
[27] Borgnia, M., Nielsen, S., Engel, A. and Agre, P. (1999) Cellular and Molecular Biology of the Aquaporin Water Channels. Annual Review of Biochemistry, 68, 425-458. [Google Scholar] [CrossRef] [PubMed]
[28] van Hall, G., Sacchetti, M., Rådegran, G. and Saltin, B. (2002) Human Skeletal Muscle Fatty Acid and Glycerol Metabolism during Rest, Exercise and Recovery. The Journal of Physiology, 543, 1047-1058. [Google Scholar] [CrossRef] [PubMed]
[29] Dai, C., Charlestin, V., Wang, M., Walker, Z.T., Miranda-Vergara, M.C., Facchine, B.A., et al. (2020) Aquaporin-7 Regulates the Response to Cellular Stress in Breast Cancer. Cancer Research, 80, 4071-4086. [Google Scholar] [CrossRef] [PubMed]
[30] Furuhashi, M. and Hotamisligil, G.S. (2008) Fatty Acid-Binding Proteins: Role in Metabolic Diseases and Potential as Drug Targets. Nature Reviews Drug Discovery, 7, 489-503. [Google Scholar] [CrossRef] [PubMed]
[31] Gharpure, K.M., Pradeep, S., Sans, M., Rupaimoole, R., Ivan, C., Wu, S.Y., et al. (2018) FABP4 as a Key Determinant of Metastatic Potential of Ovarian Cancer. Nature Communications, 9, Article No. 2923. [Google Scholar] [CrossRef] [PubMed]
[32] O’Neill, A.S.G., van den Berg, T.K. and Mullen, G.E.D. (2013) Sialoadhesin—A Macrophage‐Restricted Marker of Immunoregulation and Inflammation. Immunology, 138, 198-207. [Google Scholar] [CrossRef] [PubMed]
[33] Cassetta, L., Fragkogianni, S., Sims, A.H., Swierczak, A., Forrester, L.M., Zhang, H., et al. (2019) Human Tumor-Associated Macrophage and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets. Cancer Cell, 35, 588-602.e10. [Google Scholar] [CrossRef] [PubMed]
[34] Schonkeren, S.L., Thijssen, M.S., Vaes, N., Boesmans, W. and Melotte, V. (2021) The Emerging Role of Nerves and Glia in Colorectal Cancer. Cancers, 13, Article 152. [Google Scholar] [CrossRef] [PubMed]
[35] Liu, Y. and Cao, X. (2016) Characteristics and Significance of the Pre-Metastatic Niche. Cancer Cell, 30, 668-681. [Google Scholar] [CrossRef] [PubMed]
[36] Bader, J.E., Voss, K. and Rathmell, J.C. (2020) Targeting Metabolism to Improve the Tumor Microenvironment for Cancer Immunotherapy. Molecular Cell, 78, 1019-1033. [Google Scholar] [CrossRef] [PubMed]