Injectable Asymmetric Adhesive-Antifouling Bifunctional Hydrogel for Peritoneal Adhesion Prevention.

Peritoneal adhesion is a common problem after abdominal surgery and can lead to various medical problems. In response to the lack of in situ retention and pro-wound healing properties of existing anti-adhesion barriers, this work reports an injectable adhesive-antifouling bifunctional hydrogel (AAB-hydrogel). This AAB-hydrogel can be constructed by "two-step" injection. The tissue adhesive hydrogel based on gallic acid-modified chitosan and aldehyde-modified dextran is prepared as the bottom hydrogel (B-hydrogel) by Schiff base reaction. The aldehyde-modified zwitterionic dextran/carboxymethyl chitosan-based hydrogel is formed on the B-hydrogel surface as the antifouling top hydrogel (T-hydrogel). The AAB-hydrogel exhibits good bilayer binding and asymmetric properties, including tissue adhesive, antifouling, and antimicrobial properties. To evaluate the anti-adhesion effect in vivo, the prepared hydrogels are injected onto the wound surface of a mouse abdominal wall abrasion-cecum defect model. Results suggest that the AAB-hydrogel has antioxidant capacity and can reduce the postoperative inflammatory response by modulating the macrophage phenotype. Moreover, the AAB-hydrogel could effectively inhibit the formation of postoperative adhesions by reducing protein deposition, and resisting fibroblast adhesions and bacteria attacking. Therefore, AAB-hydrogel is a promising candidate for the prevention of postoperative peritoneal adhesions.

An Important Role in Novel Immune Mechanism and Diagnostic Model of Ankylosing Spondylitis: The CeRNA-ADRB2 Network.

Objective: The mechanism of ankylosing spondylitis (AS) remains unclear, and clinical diagnosis still pose challenges. This study aims to explore potential regulatory mechanisms underlying AS and develop a novel diagnostic model. Methods: Interspinous ligament (ISL) tissues were collected from control samples and ankylosing spondylitis with kyphotic deformity (AS-KD) samples during surgery, followed by high-throughput sequencing. By integrating gene expression profiles from publicly available AS peripheral blood (PB) samples, differentially expressed immune genes (DEIRGs) were identified. Through gene set enrichment analysis(GSEA), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, the regulatory mechanisms of the immune gene family in AS were explored. A diagnostic model for AS were constructed and validated it externally. Additionally, a competing endogenous RNA(ceRNA)-protein regulatory network was built for key immune genes. Results: Adrenergic receptor beta 2 (ADRB2) was downregulated in both ISL and PB samples. It was enriched in common pathways, including natural killer cell-mediated cytotoxicity, B cell receptor signaling pathway, Th1 and Th2 cell differentiation. Using the LASSO algorithm, 12 DEIRGs were identified, including the downregulated ADRB2. Based on the DEIRGs family, a novel diagnostic model was constructed with an AUC of 0.87 for the validation set and 0.7 for the test set. The AUC for ADRB2 alone was 0.75. Subgrouping AS based on these immune genes revealed a close association with neutrophils. GSEA and KEGG analysis of ISL, PB, and subgrouping of AS showed that ADRB2 may be involved in regulating the T cell receptor signaling pathway. Immune infiltration analysis indicated a decrease in CD8+ T cell infiltration, which was positively correlated with ADRB2. ADRB2 in AS-KD was regulated by multiple ceRNA-protein (lncRNA-[hsa-miR-513a-5p]-mRNA-protein). Conclusion: The immune gene family, especially ADRB2, participates in the mechanism and contributes to the diagnosis of AS.

Clinical model of pulmonary metastasis in patients with osteosarcoma: A new multiple machine learning-based risk prediction.

