Using Machine Learning to Predict Surgical Site Infection After Lumbar Spine Surgery.

Objective: The objective of this study was to utilize machine learning techniques to analyze perioperative factors and identify blood glucose levels that can predict the occurrence of surgical site infection following posterior lumbar spinal surgery. Methods: A total of 4019 patients receiving lumbar internal fixation surgery from an institute were enrolled between June 2012 and February 2021. First, the filtered data were randomized into the test and verification groups. Second, in the test group, specific variables were screened using logistic regression analysis, Lasso regression analysis, support vector machine, and random forest. Specific variables obtained using the four methods were intersected, and a dynamic model was constructed. ROC and calibration curves were constructed to assess model performance. Finally, internal model performance was verified in the verification group using ROC and calibration curves. Results: The data from 4019 patients were collected. In total, 1327 eligible cases were selected. By combining logistic regression analysis with three machine learning algorithms, this study identified four predictors associated with SSI, namely Modic changes, sebum thickness, hemoglobin, and glucose. Using this information, a prediction model was developed and visually represented. Then, we constructed ROC and calibration curves using the test group; the area under the ROC curve was 0.988. Further, calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index of our model was 0.986 (95% CI 0.981-0.994). Finally, we used the validation group to validate the model internally; the AUC was 0.987. Calibration curve analysis revealed favorable consistency of nomogram-predicted values compared with real measurements. The C-index was 0.982 (95% CI 0.974-0.999). Conclusion: Logistic regression analysis and machine learning were employed to select four risk factors: Modic changes, sebum thickness, hemoglobin, and glucose. Then, a dynamic prediction model was constructed to help clinicians simplify the monitoring and prevention of SSI.

Predictors of osteoporotic fracture in postmenopausal women: a meta-analysis.

Osteoporosis affects more than 200 million women worldwide, with postmenopausal women being particularly susceptible to this condition and its severe sequelae disproportionately, such as osteoporotic fractures. To date, the current focus has been more on symptomatic treatment, rather than preventive measures. To address this, we performed a meta-analysis aiming to identify potential predictors of osteoporotic fractures in postmenopausal women, with the ultimate goal of identifying high-risk patients and exploring potential therapeutic approaches. We searched Embase, MEDLINE and Cochrane with search terms (postmenopausal AND fracture) AND ("risk factor" OR "predictive factor") in May 2022 for cohort and case-control studies on the predictors of osteoporotic fracture in postmenopausal women. Ten studies with 1,287,021 postmenopausal women were found eligible for analyses, in which the sample size ranged from 311 to 1,272,115. The surveyed date spanned from 1993 to 2021. Our results suggested that age, BMI, senior high school and above, parity ≥ 3, history of hypertension, history of diabetes mellitus, history of alcohol intake, age at menarche ≥ 15, age at menopause < 40, age at menopause > 50, estrogen use and vitamin D supplements were significantly associated with osteoporotic fracture in postmenopausal women. Our findings facilitate the early prediction of osteoporotic fracture in postmenopausal women and may contribute to potential therapeutic approaches. By focusing on preventive strategies and identifying high-risk individuals, we can work toward reducing the burden of osteoporosis-related fractures in this vulnerable population.

Comprehensive AI-assisted tool for ankylosing spondylitis based on multicenter research outperforms human experts.

Introduction: The diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS. Methods: In this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients. Results: The ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care. Discussion: In this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

MEG3 alleviates ankylosing spondylitis by suppressing osteogenic differentiation of mesenchymal stem cells through regulating microRNA-125a-5p-mediated TNFAIP3.

Osteoblasts are important regulators of bone formation, but their roles in ankylosing spondylitis (AS) remain unclear. This study aims to explore the role of long non-coding RNA (lncRNA) maternally expressed 3 (MEG3) MEG3 in AS. Serum from AS patients as well as AS mesenchymal stem cells (ASMSCs) and healthy donors mesenchymal stem cells (HDMSCs) was collected. Accordingly, poorly expressed MEG3 and TNF alpha induced protein 3 (TNFAIP3) as well as overexpressed microRNA-125a-5p (miR-125a-5p) were noted in the serum of AS patients and in ASMSCs during the osteogenic induction process. Meanwhile, the interaction among MEG3, miR-125a-5p, and TNFAIP3 was determined and their effect on osteoblast activity was examined in vitro and in vivo. Overexpression of MEG3 and TNFAIP3 or inhibition of miR-125a-5p was found to inactivate the Wnt/ß-catenin pathway, thus suppressing osteogenic differentiation of MSCs. MEG3 competitively bound to miR-125a-5p to increase TNFAIP3 expression, thereby inactivating the Wnt/ß-catenin pathway and repressing the osteogenic differentiation of MSCs. In proteoglycan (PG)-induced AS mouse models, MEG3 also reduced osteogenic activity of MSCs to inhibit AS progression through the miR-125a-5p/TNFAIP3/Wnt/ß-catenin axis. Therefore, up-regulation of MEG3 or depletion of miR-125a-5p holds potential of alleviating AS, which sheds light on a new therapeutic strategy for AS treatment.

