Comprehensive analysis of a glycolysis and cholesterol synthesis-related genes signature for predicting prognosis and immune landscape in osteosarcoma

AGFG1 increases cholesterol biosynthesis by disrupting intracellular cholesterol homeostasis to promote PDAC progression

PURPOSE

Cholesterol metabolism reprograming has been acknowledged as a novel feature of cancers. Pancreatic ductal adenocarcinoma (PDAC) is a cancer with a high demand of cholesterol for rapid growth. The underlying mechanism of how cholesterol metabolism homestasis are disturbed in PDAC is explored.

EXPERIMENTAL DESIGN

The relevance between PDAC and cholesterol was confirmed in TCGA database. The expression and clinical association were discovered in TCGA and GEO datasets. Knockdown and overexpression of AGFG1 was adopted to perform function studies. RNA sequencing, cholesterol detection, transmission electron microscope, co-immunoprecipitation, and immunofluorescence et al. were utilized to reveal the underlying mechanism.

RESULTS

AGFG1 was identified as one gene positively correlated with cholesterol metabolism in PDAC as revealed by bioinformatics analysis. AGFG1 expression was then found associated with poor prognosis in PDAC. AGFG1 knockdown led to decreased proliferation of tumor cells both in vitro and in vivo. By RNA sequencing, we found AGFG1 upregulated expression leads to enhanced intracellular cholesterol biosynthesis. AGFG1 knockdown suppressed cholesterol biosynthesis and an accumulation of cholesterol in the ER. Mechanistically, we confirmed that AGFG1 interacted with CAV1 to relocate cholesterol for the proceeding of cholesterol biosynthesis, therefore causing disorders in intracellular cholesterol metabolism.

CONCLUSIONS

Our study demonstrates the tumor-promoting role of AGFG1 by disturbing cholesterol metabolism homestasis in PDAC. Our study has present a new perspective on cancer therapeutic approach based on cholerstrol metabolism in PDAC.

Lipidomics by Nuclear Magnetic Resonance Spectroscopy and Liquid Chromatography-High-Resolution Mass Spectrometry in Osteosarcoma: A Pilot Study

Cancer is a complex disease that can also affect the younger population; however, it is responsible for a relatively high mortality rate of children and youth, especially in low- and middle-income countries (LMICs). Besides that, lipidomic studies in this age range are scarce. Therefore, we analyzed blood serum samples from young patients (12 to 35 years) with bone sarcoma (osteosarcoma) and compared their lipidomics to the ones from the control group of samples, named healthy control (HC group), using NMR and LC-MS techniques. Furthermore, differences in the lipidomic profiles between OS patients with and without metastasis indicate higher glycerophosphocholine (GPC) and glycerophospholipid (GPL) levels in osteosarcoma and increased cholesterol, choline, polyunsaturated fatty acids (PUFAs), and glycerols during the metastasis. These differences, detected in the peripheral blood, could be used as biomarkers for liquid biopsy.

Identification of a pro-protein synthesis osteosarcoma subtype for predicting prognosis and treatment

Osteosarcoma (OS) is a heterogeneous malignant spindle cell tumor that is aggressive and has a poor prognosis. Although combining surgery and chemotherapy has significantly improved patient outcomes, the prognosis for OS patients with metastatic or recurrent OS has remained unsatisfactory. Therefore, it is imperative to gain a fresh perspective on OS development mechanisms and treatment strategies. After studying single-cell RNA sequencing (scRNA-seq) data in public databases, we identified seven OS subclonal types based on intra-tumor heterogeneity. Subsequently, we constructed a prognostic model based on pro-protein synthesis osteosarcoma (PPS-OS)-associated genes. Correlation analysis showed that the prognostic model performs extremely well in predicting OS patient prognosis. We also demonstrated that the independent risk factors for the prognosis of OS patients were tumor primary site, metastatic status, and risk score. Based on these factors, nomograms were constructed for predicting the 3- and 5-year survival rates. Afterward, the investigation of the tumor immune microenvironment (TIME) revealed the vital roles of γδ T-cell and B-cell activation. Drug sensitivity analysis and immune checkpoint analysis identified drugs that have potential application value in OS. Finally, the jumping translocation breakpoint (JTB) gene was selected for experimental validation. JTB silencing suppressed the proliferation, migration, and invasion of OS cells. Therefore, our research suggests that PPS-OS-related genes facilitate the malignant progression of OS and may be employed as prognostic indicators and therapeutic targets in OS.

