TNF-α: A serological marker for evaluating the severity of hippocampal sclerosis in medial temporal lobe epilepsy?

OBJECTIVE: By analysing the difference in TNF-α levels in the peripheral blood of patients with medial temporal lobe epilepsy (mTLE) with or without hippocampal sclerosis and the correlation between TNF-α and N-acetylaspartate levels in the hippocampus, we explored the relationship between TNF-α and the degree of damage to hippocampal sclerosis neurons in medial temporal lobe epilepsy. METHODS: This is a prospective, population-based study. A total of 71 Patients with medial temporal lobe epilepsy diagnosed by clinical seizures, video-EEG, epileptic sequence MRI, and other imaging examinations were recruited from October 2020 to July 2022 in the Department of Neurology, Affiliated Hospital of Xuzhou Medical University. Twenty age-matched healthy subjects were selected as the control group. The patients were divided into two groups: the medial temporal epilepsy with hippocampal sclerosis group (positive group, mTLE-HS-P group) and the medial temporal epilepsy without hippocampal sclerosis group (negative group, mTLE-HS-N group). The levels of IL-1ß, IL-5, IL-6, IL-8, IL-17, IFN-γ and TNF-α in the peripheral blood of the patients in the three groups were detected by multimicrosphere flow immunofluorescence assay. The level of N-acetylaspartate (NAA) in the hippocampus was measured by 1H-MRS. The differences in cytokine levels among the three groups were analysed, and the correlation between cytokine and NAA levels was analysed. RESULTS: The level of TNF-α in the peripheral blood of the patients in the mTLE-HS-P group was significantly higher than that of the patients in the mTLE-HS-N and healthy control groups, and the level of TNF-α in the patients in the mTLE-HS-N group was significantly higher than that of the patients in the healthy control group. The NAA level in mTLE-HS-P group patients was significantly lower than that of mTLE-HS-N patients and healthy controls, but there was no significant difference between mTLE-HS-N patients and healthy controls (P > 0.05). Spearman correlation analysis showed that TNF-α level (rs = -0.437, P < 0.05) and the longest duration of a single seizure (rs = -0.398, P < 0.05) were negatively correlated with NAA level. Logistic regression analysis showed that there was no significant correlation between the longest duration of a single seizure and hippocampal sclerosis, but TNF-α level was closely related to hippocampal sclerosis in patients with mTLE (OR = 1.315, 95 % CI 1.084-1.595, P = 0.005). CONCLUSION: The level of TNF-α in the peripheral blood of patients with medial temporal lobe epilepsy with hippocampal sclerosis was higher, and it was correlated with NAA and hippocampal sclerosis. The high expression of TNF-α may be of important value in the evaluation of hippocampal sclerosis patients.

Bioactive 3D Electrohydrodynamic Printed Lattice Architectures Augment Tenogenesis of Tendon Stem/Progenitor Cells.

Tendon defect repair remains a tough clinical procedure that hinders functional motion in patients. Electrohydrodynamic (EHD) three-dimensional (3D) printing, as a novel strategy, can controllably fabricate biomimetic micro/nanoscale architecture, but the hydrophobic and bioinert nature of polymers might be adverse to cell-material interplay. In this work, 3D EHD printed polycaprolactone (PCL) was immobilized on basic fibroblast growth factor (bFGF) using polydopamine (PDA), and the proliferation and tenogenic differentiation of tendon stem/progenitor cells (TSPCs) in vitro was researched. A subcutaneous model was established to evaluate the effects of tenogenesis and immunomodulation. We then investigated the in situ implantation and immunomodulation effects in an Achilles tendon defect model. After immobilization of bFGF, the scaffolds profoundly facilitated proliferation and tenogenic differentiation; however, PDA had only a proliferative effect. Intriguingly, the bFGF immobilized on EHD printed PCL indicated a synergistic effect on the highest expression of tenogenic gene and protein markers at 14 days, and the tenogenesis may be induced by activating the transforming growth factor-ß (TGF-ß) signal pathway in vitro. The subcutaneous engraftment study confirmed a tendon-like structure, similar to that of the native tendon, as well as an M2 macrophage polarization effect. Additionally, the bioactive scaffold exhibited superior efficacy in new collagen formation and repair of Achilles tendon defects. Our study revealed that the topographic cues alone were insufficient to trigger tenogenic differentiation, requiring appropriate chemical signals, and that appropriate immunomodulation was conducive to tenogenesis. The tenogenesis of TSPCs on the bioactive scaffold may be correlated with the TGF-ß signal pathway and M2 macrophage polarization.