BACKGROUND: Metastasis is one of the most significant prognostic factors in osteosarcoma (OS). The goal of this study was to construct a clinical prediction model for OS patients in a population cohort and to evaluate the factors influencing the occurrence of pulmonary metastasis. METHODS: We collected data from 612 patients with osteosarcoma (OS), and 103 clinical indicators were collected. After the data were filtered, the patients were randomly divided into training and validation cohorts by using random sampling. The training cohort included 191 patients with pulmonary metastasis in OS and 126 patients with non-pulmonary metastasis, and the validation cohort included 50 patients with pulmonary metastasis in OS and 57 patients with non-pulmonary metastasis. Univariate logistics regression analysis, LASSO regression analysis and multivariate logistic regression analysis were performed to identify potential risk factors for pulmonary metastasis in patients with osteosarcoma. A nomogram was developed that included risk influencing variables selected by multivariable analysis, and used the concordance index (C-index) and calibration curve to validate the model. Receiver operating characteristic curve (ROC), decision analysis curve (DCA) and clinical impact curve (CIC) were employed to assess the model. In addition, we used a predictive model on the validation cohort. RESULTS: Logistic regression analysis was used to identify independent predictors [N Stage + Alkaline phosphatase (ALP)+Thyroid stimulating hormone (TSH)+Free triiodothyronine (FT3)]. A nomogram was constructed to predict the risk of pulmonary metastasis in patients with osteosarcoma. The performance was evaluated by the concordance index (C-index) and calibration curve. The ROC curve provides the predictive power of the nomogram (AUC = 0.701 in the training cohort, AUC = 0.786 in the training cohort). Decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the clinical value of the nomogram and higher overall net benefits. CONCLUSIONS: Our study can help clinicians effectively predict the risk of lung metastases in osteosarcoma with more readily available clinical indicators, provide more personalized diagnosis and treatment guidance, and improve the prognosis of patients. MINI ABSTRACT: A new risk model was constructed to predict the pulmonary metastasis in patients with osteosarcoma based on multiple machine learning.

To infer the probability of cervical ossification of the posterior longitudinal ligament and explore its impact on cervical surgery.

The ossification of the posterior longitudinal ligament (OPLL) in the cervical spine is commonly observed in degenerative changes of the cervical spine. Early detection of cervical OPLL and prevention of postoperative complications are of utmost importance. We gathered data from 775 patients who underwent cervical spine surgery at the First Affiliated Hospital of Guangxi Medical University, collecting a total of 84 variables. Among these patients, 144 had cervical OPLL, while 631 did not. They were randomly divided into a training cohort and a validation cohort. Multiple machine learning (ML) methods were employed to screen the variables and ultimately develop a diagnostic model. Subsequently, we compared the postoperative outcomes of patients with positive and negative cervical OPLL. Initially, we compared the advantages and disadvantages of various ML methods. Seven variables, namely Age, Gender, OPLL, AST, UA, BMI, and CHD, exhibited significant differences and were used to construct a diagnostic nomogram model. The area under the curve (AUC) values of this model in the training and validation groups were 0.76 and 0.728, respectively. Our findings revealed that 69.2% of patients who underwent cervical OPLL surgery eventually required elective anterior surgery, in contrast to 86.8% of patients who did not have cervical OPLL. Patients with cervical OPLL had significantly longer operation times and higher postoperative drainage volumes compared to those without cervical OPLL. Interestingly, preoperative cervical OPLL patients demonstrated significant increases in mean UA, age, and BMI. Furthermore, 27.1% of patients with cervical anterior longitudinal ligament ossification (OALL) also exhibited cervical OPLL, whereas this occurrence was only observed in 6.9% of patients without cervical OALL. We developed a diagnostic model for cervical OPLL using the ML method. Our findings indicate that patients with cervical OPLL are more likely to undergo posterior cervical surgery, and they exhibit elevated UA levels, higher BMI, and increased age. The prevalence of cervical anterior longitudinal ligament ossification was also significantly higher among patients with cervical OPLL.

Application of machine learning in prediction of bone cement leakage during single-level thoracolumbar percutaneous vertebroplasty.