Immune cell infiltration-related clinical diagnostic model for Ankylosing Spondylitis.

Background: The pathogenesis and diagnosis of Ankylosing Spondylitis (AS) has remained uncertain due to several reasons, including the lack of studies on the local and systemic immune response in AS. To construct a clinical diagnostic model, this study identified the micro RNA-messenger RNA (miRNA-mRNA) interaction network and immune cell infiltration-related hub genes associated with AS. Materials and Methods: Total RNA was extracted and purified from the interspinous ligament tissue samples of three patients with AS and three patients without AS; miRNA and mRNA microarrays were constructed using the extracted RNA. Bioinformatic tools were used to construct an miRNA-mRNA network, identify hub genes, and analyze immune infiltration associated with AS. Next, we collected the blood samples and clinical characteristics of 359 patients (197 with AS and 162 without AS). On the basis of the clinical characteristics and results of the routine blood tests, we selected immune-related cells whose numbers were significantly different in patients with AS and patients without AS. Univariate and multivariate logistic regression analysis was performed to construct a nomogram. Immunohistochemistry staining analysis was utilized to verify the differentially expression of LYN in AS and controls. Results: A total of 225 differentially expressed miRNAs (DE miRNAs) and 406 differentially expressed mRNAs (DE mRNAs) were identified from the microarray. We selected 15 DE miRNAs and 38 DE mRNAs to construct a miRNA-mRNA network. The expression of LYN, an immune-related gene, correlated with the counts of monocytes, neutrophils, and dendritic cells. Based on the independent predictive factors of sex, age, and counts of monocytes, neutrophils, and white blood cells, a nomogram was established. Receiver operating characteristic (ROC) analysis was performed to evaluate the nomogram, with a C-index of 0.835 and AUC of 0.855. Conclusion: LYN, an immune-related hub gene, correlated with immune cell infiltration in patients with AS. In addition, the counts of monocytes and neutrophils were the independent diagnostic factors for AS. If verified in future studies, a diagnostic model based on these findings may be used to predict AS effectively.

Transfer of microRNA-22-3p by M2 macrophage-derived extracellular vesicles facilitates the development of ankylosing spondylitis through the PER2-mediated Wnt/ß-catenin axis.

Pathological osteogenesis and inflammation possess critical significance in ankylosing spondylitis (AS). The current study aimed to elucidate the mechanisms regarding extracellular vesicle (EV)-packaged microRNA-22-3p (miR-22-3p) from M2 macrophages in the osteogenic differentiation of mesenchymal stem cells (MSCs) in AS. EVs were initially isolated from M2 macrophages, which had been treated with either restored or depleted miR-22-3p. AS-BMSCs were subsequently treated with M2 macrophage-derived EVs to detect osteogenic differentiation in BMSCs using gain- or loss-of-function experiments. The binding affinity among miR-22-3p, period circadian protein 2 (PER2), and Wnt7b was identified. Finally, AS mouse models were established for testing the effects of M2-EV-miR-22-3p on the bone metastatic microenvironment in vivo. miR-22-3p from M2 macrophages could be transferred into BMSCs via EVs, which promoted the osteogenic differentiation of AS-BMSCs. miR-22-3p inhibited PER2, while PER2 blocked the Wnt/ß-catenin signaling pathway via Wnt7b inhibition. M2-EV-shuttled miR-22-3p facilitated alkaline phosphatase activity and extracellular matrix mineralization via PER2-regulated Wnt/ß-catenin axis, stimulating the BMSC osteogenic differentiation. Taken together, these findings demonstrate that miR-22-3p in M2 macrophage-released EVs downregulates PER2 to facilitate the osteogenesis of MSCs via Wnt/ß-catenin axis.

STAT3 and SPI1, may lead to the immune system dysregulation and heterotopic ossification in ankylosing spondylitis.