A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study

Purpose Primary bone and joint sarcomas of the long bone are relatively rare neoplasms with poor prognosis. An efficient clinical tool that can accurately predict patient prognosis is not available. The current study aimed to use deep learning algorithms to develop a prediction model for the prognosis of patients with long bone sarcoma. Methods Data of patients with long bone sarcoma in the extremities was collected from the Surveillance, Epidemiology, and End Results Program database from 2004 to 2014. Univariate and multivariate analyses were performed to select possible prediction features. DeepSurv, a deep learning model, was constructed for predicting cancer-specific survival rates. In addition, the classical cox proportional hazards model was established for comparison. The predictive accuracy of our models was assessed using the C-index, Integrated Brier Score, receiver operating characteristic curve, and calibration curve. Results Age, tumor extension, histological grade, tumor size, surgery, and distant metastasis were associated with cancer-specific survival in patients with long bone sarcoma. According to loss function values, our models converged successfully and effectively learned the survival data of the training cohort. Based on the C-index, area under the curve, calibration curve, and Integrated Brier Score, the deep learning model was more accurate and flexible in predicting survival rates than the cox proportional hazards model. Conclusion A deep learning model for predicting the survival probability of patients with long bone sarcoma was constructed and validated. It is more accurate and flexible in predicting prognosis than the classical CoxPH model (AU)

Machine learning and experiments revealed a novel pyroptosis-based classification linked to diagnosis and immune landscape in spinal cord injury.

Background: Rising evidence indicates the development of pyroptosis in the initiation and pathogenesis of spinal cord injury (SCI). However, the associated effects of pyroptosis-related genes (PRGs) in SCI are unclear. Methods: We obtained the gene expression profiles of SCI and normal samples in the GEO. Database: The R package limma screened for differentially expressed (DE) PRGs and performed functional enrichment analysis. Mechanical learning and PPI analysis helped filter essential PRGs to diagnose SCI. Peripheral blood was collected for validation from ten SCI patients and eight healthy individuals. The association of essential PRGs with immune infiltration was evaluated, and pyroptosis subtypes were recognized in SCI patients by unsupervised cluster analysis. Besides, a SCI model was built for in vivo validation of essential PRGs. Result: We identified 25 DE-PRGs between SCI and normal controls. Functional enrichment analysis revealed the principal involvement of DE-PRGs in pyroptosis, inflammasome complex, interleukin-1 beta production, etc. Subsequently, three essential PRGs were identified and validated, showing excellent diagnostic efficacy and significant correlation with immune cell infiltration. Additionally, we developed diagnostic nomograms to predict the occurrence of SCI. Two pyroptosis subtypes exhibited distinct biological functions and immune landscapes among SCI patients. Finally, the expression of these essential PRGswas verified in vivo. Conclusion: The current study described the vital effects of pyroptosis-related genes in SCI, providing a novel direction for effective assessment and management of SCI.

Calcium phosphate coating enhances osteointegration of melt electrowritten scaffold by regulating macrophage polarization.

