The rapid development of artificial intelligence (AI) technology has brought new opportunities and challenges to the ever-evolving field of medical education. However, the specific impact of AI on the teaching of diagnostic assessments to medical students requires further investigation. The purpose of this study was to evaluate the effectiveness of ChatGPT-assisted teaching of diagnoses. Eighty medical students were randomly divided into 2 groups; 1 group received ChatGPT-assisted training, and the other group received conventional instruction. We evaluated the effectiveness of both teaching methods using a theoretical examination and the Mini-Clinical Evaluation Exercise (Mini-CEX). We also assessed satisfaction with the ChatGPT-assisted teaching method using a structured survey. The ChatGPT-assisted students scored significantly higher on the theoretical knowledge examination compared to the traditional teaching group (t = 6.34, P < .001). The ChatGPT-assisted group showed significant improvement on the Mini-CEX examination, particularly on medical history taking (Χ2 = 5.115, P = .024), doctor-patient communication (Χ2 = 5.051, P = .025), professionalism (Χ2 = 4.073, P = .044), clinical judgment (Χ2 = 4.503, P = .034), and overall capabilities (Χ2 = 7.218, P = .007). The majority of participants rated the ChatGPT teaching method highly and provided positive feedback, highlighting the benefits of interactive learning and skill acquisition. ChatGPT-assisted teaching not only improved the students' scores on the theoretical knowledge of diagnostic assessments, but also enhanced the clinical knowledge of the medical students. The positive feedback of the ChatGPT-assisted group shows that this method has the potential to supplement and enhance conventional teaching methods in medical education. These results support further expansion and the application of AI in medical education.

The rapid development of artificial intelligence (AI) technology has brought new opportunities and challenges to the ever-evolving field of medical education. However, the specific impact of AI on the teaching of diagnostic assessments to medical students requires further investigation. The purpose of this study was to evaluate the effectiveness of ChatGPT-assisted teaching of diagnoses. Eighty medical students were randomly divided into 2 groups; 1 group received ChatGPT-assisted training, and the other group received conventional instruction. We evaluated the effectiveness of both teaching methods using a theoretical examination and the Mini-Clinical Evaluation Exercise (Mini-CEX). We also assessed satisfaction with the ChatGPT-assisted teaching method using a structured survey. The ChatGPT-assisted students scored significantly higher on the theoretical knowledge examination compared to the traditional teaching group ( t = 6.34, P < .001). The ChatGPT-assisted group showed significant improvement on the Mini-CEX examination, particularly on medical history taking (Χ 2 = 5.115, P = .024), doctor-patient communication (Χ 2 = 5.051, P = .025), professionalism (Χ 2 = 4.073, P = .044), clinical judgment (Χ 2 = 4.503, P = .034), and overall capabilities (Χ 2 = 7.218, P = .007). The majority of participants rated the ChatGPT teaching method highly and provided positive feedback, highlighting the benefits of interactive learning and skill acquisition. ChatGPT-assisted teaching not only improved the students’ scores on the theoretical knowledge of diagnostic assessments, but also enhanced the clinical knowledge of the medical students. The positive feedback of the ChatGPT-assisted group shows that this method has the potential to supplement and enhance conventional teaching methods in medical education. These results support further expansion and the application of AI in medical education.

BACKGROUND:Systemic juvenile idiopathic arthritis (sJIA) represents the most severe subtype of juvenile idiopathic arthritis and is classified as a rare autoinflammatory disease. It significantly impacts patients' quality of life. Its pathogenesis involves complex immune dysregulation and inflammatory responses, which remain incompletely understood. This study aims to identify key core genes associated with sJIA using advanced machine learning algorithms and construct an efficient diagnostic model. METHODS:We integrated chip and high-throughput datasets related to sJIA from the Gene Expression Omnibus database. Through differential expressed gene analysis and weighted gene co-expression network analysis, we selected 52 candidate genes. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis were utilized to identify differentially expressed pathways. We employed 113 machine learning algorithms to screen candidate genes and identify the most critical core genes, validated with external datasets to construct a robust diagnostic model. RESULTS:Gene Set Enrichment Analysis revealed significant activation of the complement and coagulation cascade pathway alongside notable suppression of antigen processing and presentation pathway. We identified 8 core genes: ADIPOR1, GLRX5, MXI1, SIAH2, SLC22A4, SLC25A37, SLC6A8, and YBX3. The diagnostic model constructed from these genes achieved impressive performance, with an area under the receiver operating characteristic curve exceeding 0.70 across training and validation sets. CONCLUSION:This study elucidated biomarkers associated with sJIA, highlighting the crucial influence of 8 core genes on disease progression. It also successfully developed an effective diagnostic model, potentially guiding future clinical practice in managing sJIA.