EBF1-COX4I2 signaling axis promotes a myofibroblast-like phenotype in cancer-associated fibroblasts (CAFs) and is associated with an immunosuppressive microenvironment

Immunotherapy has limited response rates in colorectal cancer (CRC) due to an immunosuppressive tumor microenvironment (TME). Combining transcriptome sequencing, clinical specimens, and functional experiments, we identified a unique group of CAF subpopulations (COX4I2 + ) with inhibited mitochondrial respiration and enhanced glycolysis. Through bioinformatics predictions and luciferase reporter assays, we determined that EBF1 can upstreamly regulate COX4I2 transcription. COX4I2 + CAFs functionally and phenotypically resemble myofibroblasts, are important for the formation of the fibrotic TME, and are capable of activating the M2 phenotype of macrophages. In vitro experiments demonstrated that COX4I2 + CAFs promote immunosuppressive TME by blocking CD8 + T cell infiltration and inducing CD8 + T cell dysfunction. Using multiple independent cohorts, we also found a strong correlation between the immunotherapy response rate of CRC patients and COX4I2 expression in their tumors. Our results identify a CAF subpopulation characterized by activation of the EBF1-COX4I2 axis, and this group of CAFs can be targeted to improve cancer immunotherapy outcomes.

Exploring the Role of Neutrophil-Related Genes in Osteosarcoma via an Integrative Analysis of Single-Cell and Bulk Transcriptome

BACKGROUND

The involvement of neutrophil-related genes (NRGs) in patients with osteosarcoma (OS) has not been adequately explored. In this study, we aimed to examine the association between NRGs and the prognosis as well as the tumor microenvironment of OS.

METHODS

The OS data were obtained from the TARGET-OS and GEO database. Initially, we extracted NRGs by intersecting 538 NRGs from single-cell RNA sequencing (scRNA-seq) data between aneuploid and diploid groups, as well as 161 up-regulated differentially expressed genes (DEGs) from the TARGET-OS datasets. Subsequently, we conducted Least Absolute Shrinkage and Selection Operator (Lasso) analyses to identify the hub genes for constructing the NRG-score and NRG-signature. To assess the prognostic value of the NRG signatures in OS, we performed Kaplan-Meier analysis and generated time-dependent receiver operating characteristic (ROC) curves. Gene enrichment analysis (GSEA) and gene set variation analysis (GSVA) were utilized to ascertain the presence of tumor immune microenvironments (TIMEs) and immunomodulators (IMs). Additionally, the KEGG neutrophil signaling pathway was evaluated using ssGSEA. Subsequently, PCR and IHC were conducted to validate the expression of hub genes and transcription factors (TFs) in K7M2-induced OS mice.

RESULTS

FCER1G and C3AR1 have been identified as prognostic biomarkers for overall survival. The findings indicate a significantly improved prognosis for OS patients. The effectiveness and precision of the NRG signature in prognosticating OS patients were validated through survival ROC curves and an external validation dataset. The results clearly demonstrate that patients with elevated NRG scores exhibit decreased levels of immunomodulators, stromal score, immune score, ESTIMATE score, and infiltrating immune cell populations. Furthermore, our findings substantiate the potential role of SPI1 as a transcription factor in the regulation of the two central genes involved in osteosarcoma development. Moreover, our analysis unveiled a significant correlation and activation of the KEGG neutrophil signaling pathway with FCER1G and C3AR1. Notably, PCR and IHC demonstrated a significantly higher expression of C3AR1, FCER1G, and SPI1 in Balb/c mice induced with K7M2.

CONCLUSIONS

Our research emphasizes the significant contribution of neutrophils within the TIME of osteosarcoma. The newly developed NRG signature could serve as a good instrument for evaluating the prognosis and therapeutic approach for OS.

Biomaterials-Boosted Immunotherapy for Osteosarcoma

Osteosarcoma (OS) is a primary malignant bone tumor that emanates from mesenchymal cells, commonly found in the epiphyseal end of long bones. The highly recurrent and metastatic nature of OS poses significant challenges to the efficacy of treatment and negatively affects patient prognosis. Currently, available clinical treatment strategies primarily focus on maximizing tumor resection and reducing localized symptoms rather than the complete eradication of malignant tumor cells to achieve ideal outcomes. The biomaterials-boosted immunotherapy for OS is characterized by high effectiveness and a favorable safety profile. This therapeutic approach manipulates the tumor microenvironments at the cellular and molecular levels to impede tumor progression. This review delves into the mechanisms underlying the treatment of OS, emphasizing biomaterials-enhanced tumor immunity. Moreover, it summarizes the immune cell phenotype and tumor microenvironment regulation, along with the ability of immune checkpoint blockade to activate the autoimmune system. Gaining a profound comprehension of biomaterials-boosted OS immunotherapy is imperative to explore more efficacious immunotherapy protocols and treatment options in this setting.