The osteoimmune microenvironment induced by implants plays a significant role in bone regeneration. It is essential to efficiently and timely switch the macrophage phenotype from M1 to M2 for optimal bone healing. This study examined the impact of a calcium phosphate (CaP) coating on the physiochemical properties of highly ordered polycaprolactone (PCL) scaffolds fabricated using melt electrowritten (MEW). Additionally, it investigated the influence of these scaffolds on macrophage polarization and their immunomodulation on osteogenesis. The results revealed that the CaP coated PCL scaffold exhibited a rougher surface topography and higher hydrophilicity in comparison to the PCL scaffold without coating. Besides, the surface morphology of the coating and the release of Ca2+ from the CaP coating were crucial in regulating the transition of macrophages from M1 to M2 phenotypes. They might activate the PI3K/AKT and cAMP-PKA pathways, respectively, to facilitate M2 polarization. In addition, the osteoimmune microenvironment induced by CaP coated PCL could not only enhance the osteogenic differentiation of bone marrow-derived mesenchymal stem cells (BMSCs) in vitro but also promote the bone regeneration in vivo. Taken together, the CaP coating can be employed to control the phenotypic switching of macrophages, thereby creating a beneficial immunomodulatory microenvironment that promotes bone regeneration.

Unraveling T cell exhaustion in the immune microenvironment of osteosarcoma via single-cell RNA transcriptome.

Osteosarcoma (OS) represents a profoundly invasive malignancy of the skeletal system. T cell exhaustion (Tex) is known to facilitate immunosuppression and tumor progression, but its role in OS remains unclear. In this study, single-cell RNA sequencing data was employed to identify exhausted T cells within the tumor immune microenvironment (TIME) of OS. We found that exhausted T cells exhibited substantial infiltration in OS samples. Pseudotime trajectory analysis revealed a progressive increase in the expression of various Tex marker genes, including PDCD1, CTLA4, LAG3, ENTPD1, and HAVCR2 in OS. GSVA showed that apoptosis, fatty acid metabolism, xenobiotic metabolism, and the interferon pathway were significantly activated in exhausted T cells in OS. Subsequently, a prognostic model was constructed using two Tex-specific genes, MYC and FCGR2B, which exhibited exceptional prognostic accuracy in two independent cohorts. Drug sensitivity analysis revealed that OS patients with a low Tex risk were responsive to Dasatinib and Pazopanib. Finally, immunohistochemistry verified that MYC and FCGR2B were significantly upregulated in OS tissues compared with adjacent tissues. This study investigates the role of Tex within the TIME of OS, and offers novel insights into the mechanisms underlying disease progression as well as the potential treatment strategies for OS.

A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study.

PURPOSE: Primary bone and joint sarcomas of the long bone are relatively rare neoplasms with poor prognosis. An efficient clinical tool that can accurately predict patient prognosis is not available. The current study aimed to use deep learning algorithms to develop a prediction model for the prognosis of patients with long bone sarcoma. METHODS: Data of patients with long bone sarcoma in the extremities was collected from the Surveillance, Epidemiology, and End Results Program database from 2004 to 2014. Univariate and multivariate analyses were performed to select possible prediction features. DeepSurv, a deep learning model, was constructed for predicting cancer-specific survival rates. In addition, the classical cox proportional hazards model was established for comparison. The predictive accuracy of our models was assessed using the C-index, Integrated Brier Score, receiver operating characteristic curve, and calibration curve. RESULTS: Age, tumor extension, histological grade, tumor size, surgery, and distant metastasis were associated with cancer-specific survival in patients with long bone sarcoma. According to loss function values, our models converged successfully and effectively learned the survival data of the training cohort. Based on the C-index, area under the curve, calibration curve, and Integrated Brier Score, the deep learning model was more accurate and flexible in predicting survival rates than the cox proportional hazards model. CONCLUSION: A deep learning model for predicting the survival probability of patients with long bone sarcoma was constructed and validated. It is more accurate and flexible in predicting prognosis than the classical CoxPH model.

Gut microbiota modification by diosgenin mediates antiepileptic effects in a mouse model of epilepsy.

