A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study

Purpose Primary bone and joint sarcomas of the long bone are relatively rare neoplasms with poor prognosis. An efficient clinical tool that can accurately predict patient prognosis is not available. The current study aimed to use deep learning algorithms to develop a prediction model for the prognosis of patients with long bone sarcoma. Methods Data of patients with long bone sarcoma in the extremities was collected from the Surveillance, Epidemiology, and End Results Program database from 2004 to 2014. Univariate and multivariate analyses were performed to select possible prediction features. DeepSurv, a deep learning model, was constructed for predicting cancer-specific survival rates. In addition, the classical cox proportional hazards model was established for comparison. The predictive accuracy of our models was assessed using the C-index, Integrated Brier Score, receiver operating characteristic curve, and calibration curve. Results Age, tumor extension, histological grade, tumor size, surgery, and distant metastasis were associated with cancer-specific survival in patients with long bone sarcoma. According to loss function values, our models converged successfully and effectively learned the survival data of the training cohort. Based on the C-index, area under the curve, calibration curve, and Integrated Brier Score, the deep learning model was more accurate and flexible in predicting survival rates than the cox proportional hazards model. Conclusion A deep learning model for predicting the survival probability of patients with long bone sarcoma was constructed and validated. It is more accurate and flexible in predicting prognosis than the classical CoxPH model (AU)

Machine learning and experiments revealed a novel pyroptosis-based classification linked to diagnosis and immune landscape in spinal cord injury.

Background: Rising evidence indicates the development of pyroptosis in the initiation and pathogenesis of spinal cord injury (SCI). However, the associated effects of pyroptosis-related genes (PRGs) in SCI are unclear. Methods: We obtained the gene expression profiles of SCI and normal samples in the GEO. Database: The R package limma screened for differentially expressed (DE) PRGs and performed functional enrichment analysis. Mechanical learning and PPI analysis helped filter essential PRGs to diagnose SCI. Peripheral blood was collected for validation from ten SCI patients and eight healthy individuals. The association of essential PRGs with immune infiltration was evaluated, and pyroptosis subtypes were recognized in SCI patients by unsupervised cluster analysis. Besides, a SCI model was built for in vivo validation of essential PRGs. Result: We identified 25 DE-PRGs between SCI and normal controls. Functional enrichment analysis revealed the principal involvement of DE-PRGs in pyroptosis, inflammasome complex, interleukin-1 beta production, etc. Subsequently, three essential PRGs were identified and validated, showing excellent diagnostic efficacy and significant correlation with immune cell infiltration. Additionally, we developed diagnostic nomograms to predict the occurrence of SCI. Two pyroptosis subtypes exhibited distinct biological functions and immune landscapes among SCI patients. Finally, the expression of these essential PRGswas verified in vivo. Conclusion: The current study described the vital effects of pyroptosis-related genes in SCI, providing a novel direction for effective assessment and management of SCI.

Unraveling T cell exhaustion in the immune microenvironment of osteosarcoma via single-cell RNA transcriptome.

Osteosarcoma (OS) represents a profoundly invasive malignancy of the skeletal system. T cell exhaustion (Tex) is known to facilitate immunosuppression and tumor progression, but its role in OS remains unclear. In this study, single-cell RNA sequencing data was employed to identify exhausted T cells within the tumor immune microenvironment (TIME) of OS. We found that exhausted T cells exhibited substantial infiltration in OS samples. Pseudotime trajectory analysis revealed a progressive increase in the expression of various Tex marker genes, including PDCD1, CTLA4, LAG3, ENTPD1, and HAVCR2 in OS. GSVA showed that apoptosis, fatty acid metabolism, xenobiotic metabolism, and the interferon pathway were significantly activated in exhausted T cells in OS. Subsequently, a prognostic model was constructed using two Tex-specific genes, MYC and FCGR2B, which exhibited exceptional prognostic accuracy in two independent cohorts. Drug sensitivity analysis revealed that OS patients with a low Tex risk were responsive to Dasatinib and Pazopanib. Finally, immunohistochemistry verified that MYC and FCGR2B were significantly upregulated in OS tissues compared with adjacent tissues. This study investigates the role of Tex within the TIME of OS, and offers novel insights into the mechanisms underlying disease progression as well as the potential treatment strategies for OS.

