Early systemic immune biomarkers predict bone regeneration after trauma.
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ABSTRACT:
SUBMITTER: Cheng A
PROVIDER: S-EPMC7923361 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Bone nonunions arising from large bone defects and composite injuries remain compelling challenges for orthopedic surgeons. Biological changes associated with nonunions, such as systemic immune dysregulation, can contribute to an adverse healing environment. Bone morphogenetic protein 2 (BMP-2), an osteoinductive and potentially immunomodulatory growth factor, is a promising strategy; however, burst release from the clinical standard collagen sponge delivery vehicle can result in adverse side effects such as heterotopic ossification (HO) and irregular bone structure, especially when using supraphysiological BMP-2 doses for complex injuries at high risk for nonunion. To address this challenge, biomaterials that strongly bind BMP-2, such as heparin methacrylamide microparticles (HMPs), may be used to limit exposure and spatially constrain proteins within the injury site. Here, we investigate moderately high dose BMP-2 delivered in HMPs within an injectable hydrogel system in two challenging nonunion models exhibiting characteristics of systemic immune dysregulation. The HMP delivery system increased total bone volume and decreased peak HO compared to collagen sponge delivery of the same BMP-2 dose. Multivariate analyses of systemic immune markers showed the collagen sponge group correlated with markers that are hallmarks of systemic immune dysregulation, including immunosuppressive myeloid-derived suppressor cells, whereas the HMP groups were associated with immune effector cells, including T cells, and cytokines linked to robust bone regeneration. Overall, our results demonstrate that HMP delivery of moderately high doses of BMP-2 promotes repair of complex bone nonunion injuries and that local delivery strategies for potent growth factors like BMP-2 may positively affect the systemic immune response to traumatic injury.
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Project description:UnlabelledWe recently reported that subantimicrobial-dose doxycycline (SDD) significantly reduced serum bone-resorption biomarkers in subgroups of post-menopausal women. We hypothesize that changes in serum bone biomarkers are associated not only with systemic bone mineral density (BMD) changes, but also with alveolar bone changes over time. One hundred twenty-eight eligible post-menopausal women with periodontitis and systemic osteopenia were randomly assigned to receive SDD or placebo tablets twice daily for two years, adjunctive to periodontal maintenance. Sera were analyzed for bone biomarkers. As expected, two-year changes in a serum bone biomarker were significantly associated with systemic BMD loss at the lumbar spine (osteocalcin, bone-turnover biomarker, p = 0.0002) and femoral neck (osteocalcin p = 0.0025). Two-year changes in serum osteocalcin and serum pyridinoline-crosslink fragment of type I collagen (ICTP; bone-resorption biomarker) were also significantly associated with alveolar bone density loss (p < 0.0001) and alveolar bone height loss (p = 0.0008), respectively. Thus, we have shown that serum bone biomarkers are associated with not only systemic BMD loss, but with alveolar bone loss as well.Clinical trial registration informationProtocol registered at ClinicalTrials.gov, NCT00066027.
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Project description:IntroductionApproximately 30% of patients diagnosed with stage Ia-b NSCLC die of recurrent disease after surgery. This study aimed to identify immune-related biomarkers that might predict tumor recurrence in stage Ia-b NSCLC within 40 months after curative resection.MethodsGene expression data of stage Ia-b NSCLC samples was retrieved from the TCGA database, the GEO databases, and the Second Xiangya hospital (XXEYY) database. 22 types of tumors infiltrating immune cells and the expression of immune-associated genes were investigated using CIBERSORT, immunohistochemical staining, and GSEA analyses in a total of 450 patients (80 in the training cohort and 370 in the validation cohorts). Recurrence-related immune features were selected based on the LASSO Cox regression model.ResultsHigh density of Tregs, Macrophages M0 and M1 cell could be observed in recurrence group while the memory B cell was more frequently enriched in controls, yet Tregs alone was significantly associated with tumor early recurrence in TCGA cohort, XYEYY cohort and GSE37745 dataset. A handful of immune-related genes were identified in the recurrence group. Based on Lasso regression analysis, the expressions of five immune-related genes, RLTPR, SLFN13, MIR4500HG, HYDIN and TPRG1 were closely correlated with tumor early recurrence. In the training cohort (TCGA), the combination of these five genes has sensitivity and specificity of 85% and 85%, with AUC of 0.91 (95% CI 0.84-0.98) for lung cancer early recurrence prediction, whereas in validation cohorts, the sensitivity and specificity using this panel was 61-89% and 54-82%, with AUC of 0.62-0.84.ConclusionOur study demonstrated that the immune microenvironment signatures were closely related to tumor early recurrence. Compared to tumor-infiltrating lymphocytes, the expression of five immune-related genes could be robust biomarkers to predict early recurrence of stage Ia-b NSCLC after curative resection.
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Project description:BackgroundEffectively predicting the risk of adverse pregnancy outcome (APO) in women with systemic lupus erythematosus (SLE) during early and mid-pregnancy is a challenge. This study was aimed to identify potential markers for early prediction of APO risk in women with SLE.MethodsThe GSE108497 gene expression dataset containing 120 samples (36 patients, 84 controls) was downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed, and differentially expressed genes (DEGs) were screened to define candidate APO marker genes. Next, three individual machine learning methods, random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator, were combined to identify feature genes from the APO candidate set. The predictive performance of feature genes for APO risk was assessed using area under the receiver operating characteristic curve (AUC) and calibration curves. The potential functions of these feature genes were finally analyzed by conventional gene set enrichment analysis and CIBERSORT algorithm analysis.ResultsWe identified 321 significantly up-regulated genes and 307 down-regulated genes between patients and controls, along with 181 potential functionally associated genes in the WGCNA analysis. By integrating these results, we revealed 70 APO candidate genes. Three feature genes, SEZ6, NRAD1, and LPAR4, were identified by machine learning methods. Of these, SEZ6 (AUC = 0.753) showed the highest in-sample predictive performance for APO risk in pregnant women with SLE, followed by NRAD1 (AUC = 0.694) and LPAR4 (AUC = 0.654). After performing leave-one-out cross validation, corresponding AUCs for SEZ6, NRAD1, and LPAR4 were 0.731, 0.668, and 0.626, respectively. Moreover, CIBERSORT analysis showed a positive correlation between regulatory T cell levels and SEZ6 expression (P < 0.01), along with a negative correlation between M2 macrophages levels and LPAR4 expression (P < 0.01).ConclusionsOur preliminary findings suggested that SEZ6, NRAD1, and LPAR4 might represent the useful genetic biomarkers for predicting APO risk during early and mid-pregnancy in women with SLE, and enhanced our understanding of the origins of pregnancy complications in pregnant women with SLE. However, further validation was required.
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