Colorectal Cancer: A Global Challenge
Colorectal cancer (CRC) is among the most common and deadly cancers worldwide. It accounts for 9.6% of all cancer cases and is the second leading cause of cancer-related death, responsible for around 900,000 deaths each year. By 2035, it is projected to affect 2.5 million people.
One major difficulty is its silent onset. Early CRC often shows no symptoms, making timely diagnosis difficult. Although therapeutic targets such as Nrf2 and ferroptosis-related genes (GSH, GPX4, and P53) have been identified, the lack of reliable biomarkers continues to hinder prognosis and treatment.
Why Telomere Maintenance Genes?
Telomeres are protective structures at the ends of chromosomes that preserve genetic stability. With each cell division, they shorten, which is strongly linked to cancer development. To prevent this, cells activate telomerase or use alternative lengthening of telomeres (ALT).
Mutations in telomere maintenance genes (TMGs), such as changes in the TERT promoter, play roles in tumor initiation. While studied in other cancers, their value in CRC prognosis and treatment response has been unclear.
The Study Design
Researchers led by Zhikai Wang, Chunyan Zhao, Yifen Huang, and Chong Li systematically examined TMGs in CRC using data from TCGA and GEO databases.
They used:
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ssGSEA to calculate TMG scores.
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Consensus clustering to identify CRC subtypes.
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Cox and Lasso regression to build a risk model.
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Kaplan–Meier curves and ROC analysis for survival predictions.
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Cell-based experiments to validate gene function.
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TIDE and pRRophetic tools to predict immunotherapy and chemotherapy responses.
Key Findings
Higher TMG Activity in Tumors
TMG scores were significantly higher in tumors compared to normal tissues. From these, 28 TMGs were linked to prognosis. Mutations were relatively rare (~24%), with SNAI1 and RBL1 most often amplified.
Two CRC Subtypes
Two subtypes emerged:
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C1: Better survival, enriched in cell cycle and DNA replication pathways.
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C2: Worse survival, enriched in metastasis-related pathways.
Seven-Gene Risk Model
A model was built with CDC25C, CXCL1, RTL8C, FABP4, ITLN1, MUC12, and ERI1.
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High-risk patients had shorter survival times.
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The model achieved an AUC of 0.72 for 5-year survival.
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Validation in an external dataset confirmed its accuracy.
Functional Role of MUC12
qRT-PCR showed that most model genes were upregulated in CRC cells, while ITLN1 and ERI1 were downregulated. Silencing MUC12 significantly reduced CRC cell migration and invasion, confirming its role in tumor spread.
Immune Landscape Differences
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Low-risk patients: Higher infiltration of immune cells (CD8+ T cells, NK cells, activated B cells).
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High-risk patients: Fewer effector cells, more regulatory T cells, indicating a “cold” immune environment.
Therapy Predictions
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Low-risk patients: More likely to respond to immunotherapy, with higher PD-L1 expression and lower TIDE scores.
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High-risk patients: Predicted to respond better to conventional drugs such as Sorafenib and Phenformin.
Implications for CRC Treatment
The results suggest TMGs can stratify patients by prognosis and therapy response.
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Low-risk patients may benefit from immune checkpoint blockade therapy.
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High-risk patients may need chemotherapy or targeted agents.
The dual behavior of MUC12 is noteworthy: higher expression correlated with longer survival in clinical data but promoted invasion in vitro, suggesting its role may vary by disease stage or environment.
Limitations
The authors note that:
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Retrospective data may introduce bias.
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Functional experiments mainly focused on MUC12.
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Mechanisms linking TMGs and immune evasion require deeper study.
Future research should include prospective clinical validation and mechanistic studies using multi-omics and animal models.
Conclusion
This is the first systematic analysis of TMGs in CRC. The seven-gene model accurately predicted survival, immune landscape, and treatment response across cohorts.
As the authors conclude, the findings “provide novel insights that could inform personalized therapeutic strategies” for CRC.