A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study.

PURPOSE: Primary bone and joint sarcomas of the long bone are relatively rare neoplasms with poor prognosis. An efficient clinical tool that can accurately predict patient prognosis is not available. The current study aimed to use deep learning algorithms to develop a prediction model for the prognosis of patients with long bone sarcoma. METHODS: Data of patients with long bone sarcoma in the extremities was collected from the Surveillance, Epidemiology, and End Results Program database from 2004 to 2014. Univariate and multivariate analyses were performed to select possible prediction features. DeepSurv, a deep learning model, was constructed for predicting cancer-specific survival rates. In addition, the classical cox proportional hazards model was established for comparison. The predictive accuracy of our models was assessed using the C-index, Integrated Brier Score, receiver operating characteristic curve, and calibration curve. RESULTS: Age, tumor extension, histological grade, tumor size, surgery, and distant metastasis were associated with cancer-specific survival in patients with long bone sarcoma. According to loss function values, our models converged successfully and effectively learned the survival data of the training cohort. Based on the C-index, area under the curve, calibration curve, and Integrated Brier Score, the deep learning model was more accurate and flexible in predicting survival rates than the cox proportional hazards model. CONCLUSION: A deep learning model for predicting the survival probability of patients with long bone sarcoma was constructed and validated. It is more accurate and flexible in predicting prognosis than the classical CoxPH model.

Deciphering the heterogeneity and immunosuppressive function of regulatory T cells in osteosarcoma using single-cell RNA transcriptome.

Osteosarcoma (OS) is a highly invasive malignant neoplasm with poor prognosis. The tumor microenvironment (TME) plays an essential role in the occurrence and development of OS. Regulatory T cells (Tregs) are known to facilitate immunosuppression, tumor progression, invasion, and metastasis. However, the effect of Tregs in the TME of OS remains unclear. In this study, single-cell RNA sequencing (scRNA-seq) data was used to identify Tregs and various other cell clusters in the TME of OS. Gene set variation analysis (GSVA) was used to investigate the signaling pathways in Tregs from OS and adjacent tissues. The CellChat and iTALK packages were used to analyze cellular communication. In addition, a prognostic model was established based on the Tregs-specific genes using bulk RNA-seq from the TARGET database, and it was verified using a Gene Expression Omnibus dataset. The pRRophetic package was used to predict drug sensitivity. Immunohistochemistry was used to verify the expression of candidate genes in OS. Based on the above methods, we showed that the OS samples were highly infiltrated with Tregs. GSVA revealed that oxidative phosphorylation, angiogenesis and mammalian target of rapamycin complex 1 (mTORC1) were highly activated in Tregs from OS compared with those from adjacent tissues. Using cellular communication analysis, we found that Tregs interacted with osteoblastic, endothelial, and myeloid cells via C-X-C motif chemokine ligand (CXCL) signaling; particularly, they strongly affected the expression of C-X-C motif chemokine receptor 4 (CXCR4) and interacted with other cell clusters through CXCL12/transforming growth factor ß1 (TGFB1) to collectively enable tumor growth and progression. Subsequently, two Tregs-specific genes-CD320 and MAF-were screened through univariate, least absolute shrinkage and selection operator regression (LASSO) and multivariate analysis to construct a prognostic model, which showed excellent prognostic accuracy in two independent cohorts. In addition, drug sensitivity analysis demonstrated that OS patients at high Tregs risk were sensitive to sunitinib, sorafenib, and axitinib. We also used immunohistochemistry to validate that CD320 and MAF were significantly upregulated in OS tissues compared with adjacent tissues. Overall, this study reveals the heterogeneity of Tregs in the OS TME, providing new insights into the invasion and treatment of this cancer.