Diosgenin, a natural steroid saponin, holds promise as a multitarget therapeutic for various diseases, including neurodegenerative conditions. Its efficacy in slowing Alzheimer's disease, Parkinson's disease, multiple sclerosis, and stroke progression has been demonstrated. However, the role of diosgenin in anti-epilepsy and its potential connection to the modulation of the intestinal microbiota remain poorly understood. In this study, exogenous diosgenin significantly mitigated pentylenetetrazole (PTZ)-induced seizures, learning and memory deficits, and hippocampal neuronal injury. 16S ribosomal RNA (16S rRNA) sequencing revealed a reversal in the decrease of Bacteroides and Parabacteroides genera in the PTZ-induced mouse epileptic model following diosgenin treatment. Fecal microbiota transplantation (FMT) experiments illustrated the involvement of diosgenin in modulating gut microbiota and providing neuroprotection against epilepsy. Our results further indicated the repression of enteric glial cells (EGCs) activation and the TLR4-MyD88 pathway, coupled with reduced production of inflammatory cytokines in the colonic lumen, and improved intestinal barrier function in epilepsy mice treated with diosgenin or FMT. This study suggests that diosgenin plays a role in modifying gut microbiota, contributing to the alleviation of intestinal inflammation and neuroinflammation, ultimately inhibiting epilepsy progression in a PTZ-induced mouse model. Diosgenin emerges as a potential therapeutic option for managing epilepsy and its associated comorbidities.

Deciphering the heterogeneity and immunosuppressive function of regulatory T cells in osteosarcoma using single-cell RNA transcriptome.

Osteosarcoma (OS) is a highly invasive malignant neoplasm with poor prognosis. The tumor microenvironment (TME) plays an essential role in the occurrence and development of OS. Regulatory T cells (Tregs) are known to facilitate immunosuppression, tumor progression, invasion, and metastasis. However, the effect of Tregs in the TME of OS remains unclear. In this study, single-cell RNA sequencing (scRNA-seq) data was used to identify Tregs and various other cell clusters in the TME of OS. Gene set variation analysis (GSVA) was used to investigate the signaling pathways in Tregs from OS and adjacent tissues. The CellChat and iTALK packages were used to analyze cellular communication. In addition, a prognostic model was established based on the Tregs-specific genes using bulk RNA-seq from the TARGET database, and it was verified using a Gene Expression Omnibus dataset. The pRRophetic package was used to predict drug sensitivity. Immunohistochemistry was used to verify the expression of candidate genes in OS. Based on the above methods, we showed that the OS samples were highly infiltrated with Tregs. GSVA revealed that oxidative phosphorylation, angiogenesis and mammalian target of rapamycin complex 1 (mTORC1) were highly activated in Tregs from OS compared with those from adjacent tissues. Using cellular communication analysis, we found that Tregs interacted with osteoblastic, endothelial, and myeloid cells via C-X-C motif chemokine ligand (CXCL) signaling; particularly, they strongly affected the expression of C-X-C motif chemokine receptor 4 (CXCR4) and interacted with other cell clusters through CXCL12/transforming growth factor ß1 (TGFB1) to collectively enable tumor growth and progression. Subsequently, two Tregs-specific genes-CD320 and MAF-were screened through univariate, least absolute shrinkage and selection operator regression (LASSO) and multivariate analysis to construct a prognostic model, which showed excellent prognostic accuracy in two independent cohorts. In addition, drug sensitivity analysis demonstrated that OS patients at high Tregs risk were sensitive to sunitinib, sorafenib, and axitinib. We also used immunohistochemistry to validate that CD320 and MAF were significantly upregulated in OS tissues compared with adjacent tissues. Overall, this study reveals the heterogeneity of Tregs in the OS TME, providing new insights into the invasion and treatment of this cancer.

Deep-Learning-Based Model for the Prediction of Cancer-Specific Survival in Patients with Spinal Chordoma.