This meta-analysis investigates the prevalence and risk factors for new vertebral compression fractures (NVCF) following percutaneous vertebral augmentation (PVA) in patients with osteoporotic vertebral compression fractures (OVCF). We searched PubMed, Cochrane Library, Embase, and Web of Science (up to June 2025) and screened case–control and cohort studies that utilized multivariate logistic regression analysis to examine the prevalence and risk factors of NVCF after PVA in OVCF patients. The meta-analysis was performed according to the PRISMA guidelines. After data extraction, STATA 15.0 was used to calculate the pooled odds ratio (OR) and 95

BACKGROUND:Advancements in educational technology have highlighted the significant potential of gamification in medical education. Herein we employed bibliometric methods to systematically analyze the development pathways and prospects of gamification in medical education from 2000 to 2024, offering data-driven insights for educators and researchers. METHODS:Using the Web of Science Core Collection database, we identified 364 relevant articles on gamification in medical education published from 2000 to 2024 through a specific search strategy. Visualization analyses of publication trends, research hotspots, collaboration networks, and highly cited literature were performed using tools such as VOSviewer, CiteSpace, HisCite Pro, Bibliometrix, and ggplot2. RESULTS:The analyzed literature spanned 796 institutions, with the United States and European countries leading in publication numbers and collaborative exchanges. The National University of Singapore made the most significant contributions, and Professor Tobias Raupach emerged as the most prolific and highly cited author. JMIR Serious Games was identified as the most influential journal in this field. Keyword clustering analysis indicated that teaching technologies in medical education are evolving from basic gamification towards more immersive, intelligent, and interactive learning experiences. CONCLUSION:Gamification enhances learner engagement and outcomes in medical education, fostering core medical competencies, and professional qualities. We report key development trends and research hotspots, emphasizing the importance of international collaboration. Future research should explore the long-term benefits of gamified learning, personalized learning paths, and the integration of gamification within ethical and standard frameworks to drive continuous innovation in medical education.

Background: KIAA1429, a member of the RNA methyltransferase complex, is involved in cancer progression; however, the clinical significance and underlying mechanism of KIAA1429 in osteosarcoma (OS) remains to be reported. Methods: We evaluated the clinical significance of KIAA1429 in OS by performing RT-qPCR, microarray, and RNA sequencing and using published data as a reference. Two KIAA1429-targeting siRNA constructs were transfected into SW1353 cells. CCK-8 assay, colony formation assays, flow cytometry and the xenograft mouse model were conducted to investigate the biological function of KIAA1429 in OS. Results: The mRNA expression of KIAA1429 was markedly upregulated in 250 OS samples as compared to that in 71 non-cancer samples (standardized mean difference = 0.67). Summary receiver operating characteristic curve analysis revealed that KIAA1429 exhibited reliable diagnostic capacity to differentiate OS samples from non-cancer samples (area under the curve = 0.83). Further, survival analysis indicated that KIAA1429 overexpression was associated with shorter overall survival time. Knocking down KIAA1429 reduced m6A methylation levels, inhibited proliferation, prevented the growth of tumors in vivo and accelerated apoptosis of OS cells. In total, 395 KIAA1429-related genes were identified among co-expressed genes and differentially expressed genes, which were enriched in the cell cycle pathway. Protein-protein interaction network analysis showed that CDK1, CCNA2, and CCNB1 were KIAA1429-related genes, serving as major network hubs in OS. Conclusions: Our findings indicate that KIAA1429 plays an oncogenic role in OS and potentially facilitates OS progression via a mechanism that involves regulating CDK1, CCNA2, and CCNB1.

