DNA damage response and repair in osteosarcoma: Defects, regulation and therapeutic implications

USP3 promotes osteosarcoma progression via deubiquitinating EPHA2 and activating the PI3K/AKT signaling pathway

Ubiquitin-specific protease 3 (USP3) plays an important role in the progression of various tumors. However, the role of USP3 in osteosarcoma (OS) remains poorly understood. The aim of this study was to explore the biological function of USP3 in OS and the underlying molecular mechanism. We found that OS had higher USP3 expression compared with that of normal bone tissue, and high expression of USP3 was associated with poor prognosis in patients with OS. Overexpression of USP3 significantly increased OS cell proliferation, migration, and invasion. Mechanistically, USP3 led to the activation of the PI3K/AKT signaling pathway in OS by binding to EPHA2 and then reducing its protein degradation. Notably, the truncation mutant USP3-F2 (159-520) interacted with EPHA2, and amino acid 203 was found to play an important role in this process. And knockdown of EPHA2 expression reversed the pro-tumour effects of USP3-upregulating. Thus, our study indicates the USP3/EPHA2 axis may be a novel potential target for OS treatment.

Construction of an oxidative phosphorylation-related gene signature for predicting prognosis and identifying immune infiltration in osteosarcoma

BACKGROUND

Osteosarcoma is a prevalent malignant tumor that originates from mesenchymal tissue. It typically affects children and adolescents. Although it is known that the growth of osteosarcoma relies on oxidative phosphorylation for energy production, limited attention has been paid to exploring the potential of oxidative phosphorylation-related genes in predicting the prognosis of individuals suffering from osteosarcoma.

METHODS

All the data were retrieved from the UCSC Xena and GEO (GENE EXPRESSION OMNIBUS). Identification of the oxidative phosphorylation genes linked to the prognosis of individuals with osteosarcoma was done by means of univariate COX and LASSO regression analyses. Following that, patients were categorized into a high-risk group and a low-risk group as per the risk score determined by the identified oxidative phosphorylation genes. Furthermore, a comparison was made in terms of the survival and immune infiltration between both groups, and the prognostic model was established.

RESULTS

Five oxidative phosphorylation genes (ATP6V0D1, LHPP, COX6A2, MTHFD2, NDUFB9) associated with the prognosis of individuals with osteosarcoma were identified and the risk prognostic models were constructed. In the current research, the analysis of the ROC curves indicated a superior predictive accuracy exhibited by the risk model. The prognosis was adversely affected by immune infiltration in the high-risk group in comparison with the low-risk group. The function of the oxidative phosphorylation-related prognostic gene set was verified by GO and KEGG analysis. Furthermore, the link between oxidative phosphorylation-related genes and osteosarcoma immune infiltration was examined by GSEA analysis.

CONCLUSIONS

In this study, a prognostic model that demonstrated good predictive performance was constructed. Additionally, this study highlighted a correlation between oxidative phosphorylation-related genes and immune infiltration.

Versatile function of AMPK signaling in osteosarcoma: An old player with new emerging carcinogenic functions

AMP-activated protein kinase (AMPK) signaling has a versatile role in Osteosarcoma (OS), an aggressive bone malignancy with a poor prognosis, particularly in cases that have metastasized or recurred. This review explores the regulatory mechanisms, functional roles, and therapeutic applications of AMPK signaling in OS. It focuses on the molecular activation of AMPK and its interactions with cellular processes like proliferation, apoptosis, and metabolism. The uncertain role of AMPK in cancer is also discussed, highlighting its potential as both a tumor suppressor and a contributor to carcinogenesis. The therapeutic potential of targeting AMPK signaling in OS treatment is examined, including direct and indirect activators like metformin, A-769662, resveratrol, and salicylate. Further research is needed to determine dosing, toxicities, and molecular mechanisms responsible for the anti-osteosarcoma effects of these compounds. This review underscores the complex involvement of AMPK signaling in OS and emphasizes the need for a comprehensive understanding of its molecular mechanisms. By elucidating the role of AMPK in OS, the aim is to pave the way for innovative therapeutic approaches that target this pathway, ultimately improving the prognosis and quality of life for OS patients.

