Wang Changjing, Tang Yujie, Ma Hongqing, Wei Sisi, Hu Xuhua, Zhao Lianmei, Wang Guiying
Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
The Second Department of Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China.
Front Genet. 2022 Jun 17;13:919389. doi: 10.3389/fgene.2022.919389. eCollection 2022.
Immunotherapy is a treatment that can significantly improve the prognosis of patients with colon cancer, but the response to immunotherapy is different in patients with colon cancer because of the heterogeneity of colon carcinoma and the complex nature of the tumor microenvironment (TME). In the precision therapy mode, finding predictive biomarkers that can accurately identify immunotherapy-sensitive types of colon cancer is essential. Hypoxia plays an important role in tumor proliferation, apoptosis, angiogenesis, invasion and metastasis, energy metabolism, and chemotherapy and immunotherapy resistance. Thus, understanding the mechanism of hypoxia-related genes (HRGs) in colon cancer progression and constructing hypoxia-related signatures will help enrich our treatment strategies and improve patient prognosis. We obtained the gene expression data and corresponding clinical information of 1,025 colon carcinoma patients from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, respectively. We identified two distinct hypoxia subtypes (subtype A and subtype B) according to unsupervised clustering analysis and assessed the clinical parameters, prognosis, and TME cell-infiltrating characteristics of patients in the two subtypes. We identified 1,132 differentially expressed genes (DEGs) between the two hypoxia subtypes, and all patients were randomly divided into the training group (n = 513) and testing groups (n = 512). Following univariate Cox regression with DEGs, we construct the prognostic model (HRG-score) including six genes (S1PR3, ETV5, CD36, FOXC1, CXCL10, and MMP12) through the LASSO-multivariate cox method in the training group. We comprehensively evaluated the sensitivity and applicability of the HRG-score model from the training group and the testing group, respectively. We explored the correlation between HRG-score and clinical parameters, tumor microenvironment, cancer stem cells (CSCs), and MMR status. In order to evaluate the value of the risk model in clinical application, we further analyzed the sensitivity of chemotherapeutics and immunotherapy between the low-risk group and high-risk group and constructed a nomogram for improving the clinical application of the HRG-score. Subtype A was significantly enriched in metabolism-related pathways, and subtype B was significantly enriched in immune activation and several tumor-associated pathways. The level of immune cell infiltration and immune checkpoint-related genes, stromal score, estimate score, and immune dysfunction and exclusion (TIDE) prediction score was significantly different in subtype A and subtype B. The level of immune checkpoint-related genes and TIDE score was significantly lower in subtype A than that in subtype B, indicating that subtype A might benefit from immune checkpoint inhibitors. Finally, an HRG-score signature for predicting prognosis was constructed through the training group, and the predictive capability was validated through the testing group. The survival analysis and correlation analysis of clinical parameters revealed that the prognosis of patients in the high-risk group was significantly worse than that in the low-risk group. There were also significant differences in immune status, mismatch repair status (MMR), and cancer stem cell index (CSC), between the two risk groups. The correlation analysis of risk scores with IC and IPS showed that patients in the low-risk group had a higher benefit from chemotherapy and immunotherapy than those in the high-risk group, and the external validation IMvigor210 demonstrated that patients with low risk were more sensitive to immunotherapy. We identified two novel molecular subgroups based on HRGs and constructed an HRG-score model consisting of six genes, which can help us to better understand the mechanisms of hypoxia-related genes in the progression of colon cancer and identify patients susceptible to chemotherapy or immunotherapy, so as to achieve precision therapy for colon cancer.
免疫疗法是一种能显著改善结肠癌患者预后的治疗方法,但由于结肠癌的异质性和肿瘤微环境(TME)的复杂性,结肠癌患者对免疫疗法的反应存在差异。在精准治疗模式下,寻找能够准确识别对免疫疗法敏感的结肠癌类型的预测性生物标志物至关重要。缺氧在肿瘤增殖、凋亡、血管生成、侵袭和转移、能量代谢以及化疗和免疫治疗耐药中起着重要作用。因此,了解缺氧相关基因(HRGs)在结肠癌进展中的机制并构建缺氧相关特征将有助于丰富我们的治疗策略并改善患者预后。我们分别从癌症基因组图谱(TCGA)和基因表达综合数据库(GEO)中获取了1025例结肠癌患者的基因表达数据和相应的临床信息。根据无监督聚类分析,我们确定了两种不同的缺氧亚型(A亚型和B亚型),并评估了这两种亚型患者的临床参数、预后和TME细胞浸润特征。我们确定了两种缺氧亚型之间的1132个差异表达基因(DEGs),所有患者被随机分为训练组(n = 513)和测试组(n = 512)。通过在训练组中使用LASSO多元cox方法对DEGs进行单变量Cox回归,我们构建了包含六个基因(S1PR3、ETV5、CD36、FOXC1、CXCL10和MMP12)的预后模型(HRG评分)。我们分别从训练组和测试组全面评估了HRG评分模型的敏感性和适用性。我们探讨了HRG评分与临床参数、肿瘤微环境、癌症干细胞(CSCs)和错配修复状态(MMR)之间的相关性。为了评估风险模型在临床应用中的价值,我们进一步分析了低风险组和高风险组之间化疗和免疫治疗的敏感性,并构建了一个列线图以改善HRG评分的临床应用。A亚型在代谢相关途径中显著富集,B亚型在免疫激活和几种肿瘤相关途径中显著富集。A亚型和B亚型在免疫细胞浸润水平、免疫检查点相关基因、基质评分、估计评分以及免疫功能障碍和排除(TIDE)预测评分方面存在显著差异。A亚型中免疫检查点相关基因水平和TIDE评分显著低于B亚型,表明A亚型可能从免疫检查点抑制剂中获益。最后,通过训练组构建了用于预测预后的HRG评分特征,并通过测试组验证了预测能力。临床参数的生存分析和相关性分析表明,高风险组患者的预后明显比低风险组差。两个风险组在免疫状态、错配修复状态(MMR)和癌症干细胞指数(CSC)方面也存在显著差异。风险评分与IC和IPS的相关性分析表明,低风险组患者比高风险组患者从化疗和免疫治疗中获益更高,外部验证IMvigor210表明低风险患者对免疫治疗更敏感。我们基于HRGs确定了两个新的分子亚组,并构建了一个由六个基因组成的HRG评分模型,这可以帮助我们更好地理解缺氧相关基因在结肠癌进展中的机制,并识别对化疗或免疫治疗敏感的患者,从而实现结肠癌的精准治疗。