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Integrated traditional Chinese medicine alleviates sciatica while regulating gene expression in peripheral blood

Abstract

Background

Although integrated traditional Chinese medicine (TCM) has long been indicated to be effective in the treatment of sciatica and is widely used in the management of this condition, the mechanism by which integrated TCM alleviates sciatica has not yet been fully defined, and the effect of integrated TCM on gene expression in the peripheral blood of patients with sciatica is still unknown. We performed this study to investigate the effect of integrated TCM on peripheral blood gene expression in patients with sciatica and to explore new clues for studying the mechanism of integrated TCM in alleviating sciatica.

Methods

We used a microarray to identify differentially expressed genes (DEGs) in the peripheral blood of patients with sciatica and healthy controls (DEGs-baseline), bioinformatic analysis to reveal the characteristics of DEGs-baseline, and the key genes that contribute to the gene dysregulation. A microarray was also used to identify DEGs in the peripheral blood of patients with sciatica after integrated TCM treatment compared with those at baseline, and the expression levels of DEGs were validated by qRT-PCR.

Results

We identified 153 DEGs-baseline, which included 131 upregulated genes and 22 downregulated genes. Bioinformatic analysis revealed that most of the DEGs-baseline were related to immunity and the inflammatory response and that TLR4, MMP9, MPO, CAMP, RETN, TLR5, and IL1RN were key genes involved in the dysregulation of genes in the peripheral blood of patients with sciatica. The expression levels of TLR5, IL1RN, SLC8A1, RBM20, GPER1, IL27, SOCS1, and GRTP1-AS1 were decreased in the peripheral blood of patients after integrated TCM treatment compared with that at baseline, which was accompanied by relief of pain.

Conclusion

Integrated TCM treatment relieved pain while regulating the gene expression of TLR5, IL1RN, SLC8A1, RBM20, GPER1, IL27, SOCS1, and GRTP1-AS1 in the peripheral blood of patients with sciatica. Our study provides new clues for studying the mechanism of TCM in treating sciatica.

Background

Sciatica is characterized by neuropathic pain in the sciatic nerve, and the lifetime incidence is up to 40% [1]. Lumbar disc herniation is a major cause of sciatica and a major concern in studies about sciatica [2]. It is generally recognized that during lumbar disc herniation, mechanical compression in combination with immunity and inflammation causes sciatica. Many studies have revealed inflammatory transcriptome characteristics of degenerated discs [3,4,5,6], and many cytokines related to immunity and inflammation were activated in herniated lumbar discs [7,8,9,10]. However, since sciatica involves a focal lesion, gene expression changes in peripheral blood caused by sciatica have rarely been studied.

Many methods have been reported to improve sciatica, and 90% of acute sciatica can be effectively relieved by nonsurgical treatment [11]. Integrated traditional Chinese medicine (TCM) has long been indicated to be effective in the treatment of sciatica and is widely used in the management of this condition [12,13,14]. However, the mechanism by which integrated TCM relieves sciatica has not yet been fully defined. Therefore, we performed the present study to investigate the gene expression changes after integrated TCM treatment in the peripheral blood of patients with sciatica, aiming to explore new clues for studying the role of traditional Chinese medicine in alleviating sciatica.

