Establishment and characterization of a highly metastatic human osteosarcoma cell line from osteosarcoma lung metastases

Scavenger receptor class B member 1 promotes lung cancer growth and metastasis through enhanced twist family BHLH transcription factor 1 signaling in vitro and in vivo: Exploration of RPPNs as a therapeutic Strategy

Objective

Lung cancer remains a leading cause of cancer-related mortality worldwide. Although scavenger receptor class B member 1 (SCARB1), a crucial cell surface receptor, plays a vital role in various cancers, its function in lung cancer remains incompletely elucidated. This study aims to investigate the role and molecular mechanisms of SCARB1 in lung cancer progression and develop a novel SCARB1-targeted nanoparticle drug delivery system.

Material and Methods

We analyzed SCARB1 expression levels in lung cancer tissues and their correlation with patient prognosis using the Cancer Genome Atlas database. In vitro experiments, including quantitative real-time polymerase chain reaction, Western blot, 5-ethynyl-2'-deoxyuridine, colony formation, and Transwell analyses, were conducted to study the effects of SCARB1 on lung cancer cell proliferation, invasion, and migration. A lung metastasis model was established through tail vein injection to evaluate the role of SCARB1 in promoting lung cancer metastasis in vivo. We also developed red blood cell membrane-coated poly (lactic-co-glycolic acid) nanocarriers loaded with paclitaxel (RPPNs) and assessed their effect on SCARB1 expression and lung cancer progression.

Results

SCARB1 was overexpressed in human lung cancer tissues and significantly associated with poor patient prognosis. In vitro experiments confirmed that silencing SCARB1 inhibited lung cancer cell growth, invasion, and migration. SCARB1 overexpression promoted lung cancer cell proliferation, migration, and epithelial-mesenchymal transition through twist family BHLH transcription factor 1 (Twist1) activation. In vivo experiments further validated the crucial role of SCARB1 in promoting lung cancer metastasis. The developed RPPNs effectively suppressed SCARB1 expression in lung cancer and demonstrated superior inhibitory effects compared with traditional RPPNs. However, SCARB1 overexpression partially antagonized the antimetastatic effects of RPPNs.

Conclusion

This work elucidates, for the 1st time, the molecular mechanism by which SCARB1 promotes lung cancer growth and metastasis through the activation of the Twist1 signaling pathway and develops a novel SCARB1-targeted nanoparticle drug delivery system, namely RPPNs. The findings of this work not only deepen our understanding of the molecular mechanisms underlying lung cancer progression but also provide new strategies for lung cancer diagnosis and treatment.

GALNT6, transcriptionally inhibited by KLF9, promotes osteosarcoma progression by increasing EFEMP1 expression via O-glycosylation modification

Osteosarcoma (OS) is one of the deadliest malignancies in adolescents and its treatment status and prognosis remain unsatisfactory. N-acetylgalactosamine transferase 6 (GALNT6), one of the key enzymes regulating O-glycosylation, functions vary in different types of cancer. Currently, the function of GALNT6 in OS is unclear. Our results showed that GALNT6 was highly expressed in OS tissues, and the patients with higher GALNT6 expression exhibited a lower overall survival rate than patients with lower GALNT6 expression. We constructed the GALNT6-knockdown and GALNT6-overexpression vectors based on Tet-on system and packaged lentiviral particles to modulate GALNT6 expression. GALNT6 silencing impaired OC cell growth and metastasis both in vivo and vitro. Kruppel-like factor 9 (KLF9), a transcription factor known to suppress OS progression, was found to block GALNT6 transcription by binding to its promoter. Meanwhile, GALNT6 overexpression restored the effects caused by KLF9 upregulation. GALNT6 was known to affect protein stability by O-glycosylation regulation, thus the label-free proteomics combined with co-immunoprecipitation/mass-spectrum (MS) analysis were conducted to identify the potential mechanism of GALNT6 in promoting OS progression. EGF-containing fibulin extracellular matrix protein 1 (EFEMP1), contained several O-glycosylation sites and was upregulated in GALNT6 overexpressing cells (Log2FC = 1.3195, p = 0.0160), attracted our attention. We demonstrated that GALNT6 interacted with EFEMP1 at protein level. The O-glycosylation of EFEMP1 was increased by GALNT6 overexpression, which slowed the degradation rate of EFEMP1. EFEMP1 knockdown reversed the effects of GALNT6 overexpression. Collectively, our observations demonstrate that KLF9/GALNT6/EFEMP1 may be a promising direction for OS treatment.