OBJECTIVE: Spinal chordomas are locally aggressive and frequently recurrent tumors with a poor prognosis. Previous studies focused on a Cox regression model to predict the survival of patients with spinal chordoma. We aimed to develop a more effective model based on deep learning for prognosis prediction in spinal chordoma. METHODS: Patients with spinal chordoma were gathered from the SEER database. Cox regression analysis was conducted to compare the influence of different clinical characteristics on cancer-specific survival. Two deep learning models, namely, DeepSurv and NMTLR, were developed, alongside 2 classic models, for the purpose of comparison. Performance of these models was evaluated by concordance index, Integrated Brier Score, receiver operating characteristic curves, Kaplan-Meier curves, and calibration curves. RESULTS: A total of 258 spinal chordoma patients were included in the current study. The median follow-up time was 94 ± 52 months. Variables used for prognosis prediction consisted of age, primary site, tumor size, histologic grade, extension of surgery, tumor invasion, and metastasis. Comparing with conventional models, each deep learning model showed superior predictive performance, the C-index on the test cohort is 0.830 for DeepSurv and 0.804 for NMTLR, respectively. The DeepSurv model represented the best performance, with area under the curve of 0.843 in predicting 5-year survival and 0.880 in predicting 10-year survival. CONCLUSIONS: We successfully constructed a deep learning model to predict the CSS of spinal chordoma patients and proved that it was more accurate and practical than conventional prediction model. Our deep learning model has the potential to guide clinicians in better care planning and decision-making.

Using network pharmacology to fabricate crosslinked hyaluronan-chondroitin sulphate-resveratrol composite hydrogels for cartilage regeneration.

Cartilage tissue engineering provides a new approach for the treatment of cartilage damage. The combination of drug system with a tissue scaffold could be highly beneficial. Resveratrol (RES) is a potent anti-inflammatory agent, but its target genes and molecular mechanism of cartilage repair remain to be further studied. We used systems biology and network pharmacology methods to explore the mechanism of RES for chondrocyte and macrophages. Meanwhile, crosslinked hyaluronan-chondroitin sulphate-RES hydrogels (cHA-CS-RES) were constructed based on the target prediction results. Byin vitroandin vivoexperiments, we investigated its anti-inflammatory and pro-chondrogenesis. The results showed there were 12 hub genes potentially interacting in the RES-chondrocyte-macrophage network.In vitroexperiments were used to further verify the validity of the predicted hub genes. The composite hydrogels were successfully fabricated, and maintenance of the characteristic was further confirmed.In vitrostudy, cHA-CS-RES showed high cell viability, anti-inflammatory and pro-chondrogenesis abilities.In vivostudy of cartilage defects confirmed that the cHA-CS-RES groups were significantly better than the control group. Network pharmacology was used to predict and screen the target proteins of RES critical to cartilage tissue engineering. Moreover, cHA-CS-RES composite hydrogel showed good cartilage repair effects, anti-inflammatory and pro-chondrogenesis abilities.

Reconstructing avascular necrotic femoral head through a bioactive ß-TCP system: From design to application.

A variety of techniques have been used for treating avascular necrosis of the femoral head (ANFH), but have frequently failed. In this study, we proposed a ß-TCP system for the treatment of ANFH by boosting revascularization and bone regeneration. The angio-conductive properties and concurrent osteogenesis of the highly interconnected porous ß-TCP scaffold were revealed and quantified through an in vivo model that simulated the ischemic environment of ANFH. Mechanical test and finite element analysis showed that the mechanical loss caused by tissue necrosis and surgery was immediately partially compensated after implantation, and the strength of the operated femoral head was adaptively increased and eventually returned to normal bone, along with continuous material degradation and bone regeneration. For translational application, we further conducted a multi-center open-label clinical trial to assess the efficacy of the ß-TCP system in treating ANFH. Two hundred fourteen patients with 246 hips were enrolled for evaluation, and 82.1% of the operated hips survived at a 42.79-month median follow-up. The imaging results, hip function, and pain scores were dramatically improved compared to preoperative levels. ARCO stage Ⅱ disease outperformed stage Ⅲ in terms of clinical effectiveness. Thus, bio-adaptive reconstruction using the ß-TCP system is a promising hip-preserving strategy for the treatment of ANFH.

