The novel prognostic risk factor STC2 can regulate the occurrence and progression of osteosarcoma via the glycolytic pathway

HOXB9 promotes osteosarcoma cell survival and malignancy under glucose starvation via upregulating SPP1 expression

Homeobox B9 (HOXB9) has been shown to play a critical role in several tumors. However, the precise biological mechanisms and functions of HOXB9 in osteosarcoma remain largely unknown. In this study, we found that HOXB9 was increased upon glucose starvation. Elevated HOXB9 suppressed osteosarcoma cell death and supported cell growth and migration under glucose starvation. Further mechanistic studies demonstrated that HOXB9 directly bound to the promoter of secreted phosphoprotein 1 (SPP1) and transcriptionally upregulated SPP1 expression which then led cell death decrease and cell growth increase under glucose deprivation environment. Clinically, HOXB9 was significantly upregulated in osteosarcoma compared with normal tissues and increase of HOXB9 expression was positively associated with the elevation of SPP1 in osteosarcoma. Overall, our study illustrates that HOXB9 contributes to malignancy in osteosarcoma and inhibits cell death through transcriptional upregulating SPP1 under glucose starvation.

Molecular characterization of Golgi apparatus-related genes indicates prognosis and immune infiltration in osteosarcoma

BACKGROUND

The Golgi apparatus (GA) is crucial for protein synthesis and modification, and regulates various cellular processes. Dysregulation of GA can lead to pathological conditions like neoplastic growth. GA-related genes (GARGs) mutations are commonly found in cancer, contributing to tumor metastasis. However, the expression and prognostic significance of GARGs in osteosarcoma are yet to be understood.

METHODS

Gene expression and clinical data of osteosarcoma patients were obtained from the TARGET and GEO databases. A consensus clustering analysis identified distinct molecular subtypes based on GARGs. Discrepancies in biological processes and immunological features among the subtypes were explored using GSVA, ssGSEA, and Metascape analysis. A GARGs signature was constructed using Cox regression. The prognostic value of the GARGs signature in osteosarcoma was evaluated using Kaplan-Meier curves and a nomogram.

RESULTS

Two GARG subtypes were identified, with Cluster A showing better prognosis, immunogenicity, and immune cell infiltration than Cluster B. A novel risk model of 3 GARGs was established using the TARGET dataset and validated with independent datasets. High-risk patients had poorer overall survival, and the GARGs signature independently predicted osteosarcoma prognosis. Combining risk scores and clinical characteristics in a nomogram improved prediction performance. Additionally, we discovered Stanniocalcin-2 (STC2) as a significant prognostic gene highly expressed in osteosarcoma and potential disease biomarker.

CONCLUSIONS

Our study revealed that patients with osteosarcoma can be divided into two GARGs subgroups. Furthermore, we have developed a GARGs prognostic signature that can accurately forecast the prognosis of osteosarcoma patients.

Reprogramming of glucose metabolism: Metabolic alterations in the progression of osteosarcoma

Metabolic reprogramming is an adaptive response of tumour cells under hypoxia and low nutrition conditions. There is increasing evidence that glucose metabolism reprogramming can regulate the growth and metastasis of osteosarcoma (OS). Reprogramming in the progress of OS can bring opportunities for early diagnosis and treatment of OS. Previous research mainly focused on the glycolytic pathway of glucose metabolism, often neglecting the tricarboxylic acid cycle and pentose phosphate pathway. However, the tricarboxylic acid cycle and pentose phosphate pathway of glucose metabolism are also involved in the progression of OS and are closely related to this disease. The research on glucose metabolism in OS has not yet been summarized. In this review, we discuss the abnormal expression of key molecules related to glucose metabolism in OS and summarize the glucose metabolism related signaling pathways involved in the occurrence and development of OS. In addition, we discuss some of the targeted drugs that regulate glucose metabolism pathways, which can lead to effective strategies for targeted treatment of OS.

