Construction and validation of an oxidative-stress-related risk model for predicting the prognosis of osteosarcoma

Integrated analysis of single-cell and bulk transcriptomics reveals cellular subtypes and molecular features associated with osteosarcoma prognosis

BACKGROUND

Osteosarcoma (OS) is the most common primary bone malignancy with variable molecular biology and prognosis. However, our understanding of the association between cell types and OS progression remains poor.

METHODS

We generated a human OS cell atlas by integrating over 110,000 single cells from 17 samples. Multiple machine learning algorithms were applied to develop tumor purity prediction models based on transcriptomic profile of OS. The Scissor algorithm and gene enrichment analyses were conducted to delve into cell-intrinsic molecular characteristics linked to OS prognosis. Moreover, the study investigated the impact of ATF6α in OS aggressiveness through genetic and pharmacological loss of function analyses. Lastly, the CellChat algorithm was employed to investigate cell-cell communications.

RESULTS

Utilizing the high-quality human OS cell atlas, we identified tumor purity as a prognostic indicator and developed a robust tumor purity prediction model. We respectively delineated cancer cell- and immune cell-intrinsic molecular characteristics associated with OS prognosis at single-cell resolution. Interestingly, tumor cells with activated unfolded protein response (UPR) pathway were significantly associated with disease aggressiveness. Notably, ATF6α emerged as the top-activated transcription factor for this tumor subcluster. Subsequently, we confirmed that ATF6α was markedly associated with OS progression, while both genetic and pharmacological inhibition of ATF6α impaired the survival of HOS cells. Lastly, we depicted the landscape of signal crosstalk between the UPR-related subcluster and other cell types within the tumor microenvironment.

CONCLUSION

In summary, our work provides novel insights into the molecular biology of OS, and offers valuable resource for OS biomarker discovery and treatment strategy development.

T cell receptor signaling pathway subgroups and construction of a novel prognostic model in osteosarcoma

Background

T cell receptor (TCR) signaling pathway is closely related to tumor progress and immunotherapy. This study aimed to explore the clinical significance, prognosis, immune infiltration and chemotherapy sensitivity of TCR in osteosarcoma (OS).

Material and methods

OS data were obtained from TARGET and GEO database. TCR signaling pathway-related genes (TCRGs) were extracted from Molecular Signatures Database. Unsupervised non-negative matrix factorization clustering analysis was used to identify OS molecular subtypes. Differential expressed TCRGs between molecular subtypes were screened with univariate Cox regression, LASSO regression and multivariate Cox regression. Subsequently, an OS-associated prognostic model was constructed and validated. Nomogram was established and verified. Immune landscape analysis including immune infiltration analysis, ESTIMATE algorithm and immune checkpoints expression levels of molecular subtypes and different risk groups were analyzed. Finally, chemotherapy sensitivity and potential therapeutic agents between different risk groups was identified.

Results

Two TCRGs related subclusters were identified. Two hundred and seventy-two Differential expressed TCRGs were screened between two subclusters. A robust prognostic model were constructed. High and low risk groups were stratified. Low risk group showed higher ESTIMATE, immune and stromal scores, while high risk group exhibited higher tumor purity and the lower expression levels of immune checkpoints. A nomogram comprising metastasis and risk score was successfully built. The sensitivity to chemotherapy agents were different across high and low risk groups.

Conclusions

Our study proposed TCR related molecular subtypes and provided a prognostic model for OS. Our findings may bring a new insight into the immunotherapy for OS patients.

UBE2L3 Suppresses Oxidative Stress-regulated Necroptosis to Accelerate Osteosarcoma Progression

BACKGROUND

Osteosarcoma is a highly invasive bone marrow stromal tumor with limited treatment options. Oxidative stress plays a crucial role in the development and progression of tumors, but the underlying regulatory mechanisms are not fully understood. Recent studies have revealed the significant involvement of UBE2L3 in oxidative stress, but its specific role in osteosarcoma remains poorly investigated.

OBJECTIVE

This study aimed to explore the molecular mechanisms by which UBE2L3 promotes oxidative stress-regulated necroptosis to accelerate the progression of osteosarcoma using in vitro cell experiments.

METHODS

Human osteoblast hFOB1.19 cells and various human osteosarcoma cell lines (MG-63, U2OS, SJSA-1, HOS, and 143B) were cultured in vitro. Plasmids silencing UBE2L3 and negative control plasmids were transfected into U2OS and HOS cells. The cells were divided into the following groups: U2OS cell group, HOS cell group, si-NC-U2OS cell group, si-UBE2L3-U2OS cell group, si-NC-HOS cell group, and si-UBE2L3-HOS cell group. Cell viability and proliferation capacity were measured using the Tunnel method and clonogenic assay. Cell migration and invasion abilities were assessed by Transwell and scratch assays. Cell apoptosis was analyzed by flow cytometry, and ROS levels were detected using immunofluorescence. The oxidative stress levels in various cell groups and the expression changes of necroptosis-related proteins were assessed by PCR and WB. Through these experiments, we aim to evaluate the impact of oxidative stress on necroptosis and uncover the specific mechanisms by which targeted regulation of oxidative stress promotes tumor cell necroptosis as a potential therapeutic strategy for osteosarcoma.