Transcriptome and single-cell analysis reveal disulfidptosis-related modification patterns of tumor microenvironment and prognosis in osteosarcoma

Osteosarcoma (OS) is the most common malignant bone tumor with high pathological heterogeneity. Our study aimed to investigate disulfidptosis-related modification patterns in OS and their relationship with survival outcomes in patients with OS. We analyzed the single-cell-level expression profiles of disulfidptosis-related genes (DSRGs) in both OS microenvironment and OS subclusters, and HMGB1 was found to be crucial for intercellular regulation of OS disulfidptosis. Next, we explored the molecular clusters of OS based on DSRGs and related immune cell infiltration using transcriptome data. Subsequently, the hub genes of disulfidptosis in OS were screened by applying multiple machine models. In vitro and patient experiments validated our results. Three main disulfidptosis-related molecular clusters were defined in OS, and immune infiltration analysis suggested high immune heterogeneity between distinct clusters. The in vitro experiment confirmed decreased cell viability of OS after ACTB silencing and higher expression of ACTB in patients with lower immune scores. Our study systematically revealed the underlying relationship between disulfidptosis and OS at the single-cell level, identified disulfidptosis-related subtypes, and revealed the potential role of ACTB expression in OS disulfidptosis.

Expression of E-cadherin and N-cadherin in Epithelial-to-Mesenchymal Transition of Osteosarcoma: A Systematic Review

Osteosarcoma (OS) is a debilitating cancer of the bone that commonly afflicts the young and old. This may be de novo or associated with tumorigenic syndromes. However, many molecular mechanisms are still being uncovered and may offer greater avenues for screening and therapy. Cadherins, including E-cadherin and N-cadherin/vimentin, are involved in epithelial-to-mesenchymal transmission (EMT), which is key for tumor invasion. A study reviewing the relationship between OS and cadherins might elucidate a potential target for therapy and screening. A robust literature review was conducted by searching PubMed with the keywords "osteosarcoma", "cadherin", "e-cadherin" and "n-cadherin". Of a preliminary 266 papers, 25 were included in the final review. Review articles and those without primary data were excluded. Loss of E-cadherin is noted in metastatic cell lines of osteosarcoma. Overexpression of E-cadherin or knockout of N-cadherin/vimentin results in loss of metastatic potential. There are several methods of gene knockout, including CRISPR-Cas9 gene editing, viral vector insertion with micro RNA complementary to long noncoding RNA within gene segments, or proteomic editing. Screening for EMT and genetic treatment of EMT is a possible avenue for the treatment of refractory osteosarcoma. Several studies were conducted ex vivo. Further testing involving in vitro therapy is necessary to validate these methods. Limitations of this study involve a lack of in vivo trials to validate methods.

Isolation and Characterization of Novel Canine Osteosarcoma Cell Lines from Chemotherapy-Naïve Patients

The present study aimed to establish novel canine osteosarcoma cell lines (COS3600, COS3600B, COS4074) and characterize the recently described COS4288 cells. The established D-17 cell line served as a reference. Analyzed cell lines differed notably in their biological characteristics. Calculated doubling times were between 22 h for COS3600B and 426 h for COS4074 cells. COS3600B and COS4288 cells produced visible colonies after anchorage-independent growth in soft agar. COS4288 cells were identified as cells with the highest migratory capacity. All cells displayed the ability to invade through an artificial basement membrane matrix. Immunohistochemical analyses revealed the mesenchymal origin of all COS cell lines as well as positive staining for the osteosarcoma-relevant proteins alkaline phosphatase and karyopherin α2. Expression of p53 was confirmed in all tested cell lines. Gene expression analyses of selected genes linked to cellular immune checkpoints (CD270, CD274, CD276), kinase activity (MET, ERBB2), and metastatic potential (MMP-2, MMP-9) as well as selected long non-coding RNA (MALAT1) and microRNAs (miR-9, miR-34a, miR-93) are provided. All tested cell lines were able to grow as multicellular spheroids. In all spheroids except COS4288, calcium deposition was detected by von Kossa staining. We believe that these new cell lines serve as useful biological models for future studies.

