Liu Cong, Liu Dingwei, Wang Fangfei, Xie Jun, Liu Yang, Wang Huan, Rong Jianfang, Xie Jinliang, Wang Jinyun, Zeng Rong, Zhou Feng, Peng Jianxiang, Xie Yong
Department of Gastroenterology, Digestive Disease Hospital, The First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
Gastroenterology Institute of Jiangxi Province, Nanchang, Jiangxi, China.
Front Cell Dev Biol. 2022 Aug 23;10:971992. doi: 10.3389/fcell.2022.971992. eCollection 2022.
Colon adenocarcinoma (COAD), a malignant gastrointestinal tumor, has the characteristics of high mortality and poor prognosis. Even in the presence of oxygen, the Warburg effect, a major metabolic hallmark of almost all cancer cells, is characterized by increased glycolysis and lactate fermentation, which supports biosynthesis and provides energy to sustain tumor cell growth and proliferation. However, a thorough investigation into glycolysis- and lactate-related genes and their association with COAD prognosis, immune cell infiltration, and drug candidates is currently lacking. COAD patient data and glycolysis- and lactate-related genes were retrieved from The Cancer Genome Atlas (TCGA) and Gene Set Enrichment Analysis (GSEA) databases, respectively. After univariate Cox regression analysis, a nonnegative matrix factorization (NMF) algorithm was used to identify glycolysis- and lactate-related molecular subtypes. Least absolute shrinkage and selection operator (LASSO) Cox regression identified twelve glycolysis- and lactate-related genes (ADTRP, ALDOB, APOBEC1, ASCL2, CEACAM7, CLCA1, CTXN1, FLNA, NAT2, OLFM4, PTPRU, and SNCG) related to prognosis. The median risk score was employed to separate patients into high- and low-risk groups. The prognostic efficacy of the glycolysis- and lactate-related gene signature was assessed using Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) curve analyses. The nomogram, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC) were employed to improve the clinical applicability of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on differentially expressed genes (DEGs) from the high- and low-risk groups. Using CIBERSORT, ESTIMATE, and single-sample GSEA (ssGSEA) algorithms, the quantities and types of tumor-infiltrating immune cells were assessed. The tumor mutational burden (TMB) and cytolytic (CYT) activity scores were calculated between the high- and low-risk groups. Potential small-molecule agents were identified using the Connectivity Map (cMap) database and validated by molecular docking. To verify key core gene expression levels, quantitative real-time polymerase chain reaction (qRT-PCR) assays were conducted. We identified four distinct molecular subtypes of COAD. Cluster 2 had the best prognosis, and clusters 1 and 3 had poor prognoses. High-risk COAD patients exhibited considerably poorer overall survival (OS) than low-risk COAD patients. The nomogram precisely predicted patient OS, with acceptable discrimination and excellent calibration. GO and KEGG pathway enrichment analyses of DEGs revealed enrichment mainly in the "glycosaminoglycan binding," "extracellular matrix," "pancreatic secretion," and "focal adhesion" pathways. Patients in the low-risk group exhibited a larger infiltration of memory CD4+ T cells and dendritic cells and a better prognosis than those in the high-risk group. The chemotherapeutic agent sensitivity of patients categorized by risk score varied significantly. We predicted six potential small-molecule agents binding to the core target of the glycolysis- and lactate-related gene signature. ALDOB and APOBEC1 mRNA expression was increased in COAD tissues, whereas CLCA1 and OLFM4 mRNA expression was increased in normal tissues. In summary, we identified molecular subtypes of COAD and developed a glycolysis- and lactate-related gene signature with significant prognostic value, which benefits COAD patients by informing more precise and effective treatment decisions.
结肠腺癌（COAD）是一种恶性胃肠道肿瘤，具有高死亡率和预后差的特点。即使在有氧的情况下，几乎所有癌细胞的主要代谢特征——瓦博格效应，其特点是糖酵解增加和乳酸发酵增加，这支持生物合成并提供能量以维持肿瘤细胞的生长和增殖。然而，目前缺乏对糖酵解和乳酸相关基因及其与COAD预后、免疫细胞浸润和候选药物的关联的深入研究。分别从癌症基因组图谱（TCGA）和基因集富集分析（GSEA）数据库中检索COAD患者数据以及糖酵解和乳酸相关基因。经过单因素Cox回归分析后，使用非负矩阵分解（NMF）算法来识别糖酵解和乳酸相关的分子亚型。最小绝对收缩和选择算子（LASSO）Cox回归确定了十二个与预后相关的糖酵解和乳酸相关基因（ADTRP、ALDOB、APOBEC1、ASCL2、CEACAM7、CLCA1、CTXN1、FLNA、NAT2、OLFM4、PTPRU和SNCG）。采用中位风险评分将患者分为高风险组和低风险组。使用Kaplan-Meier（KM）生存分析和受试者工作特征（ROC）曲线分析评估糖酵解和乳酸相关基因特征的预后效果。使用列线图、校准曲线、决策曲线分析（DCA）和临床影响曲线（CIC）来提高预后特征的临床适用性。对高风险组和低风险组的差异表达基因（DEG）进行基因本体（GO）和京都基因与基因组百科全书（KEGG）通路富集分析。使用CIBERSORT、ESTIMATE和单样本GSEA（ssGSEA）算法评估肿瘤浸润免疫细胞的数量和类型。计算高风险组和低风险组之间的肿瘤突变负荷（TMB）和细胞溶解（CYT）活性评分。使用连通性图谱（cMap）数据库识别潜在的小分子药物，并通过分子对接进行验证。为了验证关键核心基因的表达水平，进行了定量实时聚合酶链反应（qRT-PCR）检测。我们确定了COAD的四种不同分子亚型。第2组预后最佳，第1组和第3组预后较差。高风险COAD患者的总生存期（OS）明显低于低风险COAD患者。列线图精确预测了患者的OS，具有可接受的区分度和良好的校准度。对DEG的GO和KEGG通路富集分析显示主要富集在“糖胺聚糖结合”、“细胞外基质”、“胰腺分泌”和“粘着斑”通路。低风险组患者的记忆性CD4 + T细胞和树突状细胞浸润较多，预后比高风险组患者好。根据风险评分分类的患者对化疗药物的敏感性差异显著。我们预测了六种与糖酵解和乳酸相关基因特征的核心靶点结合的潜在小分子药物。ALDOB和APOBEC1 mRNA在COAD组织中的表达增加，而CLCA1和OLFM4 mRNA在正常组织中的表达增加。总之，我们确定了COAD的分子亚型，并开发了具有显著预后价值的糖酵解和乳酸相关基因特征，通过为更精确有效的治疗决策提供信息，使COAD患者受益。