Deep-Learning-Based Model for the Prediction of Cancer-Specific Survival in Patients with Spinal Chordoma.

OBJECTIVE: Spinal chordomas are locally aggressive and frequently recurrent tumors with a poor prognosis. Previous studies focused on a Cox regression model to predict the survival of patients with spinal chordoma. We aimed to develop a more effective model based on deep learning for prognosis prediction in spinal chordoma. METHODS: Patients with spinal chordoma were gathered from the SEER database. Cox regression analysis was conducted to compare the influence of different clinical characteristics on cancer-specific survival. Two deep learning models, namely, DeepSurv and NMTLR, were developed, alongside 2 classic models, for the purpose of comparison. Performance of these models was evaluated by concordance index, Integrated Brier Score, receiver operating characteristic curves, Kaplan-Meier curves, and calibration curves. RESULTS: A total of 258 spinal chordoma patients were included in the current study. The median follow-up time was 94 ± 52 months. Variables used for prognosis prediction consisted of age, primary site, tumor size, histologic grade, extension of surgery, tumor invasion, and metastasis. Comparing with conventional models, each deep learning model showed superior predictive performance, the C-index on the test cohort is 0.830 for DeepSurv and 0.804 for NMTLR, respectively. The DeepSurv model represented the best performance, with area under the curve of 0.843 in predicting 5-year survival and 0.880 in predicting 10-year survival. CONCLUSIONS: We successfully constructed a deep learning model to predict the CSS of spinal chordoma patients and proved that it was more accurate and practical than conventional prediction model. Our deep learning model has the potential to guide clinicians in better care planning and decision-making.

Using network pharmacology to fabricate crosslinked hyaluronan-chondroitin sulphate-resveratrol composite hydrogels for cartilage regeneration.

Cartilage tissue engineering provides a new approach for the treatment of cartilage damage. The combination of drug system with a tissue scaffold could be highly beneficial. Resveratrol (RES) is a potent anti-inflammatory agent, but its target genes and molecular mechanism of cartilage repair remain to be further studied. We used systems biology and network pharmacology methods to explore the mechanism of RES for chondrocyte and macrophages. Meanwhile, crosslinked hyaluronan-chondroitin sulphate-RES hydrogels (cHA-CS-RES) were constructed based on the target prediction results. Byin vitroandin vivoexperiments, we investigated its anti-inflammatory and pro-chondrogenesis. The results showed there were 12 hub genes potentially interacting in the RES-chondrocyte-macrophage network.In vitroexperiments were used to further verify the validity of the predicted hub genes. The composite hydrogels were successfully fabricated, and maintenance of the characteristic was further confirmed.In vitrostudy, cHA-CS-RES showed high cell viability, anti-inflammatory and pro-chondrogenesis abilities.In vivostudy of cartilage defects confirmed that the cHA-CS-RES groups were significantly better than the control group. Network pharmacology was used to predict and screen the target proteins of RES critical to cartilage tissue engineering. Moreover, cHA-CS-RES composite hydrogel showed good cartilage repair effects, anti-inflammatory and pro-chondrogenesis abilities.

Comprehensive landscape of TGFß-related signature in osteosarcoma for predicting prognosis, immune characteristics, and therapeutic response.