BACKGROUND: In the elderly, osteoporotic vertebral compression fractures (OVCFs) of the thoracolumbar vertebra are common, and percutaneous vertebroplasty (PVP) is a common surgical method after fracture. Machine learning (ML) was used in this study to assist clinicians in preventing bone cement leakage during PVP surgery. METHODS: The clinical data of 374 patients with thoracolumbar OVCFs who underwent single-level PVP at The First People's Hospital of Chenzhou were chosen. It included 150 patients with bone cement leakage and 224 patients without it. We screened the feature variables using four ML methods and used the intersection to generate the prediction model. In addition, predictive models were used in the validation cohort. RESULTS: The ML method was used to select five factors to create a Nomogram diagnostic model. The nomogram model's AUC was 0.646667, and its C value was 0.647. The calibration curves revealed a consistent relationship between nomogram predictions and actual probabilities. In 91 randomized samples, the AUC of this nomogram model was 0.7555116. CONCLUSION: In this study, we invented a prediction model for bone cement leakage in single-segment PVP surgery, which can help doctors in performing better surgery with reduced risk.

Combined treatment of xyloglucan derivative hydrogel and anti-C5a receptor antibody in preventing peritoneal adhesion.

Postoperative peritoneal adhesion is a common complication after surgery with high morbidity. In addition to improving surgical operations, medical therapy and physical barriers are the two main ways to prevent postoperative peritoneal adhesion. Satisfactory efficacy is not often obtained by the single antiadhesion method, and the combination of barrier therapy and antiadhesion drugs has attracted more attention. In this study, we first demonstrated that aberrant complement activation was associated with peritoneal injury and inflammatory responses. Correspondingly, blocking the C5a-C5aR axis reaction effectively reduced inflammatory reactions. Therefore, we creatively developed an integrated treatment of xyloglucan derivative (mXG) hydrogel and intravenous anti-C5a receptor antibody (anti-C5aRab) aimed at peritoneal adhesion, and then systematically evaluated the therapeutic efficacy using a sidewall defect-cecum abrasion model in mice. In vitro and in vivo experiments showed that the mXG hydrogel had good biocompatibility and degradability and could serve as a safe anti-adhesion barrier. The results showed that anti-C5aRab treatment could significantly inhibit peritoneal adhesions by reducing neutrophil infiltration and the expression of phosphorylated Smad2. Taken together, the mXG hydrogel integrated with anti-C5aRab showed superior antiadhesion performance and holds promising clinical applications in preventing peritoneal adhesion. STATEMENT OF SIGNIFICANCE: Postoperative peritoneal adhesion is an urgent problem to be solved after surgery. Previously, a biodegradable and thermoreversible xyloglucan derivative (mXG) hydrogel was developed that effectively prevented postoperative peritoneal adhesions, but obvious inflammatory responses and proliferation could still be observed. In addition, aberrant complement activation is associated with a variety of inflammatory diseases. We demonstrated that aberrant complement activation is involved in peritoneal adhesion. In this work, mXG hydrogel and intravenous anti-C5a receptor antibody (anti-C5aRab) were integrated to address peritoneal adhesions. The anti-C5aRab reduced the inflammatory responses. In addition, the mXG hydrogel was easy to use and effectively isolated the wound surface at the local injury site. Overall, this integrated treatment significantly improved the antiadhesion effect.

Predicting Surgical Site Infection Risk after Spinal Tuberculosis Surgery: Development and Validation of a Nomogram.

Background: The purpose of this study was to predict the surgical site infection risk after spinal tuberculosis surgery based on a nomogram. Patients and Methods: We collected the clinical data of patients who underwent spinal tuberculosis surgery in our hospital and included all the data in the least absolute shrinkage and selection operator (LASSO) regression analysis. Next, the selected parameters were analyzed using logistic regression. The logistic regression analysis and receiver operating characteristic (ROC) curve analysis were further used to obtain statistically significant parameters. These parameters were then used to construct a nomogram. The C-index, ROC curve, and decision curve analysis (DCA) were used to assess the predictive ability and accuracy of the nomogram, whereas internal verification was used to calculate the C-index by bootstrapping with 1,000 resamples. Results: A total of 394 patients with spinal tuberculosis surgery were included in the study, of whom 76 patients had surgical site infections whereas 318 patients did not. The predicted risk of surgical site infection in the nomogram ranged between 0.01 and 0.98. Both the value of the C-index of the nomogram (95% confidence interval [CI], 0.62-0.76) and the area under the curve (AUC) were found to be 0.69. The net benefit of the model ranged between 0.01 and 0.99. In contrast, the C-index calculated by the internal verification method of the nomogram was found to be 0.68. Conclusions: The risk factors predicting surgical site infection after spinal tuberculosis surgery included albumin, lesion segment, operation time, and incision length.