Cancer-Erythrocyte Membrane-Mimicking Fe3O4 Nanoparticles and DHJS for Ferroptosis/Immunotherapy Synergism in Tumors

Ferroptosis is characterized by iron accumulation and lipid peroxidation. However, a clinical dose of Fe3O4 nanoparticles could not cause effective ferroptosis in tumors, and the mechanism is yet to be completely understood. In this study, using RNA-seq data, we found that tumor cells could feedback-activate the antioxidant system by upregulating Nrf-2 expression, thus avoiding ferroptosis caused by Fe3O4 nanoparticles. We also found that DHJS (a probe for ROS generation) can antagonize Nrf-2 expression when it synergizes with Fe3O4 nanoparticles, thus inducing ferroptosis in tumor cells. Considering these findings, we created a biomimetic hybrid cell membrane camouflaged by PLGA-loaded Fe3O4 and DHJS to treat osteosarcoma. The hybrid cell membrane endowed the core nanoparticle with the extension of blood circulation life and enhanced homologous targeting ability. In addition, DHJS and Fe3O4 in nanoparticles prompted synergistically lethal ferroptosis in cancer cells and induced macrophage M1 polarization as well as the infiltration of CD8(+) T cells and dendritic cells in tumors. In summary, this study provides novel mechanistic insights and practical strategies for ferroptosis induction of Fe3O4 nanoparticles. Meanwhile, the synthesized biomimetic nanoparticles exhibited synergistic ferroptosis/immunotherapy against osteosarcoma.

Bad to the Bone: Emerging Approaches to Aggressive Bone Sarcomas

Bone sarcomas are rare heterogeneous tumors that affect patients of all ages including children, adolescent young adults, and older adults. They include many aggressive subtypes and patient groups with poor outcomes, poor access to clinical trials, and lack of defined standard therapeutic strategies. Conventional chondrosarcoma remains a surgical disease, with no defined role for cytotoxic therapy and no approved targeted systemic therapies. Here, we discuss promising novel targets and strategies undergoing evaluation in clinical trials. Multiagent chemotherapy has greatly improved outcomes for patients with Ewing sarcoma (ES) and osteosarcoma, but management of those with high-risk or recurrent disease remains challenging and controversial. We describe the impact of international collaborative trials, such as the rEECur study, that aim to define optimal treatment strategies for those with recurrent, refractory ES, and evidence for high-dose chemotherapy with stem-cell support. We also discuss current and emerging strategies for other small round cell sarcomas, such as CIC-rearranged, BCOR-rearranged tumors, and the evaluation of emerging novel therapeutics and trial designs that may offer a new paradigm to improve survival in these aggressive tumors with notoriously bad (to the bone) outcomes.

Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net

One of the most prevalent malignant bone tumors is osteosarcoma. The diagnosis and treatment cycle are long and the prognosis is poor. It takes a lot of time to manually identify osteosarcoma from osteosarcoma magnetic resonance imaging (MRI). Medical image processing technology has greatly alleviated the problems faced by medical diagnoses. However, MRI images of osteosarcoma are characterized by high noise and blurred edges. The complex features increase the difficulty of lesion area identification. Therefore, this study proposes an osteosarcoma MRI image segmentation method (OSTransnet) based on Transformer and U-net. This technique primarily addresses the issues of fuzzy tumor edge segmentation and overfitting brought on by data noise. First, we optimize the dataset by changing the precise spatial distribution of noise and the data-increment image rotation process. The tumor is then segmented based on the model of U-Net and Transformer with edge improvement. It compensates for the limitations of U-semantic Net by using channel-based transformers. Finally, we also add an edge enhancement module (BAB) and a combined loss function to improve the performance of edge segmentation. The method's accuracy and stability are demonstrated by the detection and training results based on more than 4,000 MRI images of osteosarcoma, which also demonstrate how well the method works as an adjunct to clinical diagnosis and treatment.

Identification of Differentially Expressed Intronic Transcripts in Osteosarcoma

Over the past decade; the discovery and characterization of long noncoding RNAs (lncRNAs) have revealed that they play a major role in the development of various diseases; including cancer. Intronic transcripts are one of the most fascinating lncRNAs that are located within intron regions of protein-coding genes, which have the advantage of encoding micropeptides. There have been several studies looking at intronic transcript expression profiles in cancer; but almost none in osteosarcoma. To overcome this problem; we have investigated differentially expressed intronic transcripts between osteosarcoma and normal bone tissues. The results highlighted that NRG1-IT1; FGF14-IT1; and HAO2-IT1 were downregulated; whereas ER3-IT1; SND1-IT1; ANKRD44-IT1; AGAP1-IT1; DIP2A-IT1; LMO7DN-IT1; SLIT2-IT1; RNF216-IT1; and TCF7L1-IT1 were upregulated in osteosarcoma tissues compared to normal bone tissues. Furthermore, we identified if the transcripts encode micropeptides and the transcripts' locations in a cell.

