Diagnostic blood RNA profiles for human acute spinal cord injury

Abstract

Diagnosis of spinal cord injury (SCI) severity at the ultra-acute stage is of great importance for emergency clinical care of patients as well as for potential enrollment into clinical trials. The lack of a diagnostic biomarker for SCI has played a major role in the poor results of clinical trials. We analyzed global gene expression in peripheral white blood cells during the acute injury phase and identified 197 genes whose expression changed after SCI compared with healthy and trauma controls and in direct relation to SCI severity. Unsupervised coexpression network analysis identified several gene modules that predicted injury severity (AIS grades) with an overall accuracy of 72.7% and included signatures of immune cell subtypes. Specifically, for complete SCIs (AIS A), ROC analysis showed impressive specificity and sensitivity (AUC: 0.865). Similar precision was also shown for AIS D SCIs (AUC: 0.938). Our findings indicate that global transcriptomic changes in peripheral blood cells have diagnostic and potentially prognostic value for SCI severity.

Peripheral white blood cell responses as emerging biomarkers for patient stratification and prognosis in acute spinal cord injury

“…Irrespective of these knowledge gaps, receiver-operator characteristics and precision-recall analyses confirmed that, when modelled appropriately, both neutrophilia and lymphopenia can serve as independent prognostic markers of long-term neurological recovery in acute SCI [ 45 ▪▪ ]. These latter findings are consistent with the reports of others that increased neutrophil-to-lymphocyte ratios may be predictive of poor neurological recovery after SCI [ 50 ▪▪ , 51 ]. Possible use of blood products in acute care may require some additional consideration here, as transfusion-related leukocytosis can occur in critically ill patients [ 52 ], although that also does not necessarily confound the use of WBC differentials as biomarkers for SCI prognostication.…”

“…Propelled by modern omics, multidimensional analysis and integrated bioinformatics approaches, this work has already begun. Specifically, and taking advantage of the fact that WBCs both sense and rapidly response to damage-associated molecular patterns (DAMPs), Kyritsis et al analysed the transcriptome of circulating WBC that were harvested from SCI patients [ 50 ▪▪ ]. Unbiased gene expression analysis indeed showed that patients with SCIs have divergent gene expression profiles, not only from healthy controls but also nonneurological trauma, with nearly 200 differentially expressed genes; these genes were also associated with SCI severity.…”

Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury

“…We focused on three classification tasks: a model predicting AIS improvement of at least 1 grade at discharge, a model predicting AIS A at discharge, and a model predicting AIS D at discharge. We chose to predict AIS A and D instead of a multiclass prediction of the AIS at discharge in concordance to our previous studies 42 and because of the low representation of other grades in our dataset (table 1). For each of the three classification tasks, we performed an exhaustive search of all possible additive models with at least one of the predictors of interest: quadratic aMAP, aHR, length of surgery, days from surgery to discharge, age, AIS grade at admission, dichotomized NLI (Cervical, non-Cervical), time of MAP out of range 76-104, and time of MAP out of range 76-117.…”

Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org)

“…Preclinical SCI research produces diverse neuromotor recovery behavioral measures in rats, mice, nonhuman primates, and pooled de-identified human data. These neuro-behavioral data are often combined with histopathological ratings of postmortem tissue, and variety of molecular endpoints with data often collected in an ad hoc fashion in the same individuals over time (e.g., Ferguson et al, 2013;Kyritsis et al, 2021). Both clinical and preclinical research have worked to promote the cultural adoption of data sharing and standardization in the SCI research community after many years of collective action.…”