OBJECTIVE: This study was aimed to identify the biomarkers for diagnosis and reveal the immune microenvironment changes in ankylosing spondylitis (AS). METHODS: GSE73754 was downloaded for the co-expression network construction and immune cell analyses. Flow cytometric analysis was performed to validate the results of bioinformatics analysis. Gene set enrichment analysis (GSEA) was performed to investigate the potential biological characteristic between different phenotypes. Pearson correlation analysis between the hub genes and the xCell score of immune cell types was performed. RESULTS: Signal transducer and activator of transcription 3 (STAT3) and Spi-1 proto-oncogene (SPI1) was identified as the hub genes in the datasets GSE73754. And the t-test showed that the expression level of STAT3 and SPI1 in the GSE73754 was significantly higher in AS and human leukocyte antigen (HLA)-B27(+) groups. Flow cytometric analysis showed that natural killer T cells (NKT) cells were upregulated, while Th1 cells were down-regulated in AS, which was consistent with the results obtained from bioinformatics analysis. STAT3 and SPI1 was correlated with the NKT cells and Th1 cells. CONCLUSION: STAT3 and SPI1 may be a key cytokine receptor in disease progression in AS.

Ferroptosis-related gene SOCS1, a marker for tuberculosis diagnosis and treatment, involves in macrophage polarization and facilitates bone destruction in tuberculosis.

This study was aimed to reveal the role of ferroptosis in tuberculosis infection. To elucidate the ferroptosis-related DEGs, GEO datasets associated with tuberculosis infection were downloaded. The two external validation GEO datasets were exploited for subsequent verification of the ferroptosis-related DEGs. We further evaluated the correlation among the ferroptosis-related DEGs, therapeutic effects, and drug resistance. Finally, we tried to reveal the engagement of the ferroptosis-related DEGs in bone destruction during TB infection. The present study identified SOCS1 as the only ferroptosis-related DEGs. Compared to the non-TB patients, up-regulation of SOCS1 was evident in the TB patients. After receiving standard anti-TB treatment, significant down-regulation of SOCS1 confirmed its acceptance as the marker for therapeutic efficacy. The involvement of SOCS1 has also been suggested in the regulation of the micro immune environment in TB. Furthermore, SOCS1 might play an important role in causing bone destruction during TB infection. FRGs-SOCS1 may be the key gene involved in the pathogenesis and progression of TB infection.

Epithelial-mesenchymal transition interaction with CD8+ T cell, dendritic cell and immune checkpoints in the development of melanoma.

BACKGROUND: Melanoma is fatal cancer originating from melanocytes, whose high metastatic potential leads to an extremely poor prognosis. OBJECTIVE: This study aimed to reveal the relationship among EMT, TIICs, and immune checkpoints in melanoma. METHODS: Gene expression data and clinical data of melanoma were downloaded from TCGA, UCSC Xena and GEO databases. EMT-related DEGs were detected for risk score calculation. "ESTIMATE" and "xCell" were used for estimating TIICs and obtaining 64 immune cell subtypes, respectively. Moreover, we evaluated the relationship between the risk score and immune cell subtypes and immune checkpoints. RESULTS: Seven EMT-related genes were selected to establish a risk scoring system because of their integrated prognostic relevance. The results of GSEA revealed that most of the gene sets focused on immune-related pathways in the low-risk score group. The risk score was significantly correlated with the xCell score of some TIICs, which significantly affected the prognosis of melanoma. Patients with a low-risk score may be associated with a better response to ICI therapy. CONCLUSION: The individualized risk score could effectively conduct risk stratification, overall survival prediction, ICI therapy prediction, and TME judgment for patients with melanoma, which would be conducive to patients' precise treatment.

STAT1 and CXCL10 involve in M1 macrophage polarization that may affect osteolysis and bone remodeling in extrapulmonary tuberculosis.

OBJECTIVE: This study was aimed to reveal the molecular mechanism of bone destruction due to macrophage polarization leading to during extrapulmonary tuberculosis (EPTB) infection. METHODS: The dataset GSE83456 was downloaded from the GEO database, and the xCell tool was used to obtain the 64 types of immune cells. The flow cytometry was performed to identified the differences between M1 and M2 macrophages between EPTB and the healthy controls (HCs). The enrichment analyses were performed on the differentially expressed genes (DEGs) and their functionally related modules. The hub genes were screened out, and their relationships with EPTB and the immune cell subtypes were further analyzed. RESULTS: The flow cytometric analysis validated this hypothesis of M1-macrophage polarization correlated with the pathogenesis of EPTB. Of the obtained 103 DEGs, 97 genes were upregulated, and 6 genes were downregulated. The GO and KEGG pathway analyses showed that the DEGs were particularly involved in the immune-related processes. The hub genes (STAT1 and CXCL10) might be involved in M1-macrophage polarization and correlated with the pathogenesis of EPTB. STAT1 and CXCL10 could also behave as biomarkers for EPTB. CONCLUSION: STAT1 and CXCL10 were involved in the M1-macrophage polarization and correlated with the pathogenesis of EPTB. Besides, both of them could also behave as biomarkers for EPTB diagnosis and provide the required clues for targeted therapy in the future.