Comprehensive landscape of TGFß-related signature in osteosarcoma for predicting prognosis, immune characteristics, and therapeutic response.

Osteosarcoma (OS) is a highly heterogeneous malignant bone tumor, and its tendency to metastasize leads to a poor prognosis. TGFß is an important regulator in the tumor microenvironment and is closely associated with the progression of various types of cancer. However, the role of TGFß-related genes in OS is still unclear. In this study, we identified 82 TGFß DEGs based on RNA-seq data from the TARGET and GETx databases and classified OS patients into two TGFß subtypes. The KM curve showed that the Cluster 2 patients had a substantially poorer prognosis than the Cluster 1 patients. Subsequently, a novel TGFß prognostic signatures (MYC and BMP8B) were developed based on the results of univariate, LASSO, and multifactorial Cox analyses. These signatures showed robust and reliable predictive performance for the prognosis of OS in the training and validation cohorts. To predict the three-year and five-year survival rate of OS, a nomogram that integrated clinical features and risk scores was also developed. The GSEA analysis showed that the different subgroups analyzed had distinct functions, particularly, the low-risk group was associated with high immune activity and a high infiltration abundance of CD8 T cells. Moreover, our results indicated that low-risk cases had higher sensitivity to immunotherapy, while high-risk cases were more sensitive to sorafenib and axitinib. scRNA-Seq analysis further revealed that MYC and BMP8B were strongly expressed mainly in tumor stromal cells. Finally, in this study, we confirmed the expression of MYC and BMP8B by performing qPCR, WB, and IHC analyses. To conclude, we developed and validated a TGFß-related signature to accurately predict the prognosis of OS. Our findings might contribute to personalized treatment and making better clinical decisions for OS patients.

Blood transcriptome and machine learning identified the crosstalk between COVID-19 and fibromyalgia: a preliminary study.

OBJECTIVES: The COVID-19 pandemic caused by SARS-CoV-2 has seriously threatened the human health. Growing evidence shows that COVID-19 patients who recovery will persist with symptoms of fibromyalgia (FM). However, the common molecular mechanism between COVID-19 and FM remains unclear. METHODS: We obtained blood transcriptome data of COVID-19 (GSE177477) and FM (GSE67311) patients from GEO database, respectively. Subsequently, we applied Limma, GSEA, Wikipathway, KEGG, GO, and machine learning analysis to confirm the common pathogenesis between COVID-19 and FM, and screened key genes for the diagnosis of COVID-19 related FM. RESULTS: A total of 2505 differentially expressed genes (DEGs) were identified in the FM dataset. Functional enrichment analysis revealed that the occurrence of FM was intimately associated with viral infection. Moreover, WGCNA analysis identified 243 genes firmly associated with the pathological process of COVID-19. Subsequently, 50 common genes were screened between COVID-19 and FM, and functional enrichment analysis of these common genes primarily involved in immunerelated pathways. Among these common genes, 3 key genes were recognised by machine learning for the diagnosis of COVID-19 related FM. We also developed a diagnostic nomogram to predict the risk of FM occurrence which showed excellent predictive performance. Finally, we found that these 3 key genes were closely relevant to immune cells and screened potential drugs that interacted with the key genes. CONCLUSIONS: Our study revealed the bridge role of immune dysregulation between COVID-19 and fibromyalgia, and screened underlying biomarkers to provide new clues for further clinical research.

Comprehensive landscape of immune-based classifier related to early diagnosis and macrophage M1 in spinal cord injury.

