Abstract
Recent studies in patients with spinal cord injuries (SCIs) have confirmed the diagnostic potential of biofluid-based biomarkers, as a topic of increasing interest in relation to SCI diagnosis and treatment. This paper reviews the research progress and application prospects of recently identified SCI-related biomarkers. Many structural proteins, such as glial fibrillary acidic protein, S100-β, ubiquitin carboxy-terminal hydrolase-L1, neurofilament light, and tau protein were correlated with the diagnosis, American Spinal Injury Association Impairment Scale, and prognosis of SCI to different degrees. Inflammatory factors, including interleukin-6, interleukin-8, and tumor necrosis factor α, are also good biomarkers for the diagnosis of acute and chronic SCI, while non-coding RNAs (microRNAs and long non-coding RNAs) also show diagnostic potential for SCI. Trace elements (Mg, Se, Cu, Zn) have been shown to be related to motor recovery and can predict motor function after SCI, while humoral markers can reflect the pathophysiological changes after SCI. These factors have the advantages of low cost, convenient sampling, and ease of dynamic tracking, but are also associated with disadvantages, including diverse influencing factors and complex level changes. Although various proteins have been verified as potential biomarkers for SCI, more convincing evidence from large clinical and prospective studies is thus required to identify the most valuable diagnostic and prognostic biomarkers for SCI.
Key Words: biomarker, diagnosis, inflammatory cytokine, motor recovery, non-coding RNA, prognosis, spinal cord injury, structural protein, trace element
Introduction
Spinal cord injuries (SCIs) are severe complications of spinal injuries caused by e.g. car accidents and falls, which are accompanied by clinical manifestations including spinal cord concussion and spinal shock (McDonald and Sadowsky, 2002; Deng et al., 2020). SCIs cause devastating and long-term disabilities, and improving the prognostication of patients has thus become a long-standing goal in clinical research (Fakhoury, 2015; Eckert and Martin, 2017; Liu et al., 2017). The main clinically proven effective treatment strategies for SCI currently include early decompression, exercise rehabilitation, drug therapy, and cell transplantation (McDonald and Sadowsky, 2002; Guo, 2020; Huang et al., 2020; Sharif and Jazaib Ali, 2020). However, there is still a lack of timely prevention strategies for secondary SCI and of effective treatment measures to achieve satisfactory recovery (Hu et al., 2021).
At present, the presence of typical neurological symptoms combined with spinal cord imaging results are considered as effective and authoritative methods for diagnosing SCI (Mortazavi et al., 2015; Fan et al., 2018). However, the accurate judgement of injury severity and SCI prognosis based on imaging performance and physical examination are limited. Among the available imaging tools, magnetic resonance imaging (MRI) remains the most objective and authoritative method for diagnosing SCI, by providing high-resolution and contrast images of the injured spinal cord (Freund et al., 2019). However, early MRI is not always suitable or accessible in patients with unstable SCIs (Wang et al., 2018; Albayar et al., 2019; Seif et al., 2019). Regarding physical examination, although the International Standards for Neurological Classification of Spinal Cord Injury examination is generally considered as the gold standard and is widely applied for the assessment of SCI severity and prognosis (Hales et al., 2015; Chun et al., 2020), this relatively subjective evaluation has certain limitations associated with its time-consuming nature and the need for patient cooperation, which is not applicable to patients in a state of coma, sedation, or spinal shock (Thomas and Murphy, 2018).
The above limitations indicate the urgent need to find other auxiliary indicators to accurately evaluate the degree of injury and clinical prognosis of patients with SCI. Numerous recent studies have revealed that some structural proteins and inflammatory factors in cerebrospinal fluid (CSF) and serum can reflect the degree of SCI (Badhiwala et al., 2018). However, although many researchers have reviewed the changes of these biomarkers in SCI, few have focused on which biomarkers are the most beneficial for the diagnosis of SCI, the grading of American Spinal Injury Association (ASIA) Impairment Scale (AIS), and for assessing the prognosis of SCI, which are the most important clinical applications of biomarkers. In this review, we summarize and review recent progress in relation to the common biofluid-based biomarkers with applications in SCI.
Search Strategy and Selection Criteria
HDW and ZJW performed a literature search of PubMed, Web of Science, and Google Scholar using the following search terms: “spinal cord injury” “biomarkers” “GFAP” “S100β” “NSE” “UCH-L1” “NF” “tau” “MMPs” “IL-6” “IL-8” “TNFα” “IL-1β” “IGF-1” “TGF-β” “non-coding RNAs” “miRNAs” “lncRNAs”. Articles focusing on the role of biomarkers in the diagnosis, AIS classification, and motor recovery of SCI were included. Overall, 75 studies published between 2002 and 2021 were included.
Structural Biomarkers for Spinal Cord Injury
During SCI, neuronal and glial cell damage results in the release of cellular components into the CSF and peripheral circulation through the ruptured blood-spinal cord barrier. The concentrations of these cellular components change over time, making them potentially useful indicators of the severity of SCI and recovery prognosis. Protein biomarkers can conventionally be divided into structural and inflammation-related biomarkers (Kwon et al., 2019). The different pathophysiological processes involved in producing these biomarkers include astroglial injury (S100 calcium-binding protein β, S100β; glial fibrillary acidic protein, GFAP), neuronal cell-body damage (neuron-specific enolase, NSE; ubiquitin C-terminal hydrolase-L1, UCH-L1), axonal damage (neurofilament proteins, NF; myelin basic protein, MBP), and dendritic damage (microtubule-associated protein) (Wang et al., 2018) (Figure 1). Based on the current research results, we divided protein biomarkers into (1) biomarkers related to the diagnosis of SCI, (2) biomarkers related to American Spinal Injury Association (ASIA) Impairment Scale (AIS) classification, and (3) biomarkers related to the prognosis of SCI. We have summarized and discussed the biomarkers in each category, with the aim of identifying two or three biomarkers or their combinations with the greatest clinical potential (Table 1).
Figure 1.

Classification of spinal cord injury (SCI) biomarkers.
SCI biomarkers can be divided into several categories, including structural proteins, inflammatory cytokines, non-coding RNAs, and microvesicles. Protein SCI biomarkers can be divided into three categories based on the processes in which they are involved: astroglial injury (S100β, GFAP), neuronal cell body injury (NSE, MBP), and axonal damage (NF). GFAP: glial fibrillary acidic protein: IL: interleukin; lncRNA: long non-coding RNA; miRNA: microRNA; NF: neurofilament proteins; TNFα: tumor necrosis factor α.
Table 1.
Summary of structural proteins and inflammatory cytokines in recent human SCI studies published during 2010–2021
| Reference | Phase of SCI | Testing method | Biomarker | Groups and sample size | Type of sample | AIS grade | Time of sampling | Role in diagnosis of SCI | Role in AIS grading of SCI | Role in the prediction of SCI recovery | Conclusion | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|
| ||||||||||||
| Control group | Study group | |||||||||||
| Kwon et al., 2010 | Acute and subacute | Bio-Plex system (Bio-Rad, Hercules, CA, USA) using a 25-plex human cytokine kit and Meso Scale Discovery® platform; ELISA | IL-6, IL-8, MCP1, Tau, S100β, GFAP | 27 patients with complete SCI (ASIA A) or incomplete SCI (ASIA B or C) | CSF | A–C | At 0–24, 24–48, 72–96 and > 96 h | Not described | CSF, IL-6, IL-8, MCP-1, tau, s100beta, and GFAP levels were elevated in a severity-dependent fashion. A biochemical model using s100beta, GFAP, and IL-8 can predict injury severity (ASIA A, B, or C) | Segmental motor recovery at 6 mon post- injury was better predicted by these CSF proteins than with the patients’ baseline ASIA grade | CSF, IL-6, IL-8, MCP-1, tau, s100beta, and GFAP can stratify injury severity and predict neurological outcome | |
| Hayakawa et al., 2012 | Acute and subacute | ELISA | Pnf-H | 14 patients with acute cervical SCI | Plasma | A–D | At 6, 12, 18, 24, 48, 72 and 96 h, and 6, 8,10, 14 and 21 d after injury | Not described | Plasma pnf-H level was elevated in accordance with the severity | Not described | Pnf-H is a potential biomarker to independently distinguish AIS A patients (complete SCI) from AIS C–E patients (incomplete SCI) | |
