An Immune-Related Long Non-Coding RNA Signature to Predict the Prognosis of Ewing's Sarcoma Based on a Machine Learning Iterative Lasso Regression.

The aim of this study was to construct a new immune-associated long non-coding RNA (lncRNA) signature to predict the prognosis of Ewing sarcoma (ES) and explore its molecular mechanisms. We downloaded transcriptome and clinical prognosis data from the Gene Expression Omnibus (GSE17679, which included 88 ES samples and 18 matched normal skeletal muscle samples), and used it as a training set to identify immune-related lncRNAs with different expression levels in ES. Univariable Cox regression was used to screen immune-related lncRNAs related to ES prognosis, and an immune-related lncRNA signature was constructed based on machine learning iterative lasso regression. An external verification set was used to confirm the predictive ability of the signature. Clinical feature subgroup analysis was used to explore whether the signature was an independent prognostic factor. In addition, CIBERSORT was used to explore immune cell infiltration in the high- and low-risk groups, and to analyze the correlations between the lncRNA signature and immune cell levels. Gene set enrichment and variation analyses were used to explore the possible regulatory mechanisms of the immune-related lncRNAs in ES. We also analyzed the expression of 17 common immunotherapy targets in the high- and low-risk groups to identify any that may be regulated by immune-related lncRNAs. We screened 35 immune-related lncRNAs by univariate Cox regression. Based on this, an immune-related 11-lncRNA signature was generated by machine learning iterative lasso regression. Analysis of the external validation set confirmed its high predictive ability. DPP10 antisense RNA 3 was negatively correlated with resting dendritic cell, neutrophil, and γδ T cell infiltration, and long intergenic non-protein coding RNA 1398 was positively correlated with resting dendritic cells and M2 macrophages. These lncRNAs may affect ES prognosis by regulating GSE17721_CTRL_VS_PAM3CSK4_12H_BMDC_UP, GSE2770_IL4_ACT_VS_ACT_CD4_TCELL_48H_UP, GSE29615_CTRL_VS_DAY3_ LAIV_IFLU_VACCINE_PBMC_UP, complement signaling, interleukin 2-signal transducer and activator of transcription 5 signaling, and protein secretion. The immune-related 11-lncRNA signature may also have regulatory effects on the immunotherapy targets CD40 molecule, CD70 molecule, and CD276 molecule. In conclusion, we constructed a new immune-related 11-lncRNA signature that can stratify the prognoses of patients with ES.

Development of a Machine Learning-Based Autophagy-Related lncRNA Signature to Improve Prognosis Prediction in Osteosarcoma Patients.

BACKGROUND: Osteosarcoma is a frequent bone malignancy in children and young adults. Despite the availability of some prognostic biomarkers, most of them fail to accurately predict prognosis in osteosarcoma patients. In this study, we used bioinformatics tools and machine learning algorithms to establish an autophagy-related long non-coding RNA (lncRNA) signature to predict the prognosis of osteosarcoma patients. METHODS: We obtained expression and clinical data from osteosarcoma patients in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases. We acquired an autophagy gene list from the Human Autophagy Database (HADb) and identified autophagy-related lncRNAs by co-expression analyses. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the autophagy-related lncRNAs were conducted. Univariate and multivariate Cox regression analyses were performed to assess the prognostic value of the autophagy-related lncRNA signature and validate the relationship between the signature and osteosarcoma patient survival in an independent cohort. We also investigated the relationship between the signature and immune cell infiltration. RESULTS: We initially identified 69 autophagy-related lncRNAs, 13 of which were significant predictors of overall survival in osteosarcoma patients. Kaplan-Meier analyses revealed that the 13 autophagy-related lncRNAs could stratify patients based on their outcomes. Receiver operating characteristic curve analyses confirmed the superior prognostic value of the lncRNA signature compared to clinically used prognostic biomarkers. Importantly, the autophagy-related lncRNA signature predicted patient prognosis independently of clinicopathological characteristics. Furthermore, we found that the expression levels of the autophagy-related lncRNA signature were significantly associated with the infiltration levels of different immune cell subsets, including T cells, NK cells, and dendritic cells. CONCLUSION: The autophagy-related lncRNA signature established here is an independent and robust predictor of osteosarcoma patient survival. Our findings also suggest that the expression of these 13 autophagy-related lncRNAs may promote osteosarcoma progression by regulating immune cell infiltration in the tumor microenvironment.

Different Types of Double-Level Degenerative Lumber Spondylolisthesis: What Is Different in the Sagittal Plane?