Methods

Enrollment and integrated TCM treatment

This study was approved by the Ethics Committee of Sichuan Provincial Orthopedics Hospital, and written informed consent was obtained. From April 2018 to December 2019, 25 patients and 25 healthy volunteers were enrolled in this observational study. Patients were enrolled from those who chose to receive nonsurgical treatment with integrated TCM in our hospital. Diagnostic criteria of sciatica was set according to the guidelines of the North American Spine Society [15]: (1) a history of pain in the distribution of sciatic nerve, (2) a positive Lasegue’s sign, and (3) had findings on magnetic resonance imaging of lumbar disc herniation at the L4/5 level or L5/S1 level with compression of the corresponding nerve root. The patient inclusion criteria were as follows: (1) aged between 18 and 60 years, (2) had sciatica secondary to single-level lumbar disc herniation at the L4/5 level or L5/S1 level, and (3) had no medication history within 3 months before the appointment. The exclusion criteria were as follows: (1) concomitant cauda equina syndrome, other neuropathy, other spinal diseases, infection, rheumatism, cardiovascular disease, metabolic disease, hematopoietic diseases, dementia, consciousness disturbance, or mental illness; (2) a history of surgery, congenital disease, tuberculosis, or tumors; (3) pregnancy or lactation; and (4) other medical conditions that might affect gene expression in peripheral blood. Healthy volunteers were recruited as healthy controls. All patients underwent standard integrated TCM treatment for sciatica according to our medical protocol, including electric acupuncture, massage, moxibustion, and Chinese medicine fumigation for waist circumference. The pain intensity score on the visual analogue scale (VAS, ranging from 0 to 10, with higher scores indicating greater pain intensity) was recorded.

Peripheral blood collection, RNA extraction, and microarray analysis

Peripheral blood was collected and frozen in PAXgene tubes, and then, total RNA was extracted and purified using the PAXgeneTM Blood RNA Kit (Qiagen, Germany). An Agilent SurePrint G3 Microarray (8 × 60 K) was used for the microarray experiment, which was performed by Shanghai Biotech Co. (Shanghai, China). These procedures were described in our previous study [16].

Differentially expressed genes (DEGs)

Two groups of DEGs were identified: DEGs in the peripheral blood of patients with sciatica and healthy controls (DEGs-baseline) and DEGs in the peripheral blood of patients with sciatica after integrated TCM treatment compared with those at baseline (DEGs-TCM).

Data from the chip scan were log2 normalized for comparative analyses. Unrecognized probes were discarded. We deleted unrecognized probes. The average value of the data from the probes corresponding to the same gene was used for analysis. Genes with an absolute fold change (FC) of ≥ 1.5 were identified as DEGs, and the FC data were filtered by t tests (P < 0.05). Gene expression data sets are accessible on the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo) under the number GSE150408 and GSE124272.

Enrichment analysis of DEGs-baseline

Enrichment analysis was performed with the web-based portal Metascape [17], which included Gene Ontology (GO) analysis of biological processes (BP), cellular components (CC), and molecular functions (MF), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.

Protein–protein interaction (PPI) network

To identify key genes in the dysregulation of gene expression in the peripheral blood of patients at baseline and in healthy controls, we used the STRING database to construct the PPI network of DEGs-baseline with a combined score > 0.4. Cytoscape software (V3.6.1) was used to visualize the PPI network. Disconnected nodes were excluded, the degree of centrality of the DEGs-baseline was calculated, and a submodule was clustered, as we previously described [16].

Quantitative real-time quantitative PCR (qRT-PCR) and statistical analysis

qRT-PCR was performed to measure the expression levels of DEGs using the 2-ΔΔCq method. β-Actin was used as the normalization control for mRNA. The design and synthesis of the primers and the qRT-PCR procedure were described previously [16]. The primers are listed in Additional file 1. Significant differences in expression levels were determined using t test with a P value < 0.05. Statistical analysis was performed and visualized as we previously described [16].

Results

Patients and leg-pain scores

We enrolled 25 patients with sciatica aged between 19 and 54 years (mean age 40 years) who were seen in our department for integrated TCM treatment and 25 healthy volunteers aged between 19 and 30 years (mean age 23 years) as healthy controls. All patients underwent standard integrated TCM treatment for sciatica for at least 14 days. The VAS score for pain in patients decreased (P < 0.000), from an average of 7.4 ± 0.81 at baseline to 2.1 ± 0.97 14 days after integrated TCM treatment, indicating that all patients achieved clinical remission.