Design, synthesis, molecular modeling and DNA-binding studies of new barbituric acid derivatives

Cancer disease is developing all over the world mainly in developing countries. We should learn more about DNA–ligand interactions to design new drugs that target biological activities like transcription, replication and translation of particular genes. To understand the mechanism of action and design-specific DNA binders, the evaluation of DNA–ligand interactions is critical. Novel barbituric acid derivatives based on (benzyloxy)benzaldehydes were synthesized and evaluated as DNA-binding agents. Among products, molecular docking studies revealed that 4j and 4m have the best interactions with the ctDNA via the minor groove binding. These results were approved by the quantum mechanics calculations. The interaction profiles of the selected compound (4j and 4m) with DNA were evaluated by UV–Visible titration. UV–Visible titration data confirm this interaction. According to the molecular modeling results, the Structure–Activity relationships for all synthesized barbituric acid derivatives were proposed. It was observed that N,N-dimethyl barbituric acid/4-hydroxybenzaldehyde derivatives have better DNA interactions than barbituric acid/vanillin and barbituric acid/3-hydroxybenzaldehyde derivatives.

m6A-Related lncRNAs Predict Overall Survival of Patients and Regulate the Tumor Immune Microenvironment in Osteosarcoma

Background

m6A-related lncRNAs have demonstrated great potential tumor diagnostic and therapeutic targets. The goal of this work was to find m6A-regulated lncRNAs in osteosarcoma patients.

Method

The Cancer Genome Atlas (TCGA) database was used to retrieve RNA sequencing and medical information from osteosarcoma sufferers. The Pearson's correlation test was used to identify the m6A-related lncRNAs. A risk model was built using univariate and multivariable Cox regression analysis. Kaplan–Meier survival analysis and receiver functional requirements were used to assess the risk model's performance (ROC). By using the CIBERSORT method, the associations between the relative risks and different immune cell infiltration were investigated. Lastly, the bioactivities of high-risk and low-risk subgroups were investigated using Gene Set Enrichment Analysis (GSEA).

Result

A total of 531 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs have demonstrated prognostic values. A total of 88 OS patients were separated into cluster 1, cluster 2, and cluster 3. The overall survival rate of OS patients in cluster 3 was more favorable than that of those in cluster 1 and cluster 2. The average Stromal score was much higher in cluster 1 than in cluster 2 and cluster 3 (P < 0.05). The expression levels of lncRNAs used in the construction of the risk prediction model in the high-risk group were generally lower than those in the low-risk group. Analysis of patient survival indicated that the survival of the low-risk group was higher than that of the high-risk group (P < 0.0001) and the area under the curve (AUC) of the ROC curve was 0.719. Using the CIBERSORT algorithm, the results revealed that Macrophages M0, Macrophages M2, and T cells CD4 memory resting accounted for a large proportion of immune cell infiltration. By GSEA analysis, our results implied that the high-risk group was mainly involved in unfolded protein response, DNA repair signaling, and epithelial-mesenchymal transition signaling pathway and glycolysis pathway; meanwhile, the low-risk group was mainly involved in estrogen response early and KRAS signaling pathway.

Conclusion

Our investigation showed that m6A-related lncRNAs remained tightly connected to the immunological microenvironment of osteosarcoma tumors, potentially influencing carcinogenesis and development. The immune microenvironment and immune-related biochemical pathways can be changed by regulating the transcription of M6A modulators or lncRNAs. In addition, we looked for risk-related signaling of m6A-related lncRNAs in osteosarcomas and built and validated the risk prediction system. The findings of our current analysis will facilitate the assessment of outcomes and the development of immunotherapies for sufferers of osteosarcomas.

Efficacy of Neoadjuvant Chemotherapy plus Limb-Sparing Surgery for Osteosarcoma and Its Impact on Long-Term Quality of Life

Purpose

To assess the efficacy of neoadjuvant chemotherapy plus limb-sparing surgery for osteosarcoma and its impact on long-term quality of life.

Methods

Between August 2016 and December 2018, 90 patients with osteosarcoma treated in Nanchong Central Hospital were recruited and divided at a ratio of 1 : 1 to receive limb-sparing surgery (control group) or limb-sparing surgery plus neoadjuvant chemotherapy (study group) by random number table methods. The clinical endpoints were clinical efficacy and long-term quality of life.

