Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
Department of Computational Biology, Indraprastha Institute of Information Technology, Okhla Phase 3, New Delhi, 110020, India.
Comput Biol Med. 2023 Dec;167:107594. doi: 10.1016/j.compbiomed.2023.107594. Epub 2023 Oct 18.
Advancements in cancer immunotherapy have shown significant outcomes in treating cancers. To design effective immunotherapy, it's important to understand immune response of a patient based on its genomic profile. However, analyses to do that requires proficiency in the bioinformatic methods. Swiftly growing sequencing technologies and statistical methods create a blockage for the scientists who want to find the biomarkers for different cancers but don't have detailed knowledge of coding or tool. Here, we are providing a web-based resource that gives scientists with no bioinformatics expertise, the ability to obtain the prognostic biomarkers for different cancer types at different levels. We computed prognostic biomarkers from 8346 cancer patients for twenty cancer types. These biomarkers were computed based on i) presence of 352 Human leukocyte antigen class-I, ii) 660959 tumor-specific HLA1 neobinders, and iii) expression profile of 153 cytokines. It was observed that survival risk of cancer patients depends on presence of certain type of HLA-I alleles; for example, liver hepatocellular carcinoma patients with HLA-A03:01 are at lower risk. Our analysis indicates that neobinders of HLA-I alleles have high correlation with overall survival of certain type of cancer patients. For example, HLA-B07:02 binders have 0.49 correlation with survival of lung squamous cell carcinoma and -0.77 with kidney chromophobe patients. Additionally, we computed prognostic biomarkers based on cytokine expressions. Higher expression of few cytokines is survival favorable like IL-2 for bladder urothelial carcinoma, whereas IL-5R is survival unfavorable for kidney chromophobe patients. Freely accessible to public, CancerHLA-I maintains raw and analysed data (https://webs.iiitd.edu.in/raghava/cancerhla1/).
癌症免疫疗法的进展在治疗癌症方面显示出了显著的效果。为了设计有效的免疫疗法，根据患者的基因组谱了解其免疫反应是很重要的。然而，进行这种分析需要精通生物信息学方法。快速发展的测序技术和统计方法为那些想要找到不同癌症生物标志物但不具备编码或工具详细知识的科学家们设置了障碍。在这里，我们提供了一个基于网络的资源，使没有生物信息学专业知识的科学家能够在不同的层次上获得不同癌症类型的预后生物标志物。我们为 20 种癌症类型的 8346 名癌症患者计算了预后生物标志物。这些生物标志物是基于以下三种情况计算的：i）352 种人类白细胞抗原 I 类的存在，ii）660959 种肿瘤特异性 HLA1 新结合物，以及 iii）153 种细胞因子的表达谱。结果表明，癌症患者的生存风险取决于某些类型 HLA-I 等位基因的存在；例如，具有 HLA-A03:01 的肝癌患者风险较低。我们的分析表明，HLA-I 等位基因的新结合物与某些类型癌症患者的总生存率有很高的相关性。例如，HLA-B07:02 结合物与肺鳞状细胞癌的生存率有 0.49 的相关性，而与肾嫌色细胞瘤患者的生存率有-0.77 的相关性。此外，我们还根据细胞因子的表达计算了预后生物标志物。少数细胞因子的高表达对生存有利，如膀胱癌中的 IL-2，而 IL-5R 对肾嫌色细胞瘤患者的生存不利。CancerHLA-I 可公开获取原始数据和分析数据（https://webs.iiitd.edu.in/raghava/cancerhla1/）。