Microarray analysis

Microarray analysis identified 153 DEGs-baseline, which included 131 upregulated genes and 22 downregulated genes (Fig. 1). After integrated TCM treatment, 8 genes were differentially expressed in the peripheral blood of patients compared with those at baseline, and all of these genes were downregulated (Table 1). When we compared the DEGs-baseline and DEGs-TCM, we found that toll-like receptor 5 (TLR5), interleukin-1 receptor antagonist (IL1RN), and solute carrier family 8 member A1 (SLC8A1) were present in both groups (Table 1) and that TLR5, IL1RN, and SLC8A1 were upregulated in sciatica patients at baseline compared with healthy controls and were then downregulated in sciatica patients after integrated TCM treatment compared with baseline.

Fig. 1
figure1

Heatmap of the DEGs in the peripheral blood of patients with sciatica and healthy controls. The expression data were normalized using the z-score for indexes between − 3 and 3. Red, upregulated; blue, downregulated. P patient with sciatica, V healthy volunteer

Table 1 Differentially expressed genes. After TCM treatment, 8 genes were differentially expressed in the peripheral blood of patients compared with those at baseline. TLR5, IL1RN, and SLC8A1 were upregulated in sciatica patients at baseline compared with healthy controls and were then downregulated in sciatica patients after TCM treatment compared with baseline.

Enrichment analysis of DEGs-baseline

Enrichment analysis by Metascape revealed that upregulated genes at baseline were enriched in 115 GO BP, 25 GO CC, 4 GO MF, and 6 KEGG pathways (Table 2). Downregulated genes at baseline were enriched in 10 GO BP, 1 GO MF, and 2 KEGG pathways (Table 3). Most of the enriched terms for both upregulated and downregulated DEGs at baseline were related to immunity and the inflammatory response. Enrichment analysis revealed immune and inflammatory response-related gene expression characteristics in the peripheral blood of patients with sciatica.

Table 2 Gene Ontology analysis of upregulated DEGs-baseline. The top 15 enriched terms of the upregulated genes in the peripheral blood of the patients with sciatica at baseline compared with the healthy controls according to the P value. Log10(P) is the log10-based P value.
Table 3 Gene Ontology analysis of downregulated DEGs-baseline. All enriched terms of the downregulated genes in the peripheral blood of the patients with sciatica at baseline compared with the healthy controls according to the P value. Log10(P) is the log10-based P value.

PPI network

The PPI network of DEGs-baseline consisted of 77 connected nodes and 255 edges (Fig. 2). To identify key genes in the PPI network, the plug-in CentiScaPe was used to calculate the centrality degree of each node. Genes with a high degree centrality included toll-like receptor (TLR4, degree 26), matrix metallopeptidase 9 (MMP9, degree 22), myeloperoxidase (MPO, degree 20), cathelicidin antimicrobial peptide (CAMP, degree 18), resistin (RETN, degree 18), TLR5 (degree, 17), etc. The top 10 genes with the highest degree centrality are listed in Table 4. MCODE analysis identified 1 cluster in the PPI network with threshold k-core = 5. TLR4, MMP9, CAMP, and IL1RN were enriched in this cluster. The PPI network suggested that TLR4, MMP9, MPO, CAMP, RETN, TLR5, and IL1RN were key genes in the dysregulation of genes in the peripheral blood of patients with sciatica.

Fig. 2
figure2

PPI network of DEGs at baseline. Seventy-seven nodes with a combined score > 0.4 and 255 edges were included. The size of the nodes represents the centrality degree. One cluster was identified by MCODE analysis with threshold k-core = 5 and is colored yellow

Table 4 Top 10 genes with the highest centrality degree in the PPI network. Centrality degree was calculated by the CentiScaPe plug-in, and one cluster was identified by MCODE analysis with threshold k-core = 5

