Osteosarcoma-Specific Genes as a Diagnostic Tool and Clinical Predictor of Tumor Progression

RARRES2 is involved in the "lock-and-key" interactions between osteosarcoma stem cells and tumor-associated macrophages

Osteosarcoma (OS) is a type of tumor. Osteosarcoma stem cells (OSCs) are responsible for drug resistance, recurrence, and immunosuppression in OS. We aimed to determine the heterogeneity of OSCs and the immunosuppression mechanisms underlying the interactions between OSCs and tumor-associated macrophages (TAMs). The cell components, trajectory changes, and cell communication profiles of OS cells were analyzed by transcriptomics at the single-cell level. The intercellular communication patterns of OSCs were verified, and the role of the cell hub genes was revealed. Hub geneS are genes that play important roles in regulating certain biological processes; they are often defined as the genes with the strongest regulatory effect on differentially expressed gene sets. Moreover, various cellular components of the OS microenvironment were identified. Malignant cells were grouped, and OSCs were identified. Further regrouping and communication analysis revealed that the genes in the stemness maintenance and differentiation subgroups were involved in communication with macrophages. Key receptor-ligand pairs and target gene sets for cell communication were obtained. Transcriptome data analysis revealed the key gene RARRES2, which is involved in intercellular communication between OSCs and TAMs. In vitro studies confirmed that macrophages promote RARRES2-mediated stemness maintenance in OSCs via the TAM-secreted cytokine insulin-like growth factor 1. Patient studies confirmed that RARRES2 could be a biomarker of OS. OSCs are highly heterogeneous, and different subgroups are responsible for proliferation and communication with other cells. The IGF-RARRES2 axis plays a key role in maintaining OSC stemness through communication with TAMs.

The clinical significance of CD44v6 in malignant and benign primary bone tumors

BACKGROUND

The objective of this study was to assess the expression profile of CD44v6, a potential cancer stem cell marker, and its diagnostic and predictive significance in three distinct types of primary bone tumors.

METHODS

In this study, we utilized real-time qRT-PCR and immunohistochemistry to examine the gene and protein levels of CD44v6 in a total of 138 fresh bone tissues. This included 69 tumor tissues comprising osteosarcoma (N = 23), chondrosarcoma (N = 23), and GCT (N = 23), as well as 69 corresponding non-cancerous tumor margins. Furthermore, we investigated the circulating level of CD44v6 by isolating peripheral blood mononuclear cells from 92 blood samples. Among these, 69 samples were obtained from patients diagnosed with primary bone tumors, while the remaining 23 samples were from healthy donors. The primary objectives of our investigation were to assess the correlation between CD44v6 expression levels and clinic-pathological features of the patients, as well as to evaluate the diagnostic and predictive values of CD44v6 in this context.

RESULTS

In patients with osteosarcoma and chondrosarcoma tumors, both the gene and protein expression of CD44v6 were found to be significantly higher compared to the GCT group. Furthermore, the circulating level of CD44v6 was notably elevated in patients diagnosed with osteosarcoma and chondrosarcoma in comparison to the GCT group and patients with malignant tumor characteristics. Additionally, we observed a strong correlation between the gene and protein levels of CD44v6 and important tumor indicators such as tumor grade, metastasis, recurrence, and size at the tumor site. CD44v6 shows potential in differentiating patients with bone tumors from both control groups and tumor groups with severe and invasive characteristics from those with non-severe features. Importantly, the expression level of CD44v6 also demonstrated predictive value for determining tumor grade and the likelihood of recurrence.

CONCLUSION

CD44v6 is likely to play a role in the development of primary bone tumors and has the potential to serve as a diagnostic biomarker for bone cancer. However, to obtain more accurate and conclusive findings, further mechanistic investigations involving larger population samples are necessary.