BACKGROUND: Degenerative lumber spondylolisthesis (DLS) is a common orthopedic condition, described as a condition that compared with the lower vertebra, the superior vertebra slides forward or backward in the sagittal plane without accompanying isthmic spondylolisthesis. Information pertaining to different types of double-level DLS is scarce. This study aims to analyze parameters of patients with different types of double-level DLS to provide a reference for guiding surgical treatment and restoring sagittal balance of patients with DLS. METHODS: From January 2014 to January 2020, records of patients with double-level DLS were retrospectively reviewed. Patients with double-level DLS were divided into 3 types: anterior, posterior, and combined; the anterior and combined types were studied. The sagittal spinopelvic parameters included C7 tilt, maximal thoracic kyphosis, maximal lumbar lordosis (LLmax), pelvic incidence (PI), pelvic tilt (PT), and sacral slope (SS). After descriptive analysis, demographic and radiographic data were compared. RESULTS: Forty and 18 patients were included in the anterior and combined type groups, respectively. Both groups had different levels of chronic low back pain, but the incidence of radiating leg pain and neurogenic claudication was significantly higher in the anterior type. Oswestry Disability Index and visual analog scale low back scores were also higher in the anterior type. In the anterior type, C7 tilt (7.14 ± 2.15 vs. 5.41 ± 2.28, P = 0.007), LLmax (50.02 ± 14.76 vs. 36.96 ± 14.56, P = 0.003), PI (68.28 ± 9.16 vs. 55.53 ± 14.19, P < 0.001), PT (28.68 ± 7.31 vs. 19.38 ± 4.70, P < 0.001), and PT/PI (42.45 ± 11.22 vs. 36.04 ± 9.87, P = 0.041) were significantly higher. In the anterior type, PI correlated positively with LLmax (r = 0.59) and SS (r = 0.71). LLmax and SS (r = 0.65) had a positive correlation. PT/PI and SS (r = -0.77) had a negative correlation. In the combined type, PI correlated positively with LLmax (r = 0.61) and SS (r = 0.88), and PT/PI correlated negatively with SS (r = -0.81). CONCLUSIONS: In patients with double-level DLS, the sagittal spinopelvic parameters differed between the anterior and combined types. Overall, spinal surgeons should focus on correcting sagittal deformities, relieving postoperative clinical symptoms, and improving quality of life during fusion surgery.

An immune-related gene signature for determining Ewing sarcoma prognosis based on machine learning.

PURPOSE: Ewing sarcoma (ES) is one of the most common malignant bone tumors in children and adolescents. The immune microenvironment plays an important role in the development of ES. Here, we developed an optimal signature for determining ES patient prognosis based on immune-related genes (IRGs). METHODS: We analyzed the ES gene expression profile dataset, GSE17679, from the GEO database and extracted differential expressed IRGs (DEIRGs). Then, we conducted functional correlation and protein-protein interaction (PPI) analyses of the DEIRGs and used the machine learning algorithm-iterative Lasso Cox regression analysis to build an optimal DEIRG signature. In addition, we applied ES samples from the ICGC database to test the optimal gene signature. We performed univariate and multivariate Cox regressions on clinicopathological characteristics and optimal gene signature to evaluate whether signature is an important prognostic factor. Finally, we calculated the infiltration of 24 immune cells in ES using the ssGSEA algorithm, and analyzed the correlation between the DEIRGs in the optimal gene signature and immune cells. RESULTS: A total of 249 DEIRGs were screened and an 11-gene signature with the strongest correlation with patient prognoses was analyzed using a machine learning algorithm. The 11-gene signature also had a high prognostic value in the ES external verification set. Univariate and multivariate Cox regression analyses showed that 11-gene signature is an independent prognostic factor. We found that macrophages and cytotoxic, CD8 T, NK, mast, B, NK CD56bright, TEM, TCM, and Th2 cells were significantly related to patient prognoses; the infiltration of cytotoxic and CD8 T cells in ES was significantly different. By correlating prognostic biomarkers with immune cell infiltration, we found that FABP4 and macrophages, and NDRG1 and Th2 cells had the strongest correlation. CONCLUSION: Overall, the IRG-related 11-gene signature can be used as a reliable ES prognostic biomarker and can provide guidance for personalized ES therapy.

Development of a novel immune-related genes prognostic signature for osteosarcoma.

Immune-related genes (IRGs) are responsible for osteosarcoma (OS) initiation and development. We aimed to develop an optimal IRGs-based signature to assess of OS prognosis. Sample gene expression profiles and clinical information were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Genotype-Tissue Expression (GTEx) databases. IRGs were obtained from the ImmPort database. R software was used to screen differentially expressed IRGs (DEIRGs) and functional correlation analysis. DEIRGs were analyzed by univariate Cox regression and iterative LASSO Cox regression analysis to develop an optimal prognostic signature, and the signature was further verified by independent cohort (GSE39055) and clinical correlation analysis. The analyses yielded 604 DEIRGs and 10 hub IRGs. A prognostic signature consisting of 13 IRGs was constructed, which strikingly correlated with OS overall survival and distant metastasis (p < 0.05, p < 0.01), and clinical subgroup showed that the signature's prognostic ability was independent of clinicopathological factors. Univariate and multivariate Cox regression analyses also supported its prognostic value. In conclusion, we developed an IRGs signature that is a prognostic indicator in OS patients, and the signature might serve as potential prognostic indicator to identify outcome of OS and facilitate personalized management of the high-risk patients.

Sirtuins and intervertebral disc degeneration: Roles in inflammation, oxidative stress, and mitochondrial function.