Numerous studies have documented that immune responses are crucial in the pathophysiology of spinal cord injury (SCI). Our study aimed to uncover the function of immune-related genes (IRGs) in SCI. Here, we comprehensively evaluated the transcriptome data of SCI and healthy controls (HC) obtained from the GEO Database integrating bioinformatics and experiments. First, a total of 2067 DEGs were identified between the SCI and HC groups. Functional enrichment analysis revealed substantial immune-related pathways and functions that were abnormally activated in the SCI group. Immune analysis revealed that myeloid immune cells were predominantly upregulated in SCI patients, while a large number of lymphoid immune cells were dramatically downregulated. Subsequently, 51 major IRGs were screened as key genes involved in SCI based on the intersection of the results of WGCNA analysis, DEGs, and IRGs. Based on the expression profiles of these genes, two distinct immune modulation patterns were recognized exhibiting opposite immune characteristics. Moreover, 2 core IRGs (FCER1G and NFATC2) were determined to accurately predict the occurrence of SCI via machine learning. qPCR analysis was used to validate the expression of core IRGs in an external independent cohort. Finally, the expression of these core IRGs was validated by sequencing, WB, and IF analysis in vivo. We found that these two core IRGs were closely associated with immune cells and verified the co-localization of FCER1G with macrophage M1 via IF analysis. Our study revealed the key role of immune-related genes in SCI and contributed to a fresh perspective for early diagnosis and treatment of SCI.

Anoikis patterns exhibit distinct prognostic and immune landscapes in Osteosarcoma.

OBJECTIVES: Osteosarcoma is highly aggressive and prone to metastasis, with a poor prognosis. Increasing evidence identified anoikis has a critical effect in tumor metastasis and invasion. However, the prognostic value of anoikis-related genes (ANRGs) in osteosarcoma and their role in the immune landscape of osteosarcoma remain unclear. METHODS: The RNA sequencing and clinical data of patients with osteosarcoma were extracted from the TARGET and GEO databases, and ANRGs were identified from the GeneCards database. Unsupervised clustering analysis was employed to identify anoikis-related patterns. The ESTIMATE, TIMER and ssGSEA algorithms were used to assess the immune microenvironment of different subtypes. A prognostic signature based on the identified ANRGs was constructed via univariate, LASSO and multivariate Cox regression analyses. KEGG, GO and GSEA were used for functional enrichment of genes associated with different risk subtypes. qPCR, WB and IHC were used to validate the expression of candidate genes. RESULTS: Two anoikis-related patterns with distinct clinical features and immune statuses were identified based on prognosis-related ANRGs. Cluster 2 had more active immunogenicity and a better prognosis than Cluster 1. Subsequently, we developed and validated an anoikis prognostic signature demonstrating excellent predictive ability for the prognosis of osteosarcoma. Anoikis risk score was positively associated with osteosarcoma metastasis and was identified as an independent prognostic marker. Additionally, a nomogram was established to predict the 3- and 5-year survival probability of patients with osteosarcoma. Functional enrichment analysis revealed that immune dysregulation was correlated with poor prognosis. Besides, patients in the low-risk group had higher infiltration levels of immune cells and more active immune function than patients in the high-risk group. Drug sensitivity analysis revealed several chemotherapeutic agents for the treatment of different subtypes of osteosarcoma. CONCLUSION: Our study demonstrated the role of ANRGs in osteosarcoma progression, providing insights into clinical decision making in osteosarcoma.

Identification of key biomarkers related to epithelial-mesenchymal transition and immune infiltration in ameloblastoma using integrated bioinformatics analysis.