| Pouw et al., 2014 | Acute | ELISA | NSE, S100β, NF-H, GFAP | 16 patients with traumatic SCI | CSF | A–D | Within 24 h after injury | Not described | CSF, NSE, S100β, NF-H concentration in ’motor complete’ (AIS A, B) patients than ’motor incomplete’ (AIS C, D) patients | Not described | Structural CSF biomarkers NSE, S-100β and NFH were different between motor complete and motor incomplete SCI patients | |
| Wolf et al., 2014 | Acute | ELISA | NSE, S100β | 29 patients with proximal femur fractures | 34 patients with acute vertebral fractures | Serum | Not described | Within 24 h after injury | Serum S00β level was significantly increased in patients with vertebral spine fractures | Not described | Not described | |
| Ahadi et al., 2015 | Acute and subacute | ELISA | GFAP, pnf-H, NSE | 9 patients with spinal fractures but without neurological symptoms | 35 patients | Serum | A–D | At 24, 48 and 72 h after injury | Serum GFAP, pnf-H and NSE levels were significantly higher in SCI patients than in controls. | Serum level of GFAP differed between motor-complete SCI (AIS A and B) and motor-incomplete SCI (AIS C and D) | The serum level of GFAP was appropriate for estimating the severity of SCI in the first 24 hours after injury. | |
| Bank et al., 2015 | Acute and subacute | Multiplex assays (Bio-Plex Pro Human Cytokine 21-Plex, Group II, #MFO-005KMII and 27-Plex, Group I #M500KCAF0Yb) | MIF and 47 other factors | 18 uninjured participants | 18 adults with acute traumatic SCI | Plasma | A–D | At 0–3, 4–7, 8–11 and 12–15 d | MIF, IL-6, IL-9, IL-16, IL-18, chemokines, growth-related oncogene α/chemokine (C-X-C motif) ligand1, macrophage inflammatory protein 1-β/chemokine (C-C motif) ligand 4, hepatocyte growth factor and stem cell growth factor-β increased significantly after injury. | Not described | Not described | |
| Biglari et al., 2015 | Acute, subacute and intermediate | The Quantikine Human Immunoassay (R&D Systems, Inc, Minneapolis, MN,USA) | IL-1β, TNF-α | 23 patients (group 1: with AIS improvement; group 2: without AIS improvement) | Serum | A–D | At 0, 4, 9 and 12 h, 1 and 3 d and at 1, 2, 4, 8 and 12 wk | Not described | Not described | Patients with AIS improvement (group 1) had significantly lower TNF-α levels at 9 hours compared with patients without AIS improvement (group 2; P < 0.01) | Measuring serum levels of TNF-α and IL-1β over time can be useful in tracking the course of SCI. | |
| Kuhle et al., 2015 | Acute and subacute | Nflumea47:3 (an immunoassay developed in-house for nfl) | NF-L | 67 healthy controls | Patients with central cord syndrome (n = 4), motor incomplete SCI (n = 10), motor complete SCI (n = 13) | Serum | A–D | At 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120, 132, 144, 156 and 168 h after injury | Baseline nfl level was higher in patients with motor incomplete SCI (21 pg/mL) and in patients with motor complete SCI (70 pg/mL) than in healthy controls (5 pg/mL) and central cord syndrome (6 pg/mL). | Nfl level was correlated with ASIA motor score at baseline (r = –0.53) and after 24 h (r = –0.69). | Nfl level was correlated with 3-12-mon motor outcome (baseline nfl: r = –0.43, P = 0.026 and 24-h nfl: r = –0.72, P < 0.001). | |
| Moghaddam et al., 2016 | Acute, subacute and intermediate | ELISA | IGF-1 | 45 patients (G1: remission; G2: no remission) | Serum | A–D | At 0, 4, 9 and 12 h, 1, 3, 7 and 14 d and 1, 2 and 3 mon | Not described | Patients with AIS A grade had higher level of IGF-1 than patients with B-D grade at 7 d and 2 mon. | Patients with clinically documented neurological remission showed consistently higher IGF-1 level than patients without neurological remission. | Serum sustained increased IGF-1 indicted neurological remission. | |
| Kwon et al., 2017 | Acute | Bio-plex (Bio-Rad) multiple magnetic bead antibody detection assay on inflammatory cytokines; ELISA for tau, GFAP, and S100β detection | IL-6, IL-8, MCP-1, Tau, S100β, GFAP | 50 acute SCI patients | CSF | A–C | At 24 h after injury | Not described | IL-6, tau, S100β, and GFAP levels were significantly different between patients with baseline AIS A, B, or C | IL-6, IL-8, MCP-1, tau, S100β, and GFAP levels were significantly different between those who improved an AIS grade over 6 mon and those who did not improve. | The analysis of CSF can provide valuable biological information about injury severity and recovery potential after acute SCI. | |
| Moghaddam et al., 2017 | Acute, subacute and intermediate | Luminex Performance Human High Sensi- tivity Cytokine Panel | MMP-2, MMP-8, MMP-9, MMP-10, MMP-12 | Control (10 fracture patients without neurological impairment) | 10 patients with traumatic SCI with neurological remission (group 1) and 10 patients with traumatic SCI without neurological remission (group 0) | Serum | Not described | At 0, 4, 9 and 12 h, 1 and 3 d and 1, 2, 4, 8 and 12 wk after injury | Not described | Not described | Favored predictive model including MMP-8 value 1 day and MMP-9 value 1 month after SCI enables a prediction of neurological remission in 97% of cases. | |
| Dalkilic et al., 2018 | Acute | Bio-plex (Bio-Rad) multiple magnetic bead antibody detection assay on inflammatory cytokines; ELISA for tau, GFAP, and S100β detection | IL-6, IL-8, MCP-1, Tau, GFAP, S100β | 36 acute cervical SCI patients | CSF | A–C | At 24 h after injury | Not described | CSF biomarker level was found to correlate with baseline injury grade. Combination of MRI and CSF biomarkers provides a stronger model for classifying baseline AIS grade than CSF or MRI biomarkers alone. | Inflammatory CSF biomarkers best predicted AIS grade conversion, whereas structural biomarker levels best predicted motor score improvement. | The CSF biomarker discriminated better between different injury severities, and is a stronger predictor of neurological recovery in terms of AIS grade and motor score improvement than MRI | |
| Du et al., 2018 | Acute and subacute | ELISA | NSE, S100β | 60 patients with acute SCI were divided into group A (1 or more than 1 ASIA grade improved at 6 months after the injury) and group B (ASIA grades changed < 1 at 6 months after injury) | Serum | Not described | At 0, 2, 4, 6, 8, 10, 12, 14 d after injury | Not described | Not described | Serum NSE and S100β levels predicted the prognosis of acute SCI patients with the sensitivity of 74.35% and 71.79%, the specificity of 71.43% and 66.67%, respectively. | Serum NSE and S100B protein levels can reflect the degree of SCI and could be potential biomarkers for the prognosis of acute SCI. | |
| De Mello Rieder et al., 2019 | Acute and subacute | ELISA | NSE, IL-6, NGF, GDNF | 36 healthy subjects | 52 acute SCI patients | Serum | Not described | Within 48 h and 7 d after injury | Serum NSE level increased while NGF decreased at 48 h and 7 d; serum IL-6 level increased only at 48 h. | Not described | Not described | |
| Holmström et al., 2020 | Intermediate and chronic | ELISA | GFAP, UCH-L1, Pnf-H | 7 patients with benign lumbar intradural tumors and 7 patients with cervical radiculopathy | 12 patients with spinal cord tethering | CSF and serum | A–D | 1-24 m | CSF GFAP (P ≤ 0.01) and CSF pnf-H (P ≤ 0.05) levels were increased in patients compared with radiculopathy controls. | Not described | Not described | |
| Ogurcov et al., 2021 | Subacute | Bio-Plex Pro™ Human Cytokine 40-plex Assay #171AK99MR2 (Bio-Rad) on cytokines; ELISA for NSE and VEGF detection | Interferon-γ, CCL27, CCL26, CXCL5, CCL11, CXCL11, IL-10, TNF-α, MIF, NSE, VEGF | 16 healthy subjects | 28 patients with SCI | Serum | A–B | 2 wk after injury | Serum interferon-γ (> 52 fold), CCL27 (> 13 fold), and CCL26 (> 8 fold) increased; serum NSE increased after SCI. | Serum CXCL5, CCL11, CXCL11, IL-10, TNF-α, and MIF were different between patients with AIS A and B. | Not described | |
| Stukas et al., 2021 | Acute and subacute | UCH-L1 Discovery Assay (102343) from Quanterix Corporation (Billerica, MA, USA) | UCH-L1 | 10 patients with spinal stenosis and lumbar disc herniations | 32 acute SCI patients | CSF and serum | A–C | At 24, 48, 72, and 96 h after injury | CSF UCH-L1 elevated by 10- to 100-fold over laminectomy controls | CSF UCH-L1 increased in an injury severity- dependent manner. | 100% sensitivity and 86% specificity in the prediction of motor recovery at 6 mon after injury | |
AIS: American Spinal Injury Association (ASIA) Impairment Scale; ASIA: American Spinal Injury Association; CSF: cerebrospinal fluid; Cu: copper; ELISA: enzyme-linked immunosorbent assay; GFAP: glial fibrillary acidic protein; IGF-1: insulin-like growth factor 1; IL: interleukin; MCP-1: monocyte chemotactic protein-1; Mg: magnesium; MIF: macrophage migration inhibition factor; MMP: matrix metalloproteinase; NF-L: neurofilament protein-light; NSE: neuron-specific enolase; pnf-H: phosphorylated neurofilament-heavy; S100β: S100 calcium-binding protein β; SCI: spinal cord injury; Se: selenium; TGF-β: transforming growth factor β; TNF-α: tumor necrosis factor α; UCH-L1: ubiquitin carboxy-terminal hydrolase L1; Zn: zinc.