Expression of DEGs

We examined the expression levels of the key DEGs-baseline (TLR4, MMP9, MPO, CAMP, RETN, TLR5, and IL1RN) in the peripheral blood of patients at baseline and in the peripheral blood of healthy controls, as well as expression levels of DEGs-TCM in the peripheral blood of patients at baseline and in the peripheral blood of patients after integrated TCM treatment.

qRT-PCR showed that the expression levels of TLR4, MMP9, MPO, CAMP, RETN, TLR5, and IL1RN were increased in the peripheral blood of patients at baseline compared with the healthy controls (Fig. 3); the expression levels of TLR5, IL1RN, SLC8A1, RNA binding motif protein 20 (RBM20), G protein-coupled estrogen receptor 1 (GPER1), interleukin 27 (IL27), suppressor of cytokine signalling 1 (SOCS1), and GRTP1 antisense RNA 1 (GRTP1-AS1) were decreased in the peripheral blood of the patients after integrated TCM treatment compared with that at baseline (Fig. 4).

Fig. 3
figure3

Expression of the key DEGs contributing to dysregulation of gene expression in patients with sciatica. *P < 0.05

Fig. 4
figure4

Expression of DEGs of patients with sciatica after integrated TCM treatment compared with those at baseline. *P < 0.05

Discussion

With microarray analysis, we identified 153 DEGs in the peripheral blood of sciatica patients and healthy controls. Bioinformatic analysis revealed that these DEGs were mainly related to immunity and the inflammatory response and that TLR4, MMP9, MPO, CAMP, RETN, TLR5, and IL1R were the most important genes contributing to the dysregulation of these DEGs.

In the PPI network of DEGs-baseline, TLR4 had the highest centrality degree, which was consistent with our previous study [16]. TLR4 is the most studied Toll-like receptor because it plays a broad role in the inflammatory response [18]. It is also involved in innate neuroimmunity and neuropathy and mediates inflammatory and neuropathic pain [19]. An animal model confirmed that the expression of TLR4 in intervertebral discs and activation of TLR4 in intervertebral discs induced an inflammatory response, including the upregulation of tumor necrosis factor α, IL-1β, IL-6, and nitric oxide [20]. The inhibition of TLR4 in discs reduced inflammation and reversed pain-related neuroplasticity, suggesting that TLR4 is a potential target for treating disc-related inflammatory and neuropathic pain [21]. MMP9 is associated with sciatica in both peripheral blood and local discs. MMP9 has long been known to degrade collagen. It is expressed in intervertebral discs, which are full of collagen [22], and the expression levels of MMP9 decrease in free, protruded, and extruded discs [23]. MMP 9 is also involved in the activation and inactivation of inflammation, although the mechanism has not been completely elucidated [24]. Focal MMP9 causes leukocytes to migrate from peripheral blood to tissues by promoting a chemotactic gradient, which has a role in the neuroinflammatory process [25]. MPO is mainly produced by circulating neutrophils and is linked to inflammatory conditions and degenerative neurological disorders. By producing hypochlorous acid-sphingomyelinase, which is released as a source of reactive oxygen species, MPO indirectly mediates inflammatory injury [26]. In stroke patients, MPO expression is increased in both the plasma and the serum [27]. The expression of MPO is increased in the brains of patients with Alzheimer’s or Parkinson’s disease [28, 29], and MPO has been reported to be activated in human multiple sclerosis plaques [30]. The exact mechanism of MPO in these neurological diseases is not completely clear. RETN levels were reported to be associated with inflammatory cytokines, including TNF-α receptor-2, IL-6, ICAM-1, lipoprotein-associated PLA2, and C-reactive protein [31, 32]. RETN also upregulates TNF-α and IL-6 in human peripheral blood mononuclear cells as well as TNF-α and IL-12 in human macrophages [33, 34]. Combining these studies, a potential link between TLR4, MMP9, MPO, CAMP, RETN, and sciatica can be established, but microarray analysis showed that the expression of these genes was not changed after integrated TCM treatment. Integrated TCM treatment does not alleviate sciatica by regulating these genes.