Osteosarcoma (OS) is a highly heterogeneous malignant bone tumor, and its tendency to metastasize leads to a poor prognosis. TGFß is an important regulator in the tumor microenvironment and is closely associated with the progression of various types of cancer. However, the role of TGFß-related genes in OS is still unclear. In this study, we identified 82 TGFß DEGs based on RNA-seq data from the TARGET and GETx databases and classified OS patients into two TGFß subtypes. The KM curve showed that the Cluster 2 patients had a substantially poorer prognosis than the Cluster 1 patients. Subsequently, a novel TGFß prognostic signatures (MYC and BMP8B) were developed based on the results of univariate, LASSO, and multifactorial Cox analyses. These signatures showed robust and reliable predictive performance for the prognosis of OS in the training and validation cohorts. To predict the three-year and five-year survival rate of OS, a nomogram that integrated clinical features and risk scores was also developed. The GSEA analysis showed that the different subgroups analyzed had distinct functions, particularly, the low-risk group was associated with high immune activity and a high infiltration abundance of CD8 T cells. Moreover, our results indicated that low-risk cases had higher sensitivity to immunotherapy, while high-risk cases were more sensitive to sorafenib and axitinib. scRNA-Seq analysis further revealed that MYC and BMP8B were strongly expressed mainly in tumor stromal cells. Finally, in this study, we confirmed the expression of MYC and BMP8B by performing qPCR, WB, and IHC analyses. To conclude, we developed and validated a TGFß-related signature to accurately predict the prognosis of OS. Our findings might contribute to personalized treatment and making better clinical decisions for OS patients.

Comprehensive landscape of immune-based classifier related to early diagnosis and macrophage M1 in spinal cord injury.

Numerous studies have documented that immune responses are crucial in the pathophysiology of spinal cord injury (SCI). Our study aimed to uncover the function of immune-related genes (IRGs) in SCI. Here, we comprehensively evaluated the transcriptome data of SCI and healthy controls (HC) obtained from the GEO Database integrating bioinformatics and experiments. First, a total of 2067 DEGs were identified between the SCI and HC groups. Functional enrichment analysis revealed substantial immune-related pathways and functions that were abnormally activated in the SCI group. Immune analysis revealed that myeloid immune cells were predominantly upregulated in SCI patients, while a large number of lymphoid immune cells were dramatically downregulated. Subsequently, 51 major IRGs were screened as key genes involved in SCI based on the intersection of the results of WGCNA analysis, DEGs, and IRGs. Based on the expression profiles of these genes, two distinct immune modulation patterns were recognized exhibiting opposite immune characteristics. Moreover, 2 core IRGs (FCER1G and NFATC2) were determined to accurately predict the occurrence of SCI via machine learning. qPCR analysis was used to validate the expression of core IRGs in an external independent cohort. Finally, the expression of these core IRGs was validated by sequencing, WB, and IF analysis in vivo. We found that these two core IRGs were closely associated with immune cells and verified the co-localization of FCER1G with macrophage M1 via IF analysis. Our study revealed the key role of immune-related genes in SCI and contributed to a fresh perspective for early diagnosis and treatment of SCI.

Anoikis patterns exhibit distinct prognostic and immune landscapes in Osteosarcoma.

OBJECTIVES: Osteosarcoma is highly aggressive and prone to metastasis, with a poor prognosis. Increasing evidence identified anoikis has a critical effect in tumor metastasis and invasion. However, the prognostic value of anoikis-related genes (ANRGs) in osteosarcoma and their role in the immune landscape of osteosarcoma remain unclear. METHODS: The RNA sequencing and clinical data of patients with osteosarcoma were extracted from the TARGET and GEO databases, and ANRGs were identified from the GeneCards database. Unsupervised clustering analysis was employed to identify anoikis-related patterns. The ESTIMATE, TIMER and ssGSEA algorithms were used to assess the immune microenvironment of different subtypes. A prognostic signature based on the identified ANRGs was constructed via univariate, LASSO and multivariate Cox regression analyses. KEGG, GO and GSEA were used for functional enrichment of genes associated with different risk subtypes. qPCR, WB and IHC were used to validate the expression of candidate genes. RESULTS: Two anoikis-related patterns with distinct clinical features and immune statuses were identified based on prognosis-related ANRGs. Cluster 2 had more active immunogenicity and a better prognosis than Cluster 1. Subsequently, we developed and validated an anoikis prognostic signature demonstrating excellent predictive ability for the prognosis of osteosarcoma. Anoikis risk score was positively associated with osteosarcoma metastasis and was identified as an independent prognostic marker. Additionally, a nomogram was established to predict the 3- and 5-year survival probability of patients with osteosarcoma. Functional enrichment analysis revealed that immune dysregulation was correlated with poor prognosis. Besides, patients in the low-risk group had higher infiltration levels of immune cells and more active immune function than patients in the high-risk group. Drug sensitivity analysis revealed several chemotherapeutic agents for the treatment of different subtypes of osteosarcoma. CONCLUSION: Our study demonstrated the role of ANRGs in osteosarcoma progression, providing insights into clinical decision making in osteosarcoma.