Astroglial Injury Biomarkers for Spinal Cord Injury
GFAP exists in the astrocyte matrix and is a single-molecule intermediate filament protein with high specificity. It has been confirmed as a robust biomarker of traumatic brain injury (TBI) (Wang et al., 2018) and may also have great value for the assessment of SCI. Kwon et al. (2017) performed CSF GFAP testing and AIS grading in 15 patients with acute SCI at 24 hours and 6 months post-injury. They demonstrated that CSF GFAP concentrations were significantly different among patients diagnosed with AIS grades A, B, and C at 24 hours after SCI injury, and GFAP levels were also significantly different between patients with an improved AIS grade after 6 months of treatment compared with those with no improvement. In their study, GFAP had an 83% probability of predicting neurological improvement in patients with AIS grade A at 6 months after injury, indicating that GFAP might be a useful index for assessing the severity of SCI and for predicting future neurological functional recovery (Kwon et al., 2017). Guéz et al. (2003) showed that GFAP levels were higher in patients with complete motor loss compared with patients with incomplete motor loss. Ahadi et al. (2015) also reported that a higher degree of SCI was associated with higher serum concentrations of GFAP in a study of 35 patients. Obly et al. (2019) recently found that serum GFAP concentrations rose in the first 1–3 days in 31 dogs with simulated SCI, and then gradually decreased and became undetectable by 14 days. Importantly, the serum level of GFAP in the first 3 days predicted recovery with an accuracy of 76.7–86%. Regarding sequelae for patients with chronic SCI, Holmstrom et al. (2020) found that CSF GFAP and phosphorylated neurofilament-heavy (pNF-H) levels were significantly higher in 12 patients with spinal cord tethering compared with radiculopathy controls. Collectively, GFAP has thus been shown to be a strong prognostic biomarker for neurological amelioration after SCI.
S100β is a calcium-binding protein that is mainly present in astrocytes and Schwann cells, and participates in calcium homeostasis, cell proliferation, and differentiation (Donato et al., 2009). Similarly, S100β has been confirmed as a biomarker of TBI and also has an essential role in SCI diagnosis (Hulme et al., 2017). Kwon et al. (2010) detected CSF expression levels of S100β and other cytokines in 27 patients with SCI, and found that combining GFAP with IL-8 and S100β predicted the observed ASIA grade with 89% accuracy at 24 hours and better predicted segmental motor function improvement after 6 months of SCI injury compared with baseline ASIA grade. Similar conclusions were reached in a series of subsequent studies (Wolf et al., 2014). Kwon et al. (2017) verified that CSF S100β levels differed significantly among patients with different AIS grades of SCI at 24 hours post-injury, and S100β was also significantly correlated with improvements in sports scores and the prediction of ASI conversion over 6 months. In a recent study in 60 patients, serum S100β increased after SCI and reached a peak 4 days after injury, and then declined to a relatively low level 2 weeks later (Du et al., 2018). Du et al. (2018) also showed that SCI patients who showed improvements of at least one ASIA grade after 6 months had lower S100β levels than those whose ASIA grade changed by less than one grade throughout the course of the disease. However, despite the diagnostic sensitivity of S100β for SCI, S100β also exists in adipocytes and chondrocytes, and increased serum levels of S100β alone thus lack specificity for diagnosing SCI (Faridaalee and Keyghobadi Khajeh, 2019).
Neuronal Cell Body Injury Biomarkers for Spinal Cord Injury
The glycolytic enzyme NSE is mainly distributed in the cytoplasm of mature neurons and neuroendocrine cells, and is released into the extracellular environment following neuronal axon damage. In an early study of 34 patients with vertebral spine fractures, Wolf et al. (2014) found no difference in serum NSE levels between experimental and normal subjects or among SCI patients classified with paresthesia, incomplete paraplegia, and complete paraplegia. However, Ahadi et al. (2015) subsequently arrived at different conclusions based on significantly higher average serum NSE levels in 35 patients with SCI, compared with the control group at 24 and 48 hours post-injury. Similarly, the results of a study by Pouw et al. (2014) based on 16 patients with acute traumatic SCI showed that CSF levels of NSE were significantly correlated with baseline neurological functions, such as complete motor loss (AIS grade A or B) or incomplete motor loss (AIS C or D). However, recent studies have produced conflicting results: a study of 52 patients by de Mello Rieder et al. (2019) found that serum NSE levels were significantly increased at 48 hours and 7 days after SCI, but found no significant difference between patients with different ASIA grades and no significant correlation between post-injury serum NSE and future neurologic melioration.
However, in a study of 60 patients with acute SCI by Du et al. (2018), serum NSE levels were significantly increased after injury and reached a peak on the 2nd day, and then declined gradually. Furthermore, patients with an improvement of at least one ASIA grade 6 months later had lower serum NSE levels than those who did not recover well (less than one grade improvement). In summary, NSE could serve as a diagnostic biomarker for SCI, and possibly also for severity assessment and prognostic evaluation.
UCH-L1 is a deubiquitinase involved in the addition or removal of ubiquitin in proteins that primarily exists in the cytoplasm of neuronal cells. Yang et al. (2018) showed that CSF and serum UCH-L1 levels peaked rapidly at 4 hours after injury then declined at 24 hours in a rat SCI model. In addition, a recent study of 32 patients with acute SCI revealed that CSF UCH-L1 levels increased dramatically after injury in a severity- and time-dependent way, with 100% sensitivity and 86% specificity for predicting ASIA conversion, despite no differences in serum UCH-L1 levels. Furthermore, a consistent increase in CSF UCH-L1 up to 96 hours and relatively higher CSF levels 24 hours after injury were correlated with a lack of AIS grade improvement and motor function amelioration, respectively (Stukas et al., 2021). An ultrasensitive immunosensor has recently been developed for the rapid detection of UCH-L1 in biofluids in patients with TBI or SCI (Khetani et al., 2019). Overall, CSF levels of UCH-L1 have great potential for the diagnosis of acute SCI, and might qualify as a sensitive biomarker for predicting future neurological recovery.
Axonal Damage Biomarkers for Spinal Cord Injury
NF, as a major component of the axon cytoskeleton, interacts with other cytoskeletal proteins and regulates axonal transportation and neuronal signaling (Yuan et al., 2017). NF exists exclusively in neurons but is released and increased in the extracellular environment following neuronal cell damage, and is thus a potential biomarker for SCI (Al-Chalabi and Miller, 2003). The basic structural components of NF include three polypeptide subunits: neurofilament-light (NF-L, 68K), neurofilament-medium (150K), and neurofilament-heavy (NF-H, 200K), among which phosphorylated NF-H (pNF-H) is most easily detectable in serum and CSF due to its relative resistance to proteases (Shaw et al., 2005). Hayakawa et al. (2012) recruited 14 patients with acute cervical SCI and found that serum pNF-H was detectably increased from 12 hours to 21 days after injury in patients with complete SCI, and there were significant differences in serum pNF-H levels between patients with AIS grade A (completely paralyzed) and AIS grade C (incompletely paralyzed). A later study involving 35 patients also found that serum pNF-H levels were significantly higher in patients with SCI at 24 and 48 hours after injury compared with controls (Ahadi et al., 2015). In addition, serum NF-L has been identified as a potential biomarker in various neurological diseases (Gaetani et al., 2019). In an early clinical study of SCI biomarkers, Guez et al. (2003) examined CSF samples from six patients with acute SCI and found that patients with complete motor loss had higher levels of NF-L than those with partial motor loss, and NF-L levels in the CSF of patients without neurological improvement were 10 times higher than those in patients with improvement, indicating that SCI could be diagnosed based on CSF levels of NF-L. In a subsequent study with a larger sample (23 SCI patients and 67 healthy controls). Kuhle et al. (2015) confirmed that baseline serum NF-L levels were significantly higher in patients with motor-complete SCI (70 pg/mL) and motor-incomplete SCI (21 pg/mL) than in healthy controls (5 pg/mL), and serum NF-L was significantly correlated with ASIA grade and motor score at baseline, 24 hours, and 3–12 months. They also showed that minocycline treatment reduced serum NF-L levels in the subgroup of patients with motor-complete SCI, suggesting that NF-L may help to guide treatment, as well as being a diagnostic and prognostic indicator (Kuhle et al., 2015).
Other Structural Biomarkers for Spinal Cord Injury
Tau, as the most abundant tubule-associated protein, contributes to microtubule formation and stabilization inside neuronal cells. Kwon et al. (2010) showed that CSF tau levels increased in a severity-dependent manner among patients with AIS grades A, B, and C, and that a lower tau level predicted an improvement of one AIS grade at 6 months after SCI injury (Kwon et al., 2017). Dalkilic et al. (2018) confirmed that CSF tau levels differed significantly among SCI patients with AIS grades A, B, and C, and motor score improvement 6 months later was best predicted by structural proteins, such as CSF tau. A recent study demonstrated that CSF and serum tau levels peaked rapidly at 12 hours after injury in a severity-dependent SCI rat model, and Basso, Beattie, and Bresnahan locomotor rating scale scores were positively linearly correlated with tau protein levels (Tang et al., 2019). Because tau is hyperphosphorylated under conditions of pathological axonal damage, such as in TBI (Rubenstein et al., 2015), recent studies have examined the potential role of hyperphosphorylated tau (p-tau) as a biomarker for SCI. Caprelli et al. (2018) proved that the presence of p-tau was in line with the temporal and spatial distribution of axonal damage in SCI model rats, and CSF and serum levels of p-tau were greatly increased after SCI. However, there are currently no clinical data regarding dynamic changes in p-tau in biofluids in SCI patients, and the relationship between p-tau protein and AIS grading remains unknown. Overall, evidence suggests that total tau can serve as a diagnostic and prognostic biomarker for SCI, while p-tau may also have a certain diagnostic value for SCI.
Matrix metalloproteinases (MMPs) comprise a large family of zinc-bound proteases with strong effects on the degradation of protein components in the extracellular matrix. Moghaddam et al. (2017) analyzed the data sets for 115 patients with traumatic SCI (33 women, 82 men) published from 2010 to 2015, including serum MMP levels at different time points from admission to 12 weeks after injury. They found that MMP-8 and MMP-9 levels differed significantly between SCI patients and controls, and the favored predictive model consisting of MMP-8 levels 1 day and MMP-9 levels 1 month after injury could predict neurological remission in 97% of cases (Moghaddam et al., 2017).