After integrated TCM treatment, the pain of the patients was relieved, and the expression levels of TLR5, IL1RN, SLC8A1, RBM20, GPER1, IL27, SOCS1, and GRTP1-AS1 in peripheral blood were downregulated compared with baseline. We also found that TLR5, IL1RN, and SLC8A1 were upregulated in sciatica patients at baseline compared with healthy controls and downregulated after integrated TCM treatment compared with baseline, indicating that these genes played an important role in the pathogenesis and remission of sciatica. In the future, the expression of these genes can be considered as an objective indicator for evaluating the efficacy of integrated TCM in treating sciatica in the clinical setting.

Animal experiments have shown that the knockout or blockade of TLR5 can reduce pain [35, 36], which is consistent with our finding that TLR5 expression was downregulated in patients with pain relief after integrated TCM treatment. IL1RN polymorphism and genetic variability were reported to be associated with outcomes of intervetebral disc disease [37, 38], but no studies have elaborated the role of IL1RN mRNA expression in sciatica. The roles of SLC8A1, RBM20, GPER1, IL27, SOCS1, and GRTP1-AS1 in sciatica or in relieving sciatica are currently unknown. Future studies should further investigate the role of these genes in sciatica remission.

Our study had limitations. Some other factors, such as miRNAs and lncRNAs, are involved in the process of protein translation from mRNA, and protein expression is not only determined by mRNA. We did not perform western blotting to measure the protein levels of these genes, and the protein levels and mRNA levels of these genes may not be consistent. Changes in gene expression and alleviation of sciatica were simultaneously observed after integrated TCM treatment in the present study; whether integrated TCM treatment relieved sciatica by regulating the expression of these genes still needs further investigation.

Conclusions

In conclusion, we found that integrated TCM treatment alleviated sciatica while regulating the gene expression of TLR5, IL1RN, SLC8A1, RBM20, GPER1, IL27, SOCS1, and GRTP1-AS1 in the peripheral blood of patients. Our study provides new clues for studying the mechanism of TCM in treating sciatica.

Availability of data and materials

The gene expression datasets generated and analyzed during the current study are available in the GEO database (http://www.ncbi.nlm.nih.gov/geo) under the number GSE150408 and GSE124272. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

TCM:

Traditional Chinese medicine

DEGs:

Differentially expressed genes

DEGs-baseline:

DEGs in the peripheral blood of patients with sciatica and healthy controls

DEGs-TCM:

DEGs in the peripheral blood of patients with sciatica after integrated TCM treatment compared with those at baseline

GO:

Gene ontology

BP:

Biological processes

CC:

Cellular components

MF:

Molecular functions

KEGG:

Kyoto Encyclopedia of Genes and Genomes

PPI:

Protein–protein interaction

qRT-PCR:

Quantitative real-time quantitative PCR

TLR5:

Toll-like receptor 5

IL1RN:

Interleukin 1 receptor antagonist

SLC8A1:

Solute carrier family 8 member A1

TLR4:

Toll-like receptor

MMP9:

Matrix metallopeptidase 9

MPO:

Myeloperoxidase

CAMP:

Cathelicidin antimicrobial peptide

RETN:

Resistin

RBM20:

RNA-binding motif protein 20

GPER1:

G protein-coupled estrogen receptor 1

IL27:

Interleukin 27

SOCS1:

Suppressor of cytokine signalling 1

GRTP1-AS1:

GRTP1 antisense RNA 1

References

  1. 1.

    Stafford MA, Peng P, Hill DA. Sciatica: a review of history, epidemiology, pathogenesis, and the role of epidural steroid injection in management. Br J Anaesth. 2007;99(4):461–73.

    CAS  Article  Google Scholar 

  2. 2.

    Valat JP, Genevay S, Marty M, Rozenberg S, Koes B. Sciatica. Best Pract Res Clin Rheumatol. 2010;24(2):241–52.

