COL5A2 Inhibits the TGF-β and Wnt/β-Catenin Signaling Pathways to Inhibit the Invasion and Metastasis of Osteosarcoma

The Role of Epithelial-Mesenchymal Transition in Osteosarcoma Progression: From Biology to Therapy

Osteosarcoma (OS) is the most common primary malignant bone tumor, predominantly affecting children, adolescents, and young adults. Epithelial-mesenchymal transition (EMT), a process in which epithelial cells lose their cell-cell adhesion and gain migratory and invasive properties, has been extensively studied in various carcinomas. However, its role in mesenchymal tumors like osteosarcoma remains less explored. EMT is increasingly recognized as a key factor in the progression of osteosarcoma, contributing to tumor invasion, metastasis, and resistance to chemotherapy. This narrative review aims to provide a comprehensive overview of the molecular mechanisms driving EMT in osteosarcoma, highlighting the involvement of signaling pathways such as TGF-β, transcription factors like Snail, Twist, and Zeb, and the role of microRNAs in modulating EMT. Furthermore, we discuss how EMT correlates with poor prognosis and therapy resistance in osteosarcoma patients, emphasizing the potential of targeting EMT for therapeutic intervention. Recent advancements in understanding EMT in osteosarcoma have opened new avenues for treatment, including EMT inhibitors and combination therapies aimed at overcoming drug resistance. By integrating biological insights with clinical implications, this review underscores the importance of EMT as a critical process in osteosarcoma progression and its potential as a therapeutic target.

Prognostic and Therapeutic Significance of Cancer-Associated Fibroblasts Genes in Osteosarcoma Based on Bulk and Single-Cell RNA Sequencing Data

Osteosarcoma (OS) is the most frequent primary solid malignancy of bone, whose course is usually dismal without efficient treatments. The aim of the study was to discover novel risk models to more accurately predict and improve the prognosis of patients with osteosarcoma. The single-cell RNA sequencing (scRNA-seq) data was obtained from the GEO database. Bulk RNA-seq data and microarray data of OS were obtained from the TARGET and GEO databases respectively. A clustering tree was plotted to classify all cells into different clusters. The "cellchat" R package was used to establish and visualise cell-cell interaction networks. Then Univariate COX regression analysis was used to determine the prognostic CAF-related genes, followed by the Lasso-Cox regression analysis to build a risk on the prognostic CAF-related genes. Finally, from multiple perspectives, the signature was validated as an accurate and dependable tool in predicting the prognosis and guiding treatment therapies in OS patients. From the single-cell dataset, six OS patients and 46,544 cells were enrolled. All cells were classified into 22 clusters, and the clusters were annotated to 14 types of cells. Subsequently, CAFs were observed as a vital TME components. In cell-cell interaction networks in OS cells, CAFs had a profound impact as four roles. Via the Univariate COX regression analysis, 14 CAF-related genes were screened out. By the Lasso-Cox regression analyses, 11 key CAF-related genes were obtained, based on which an 11-gene signature that could predict the prognosis of osteosarcoma patients was constructed. According to the median of risk scores, all patients were grouped in to the high- and low-risk group, and their overall survival, activated pathways, immune cell infiltrations, and drug sensitivity were significantly differential, which may have important implications for the clinical treatment of patients with osteosarcoma. Our study, a systematic analysis of gene and regulatory genes, has proven that CAF-related genes had excellent diagnostic and prognostic capabilities in OS, and it may reshape the TME in OS. The novel CAF-related risk signature can effectively predict the prognosis of OS and provide new strategies for cancer treatment.

Daidzin improves the cisplatin chemosensitivity for osteosarcoma via binding to β-catenin protein and suppressing the wnt pathway

Osteosarcoma (OS) mainly occurs in adolescents and children, accompanied with chemotherapy resistance as well as unsatisfactory treatment outcome. Recent studies have discovered a series of natural products with anticancer activity, which have important translational value. Daidzin has been reported with a variety of biological activities. We aim to explore whether daidzin has therapeutic potential for OS. In this study, cell based and in vivo studies showed that daidzin can inhibit the ability of OS cells to proliferate and metastasize. Compared to single treatment arms, combined treatment of daidzin and cisplatin, a classic recommendation for OS treatment, showed a further suppression in OS cell viability, migration and invasion ability, and led to further apoptosis. Mechanistically, daidzin was found to downregulate the β-catenin expression and further inhibited the Wnt pathway. However, daidzin did not alter the transcriptional level of β-catenin. Molecular docking showed a potential interaction between daidzin and β-catenin protein, and their combination was further confirmed by thermal shift assay and MST assay. Daidzin was found to reduced protein stability of β-catenin, and only transfection of β-catenin mutant (a mutant that targets the binding sites to daidzin) at the same time as daidzin treatment could effectively attenuate the antitumor effect of daidzin. Our study suggests that daidzin has translational value as a potential adjuvant to improve treatment outcomes for OS.