Identification of allograft inflammatory factor-1 suppressing the progression and indicating good prognosis of osteosarcoma

BACKGROUND

Osteosarcoma is one of the most common cancers worldwide. Intense efforts have been made to elucidate the pathogeny, but the mechanisms of osteosarcoma are still not well understood. We aimed to investigate the potential biomarker, allograft inflammatory factor-1 (AIF1), affecting the progression and prognosis of osteosarcoma.

METHODS

Three microarray datasets were downloaded from GEO datasets and one was obtained from the TCGA dataset. The differentially expressed genes (DEGs) were identified. GO and KEGG functional enrichment analyses of overlapped DEGs were performed. The PPI network of overlapped DEGs was constructed by STRING and visualized with Cytoscape. Overall survival (OS) and Metastasis free survival (MFS) were analyzed from GSE21257. Finally, the effect of the most relevant core gene affecting the progression of osteosarcoma was examined in vitro.

RESULTS

One hundred twenty six DEGs were identified, consisting of 65 upregulated and 61 downregulated genes. Only AIF1 was significantly associated with OS and MFS. It was found that AIF1 could be enriched into the NF-κB signaling pathway. GSEA and ssGSEA analyses showed that AIF1 was associated with the immune invasion of tumors. Cell experiments showed that AIF1 was underexpressed in osteosarcoma cell lines, while the malignant propriety was attenuated after overexpressing the expression of AIF1. Moreover, AIF1 also affects the expression of the NF-κB pathway.

CONCLUSION

In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of osteosarcoma, and provide candidate targets for diagnosis and treatment of osteosarcoma.

Identifying squalene epoxidase as a metabolic vulnerability in high-risk osteosarcoma using an artificial intelligence-derived prognostic index

BACKGROUND

Osteosarcoma (OSA) presents a clinical challenge and has a low 5-year survival rate. Currently, the lack of advanced stratification models makes personalized therapy difficult. This study aims to identify novel biomarkers to stratify high-risk OSA patients and guide treatment.

METHODS

We combined 10 machine-learning algorithms into 101 combinations, from which the optimal model was established for predicting overall survival based on transcriptomic profiles for 254 samples. Alterations in transcriptomic, genomic and epigenomic landscapes were assessed to elucidate mechanisms driving poor prognosis. Single-cell RNA sequencing (scRNA-seq) unveiled genes overexpressed in OSA cells as potential therapeutic targets, one of which was validated via tissue staining, knockdown and pharmacological inhibition. We characterized changes in multiple phenotypes, including proliferation, colony formation, migration, invasion, apoptosis, chemosensitivity and in vivo tumourigenicity. RNA-seq and Western blotting elucidated the impact of squalene epoxidase (SQLE) suppression on signalling pathways.

RESULTS

The artificial intelligence-derived prognostic index (AIDPI), generated by our model, was an independent prognostic biomarker, outperforming clinicopathological factors and previously published signatures. Incorporating the AIDPI with clinical factors into a nomogram improved predictive accuracy. For user convenience, both the model and nomogram are accessible online. Patients in the high-AIDPI group exhibited chemoresistance, coupled with overexpression of MYC and SQLE, increased mTORC1 signalling, disrupted PI3K-Akt signalling, and diminished immune infiltration. ScRNA-seq revealed high expression of MYC and SQLE in OSA cells. Elevated SQLE expression correlated with chemoresistance and worse outcomes in OSA patients. Therapeutically, silencing SQLE suppressed OSA malignancy and enhanced chemosensitivity, mediated by cholesterol depletion and suppression of the FAK/PI3K/Akt/mTOR pathway. Furthermore, the SQLE-specific inhibitor FR194738 demonstrated anti-OSA effects in vivo and exhibited synergistic effects with chemotherapeutic agents.

CONCLUSIONS

AIDPI is a robust biomarker for identifying the high-risk subset of OSA patients. The SQLE protein emerges as a metabolic vulnerability in these patients, providing a target with translational potential.