    Article  Google Scholar 

  3. 3.

    Wang Y, Jiang L, Dai G, Li S, Mu X. Bioinformatics analysis reveals different gene expression patterns in the annulus fibrosis and nucleus pulpous during intervertebral disc degeneration. Exp Ther Med. 2018;16(6):5031–40.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Zhu Z, Chen G, Jiao W, Wang D, Cao Y, Zhang Q, et al. Identification of critical genes in nucleus pulposus cells isolated from degenerated intervertebral discs using bioinformatics analysis. Mol Med Rep. 2017;16(1):553–64.

    CAS  Article  Google Scholar 

  5. 5.

    Guo W, Zhang B, Li Y, Duan HQ, Sun C, Xu YQ, et al. Gene expression profile identifies potential biomarkers for human intervertebral disc degeneration. Mol Med Rep. 2017;16(6):8665–72.

    CAS  Article  Google Scholar 

  6. 6.

    Xu F, Gao F, Liu Y, Wang Z, Zhuang X, Qu Z, et al. Bioinformatics analysis of molecular mechanisms involved in intervertebral disc degeneration induced by TNF-alpha and IL-1beta. Mol Med Rep. 2016;13(3):2925–31.

    CAS  Article  Google Scholar 

  7. 7.

    Purmessur D, Walter BA, Roughley PJ, Laudier DM, Hecht AC, Iatridis J. A role for TNFalpha in intervertebral disc degeneration: a non-recoverable catabolic shift. Biochem Biophys Res Commun. 2013;433(1):151–6.

    CAS  Article  Google Scholar 

  8. 8.

    Matsui Y, Maeda M, Nakagami W, Iwata H. The involvement of matrix metalloproteinases and inflammation in lumbar disc herniation. Spine (Phila Pa 1976). 1998;23(8):863–8 discussion 8-9.

    CAS  Article  Google Scholar 

  9. 9.

    Shamji MF, Setton LA, Jarvis W, So S, Chen J, Jing L, et al. Proinflammatory cytokine expression profile in degenerated and herniated human intervertebral disc tissues. Arthritis Rheum. 2010;62(7):1974–82.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    McCarron RF, Wimpee MW, Hudkins PG, Laros GS. The inflammatory effect of nucleus pulposus. A possible element in the pathogenesis of low-back pain. Spine (Phila Pa 1976). 1987;12(8):760–4.

    CAS  Article  Google Scholar 

  11. 11.

    Bailey CS, Rasoulinejad P, Taylor D, Sequeira K, Miller T, Watson J, et al. Surgery versus conservative care for persistent sciatica lasting 4 to 12 months. N Engl J Med. 2020;382(12):1093–102.

    Article  Google Scholar 

  12. 12.

    Mo Z, Li D, Zhang R, Chang M, Yang B, Tang S. Comparisons of the effectiveness and safety of tuina, acupuncture, traction, and Chinese herbs for lumbar disc herniation: a systematic review and network meta-analysis. Evid Based Complement Alternat Med. 2019;2019:6821310.

    Article  Google Scholar 

  13. 13.

    Tang S, Mo Z, Zhang R. Acupuncture for lumbar disc herniation: a systematic review and meta-analysis. Acupunct Med. 2018;36(2):62–70.

    Article  Google Scholar 

  14. 14.

    Long Y. Treatment of protrusion of the lumbar intervertebral disc by TCM massage. J Tradit Chin Med. 2002;22(2):126–7.

    PubMed  Google Scholar 

  15. 15.

    Kreiner DS, Hwang SW, Easa JE, Resnick DK, Baisden JL, Bess S, et al. An evidence-based clinical guideline for the diagnosis and treatment of lumbar disc herniation with radiculopathy. Spine J. 2014;14(1):180–91.

    Article  Google Scholar 

  16. 16.