Intervertebral disc degeneration (IDD) is one of the main causes of low back pain, which seriously reduces the quality of life of patients and places a heavy economic burden on their families. Cellular senescence is considered to be an important factor leading to IDD, and inflammatory response, oxidative stress, and mitochondrial dysfunction are closely related to intervertebral disc (IVD) senescence. Therefore, inhibition of the inflammatory response and oxidative stress, along with maintaining mitochondrial function, may be useful in treating IDD. The sirtuins are a family of evolutionarily conserved nicotinamide adenine dinucleotide (NAD+)-dependent histone deacetylases, which are the major molecules mediating life extension or delay of aging-related diseases. The sirtuin protein family consist of seven members (SIRT1 - 7), which are mainly involved in various aging-related diseases by regulating inflammation, oxidative stress, and mitochondrial function. Among them, SIRT1, SIRT2, SIRT3, and SIRT6 are closely related to IDD. In addition, some activators of sirtuin proteins, such as resveratrol, melatonin, magnolol, 1,4-dihydropyridine (DHP), SRT1720, and nicotinamide mononucleotide (NMN), have been evaluated in preclinical studies for their effects in preventing IDD. This review described the biological functions of sirtuins and the important roles of SIRT1, SIRT2, SIRT3, and SIRT6 in IDD by regulating oxidative stress, inflammatory response, and mitochondrial function. In addition, we introduce the status of some sirtuin activators in IDD preclinical studies. This review will provide a background for further clarification of the molecular mechanism underlying IDD and the development of potential therapeutic drugs.

GRB10 and E2F3 as Diagnostic Markers of Osteoarthritis and Their Correlation with Immune Infiltration.

This study aimed to find potential diagnostic markers for osteoarthritis (OA) and analyze the role of immune cells infiltration in this pathology. We used OA datasets from the Gene Expression Omnibus database. First, R software was used to identify differentially expressed genes (DEGs) and perform functional correlation analysis. Then least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination algorithms were used to screen and verify the diagnostic markers of OA. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in OA tissues, and the correlation between diagnostic markers and infiltrating immune cells was analyzed. A total of 458 DEGs were screened in this study. GRB10 and E2F3 (AUC = 0.962) were identified as diagnostic markers of OA. Immune cell infiltration analysis found that resting mast cells, T regulatory cells, CD4 memory resting T cells, activated NK cells, and eosinophils may be involved in the OA process. In addition, GRB10 was correlated with NK resting cells, naive CD4 + T cells, and M1 macrophages, while E2F3 was correlated with resting mast cells. In conclusion, GRB10 and E2F3 can be used as diagnostic markers of osteoarthritis, and immune cell infiltration plays an important role in the occurrence and progression of OA.

[Anterior versus posterior decompression for the treatment of thoracolumbar fractures with spinal cord injury:a Meta-analysis].

OBJECTIVE: To systematically evaluate the efficacy and safety of anterior decompression and posterior decompression in the treatment of thoracolumbar fractures with spinal cord injury, so as to provide a good scientific basis for more effective treatment of thoracolumbar fractures with spinal cord injury. METHODS: A clinical data about comparative study of anterior decompression and posterior decompression in the treatment of thoracolumbar fractures with spinal cord injury was searched and collected. The databases of Pubmed, Embase, Cochrane Library, CNKI, CBM, Wanfang Medical Network were searched by computer. Artificially collected journals included Spine, European Spine Journal, The Journal of Bone and Joint Surgery. Two spine surgeons independently screened the literature according to established inclusion and exclusion criteria and assessed the quality of the included studies. Meta-analysis was performed on the data using Review Manager 5.3 software, the indicators included operative time, intraoperative blood loss, postoperative tactile score, postoperative motor score, postoperative vertebral height, hospitalization time, neurological function recovery, efficiency of treatment, postoperative complications. RESULTS: Fifteen randomized controlled trials (RCTs) were enrolled in a total of 1 360 patients, including 680 anterior decompression and 680 posterior decompression. The results of Meta-analysis showed that the anterior decompression group had longer operation time [MD=80.09, 95% CI(36.83, 123.34), P=0.000 3], more intraoperative blood loss [MD=225.21, 95%CI(171.07, 279.35), P<0.000 01], longer hospitalization time [MD=2.31, 95% CI(0.32, 4.31), P=0.02]. And the postoperative tactile score [MD=13.39, 95% CI(9.86, 16.92), P<0.000 01], postoperative motor score [MD=13.15, 95% CI(7.02, 19.29), P<0.000 1], vertebral height [MD=1.36, 95% CI(0.79, 1.92), P<0.000 01] in anterior decompression were higher than that in posterior decompression. There was no statistically significant differences in the efficacy of treatment [OR=1.14, 95% CI(0.56, 2.31), P=0.72], neurological recovery [OR=0.87, 95% CI(0.57, 1.33), P=0.52] between two groups. CONCLUSIONS: Compared with posterior decompression, the anterior decompression has the advantages of longer operating time, more intraoperative blood loss, longer hospitalization time, higher postoperative tactile score, higher postoperative motor score, and higher injury vertebral height, But there was no significant difference in the treatment efficiency and nerve function recovery between two groups.