The lncRNAs RP1-261G23.7, RP11-69E11.4 and SATB2-AS1 are a novel clinical signature for predicting recurrent osteosarcoma

Novel pyroptosis-related lncRNAs and ceRNAs predict osteosarcoma prognosis and indicate immune microenvironment signatures

Objective

To study pyroptosis-related biomarkers that are associated with the prognosis and immune microenvironment characteristics of osteosarcoma (OS). The goal is to establish a foundation for the prognosis and treatment of OS.

Methods

We retrieved transcriptome and clinical data from The Cancer Genome Atlas (TCGA) database for 88 OS patients. Using this data, we constructed a prognostic model to identify pyroptosis-related genes (PRGs) associated with OS prognosis. To further explore the biological function of these PRGs, we performed enrichment analysis. To identify pyroptosis-related long non-coding RNAs (PRLncs) associated with the prognosis of OS, we performed co-expression analysis. Subsequently, a risk prognostic model was constructed using these PRLncs to generate a risk score, termed as PRLncs-score, thereby obtaining PRLncs associated with the prognosis of OS. The accuracy of the prognostic model was verified through survival analysis, risk curve, independent prognostic analysis, receiver operating characteristic (ROC) curve, difference analysis between high- and low-risk groups, and clinical correlation analysis. And to determine whether PRLncs-score is independent prognostic factor for OS. In addition, we further conducted external and internal validation for the risk prognosis model. Further analyses of immune cell infiltration and tumor microenvironment were performed. A pyroptosis-related competitive endogenous RNA (PRceRNA) network was constructed to obtain PRceRNAs associated with the prognosis of OS and performed gene set enrichment analysis (GSEA) on PRceRNA genes.

Results

We obtained five PRGs (CHMP4C, BAK1, GSDMA, CASP1, and CASP6) that predicted OS prognosis and seven PRLncs (AC090559.1, AP003119.2, CARD8-AS1, AL390728.4, SATB2-AS1, AL133215.2, and AC009495.3) and one PRceRNA (CARD8-AS1-hsa-miR-21-5p-IL1B) that predicted OS prognosis and indicated characteristics of the OS immune microenvironment. The PRLncs-score, in combination with other clinical features, was established as an independent prognostic factor for OS patients. Subsequent scrutiny of the tumor microenvironment and immune infiltration indicated that patients with low-PRLncs-scores were associated with reduced metastatic risk, improved survival rates, heightened levels of immune cells and stroma, and increased immune activity compared to those with high-PRLncs-scores.

Conclusion

The study's findings offer insight into the prognosis of OS and its immune microenvironment, and hold promise for improving early diagnosis and immunotherapy.

LncRNA SATB2-AS1 promotes tumor growth and metastasis and affects the tumor immune microenvironment in osteosarcoma by regulating SATB2

Our previous report has identified a lncRNA SATB2-AS1, which was significantly up-regulated in osteosarcoma tissue and promotes the proliferation of osteosarcoma cells in vitro. However, the mechanisms of SATB2-AS1 regulating the growth and metastasis of osteosarcoma cells in vivo and its role in the prognosis of osteosarcoma patients are still unclear. In this study, the transcriptome sequencing data of 87 patients with osteosarcoma from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database and 7 patients from our clinical center (GZFPH) was used to evaluate the importance of SATB2-AS1 and SATB2 on the prognosis. The effect of SATB2-AS1 on the growth and metastasis of osteosarcoma cells in vivo was verified by a mouse tumor model. The potential mechanisms of SATB2-AS1 regulating SATB2 were further explored by dual-luciferase reporter gene assay, RNA pull-down assay, and bioinformatics analysis. The results suggested that increased co-expression of SATB2-AS1 and SATB2 was significantly associated with poor overall survival (OS) and relapse-free survival (RFS), and was a biomarker for risk stratification in patients with osteosarcoma. Mechanistically, SATB2-AS1 promotes tumor growth and lung metastasis by regulating SATB2 in vivo. SATB2-AS1 directly binds to POU3F1 for mediating SATB2 expression in MNNG/HOS cells. In addition, SATB2-AS1 and SATB2 might be potential immunomodulators for negatively affecting immune cell infiltration by the IL-17 signaling pathway. In summary, SATB2-AS1 promoted tumor cell growth and lung metastasis by activating SATB2, thereby associated with poor prognosis in patients with osteosarcoma, which indicated that SATB2-AS1 and SATB2 might be novel biomarkers for risk stratification and promising therapeutic targets for osteosarcoma.