Interactions between circRNAs and miR-141 in Cancer: From Pathogenesis to Diagnosis and Therapy

The function of non-coding RNAs (ncRNAs) in the pathogenesis and development of cancer is indisputable. Molecular mechanisms underlying carcinogenesis involve the aberrant expression of ncRNAs, including circular RNAs (circRNAs), and microRNAs (miRNAs). CircRNAs are a class of single-stranded, covalently closed RNAs responsible for maintaining cellular homeostasis through their diverse functions. As a part of the competing endogenous RNA (ceRNAs) network, they play a central role in the regulation of accessibility of miRNAs to their mRNA targets. The interplay between these molecular players is based on the primary role of circRNAs that act as miRNAs sponges, and the circRNA/miRNA imbalance plays a central role in different pathologies including cancer. Herein, we present the latest state of knowledge about interactions between circRNAs and miR-141, a well-known member of the miR-200 family, in malignant transformation, with emphasis on the biological role of circRNA/miR-141/mRNA networks as a future target for novel anti-cancer therapies.

Psoralidin inhibits osteosarcoma growth and metastasis by downregulating ITGB1 expression via the FAK and PI3K/Akt signaling pathways

BACKGROUND

Psoralea corylifolia is a medicinal leguminous plant that has long been used to treat various diseases. Psoralidin (PSO) is the main extract compound of P. corylifolia and exhibits antibacterial, antitumor, anti-inflammatory, antioxidant, and other pharmacological activities. PSO has demonstrated inhibitory effects in several cancers; however, its inhibitory effect on osteosarcoma has not been reported. This study aimed to evaluate the inhibitory effect of PSO on osteosarcoma and elucidate the underlying molecular mechanisms.

METHODS

Crystal violet, cell counting kit-8 (CCK8), and 5-Ethynyl-2'-deoxyuridine (EdU) staining assays were used to assess the inhibitory effect of PSO on the proliferation of 143B and MG63 osteosarcoma cells. Wound healing and Transwell assays were conducted to evaluate the effects of PSO on osteosarcoma cell migration and invasion. The cell cycle and apoptosis were analyzed using flow cytometry. To determine the possible molecular mechanisms, RNA-sequencing was performed and protein expression was analyzed by western blotting. The inhibitory effect of PSO on osteosarcoma in vivo was analyzed using a mouse model of orthotopic osteosarcoma and immunohistochemistry.

RESULTS

PSO inhibited osteosarcoma cell proliferation in a concentration-dependent manner, inhibited cell migration and invasion, and induced cell-cycle arrest and apoptosis. Mechanistically, PSO treatment significantly inhibited the focal adhesion kinase (FAK) and phosphatidylinositol 3-kinase (PI3K)/Akt signaling pathways by downregulating ITGB1 expression in both MG63 and 143B cells. Furthermore, we demonstrated that PSO restrained osteosarcoma growth in vivo.

CONCLUSION

PSO may suppress osteosarcoma via the FAK and PI3K/Akt signaling pathways by downregulating ITGB1 expression.

Construction of Osteosarcoma Diagnosis Model by Random Forest and Artificial Neural Network

Osteosarcoma accounts for 28% of primary bone malignancies in adults and up to 56% in children and adolescents (<20 years). However, early diagnosis and treatment are still inadequate, and new improvements are still needed. Missed diagnoses exist due to fewer traditional diagnostic methods, and clinical symptoms are often already present before diagnosis. This study aimed to develop novel and efficient predictive models for the diagnosis of osteosarcoma and to identify potential targets for exploring osteosarcoma markers. First, osteosarcoma and normal tissue expression microarray datasets were downloaded from the Gene Expression Omnibus (GEO). Then we screened the differentially expressed genes (DEGs) in the osteosarcoma and normal groups in the training group. Next, in order to explore the biologically relevant role of DEGs, Metascape and enrichment analyses were also performed on DEGs. The “randomForest” and “neuralnet” packages in R software were used to select representative genes and construct diagnostic models for osteosarcoma. The next step is to validate the model of the artificial neural network. Then, we performed an immune infiltration analysis by using the training set data. Finally, we constructed a prognostic model using representative genes for prognostic analysis. The copy number of osteosarcoma was also analyzed. A random forest classifier identified nine representative genes (ANK1, TGFBR3, RSF21, HSPB8, ITGA7, RHD, AASS, GREM2, NFASC). HSPB8, RHD, AASS, and NFASC were genes we identified that have not been previously reported to be associated with osteosarcoma. The osteosarcoma diagnostic model we constructed has good performance with areas under the curves (AUCs) of 1 and 0.987 in the training and validation groups, respectively. This study opens new horizons for the early diagnosis of osteosarcoma and provides representative markers for the future treatment of osteosarcoma. This is the first study to pioneer the establishment of a genetic diagnosis model for osteosarcoma and advance the development of osteosarcoma diagnosis and treatment.