Construction of a cuproptosis-related lncRNA signature for predicting prognosis and immune landscape in osteosarcoma patients

BACKGROUND

Long noncoding RNAs (lncRNAs) influence the onset of osteosarcoma. Cuproptosis is a novel cell death mechanism. We attempted to identify a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape in osteosarcoma patients.

METHODS

Transcriptional and clinical data of 85 osteosarcoma patients were derived from the TARGET database and randomly categorized into the training and validation cohorts. We implemented the univariate and multivariate Cox regression, along with LASSO regression analyses for developing a cuproptosis-related lncRNA risk model. Kaplan-Meier curves, C-index, ROC curves, univariate and multivariate Cox regression, and nomogram were used to assess the capacity of this risk model to predict the osteosarcoma prognosis. Gene ontology, KEGG, and Gene Set Enrichment (GSEA) analyses were conducted for determining the potential functional differences existing between the high-risk and low-risk patients. We further conducted the ESTIMATE, single-smaple GSEA, and CIBERSORT analyses for identifying the different immune microenvironments and immune cells infiltrating both the risk groups.

RESULTS

We screened out four cuproptosis-related lncRNAs (AL033384.2, AL031775.1, AC110995.1, and LINC00565) to construct the risk model in the training cohort. This risk model displayed a good performance to predict the overall survival of osteosarcoma patients, which was confirmed by using the validation and the entire cohort. Further analyses showed that the low-risk patients have more immune activation and immune cells infiltrating as well as a good response to immunotherapy.

CONCLUSIONS

We developed a novel cuproptosis-related lncRNA signature with high reliability and accuracy for predicting outcome and immunotherapy response in osteosarcoma patients, which provides new insights into the personalized treatment of osteosarcoma.

Modeling Ewing Sarcoma Lung Metastasis

Ewing Sarcoma (EwS) is the second most common malignant bone tumor in adolescents and young adults. The single-most powerful predictor of outcome in EwS is presence of metastatic burden at the time of diagnosis. Patients with metastatic Ewing Sarcoma have an abysmal 5-year survival rate of 10%-25%, which has not changed over the past 30-40 years. Thus, unraveling underlying mechanisms of EwS metastasis are imperative for developing effective therapeutic measures. Investigations towards this goal are limited by the lack of reliable genetically engineered mouse models and specialized metastatic models. Using two established cell lines, A673 and TC71, we generated lung specific metastatic cell lines by serial orthotopic intra-tibial injection followed by isolation of cells from lung metastases. The lung metastatic lines generated exhibit distinct differential molecular signatures from the parental cells when analyzed using a multi-omics approach. These signatures overlapped with EwS patient primary bone and metastatic lung specimens supporting the clinical relevance of these preclinical models of EwS. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Intra-Tibial injection in NSG mice Basic Protocol 2: Development and characterization of lung metastatic cell line.

Machine Learning-Based Prediction of Lymph Node Metastasis Among Osteosarcoma Patients

BACKGROUND

Regional lymph node metastasis is a contributor for poor prognosis in osteosarcoma. However, studies on risk factors for predicting regional lymph node metastasis in osteosarcoma are scarce. This study aimed to develop and validate a model based on machine learning (ML) algorithms.

METHODS

A total of 1201 patients, with 1094 cases from the surveillance epidemiology and end results (SEER) (the training set) and 107 cases (the external validation set) admitted from four medical centers in China, was included in this study. Independent risk factors for the risk of lymph node metastasis were screened by the multifactorial logistic regression models. Six ML algorithms, including the logistic regression (LR), the gradient boosting machine (GBM), the extreme gradient boosting (XGBoost), the random forest (RF), the decision tree (DT), and the multilayer perceptron (MLP), were used to evaluate the risk of lymph node metastasis. The prediction model was developed based on the bestpredictive performance of ML algorithm and the performance of the model was evaluatedby the area under curve (AUC), prediction accuracy, sensitivity and specificity. A homemade online calculator was capable of estimating the probability of lymph node metastasis in individuals.