NAT10 mediated ac4C acetylation driven m6A modification via involvement of YTHDC1-LDHA/PFKM regulates glycolysis and promotes osteosarcoma

The dynamic changes of RNA N6-methyladenosine (m6A) during cancer progression participate in various cellular processes. However, less is known about a possible direct connection between upstream regulator and m6A modification, and therefore affects oncogenic progression. Here, we have identified that a key enzyme in N4-acetylcytidine (ac4C) acetylation NAT10 is highly expressed in human osteosarcoma tissues, and its knockdown enhanced m6A contents and significantly suppressed osteosarcoma cell growth, migration and invasion. Further results revealed that NAT10 silence inhibits mRNA stability and translation of m6A reader protein YTHDC1, and displayed an increase in glucose uptake, a decrease in lactate production and pyruvate content. YTHDC1 recognizes differential m6A sites on key enzymes of glycolysis phosphofructokinase (PFKM) and lactate dehydrogenase A (LDHA) mRNAs, which suppress glycolysis pathway by increasing mRNA stability of them in an m6A methylation-dependent manner. YTHDC1 partially abrogated the inhibitory effect caused by NAT10 knockdown in tumor models in vivo, lentiviral overexpression of YTHDC1 partially restored the reduced stability of YTHDC1 caused by lentiviral depleting NAT10 at the cellular level. Altogether, we found ac4C driven RNA m6A modification can positively regulate the glycolysis of cancer cells and reveals a previously unrecognized signaling axis of NAT10/ac4C-YTHDC1/m6A-LDHA/PFKM in osteosarcoma. Video Abstract.

Dissecting the role of lactate metabolism LncRNAs in the progression and immune microenvironment of osteosarcoma

BACKGROUND

The process of lactate metabolism has been proved to play a critical role in the progression of various cancers and to influence the immune microenvironment, but its potential role in osteosarcoma remains unclear.

METHODS

We have acquired transcriptomic and clinical data from 84 osteosarcoma samples and 70 normal bone samples from the TARGET and GTEx databases. We identified differentially expressed lactate metabolism-related LncRNAs (LRLs) in osteosarcoma and performed Cox regression and LASSO regression to establish LRLs prognostic signature (LRPS). The reliability of LRPS performance was examined by separate prognostic analysis, viability curves and receiver operating characteristic (ROC) curves. Furthermore, the effects of LRPS on the immune microenvironment of osteosarcoma were investigated, and the functions of the focal genes were experimentally validated.

RESULT

A total of 856 differentially expressed LRLs were identified and 5 of them were selected to construct LRPS, which was a better prognostic predictor for osteosarcoma compared with other published prognostic signatures (AUC up to 0.947 and 0.839 in the training and test groups, respectively, with adj-p<0.05 for KM curves). We found that LRPS significantly affected the immune infiltration of osteosarcoma, while RP11-472M19.2 significantly promoted the metastasis of osteosarcoma, which was well validated experimentally. Encouragingly, a number of sensitive drugs were identified for LRPS and RP11-472M19.2 high-risk groups.

CONCLUSION

Our study shows that lactate metabolism plays a crucial role in the development of osteosarcoma and has been well validated experimentally, providing extremely important insights into the clinical treatment and in-depth research of osteosarcoma.

DNA-Methylome-Based Tumor Hypoxia Classifier Identifies HPV-Negative Head and Neck Cancer Patients at Risk for Locoregional Recurrence after Primary Radiochemotherapy

PURPOSE

Tumor hypoxia is a paradigmatic negative prognosticator of treatment resistance in head and neck squamous cell carcinoma (HNSCC). The lack of robust and reliable hypoxia classifiers limits the adaptation of stratified therapies. We hypothesized that the tumor DNA methylation landscape might indicate epigenetic reprogramming induced by chronic intratumoral hypoxia.

EXPERIMENTAL DESIGN

A DNA-methylome-based tumor hypoxia classifier (Hypoxia-M) was trained in the TCGA (The Cancer Genome Atlas)-HNSCC cohort based on matched assignments using gene expression-based signatures of hypoxia (Hypoxia-GES). Hypoxia-M was validated in a multicenter DKTK-ROG trial consisting of human papillomavirus (HPV)-negative patients with HNSCC treated with primary radiochemotherapy (RCHT).