    Wang Y, Dai G, Li L, Liu L, Jiang L, Li S, et al. Transcriptome signatures reveal candidate key genes in the whole blood of patients with lumbar disc prolapse. Exp Ther Med. 2019;18(6):4591–602.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.

    Article  Google Scholar 

  18. 18.

    Lacagnina MJ, Watkins LR, Grace PM. Toll-like receptors and their role in persistent pain. Pharmacol Ther. 2018;184:145–58.

    CAS  Article  Google Scholar 

  19. 19.

    Ji RR, Chamessian A, Zhang YQ. Pain regulation by non-neuronal cells and inflammation. Science. 2016;354(6312):572–7.

    CAS  Article  Google Scholar 

  20. 20.

    Rajan NE, Bloom O, Maidhof R, Stetson N, Sherry B, Levine M, et al. Toll-like receptor 4 (TLR4) expression and stimulation in a model of intervertebral disc inflammation and degeneration. Spine (Phila Pa 1976). 2013;38(16):1343–51.

    Article  Google Scholar 

  21. 21.

    Krock E, Millecamps M, Currie JB, Stone LS, Haglund L. Low back pain and disc degeneration are decreased following chronic toll-like receptor 4 inhibition in a mouse model. Osteoarthr Cartil. 2018;26(9):1236–46.

    Article  Google Scholar 

  22. 22.

    Basaran R, Senol M, Ozkanli S, Efendioglu M, Kaner T. Correlation of matrix metalloproteinase (MMP)-1, -2, -3, and -9 expressions with demographic and radiological features in primary lumbar intervertebral disc disease. J Clin Neurosci. 2017;41:46–9.

    Article  Google Scholar 

  23. 23.

    Li PB, Tang WJ, Wang K, Zou K, Che B. Expressions of IL-1alpha and MMP-9 in degenerated lumbar disc tissues and their clinical significance. Eur Rev Med Pharmacol Sci. 2017;21(18):4007–13.

    PubMed  Google Scholar 

  24. 24.

    Parks WC, Wilson CL, Lopez-Boado YS. Matrix metalloproteinases as modulators of inflammation and innate immunity. Nat Rev Immunol. 2004;4(8):617–29.

    CAS  Article  Google Scholar 

  25. 25.

    Hannocks MJ, Zhang X, Gerwien H, Chashchina A, Burmeister M, Korpos E, et al. The gelatinases, MMP-2 and MMP-9, as fine tuners of neuroinflammatory processes. Matrix Biol. 2019;75-76:102–13.

    CAS  Article  Google Scholar 

  26. 26.

    Pravalika K, Sarmah D, Kaur H, Wanve M, Saraf J, Kalia K, et al. Myeloperoxidase and neurological disorder: a crosstalk. ACS Chem Neurosci. 2018;9(3):421–30.

    CAS  Article  Google Scholar 

  27. 27.

    Tay A, Tamam Y, Yokus B, Ustundag M, Orak M. Serum myeloperoxidase levels in predicting the severity of stroke and mortality in acute ischemic stroke patients. Eur Rev Med Pharmacol Sci. 2015;19(11):1983–8.

    CAS  PubMed  Google Scholar 

  28. 28.

    Green PS, Mendez AJ, Jacob JS, Crowley JR, Growdon W, Hyman BT, et al. Neuronal expression of myeloperoxidase is increased in Alzheimer's disease. J Neurochem. 2004;90(3):724–33.

    CAS  Article  Google Scholar 

  29. 29.

    Gellhaar S, Sunnemark D, Eriksson H, Olson L, Galter D. Myeloperoxidase-immunoreactive cells are significantly increased in brain areas affected by neurodegeneration in Parkinson's and Alzheimer's disease. Cell Tissue Res. 2017;369(3):445–54.

    CAS  Article  Google Scholar 

  30. 30.

    Sospedra M, Martin R. Immunology of multiple sclerosis. Semin Neurol. 2016;36(2):115–27.

    Article  Google Scholar 

  31. 31.