Bio-Inspired Antibacterial Hydrogel Adhesives with High Adhesion Strength.

Traditional adhesives such as cyanoacrylate glue are mostly solvent-based. They are facing the problem of insufficient adhesion to some substrates, and also from the drawback of volatilization and release of small organic molecules in the process of usage. Therefore, a novel adhesive with non-irritating, high adhesive strength, and antibacterial properties is highly required. In this study, a full physically crosslinked zwitterionic poly(betaine sulfonate methacrylate) (PSBMA) hydrogel is proposed. The physical crosslinking interactions endow the hydrogel with good self-healing properties. Furthermore, the pure physical crosslinking hydrogel can form PSBMA powder adhesive after lyophilization and return to the hydrogel state after hydration. The mechanical properties of PSBMA adhesive can be modulated via adjusting the solid content and initiator dosage. Following the cure process similar to that of snail mucus or insect exoskeletons in nature, the adhesion of the PSBMA adhesive is improved at least 100 times than its wet state. In addition, the PSBMA adhesive is easy to be removed due to the dissociation of cross-linked structures in saltwater environments. Moreover, PSBMA adhesive with antifouling properties can effectively prevent the adhesion of proteins and bacteria, which shows potential applications in the assembly of medical devices.

Comparison of Clinical Data Between Patients With Complications and Without Complications After Spinal Tuberculosis Surgery: A Propensity Score Matching Analysis.

Purpose: This study used a propensity score matching (PSM) analysis to explore the risk factors of post-operative complications and compared the differences in clinical data between them following spinal tuberculosis surgery. Methods: The clinical data of patients with spinal tuberculosis were collected in our hospital from June 2012 to June 2021, including general information, laboratory results, surgical information, and hospitalization costs. The data were divided into two groups: complication and without complication groups. The baseline data of the two groups were obtained using the PSM analysis. Univariate and multivariate logistic analyses were used to analyze the differences between the two groups. Results: A total of 292 patients were included in the PSM analysis: 146 patients with complications and 146 patients without complications. The operation time, incision length, hospital stay, and albumin quantity in the complications group were 162 ± 74.1, 11.2 ± 4.76, 14.7 ± 9.34, and 1.71 ± 2.82, respectively, and those in the without complication group were 138 ± 60.5, 10.2 ± 3.56, 11.7 ± 7.44, and 0.740 ± 2.44, respectively. The laboratory costs, examination costs, guardianship costs, oxygen costs, and total costs in the complications group were higher than those in the without complication group. A significant difference was observed in the albumin quantity by logistic regression analysis (P < 0.05). Conclusion: Several costs in the complication group were higher than in the without complication group. The albumin quantity may be an independent factor to predict post-operative complications of spinal tuberculosis by logistic regression analysis.

Blood transfusion risk prediction in spinal tuberculosis surgery: development and assessment of a novel predictive nomogram.

OBJECTIVE: The present study attempted to predict blood transfusion risk in spinal tuberculosis surgery by using a novel predictive nomogram. METHODS: The study was conducted on the clinical data of 495 patients (167 patients in the transfusion group and 328 patients in the non-transfusion group) who underwent spinal tuberculosis surgery in our hospital from June 2012 to June 2021. The least absolute shrinkage and selection operator (LASSO) and multivariable logistic regression analyses were used to screen out statistically significant parameters, which were included to establish a novel predictive nomogram model. The receiver operating characteristic (ROC) curve, calibration curves, C-index, and decision curve analysis (DCA) were used to evaluate the model. Finally, the nomogram was further assessed through internal validation. RESULTS: The C-index of the nomogram was 0.787 (95% confidence interval: 74.6%-.82.8%). The C-value calculated by internal validation was 0.763. The area under the curve (AUC) of the predictive nomogram was 0.785, and the DCA was 0.01-0.79. CONCLUSION: A nomogram with high accuracy, clinical validity, and reliability was established to predict blood transfusion risk in spinal tuberculosis surgery. Surgeons must prepare preoperative surgical strategies and ensure adequate availability of blood before surgery.