RESULTS

The mRNA expression levels of UBE2L3 in human osteosarcoma cell lines were significantly higher than those in human osteoblast hFOB1.19 cells (p <0.01). UBE2L3 expression was significantly decreased in U2OS and HOS cells transfected with si-UBE2L3, indicating the successful construction of stable cell lines with depleted UBE2L3. Tunnel assay results showed a significant increase in the number of red fluorescent-labeled cells in si-UBE2L3 groups compared to si-NC groups in both cell lines, suggesting a pronounced inhibition of cell viability. Transwell assay demonstrated a significant reduction in invasion and migration capabilities of si-UBE2L3 groups in osteosarcoma cells. The clonogenic assay revealed significant suppression of proliferation and clonogenic ability in both U2OS and HOS cells upon UBE2L3 knockdown. Flow cytometry confirmed that UBE2L3 knockdown significantly enhanced apoptosis in U2OS and HOS cells. Immunofluorescence results showed that UBE2L3 silencing promoted oxidative stress levels in osteosarcoma cells and facilitated tumor cell death. WB analysis indicated a significant increase in phosphorylation levels of necroptosis-related proteins, RIP1, RIP3, and MLKL, in both osteosarcoma cell lines after UBE2L3 knockdown. In addition, the expression of necrosis-associated proteins was inhibited by the addition of the antioxidant N-acetylcysteine (NAC).

CONCLUSION

UBE2L3 is upregulated in osteosarcoma cells, and silencing of UBE2L3 promotes oxidative stress in these cells, leading to enhanced necroptosis and delayed progression of osteosarcoma.

A novel prognostic signature related to programmed cell death in osteosarcoma

Background

Osteosarcoma primarily affects children and adolescents, with current clinical treatments often resulting in poor prognosis. There has been growing evidence linking programmed cell death (PCD) to the occurrence and progression of tumors. This study aims to enhance the accuracy of OS prognosis assessment by identifying PCD-related prognostic risk genes, constructing a PCD-based OS prognostic risk model, and characterizing the function of genes within this model.

Method

We retrieved osteosarcoma patient samples from TARGET and GEO databases, and manually curated literature to summarize 15 forms of programmed cell death. We collated 1621 PCD genes from literature sources as well as databases such as KEGG and GSEA. To construct our model, we integrated ten machine learning methods including Enet, Ridge, RSF, CoxBoost, plsRcox, survivalSVM, Lasso, SuperPC, StepCox, and GBM. The optimal model was chosen based on the average C-index, and named Osteosarcoma Programmed Cell Death Score (OS-PCDS). To validate the predictive performance of our model across different datasets, we employed three independent GEO validation sets. Moreover, we assessed mRNA and protein expression levels of the genes included in our model, and investigated their impact on proliferation, migration, and apoptosis of osteosarcoma cells by gene knockdown experiments.

Result

In our extensive analysis, we identified 30 prognostic risk genes associated with programmed cell death (PCD) in osteosarcoma (OS). To assess the predictive power of these genes, we computed the C-index for various combinations. The model that employed the random survival forest (RSF) algorithm demonstrated superior predictive performance, significantly outperforming traditional approaches. This optimal model included five key genes: MTM1, MLH1, CLTCL1, EDIL3, and SQLE. To validate the relevance of these genes, we analyzed their mRNA and protein expression levels, revealing significant disparities between osteosarcoma cells and normal tissue cells. Specifically, the expression levels of these genes were markedly altered in OS cells, suggesting their critical role in tumor progression. Further functional validation was performed through gene knockdown experiments in U2OS cells. Knockdown of three of these genes-CLTCL1, EDIL3, and SQLE-resulted in substantial changes in proliferation rate, migration capacity, and apoptosis rate of osteosarcoma cells. These findings underscore the pivotal roles of these genes in the pathophysiology of osteosarcoma and highlight their potential as therapeutic targets.

Conclusion

The five genes constituting the OS-PCDS model-CLTCL1, MTM1, MLH1, EDIL3, and SQLE-were found to significantly impact the proliferation, migration, and apoptosis of osteosarcoma cells, highlighting their potential as key prognostic markers and therapeutic targets. OS-PCDS enables accurate evaluation of the prognosis in patients with osteosarcoma.

