Shenzhen People's Hospital, First Affiliated Hospital of Southern University of Science and Technology, Second Clinical Medicine College of Jinan University, Shenzhen, 518020, China.
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
BMC Genomics. 2021 Apr 16;22(1):275. doi: 10.1186/s12864-021-07576-4.
Sepsis is the major cause of death in Intensive Care Unit (ICU) globally. Molecular detection enables rapid diagnosis that allows early intervention to minimize the death rate. Recent studies showed that long non-coding RNAs (lncRNAs) regulate proinflammatory genes and are related to the dysfunction of organs in sepsis. Identifying lncRNA signature with absolute abundance is challenging because of the technical variation and the systematic experimental bias.
Cohorts (n = 768) containing whole blood lncRNA profiling of sepsis patients in the Gene Expression Omnibus (GEO) database were included. We proposed a novel diagnostic strategy that made use of the relative expressions of lncRNA pairs, which are reversed between sepsis patients and normal controls (eg. lncRNA > lncRNA in sepsis patients and lncRNA < lncRNA in normal controls), to identify 14 lncRNA pairs as a sepsis diagnostic signature. The signature was then applied to independent cohorts (n = 644) to evaluate its predictive performance across different ages and normalization methods. Comparing to common machine learning models and existing signatures, SepSigLnc consistently attains better performance on the validation cohorts from the same age group (AUC = 0.990 & 0.995 in two cohorts) and across different groups (AUC = 0.878 on average), as well as cohorts processed by an alternative normalization method (AUC = 0.953 on average). Functional analysis demonstrates that the lncRNA pairs in SepsigLnc are functionally similar and tend to implicate in the same biological processes including cell fate commitment and cellular response to steroid hormone stimulus.
Our study identified 14 lncRNA pairs as signature that can facilitate the diagnosis of septic patients at an intervenable point when clinical manifestations are not dramatic. Also, the computational procedure can be generalized to a standard procedure for discovering diagnostic molecule signatures.
脓毒症是全球重症监护病房（ICU）死亡的主要原因。分子检测可实现快速诊断，从而进行早期干预以最大限度地降低死亡率。最近的研究表明，长非编码 RNA（lncRNA）可调节促炎基因，并与脓毒症器官功能障碍有关。由于技术差异和系统实验偏差，确定 lncRNA 特征的绝对丰度具有挑战性。
纳入了基因表达综合数据库（GEO）中包含脓毒症患者全血 lncRNA 谱的队列（n=768）。我们提出了一种新的诊断策略，该策略利用 lncRNA 对的相对表达，即脓毒症患者与正常对照之间的逆转（例如，lncRNA 在脓毒症患者中>lncRNA，lncRNA 在正常对照中<lncRNA），从而鉴定出 14 个 lncRNA 对作为脓毒症诊断特征。然后将该特征应用于独立队列（n=644），以评估其在不同年龄和归一化方法下的预测性能。与常见的机器学习模型和现有特征相比，SepSigLnc 在来自同一年龄组的验证队列中始终表现出更好的性能（两个队列中的 AUC=0.990 和 0.995），并且在不同组中也表现出更好的性能（平均 AUC=0.878），以及通过替代归一化方法处理的队列（平均 AUC=0.953）。功能分析表明，SepsigLnc 中的 lncRNA 对功能相似，并且倾向于涉及相同的生物学过程，包括细胞命运决定和细胞对类固醇激素刺激的反应。
我们的研究确定了 14 个 lncRNA 对作为特征，可在临床表现不明显时有助于诊断脓毒症患者。此外，计算过程可以推广为发现诊断分子特征的标准程序。