A glycometabolic gene signature associating with immune infiltration and chemosensitivity and predicting the prognosis of patients with osteosarcoma

Background

Accumulating evidence has suggested that glycometabolism plays an important role in the pathogenesis of tumorigenesis. However, few studies have investigated the prognostic values of glycometabolic genes in patients with osteosarcoma (OS). This study aimed to recognize and establish a glycometabolic gene signature to forecast the prognosis, and provide therapeutic options for patients with OS.

Methods

Univariate and multivariate Cox regression, LASSO Cox regression, overall survival analysis, receiver operating characteristic curve, and nomogram were adopted to develop the glycometabolic gene signature, and further evaluate the prognostic values of this signature. Functional analyses including Gene Ontology (GO), kyoto encyclopedia of genes and genomes analyses (KEGG), gene set enrichment analysis, single-sample gene set enrichment analysis (ssGSEA), and competing endogenous RNA (ceRNA) network, were used to explore the molecular mechanisms of OS and the correlation between immune infiltration and gene signature. Moreover, these prognostic genes were further validated by immunohistochemical staining.

Results

A total of four genes including PRKACB, SEPHS2, GPX7, and PFKFB3 were identified for constructing a glycometabolic gene signature which had a favorable performance in predicting the prognosis of patients with OS. Univariate and multivariate Cox regression analyses revealed that the risk score was an independent prognostic factor. Functional analyses indicated that multiple immune associated biological processes and pathways were enriched in the low-risk group, while 26 immunocytes were down-regulated in the high-risk group. The patients in high-risk group showed elevated sensitivity to doxorubicin. Furthermore, these prognostic genes could directly or indirectly interact with other 50 genes. A ceRNA regulatory network based on these prognostic genes was also constructed. The results of immunohistochemical staining showed that SEPHS2, GPX7, and PFKFB3 were differentially expressed between OS tissues and adjacent normal tissues.

Conclusion

The preset study constructed and validated a novel glycometabolic gene signature which could predict the prognosis of patients with OS, identify the degree of immune infiltration in tumor microenvironment, and provide guidance for the selection of chemotherapeutic drugs. These findings may shed new light on the investigation of molecular mechanisms and comprehensive treatments for OS.

Evaluating a Panel of Autoantibodies Against Tumor-Associated Antigens in Human Osteosarcoma

Background: The aim of this study was to identify a panel of candidate autoantibodies against tumor-associated antigens in the detection of osteosarcoma (OS) so as to provide a theoretical basis for constructing a non-invasive serological diagnosis method in early immunodiagnosis of OS. Methods: The serological proteome analysis (SERPA) approach was used to select candidate anti-TAA autoantibodies. Then, indirect enzyme-linked immunosorbent assay (ELISA) was used to verify the expression levels of eight candidate autoantibodies in the serum of 51 OS cases, 28 osteochondroma (OC), and 51 normal human sera (NHS). The rank-sum test was used to compare the content of eight autoantibodies in the sera of three groups. The diagnostic value of each indicator for OS was analyzed by an ROC curve. Differential autoantibodies between OS and NHS were screened. Then, a binary logistic regression model was used to establish a prediction logistical regression model. Results: Through ELISA, the expression levels of seven autoantibodies (ENO1, GAPDH, HSP27, HSP60, PDLIM1, STMN1, and TPI1) in OS patients were identified higher than those in healthy patients (p < 0.05). By establishing a binary logistic regression predictive model, the optimal panel including three anti-TAAs (ENO1, GAPDH, and TPI1) autoantibodies was screened out. The sensitivity, specificity, Youden index, accuracy, and AUC of diagnosis of OS were 70.59%, 86.27%, 0.5686, 78.43%, and 0.798, respectively. Conclusion: The results proved that through establishing a predictive model, an optimal panel of autoantibodies could help detect OS from OC or NHS at an early stage, which could be used as a promising and powerful tool in clinical practice.