Mechanism of Hip Arthropathy in Ankylosing Spondylitis: Abnormal Myeloperoxidase and Phagosome.

Background: The pathogenesis of Ankylosing spondylitis (AS) has not been elucidated, especially involving hip joint disease. The purpose of this study was to analyze the proteome of diseased hip in AS and to identify key protein biomarkers. Material and Methods: We used label-free quantification combined with liquid chromatography mass spectrometry (LC-MS/MS) to screen for differentially expressed proteins in hip ligament samples between AS and No-AS groups. Key protein was screened by Bioinformatics methods. and verified by in vitro experiments. Results: There were 3,755 identified proteins, of which 92.916% were quantified. A total of 193 DEPs (49 upregulated proteins and 144 downregulated proteins) were identified according to P < 0.01 and Log|FC| > 1. DEPs were mainly involved in cell compartment, including the vacuolar lumen, azurophil granule, primary lysosome, etc. The main KEGG pathway included Phagosome, Glycerophospholipid metabolism, Lysine degradation, Pentose phosphate pathway. Myeloperoxidase (MPO) was identified as a key protein involved in Phagosome pathway. The experiment of siRNA interfering with cells further confirmed that the upregulated MPO may promote the inflammatory response of fibroblasts. Conclusions: The overexpression of MPO may contribute to the autoimmune inflammatory response of AS-affected hip joint through the phagosome pathway.

Platelet-to-Lymphocyte Ratio as an Independent Factor Was Associated With the Severity of Ankylosing Spondylitis.

The study was aimed to determine the association of the platelet-lymphocyte ratio (PLR) with the disease activity of ankylosing spondylitis (AS). A total of 275 patients, including 180 AS patients and 95 non-AS patients, participated in the study. We assessed a full blood count for each participant. Platelet to monocyte ratio (PMR), monocytes to lymphocyte ratio (MLR), monocyte to neutrophil ratio (MNR), platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), and platelet to neutrophil ratio (PNR) were calculated. LASSO and logistic regression analyses were performed to establish the nomogram. Receiver operating characteristic (ROC) analysis was performed to evaluate the clinical value of the nomogram. We constructed a novel nomogram, which incorporated easily accessible clinical characteristics like sex, PLR, WBC, EOS, and ESR for AS diagnosis. The AUC value of this nomogram was 0.806; also, the calibration curves indicated a satisfactory agreement between nomogram prediction and actual probabilities. Furthermore, PLR was positively correlated with the severity of AS. PLR was identified as an independent factor for the diagnosis of AS and was associated with the severity of AS.

TYROBP, TLR4 and ITGAM regulated macrophages polarization and immune checkpoints expression in osteosarcoma.

We established a relationship among the immune-related genes, tumor-infiltrating immune cells (TIICs), and immune checkpoints in patients with osteosarcoma. The gene expression data for osteosarcoma were downloaded from UCSC Xena and GEO database. Immune-related differentially expressed genes (DEGs) were detected to calculate the risk score. "Estimate" was used for immune infiltrating estimation and "xCell" was used to obtain 64 immune cell subtypes. Furthermore, the relationship among the risk scores, immune cell subtypes, and immune checkpoints was evaluated. The three immune-related genes (TYROBP, TLR4, and ITGAM) were selected to establish a risk scoring system based on their integrated prognostic relevance. The GSEA results for the Hallmark and KEGG pathways revealed that the low-risk score group exhibited the most gene sets that were related to immune-related pathways. The risk score significantly correlated with the xCell score of macrophages, M1 macrophages, and M2 macrophages, which significantly affected the prognosis of osteosarcoma. Thus, patients with low-risk scores showed better results with the immune checkpoints inhibitor therapy. A three immune-related, gene-based risk model can regulate macrophage activation and predict the treatment outcomes the survival rate in osteosarcoma.

Comprehensive analyses of potential key genes in active tuberculosis: A systematic review.