    Pang SS, Le YY. Role of resistin in inflammation and inflammation-related diseases. Cell Mol Immunol. 2006;3(1):29–34.

    CAS  PubMed  Google Scholar 

  32. 32.

    Filkova M, Haluzik M, Gay S, Senolt L. The role of resistin as a regulator of inflammation: implications for various human pathologies. Clin Immunol. 2009;133(2):157–70.

    CAS  Article  Google Scholar 

  33. 33.

    Silswal N, Singh AK, Aruna B, Mukhopadhyay S, Ghosh S, Ehtesham NZ. Human resistin stimulates the pro-inflammatory cytokines TNF-alpha and IL-12 in macrophages by NF-kappaB-dependent pathway. Biochem Biophys Res Commun. 2005;334(4):1092–101.

    CAS  Article  Google Scholar 

  34. 34.

    Bokarewa M, Nagaev I, Dahlberg L, Smith U, Tarkowski A. Resistin, an adipokine with potent proinflammatory properties. J Immunol. 2005;174(9):5789–95.

    CAS  Article  Google Scholar 

  35. 35.

    Stokes JA, Cheung J, Eddinger K, Corr M, Yaksh TL. Toll-like receptor signaling adapter proteins govern spread of neuropathic pain and recovery following nerve injury in male mice. J Neuroinflammation. 2013;10:148.

    Article  Google Scholar 

  36. 36.

    Das N, Dewan V, Grace PM, Gunn RJ, Tamura R, Tzarum N, et al. HMGB1 activates proinflammatory signaling via TLR5 leading to allodynia. Cell Rep. 2016;17(4):1128–40.

    CAS  Article  Google Scholar 

  37. 37.

    Kim DH, Lee SH, Kim KT, Yu SD. Association of interleukin-1 receptor antagonist gene polymorphism with response to conservative treatment of lumbar herniated nucleus pulposus. Spine (Phila Pa 1976). 2010;35(16):1527–31.

    Article  Google Scholar 

  38. 38.

    Moen A, Schistad EI, Rygh LJ, Roe C, Gjerstad J. Role of IL1A rs1800587, IL1B rs1143627 and IL1RN rs2234677 genotype regarding development of chronic lumbar radicular pain; a prospective one-year study. PLoS One. 2014;9(9):e107301.

    Article  Google Scholar 

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Acknowledgements

Not applicable

Funding

This study was supported by the Special Scientific Research Project of Orthopedics (Shang An Tong) of Sichuan Medical Association (2019SAT06), Sichuan Science and Technology Program (2018SZ0075), and Sichuan Provincial Administration of Traditional Chinese Medicine (2020JC0103). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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Authors

Contributions

Yi Wang: conceptualization, project administration, methodology, investigation, software, validation, formal analysis, writing—original draft, visualization, and funding acquisition. Guogang Dai and Yan Xu: supervision, project administration, resources, validation, funding acquisition, investigation, and writing—review & editing. Ling Jiang and Zhibin Fu: methodology, software, validation, methodology, data curation, formal analysis, investigation, visualization, and writing—original draft. Jiao Xia and Guogang Tian: formal analysis, investigation, resources, data curation, and writing—original draft. Wanli Du: formal analysis, investigation, resources, data curation, and writing—original draft. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yi Wang.

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Ethical approval for this study was obtained from the Ethics Committee of the Sichuan Provincial Orthopedics Hospital. All participants provided informed written consent.

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The authors declare no conflict of interests.

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Supplementary Information

Additional file 1.

Sequences of primers used for qRT-PCR

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Wang, Y., Dai, G., Xu, Y. et al. Integrated traditional Chinese medicine alleviates sciatica while regulating gene expression in peripheral blood. J Orthop Surg Res 16, 130 (2021). https://doi.org/10.1186/s13018-021-02280-1

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Keywords

  • Sciatica
  • Peripheral blood
  • Gene expression
  • Traditional Chinese medicine