Inflammatory Biomarkers for Spinal Cord Injury
Damaged tissues and a ruptured blood-spinal cord barrier following SCI lead to the accumulation of pro-inflammatory cytokines and chemokines in the CSF or peripheral blood, enabling these molecules to serve as latent biomarkers. Among the inflammatory mediators, interleukins have been studied extensively in clinical research and have shown great potential as SCI biomarkers. Davies et al. (2007) conducted a cross-sectional study of 56 SCI patients and 35 age-matched controls, and found that serum interleukin-6 (IL-6) and IL-1 levels were significantly higher in SCI patients than in controls at 2–52 weeks post-injury; however, increased cytokine levels were not correlated with injury level or AIS classification. However, Bank et al. (2015) found that serum IL-6 levels were significantly elevated in patients with acute SCI, but only in the first week of injury. Similarly, de Mello Rieder et al. (2019) reported that serum IL-6 levels were only increased 48 hours post-injury, and there was no correlation between IL-6 levels and short- or long-term prognosis (i.e., survival rate or sensory and motor function improvement).
CSF levels of interleukins differ from plasma levels in patients with SCI. Kwon et al. (2010) demonstrated that CSF IL-6 and IL-8 increased in severity-dependent manners in SCI patients, and IL-8, together with S100β and GFAP, was used to establish a biochemical model that could predict ASIA grade with 89% accuracy after 24 hours of injury. Furthermore, these CSF proteins were more predictive of segmental motor recovery than baseline ASIA grade at 6 months after injury. Kwon et al. (2017) also verified that CSF IL-6 expression levels differed significantly among patients with acute SCI with baseline ASIA grades of A, B, and C. Moreover, IL-6 was strongly correlated with ASIA conversion and motor improvement 6 months after SCI. In another clinical study comparing CSF and MRI biomarkers for acute SCI. Dalkilic et al. (2018) showed that CSF IL-6 and IL-8 levels were correlated with baseline ASIA grades. In addition, CSF levels of inflammatory cytokines (IL-6, IL-8, monocyte chemotactic protein-1) performed best for predicting ASIA grade conversion, while structural biomarkers (tau, GFAP, S100β) performed best for predicting motor score improvement.
Other inflammatory cytokines, such as tumor necrosis factor α (TNFα) and IL-1β, which are secreted mainly by monocytes or macrophages, also participate in the secondary inflammatory response in SCI. Hayes et al. (2002) showed that serum TNFα levels were significantly higher in patients with chronic SCI (>12 months) compared with normal subjects. In addition, Davies et al. (2007) found that higher serum TNFα levels were sustained in SCI patients from 2–52 weeks post-injury, and TNFα levels differed among patients with different ASIA classifications. However, Biglari et al. (2015) found that serum TNFα levels were significantly lower in patients with AIS improvement at 9 hours after SCI than in those without. Meanwhile, serum IL-β levels exhibited a stable decline from 2–12 weeks in SCI patients, despite showing no distinction in relation to ASIA conversion. A recent study by Ogurcov et al. (2021) confirmed that serum TNFα levels differed between patients with ASIA grades A and B. In another study based on 140 participants, Xu et al. (2015) revealed that SCI patients with neuropathic pain had significantly higher serum TNFα levels. In conclusion, serum TNFα could play a part in the diagnosis of SCI, classification of ASIA grade, and prognosis of neurological improvement, and dynamic measurement of serum IL-1 might be useful for tracking the progression of SCI.
Other cytokines, such as growth factors, have also proven useful for the assessment of patients with SCI. Ferbert et al. (2017) found that serum levels of insulin-like growth factor 1 (IGF-1) and transforming growth factor-β (TGF-β) were both elevated during the acute and sub-acute phases after injury in 23 SCI patients, and higher serum IGF-1 levels predicted insufficient neurological remission 12 weeks later, while TGF-β showed no such effect. However, a study of 45 SCI patients by Moghaddam et al. (2016) drew different conclusions, and found that high serum IGF-1 levels were related to neurological recovery. Information on the relationships between these cytokines and AIS classification is also lacking, and more studies are needed.
Spinal Cord Injury RNA Biomarkers
Studies of non-coding RNA (ncRNA) sequences after SCI are shown in Table 2.
Table 2.
Non-coding rnas sequencing after SCI
| Category | Reference | Method | Species | Group | Sample size (n) | Bio-sample | Time of sampling | Key mirnas | Role in SCI |
|---|---|---|---|---|---|---|---|---|---|
| miRNAs | Hachisuka et al., 2014 | Taqman low-density array analysis | Mouse | Normal (without surgery); sham (laminectomy only); mild (50 kdyn); severe (70 kdyn) | 1 | Serum | 12 h after SCI | mir-9*, mir-219 and mir-384-5p | Positively related with SCI severity |
| Tigchelaar et al., 2017 | Next-generation sequencing | Female Yucatan miniature pigs | Sham (laminectomy only); mild (10 cm of 50 g weight); moderate (20 cm of 50 g weight); severe (40 cm of 50 g weight) | 4 | CSF and serum | 15 min before injury; 1, 3, and 5 d after injury | 58, 21, 9 and 7 altered mirnas were identified in severe, moderate, mild SCI and sham surgery groups | Overall mirna expression at 1 and 3 d post injury was strongly correlated with SCI outcome | |
| Ding et al., 2019 | Next-generation sequencing | Rat | Sham (laminectomy only); SCI (120 kdyn) | 3 | Serum exosomes | 6 h after injury | mir-125b-5p, mir-152-3p, and mir-130a-3p | Acute SCI diagnosis | |
| Ding et al., 2020 | Next-generation sequencing | Rat | Sham (laminectomy only); SCI (120 kdyn) | 3 | Serum exosomes | 7 d after injury | mir-485, mir-30b, mir-26b, mir-23b, mir-223, mir-21, mir-211, mir-200c, mir-195, mir-17, mir-133a, mir-125b-1, mir-124, mir-103, mir-672 and mir-15b | Subacute SCI diagnosis | |
| LncRNAs | Ding et al., 2016 | Microarray analysis | Mouse | Five groups (sham operation, 1 3 d after injury, 1 and 3 wk after injury) | 3 | Spinal cord | At 1 and 3 d, 1 and 3 wk after injury | 345, 502, 891, and 181 lncRNAs were differentially expressed at 1, 3, 7, and 21 d after injury; 458, 1073, 2177 and 850 mRNAs were differentially expressed at 1, 3, 7, and 21 d after injury | Lncrnas and mirnas expression peaked 1 wk after SCI |
| Duran et al., 2017 | RNA sequencing | Rat | Four groups (sham operation, 1, 3 and 6 mon after SCI), 150 kdyn | 3 | Spinal cord | At 1, 3 and 6 mon after injury | 2935, 3981 and 3206 protein-coding genes were differentially expressed at 1, 3 and 6 mon after injury; 137, 239 and 179 lncRNAs was differentially expressed at 1, 3 and 6 mon after injury. | Sub-chronic and chronic stages of SCI | |
| Zhou et al., 2018 | Microarray analysis | Rat | Control (without surgery); SCI group (10 g × 50 mm) | 9 | Spinal cord | 2 h after SCI | 772 lncRNAs and 992 mRNAs were differentially expressed | Immediate phase of SCI | |
| Shi et al., 2019 | Microarray analysis | Rat | Control (without surgery); SCI group (10 g × 50 mm) | 9 | Spinal cord | 2 d after SCI | 3193 lncRNAs and 4308 mRNAs were differentially expressed | Early acute phase of SCI |
1 dyn = 10–5 N. CSF: Cerebrospinal fluid; lncRNAs: long-coding RNAs; miRNAs: microRNAs; SCI: spinal cord injury.
Use of microRNAs as biomarkers for SCI
Single-stranded ncRNA molecules with a length of 22 nucleotides, known as microRNAs (miRNAs), participate in the regulation of post-transcriptional gene expression. Differentially expressed miRNAs have been shown to contribute to pathological reactions (including apoptosis, inflammatory response, angiogenesis, etc.) in SCI, making them potential therapeutic targets for SCI intervention (Nieto-Diaz et al., 2014; Shi et al., 2017). Hachisuka et al. (2014) established a mouse SCI model and found that serum expression levels of miR-9*, miR-219, and miR-384-5p were significantly increased in the normal, sham, mild, and severe groups in a severity-dependent manner at 12 hours after injury. Tigchelaar et al. (2017) evaluated serum miRNA levels at 1, 3, and 5 days after injury in a porcine model of SCI, and quantified seven, nine, 21, and 58 differentially expressed miRNAs in the sham surgery, mild, moderate, and severe injury groups, respectively. They also found that SCI functional outcome was significantly correlated with serum miRNA levels at 1 and 3 days after injury, supporting a role for serum miRNAs as promising biomarkers for the assessment of SCI severity and future functional amelioration.
Although circulating miRNAs have potential as biomarkers in SCI, their concentration and stability in body fluids, such as blood and CSF, are limited. However, exosomes are lipid-bilayer-encapsulated particles containing various proteins and miRNAs that are released from cells into the extracellular fluid, and exosomal miRNAs have exhibited greater effects in terms of the pathobiology and therapy of SCI than free miRNAs (Dutta et al., 2021). Ding et al. (2019) analyzed miRNA profiles in serum exosomes at 6 hours after injury in a rat SCI model, and found that serum exosomal miR-125b-5p, miR-152-3p, and miR-130a-3p had sufficient specificity and detectability to act as diagnostic biomarkers for acute SCI. They also confirmed that 141 miRNAs in serum exosomes were differentially expressed between SCI and sham rats at 7 days after injury, showing potential diagnostic and prognostic values for subacute SCI (Ding et al., 2020). Overall, circulating miRNAs, especially exosomal miRNAs, could play a vital role as potential biomarkers for SCI.
