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中华结直肠疾病电子杂志

中华结直肠疾病电子杂志 ›› 2026, Vol. 15 ›› Issue (02) : 146 -159. doi: 10.3877/cma.j.issn.2095-3224.2026.02.006

论著

基于溶酶体相关基因构建结直肠癌预后模型的研究
王欢1, 王伟彪1, 李多2,†(), 于永强2, 武雪亮3, 樊建春4   
  1. 1　075000　张家口，河北北方学院
    2　075000　张家口，河北北方学院附属第一医院消化科
    3　075000　张家口，河北北方学院附属第一医院血管腺体外科
    4　075000　张家口，河北北方学院附属第一医院肿瘤研究所
  • 收稿日期:2025-12-17 出版日期:2026-04-25
  • 通信作者: 李多
  • 基金资助:
    河北省财政厅临床医学优秀人才培养项目(ZF2023239); 河北省科技厅重点研发计划项目(22377784D); 河北省财政厅优秀人才资助项目(361009)

Research on the construction of a prognostic model for colorectal cancer based on lysosome-related genes

Huan Wang1, Weibiao Wang1, Duo Li2,†(), Yongqiang Yu2, Xueliang Wu3, Jianchun Fan4   

  1. 1 Hebei North University, Zhangjiakou 075000, China
    2 Gastroenterology Department, the First Hospital Affiliated to Hebei North University, Zhangjiakou 075000, China
    3 Vascular Gland Surgery, the First Hospital Affiliated to Hebei North University, Zhangjiakou 075000, China
    4 Tumor Research Institute, the First Hospital Affiliated to Hebei North University, Zhangjiakou 075000, China
  • Received:2025-12-17 Published:2026-04-25
  • Corresponding author: Duo Li
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引用本文：

王欢, 王伟彪, 李多, 于永强, 武雪亮, 樊建春. 基于溶酶体相关基因构建结直肠癌预后模型的研究[J/OL]. 中华结直肠疾病电子杂志, 2026, 15(02): 146-159.

Huan Wang, Weibiao Wang, Duo Li, Yongqiang Yu, Xueliang Wu, Jianchun Fan. Research on the construction of a prognostic model for colorectal cancer based on lysosome-related genes[J/OL]. Chinese Journal of Colorectal Diseases(Electronic Edition), 2026, 15(02): 146-159.

目的

本研究利用生物信息学方法筛选溶酶体相关结直肠癌基因构建预后模型。

方法

从TCGA和GEO数据库中分别下载结、直肠癌数据集。利用R包从中筛选出与结直肠癌生存相关的差异表达的LRGs，并对这些基因采用单因素Cox回归及LASSO回归进行相关预后基因筛选，构建预后风险模型。同时使用单因素Cox回归分析方法构建临床预后模型，并进行独立预后及生存分析。通过K-M生存曲线及ROC曲线分析对模型的相关性能进行验证。最后通过体外实验进一步验证ATP6V1G2、DLG4、LZTS1基因及蛋白在结直肠癌中的表达与病理特征及预后的关系。

结果

筛选得到7个相关预后基因分别是ATP6V1G2、LZTS1、CLU、PDGFRA、CLVS2、DLG4、RAMP1，以此为特征构建结直肠癌预后模型。K-M生存曲线显示低风险组预后明显优于高风险组（χ2=15.14，P=0.0001），并在GEO数据库中得到验证。ROC曲线显示5年及总生存时间的AUC值均大于0.65（0.67，0.66），说明该模型具有一定的预测能力。GO富集分析显示，LRGs主要参与自噬、内吞、巨自噬、细胞降解等过程进而影响结直肠癌的发生、发展和转化等。KEGG富集分析主要富集到Rap1信号通路、内吞作用、唾液分泌三条通路。体外实验结果显示：在结直肠癌中ATP6V1G2及DLG4呈高表达（ATP6V1G2：t=−6.847，P＜0.05；DLG4：t=−6.324，P＜0.05），LZTS1呈低表达（t=5.568，P＜0.05）。ATP6V1G2、DLG4高表达及LZTS1低表达均与肿瘤浸润深度（ATP6V1G2：χ2=5.333，P=0.021；DLG4：χ2=6.522，P=0.011；LZTS1：χ2=6.095，P=0.014）、淋巴结转移（ATP6V1G2：χ2=7.065，P=0.008；DLG4：χ2=5.265，P=0.022；LZTS1：χ2=5.224，P=0.022）及患者年龄显著相关（ATP6V1G2：χ2=4.844，P=0.028；DLG4：χ2=6.332，P=0.012；LZTS1：χ2=4.969，P=0.026）。ATP6V1G2及DLG4表达越高，LZTS1表达越低，患者预后越差。ROC曲线显示ATP6V1G2、DLG4及LZTS1三者单独及联合对结直肠癌的诊断及预后均有预测价值（ATP6V1G2：AUC=0.886，P＜0.001；DLG4：AUC=0.781，P=0.003；LZTS1：AUC：0.667，P=0.029；三者联合：AUC=0.894，P＜0.001）。