Selection of lncRNAs That Influence the Prognosis of Osteosarcoma Based on Copy Number Variation Data

Osteosarcoma is the most common primary malignancy in the musculoskeletal system. It is reported that copy number variation- (CNV-) derived lncRNAs contribute to the progression of osteosarcoma. However, whether CNV-derived lncRNAs affect the prognosis of osteosarcoma remains unclear. Here, we obtained osteosarcoma-related CNV data and gene expression profiles from The Cancer Genome Atlas (TCGA) database. CNV landscape analysis indicated that copy number amplification of lncRNAs was more frequent than deletion in osteosarcoma samples. Thirty-four CNV-lncRNAs with DNA-CNV frequencies greater than 30% and their corresponding 294 mRNAs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses revealed that these mRNAs were mainly enriched in olfaction, olfactory receptor activity, and olfactory transduction processes. Furthermore, we predicted that a total of 23 genes were cis-regulated by 16 CNV-lncRNAs, while 30 transcription factors (TFs) were trans-regulated by 5 CNV-lncRNAs. Through $t$ -tests, univariate Cox regression analysis, and the least absolute shrinkage and selection operator (LASSO), we constructed a CNV-related risk model including 3 lncRNAs (AC129492.1, PSMB1, and AC037459.4). The Kaplan-Meier (K-M) curves indicated that patients with high-risk scores showed poor prognoses. The areas under the receiver operating characteristic (ROC) curves (AUC) for predicting 3-, 5-, and 7-year overall survival (OS) were greater than 0.7, showing a satisfactory predictive efficiency. Gene set enrichment analysis (GSEA) revealed that the prognostic signature was intimately linked to skeletal system development, immune regulation, and inflammatory response. Collectively, our study developed a novel 3-CNV-lncRNA prognostic signature that would provide theoretical guidance for the clinical prognostic management of osteosarcoma.

Metastatic Progression of Osteosarcomas: A Review of Current Knowledge of Environmental versus Oncogenic Drivers

Simple Summary

Osteosarcomas are heterogeneous bone tumors with complex genetic and chromosomic alterations. The numerous patients with metastatic osteosarcoma have a very poor prognosis, and only those who can have full surgical resection of the primary tumor and of all the macro metastasis can survive. Despite the recent improvements in prediction and early detection of metastasis, big efforts are still required to understand the specific mechanisms of osteosarcoma metastatic progression, in order to reveal novel therapeutic targets.

Abstract

Metastases of osteosarcomas are heterogeneous. They may grow simultaneously with the primary tumor, during treatment or shortly after, or a long time after the end of the treatment. They occur mainly in lungs but also in bone and various soft tissues. They can have the same histology as the primary tumor or show a shift towards a different differentiation path. However, the metastatic capacities of osteosarcoma cells can be predicted by gene and microRNA signatures. Despite the identification of numerous metastasis-promoting/predicting factors, there is no efficient therapeutic strategy to reduce the number of patients developing a metastatic disease or to cure these metastatic patients, except surgery. Indeed, these patients are generally resistant to the classical chemo- and to immuno-therapy. Hence, the knowledge of specific mechanisms should be extended to reveal novel therapeutic approaches. Recent studies that used DNA and RNA sequencing technologies highlighted complex relations between primary and secondary tumors. The reported results also supported a hierarchical organization of the tumor cell clones, suggesting that cancer stem cells are involved. Because of their chemoresistance, their plasticity, and their ability to modulate the immune environment, the osteosarcoma stem cells could be important players in the metastatic process.

Urinary exosomal long noncoding RNAs serve as biomarkers for early detection of non-small cell lung cancer

Objective: Increasing the efficiency of early diagnosis using noninvasive biomarkers is crucial for enhancing the survival rate of lung cancer patients. We explore the differential expression of non-small cell lung cancer (NSCLC)-related long noncoding RNAs (lncRNAs) in urinary exosomes in NSCLC patients and normal controls to diagnose lung cancer.

Methods: A differential expression analysis between NSCLC patients and healthy controls was performed using microarrays. Gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to predict potential functions of lncRNAs in NSCLC. quantitative real-time PCR (QT-PCR) was used to verify microarray results.