Results

Limb-sparing surgery plus neoadjuvant chemotherapy was associated with a significantly higher efficacy versus limb-sparing surgery alone. Limb-sparing surgery plus neoadjuvant chemotherapy resulted in a significantly higher Enneking score and a higher good function rating of patients versus limb-sparing surgery. The two groups showed a high but similar 1-year survival rate. Patients given limb-sparing surgery plus neoadjuvant chemotherapy showed significantly higher 2-year and 3-year survival and a longer mean survival versus those receiving limb-sparing surgery alone. Limb-sparing surgery plus neoadjuvant chemotherapy resulted in significantly higher scores of role emotional, mental health, physical function, and social function and a lower bodily pain score than limb-sparing surgery alone. Limb-sparing surgery plus neoadjuvant chemotherapy was associated with significantly lower fatigue, nausea and vomiting, dyspnea, constipation, and diarrhea scores and a significantly higher health status score versus monotherapy of limb-sparing surgery.

Conclusion

Neoadjuvant chemotherapy plus limb-sparing surgery improves the postoperative limb function and long-term quality of life of patients with osteosarcoma, which shows great potential for clinical promotion.

BA-GCA Net: Boundary-Aware Grid Contextual Attention Net in Osteosarcoma MRI Image Segmentation

Osteosarcoma is one of the most common bone tumors that occurs in adolescents. Doctors often use magnetic resonance imaging (MRI) through biosensors to diagnose and predict osteosarcoma. However, a number of osteosarcoma MRI images have the problem of the tumor shape boundary being vague, complex, or irregular, which causes doctors to encounter difficulties in diagnosis and also makes some deep learning methods lose segmentation details as well as fail to locate the region of the osteosarcoma. In this article, we propose a novel boundary-aware grid contextual attention net (BA-GCA Net) to solve the problem of insufficient accuracy in osteosarcoma MRI image segmentation. First, a novel grid contextual attention (GCA) is designed to better capture the texture details of the tumor area. Then the statistical texture learning block (STLB) and the spatial transformer block (STB) are integrated into the network to improve its ability to extract statistical texture features and locate tumor areas. Over 80,000 MRI images of osteosarcoma from the Second Xiangya Hospital are adopted as a dataset for training, testing, and ablation studies. Results show that our proposed method achieves higher segmentation accuracy than existing methods with only a slight increase in the number of parameters and computational complexity.

Expression of the Circadian Clock Gene ARNTL associated with DNA repair gene and prognosis of patient with osteosarcoma

PURPOSE

The study objects were to explore the correlation between the biological role of clock genes and clinical indicators in patients with osteosarcoma (OS).

METHODS

We acquired the clinical information and RNA sequencing data of OS samples from the TARGET database. The protein-protein interaction (PPI) network and expression correlation analysis of clock genes were performed. Then, the functional enrichment analysis of clock genes was analyzed. The survival analysis of clock genes in patients of OS was carried out by univariate cox regression, Kaplan-Meier (KM) curve and multivariate cox regression methods. Moreover, the spearmen correlation analysis was performed to explore the correlation between clock genes and DNA repair genes in patients with OS.

RESULTS

The PPI network and expression correlation analysis of clock genes indicated that the clock genes were highly correlated with each other. The survival analysis of clock genes found that clock gene ARNTL is a protective factor for the prognosis of patients with OS. We found that ARNTL was positively related to DNA repair genes and was involved in the biological process of DNA damage repair in patients with OS.

CONCLUSIONS

ARNTL may affect the prognosis and chemotherapy response of patients with OS by regulating DNA repair pathways.

Quercetin Augments Cisplatin-Induced Apoptosis, DNA Damage Response, and MiR-22 Expression While It Prevents DNA Repair in Osteosarcoma Cells

BACKGROUND

Osteosarcoma (OS) patients are commonly treated with chemotherapeutic agents like cisplatin (Cis). Quercetin with fewer side effects can improve the potency of chemotherapy and be used in combinational therapies. Herein, we aimed to evaluate the effects of Cis plus quercetin on DNA damage response (DDR), DNA repair, and apoptosis in Saos-2 cells.

METHODS

The effects of Cis and quercetin single or in combination on Saos-2 cell viability and the cytotoxicity of the drugs were measured by MTT assay. The expression of DDR and repair components including P53, ATM, ATR, RAD51, and H2AX, and also miR-22 were analyzed by real-time PCR. The rate of apoptosis was measured by flow cytometry.

RESULTS

Quercetin potentiated the cytotoxic effects of Cis in Saos-2 cells. The IC50 of Cis reduced from 6.12 µM to 4.25 µM. The combination of quercetin and Cis was associated with the up-regulation of miR-22 and DDR components, including P53, ATM, ATR, and H2AX as well as the down-regulation of RAD51. Moreover, this combined regimen significantly induced apoptosis in Saos-2 cells compared to mono drugs.