结论

本研究成功构建基于7个溶酶体相关基因的结直肠癌预后模型，该模型能较好地预测患者预后，且ATP6V1G2、DLG4高表达与LZTS1低表达可作为结直肠癌预后不良的潜在分子标志物。

关键词: 生物信息学, 结直肠癌, 溶酶体相关基因, 临床病理特征, 预后模型
Objective

This study utilized bioinformatics methods to screen out genes related to lysosomes in colorectal cancer and constructed a prognostic model.

Methods

The colorectal cancer data sets were downloaded from TCGA and GEO databases, respectively. The R package was used to screen out the differentially expressed LRGs related to CRC survival, and univariate Cox regression and LASSO regression were used to screen the related prognostic genes to construct a prognostic risk model. Univariate Cox regression analysis was used to construct a clinical prognostic model, and independent prognostic and survival analysis was performed. The performance of the model was verified by K-M survival curve and ROC curve analysis. Finally, the relationship between ATP6V1G2, DLG4, LZTS1 gene and protein expression and pathological features and prognosis of colorectal cancer was further verified by in vitro experiments.

Results

Seven related prognostic genes were screened out, including ATP6V1G2, LZTS1, CLU, PDGFRA, CLVS2, DLG4 and RAMP1, and a prognostic model of colorectal cancer was constructed based on these genes. K-M survival curve showed that the prognosis of low-risk group was significantly better than that of high-risk group (χ2=15.14, P=0.0001), which was verified in GEO database. The ROC curve showed that the AUC values of 5-year and overall survival time were greater than 0.65 (0.67, 0.66), indicating that the model had certain predictive ability. GO enrichment analysis showed that LRGs were mainly involved in autophagy, endocytosis, macroautophagy, cell degradation and other processes, thereby affecting the occurrence, development and transformation of colorectal cancer. KEGG enrichment analysis showed that Rap1 signaling pathway, endocytosis and salivary secretion were mainly enriched. The results of in vitro experiments showed that ATP6V1G2 and DLG4 were highly expressed (ATP6V1G2: t=−6.847, P＜0.05; DLG4: t=−6.324, P＜0.05) and LZTS1 was lowly expressed (t=5.568, P＜0.05) in colorectal cancer. The high expression of ATP6V1G2 and DLG4 and low expression of LZTS1 were significantly correlated with the depth of tumor invasion (ATP6V1G2: χ2=5.333, P=0.021; DLG4: χ2=6.522, P=0.011; LZTS1: χ2=6.095, P=0.014), lymph node metastasis (ATP6V1G2: χ2=7.065, P=0.008; DLG4: χ2=5.265, P=0.022; LZTS1: χ2=5.224, P=0.022) and patient age (ATP6V1G2: χ2=4.844, P=0.028; DLG4: χ2=6.332, P=0.012; LZTS1: χ2=4.969, P=0.026). The higher the expression levels of ATP6V1G2 and DLG4, and the lower the expression level of LZTS1, the worse the prognosis of the patients will be. ROC curve showed that ATP6V1G2, DLG4 and LZTS1 alone and in combination had predictive value for the diagnosis and prognosis of colorectal cancer (ATP6V1G2: AUC=0.886, P＜0.001; DLG4: AUC=0.781, P=0.003; LZTS1: AUC: 0.667, P=0.029; Combination of the three: AUC=0.894, P＜0.001).

Conclusion

This study successfully constructed a prognostic model of colorectal cancer based on 7 lysosome-related genes, which can better predict the prognosis of patients. The high expression of ATP6V1G2 and DLG4 and low expression of LZTS1 can be used as potential molecular markers for poor prognosis of colorectal cancer.