RESULTS

Of all included patients, 9.41% (113/1201) patients developed regional lymph node metastasis. ML prediction models were developed based on nine variables: age, tumor (T) stage, metastasis (M) stage, laterality, surgery, radiation, chemotherapy, bone metastases, and lung metastases. In multivariate logistic regression analysis, T and M stage, surgery, and chemotherapy were significantly associated with lymph node metastasis. In the six ML algorithms, XGB had the highest AUC (0.882) and was utilized to develop as prediction model. A homemade online calculator was capable of estimating the probability of CLNM in individuals.

CONCLUSIONS

T and M stage, surgery and Chemotherapy are independent risk factors for predicting lymph node metastasis among osteosarcoma patients. XGB algorithm has the best predictive performance, and the online risk calculator can help clinicians to identify the risk probability of lymph node metastasis among osteosarcoma patients.

Integrated analyses of an RNA binding protein-based signature related to tumor immune microenvironment and candidate drugs in osteosarcoma

OBJECTIVE

Osteosarcoma is the most frequent primary bone malignancy, associated with frequent recurrence and lung metastasis. RNA-binding proteins (RBPs) are pivotal in regulating several aspects of cancer biology. Nonetheless, interaction between RBPs and the osteosarcoma immune microenvironment is poorly understood. We investigated whether RBPs can predict prognosis and immunotherapy response in osteosarcoma patients.

METHODS

We constructed an RBP-related prognostic signature (RRPS) by univariate coupled with multivariate analyses and verified the independent prognostic efficacy of the signature. Single-sample Gene Set Enrichment Analysis (ssGSEA) along with ESTIMATE analysis were carried out to investigate the variations in immune characteristics between subgroups with various RRPS-scores. Furthermore, we investigatedpossible small molecule drugs using the connectivity map database and validated the expression of hub RBPs by qRT-PCR.

RESULTS

The RRPS, consisting of seven hub RBPs, was an independent prognostic factor compared to traditional clinical features. The RRPS could distinguish immune functions, immune score, stromal score, tumor purity and tumor infiltration by immune cells in different osteosarcoma subjects. Additionally, patients with high RRPS-scores had lower expression of immune checkpoint genes than patients with low RRPS-scores. We finally identified six small molecule drugs that may improve prognosis in osteosarcoma patients and substantiated notable differences in the contents of these RBPs.

CONCLUSION

We evaluated the prognostic value and clinical application of an RBPs-based prognostic signature and identified promising biomarkers to predict immune cell infiltration and immunotherapy response in osteosarcoma.

A Novel Pyroptosis-Related Signature for Predicting Prognosis and Indicating Immune Microenvironment Features in Osteosarcoma

Osteosarcoma is a common malignant bone tumor with a propensity for drug resistance, recurrence, and metastasis. A growing number of studies have elucidated the dual role of pyroptosis in the development of cancer, which is a gasdermin-regulated novel inflammatory programmed cell death. However, the interaction between pyroptosis and the overall survival (OS) of osteosarcoma patients is poorly understood. This study aimed to construct a prognostic model based on pyroptosis-related genes to provide new insights into the prognosis of osteosarcoma patients. We identified 46 differentially expressed pyroptosis-associated genes between osteosarcoma tissues and normal control tissues. A total of six risk genes affecting the prognosis of osteosarcoma patients were screened to form a pyroptosis-related signature by univariate and LASSO regression analysis and verified using GSE21257 as a validation cohort. Combined with other clinical characteristics, including age, gender, and metastatic status, we found that the pyroptosis-related signature score, which we named “PRS-score,” was an independent prognostic factor for patients with osteosarcoma and that a low PRS-score indicated better OS and a lower risk of metastasis. The result of ssGSEA and ESTIMATE algorithms showed that a lower PRS-score indicated higher immune scores, higher levels of tumor infiltration by immune cells, more active immune function, and lower tumor purity. In summary, we developed and validated a pyroptosis-related signature for predicting the prognosis of osteosarcoma, which may contribute to early diagnosis and immunotherapy of osteosarcoma.