Circadian rhythm-associated lncRNA RP11-414H17.5 as a key therapeutic target in osteosarcoma affects the tumor immune microenvironment and enhances malignancy

BACKGROUND

It has previously been proven that circadian rhythm disruption is associated with the incidence and deterioration of several tumors, which potentially leads to increased tumor susceptibility and a worse prognosis for tumor-bearing patients. However, their potential role in osteosarcoma has yet to be sufficiently investigated.

METHODS

Transcriptomic and clinical data of 84 osteosarcoma samples and 70 normal bone tissue samples were obtained from the TARGET and GTEx databases, circadian rhythm-related genes were obtained from Genecards, and circadian rhythm-related lncRNAs (CRLs) were obtained by Pearson correlation analysis, differential expression analysis, and protein-protein interaction (PPI) analysis. COX regression and LASSO regression were performed on the CRLs in order to construct a circadian rhythm-related prognostic prediction signature (CRPS). CRPS reliability was verified by Kaplan-Meier (KM), principal component analysis (PCA), nomogram, and receiver operating characteristic (ROC) curve. CRPS effects on the immune microenvironment of osteosarcoma were explored by enrichment analysis and immune infiltration analysis, and the effect of critical gene RP11-414H17.5 on osteosarcoma was experimentally verified.

RESULT

CRPS consisting of three CRLs was constructed and its area under the curve (AUC) values predicted that osteosarcoma prognosis reached 0.892 in the training group and 0.843 in the test group, with a p value of < 0.05 for the KM curve and stable performance across different clinical subgroups. PCA analysis found that CRPS could significantly distinguish between different risk subgroups, and exhibited excellent performance in the prediction of the immune microenvironment. The experiment verified that RP11-414H17.5 can promote metastasis and inhibit apoptosis of osteosarcoma cells.

CONCLUSION

The study revealed that circadian rhythm plays a crucial role in osteosarcoma progression and identified the impact of the key gene RP11-414H17.5 on osteosarcoma, which provides novel insights into osteosarcoma diagnosis and therapy.

Ferroptosis-related lncRNAs guiding osteosarcoma prognosis and immune microenvironment

OBJECTIVE

To investigate the ferroptosis-related long non-coding RNAs (FRLncs) implicated in influencing the prognostic and immune microenvironment in osteosarcoma (OS), and to establish a foundational framework for informing clinical decision making pertaining to OS management.

METHODS

Transcriptome data and clinical data pertaining to 86 cases of OS, the GSE19276, GSE16088 and GSE33382 datasets, and a list of ferroptosis-related genes (FRGs) were used to establish a risk prognostic model through comprehensive analysis. The identification of OS-related differentially expressed FRGs was achieved through an integrated analysis encompassing the aforementioned 86 OS transcriptome data and the GSE19276, GSE16088 and GSE33382 datasets. Concurrently, OS-related FRLncs were ascertained via co-expression analysis. To establish a risk prognostic model for OS, Univariate Cox regression analysis and Lasso Cox regression analysis were employed. Subsequently, a comprehensive evaluation was conducted, comprising risk curve analysis, survival analysis, receiver operating characteristic curve analysis and independent prognosis analysis. Model validation with distinct clinical subgroups was performed to assess the applicability of the risk prognostic model to diverse patient categories. Moreover, single sample gene set enrichment analysis (ssGSEA) was conducted to investigate variations in immune cell populations and immune functions within the context of the risk prognostic model. Furthermore, an analysis of immune checkpoint differentials yielded insights into immune checkpoint-related genes linked to OS prognosis. Finally, the risk prognosis model was verified by dividing the samples into train group and test group.

RESULTS

We identified a set of seven FRLncs that exhibit potential as prognostic markers and influence factors of the immune microenvironment in the context of OS. This ensemble encompasses three high-risk FRLncs, denoted as APTR, AC105914.2 and AL139246.5, alongside four low-risk FRLncs, designated as DSCR8, LOH12CR2, AC027307.2 and AC025048.2. Furthermore, our analysis revealed notable down-regulation in the high-risk group across four distinct immune cell types, namely neutrophils, natural killer cells, plasmacytoid dendritic cells and tumor-infiltrating lymphocytes. This down-regulation was also reflected in four key immune functions, antigen-presenting cell (APC)-co-stimulation, checkpoint, cytolytic activity and T cell co-inhibition. Additionally, we identified seven immune checkpoint-associated genes with significant implications for OS prognosis, including CD200R1, HAVCR2, LGALS9, CD27, LAIR1, LAG3 and TNFSF4.

CONCLUSION

The findings of this study have identified FRLncs capable of influencing OS prognosis and immune microenvironment, as well as immune checkpoint-related genes that are linked to OS prognosis. These discoveries establish a substantive foundation for further investigations into OS survival and offer valuable insights for informing clinical decision making in this context.