CONCLUSION

The co-treatment of quercetin and Cis can accelerate DNA damage, DNA damage response, and apoptosis while interfering with the DNA repair process in Saos-2 cells. Moreover, this combination provokes the tumor suppressor miR-22 expression in these cells.

Rethinking U-Net from an Attention Perspective with Transformers for Osteosarcoma MRI Image Segmentation

Osteosarcoma is one of the most common primary malignancies of bone in the pediatric and adolescent populations. The morphology and size of osteosarcoma MRI images often show great variability and randomness with different patients. In developing countries, with large populations and lack of medical resources, it is difficult to effectively address the difficulties of early diagnosis of osteosarcoma with limited physician manpower alone. In addition, with the proposal of precision medicine, existing MRI image segmentation models for osteosarcoma face the challenges of insufficient segmentation accuracy and high resource consumption. Inspired by transformer's self-attention mechanism, this paper proposes a lightweight osteosarcoma image segmentation architecture, UATransNet, by adding a multilevel guided self-aware attention module (MGAM) to the encoder-decoder architecture of U-Net. We successively perform dataset classification optimization and remove MRI image irrelevant background. Then, UATransNet is designed with transformer self-attention component (TSAC) and global context aggregation component (GCAC) at the bottom of the encoder-decoder architecture to perform integration of local features and global dependencies and aggregation of contexts to learned features. In addition, we apply dense residual learning to the convolution module and combined with multiscale jump connections, to improve the feature extraction capability. In this paper, we experimentally evaluate more than 80,000 osteosarcoma MRI images and show that our UATransNet yields more accurate segmentation performance. The IOU and DSC values of osteosarcoma are 0.922 ± 0.03 and 0.921 ± 0.04, respectively, and provide intuitive and accurate efficient decision information support for physicians.

Identification of Epigenetic-Dysregulated lncRNAs Signature in Osteosarcoma by Multi-Omics Data Analysis

Background

Alterations of epigenetic modification patterns are potential markers of cancer. The current study characterized six histone modifications in osteosarcoma and identified epigenetically dysregulated long non-coding RNAs (epi-lncRNAs).

Methods

Multi-omics data were obtained from osteosarcoma cell line SJSA1 and a normal cell line. Differentially expressed lncRNAs (DElncRNAs) between osteosarcoma and normal skeletal muscle were analyzed using Limma. MACS2 was applied to identify the “peaks” modified by each histone in the cell. Promoters or enhancers of DElncRNA were overlapped with differential histone-modified regions (DHMR) to screen epi-lncRNAs. Univariate and multivariate Cox regression analysis were performed to detect the genes closely related to the prognosis of osteosarcoma and to construct risk models.

Results

A total of 17 symbolic epi-lncRNA in osteosarcoma were screened, and 13 of them were differentially expressed between osteosarcoma and normal samples. Eight epi-lncRNAs were retained by Univariate Cox regression analysis. Four of these epi-lncRNAs were used to construct an epi-lncRNA signature. The risk score of each osteosarcoma sample in the high- or low-risk group was estimated according to the epi-lncRNA signature. The overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The area under the receiver operating characteristic (ROC) curve of the model was 0.79 and 0.82 for 1-, 3-, and 5-year OS, respectively.

Conclusion

Our results revealed the histone modification pattern in osteosarcoma and developed 4-epi-lncRNA signature to predict the prognosis of osteosarcoma, laying a foundation for the identification of highly specific epigenetic biomarkers for osteosarcoma.

DNA damage response signaling pathways as important targets for combination therapy and chemotherapy sensitization in osteosarcoma

Osteosarcoma (OS) is the most common bone malignancy that occurs most often in young adults, and adolescents with a survival rate of 20% in its advanced stages. Nowadays, increasing the effectiveness of common treatments used in OS has become one of the main problems for clinicians due to cancer cells becoming resistant to chemotherapy. One of the most important mechanisms of resistance to chemotherapy is through increasing the ability of DNA repair because most chemotherapy drugs damage the DNA of cancer cells. DNA damage response (DDR) is a signal transduction pathway involved in preserving the genome stability upon exposure to endogenous and exogenous DNA-damaging factors such as chemotherapy agents. There is evidence that the suppression of DDR may reduce chemoresistance and increase the effectiveness of chemotherapy in OS. In this review, we aim to summarize these studies to better understand the role of DDR in OS chemoresistance in pursuit of overcoming the obstacles to the success of chemotherapy.

Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries

Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model's segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.