Identification of the shared gene signatures in retinoblastoma and osteosarcoma by machine learning

Osteosarcoma (OS) is the most prevalent secondary sarcoma associated with retinoblastoma (RB). However, the molecular mechanisms driving the interactions between these two diseases remain incompletely understood. This study aims to explore the transcriptomic commonalities and molecular pathways shared by RB and OS, and to identify biomarkers that predict OS prognosis effectively. RNA sequences and patient information for OS and RB were obtained from the University of California Santa Cruz (UCSC) Xena and Gene Expression Omnibus databases. When RB and OS were first identified, a common gene expression profile was discovered. Weighted Gene Co-expression Network Analysis (WGCNA) revealed co-expression networks associated with OS after immunotyping patients. To evaluate the genes shared by RB and OS, univariate and multivariate Cox regression analysis were then carried out. Three machine learning methods were used to pick key genes, and risk models were created and verified. Next, medications that target independent prognostic genes were found using the Cellminer database. The comparison of differential gene expression between OS and RB revealed 1216 genes, primarily linked to the activation and proliferation of immune cells. WGCNA identified 12 modules related to OS immunotyping, with the grey module showing a strong correlation with the immune-inflamed phenotype. This module intersected with differential genes from RB, producing 65 RB-associated OS Immune-inflamed Genes (ROIGs). Analysis identified 6 hub genes for model construction through univariate Cox regression and three machine learning techniques. A risk model based on these hub genes was established, demonstrating significant prognostic value for OS. Genes shared between OS and RB contribute to the progression of both cancers through multiple pathways. The ROIGs risk score model independently predicts the overall survival of OS patients. Additionally, this study highlights genes with potential as therapeutic targets or biomarkers for clinical use.

Integrated analysis of gut microbiota and metabolomic profiling in colorectal cancer metastasis

Colorectal cancer (CRC) is characterized by its heterogeneity and complex metastatic mechanisms, presenting significant challenges in treatment and prognosis. This study aimed to unravel the intricate interplay between the gut microbiota and metabolic alterations associated with CRC metastasis. By employing high-throughput sequencing and advanced metabolomic techniques, we identified distinct patterns in the gut microbiome and fecal metabolites across different CRC metastatic sites. The differential gene analysis highlighted significant enrichment in biological processes related to immune response and extracellular matrix organization, with key genes playing roles in the complement and clotting cascades, and staphylococcus aureus infections. Protein-protein interaction networks further elucidated the potential mechanisms driving CRC spread, emphasizing the importance of extracellular vesicles and the PPAR signaling pathway in tumor metastasis. Our comprehensive microbiota analysis revealed a relatively stable alpha diversity across groups but identified specific bacterial genera associated with metastatic stages. Metabolomic profiling using OPLS-DA models unveiled distinct metabolic signatures, with differential metabolites enriched in pathways crucial for cancer metabolism and immune modulation. Integrative analysis of the gut microbiota and metabolic profiles highlighted significant correlations, suggesting a complex interplay that may influence CRC progression and metastasis. These findings offer novel insights into the microbial and metabolic underpinnings of CRC metastasis, paving the way for innovative diagnostic and therapeutic strategies targeting the gut microbiome and metabolic pathways.

Development and experimental verification of novel angiogenesis related prognostic model and immune infiltration characterization in osteosarcoma

BACKGROUND

As the most common primary bone cancer, osteosarcoma (OS) still lacks satisfactory therapeutic outcomes. Therefore, it is crucial to further evaluate OS at different risk levels and identify new intervention targets. Many evidences suggest the important role of angiogenesis in OS, but further exploration is needed.