RESULTS

Although hypoxia-GES failed to stratify patients in the DKTK-ROG, Hypoxia-M was independently prognostic for local recurrence (HR, 4.3; P = 0.001) and overall survival (HR, 2.34; P = 0.03) but not distant metastasis after RCHT in both cohorts. Hypoxia-M status was inversely associated with CD8 T-cell infiltration in both cohorts. Hypoxia-M was further prognostic in the TCGA-PanCancer cohort (HR, 1.83; P = 0.04), underscoring the breadth of this classifier for predicting tumor hypoxia status.

CONCLUSIONS

Our findings highlight an unexplored avenue for DNA methylation-based classifiers as biomarkers of tumoral hypoxia for identifying high-risk features in patients with HNSCC tumors. See related commentary by Heft Neal and Brenner, p. 2954.

Assessment of Colorectal Cancer Risk Factors through the Application of Network-Based Approaches in a Racially Diverse Cohort of Colon Organoid Stem Cells

Numerous demographic factors have been associated with colorectal cancer (CRC) risk. To better define biological mechanisms underlying these associations, we performed RNA sequencing of stem-cell-enriched organoids derived from the healthy colons of seven European Americans and eight African Americans. A weighted gene co-expression network analysis was performed following RNA sequencing. Module-trait relationships were determined through the association testing of each module and five CRC risk factors (age, body mass index, sex, smoking history, and race). Only modules that displayed a significantly positive correlation for gene significance and module membership were considered for further investigation. In total, 16 modules were associated with known CRC risk factors (p < 0.05). To contextualize the role of risk modules in CRC, publicly available RNA-sequencing data from TCGA-COAD were downloaded and re-analyzed. Differentially expressed genes identified between tumors and matched normal-adjacent tissue were overlaid across each module. Loci derived from CRC genome-wide association studies were additionally overlaid across modules to identify robust putative targets of risk. Among them, MYBL2 and RXRA represented strong plausible drivers through which cigarette smoking and BMI potentially modulated CRC risk, respectively. In summary, our findings highlight the potential of the colon organoid system in identifying novel CRC risk mechanisms in an ancestrally diverse and cellularly relevant population.

Osteosarcoma transcriptome data exploration reveals STC2 as a novel risk indicator in disease progression

BACKGROUND

Osteosarcoma has been the most common primary bone malignant tumor in children and adolescents. Despite the considerable improvement in the understanding of genetic events attributing to the rapid development of molecular pathology, the current information is still lacking, partly due to the comprehensive and highly heterogeneous nature of osteosarcoma. The study is to identify more potential responsible genes during the development of osteosarcoma, thus identifying promising gene indicators and aiding more precise interpretation of the disease.

METHODS

Firstly, from GEO database, osteosarcoma transcriptome microarrays were used to screen the differential expression genes (DEGS) in cancer comparing to normal bone samples, followed by GO/KEGG interpretation, risk score assessment and survival analysis of the genes, for the purpose of selecting a credible key gene. Further, the basic physicochemical properties, predicted cellular location, gene expression in human cancers, the association with clinical pathological features and potential signaling pathways involved in the key gene's regulation on osteosarcoma development were in succession explored.

RESULTS

Based on the selected GEO osteosarcoma expression profiles, we identified the differential expression genes in osteosarcoma versus normal bone samples, and the genes were classified into four groups based on the difference level, further genes interpretation indicated that the high differently level (> 8 fold) genes were mainly located extracellular and related to matrix structural constituent regulation. Meanwhile, module function analysis of the 67 high differential level (> 8 fold) DEGS revealed a 22-gene containing extracellular matrix regulation associated hub gene cluster. Further survival analysis of the 22 genes revealed that STC2 was an independent prognosis indicator in osteosarcoma. Moreover, after validating the differential expression of STC2 in cancer vs. normal tissues using local hospital osteosarcoma samples by IHC and qRT-PCR experiment, the gene's physicochemical property revealed STC2 as a cellular stable and hydrophilic protein, and the gene's association with osteosarcoma clinical pathological parameters, expression in pan-cancers and the probable biological functions and signaling pathways it involved were explored.