Results: A total of 640 lncRNAs (70 up- and 570 down-regulated) were differentially expressed in NSCLC patients in comparison to healthy controls. Six lncRNAs were detected by QT-PCR. GO term and KEGG pathway analyses showed that differential lncRNAs were enriched in cellular component organization or biogenesis, as well as other biological processes and signaling pathways, such as the PI3K-AKT, FOXO, p53, and fatty acid biosynthesis.

Conclusions: The differential lncRNAs in urinary exosomes are potential diagnostic biomarkers of NSCLC. The lncRNAs enriched in specific pathways may be associated with tumor cell proliferation, tumor cell apoptosis, and the cell cycle involved in the pathogenesis of NSCLC.

LncRNA MATN1-AS1 for Prediction of Prognosis in Osteosarcoma Patients and Its Cellular Function

Long non-coding RNAs show essential roles in various cancer processes. This study aimed at the expression features, prognosis significance, and biological effect of lnc MATN1-AS1 in osteosarcoma (OS). Five kinds of cell lines and 117 pairs of tissues were analyzed by qRT-PCR for quantification of lnc MATN1-AS1 and miR-1299 level. Clinical data were analyzed using Chi-Square Tests to show the association with lnc MATN1-AS1 level. Kaplan-Meier analysis and Cox regression were used to judge the prognostic value. Cell counting kit-8 and Transwell assay were conducted, respectively, to analyze the effect of lnc MATN1-AS1 on cell proliferation and metastasis. The target miRNA was predicted. lnc MATN1-AS1 level was significantly elevated in OS cells and tissues and related to Enneking staging, lung metastasis, and histologic type. Patients with high lnc MATN1-AS1 level showed a shorter overall survival and recurrence-free survival. Lnc MATN1-AS1 knockdown inhibited OS cell proliferation, migration, and invasion by sponging miR-1299. Lnc MATN1-AS1 has oncogenic features and prognostic significance in OS and is a novel therapeutic strategy for OS.

Long non-coding RNA CCHE1 participates in postoperative distant recurrence but not local recurrence of osteosarcoma possibly by interacting with ROCK1

BACKGROUND

Clinical treatment of osteosarcoma suffers from high recurrence rate. Therefore, is of great clinical values to develop predictive markers for recurrent osteosarcoma. Cervical carcinoma high-expressed lncRNA 1 (lncRNA CCHE1) participates in several types of malignancies, while its functionality in osteosarcoma is unknown. This study was therefore carried out to explore the involvement of lncRNA CCHE1 in recurrent osteosarcoma.

METHODS

A total of 87 osteosarcoma patients received surgical resection and 38 healthy volunteers were included in this study. The 87 osteosarcoma patients were followed up for 5 years to record the recurrence of osteosarcoma. Plasma levels of lncRNA CCHE1 and ROCK1 on the day of discharge and during follow-up were measured by real-time quantitative PCR and ELISA, respectively. The effects of CCHE1 siRNA silencing on ROCK1 expression were analyzed by real-time quantitative PCR and western blot. Transwell assay was performed to analyze the role of lncRNA CCHE1 and ROCK1 in regulating cell invasion and migration.

RESULTS

We observed that, on the day of discharge, plasma lncRNA CCHE1 was upregulated in osteosarcoma patients who developed distant recurrence (DR) during follow-up than in osteosarcoma patients who developed local recurrence (LR), patients with non-recurrence (NR) and healthy controls. On the day of discharge, plasma levels of ROCK1 were higher in DR, LR and NR groups in comparison to healthy controls. On the day of discharge, plasma levels of lncRNA CCHE1 were positively correlated with plasma levels of ROCK1 only in patients who developed DR during follow-up, but not in patients who developed LR, NR and control groups. During follow-up, plasma levels of lncRNA CCHE1 were further increased in DR group but slightly decreased in LR and NR groups. LncRNA CCHE1 siRNA silencing inhibited, while ROCK1 overexpression promoted osteosarcoma cell invasion and migration. ROCK1 overexpression attenuated the role of CCHE1. LncRNA CCHE1 siRNA silencing led to inhibited ROCK1 expression in cancer cells.

CONCLUSION

Therefore, lncRNA CCHE1 may participate in postoperative distant recurrence of osteosarcoma caner possibly by interacting with ROCK1 to promote cancer cell invasion and migration.