METHODS

We utilized public databases TARGET and GEO and employed bioinformatics algorithms such as LASSO, univariate and multivariate Cox regression analyses, and unsupervised consensus clustering to explore the role of angiogenesis-related genes (AGRGs) in OS. By calculating AGRG scores, we further analyzed OS molecular subtypes based on AGRGs. The correlation between AGRG scores and immune infiltration was subsequently examined. In vitro experiments, including WB, PCR, siRNA, migration, and invasion assays, were used to determine the value of the selected targets for OS.

RESULTS

Ultimately, we established an OS prognosis model based on five AGRGs (COL5A2, CXCL6, FSTL1, NRP1, and TNFRSF21) that can independently validate prognosis levels. In vitro experiments confirmed the aberrant expression of CXCL6 in OS and its potential role in migration and invasion.

CONCLUSION

Our study reveals the impact of angiogenesis on OS from a novel perspective and provides potential intervention targets.

EMT-related gene classifications predict the prognosis, immune infiltration, and therapeutic response of osteosarcoma

BACKGROUND

Osteosarcoma (OS), a bone tumor with high ability of invasion and metastasis, has seriously affected the health of children and adolescents. Many studies have suggested a connection between OS and the epithelial-mesenchymal transition (EMT). We aimed to integrate EMT-Related genes (EMT-RGs) to predict the prognosis, immune infiltration, and therapeutic response of patients with OS.

METHODS

We used consensus clustering to identify potential EMT-Related OS molecular subtypes. Somatic mutation, tumor immune microenvironment, and functional enrichment analyses were performed for each subtype. We next constructed an EMT-Related risk signature and evaluated it by Kaplan-Meier (K-M) analysis survival and receiver operating characteristic (ROC) curves. Moreover, we constructed a nomogram to more accurately predict OS patients' clinical outcomes. Response effects of immunotherapy in OS patients was analyzed by Tumor Immune Dysfunction and Exclusion (TIDE) analysis, while sensitivity for chemotherapeutic agents was analyzed using oncoPredict. Finally, the expression patterns of hub genes were investigated by single-cell RNA sequencing (scRNA-seq) data analysis.

RESULTS

A total of 53 EMT-RDGs related to prognosis were identified, separating OS samples into two separate subgroups. The EMT-high subgroup showed favourable overall survival and more active immune response. Significant correlations were found between EMT-Related DEGs and functions as well as pathways linked to the development of OS. Additionally, a risk signature was established and OS patients were divided into two categories based on the risk scores. The signature presented a good predictive performance and could be recognized as an independent predictive factor for OS. Furthermore, patients with higher risk scores exhibited better sensitivity for five drugs, while no significant difference existed in immunotherapy response between the two risk subgroups. scRNA-seq data analysis displayed different expression patterns of the hub genes.

CONCLUSION

We developed a novel EMT-Related risk signature that can be considered as an independent predictor for OS, which may help improve clinical outcome prediction and guide personalized treatments for patients with OS.

A novel cancer-associated fibroblasts risk score model predict survival and immunotherapy in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. Cancer-associated fibroblasts (CAFs) are a special type of fibroblasts, which play an important role in the development and immune escape of tumors. Weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression module. In combination with univariate Cox regression and analysis of least absolute shrinkage operator (LASSO), characteristics associated with CAFs were developed for a prognostic model. The migration and proliferation of lung cancer cells were evaluated in vitro. Finally, the expression levels of proteins were analyzed by Western blot. LASSO Cox regression algorithm was then performed to select hub genes. Finally, a total of 2 Genes (COL5A2, COL6A2) were obtained. We then divided LUAD patients into high- and low-risk groups based on CAFs risk scores. Survival analysis, CAFs score correlation analysis and tumor mutation load analysis showed that COL5A2 and COL6A2 were high-risk genes for LUAD. Human Protein Atlas (HPA), western blot and PCR results showed that COL5A2 and COL6A2 were up-regulated in LUAD tissues. When COL5A2 and COL6A2 were knocked down, the proliferation, invasion and migration of lung cancer cells were significantly decreased. Finally, COL5A2 can affect LUAD progression through the Wnt/β-Catenin and TGF-β signaling pathways. Our CAFs risk score model offers a new approach for predicting the prognosis of LUAD patients. Furthermore, the identification of high-risk genes COL5A2 and COL6A2 and drug sensitivity analysis can provide valuable candidate clues for clinical treatment of LUAD.