Feasibility of 3.5 mm C2 pedicle screws in children: A computerized tomography analysis.

There have been no studies with large sample sizes on growth of the pedicle of C2 in children. In the present study we measured the pedicle of C2 through computed tomography (CT) imaging in children aged less than 14 years and evaluated the suitability of the 3.5-mm screw for the pedicle in such children. The study was conducted on CT morphometric images of 420 children in our hospital between June 2018 and June 2020. The width (D1), length (D2), height (D3), inclination angle (α), and tail angle (ß) of the C2 pedicle were measured. One-way analysis of variance and Student's t test were used for statistical analyses. The least-square method was used to analyze the curve fitting the trend of anatomical change in the pedicle. The largest degree of goodness of fit determined the best-fitting curve. The size of the pedicle of C2 increased with age. The median ranges of D1, D2, D3, α, and ß were 3.312-5.431 mm, 11.732-23.645 mm, 3.597-8.038 mm, 32.583°-36.640°, and 24.867°-31.567°, respectively. The curves fitting the trends of D1 and D3 were power functions, whereas D2 was fitted by a logarithmic curve. However, no curve fitted α or ß. A 3.5-mm screw can be placed in the pedicle of C2 in children aged more than 1 year. The growth and development trend of this pedicle can provide an anatomical reference for deciding on posterior cervical surgery and for selecting and designing pedicle screws for children.

Pulmonary embolism following the third thoracic tuberculosis surgery: A case report and literature review.

BACKGROUND: The study aimed to analyze the clinical effects of pulmonary embolism succeeding a third surgery conducted for multiple recurrences in thoracic tuberculosis (TB). CASE REPORT: A 74-year-old female patient developed thoracic tuberculosis and was subsequently treated in our hospital in March 2019, October 2020, and February 2021. The third surgical intervention included anterolateral thoracic lesion resection, internal fixation, posterior spinal tuberculous sinus resection, and debridement with suture. The operative time was 172 min resulting in a substantial intraoperative blood loss (2321 ml). Postoperative re-examination of chest CTPA indicated a strip filling defect and pulmonary embolism in the external branch of the right middle lobe of the lung. After completing the active treatment, the D-dimer quantification, WBC, CRP, and ESR values were 1261 ng/ml, 7.71 × 109 /L, 74.66 mg/L, and 63 mm, respectively. Chest CTPA re-examination after the treatment showed no signs of pulmonary embolism. CONCLUSION: Patients with a long-term history of multiple operations, high BMI, cerebral infarction, diabetes, and older age group were more likely to develop pulmonary embolism after spinal tuberculosis surgery. Thus, the possibility of postoperative pulmonary embolism should be thoroughly analyzed before any subsequent surgical treatment in patients with recurrent spinal tuberculosis.

Development and Validation of a Nomogram for Predicting Albumin Transfusion After Spinal Tuberculosis Surgery: Based on Propensity Score Matching Analysis.

