Singh Anjana, Prakash Ved, Gupta Nikhil, Kumar Ashish, Kant Ravi, Kumar Dinesh
All India Institute of Medical Sciences (AIIMS), Rishikesh, Uttarakhand 249201, India.
Pulmonary & Critical Care Medicine, King George's Medical University, Lucknow, Uttar Pradesh 226003, India.
ACS Omega. 2022 Jan 31;7(6):5510-5520. doi: 10.1021/acsomega.1c06941. eCollection 2022 Feb 15.
Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89-1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with -value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.
肺癌（LC）中代谢紊乱的检测有助于早期诊断/预后评估，从而通过可靠的分级、分期以及确定LC的新辅助状态来改善疾病管理策略。然而，以前的大多数代谢组学研究比较的是归一化光谱特征，这不仅提供的信息模糊，还进一步限制了这些信息的临床转化。通过使用商业软件Chenomx以甲酸作为内标对各种代谢物进行浓度分析，可以解决各种此类问题。我们继续在这一方向上努力，使用高场800 MHz核磁共振波谱对39例LC患者和42例正常对照（NCs，年龄/性别匹配）进行血清代谢谱测定，并使用多元统计分析工具进行比较，以识别代谢紊乱和具有诊断潜力的代谢物。偏最小二乘判别分析（PLS-DA）模型显示LC组和NC组之间有明显区分，且具有出色的判别能力，受试者操作特征曲线下面积（AUROC）= 0.97 [95% CI = 0.89 - 1.00]。首先使用投影变量重要性（VIP）评分分析确定有助于区分LC和NC样本的代谢特征，然后使用ROC曲线分析检查其统计学意义（P值< 0.05）和诊断潜力。分析揭示了与LC相关的相关代谢紊乱。在各种循环代谢物中，发现包括组氨酸、谷氨酰胺、甘氨酸、苏氨酸、丙氨酸和缬氨酸在内的六种代谢物具有适用于临床意义的诊断潜力。这些代谢改变表明LC中葡萄糖代谢改变、脂肪酸合成异常以及包括活跃谷氨酰胺分解在内的各种氨基酸利用增加。