COL5A2 drives regorafenib resistance-induced metastatic phenotype via reducing LIFR expression in hepatocellular carcinoma

Systemic therapies, the ultimate strategies for patients with advanced hepatocellular carcinoma (HCC), are suffering from serious clinical challenges, such as the occurrence and development of drug resistance. Treatment resistance aggravates tumor progression partly by inducing tumor metastasis. Regorafenib-resistant HCC cells exhibit a highly striking metastatic phenotype, but the detailed mechanisms underlying these aggressive behaviors remain elusive. Here, we conduct transcriptome sequencing analysis to identify COL5A2 as a crucial driver of the metastatic characteristics of regorafenib-resistant HCC cells. COL5A2 is aberrantly highly expressed in resistant cells, and its genetic depletion significantly suppresses proliferation, migration, invasion, vasculogenic mimicry (VM) formation and lung metastasis in vitro and in vivo, concomitant with the downregulation of VE-cadherin, EphA2, Twist1, p-p38 and p-STAT3 expressions. LIFR is confirmed to be an essential downstream molecule of COL5A2, and its expression is observably elevated by COL5A2 depletion. Ectopic overexpression of LIFR drastically attenuates the proliferation, migration, invasion and VM of regorafenib-resistant cells and represses the expressions of VM-related molecules and the activation of p38/STAT3 signaling pathway. Interestingly, rescue experiments show that the inhibition of the above aggressive features of resistant cells by COL5A2 loss is clearly alleviated by silencing of LIFR. Collectively, our results reveal that COL5A2 promotes the ability of regorafenib-resistant HCC cells to acquire a metastatic phenotype by attenuating LIFR expression and suggest that therapeutic regimens targeting the COL5A2/LIFR axis may be beneficial for HCC patients with therapeutic resistance.

Immune Features of Tumor Microenvironment: A Genetic Spotlight

A tumor represents a highly intricate tissue entity, characterized by an exceptionally complex microenvironment that starkly contrasts with the typical physiological surroundings of healthy tissues. Within this tumor microenvironment (TME), every component and factor assume paramount importance in the progression of malignancy and exerts a pivotal influence on a patient's clinical outcome. One of the remarkable aspects of the TME is its remarkable heterogeneity, not only across different types of cancers but even within the same histological category of tumors. In-depth research has illuminated the intricate interplay between specific immune cells and molecules and the dynamic characteristics of the TME. Recent investigations have yielded compelling evidence that several mutations harbored by tumor cells possess the capacity to instigate substantial alterations in the TME. These mutations, often acting as drivers of tumorigenesis, can orchestrate a cascade of events that remodel the TME, thereby influencing crucial aspects of cancer behavior, including its invasiveness, immune evasion, and response to therapies. It is within this nuanced context that the present study endeavors to provide a concise yet comprehensive summary of how specific mutations, within the genetic landscape of cancer cells, can instigate profound changes in TME features. By elucidating the intricate relationship between genetic mutations and the TME, this research aims to contribute to a deeper understanding of cancer biology. Ultimately, the knowledge gained from this study holds the potential to inform the development of more targeted and effective treatments, thereby offering new hope to patients grappling with the complexities of cancer.

Deciphering the Signaling Mechanisms of Osteosarcoma Tumorigenesis

Osteosarcoma (OS) is the predominant primary bone tumor in the pediatric and adolescent populations. It has high metastatic potential, with the lungs being the most common site of metastasis. In contrast to many other sarcomas, OS lacks conserved translocations or genetic mutations; instead, it has heterogeneous abnormalities, including somatic DNA copy number alteration, ploidy, chromosomal amplification, and chromosomal loss and gain. Unfortunately, clinical outcomes have not significantly improved in over 30 years. Currently, no effective molecularly targeted therapies are available for this disease. Several genomic studies showed inactivation in the tumor suppressor genes, including p53, RB, and ATRX, and hyperactivation of the tumor promoter genes, including MYC and MDM2, in OS. Alterations in the major signaling pathways, including the PI3K/AKT/mTOR, JAK/STAT, Wnt/β-catenin, NOTCH, Hedgehog/Gli, TGF-β, RTKs, RANK/RANKL, and NF-κB signaling pathways, have been identified in OS development and metastasis. Although OS treatment is currently based on surgical excision and systematic multiagent therapies, several potential targeted therapies are in development. This review focuses on the major signaling pathways of OS, and we propose a biological rationale to consider novel and targeted therapies in the future.