BACKGROUND: There have been few literature reports on the use of perioperative parameters to predict the risk of albumin transfusion after spinal tuberculosis surgery based on the application of nomogram and propensity score matching (PSM) analysis. OBJECTIVE: The purpose was to predict the risk of albumin transfusion after spinal tuberculosis surgery based on a combination of PSM and nomogram. METHODS: The clinical data of the patients were collected in our hospital, including preoperative clinical data, preoperative laboratory tests, and postoperative clinical data. All data were divided into 2 groups, including the albumin transfusion group and the non-albumin transfusion group. The PSM analysis was used to adjust the baseline data of the 2 groups. The nomogram was further constructed. The practicability and predictive ability of the model were evaluated. RESULTS: A total of 494 cases were collected in this article; 102 pairs by PSM analysis were used to construct the nomogram. There were statistical differences in surgical approach, aspartate aminotransferase/alanine aminotransferase levels, drainage, and kyphosis by logistic analysis, and these parameters were included in the construction of the nomogram. The C-index of the prediction model was 0.734. The area under the curve was 0.73 and the net benefit was between 0.13 and 0.99. The calculated C-index was 0.71 by the internal verification method. CONCLUSIONS: The PSM analysis had a good matching effect and the nomogram had a good predictive ability. Surgical approach, aspartate aminotransferase/alanine aminotransferase levels, drainage, and kyphosis might be predictors of albumin transfusion after spinal tuberculosis surgery.

Rational design of injectable conducting polymer-based hydrogels for tissue engineering.

Recently, injectable conducting polymer-based hydrogels (CPHs) have received increasing attention in tissue engineering owing to their controlled conductivity and minimally invasive procedures. Conducting polymers (CPs) are introduced into hydrogels to improve the electrical integration between hydrogels and host tissues and promote the repair of damaged tissues. Furthermore, endowing CPHs with in situ gelation or shear-thinning properties can reduce the injury size and inflammation caused by implanted surgery materials, which approaches the clinical transformation target of conductive biomaterials. Notably, functional CPs, including hydrophilic CP complexes, side-chain modified CPs, and conducting graft polymers, improve the water-dispersible and biocompatible properties of CPs and exhibit significant advantages in fabricating injectable CPHs under physiological conditions. This review discusses the recent progress in designing injectable hydrogels based on functional CPs. Their potential applications in neurological treatment, myocardial repair, and skeletal muscle regeneration are further highlighted. STATEMENT OF SIGNIFICANCE: Conducting polymer-based hydrogels (CPHs) have broad application prospects in the biomedical field. However, the low water dispersibility and processability of conducting polymers (CPs) make them challenging to form injectable CPHs uniformly. For the first time, this review summarizes the functionalization strategies to improve the hydrophilicity and biocompatibility of CPs, which provides unprecedented advantages for designing and fabricating the physical/chemical crosslinked injectable CPHs. Besides, future challenges and prospects for further clinical transformation of injectable CPHs for tissue engineering are presented. This review's content is of great significance for the treatment of electroactive tissues with limited self-regeneration, including neurological treatment, myocardial repair, and skeletal muscle regeneration.  Therefore, it is inspiring for the tissue engineering research of biomaterials and medical practitioners.

Difference between the blood samples of patients with bone and joint tuberculosis and patients with tuberculosis studied using machine learning.

Background: Tuberculosis (TB) is a chronic infectious disease. Bone and joint TB is a common type of extrapulmonary TB and often occurs secondary to TB infection. In this study, we aimed to find the difference in the blood examination results of patients with bone and joint TB and patients with TB by using machine learning (ML) and establish a diagnostic model to help clinicians better diagnose the disease and allow patients to receive timely treatment. Methods: A total of 1,667 patients were finally enrolled in the study. Patients were randomly assigned to the training and validation cohorts. The training cohort included 1,268 patients: 158 patients with bone and joint TB and 1,110 patients with TB. The validation cohort included 399 patients: 48 patients with bone and joint TB and 351 patients with TB. We used three ML methods, namely logistic regression, LASSO regression, and random forest, to screen the differential variables, obtained the most representative variables by intersection to construct the prediction model, and verified the performance of the proposed prediction model in the validation group. Results: The results revealed a great difference in the blood examination results of patients with bone and joint TB and those with TB. Infectious markers such as hs-CRP, ESR, WBC, and NEUT were increased in patients with bone and joint TB. Patients with bone and joint TB were found to have higher liver function burden and poorer nutritional status. The factors screened using ML were PDW, LYM, AST/ALT, BUN, and Na, and the nomogram diagnostic model was constructed using these five factors. In the training cohort, the area under the curve (AUC) value of the model was 0.71182, and the C value was 0.712. In the validation cohort, the AUC value of the model was 0.6435779, and the C value was 0.644. Conclusion: We used ML methods to screen out the blood-specific factors-PDW, LYM, AST/ALT, BUN, and Na+-of bone and joint TB and constructed a diagnostic model to help clinicians better diagnose the disease in the future.