CONCLUSION

Using multiple bioinformatic analysis and local hospital samples validation, we revealed the gain of expression of STC2 in osteosarcoma, which associated statistical significantly with patients survival, and the gene's clinical features and potential biological functions were also explored. Although the results shall provide inspiring insights into further understanding of the disease, further experiments and detailed rigorous clinical trials are needed to reveal its potential drug-target role in clinical medical use.

Combining bulk RNA-sequencing and single-cell RNA-sequencing data to reveal the immune microenvironment and metabolic pattern of osteosarcoma

Background: Osteosarcoma (OS) is a kind of solid tumor with high heterogeneity at tumor microenvironment (TME), genome and transcriptome level. In view of the regulatory effect of metabolism on TME, this study was based on four metabolic models to explore the intertumoral heterogeneity of OS at the RNA sequencing (RNA-seq) level and the intratumoral heterogeneity of OS at the bulk RNA-seq and single cell RNA-seq (scRNA-seq) level.

Methods: The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a glycolysis, pentose phosphate pathway (PPP), fatty acid oxidation (FAO) and glutaminolysis gene sets score. ConsensusClusterPlus was employed to cluster OS samples downloaded from the Target database. The scRNA-seq and bulk RNA-seq data of immune cells from GSE162454 dataset were analyzed to identify the subsets and types of immune cells in OS. Malignant cells and non-malignant cells were distinguished by large-scale chromosomal copy number variation. The correlations of metabolic molecular subtypes and immune cell types with four metabolic patterns, hypoxia and angiogenesis were determined by Pearson correlation analysis.

Results: Two metabolism-related molecular subtypes of OS, cluster 1 and cluster 2, were identified. Cluster 2 was associated with poor prognosis of OS, active glycolysis, FAO, glutaminolysis, and bad TME. The identified 28608 immune cells were divided into 15 separate clusters covering 6 types of immune cells. The enrichment scores of 5 kinds of immune cells in cluster-1 and cluster-2 were significantly different. And five kinds of immune cells were significantly correlated with four metabolic modes, hypoxia and angiogenesis. Of the 28,608 immune cells, 7617 were malignant cells. The four metabolic patterns of malignant cells were significantly positively correlated with hypoxia and negatively correlated with angiogenesis.

Conclusion: We used RNA-seq to reveal two molecular subtypes of OS with prognosis, metabolic pattern and TME, and determined the composition and metabolic heterogeneity of immune cells in OS tumor by bulk RNA-seq and single-cell RNA-seq.

A novel risk score model based on glycolysis-related genes and a prognostic model for predicting overall survival of osteosarcoma patients

This study aims to construct a novel risk score model based on glycolysis-related genes in osteosarcoma and to build and validate a prognostic model for predicting overall survival of patients with osteosarcoma. The transcriptome data and corresponding clinical data of patients with osteosarcoma were obtained from The Cancer Genome Atlas (TCGA) as the training set, and from Gene Expression Omnibus (GEO) database as the validation set. Univariate Cox regression analysis was used to screen the prognostic glycolysis-related genes. The risk coefficient of each glycolysis-related gene was calculated using LASSO regression analysis. Using the median risk score as the cut-off point, patients were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis was used to determine whether there was a significant difference in the overall survival between the two groups. The nomogram was constructed according to the results of multivariate Cox regression. The C-index was calculated, the calibration chart, clinical decision curve and receiver operating characteristic curve were drawn to evaluate the predictive performance of the nomogram. We performed Gene Ontology and Kyoto encyclopedia of genes and genomics enrichment analysis to explore the potential mechanism of prognostic-related glycolysis genes in osteosarcoma. A total of 88 and 53 cases were obtained from the TCGA and GEO database, respectively. A total of 10 key glycolytic genes related to prognosis were screened out. The Kaplan-Meier survival curve revealed that the overall survival of the high-risk group was significantly shorter than that of the low-risk group. The C indices of the training set and the verification set were 0.882 and 0.828, respectively. Our findings will provide further understanding of clinical prognostic outcomes of osteosarcoma patients.