Osteosarcoma neutrophil extracellular trap network-associated gene recurrence and metastasis model

Osteosarcoma (OS) is the most common malignancy in children and adolescents and has a high probability of recurrence and metastasis. A growing number of studies have shown that neutrophil extracellular traps (NETs) are strongly associated with cancer metastasis, but in osteosarcoma, genes associated with NETs that promote osteosarcoma recurrence and metastasis remain to be explored. We systematically investigated the gene expression patterns of NETs in OS samples from the GEO database. NETs molecular typing was evaluated based on NETs expression profiles, and the association between NETs molecular subtypes and immune microenvironment and metastatic features were explored. Ultimately, we constructed a signature model and column line graph associated with metastasis prediction and screened possible potential drugs for metastatic osteosarcoma. We established two different molecular subtypes of NETs, which showed significant differences in metastatic status, metastasis time, tumor immune microenvironment, and biological effects. We also constructed a NETs-related gene metastasis signature(NRGMS) to assess the expression pattern of NETs in patients to predict metastatic recurrence in osteosarcoma patients. We screened for TOMM40 and FH associated with metastatic recurrence in osteosarcoma patients. Overall, this study constructs a predictive model for osteosarcoma metastasis of NETs-related genes, which is expected to provide new insights into the metastasis of osteosarcoma.

Comprehensive bioinformatics analysis reveals the oncogenic role of FoxM1 and its impact on prognosis, immune microenvironment, and drug sensitivity in osteosarcoma

Osteosarcoma, a highly malignant bone tumor primarily affecting adolescents, presents a significant challenge in cancer therapy due to its resistance to chemotherapy. This study explores the multifaceted impact of the transcription factor FoxM1 on osteosarcoma, shedding light on its pivotal role in tumor progression, immune microenvironment modulation, and drug response. Utilizing publicly available datasets from the Gene Expression Omnibus (GEO) and Therapeutically Applicable Research To Generate Effective Treatments (TARGET) databases, we conducted an in-depth bioinformatics analysis. Our findings illuminate the far-reaching implications of FoxM1 in osteosarcoma, emphasizing its significance as a potential therapeutic target. Differential expression analysis and Gene Set Enrichment Analysis (GSEA) revealed FoxM1's influence on critical pathways related to apoptosis, cell cycle regulation, and DNA repair. Notably, FoxM1 expression correlated with poor clinical outcomes in osteosarcoma patients, highlighting its prognostic relevance. Additionally, FoxM1 was found to modulate the immune microenvironment within tumor tissues, impacting immune cell infiltration, immunomodulators, immune checkpoints, and chemokines. Furthermore, a prognostic model based on FoxM1-coexpressed genes demonstrated its effectiveness in predicting patient survival. Drug sensitivity analysis indicated FoxM1's association with drug response, potentially guiding personalized treatment approaches. Hub gene screening identified RAB23 as a key target regulated by FoxM1, with RAB23 shown to influence osteosarcoma cell growth. This study also confirmed FoxM1's overexpression in osteosarcoma tissues compared to normal tissues, and its association with clinicopathological characteristics, including clinical stage, pathological type, and lung metastasis. In conclusion, FoxM1 emerges as a central player in the pathogenesis of osteosarcoma, impacting gene expression, immune responses, and therapeutic outcomes. This comprehensive analysis deepens our understanding of FoxM1's role in osteosarcoma and offers potential avenues for improved diagnosis and treatment.

The Effect of Cholesterol in MCF7 Human Breast Cancer Cells

In the last decade, cholesterol level has been implicated in several types of cancer, including breast cancer. In the current study, we aimed to investigate the condition of lipid depletion, hypocholesterolemia or hypercholesterolemia reproduced in vitro to analyze the response of different human breast cancer cells. Thus, MCF7 as the luminal A model, MB453 as the HER2 model and MB231 as the triple-negative model were used. No effect on cell growth and viability was detected in MB453 and MB231 cells. In MCF7 cells, hypocholesterolemia (1) reduced cell growth, and Ki67 expression; (2) increased ER/PgR expression; (3) stimulated the 3-Hydroxy-3-Methylglutaryl-CoA reductase and neutral sphingomyelinase and; (4) stimulated the expression of CDKN1A gene coding cyclin-dependent kinase inhibitor 1A protein, GADD45A coding growth arrest and DNA-damage-inducible alpha protein and, PTEN gene coding phosphatase and tensin homolog. All these effects were exacerbated by the lipid-depleted condition and reversed by the hypercholesterolemic condition. The relationship between cholesterol level and sphingomyelin metabolism was demonstrated. In summary, our data suggest that cholesterol levels should be controlled in luminal A breast cancer.