Use of long non-coding RNAs as biomarkers for SCI
As a class of ncRNA molecules with more than 200 nucleotides, long non-coding RNAs (lncRNAs) are involved in many cellular activities, including epigenetic regulation, the cell cycle, and differentiation regulation. These lncRNAs have been a recent research hot spot, and they have been confirmed to be related to various diseases such as cancer and nervous system diseases (Quan et al., 2017), including SCI (Wang et al., 2019). Studies in rats and mice have revealed a relationship between lncRNAs and SCI (Wang et al., 2019). Ding et al. analyzed the lncRNA profile of the spinal cord in an SCI mouse model and found differential expression of lncRNAs from 1 day after injury, reaching a peak at 1 week and then decreasing until 3 weeks after SCI (Ding et al., 2016). Duan et al. (2017) found that the expression profile of spinal cord lncRNAs changed dynamically during the chronic phase of SCI (1, 3, and 6 months). In another study using SCI rat models, Shi et al. (2019) quantified 3193 differentially expressed lncRNAs and 4308 differentially expressed miRNAs at 2 days after injury, while regarding the immediate phase of SCI, Zhou et al. (2018) identified 772 differentially expressed lncRNAs and 992 differentially expressed miRNAs at 2 hours after injury in a rat model. In a weighted gene co-expression network analysis, Chen et al. (2021) identified six genes (CCNB2, CCNB1, CKS2, COL5A1, KIF20A, and RACGAP1) that could act as potential biomarkers to differentiate among SCI subtypes. However, despite the use of animal models to reveal the specific molecular mechanisms of several lncRNAs in the pathogenesis and progression of SCI (Jiang and Zhang, 2018; Zhang et al., 2018; Guan and Wang, 2021), clinical data regarding the potential role of circulating lncRNAs as biomarkers for the diagnosis or prognosis of SCI are extremely limited. The current findings provide a promising basis for future research into the role of lncRNAs in SCI.
Trace Elements as Prognostic Biomarkers for Spinal Cord Injury
Oxidative stress and inflammatory responses are critical factors in the occurrence of secondary injury during the pathological progression of traumatic SCI. Essential trace elements might play a protective role as antioxidants during this process, and may thus have a close relationship with future neurological recovery. Sperl et al. (2019) studied 29 patients with acute traumatic SCI and found that patients without neurological remission (G1) had significantly higher serum Mg levels than patients with neurological remission (G1) within the first 7 days. Heller et al. (2019) and Seeling et al. (2020) found that patients with traumatic SCI with neurological remission (G1) who had a higher serum Se levels on admission exhibited a significantly more rapid decline within the first week compared with patients without neurological improvement, with a similar effect for serum Cu. However, regarding Zn levels, Kijima et al. (2019) reported that a dramatic decrease in serum Zn levels was related to the severity of SCI, and Zn levels 12 hours after injury could predict neurological functional recovery in a mouse model. In addition, they demonstrated that the severity-dependent decrease in serum Zn was due to a proportional increase in Zn uptake by circulating activated monocytes. Heller et al. (2020) also claimed that early changes in serum Zn levels from 0–9 hours after SCI had an accuracy of 82.2% for predicting neurological impairment. In brief, changes in serum levels of essential trace elements may reflect the consistent redistribution of these antioxidants, and temporal fluctuations in these microelements might thus have predictive value in terms of neurological regeneration (Table 3).
Table 3.
Summary of trace element in SCI
| Reference | Phase of SCI | Testing method | Biomarker | Groups and sample size | Type of sample | AIS grade | Time of sampling | Role in prediction of SCI recovery | Conclusion |
|---|---|---|---|---|---|---|---|---|---|
| Heller et al., 2019 | Acute, subacute and intermediate | Total reflection X-ray fluorescence and ELISA | Selenium Selenoprotein P (SELENOP) | 39 TSCI patients (group C: 10 patients without neurological impairment; group G0: 9 patients without remission; group G1: 10 patients with remission-AIS conversion within 3 mon) | Serum | A–C, E | On admission, at 4, 9 and 12 h, 1 and 3 d, 1 and 2 wk, and 1, 2 and 3 mon | Binary logistic regression analysis including the delta of Se and SELENOP within the first 24 h indicated an area under the curve (AUC) value of 90.0% (CI: 67.4–100.0%) with regards to predicting the outcome after TSCI | A Se deficit might constitute a risk factor for poor outcome after TSCI |
| Kijima et al., 2019 | Acute and subacute | Metallo assay Zn LS Kit | Zn | 38 SCI patients | Serum | A–D | Within 3 d after SCI | Serum zinc concentrations in the acute-phase accurately predicted the long-term functional outcome (R2 = 0.84) | The acute-phase serum zinc concentration can be a useful biomarker for predicting the functional prognosis |
| Sperl et al., 2019 | Acute, subacute and intermediate | ADVIA chemistry XPT system by Siemens Healthineer® | Magnesium | 29 patients were divided into two groups (group G0: 11 patients without remission; group G1: 10 patients with remission-AIS conversion within 3 months) | Serum | A–D | On admission, and at 4, 9 and 12 h, 1 and 3 d, 1 and 2 wk, and 1, 2 and 3 mon | At 7 d, Mg concentrations in G1 and G0 were significantly different (G0 > G1). Significant differences were detected between patients in G1 that presented an AIS (ASIA Impairment Scale) conversion of 1 level versus those with more than 1 level (G1 AIS imp. =+1 > G1 AI imp. > +1). | Low and decreasing levels of Mg within the first 7 d are indicative of a high probability of neurological remission, whereas increasing levels are associated with poor neurological outcome |
| Heller et al., 2020 | Acute and subacute | Total reflection X-ray fluorescence (TXRF) | Zn | 42 patients with TSCI [two groups: a study group S (n = 33) with neurological impairment and a control group C (n = 9) without neurological impairment] | Serum | A–C | On admission, and at 4, 9, 12 and 24 h, and 3 d after SCI | In comparison to the initial Zn Concentration, the AIS imp > + 1 patients reached their minimum of 58% at 4 h after trauma, whereas patients with an AIS imp = +1 reached their minimum of 56% at 9 h after TSCI | Early Zn concentration dynamics differed in relation to the outcome and may constitute a helpful diagnostic indicator for patients with SCI |
| Seelig et al., 2020 | Acute | Total reflection X-ray fluorescence and ELISA | Selenium, Copper, Selenoprotein P (SELENOP), Ceruloplasmin (CP) | 52 TSCI patients (G1: 21 patients with AIS conversion within 3 mon; G0: 21 patients without AIS conversion within 3 mon; control: without neurological impairment) | Serum | A–C | On admission, and at 4, 9, 12, and 24 h after injury | Binary logistic regression analysis including Cu and Se levels on admission in combination with Se and CP levels at 24 h after SCI allowed a prediction for potential remission | These data indicate a strong association between temporal changes of the Se and Cu status and the clinical outcome after TSCI |
AIS: American Spinal Injury Association Impairment Scale; SCI: spinal cord injury; TSCI: traumatic SCI.
Limitations
At present, although many potentially effective biomarkers have been discovered, few clinical studies have been conducted to determine which biomarkers are most closely related to the severity and prognosis of SCI, which is a major issue in their clinical application. This review did not consider all the possible biomarkers, and only listed those with the highest correlations with SCI. Regarding the acquisition of repeated CSF samples, careful attention must be paid to aseptic procedures and to ensuring an adequate time interval between samples.
Conclusion
Biomarkers should ideally possess as many of the following properties as possible: (1) changes (preferably increases) in CSF, serum, plasma, or whole-blood levels after SCI, with low levels in normal people; (2) levels in biological fluids easy to measure and quantify using sandwich enzyme-linked immunosorbent assays or similar immunoassays, with at least two detection formats or platforms; (3) ability to measure biomarker levels repeatedly within a 48-hour window after SCI; and (4) levels of the biomarker in the biofluid should respond to treatment (Wang et al., 2018).
According to the current summary, GFAP, S100β, UCH-L1, NF-L, tau, IL-6/8, TNFα, and Zn could be useful diagnostic and prognostic biomarkers, and indicators of AIS classification in SCI; NSE, pNF-H, and miRNAs have possible roles in the diagnosis and severity assessment of SCI; IGF-1 and MMPs may be involved in the diagnosis and neurological recovery of SCI; TGF-β and lncRNAs are only useful for the recognition of SCI; and Mg, Se, and Cu are mainly related to the prognosis of SCI (Figure 2).
Figure 2.

Venn diagram of current research results for potential biomarkers of spinal cord injury (SCI).
AIS: American Spinal Injury Association Impairment Scale; GFAP: glial fibrillary acidic protein; IGF-1: insulin-like growth factor-1; IL: interleukin; lncRNA: long non-coding RNA; miRNA: microRNA; MMP: matrix metalloproteinase; NSE: neuron-specific enolase: pNF-H: phosphorylated neurofilament-heavy; TGF-β: transforming growth factor-β; TNFα: tumor necrosis factor α; UCH-L1: ubiquitin C-terminal hydrolase-L1.
