The First Affiliated Hospital, Cardiovascular Lab of Big Data and lmaging Artificial Intelligence, Hengyang Medical School, University of South China Hengyang, Hunan, 421001, China; School of Computer, University of South China, Hengyang, Hunan, 421001, China; Department of Gastroenterology and Hepatology, Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, 150081, China.
The First Affiliated Hospital, Cardiovascular Lab of Big Data and lmaging Artificial Intelligence, Hengyang Medical School, University of South China Hengyang, Hunan, 421001, China; College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.
Comput Biol Med. 2023 Sep;163:107078. doi: 10.1016/j.compbiomed.2023.107078. Epub 2023 May 29.
TP53 mutation and hypoxia play an essential role in cancer progression. However, the metabolic reprogramming and tumor microenvironment (TME) heterogeneity mediated by them are still not fully understood.
The multi-omics data of 32 cancer types and immunotherapy cohorts were acquired to comprehensively characterize the metabolic reprogramming pattern and the TME across cancer types and explore immunotherapy candidates. An assessment model for metabolic reprogramming was established by integration of multiple machine learning methods, including lasso regression, neural network, elastic network, and survival support vector machine (SVM). Pharmacogenomics analysis and in vitro assay were conducted to identify potential therapeutic drugs.
First, we identified metabolic subtype A (hypoxia-TP53 mutation subtype) and metabolic subtype B (non-hypoxia-TP53 wildtype subtype) in hepatocellular carcinoma (HCC) and showed that metabolic subtype A had an "immune inflamed" microenvironment. Next, we established an assessment model for metabolic reprogramming, which was more effective compared to the traditional prognostic indicators. Then, we identified a potential targeting drug, teniposide. Finally, we performed the pan-cancer analysis to illustrate the role of metabolic reprogramming in cancer and found that the metabolic alteration (MA) score was positively correlated with tumor mutational burden (TMB), neoantigen load, and homologous recombination deficiency (HRD) across cancer types. Meanwhile, we demonstrated that metabolic reprogramming mediated a potential immunotherapy-sensitive microenvironment in bladder cancer and validated it in an immunotherapy cohort.
Metabolic alteration mediated by hypoxia and TP53 mutation is associated with TME modulation and tumor progression across cancer types. In this study, we analyzed the role of metabolic alteration in cancer and propose a predictive model for cancer prognosis and immunotherapy responsiveness. We also explored a potential therapeutic drug, teniposide.
TP53 突变和缺氧在癌症进展中起着至关重要的作用。然而，它们介导的代谢重编程和肿瘤微环境（TME）异质性仍未被充分理解。
获取 32 种癌症类型和免疫治疗队列的多组学数据，全面描述癌症类型之间的代谢重编程模式和 TME，并探索免疫治疗候选物。通过整合多种机器学习方法，包括lasso 回归、神经网络、弹性网络和生存支持向量机（SVM），建立代谢重编程评估模型。进行药物基因组学分析和体外检测，以鉴定潜在的治疗药物。
首先，我们在肝细胞癌（HCC）中鉴定出代谢亚型 A（缺氧-TP53 突变亚型）和代谢亚型 B（非缺氧-TP53 野生型亚型），并表明代谢亚型 A 具有“免疫炎症”的微环境。然后，我们建立了一个代谢重编程评估模型，与传统预后指标相比，该模型更有效。接下来，我们鉴定出一种潜在的靶向药物替尼泊苷。最后，我们进行了泛癌症分析，以说明代谢重编程在癌症中的作用，发现代谢改变（MA）评分与癌症类型之间的肿瘤突变负担（TMB）、新抗原负荷和同源重组缺陷（HRD）呈正相关。同时，我们证明代谢重编程在膀胱癌中介导了一种潜在的免疫治疗敏感的微环境，并在免疫治疗队列中进行了验证。
缺氧和 TP53 突变介导的代谢改变与肿瘤类型之间的 TME 调节和肿瘤进展有关。在这项研究中，我们分析了代谢改变在癌症中的作用，并提出了一个用于癌症预后和免疫治疗反应性的预测模型。我们还探索了一种潜在的治疗药物，替尼泊苷。