A seven-gene signature and the C-C motif chemokine receptor family genes are the sarcoma-related immune genes.

Cells of the tumor microenvironment exert a vital influence on sarcoma prognosis. This study aimed to analyze and identify differentially expressed genes (DEGs) related to immunity and their significance as immune biomarkers for the accurate prediction of overall survival of patients with sarcoma. The Cancer Genome Atlas was adopted for obtaining sarcoma gene microarray and corresponding clinical information. ESTIMATE algorithm was used to calculate tumor immune microenvironment indices. Immune-associated DEGs were identified using the limma packages and were further analyzed using the ClusterProfiler package and STRING website. Based on the results of these analyses, we constructed a prognostic model. Furthermore, we assessed the prognosis prediction model through functional evaluation and analysis of GSE17674. The functional analysis revealed that the upregulated immune DEGs were related to immune-related aspects. Chemokine ligands/receptors and immune-related genes were found to be vital for sarcoma formation and progression. We established a prognostic signature of seven genes, which indicated that high-risk cases exhibit poor prognostic outcome. The prognostic signature constructed in this study can accurately predict the overall prognosis in patients with sarcoma. Moreover, the novel immune gene expression analysis may provide clinical guidance for predicting prognosis in patients with sarcoma.

Glycolysis- and immune-related novel prognostic biomarkers of Ewing's sarcoma: glucuronic acid epimerase and triosephosphate isomerase 1.

INTRODUCTION: Owing to the poor prognosis of Ewing's sarcoma, reliable prognostic biomarkers are highly warranted for clinical diagnosis of the disease. MATERIALS AND METHODS: A combination of the weighted correlation network analysis and differentially expression analysis was used for initial screening; glycolysis-related genes were extracted and subjected to univariate Cox, LASSO regression, and multivariate Cox analyses to construct prognostic models. The immune cell composition of each sample was analysed using CIBERSORT software. Immunohistochemical analysis was performed for assessing the differential expression of modelled genes in Ewing's sarcoma and paraneoplastic tissues. RESULTS: A logistic regression model constructed for the prognosis of Ewing's sarcoma exhibited that the patient survival rate in the high-risk group is much lower than in the low-risk group. CIBERSORT analysis exhibited a strong correlation of Ewing's sarcoma with naïve B cells, CD8+ T cells, activated NK cells, and M0 macrophages (P < 0.05). Immunohistochemical analysis confirmed the study findings. CONCLUSIONS: GLCE and TPI1 can be used as prognostic biomarkers to predict the prognosis of Ewing's sarcoma, and a close association of Ewing's sarcoma with naïve B cells, CD8+ T cells, activated NK cells, and M0 macrophages provides a novel approach to the disease immunotherapy.

Construction of autophagy prognostic signature and analysis of prospective molecular mechanisms in skin cutaneous melanoma patients.