Growth factor holds great promise for bone regeneration, and spatiotemporal control of their expressing through site-specific reactions is crucial but challenging for on-demand therapy. In this study, we present the development of a novel unnatural amino acids (UAAs)-triggered therapeutic switch (UATS) system, composed of an orthogonal aminoacyl-tRNA-synthase (aaRS)-tRNA pair and a bone morphogenetic protein 2 (BMP2) gene harboring premature stop codon, which enable in situ and on-demand initiation of the expression of BMP2. The resulting UATS system allowed specifically control of base expressing on the BMP2 mRNA that switched to the BMP2 protein with complete structure and function to facilitate bone regeneration. Our investigations showed that the UATS system exhibits remarkable attributes of rapid, sensitive, reversible, and sustained BMP2 expression both in vitro and in vivo settings. Moreover, the implantation of microencapsulated cells with UATS system is applied to a mouse femur defect model, demonstrating high effciency in controlled expressing of BMP2 protein and substantial repair of bone defect following oral administration of UAAs. Therefore, our findings underscore the great potential of UATS system for on-demand awakening of functional growth factor, thus offering promising prospects in the realm of regenerative medicine.

Cardiovascular diseases, particularly those involving arterial stenosis and smooth muscle cell proliferation, pose significant health risks. This study aimed to investigate the therapeutic potential of curcumol in inhibiting platelet-derived growth factor-BB (PDGF-BB)-induced human aortic smooth muscle cell (HASMC) proliferation, migration and autophagy. Using cell viability assays, 5-ethynyl-2 '-deoxyuridine (EdU) incorporation assays and Western Blot analyses, we observed that curcumol effectively attenuated PDGF-BB-induced HASMC proliferation and migration in a concentration-dependent manner. Furthermore, curcumol mitigated PDGF-BB-induced autophagy, as evidenced by the downregulation of LC3-II/LC3-I ratio and upregulation of P62. In vivo experiments using an arteriosclerosis obliterans model demonstrated that curcumol treatment significantly ameliorated arterial morphology and reduced stenosis. Additionally, curcumol inhibited the activity of the KLF5/COX2 axis, a key pathway in vascular diseases. These findings suggest that curcumol has the potential to serve as a multi-target therapeutic agent for vascular diseases.

Background: T-box transcription factor 3 (TBX3) has been implicated in various malignant tumors, while its exact involvement in osteosarcoma (OS) remains unknown. Methods: Utilizing microarray data and bulk and single-cell RNA-seq data and qRT-PCR, we compared TBX3 mRNA expression levels in different stages of OS. Diagnostic ability testing and prognosis analysis were conducted to better understand the clinical importance of TBX3. Enrichment analysis was performed using gene groups with biological functions similar to TBX3 in different stages of OS to investigate the potential role of TBX3 in OS progression. In addition, we predicted medications targeted at TBX3 and identified downstream target genes to gain a comprehensive understanding of its therapeutic direction and regulatory mechanism. Results: TBX3 expression was highly upregulated in OS and was predominantly expressed in osteoblastic OS cells, with higher expression levels in metastatic tissues. TBX3 expression appeared somewhat suitable for discriminating between OS and normal samples, as well as different stages of OS. We found that TBX3 increased the malignant development of OS by altering cell cycle and cell adhesion molecules; exisulind and tacrolimus, which are targeted small-molecule medicines, were anticipated to counteract this dysregulation. The expression of CCNA2 could potentially be regulated by TBX3, contributing to OS advancement. Conclusion: TBX3 emerges as a potential biomarker for OS. In-depth research into its underlying molecular processes may offer new perspectives on treating OS.

Objective To explore the metabolism-related lncRNAs in the tumorigenesis of lung adenocarcinoma. Methods The transcriptome data and clinical information about lung adenocarcinoma patients were acquired in TCGA (The Cancer Genome Atlas). Metabolism-related genes were from the GSEA (Gene Set Enrichment Analysis) database. Through differential expression analysis and Pearson correlation analysis, lncRNAs about lung adenocarcinoma metabolism were identified. The samples were separated into the training and validation sets in the proportion of 2:1. The prognostic lncRNAs were determined by univariate Cox regression analysis and LASSO (Least absolute shrinkage and selection operator) regression. A risk model was built using Multivariate Cox regression analysis, evaluated by the internal validation data. The model prediction ability was assessed by subgroup analysis. The Nomogram was constructed by combining clinical indicators with independent prognostic significance and risk scores. C-index, calibration curve, DCA (Decision Curve Analysis) clinical decision and ROC (Receiver Operating Characteristic Curve) curves were obtained to assess the prediction ability of the model. Based on the CIBERSORT analysis, the correlation between lncRNAs and tumor infiltrating lymphocytes was obtained. Results From 497 lung adenocarcinoma and 54 paracancerous samples, 233 metabolic-related and 11 prognostic-related lncRNAs were further screened. According to the findings of the survival study, the low-risk group had a greater OS (Overall survival) than the high-risk group. ROC analysis indicated AUC (Area Under Curve) value was 0.726. Then, a nomogram with T, N stage and risk ratings was developed according to COX regression analysis. The C-index was 0.743, and the AUC values of 3- and 5-year survival were 0.741 and 0.775, respectively. The above results suggested the nomogram had a good prediction ability. The results based on the CIBERSORT algorithm demonstrated the lncRNAs used to construct the model had a strong correlation with the polarization of immune cells. Conclusions The study identified 11 metabolic-related lncRNAs for lung adenocarcinoma prognosis, on which basis a prognostic risk scoring model was created. This model may have a good predictive potential for lung adenocarcinoma.