The roles of glycolysis in osteosarcoma

Metabolic reprogramming is of great significance in the progression of various cancers and is critical for cancer progression, diagnosis, and treatment. Cellular metabolic pathways mainly include glycolysis, fat metabolism, glutamine decomposition, and oxidative phosphorylation. In cancer cells, reprogramming metabolic pathways is used to meet the massive energy requirement for tumorigenesis and development. Metabolisms are also altered in malignant osteosarcoma (OS) cells. Among reprogrammed metabolisms, alterations in aerobic glycolysis are key to the massive biosynthesis and energy demands of OS cells to sustain their growth and metastasis. Numerous studies have demonstrated that compared to normal cells, glycolysis in OS cells under aerobic conditions is substantially enhanced to promote malignant behaviors such as proliferation, invasion, metastasis, and drug resistance of OS. Glycolysis in OS is closely related to various oncogenes and tumor suppressor genes, and numerous signaling pathways have been reported to be involved in the regulation of glycolysis. In recent years, a vast number of inhibitors and natural products have been discovered to inhibit OS progression by targeting glycolysis-related proteins. These potential inhibitors and natural products may be ideal candidates for the treatment of osteosarcoma following hundreds of preclinical and clinical trials. In this article, we explore key pathways, glycolysis enzymes, non-coding RNAs, inhibitors, and natural products regulating aerobic glycolysis in OS cells to gain a deeper understanding of the relationship between glycolysis and the progression of OS and discover novel therapeutic approaches targeting glycolytic metabolism in OS.

S100 calcium-binding protein A10 contributes to malignant traits in osteosarcoma cells by regulating glycolytic metabolism via the AKT/mTOR pathway

As an aggressive musculoskeletal malignancy, osteosarcoma (OSa) is popular among young adults and teenagers worldwide. S100 calcium-binding protein A10 (S100A10) functioned as a novel tumor-promoting protein in several human cancers. However, its role in OSa remains obscure. In this study, gene and protein levels were respectively determined by RT-qPCR or Western blotting. OSa cell proliferation, apoptosis, and metastasis were evaluated via CCK-8, colony formation, flow cytometry, and Transwell assays. To assess the glycolysis level, glucose consumption and lactate production were detected. It was found S100A10 was highly expressed in OSa tissues and cell lines. Besides, S100A10 facilitated proliferation and metastasis, and inhibited apoptosis in OSa cells. In addition, S100A10 regulated OSa cell proliferation, metastasis and apoptosis via mediating the glycolysis process. Furthermore, S100A10-mediated AKT/mTOR signaling accelerated glycolysis, thereby promoting malignant behaviors in OSa cells. Taken together, our findings indicated that S100A10 might promote malignant phenotypes of OSa cells by accelerating glycolysis and activating the AKT/mTOR signaling, providing a promising target for OSa treatment.

Stanniocalcin 2 (STC2): a universal tumour biomarker and a potential therapeutical target

Stanniocalcin 2 (STC2) is a glycoprotein which is expressed in a broad spectrum of tumour cells and tumour tissues derived from human breast, colorectum, stomach, esophagus, prostate, kidney, liver, bone, ovary, lung and so forth. The expression of STC2 is regulated at both transcriptional and post-transcriptional levels; particularly, STC2 is significantly stimulated under various stress conditions like ER stress, hypoxia and nutrient deprivation. Biologically, STC2 facilitates cells dealing with stress conditions and prevents apoptosis. Importantly, STC2 also promotes the development of acquired resistance to chemo- and radio- therapies. In addition, multiple groups have reported that STC2 overexpression promotes cell proliferation, migration and immune response. Therefore, the overexpression of STC2 is positively correlated with tumour growth, invasion, metastasis and patients' prognosis, highlighting its potential as a biomarker and a therapeutic target. This review focuses on discussing the regulation, biological functions and clinical importance of STC2 in human cancers. Future perspectives in this field will also be discussed.