Expression of lactate-related signatures correlates with immunosuppressive microenvironment and prognostic prediction in ewing sarcoma.

Objectives: Ewing sarcoma (EWS) is an aggressive tumor of bone and soft tissue. Growing evidence indicated lactate as a pivotal mediator of crosstalk between tumor energy metabolism and microenvironmental regulation. However, the contribution of lactate-related genes (LRGs) in EWS is still unclear. Methods: We obtained the transcriptional data of EWS patients from the GEO database and identified differentially expressed-LRGs (DE-LRGs) between EWS patient samples and normal tissues. Unsupervised cluster analysis was utilized to recognize lactate modulation patterns based on the expression profile of DE-LRGs. Functional enrichment including GSEA and GSVA analysis was conducted to identify molecular signaling enriched in different subtypes. ESTIMATE, MCP and CIBERSORT algorithm was used to explore tumor immune microenvironment (TIME) between subtypes with different lactate modulation patterns. Then, lactate prognostic risk signature was built via univariate, LASSO and multivariate Cox analysis. Finally, we performed qPCR analysis to validate candidate gene expression. Result: A total of 35 DE-LRGs were identified and functional enrichment analysis indicated that these LRGs were involved in mitochondrial function. Unsupervised cluster analysis divided EWS patients into two lactate modulation patterns and we revealed that patients with Cluster 1 pattern were linked to poor prognosis and high lactate secretion status. Moreover, TIME analysis indicated that the abundance of multiple immune infiltrating cells were dramatically elevated in Cluster 1 to Cluster 2, including CAFs, endothelial cells, Macrophages M2, etc., which might contribute to immunosuppressive microenvironment. We also noticed that expression of several immune checkpoint proteins were clearly increased in Cluster 1 to Cluster 2. Subsequently, seven genes were screened to construct LRGs prognostic signature and the performance of the resulting signature was validated in the validation cohort. Furthermore, a nomogram integrating LRGs signature and clinical characteristics was developed to predict effectively the 4, 6, and 8-year prognosis of EWS patients. Conclusion: Our study revealed the role of LRGs in immunosuppressive microenvironment and predicting prognosis in EWS and provided a robust tool to predict the prognosis of EWS patients.

Effect of electrohydrodynamic printing scaffold with different spacing on chondrocyte dedifferentiation.

Background: Osteoarthritis (OA) is a common degenerative disease. Chondrocyte dedifferentiation can accelerate the progress of OA. Three-dimensional printing (3DP) is widely used in tissue regeneration applications. A three-dimensional (3D) culture system with 3D printed scaffolds could reduce the dedifferentiation of chondrocytes during passages, which would be a potential method for chondrocyte expansion. Methods: The viability and proliferation of chondrocytes on scaffolds and effects of scaffolds with 100, 150, 200, 250 or 300 µm spacing on chondrocyte dedifferentiation were analyzed in vitro. The morphology of scaffolds and cell/scaffold constructs was observed by scanning electron microscopy (SEM). Glycosaminoglycan (GAG) was evaluated by Alcian blue staining. The effects of different spacing on chondrocyte dedifferentiation were evaluated by the messenger RNA (mRNA) and protein levels of cartilage-related genes. Results: With more binding sites, the proliferation and viability of chondrocytes on scaffolds with 100 and 150 µm spacing were better than those with 200, 250 and 300 µm spacing on day 1, but this advantage diminished over time. The histology and quantitative real-time polymerase chain reaction (qRT-PCR) results showed that 200 µm spacing inhibits chondrocyte dedifferentiation better. Conclusions: 3D printed scaffolds with 200 µm spacing can inhibit chondrocyte dedifferentiation, providing a basis for the future study of 3D printed scaffolds as an effective method for chondrocyte expansion.