Background Tumor infiltrating lymphocytes (TILs), the main component in the tumor microenvironment, play a critical role in the antitumor immune response. Few studies have developed a prognostic model based on TILs in osteosarcoma. Methods ScRNA-seq data was obtained from our previous research and bulk RNA transcriptome data was from TARGET database. WGCNA was used to obtain the immune-related gene modules. Subsequently, we applied LASSO regression analysis and SVM algorithm to construct a prognostic model based on TILs marker genes. What’s more, the prognostic model was verified by external datasets and experiment in vitro . Results Eleven cell clusters and 2044 TILs marker genes were identified. WGCNA results showed that 545 TILs marker genes were the most strongly related with immune. Subsequently, a risk model including 5 genes was developed. We found that the survival rate was higher in the low-risk group and the risk model could be used as an independent prognostic factor. Meanwhile, high-risk patients had a lower abundance of immune cell infiltration and many immune checkpoint genes were highly expressed in the low-risk group. The prognostic model was also demonstrated to be a good predictive capacity in external datasets. The result of RT-qPCR indicated that these 5 genes have differential expression which accorded with the predicting outcomes. Conclusions This study developed a new molecular signature based on TILs marker genes, which is very effective in predicting OS prognosis and immunotherapy response.

Background:T-cell Activation GTPase Activating Protein (TAGAP) plays a role in immune cell regulation. This study aimed to investigate TAGAP's expression and its potential impact on CD4+ T cell function and prognosis in lung adenocarcinoma (LUAD).Methods:We analyzed TAGAP expression and its correlation with immune infiltration and clinical data in LUAD patients using multiple datasets, including The Cancer Genome Atlas (TCGA-LUAD), Gene Expression Omnibus (GEO), and scRNA-seq datasets. In vitro and in vivo experiments were conducted to explore the role of TAGAP in CD4+ T cell function, chemotaxis, and cytotoxicity.Results:TAGAP expression was significantly lower in LUAD tissues compared to normal tissues, and high TAGAP expression correlated with better prognosis in LUAD patients. TAGAP was positively correlated with immune/stromal/ESTIMATE scores and immune cell infiltration in LUAD. Single-cell RNA sequencing revealed that TAGAP was primarily distributed in CD4+/CD8+ T cells. In vitro experiments showed that TAGAP overexpression enhanced CD4+ T cell cytotoxicity, proliferation, and chemotaxis. Gene Set Enrichment Analysis (GSEA) indicated that TAGAP was enriched in the JAK-STAT signaling pathway. In vivo experiments in a xenograft tumor model demonstrated that TAGAP overexpression suppressed tumor growth and promoted CD4+ T cell cytotoxicity.Conclusions:TAGAP influences CD4+ T cell differentiation and function in LUAD through the STAT pathway, promoting immune infiltration and cytotoxicity. This study provides a scientific basis for developing novel LUAD immunotherapy strategies and exploring new therapeutic targets.

Background: The most frequent primary bone cancer in teenagers, osteosarcoma (OS), is particularly aggressive with a high mortality rate.Methods: By combining public databases, OS and non-cancer samples were obtained. The Wilcoxon test and standardized mean difference (SMD) were utilized to evaluate the mRNA expression level of TATA-box binding protein associated factor, RNA polymerase 1 subunit D (TAF1D). The potential of TAF1D to discriminate OS samples from non-cancer samples was revealed by summary receiver operating characteristic curve (sROC). To investigate the prognostic significance, Kaplan-Meier curve and univariate Cox analysis were performed. Immunohistochemistry (IHC) was used to determine the TAF1D protein expression level. ESTIMATE algorithm and TIMER2.0 database were used to reveal the association between TAF1D expression and the immune microenvironment. Enrichment analysis and potential drug prediction were performed to clarify the underlying molecular mechanisms and possible therapeutic directions of TAF1D. Ultimately, the transcription factors (TFs) and the TAF1D binding site were predicted based on the Cistrome and JASPAR databases. Results: TAF1D was upregulated in OS at the mRNA and protein levels and possessed robust discriminatory power. TAF1D upregulation was suggestive of worse prognosis and enhancement of tumor purity in OS patients. The cell cycle was the most significantly enriched pathway, and NU.1025 was considered to be the potential target agent. Finally, MYC was identified as a TF that regulates the expression of TAF1D.Conclusions: Altogether, TAF1D has the potential to serve as a biological marker and therapeutic target in OS, which could offer new perspectives for OS treatment.