We assume that the combination of two or three of these recently identified biomarkers may have useful potential in the diagnosis, prognosis, and even treatment of SCI. However, further, high-quality prospective clinical studies are needed to rigorously analyze the sensitivity, specificity, and predictability of the available biomarkers.
Additional file: Open peer review report 1 (79.9KB, pdf) .
Footnotes
Conflicts of interest: The authors declare that there are no conflicts of interest associated with this manuscript.
Financial support: This work was financially supported by the National Key Research and Development Project of Stem Cell and Transformational Research, No. 2019YFA0112100 (to SQF). The funding source had no role in manuscript conception and design, data analysis or interpretation, manuscript writing or deciding to submit this manuscript for publication.
Copyright license agreement: The Copyright License Agreement has been signed by all authors before publication.
Plagiarism check: Checked twice by iThenticate.
Peer review: Externally peer reviewed.
Open peer reviewer: Mitsuhiro Enomoto, Tokyo Medical and Dental University, Japan.
Funding: This work was financially supported by the National Key Research and Development Project of Stem Cell and Transformational Research, No. 2019YFA0112100 (to SQF).
P-Reviewer: Enomoto M; C-Editor: Zhao M; S-Editors: Wang J, Li CH; L-Editors: Susan F, Song LP; T-Editor: Jia Y
References
- 1.Ahadi R, Khodagholi F, Daneshi A, Vafaei A, Mafi AA, Jorjani M. Diagnostic value of serum levels of GFAP, pNF-H, and NSE compared with clinical findings in severity assessment of human traumatic spinal cord injury. Spine (Phila Pa 1976) 2015;40:E823–830. doi: 10.1097/BRS.0000000000000654. [DOI] [PubMed] [Google Scholar]
- 2.Albayar AA, Roche A, Swiatkowski P, Antar S, Ouda N, Emara E, Smith DH, Ozturk AK, Awad BI. Biomarkers in spinal cord injury: prognostic insights and future potentials. Front Neurol. 2019;10:27. doi: 10.3389/fneur.2019.00027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Al-Chalabi A, Miller CC. Neurofilaments and neurological disease. Bioessays. 2003;25:346–355. doi: 10.1002/bies.10251. [DOI] [PubMed] [Google Scholar]
- 4.Badhiwala JH, Wilson JR, Kwon BK, Casha S, Fehlings MG. A review of clinical trials in spinal cord injury including biomarkers. J Neurotrauma. 2018;35:1906–1917. doi: 10.1089/neu.2018.5935. [DOI] [PubMed] [Google Scholar]
- 5.Bank M, Stein A, Sison C, Glazer A, Jassal N, McCarthy D, Shatzer M, Hahn B, Chugh R, Davies P, Bloom O. Elevated circulating levels of the pro-inflammatory cytokine macrophage migration inhibitory factor in individuals with acute spinal cord injury. Arch Phys Med Rehabil. 2015;96:633–644. doi: 10.1016/j.apmr.2014.10.021. [DOI] [PubMed] [Google Scholar]
- 6.Biglari B, Swing T, Child C, Büchler A, Westhauser F, Bruckner T, Ferbert T, Jürgen Gerner H, Moghaddam A. A pilot study on temporal changes in IL-1β and TNF-α serum levels after spinal cord injury: the serum level of TNF-α in acute SCI patients as a possible marker for neurological remission. Spinal Cord. 2015;53:510–514. doi: 10.1038/sc.2015.28. [DOI] [PubMed] [Google Scholar]
- 7.Caprelli MT, Mothe AJ, Tator CH. Hyperphosphorylated tau as a novel biomarker for traumatic axonal injury in the spinal cord. J Neurotrauma. 2018;35:1929–1941. doi: 10.1089/neu.2017.5495. [DOI] [PubMed] [Google Scholar]
- 8.Chen Q, Zhao Z, Yin G, Yang C, Wang D, Feng Z, Ta N. Identification and analysis of spinal cord injury subtypes using weighted gene co-expression network analysis. Ann Transl Med. 2021;9:466. doi: 10.21037/atm-21-340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Chun A, Delgado AD, Tsai CY, Spielman L, Taylor K, Ramirez A, Huang V, Kolakowsky-Hayner SA, Escalon MX, Bryce TN. An interview based approach to the anorectal portion of the international standards of neurological classification of spinal cord injury exam (I-A-ISNCSCI): a pilot study. Spinal Cord. 2020;58:553–559. doi: 10.1038/s41393-019-0399-5. [DOI] [PubMed] [Google Scholar]
- 10.Dalkilic T, Fallah N, Noonan VK, Salimi Elizei S, Dong K, Belanger L, Ritchie L, Tsang A, Bourassa-Moreau E, Heran MKS, Paquette SJ, Ailon T, Dea N, Street J, Fisher CG, Dvorak MF, Kwon BK. Predicting injury severity and neurological recovery after acute cervical spinal cord injury: a comparison of cerebrospinal fluid and magnetic resonance imaging biomarkers. J Neurotrauma. 2018;35:435–445. doi: 10.1089/neu.2017.5357. [DOI] [PubMed] [Google Scholar]
- 11.Davies AL, Hayes KC, Dekaban GA. Clinical correlates of elevated serum concentrations of cytokines and autoantibodies in patients with spinal cord injury. Arch Phys Med Rehabil. 2007;88:1384–1393. doi: 10.1016/j.apmr.2007.08.004. [DOI] [PubMed] [Google Scholar]
- 12.de Mello Rieder M, Oses JP, Kutchak FM, Sartor M, Cecchini A, Rodolphi MS, Wiener CD, Kopczynski A, Muller AP, Strogulski NR, Carteri RB, Hansel G, Bianchin MM, Portela LV. Serum biomarkers and clinical outcomes in traumatic spinal cord injury: prospective cohort study. World Neurosurg. 2019;122:e1028–1036. doi: 10.1016/j.wneu.2018.10.206. [DOI] [PubMed] [Google Scholar]
- 13.Deng WS, Ma K, Liang B, Liu XY, Xu HY, Zhang J, Shi HY, Sun HT, Chen XY, Zhang S. Collagen scaffold combined with human umbilical cord-mesenchymal stem cells transplantation for acute complete spinal cord injury. Neural Regen Res. 2020;15:1686–1700. doi: 10.4103/1673-5374.276340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Ding SQ, Chen J, Wang SN, Duan FX, Chen YQ, Shi YJ, Hu JG, Lü HZ. Identification of serum exosomal microRNAs in acute spinal cord injured rats. Exp Biol Med (Maywood) 2019;244:1149–1161. doi: 10.1177/1535370219872759. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Ding SQ, Chen YQ, Chen J, Wang SN, Duan FX, Shi YJ, Hu JG, Lü HZ. Serum exosomal microRNA transcriptome profiling in subacute spinal cord injured rats. Genomics. 2020;112:2092–2105. doi: 10.1016/j.ygeno.2019.12.003. [DOI] [PubMed] [Google Scholar]
- 16.Ding Y, Song Z, Liu J. Aberrant lncRNA expression profile in a contusion spinal cord injury mouse model. Biomed Res Int 2016. 2016 doi: 10.1155/2016/9249401. 9249401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Donato R, Sorci G, Riuzzi F, Arcuri C, Bianchi R, Brozzi F, Tubaro C, Giambanco I. S100B's double life: intracellular regulator and extracellular signal. Biochim Biophys Acta. 2009;1793:1008–1022. doi: 10.1016/j.bbamcr.2008.11.009. [DOI] [PubMed] [Google Scholar]
- 18.Du W, Li H, Sun J, Xia Y, Zhu R, Zhang X, Tian R. The prognostic value of serum neuron specific enolase (nse) and s100b level in patients of acute spinal cord injury. Med Sci Monit. 2018;24:4510–4515. doi: 10.12659/MSM.907406. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Duran RC, Yan H, Zheng Y, Huang X, Grill R, Kim DH, Cao Q, Wu JQ. The systematic analysis of coding and long non-coding RNAs in the sub-chronic and chronic stages of spinal cord injury. Sci Rep. 2017;7:41008. doi: 10.1038/srep41008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Dutta D, Khan N, Wu J, Jay SM. Extracellular vesicles as an emerging frontier in spinal cord injury pathobiology and therapy. Trends Neurosci. 2021;44:492–506. doi: 10.1016/j.tins.2021.01.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Eckert MJ, Martin MJ. Trauma: spinal cord injury. Surg Clin North Am. 2017;97:1031–1045. doi: 10.1016/j.suc.2017.06.008. [DOI] [PubMed] [Google Scholar]
- 22.Fakhoury M. Spinal cord injury: overview of experimental approaches used to restore locomotor activity. Rev Neurosci. 2015;26:397–405. doi: 10.1515/revneuro-2015-0001. [DOI] [PubMed] [Google Scholar]
- 23.Fan B, Wei Z, Yao X, Shi G, Cheng X, Zhou X, Zhou H, Ning G, Kong X, Feng S. Microenvironment imbalance of spinal cord injury. Cell Transplant. 2018;27:853–866. doi: 10.1177/0963689718755778. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Faridaalee G, Keyghobadi Khajeh F. Serum and cerebrospinal fluid levels of s-100β is a biomarker for spinal cord injury; a systematic review and meta-analysis. Arch Acad Emerg Med. 2019;7:e19. [PMC free article] [PubMed] [Google Scholar]