Unfolded Protein Response-Related Signature Associates With the Immune Microenvironment and Prognostic Prediction in Osteosarcoma.

Background: Osteosarcoma is a highly malignant bone tumor commonly occurring in adolescents with a poor 5-year survival rate. The unfolded protein response (UPR) can alleviate the accumulation of misfolded proteins to maintain homeostasis under endoplasmic reticulum stress. The UPR is linked to the occurrence, progression, and drug resistance of tumors. However, the function of UPR-related genes (UPRRGs) in disease progression and prognosis of osteosarcoma remains unclear. Methods: The mRNA expression profiling and corresponding clinical features of osteosarcoma were acquired from TARGET and GEO databases. Consensus clustering was conducted to confirm different UPRRG subtypes. Subsequently, we evaluated the prognosis and immune status of the different subtypes. Functional analysis of GO, GSEA, and GSVA was used to reveal the molecular mechanism between the subtypes. Finally, four genes (STC2, PREB, TSPYL2, and ATP6V0D1) were screened to construct and validate a risk signature to predict the prognosis of patients with osteosarcoma. Result: We identified two subtypes according to the UPRRG expression patterns. The subgroup with higher immune scores, lower tumor purity, and active immune status was linked to a better prognosis. Meanwhile, functional enrichment revealed that immune-related signaling pathways varied markedly in the two subtypes, suggesting that the UPR might influence the prognosis of osteosarcoma via influencing the immune microenvironment. Moreover, prognostic signature and nomogram models were developed based on UPRRGs, and the results showed that our model has an excellent performance in predicting the prognosis of osteosarcoma. qPCR analysis was also conducted to verify the expression levels of the four genes. Conclusion: We revealed the crucial contribution of UPRRGs in the immune microenvironment and prognostic prediction of osteosarcoma patients and provided new insights for targeted therapy and prognostic assessment of the disease.

[Biomechanical analysis and effectiveness evaluation of zone â +â ¡+â ¢ reconstruction of hemipelvis with rod-screw prosthesis].