Background Osteosarcoma (OS) is a highly malignant primary bone tumor. Family of homology 60A (FAM60A) reportedly contributes to the malignant growth of some tumors.Methods Herein we investigated the mRNA expression level of FAM60A by combining OS and non-cancer samples from public databases. Immunohistochemistry was performed to determine protein expression levels of FAM60A in patients with OS. Further, RT-qPCR and western blotting were conducted to evaluate FAM60A expression in various OS cell lines. CCK-8 assay, colony formation assay, and flow cytometry were applied to determine the function of FAM60A. Finally, functional enrichment analysis was performed based on FAM60A co-expressed genes.Results FAM60A mRNA expression level was found to be significantly upregulated (standardized mean difference = 1.27, 95% CI [0.67-1.88]). Survival analyses suggested that higher expression of FAM60A was indicative of poor prognoses. Similarly, FAM60A protein expression level was also observed to be upregulated. Knocking down FAM60A expression inhibited OS cell proliferation, increased apoptosis, and blocked cells from entering the S phase. Besides, cell cycle was the most prominently enriched pathway, and BUB1, DTL, and EXO1 were identified as hub genes.Conclusions FAM60A expression was found to be markedly upregulated in OS; furthermore, FAM60A was observed to promote OS cell proliferation, inhibit apoptosis, and participate in cell cycle regulation. Besides, FAM60A may interact with hub genes to participate in the progress of OS.

Background:The aetiology of osteosarcoma (OS) remains unclear.Desmocollin-2 (DSC2) mediates intercellular adhesion and is involved in tumour progression.Therefore, we aim to investigate the potential role of DSC2 in OS.Methods: We analyzed the expression, prognostic value and immune infiltration of DSC2 in OS via single cell and bulk RNA seq data.Besides, the expression and function of DSC2 in OS were further verified by in vitro experiment.Results: We preliminarily determined that DSC2 was high expressed in OS, which was a risk factor for survival and had a strong relationship with immune cell infiltration.What's more, in vitro experiments also demonstrated that DSC2 was high expressed in OS cells, and silencing DSC2 would suppress proliferation, migration and invasion of OS cells.Conclusions: DSC2 may serve as an oncogene, which exerts a crucial role in tumor progression, predicting prognosis and immune cell infiltration in OS.

The clinicopathological implication and prospective molecular mechanisms of miRNA-145-5p in the metastasis of prostate cancer (PCa) stand unclear. Herein, it is found that miRNA-145-5p expression was remarkably reduced in 131 cases of metastatic PCa than 1371 cases of localised ones, as the standardised mean differences (SMD) was -1.26 and the area under the curve (AUC) was 0.86, based on miRNA-chip and miRNA-sequencing datasets. The potential targets of miRNA-145-5p in metastatic PCa (n = 414) was achieved from the intersection of miRNA-145-5p transfected metastatic PCa cell line data, differential expression of metastatic PCa upregulated genes and online prediction databases. TOP2A was screened as one of the target hub genes by PPI network analysis, which was adversely related to miRNA-145-5p expression in both metastatic PCa (r = -0.504) and primary PCa (r = -0.281). Gene-chip and RNA-sequencing datasets, as well as IHC performed on clinical PCa samples, showed consistent upregulated expression of TOP2A mRNA and protein in PCa compared with non-PCa. The expression of TOP2A mRNA was also significantly higher in metastatic than localised PCa with the SMD being 1.72 and the AUC of sROC being 0.91. In summary, miRNA-145-5p may participate in PCa metastasis by binding TOP2A and be useful as a biomarker for the detection of metastatic PCa.