Key words: Bioinformatics, Colorectal cancer, Lysosome related genes, Clinical pathological features, Prognostic model
图1 基因的差异表达分析。1A：差异分析火山图；1B：密度热图；1C：模块-结直肠癌相关性热图
图2 共有基因的富集。2A：韦恩图；2B：GO富集弦图；2C：KEGG富集网络图
图3 预后风险模型的构建及验证。3A：单因素Cox回归分析森林图；3B：lambda系数与回归系数和偏差值的关系图；3C：风险分数分布图；3D：生存状态分布图；3E：K-M曲线；3F：ROC曲线
图4 临床预后风险模型构建及评估。4A~4E：预后基因表达热图；4F：单因素Cox回归分析森林图；4G：多因素Cox回归分析森林图；4H：列线图；4I：DCA曲线
图5 肿瘤微环境、免疫细胞生存分析的评价。5A：免疫细胞浸润丰度柱状图；5B：雷达图；5C~5G：不同免疫细胞浸润水平分层的总生存期K-M曲线：5C表示M0型巨噬细胞，5D表示浆细胞，5E表示记忆活化CD4+T细胞，5F表示记忆静息CD4+T细胞，5G表示调节性T细胞
图6 ATP6V1G2、DLG4及LZTS1免疫组化结果图。6A：癌旁组织中ATP6V1G2蛋白低表达；6B：癌组织中ATP6V1G2蛋白高表达；6C：癌旁组织中DLG4蛋白低表达；6D：癌组织中DLG4蛋白高表达；6E：癌旁组织中LZTS1蛋白高表达；6F：癌组织中LZTS1蛋白低表达
图7 结直肠癌癌组织及癌旁正常组织中ATP6V1G2、DLG4及LZTS1蛋白表达量分析。7A：ATP6V1G2在结直肠癌及癌旁正常组织中的相对蛋白表达；7B：DLG4在结直肠癌及癌旁正常组织中的相对蛋白表达；7C：LZTS1在结直肠癌及癌旁正常组织中的相对蛋白表达
图8 结直肠癌癌组织及癌旁正常组织中ATP6V1G2、DLG4及LZTS1mRNA表达水平分析。8A：ATP6V1G2在结直肠癌及癌旁正常组织中的相对mRNA表达；8B：DLG4在结直肠癌及癌旁正常组织中的相对mRNA表达；8C：LZTS1在结直肠癌及癌旁正常组织中的相对mRNA表达
表1 ATP6V1G2、DLG4及LZTS1与结直肠癌临床病理特征的关系
临床病理 ATP6V1G2 χ2值 P值 DLG4 χ2值 P值 LZTS1 χ2值 P值
高表达 低表达 高表达 低表达 高表达 低表达
（n=30） （n=20） （n=27） （n=23） （n=15） （n=35）
分化 0.347 0.556 1.087 0.297 0.397 0.529
低-中分化 11 9 9 11 5 15
高分化 19 11 18 12 10 20
年龄（岁） 4.844 0.028 6.332 0.012 6.445 0.011
≤60 10 13 8 15 11 12
＞60 20 7 19 8 4 23
性别 0.855 0.355 1.342 0.247 0.244 0.621
男性 14 12 12 14 7 19
女性 16 8 15 9 8 16
肿瘤大小（cm） 0.215 0.643 0.057 0.811 0.464 0.496
≤5 13 10 12 11 8 15
＞5 17 10 15 12 7 20
浸润深度 5.333 0.021 6.522 0.011 6.095 0.014
T1+T2 11 14 9 16 12 13
T3+T4 19 6 18 7 3 22
有无淋巴结转移 7.065 0.008 5.265 0.022 5.224 0.022
无 11 15 10 16 12 14
有 19 5 17 7 3 21
图9 ATP6V1G2、DLG4高表达及LZTS1低表达与患者预后的关系。9A~9C：分别为ATP6V1G2、DLG4、LZTS1高低表达组的总生存期曲线
图10 ATP6V1G2、DLG4及LZTS1三者单独及联合诊断结直肠癌的受试者操作曲线
表2 ATP6V1G2、DLG4及LZTS1表达对结直肠癌的诊断价值
检测指标 截断值 AUC P值 95%置信区间 敏感度（%） 特异度（%）
ATP6V1G2 3.5 0.821 ＜0.001 0.741~0.902 60 86
DLG4 2.5 0.818 ＜0.001 0.736~0.901 72 78
LZTS1 1.5 0.775 ＜0.001 0.686~0.865 46 96
三者联合 0.855 ＜0.001 0.783~0.928 78 84
图11 ATP6V1G2、DLG4及LZTS1三者单独及联合预测结直肠癌预后的受试者操作曲线
表3 ATP6V1G2、DLG4及LZTS1表达对结直肠癌预后的预测价值
检测指标 截断值 AUC P值 95%置信区间 敏感度（%） 特异度（%）
ATP6V1G2 5 0.886 ＜0.001 0.802~0.991 90 77
DLG4 3.5 0.781 0.003 0.620~0.890 84 65
LZTS1 3.5 0.667 0.029 0.537~0.834 95 45
三者联合 0.894 ＜0.001 0.801~0.988 90 77
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