Citronellal alleviates doxorubicin-induced cardiotoxicity by suppressing oxidative stress and apoptosis via Na+ /H+ exchanger-1 inhibition

The medical usage of Doxorubicin (DOX) as a chemotherapeutic agent is restricted owing to its cardiotoxic properties. This study was designed to explore the effect and underlying mechanisms of Citronellal (CT) on DOX-related cardiotoxicity in rats. Rats were divided into six groups: control, DOX, CT, Lithium chloride (LiCl) (a Na+/H+exchanger-1 [NHE1] activator), DOX + CT, and DOX + CT + LiCl. To induce cardiotoxicity, a cumulative dose of 15 mg/kg DOX was intraperitoneally injected into rats. CT (150 mg/kg) and LiCl (1 mg/kg) were given daily by oral gavage for 6 weeks. CT improved cardiac functional parameters and attenuated the cardiac pathological changes induced by DOX. Further study indicated that CT administration regulated the levels of oxidative stress and apoptosis-related factors and in myocardial tissues, reducing cell per-oxidative damage and apoptosis. Besides this, CT attenuated DOX-induced NHE1 upregulation, and the preventive effects of CT against DOX-induced cardiotoxicity were abrogated by the concurrent administration of LiCl. These results demonstrate that CT could ameliorate DOX-induced cardiotoxicity by inhibiting the NHE1-mediated oxidative stress, apoptosis in rats.

Construction of a circRNA-miRNA-mRNA Regulatory Network Reveals Potential Mechanism and Treatment Options for Osteosarcoma

Background

Osteosarcoma is a common malignant primary bone tumor in adolescents and children. Numerous studies have shown that circRNAs were involved in the proliferation and invasion of various tumors. However, the role of circRNAs in osteosarcoma remains unclear. Here, we aimed to explore the regulatory network among circRNA-miRNA-mRNA in osteosarcoma.

Methods

The circRNA (GSE140256), microRNA (GSE28423), and mRNA (GSE99671) expression profiles of osteosarcoma were collected from the Gene Expression Omnibus (GEO) database. Differentially expressed circRNAs, miRNAs and mRNAs were identified. CircRNA-miRNA interactions and miRNA-mRNA interactions were determined by Circular RNA Interactome (CircInteractome) database and microRNA Data Integration Portal (mirDIP) database, respectively. Then, we constructed a regulatory network. Function enrichment analysis of miRNA and mRNA was performed by DIANA-miRPath v3.0 and Metascape database, respectively. mRNAs with significant prognostic value were identified based on expression profiles from The Cancer Genome Atlas (TCGA) database, and we constructed a subnetwork for them. To make the most of the network, we used the CLUE database to predict potential drugs for the treatment of osteosarcoma based on mRNA expression in the network. And we used the STITCH database to analyze and validate the interactions among these drugs and mRNAs, and to further screen for potential drugs.

Results

A total of 9 circRNAs, 19 miRNAs, 67 mRNAs, 54 pairs of circRNA-miRNA interactions and 110 pairs of miRNA-mRNA interactions were identified. A circRNA-miRNA-mRNA network was constructed. Function enrichment analysis indicated that these miRNAs and mRNAs in the network were involved in the process of tumorigenesis and immune response. Among these mRNAs, STC2 and RASGRP2 with significantly prognostic value were identified, and we constructed a subnetwork for them. Based on mRNA expression in the network, three potential drugs, quinacridine, thalidomide and zonisamide, were screened for the treatment of osteosarcoma. Among them, quinacridine and thalidomide have been proved to have anti-tumor effects in previous studies, while zonisamide has not been reported. And a corresponding drug-protein interaction network was constructed.

Conclusion

Overall, we constructed a circRNA-miRNA-mRNA regulatory network to investigate the possible mechanism in osteosarcoma, and predicted that quinacridine, thalidomide and zonisamide could be potential drugs for the treatment of osteosarcoma.