BackgroundSCL/TAL1 interrupting locus (STIL) is associated with the progression of several tumors; however, the biological role of STIL in osteosarcoma remains poorly understood.MethodsIn this study, the clinical significance of STIL in osteosarcoma was analyzed by gene chip data recorded in public databases. STIL expression was silenced in osteosarcoma cell lines to observe the effects on proliferation, apoptosis, invasion, and migration. Differentially expressed genes (DEGs) in the osteosarcoma chip were analyzed using The Limma package, and STIL co-expressed genes were obtained via the Pearson correlation coefficient. The potential molecular mechanism of STIL in osteosarcoma was further explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.ResultsMeta-analysis revealed that STIL was highly expressed in osteosarcoma, and the standardized mean difference (SMD) was 1.52. STIL also had a good ability to distinguish osteosarcoma from non-osteosarcoma samples (area under the curve [AUC] = 0.96). After silencing STIL, osteosarcoma cell proliferation decreased, apoptosis increased, and the migratory and invasion ability decreased. A total of 294 STIL differentially co-expressed genes were screened, and a bioinformatics analysis found that differentially co-expressed genes were primarily enriched in the cell signaling pathways. The protein-protein interaction (PPI) network indicated that the hub differentially co-expressed genes of STIL were CDK1, CCNB2, CDC20, CCNA2, BUB1, and AURKB.ConclusionsSTIL is associated with osteosarcoma proliferation and invasion, and may represent a novel target for its diagnosis and treatment.

Rapamycin, a secondary metabolite produced by Streptomyces hygroscopicus, is known for its pharmacological effects, especially antitumor and immunosuppressive activities. However, the antitumoral effects of rapamycin in human esophageal cancer (EC) are still poorly understood. To investigate the potential of rapamycin in EC treatment, sirtuin 1 (SIRT1) mRNA expression was quantified in the tissue of patients with EC or in EC cell lines using reverse transcription-quantitative PCR. The protein levels of SIRT1 and PI3K/AKT/mTOR were measured via western blotting. Furthermore, cell viability, migration and invasion were investigated by Cell Counting Kit-8, wound healing and Transwell assays, respectively. The present results suggested that SIRT1 expression was upregulated in EC. In vitro, the inhibitory effect of rapamycin on cell viability in EC was strengthened or weakened after small interfering (si)-SIRT1 or pcDNA3.1/SIRT1 transfection. Furthermore, SIRT1 rescued the inhibitory effect of rapamycin on the migration and invasion of EC cells. In vivo, si-SIRT1 or SIRT1 overexpression in mice could enhance or rescue the inhibitory effects of rapamycin on tumor growth. In addition, SIRT1 transfection rescued the decreased level of phosphorylated (p)-PI3K, p-AKT and p-mTOR induced by rapamycin treatment. Taken together, the present results suggested that rapamycin suppressed the cell viability, migration, invasion and PI3K/AKT/mTOR signaling pathway in EC by negatively regulating SIRT1.

Background SCL/TAL1 interrupting locus (STIL) is associated with the progression of several tumors; however, the biological role of STIL in osteosarcoma remains poorly understood. Methods In this study, the clinical significance of STIL in osteosarcoma was analyzed by gene chip data recorded in public databases. STIL expression was silenced in osteosarcoma cell lines to observe the effects on proliferation, apoptosis, invasion, and migration. Differentially expressed genes (DEGs) in the osteosarcoma chip were analyzed using The Limma package, and STIL co-expressed genes were obtained via the Pearson correlation coefficient. The potential molecular mechanism of STIL in osteosarcoma was further explored by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results Osteosarcoma was associated with higher STIL expression compared to the control samples, and the standardized mean difference (SMD) was 1.52. STIL also had a good ability to distinguish osteosarcoma from non-osteosarcoma samples [area under the curve (AUC) = 0.96]. After silencing STIL, osteosarcoma cell proliferation decreased, apoptosis increased, and the migratory and invasion ability decreased. A total of 294 STIL differentially co-expressed genes were screened, and a bioinformatics analysis found that differentially co-expressed genes were primarily enriched in the cell signaling pathways. The protein-protein interaction (PPI) network indicated that the hub differentially co-expressed genes of STIL were CDK1, CCNB2, CDC20, CCNA2, BUB1, and AURKB. Conclusions STIL is associated with osteosarcoma proliferation and invasion, and may be promote the progression of osteosarcoma by regulating the expression of CDK1, CCNB2, CDC20, CCNA2, BUB1 and AURKB.