BACKGROUND: Autophagy is closely related to skin cutaneous melanoma (SKCM), but the mechanism involved is unclear. Therefore, exploration of the role of autophagy-related genes (ARGs) in SKCM is necessary. MATERIALS AND METHODS: Differential expression autophagy-related genes (DEARGs) were first analysed. Univariate and multivariate Cox regression analyses were used to evaluate the expression of DEARGs and prognosis of SKCM. Further, the expression levels of prognosis-related DEARGs were verified by immunohistochemical (IHC) staining. Finally, gene set enrichment analysis (GSEA) was used to explore the underlying molecular mechanisms of SKCM. RESULTS: Five ARGs (APOL1, BIRC5, EGFR, TP63, and SPNS1) were positively correlated with the prognosis of SKCM. IHC verified the results of the differential expression of these 5 ARGs in the bioinformatics analysis. According to the receiver operating characteristic curve, the signature had a good performance at predicting overall survival in SKCM. The signature could classify SKCM patients into high-risk or low-risk groups according to distinct overall survival. The nomogram confirmed that the risk score has a particularly large impact on the prognosis of SKCM. Calibration plot displayed excellent agreement between nomogram predictions and actual observations. Principal component analysis indicated that patients in the high-risk group could be distinguished from those in low-risk group. Results of GSEA indicated that the low-risk group is enriched with aggressiveness-related pathways such as phosphatidylinositol-3-kinase/protein kinase B and mitogen-activated protein kinase signalling pathways. CONCLUSION: Our study identified a 5-gene signature. It revealed the mechanisms of autophagy that lead to the progression of SKCM and established a prognostic nomogram that can predict overall survival of patients with SKCM. The findings of this study provide novel insights into the relationship between ARGs and prognosis of SKCM.

TRIM68, PIKFYVE, and DYNLL2: The Possible Novel Autophagy- and Immunity-Associated Gene Biomarkers for Osteosarcoma Prognosis.

INTRODUCTION: Osteosarcoma is among the most common orthopedic neoplasms, and currently, there are no adequate biomarkers to predict its prognosis. Therefore, the present study was aimed to identify the prognostic biomarkers for autophagy-and immune-related osteosarcoma using bioinformatics tools for guiding the clinical diagnosis and treatment of this disease. MATERIALS AND METHODS: The gene expression and clinical information data were downloaded from the Public database. The genes associated with autophagy were extracted, followed by the development of a logistic regression model for predicting the prognosis of osteosarcoma using univariate and multivariate COX regression analysis and LASSO regression analysis. The accuracy of the constructed model was verified through the ROC curves, calibration plots, and Nomogram plots. Next, immune cell typing was performed using CIBERSORT to analyze the expression of the immune cells in each sample. For the results obtained from the analysis, we used qRT-PCR validation in two strains of human osteosarcoma cells. RESULTS: The screening process identified a total of three genes that fulfilled all the screening criteria. The survival curves of the constructed prognostic model revealed that patients with the high risk presented significantly lower survival than the patients with low risk. Finally, the immune cell component analysis revealed that all three genes were significantly associated with the immune cells. The expressions of TRIM68, PIKFYVE, and DYNLL2 were higher in the osteosarcoma cells compared to the control cells. Finally, we used human pathological tissue sections to validate the expression of the genes modeled in osteosarcoma and paracancerous tissue. CONCLUSION: The TRIM68, PIKFYVE, and DYNLL2 genes can be used as biomarkers for predicting the prognosis of osteosarcoma.

Identification of Hub Genes Associated With Melanoma Development by Comprehensive Bioinformatics Analysis.

INTRODUCTION: This study aimed to identify important genes associated with melanoma to further develop new target gene therapies and analyze their significance concerning prognosis. MATERIALS AND METHODS: Gene expression data for melanoma and normal tissue were downloaded from three databases. Differentially co-expressed genes were identified by WGCNA and DEGs analysis. These genes were subjected to GO, and KEGG enrichment analysis and construction of the PPI visualized with Cytoscape and screened for the top 10 Hub genes using CytoHubba. We validated the Hub gene's protein levels with an immunohistochemical assay to confirm the accuracy of our analysis. RESULTS: A total of 435 differentially co-expressed genes were obtained. Survival curves showed that high expression of FOXM1,\ EXO1, KIF20A, TPX2, and CDC20 in melanoma patients with 5 of the top 10 hub genes was associated with reduced overall survival (OS). Immunohistochemistry showed that all five genes were expressed at higher protein levels in melanoma than in paracancerous tissues. CONCLUSION: FOXM1, EXO1, KIF20A, TPX2, and CDC20 are prognosis-associated core genes of melanoma, and their high expression correlates with the low prognosis of melanoma patients and can be used as biomarkers for melanoma diagnosis, treatment, and prognosis prediction.