The translation of the preceding English text in Chinese:
结直肠癌：全球挑战
结直肠癌（CRC）是全球最常见和最致命的癌症之一，占所有癌症病例的9.6%，并且是癌症相关死亡的第二大原因，每年约有90万人死于此病。到2035年，预计将有250万人受到影响。
其中一个主要困难是其隐匿性发病。早期结直肠癌通常没有症状，导致及时诊断困难。尽管已经确定了如Nrf2和与铁死亡相关的基因（如GSH、GPX4和P53）等治疗靶点，但缺乏可靠的生物标志物仍然阻碍了预后和治疗。
为什么选择端粒维持基因？
端粒是位于染色体末端的保护性结构，保持遗传稳定性。每次细胞分裂时，端粒都会缩短，这与癌症的发生密切相关。为防止这一现象，细胞会激活端粒酶或使用端粒的替代延长（ALT）机制。
端粒维持基因（TMGs）的突变，如TERT启动子的变化，在肿瘤的启动中起作用。虽然在其他癌症中已有研究，但它们在结直肠癌预后和治疗反应中的价值尚不明确。
研究设计
由Zhikai Wang、Chunyan Zhao、Yifen Huang和Chong Li领导的研究人员，系统地使用TCGA和GEO数据库的数据，研究了结直肠癌中的端粒维持基因。
他们使用了以下方法：
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ssGSEA 计算TMG评分。
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共识聚类识别结直肠癌亚型。
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Cox和Lasso回归建立风险模型。
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Kaplan-Meier曲线和ROC分析用于生存预测。
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基于细胞的实验验证基因功能。
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使用TIDE和pRRophetic工具预测免疫治疗和化疗反应。
主要发现
肿瘤中的TMG活性较高
TMG评分在肿瘤中的显著高于正常组织。从这些基因中，28个TMG与预后相关。突变相对较少（约24%），其中SNAI1和RBL1最常见的是扩增。
两个结直肠癌亚型
出现了两个亚型：
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C1：生存较好，富含细胞周期和DNA复制通路。
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C2：生存较差，富含转移相关通路。
七基因风险模型
建立了一个包括CDC25C、CXCL1、RTL8C、FABP4、ITLN1、MUC12和ERI1的模型。
高风险患者的生存时间较短。
该模型在5年生存期的AUC为0.72。
在外部数据集中的验证证实了其准确性。
MUC12的功能角色
qRT-PCR显示，大多数模型基因在结直肠癌细胞中上调，而ITLN1和ERI1下调。沉默MUC12显著降低了结直肠癌细胞的迁移和侵袭，确认了其在肿瘤扩散中的作用。
免疫环境差异
低风险患者：免疫细胞（CD8+ T细胞、NK细胞、激活的B细胞）浸润较高。
高风险患者：效应细胞较少，调节性T细胞较多，表明“冷”免疫环境。
治疗预测
低风险患者：更可能对免疫治疗产生反应，具有较高的PD-L1表达和较低的TIDE评分。
高风险患者：预计对常规药物如索拉非尼和非那雄胺反应较好。
结直肠癌治疗的意义
研究结果表明，TMGs可以根据预后和治疗反应对患者进行分层。
低风险患者可能从免疫检查点抑制治疗中受益。
高风险患者可能需要化疗或靶向药物。
MUC12的双重行为值得注意：在临床数据中，较高的表达与更长的生存期相关，但在体外促进侵袭，表明其作用可能因疾病阶段或环境而异。
局限性
作者指出：
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回顾性数据可能引入偏倚。
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功能实验主要集中在MUC12。
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端粒维持基因与免疫逃逸机制之间的联系需要更深入的研究。
未来的研究应包括前瞻性的临床验证和机制研究，结合多组学和动物模型。
结论
这是首个系统分析结直肠癌中端粒维持基因的研究。七基因模型准确预测了各队列中的生存、免疫环境和治疗反应。
正如作者总结的，“这些发现为结直肠癌的个性化治疗策略提供了新的见解。”
Reference:
Zhikai Wang, Chunyan Zhao, Yifen Huang, Chong Li
Role of telomere maintenance genes as a predictive biomarker for colorectal cancer immunotherapy response and prognosis.
Biomol Biomed [Internet]. 2025 Jul. 2 [cited 2025 Sep. 10];
Available from: https://www.bjbms.org/ojs/index.php/bjbms/article/view/12053
Additional information:
We invite submissions for our upcoming thematic issues, including:
- Immune Prediction and Prognostic Biomarkers in Immuno-Oncology
- Artificial Intelligence and Machine Learning in disease diagnosis and treatment target identification
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Editor: Merima Hadžić
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