- 25.Ferbert T, Child C, Graeser V, Swing T, Akbar M, Heller R, Biglari B, Moghaddam A. Tracking spinal cord injury: differences in cytokine expression of igf-1, tgf- b1, and scd95l can be measured in blood samples and correspond to neurological remission in a 12-week follow-up. J Neurotrauma. 2017;34:607–614. doi: 10.1089/neu.2015.4294. [DOI] [PubMed] [Google Scholar]
- 26.Freund P, Seif M, Weiskopf N, Friston K, Fehlings MG, Thompson AJ, Curt A. MRI in traumatic spinal cord injury: from clinical assessment to neuroimaging biomarkers. Lancet Neurol. 2019;18:1123–1135. doi: 10.1016/S1474-4422(19)30138-3. [DOI] [PubMed] [Google Scholar]
- 27.Gaetani L, Blennow K, Calabresi P, Di Filippo M, Parnetti L, Zetterberg H. Neurofilament light chain as a biomarker in neurological disorders. J Neurol Neurosurg Psychiatry. 2019;90:870–881. doi: 10.1136/jnnp-2018-320106. [DOI] [PubMed] [Google Scholar]
- 28.Guan C, Wang Y. LncRNA CASC9 attenuates lactate dehydrogenase-mediated oxidative stress and inflammation in spinal cord injury via sponging miR-383-5p. Inflammation. 2021;44:923–933. doi: 10.1007/s10753-020-01387-7. [DOI] [PubMed] [Google Scholar]
- 29.Guez M, Hildingsson C, Rosengren L, Karlsson K, Toolanen G. Nervous tissue damage markers in cerebrospinal fluid after cervical spine injuries and whiplash trauma. J Neurotrauma. 2003;20:853–858. doi: 10.1089/089771503322385782. [DOI] [PubMed] [Google Scholar]
- 30.Guo X. Clinical diagnostic and therapeutic guidelines of stroke neurorestoration (2020 China version) J Neurorestoratol. 2020;8:241–251. [Google Scholar]
- 31.Hachisuka S, Kamei N, Ujigo S, Miyaki S, Yasunaga Y, Ochi M. Circulating microRNAs as biomarkers for evaluating the severity of acute spinal cord injury. Spinal Cord. 2014;52:596–600. doi: 10.1038/sc.2014.86. [DOI] [PubMed] [Google Scholar]
- 32.Hales M, Biros E, Reznik JE. Reliability and validity of the sensory component of the international standards for neurological classification of spinal cord injury (isncsci): a systematic review. Top Spinal Cord Inj Rehabil. 2015;21:241–249. doi: 10.1310/sci2103-241. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Hayakawa K, Okazaki R, Ishii K, Ueno T, Izawa N, Tanaka Y, Toyooka S, Matsuoka N, Morioka K, Ohori Y, Nakamura K, Akai M, Tobimatsu Y, Hamabe Y, Ogata T. Phosphorylated neurofilament subunit NF-H as a biomarker for evaluating the severity of spinal cord injury patients, a pilot study. Spinal Cord. 2012;50:493–496. doi: 10.1038/sc.2011.184. [DOI] [PubMed] [Google Scholar]
- 34.Hayes KC, Hull TC, Delaney GA, Potter PJ, Sequeira KA, Campbell K, Popovich PG. Elevated serum titers of proinflammatory cytokines and CNS autoantibodies in patients with chronic spinal cord injury. J Neurotrauma. 2002;19:753–761. doi: 10.1089/08977150260139129. [DOI] [PubMed] [Google Scholar]
- 35.Heller RA, Seelig J, Bock T, Haubruck P, Grützner PA, Schomburg L, Moghaddam A, Biglari B. Relation of selenium status to neuro-regeneration after traumatic spinal cord injury. J Trace Elem Med Biol. 2019;51:141–149. doi: 10.1016/j.jtemb.2018.10.006. [DOI] [PubMed] [Google Scholar]
- 36.Heller RA, Sperl A, Seelig J, Haubruck P, Bock T, Werner T, Besseling A, Sun Q, Schomburg L, Moghaddam A, Biglari B. Zinc concentration dynamics indicate neurological impairment odds after traumatic spinal cord injury. Antioxidants (Basel) 2020;9:21. doi: 10.3390/antiox9050421. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Holmström U, Tsitsopoulos PP, Holtz A, Salci K, Shaw G, Mondello S, Marklund N. Cerebrospinal fluid levels of GFAP and pNF-H are elevated in patients with chronic spinal cord injury and neurological deterioration. Acta Neurochir (Wien) 2020;162:2075–2086. doi: 10.1007/s00701-020-04422-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Hu XC, Lu YB, Yang YN, Kang XW, Wang YG, Ma B, Xing S. Progress in clinical trials of cell transplantation for the treatment of spinal cord injury: how many questions remain unanswered? Neural Regen Res. 2021;16:405–413. doi: 10.4103/1673-5374.293130. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Huang H, Young W, Skaper S, Chen L, Moviglia G, Saberi H, Al-Zoubi Z, Sharma HS, Muresanu D, Sharma A, El Masry W, Feng S. Clinical neurorestorative therapeutic guidelines for spinal cord injury (IANR/CANR version 2019) J Orthop Translat. 2020;20:14–24. doi: 10.1016/j.jot.2019.10.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Hulme CH, Brown SJ, Fuller HR, Riddell J, Osman A, Chowdhury J, Kumar N, Johnson WE, Wright KT. The developing landscape of diagnostic and prognostic biomarkers for spinal cord injury in cerebrospinal fluid and blood. Spinal Cord. 2017;55:114–125. doi: 10.1038/sc.2016.174. [DOI] [PubMed] [Google Scholar]
- 41.Jiang ZS, Zhang JR. LncRNA SNHG5 enhances astrocytes and microglia viability via upregulating KLF4 in spinal cord injury. Int J Biol Macromol. 2018;120:66–72. doi: 10.1016/j.ijbiomac.2018.08.002. [DOI] [PubMed] [Google Scholar]
- 42.Khetani S, Kollath VO, Eastick E, Debert C, Sen A, Karan K, Sanati-Nezhad A. Single-step functionalization of poly-catecholamine nanofilms for ultra-sensitive immunosensing of ubiquitin carboxyl terminal hydrolase-L1 (UCHL-1) in spinal cord injury. Biosens Bioelectron. 2019;145:111715. doi: 10.1016/j.bios.2019.111715. [DOI] [PubMed] [Google Scholar]
- 43.Kijima K, Kubota K, Hara M, Kobayakawa K, Yokota K, Saito T, Yoshizaki S, Maeda T, Konno D, Matsumoto Y, Nakashima Y, Okada S. The acute phase serum zinc concentration is a reliable biomarker for predicting the functional outcome after spinal cord injury. EBioMedicine. 2019;41:659–669. doi: 10.1016/j.ebiom.2019.03.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Kuhle J, Gaiottino J, Leppert D, Petzold A, Bestwick JP, Malaspina A, Lu CH, Dobson R, Disanto G, Norgren N, Nissim A, Kappos L, Hurlbert J, Yong VW, Giovannoni G, Casha S. Serum neurofilament light chain is a biomarker of human spinal cord injury severity and outcome. J Neurol Neurosurg Psychiatry. 2015;86:273–279. doi: 10.1136/jnnp-2013-307454. [DOI] [PubMed] [Google Scholar]
- 45.Kwon BK, Bloom O, Wanner IB, Curt A, Schwab JM, Fawcett J, Wang KK. Neurochemical biomarkers in spinal cord injury. Spinal Cord. 2019;57:819–831. doi: 10.1038/s41393-019-0319-8. [DOI] [PubMed] [Google Scholar]
- 46.Kwon BK, Stammers AM, Belanger LM, Bernardo A, Chan D, Bishop CM, Slobogean GP, Zhang H, Umedaly H, Giffin M, Street J, Boyd MC, Paquette SJ, Fisher CG, Dvorak MF. Cerebrospinal fluid inflammatory cytokines and biomarkers of injury severity in acute human spinal cord injury. J Neurotrauma. 2010;27:669–682. doi: 10.1089/neu.2009.1080. [DOI] [PubMed] [Google Scholar]
- 47.Kwon BK, Streijger F, Fallah N, Noonan VK, Belanger LM, Ritchie L, Paquette SJ, Ailon T, Boyd MC, Street J, Fisher CG, Dvorak MF. Cerebrospinal fluid biomarkers to stratify injury severity and predict outcome in human traumatic spinal cord injury. J Neurotrauma. 2017;34:567–580. doi: 10.1089/neu.2016.4435. [DOI] [PubMed] [Google Scholar]
- 48.Liu XG, Li T, Chen FM, Han DF, Shi M. Effects of MRI in the diagnosis and prognosis of cervical spinal cord injury without fracture or dislocation. Zhongguo Zuzhi Gongcheng Yanjiu. 2017;21:5036–5041. [Google Scholar]
- 49.McDonald JW, Sadowsky C. Spinal-cord injury. Lancet. 2002;359:417–425. doi: 10.1016/S0140-6736(02)07603-1. [DOI] [PubMed] [Google Scholar]
- 50.Moghaddam A, Heller R, Daniel V, Swing T, Akbar M, Gerner HJ, Biglari B. Exploratory study to suggest the possibility of MMP-8 and MMP-9 serum levels as early markers for remission after traumatic spinal cord injury. Spinal Cord. 2017;55:8–15. doi: 10.1038/sc.2016.104. [DOI] [PubMed] [Google Scholar]