Identification of Potential Diagnostic and Prognostic Biomarkers for Gastric Cancer Based on Bioinformatic Analysis

Gastric cancer (GC) ranks third for cancer-related fatalities worldwide. It is still unclear what causes GC to progress. Using integrated bioinformatics analysis, COL5A2 has been proved to be related to GC development, which may identify the likely pathogenic mechanism. Data from GC patients were gathered using The Cancer Gene Atlas (TCGA) and the gene expression omnibus (GEO). The level of COL5A2 expression was compared between paired GC and normal tissues. The differentially expressed genes (DEGs) in GC patients with high and low COL5A2 expression were identified using functional enrichment analysis to identify the signature pathways linked to the DEGs. The clinical pathologic traits connected to overall survival (OS) of GC patients were examined utilizing Cox regression and the Kaplan-Meier method. To assess the prognostic significance of COL5A2, receiver operating characteristic (ROC) curves was drawn. How the immune system infiltrate both normal gastric and GC tumor tissues was investigated. Using the human protein atlas (HPA) database, regression, and the Kaplan-Meier method, immunohistochemical analysis of DEG COL5A2 expression in GC tissues was carried out. The correlation between COL5A2 expression and the GC grouping was found to be highly significant. Functional annotations revealed that COL5A2 participates in extracellular matrix structure, collagen metabolism, and other biological processes (BPs). High COL5A2 expression was associated with poor prognostic and clinical features, such as clinical T, N, and M stages. ROC curves exhibited that COL5A2 might predict the occurrence of gastric cancer. The infiltration degree of 21 immune cell subsets, including activated dendritic cells (aDCs), CD8+ T cells, and cytotoxic cells, was found to be dramatically relevant to COL5A2. Immunohistochemical analysis indicated that the expression of COL5A2 in tumor tissues is higher than that in normal tissues. The COL5A2 gene may offer fresh perspectives on the pathogenic mechanism underlying GC, as well as potential biomarkers for estimating GC patient prognosis. As a result, COL5A2 may be a useful biomarker for predicting patient survival.

Integrated microfluidic-SERS for exosome biomarker profiling and osteosarcoma diagnosis

Osteosarcoma is one of the most frequent primary sarcoma of bone among adolescents. Early diagnosis of osteosarcoma is the key factor to achieve high survival rate of patients. Nevertheless, traditional histological biopsy is highly invasive and associated with the risk of arousing tumor spread. Herein, we develop a method integrating microfluidics and surface-enhanced Raman spectroscopy (SERS) to isolate plasma-derived exosomes and profile multiple exosomal biomarkers for the diagnosis of osteosarcoma. The method showed highly efficient isolation of exosomes directly from human plasma and can profile exosomes based on protein biomarkers, with the detection limit down to 2 exosomes per μL. The whole assay can be performed in 5 h and only consumed 50 μL of plasma for one analysis. With the method, we analyzed the level of three protein biomarkers, i.e., CD63, vimentin (VIM) and epithelial cell adhesion molecule (EpCAM), on plasma-derived exosomes from 20 osteosarcoma patients and 20 heathy controls. Significantly higher levels of CD63, VIM and EpCAM were observed on plasma exosomes from the osteosarcoma patients compared to the healthy controls. Based on the level of the exosomal biomarkers, a classification model was built for the rapid diagnosis of osteosarcoma, with the sensitivity, specificity and accuracy of 100%, 90% and 95%, respectively. The proposed method does not require complex operations nor expensive equipment, and has great promise in clinical diagnosis of cancer as a liquid biopsy technique.