Collagen type V alpha 2 promotes the development of gastric cancer via M2 macrophage polarization

Gastric cancer is a type of digestive tract cancer with a high morbidity and mortality, which leads to a major health burden worldwide. More research into the functions of the immune system will improve therapy and survival in gastric cancer patients. We attempted to identify potential biomarkers or targets in gastric cancer via bioinformatical analysis approaches. Three gene expression profile datasets (GSE79973, GSE103236, and GSE118916) of gastric tissue samples were obtained from the Gene Expression Omnibus database. There were 65 overlapping differentially expressed genes (DEGs) identified from three microarrays. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway were carried out for the key functions and pathways enriched in the DEGs. Then, ten hub genes were identified by protein-protein interaction network. In addition, we observed that collagen type V alpha 2 (COL5A2) was linked to gastric cancer prognosis as well as M2 macrophage infiltration. Furthermore, COL5A2 enhanced gastric cancer cell proliferation through the PI3K-AKT signaling pathway and polarized M2 macrophage cells. Therefore, in this study, we found that COL5A2 was associated with the development of gastric cancer which might function as a potential therapeutic target for the disease.

Cellular Transcriptomics of Carboplatin Resistance in a Metastatic Canine Osteosarcoma Cell Line

Osteosarcoma prognosis has remained unchanged for the past three decades. In both humans and canines, treatment is limited to excision, radiation, and chemotherapy. Chemoresistance is the primary cause of treatment failure, and the trajectory of tumor evolution while under selective pressure from treatment is thought to be the major contributing factor in both species. We sought to understand the nature of platinum-based chemotherapy resistance by investigating cells that were subjected to repeated treatment and recovery cycles with increased carboplatin concentrations. Three HMPOS-derived cell lines, two resistant and one naïve, underwent single-cell RNA sequencing to examine transcriptomic perturbation and identify pathways leading to resistance and phenotypic changes. We identified the mechanisms of acquired chemoresistance and inferred the induced cellular trajectory that evolved with repeated exposure. The gene expression patterns indicated that acquired chemoresistance was strongly associated with a process similar to epithelial–mesenchymal transition (EMT), a phenomenon associated with the acquisition of migratory and invasive properties associated with metastatic disease. We conclude that the observed trajectory of tumor adaptability is directly correlated with chemoresistance and the phase of the EMT-like phenotype is directly affected by the level of chemoresistance. We infer that the EMT-like phenotype is a critical component of tumor evolution under treatment pressure and is vital to understanding the mechanisms of chemoresistance and to improving osteosarcoma prognosis.

In Silico Analysis of Ferroptosis-Related Genes and Its Implication in Drug Prediction against Fluorosis

Fluorosis is a serious global public health problem. Interestingly, so far, there is no specific drug treatment for the treatment of fluorosis. In this paper, the potential mechanisms of 35 ferroptosis-related genes in U87 glial cells exposed to fluoride were explored by bioinformatics methods. Significantly, these genes are involved in oxidative stress, ferroptosis, and decanoate CoA ligase activity. Ten pivotal genes were found by the Maximal Clique Centrality (MCC) algorithm. Furthermore, according to the Connectivity Map (CMap) and the Comparative Toxicogenomics Database (CTD), 10 possible drugs for fluorosis were predicted and screened, and a drug target ferroptosis-related gene network was constructed. Molecular docking was used to study the interaction between small molecule compounds and target proteins. Molecular dynamics (MD) simulation results show that the structure of the Celestrol-HMOX1 composite is stable and the docking effect is the best. In general, Celastrol and LDN-193189 may target ferroptosis-related genes to alleviate the symptoms of fluorosis, which may be effective candidate drugs for the treatment of fluorosis.

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.