- 51.Moghaddam A, Sperl A, Heller R, Kunzmann K, Graeser V, Akbar M, Gerner HJ, Biglari B. Elevated serum insulin-like growth factor 1 levels in patients with neurological remission after traumatic spinal cord injury. PLoS One. 2016;11:e0159764. doi: 10.1371/journal.pone.0159764. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Mortazavi MM, Verma K, Harmon OA, Griessenauer CJ, Adeeb N, Theodore N, Tubbs RS. The microanatomy of spinal cord injury: a review. Clin Anat. 2015;28:27–36. doi: 10.1002/ca.22432. [DOI] [PubMed] [Google Scholar]
- 53.Nieto-Diaz M, Esteban FJ, Reigada D, Munoz-Galdeano T, Yunta M, Caballero-Lopez M, Navarro-Ruiz R, Del Aguila A, Maza RM. MicroRNA dysregulation in spinal cord injury: causes, consequences and therapeutics. Front Cell Neurosci. 2014;8:53. doi: 10.3389/fncel.2014.00053. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Ogurcov S, Shulman I, Garanina E, Sabirov D, Baichurina I, Kuznetcov M, Masgutova G, Kostennikov A, Rizvanov A, James V, Mukhamedshina Y. Blood serum cytokines in patients with subacute spinal cord injury: a pilot study to search for biomarkers of injury severity. Brain Sci. 2021;11:322. doi: 10.3390/brainsci11030322. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Olby NJ, Lim JH, Wagner N, Zidan N, Early PJ, Mariani CL, Muñana KR, Laber E. Time course and prognostic value of serum GFAP, pNFH, and S100β concentrations in dogs with complete spinal cord injury because of intervertebral disc extrusion. J Vet Intern Med. 2019;33:726–734. doi: 10.1111/jvim.15439. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Pouw MH, Kwon BK, Verbeek MM, Vos PE, van Kampen A, Fisher CG, Street J, Paquette SJ, Dvorak MF, Boyd MC, Hosman AJ, van de Meent H. Structural biomarkers in the cerebrospinal fluid within 24 h after a traumatic spinal cord injury: a descriptive analysis of 16 subjects. Spinal Cord. 2014;52:428–433. doi: 10.1038/sc.2014.26. [DOI] [PubMed] [Google Scholar]
- 57.Quan Z, Zheng D, Qing H. Regulatory roles of long non-coding RNAs in the central nervous system and associated neurodegenerative diseases. Front Cell Neurosci. 2017;11:175. doi: 10.3389/fncel.2017.00175. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Rubenstein R, Chang B, Davies P, Wagner AK, Robertson CS, Wang KK. A novel, ultrasensitive assay for tau: potential for assessing traumatic brain injury in tissues and biofluids. J Neurotrauma. 2015;32:342–352. doi: 10.1089/neu.2014.3548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Seelig J, Heller RA, Hackler J, Haubruck P, Moghaddam A, Biglari B, Schomburg L. Selenium and copper status -potential signposts for neurological remission after traumatic spinal cord injury. J Trace Elem Med Biol. 2020;57:126415. doi: 10.1016/j.jtemb.2019.126415. [DOI] [PubMed] [Google Scholar]
- 60.Seif M, Gandini Wheeler-Kingshott CA, Cohen-Adad J, Flanders AE, Freund P. Guidelines for the conduct of clinical trials in spinal cord injury: neuroimaging biomarkers. Spinal Cord. 2019;57:717–728. doi: 10.1038/s41393-019-0309-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Sharif S, Jazaib Ali MY. Outcome prediction in spinal cord injury: myth or reality. World Neurosurg. 2020;140:574–590. doi: 10.1016/j.wneu.2020.05.043. [DOI] [PubMed] [Google Scholar]
- 62.Shaw G, Yang C, Ellis R, Anderson K, Parker Mickle J, Scheff S, Pike B, Anderson DK, Howland DR. Hyperphosphorylated neurofilament NF-H is a serum biomarker of axonal injury. Biochem Biophys Res Commun. 2005;336:1268–1277. doi: 10.1016/j.bbrc.2005.08.252. [DOI] [PubMed] [Google Scholar]
- 63.Shi Z, Ning G, Zhang B, Yuan S, Zhou H, Pan B, Li J, Wei Z, Cao F, Kong X, Feng S. Signatures of altered long noncoding RNAs and messenger RNAs expression in the early acute phase of spinal cord injury. J Cell Physiol. 2019;234:8918–8927. doi: 10.1002/jcp.27560. [DOI] [PubMed] [Google Scholar]
- 64.Shi Z, Zhou H, Lu L, Li X, Fu Z, Liu J, Kang Y, Wei Z, Pan B, Liu L, Kong X, Feng S. The roles of microRNAs in spinal cord injury. Int J Neurosci. 2017;127:1104–1115. doi: 10.1080/00207454.2017.1323208. [DOI] [PubMed] [Google Scholar]
- 65.Sperl A, Heller RA, Biglari B, Haubruck P, Seelig J, Schomburg L, Bock T, Moghaddam A. The role of magnesium in the secondary phase after traumatic spinal cord injury: a prospective clinical observer study. Antioxidants (Basel) 2019;8:509. doi: 10.3390/antiox8110509. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Stukas S, Gill J, Cooper J, Belanger L, Ritchie L, Tsang A, Dong K, Streijger F, Street J, Paquette S, Ailon T, Dea N, Charest-Morin R, Fisher CG, Dhall S, Mac-Thiong JM, Wilson JR, Bailey C, Christie S, Dvorak MF, et al. Characterization of cerebrospinal fluid ubiquitin c-terminal hydrolase L1 (UCH-L1) as a biomarker of human acute traumatic spinal cord injury. J Neurotrauma. 2021 doi: 10.1089/neu.2020.7352. doi: 10.1089/neu.2020.7352. [DOI] [PubMed] [Google Scholar]
- 67.Tang Y, Liu HL, Min LX, Yuan HS, Guo L, Han PB, Lu YX, Zhong JF, Wang DL. Serum and cerebrospinal fluid tau protein level as biomarkers for evaluating acute spinal cord injury severity and motor function outcome. Neural Regen Res. 2019;14:896–902. doi: 10.4103/1673-5374.249238. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Thomas Fp Md MAPMS, Murphy CP. From ISNCSCI to outcomes: exploring topics in injury and dysfunction of the spinal cord. J Spinal Cord Med. 2018;41:623. doi: 10.1080/10790268.2018.1527579. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Tigchelaar S, Streijger F, Sinha S, Flibotte S, Manouchehri N, So K, Shortt K, Okon E, Rizzuto MA, Malenica I, Courtright-Lim A, Eisen A, Keuren-Jensen KV, Nislow C, Kwon BK. Serum micrornas reflect injury severity in a large animal model of thoracic spinal cord injury. Sci Rep. 2017;7:1376. doi: 10.1038/s41598-017-01299-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Wang F, Liu J, Wang X, Chen J, Kong Q, Ye B, Li Z. The emerging role of lncrnas in spinal cord injury. Biomed Res Int 2019. 2019 doi: 10.1155/2019/3467121. 3467121. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Wang KK, Yang Z, Zhu T, Shi Y, Rubenstein R, Tyndall JA, Manley GT. An update on diagnostic and prognostic biomarkers for traumatic brain injury. Expert Rev Mol Diagn. 2018;18:165–180. doi: 10.1080/14737159.2018.1428089. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 72.Wolf H, Krall C, Pajenda G, Leitgeb J, Bukaty AJ, Hajdu S, Sarahrudi K. Alterations of the biomarker S-100B and NSE in patients with acute vertebral spine fractures. Spine J. 2014;14:2918–2922. doi: 10.1016/j.spinee.2014.04.027. [DOI] [PubMed] [Google Scholar]
- 73.Xu J, E X, Liu H, Li F, Cao Y, Tian J, Yan J. Tumor necrosis factor-alpha is a potential diagnostic biomarker for chronic neuropathic pain after spinal cord injury. Neurosci Lett. 2015;595:30–34. doi: 10.1016/j.neulet.2015.04.004. [DOI] [PubMed] [Google Scholar]
- 74.Yang Z, Bramlett HM, Moghieb A, Yu D, Wang P, Lin F, Bauer C, Selig TM, Jaalouk E, Weissman AS, Rathore DS, Romo P, Zhang Z, Hayes RL, Wang MY, Dietrich WD, Wang KKW. Temporal profile and severity correlation of a panel of rat spinal cord injury protein biomarkers. Mol Neurobiol. 2018;55:2174–2184. doi: 10.1007/s12035-017-0424-7. [DOI] [PubMed] [Google Scholar]
- 75.Yuan A, Rao MV, Veeranna , Nixon RA. Neurofilaments and neurofilament proteins in health and disease. Cold Spring Harb Perspect Biol. 2017;9:a018309. doi: 10.1101/cshperspect.a018309. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Zhang H, Wang W, Li N, Li P, Liu M, Pan J, Wang D, Li J, Xiong Y, Xia L. LncRNA DGCR5 suppresses neuronal apoptosis to improve acute spinal cord injury through targeting PRDM5. Cell Cycle. 2018;17:1992–2000. doi: 10.1080/15384101.2018.1509622. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.Zhou H, Shi Z, Kang Y, Wang Y, Lu L, Pan B, Liu J, Li X, Liu L, Wei Z, Kong X, Feng S. Investigation of candidate long noncoding RNAs and messenger RNAs in the immediate phase of spinal cord injury based on gene expression profiles. Gene. 2018;661:119–125. doi: 10.1016/j.gene.2018.03.074. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