OBJECTIVE: This study aimed to elucidate the underlying mechanisms of ameloblastoma (AM) through integrated bioinformatics analysis. METHODS: We downloaded two microarrays of AMs from the GEO database and identified differentially expressed genes (DEGs) by integrated bioinformatics analysis. The enrichment analysis of DEGs was conducted to characterize GO and KEGG pathways. Protein-protein interaction (PPI) network and hub genes were screened via STRING and Cytoscape. CIBERSORT algorithm was utilized to analyze immune infiltration in AMs. We also verified the diagnostic and therapeutic value of hub genes. RESULTS: Overall, 776 DEGs were identified in AMs through bioinformatics analysis. The function enrichment analysis shed light on pathways involved in AMs. Subsequently, we screened six hub genes via PPI network. Furthermore, we evaluated immune infiltration in AMs and found that macrophages may be participating in the progression of AMs. The upregulated expression of FN1 was related to the macrophages M2 polarization. Finally, ROC analysis indicated that six hub genes had high diagnostic value for AMs and 11 drugs interacted with upregulated hub genes were identified by screening the DGIdb database. CONCLUSION: This study revealed the underlying mechanisms of pathogenesis and biological behavior of AMs and provided candidate targets for the diagnosis and treatment of AMs.

Characterization of myeloid signature genes for predicting prognosis and immune landscape in Ewing sarcoma.

Myeloid cells as a highly heterogeneous subpopulation of the tumor microenvironment (TME) are intimately associated with tumor development. Ewing sarcoma (EWS) is characterized by abundant myeloid cell infiltration in the TME. However, the correlation between myeloid signature genes (MSGs) and the prognosis of EWS patients was unclear. In this research, we synthetically characterized the expression of MSGs in a training cohort and classified EWS patients into two subtypes. Immune cell infiltration analysis revealed that MSGs subtypes correlated closely with different immune statuses. Furthermore, a three-gene prognostic model (CTSD, SIRPA, and FN1) was constructed by univariate, LASSO, and multivariate Cox analysis, and it showed excellent prognostic accuracy in EWS patients. We also developed a nomogram for better predicting the long-term survival of EWS. Functional enrichment analysis showed immune-related pathways were distinctly different in the high- and low-risk groups. Further analysis revealed that patients in the high-risk group were tightly associated with an immunosuppressive microenvironment. Finally, we validated the expression of these candidate genes by Western blot (WB), qPCR, and immunohistochemistry (IHC) analysis. To sum up, our study identified that the MSGs model was strongly linked to prognostic prediction and immune infiltration in EWS patients, providing novel insights into the clinical treatment and management of EWS patients.

Comparison of customized 3D-printed prosthesis and screw-rod-cage system reconstruction following resection of periacetabular tumors.

Background and purpose: Various operative methods are used for reconstructing pelvic girdle after resection of primary malignant periacetabular tumor has been reported. The objective of this study was to evaluate the accuracy, effectiveness, and safety of customized three dimensional-printed prosthesis (3DP) in the reconstruction of bone defects compared with conventional reconstruction using the screw-rod-cage system. Methods: A retrospective case-control analysis of 40 patients who underwent pelvic tumor resection and reconstruction with a customized 3D-printed prosthesis (3DP), or screw-rod-cage system (SRCS) between January 2010 and December 2019 was performed. The minimum follow-up time for patients alive was 2 years. Blood loss, operation time, complications, surgical margin, local recurrence, distant metastases, status at time of latest follow-up, MSTS-93 score, Harris hip score, and postoperative radiographic parameters were recorded. Moreover, overall survival, tumor-free survival, and prosthesis survival rates in both groups were compared. Results: Customized 3DP reconstruction was performed in 15 patients, and SRCS reconstruction was done in 25 patients. The group of patients treated with customized 3DP reconstruction had significantly shorter operation time (323.7 ± 83.7 vs. 393.6 ± 98.8 min; P = 0.028) and more precise (all P < 0.05) radiographic reconstruction parameters than patients in the SRCS group. Fewer complications (P = 0.026), better MSTS score (P = 0.030), and better Harris hip score (P = 0.016) were achieved in the 3DP group. Furthermore, the survival rate of prosthesis was also significantly better in the 3DP group (P = 0.039). However, blood loss, surgical margin, local recurrence, distant metastases, and status at time of latest follow-up had no significant difference between two groups. Conclusion: Compared with the screw-rod-cage system reconstruction, the customized 3D-printed prosthesis reconstruction is equally safe and effective, but it is more accurate and time-saving and is associated with fewer complications.