BACKGROUND: Tuberculosis (TB) is a global health problem that brings us numerous difficulties. Diverse genetic factors play a significant role in the progress of TB disease. However, still no key genes for TB susceptibility have been reported. This study aimed to identify the key genes of TB through comprehensive bioinformatics analysis. METHODS: The series microarray datasets from the gene expression omnibus (GEO) database were analyzed. We used the online tool GEO2R to filtrate differentially expressed genes (DEGs) between TB and health control. Database for annotation can complete gene ontology function analysis as well as Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein-protein interaction (PPI) networks of DEGs were established by STRING online tool and visualized by Cytoscape software. Molecular Complex Detection can complete the analysis of modules in the PPI networks. Finally, the significant hub genes were confirmed by plug-in Genemania of Cytoscape, and verified by the verification cohort and protein test. RESULTS: There are a total of 143 genes were confirmed as DEGs, containing 48 up-regulated genes and 50 down-regulated genes. The gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis show that upregulated DEGs were associated with cancer and phylogenetic, whereas downregulated DEGs mainly concentrate on inflammatory immunity. PPI networks show that signal transducer and activator of transcription 1 (STAT1), guanylate binding protein 5 (GBP5), 2'-5'-oligoadenylate synthetase 1 (OAS1), catenin beta 1 (CTNNB1), and guanylate binding protein 1 (GBP1) were identified as significantly different hub genes. CONCLUSION: We conclude that these genes, including TAT1, GBP5, OAS1, CTNNB1, GBP1 are a candidate as potential core genes in TB and treatment of TB in the future.

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.

Immune status changing helps diagnose osteoarticular tuberculosis.

OBJECTIVE: This study is aimed to develop a new nomogram for the clinical diagnosis of osteoarticular tuberculosis (TB). METHODS: xCell score estimation to obtained the immune cell type abundance scores. We downloaded the expression profile of GSE83456 from GEO and proceed xCell score estimation. The routine blood examinations of 326 patients were collected for further validation. We analyzed univariate and multivariate logistic regression to identified independent predicted factor for developing the nomogram. The performance of the nomogram was assessed using the receiver operating characteristic (ROC) curves. The correlation of ESR with lymphocytes, monocytes, and ML ratio was performed and visualized in osteoarticular TB patients. RESULTS: Compared with the healthy control group in the dataset GSE83456, the xCell score of basophils, monocytes, neutrophils, and platelets was higher, while lymphoid was lower in the EPTB group. The clinical data showed that the cell count of monocytes were much higher, while the cell counts of lymphocytes were lower in the osteoarticular TB group. AUCs of the nomogram was 0.798 for the dataset GSE83456, and 0.737 for the clinical data. We identified the ML ratio, BMI, and ESR as the independent predictive factors for osteoarticular TB diagnosis and constructed a nomogram for the clinical diagnosis of osteoarticular TB. AUCs of this nomogram was 0.843. CONCLUSIONS: We demonstrated a significant change between the ML ratio of the EPTB and non-TB patients. Moreover, we constructed a nomogram for the clinical diagnosis of the osteoarticular TB diagnosis, which works satisfactorily.

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.

Predicting the Failure Risk of Internal Fixation Devices in Chinese Patients Undergoing Spinal Internal Fixation Surgery: Development and Assessment of a New Predictive Nomogram.

The current study is aimed at developing and validating a nomogram of the risk of failure of internal fixation devices in Chinese patients undergoing spinal internal fixation. We collected data from a total of 1139 patients admitted for spinal internal fixation surgery at the First Affiliated Hospital of Guangxi Medical University from May 2012 to February 2019. Of these, 1050 patients were included in the spinal internal fixation group and 89 patients in the spinal internal fixation device failure group. Patients were divided into training and validation tests. The risk assessment of the failure of the spinal internal fixation device used 14 characteristics. In the training test, the feature selection of the failure model of the spinal internal fixation device was optimized using the least absolute shrinkage and selection operator (LASSO) regression model. Based on the characteristics selected in the LASSO regression model, multivariate logistic regression analysis was used for constructing the model. Identification, calibration, and clinical usefulness of predictive models were assessed using C-index, calibration curve, and decision curve analysis. A validation test was used to validate the constructed model. In the training test, the risk prediction nomogram included gender, age, presence or absence of scoliosis, and unilateral or bilateral fixation. The model demonstrated moderate predictive power with a C-index of 0.722 (95% confidence interval: 0.644-0.800) and the area under the curve (AUC) of 0.722. Decision curve analysis depicted that the failure risk nomogram was clinically useful when the probability threshold for internal fixation device failure was 3%. The C-index of the validation test was 0.761. This novel nomogram of failure risk for spinal instrumentation includes gender, age, presence or absence of scoliosis, and unilateral or bilateral fixation. It can be used for evaluating the risk of instrumentation failure in patients undergoing spinal instrumentation surgery.