Objective: To analyze the biomechanical properties of the rod-screw prosthesis based on a pelvic three-dimensional finite element model including muscle and ligament, and evaluate the effectiveness of zoneⅠ+Ⅱ+Ⅲ reconstruction of hemipelvis with rod-screw prosthesis in combination with clinical applications. Methods: A total of 21 patients who underwent hemipelvic tumor resection (zoneⅠ+Ⅱ+Ⅲ) and rod-screw prosthesis reconstruction between January 2015 and December 2020 were selected as the research subjects. Among them, there were 11 males and 10 females; the age ranged from 16 to 64 years, with an average age of 39.2 years. There were 9 cases of chondrosarcoma, 7 cases of osteosarcoma, 3 cases of Ewing sarcoma, and 2 cases of undifferentiated pleomorphic sarcoma. According to the Musculoskeletal Tumor Society Score (MSTS) staging, there were 19 cases of stage ⅡB and 2 cases of stage Ⅲ. Preoperative Harris Hip Score (HHS) and MSTS score were 54.4±3.1 and 14.1±2.0, respectively. Intraoperative 15 cases underwent extensive resection, 5 cases underwent marginal resection, and 1 case underwent intralesional resection. The CT image of 1 patient after reconstruction was used to establish a three-dimensional solid model of the pelvis via Mimics23Suite and 3-matic softwares. At the same time, a mirror operation was used to obtain a normal pelvis model, then the two solid models were imported into the finite element analysis software Workbench 2020R1 to establish three-dimensional finite element models, and the biomechanical properties of the standing position were analyzed. The operation time, intraoperative blood loss, and operation-related complications were recorded, and the postoperative evaluation was carried out with HHS and MSTS scores. Finally, the local recurrence and metastasis were reviewed. Results: Finite element analysis showed that the peak stress of the reconstructed pelvis appeared at the fixed S1, 2 rod-screw connections; the peak stress without muscles was higher than that after muscle construction, but much smaller than the yield strength of titanium alloy. The operation time was 250-370 minutes, with an average of 297 minutes; the amount of intraoperative blood loss was 3 200-5 500 mL, with an average of 4 009 mL. All patients were followed up 8-72 months, with an average of 42 months. There were 7 cases of pulmonary metastasis, of which 2 cases were preoperative metastasis; 5 cases died, 16 cases survived, and the 5-year survival rate was 72.1%. There were 3 cases of local recurrence, all of whom did not achieve extensive resection during operation. The function of the affected limbs significantly improved, and the walking function was restored. The HHS and MSTS scores were 75.2±3.0 and 20.4±2.0 at last follow-up, respectively, and the differences were significant when compared with those before operation (t=22.205, P<0.001; t=11.915, P<0.001). During follow-up, 2 cases of delayed incision healing, 2 cases of deep infection, 1 case of screw loosening, and 1 case of prosthesis dislocation occurred, and no other complication such as prosthesis or screw fracture occurred. Conclusion: The stress and deformation distribution of the reconstructed pelvis are basically the same as normal pelvis. The rod-screw prosthesis is an effective reconstruction method for pelvic malignant tumors.

Clinical application of 3D-printed patient-specific guide plate combined with computer navigation in acetabular reconstruction following resection of periacetabular tumors.

Background: The precise acetabular reconstruction has historically been a challenging procedure. 3D-printed patient-specific guide (PSG) and computer navigation (CN) technologies have been used to assist acetabular component positioning and pelvic reconstruction. This precise reconstruction approach may translate into clinical benefit. Methods: The clinical data of 84 patients who underwent periacetabular malignant tumor resection and screw-rod-acetabular cage system reconstruction in our center from January 2013 to December 2020 were retrospectively analyzed. Patients were divided into four groups: free hand (FH) group, PSG group, CN group, and PSG combined with computer navigation (PSG + CN) group. The operation time, intraoperative blood loss, and number of fluoroscopy views were recorded. The oncological prognosis, radiographic measurements of the acetabulum, limb function data, and postoperative complications were compared among groups. And finally, we evaluated the risk factors for mechanical failure of the prosthesis. Results: The postoperative X-ray and computed tomography (CT) scan revealed that the vertical offset discrepancy (VOD) between affected side and contralateral side was 8.4±1.9, 5.9±2.2, 4.1±1.3, and 2.4±1.2 mm in each groups; the horizontal offset discrepancy (HOD) was 9.0±1.9, 6.1±2.2, 3.2±1.3, and 2.1±1.2 mm, correspondingly; the abduction angle discrepancy (ABAD) was 8.6°±1.8°, 5.6°±2.0°, 2.5°±1.3°, and 1.8°±0.9°, respectively; the anteversion angle discrepancy (ANAD) was 5.9°±1.6°, 3.6°±1.7°, 2.9°±1.6°, and 1.9°±0.9°, correspondingly. Statistical results show that the PSG + CN group was superior to the FH group and the PSG group in terms of acetabular position and limb function (P<0.05). Body mass index (P=0.040) and resection type (P=0.042) were found to be the high-risk factors for mechanical failure of the prosthesis. Conclusions: PSG + CN has potential advantages in improving the accuracy and safety of acetabular positioning and reconstruction.