Construction of a 5-gene prognostic signature based on oxidative stress related genes for predicting prognosis in osteosarcoma.

BACKGROUND: The understanding of the complex biological scenario of osteosarcoma will open the way to identifying new strategies for its treatment. Oxidative stress is a cancer-related biological scenario. At present, it is not clear the oxidative stress genes in affecting the prognosis and progression of osteosarcoma, the underlying mechanism as well as their impact on the classification of osteosarcoma subtypes. METHODS: We selected samples and sequencing data from TARGET data set and GSE21257 data set, and downloaded oxidative stress related-genes (OSRGs) from MsigDB. Univariate Cox analysis of OSRG was conducted using TARGET data, and the prognostic OSRG was screened to conduct unsupervised clustering analysis to identify the molecular subtypes of osteosarcoma. Through least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression analysis of differentially expressed genes (DEGs) between subgroups, a risk assessment system for osteosarcoma was developed. RESULTS: 45 prognosis-related OSRGs genes were acquired, and two molecular subtypes of osteosarcoma were clustered. C2 cluster displayed prolonged overall survival (OS) accompanied with high degree of immune infiltration and enriched immune pathways. While cell cycle related pathways were enriched in C2 cluster. Based on DEGs between subgroups and Lasso analysis, 5 hub genes (ZYX, GJA5, GAL, GRAMD1B, and CKMT2) were screened to establish a robust prognostic risk model independent of clinicopathological features. High-risk group had more patients with cancer metastasis and death as well as C1 subtype with poor prognosis. Low-risk group exhibited favorable OS and high immune infiltration status. Additionally, the risk assessment system was optimized by building decision tree and nomogram. CONCLUSIONS: This study defined two molecular subtypes of osteosarcoma with different prognosis and tumor immune microenvironment status based on the expression of OSRGs, and provided a new risk assessment system for the prognosis of osteosarcoma.

A nomogram to predict postoperative surgical site infection of adult patients who received orthopaedic surgery: a retrospective study.

Surgical site infection is a common postoperative complication with serious consequences. This study developed a nomogram to estimate the probability of postoperative surgical site infection for orthopaedic patients. Adult patients following orthopaedic surgery during hospitalization were included in this study. We used univariate and multivariate logistic regression analyses to establish the predictive model, which was also visualized by nomogram. To evaluate the model performance, we applied the receiver operating characteristic curve, calibration curve, and decision curve analysis, which were utilized in external validation and internal validation. From January 2021 to June 2022, a total of 787 patients were enrolled in this study. After statistical analysis, five variables were enrolled in the predictive model, including age, operation time, diabetes, WBC, and HGB. The mathematical formula has been established as follows: Logit (SSI) = - 6.301 + 1.104 * (Age) + 0.669 * (Operation time) + 2.009 * (Diabetes) + 1.520 * (WBC) - 1.119 * (HGB). The receiver Operating Characteristic curve, calibration curve, and decision curve analysis presented a good performance of this predictive model. Our nomogram showed great discriminative ability, calibration, and clinical practicability in the training set, external validation, and internal validation.

Discovery of New Therapeutic Targets for Osteosarcoma Treatment Based on Immune-Related lncRNAs in the Tumor Microenvironment.

Background: Long noncoding RNAs (lncRNAs) play an important role in osteosarcoma development, but their role in the tumor microenvironment (TME) is not fully understood. This study associated lncRNAs with immune-related genes and explored the mechanism of lncRNAs in osteosarcoma progression. Methods: Unsupervised consensus clustering was applied to construct immune subtypes based on immune-related lncRNAs identified by Pearson's correlation analysis. A series of functional analysis was performed to reveal the links among lncRNAs, immune subtypes, TME, and osteosarcoma prognosis. Results: We identified two immune subtypes C1 and C2 showing distinct overall survival. ECM-receptor interaction pathway was more activated in C2 subtype, while immune response pathways were more enriched in C2 subtype. Differential TME and response to chemotherapeutic drugs were observed between the two subtypes. Four metagenes of costimulation, cytolytic activity (CYT), immune score, and STAT1 were differentially enriched in the two subtypes. Based on 26-paired lncRNAs, we constructed a 4-paired lncRNA prognostic signature for predicting prognosis of osteosarcoma prognosis. Conclusions: This study focused on immune-related lncRNAs and TME, showing the possible role and mechanisms of lncRNAs in tumor growth and metastasis. ECM may be the new therapeutic target for treating osteosarcoma, and 26-paired lncRNAs could serve as a basis for further studying the mechanisms of CYT and STAT1 in immune response, cancer cell proliferation, and migration. The two subtypes and prognostic signature could promote the design of personalized osteosarcoma treatment.