Identification of a novel gene signature with regard to ferroptosis, prognosis prediction, and immune microenvironment in osteosarcoma

Ferroptosis is a novel form of non-apoptotic cell death that mainly results from the iron-dependent lethal accumulation of lipid peroxidation products. Here, we defined differentially expressed genes between control and RSL3-treated osteosarcoma cells as ferroptosis-associated genes (FAGs). These FAGs were then subjected to weighted gene correlation network analysis (WGCNA), and we found that the turquoise module, containing 71 FAGs, was markedly related to the patient’s vital status. After that, FAGs in the turquoise module were utilized to construct a prognostic multigene (COL5A2, HOXB4, and UNC5B) signature for risk stratification in osteosarcoma. Validation in internal and external cohorts indicated the accuracy and clinical applicability of this signature in predicting the prognosis of patients with osteosarcoma. Univariate and multivariate Cox regression analyses suggested that the signature-derived risk score is an independent indicator of patient prognosis. Immunological analysis indicated that significant variations in stromal and ESTIMATE scores, as well as tumor purity, were found when the high- and low-risk groups were compared. Regarding immune cell infiltration, the proportion of activated CD4 memory T cells was significantly lower in the high-risk group than that in the low-risk group. The ssGSEA results suggested that CD8⁺ T, Tfh, and Th1 cell scores were consistently lower in the high-risk group than those in the low-risk group. In terms of immune-related activities, the high-risk group had considerably lower scores for promoting inflammation, T-cell co-inhibition, and T-cell co-stimulation than the low-risk group, indicating the differential immunological state of the high- and low-risk groups. Of the three FAGs included in the signature, the expression of COL5A2, HOXB4, and UNC5B was higher in the high-risk groups, and the expression of COL5A2 and UNC5B was negatively associated with patient prognosis. Additionally, the mRNA levels of COL5A2 and HOXB4 were lower and those of UNC5B were higher in RSL3-treated cells than in control cells. In all, we systematically analyzed the transcriptional changes of osteosarcoma cells induced by RSL3 and constructed a novel three-gene signature with regard to ferroptosis, prognosis prediction, and immune microenvironment. We also identified COL5A2, HOXB4, and UNC5B as potential therapeutic targets and important regulators of ferroptosis in osteosarcoma.

Curcumol Synergizes with Cisplatin in Osteosarcoma by Inhibiting M2-like Polarization of Tumor-Associated Macrophages

Osteosarcoma is the most prevalent bone cancer, and chemotherapy is still an indispensable treatment in its clinical practice. Cisplatin (CDDP) has become the most commonly used agent for osteosarcoma, although the outcomes of CDDP chemotherapy remain unsatisfactory because of frequent resistance. Here, we report on a promising combination therapy where curcumol, a bioactive sesquiterpenoid, enhanced CDDP-induced apoptosis to eradicate osteosarcoma cells, and revealed that M2-like macrophages might be the underlying associated mechanisms. First, we observed that curcumol enhanced the CDDP-mediated inhibition of cell proliferation and augmented the apoptosis in osteosarcoma cell lines. Curcumol contributed to preventing the migration of osteosarcoma cells when combined with CDDP. Moreover, this drug combination showed more potent tumor-growth suppression in the orthotopic transplantation of osteosarcoma K7M2 WT cells. We then estimated chemotherapy-associated drug-resistant genes, including ABCB1, ABCC1 and ABCG2, and found that curcumol significantly reversed the mRNA levels of CDDP-induced ABCB1, ABCC1 and ABCG2 genes in the tumor tissue. Moreover, M2-like macrophages were enriched in osteosarcoma tissues, and were largely decreased after curcumol and CDDP treatment. Taken together, these findings suggest that curcumol inhibits the polarization of M2-like macrophages and could be a promising combination strategy to synergize with CDDP in the osteosarcoma.

Convallatoxin suppresses osteosarcoma cell proliferation, migration, invasion, and enhances osteogenic differentiation by downregulating parathyroid hormone receptor 1 (PTHR1) expression and inactivating Wnt/β-catenin pathway

Osteosarcoma (OS) is the most common primary malignant bone tumor in children and adolescents. Convallatoxin, a natural cardiac glycoside, exhibits potent anti-tumor activities. Literature has confirmed that PTHR1 is highly expressed in OS tissues and cells and downregulation of PTHR1 could decrease the invasion and growth of OS cells and increase tumor differentiation. In addition, PTHR1 could activate Wnt signaling pathway to promote the malignant functions of OS. In the present study, MG63 and U2OS cells were treated with 0, 12.5, 25, and 50 nM convallatoxin in order to elucidate the precise function of convallatox on the malignant behaviors of OS cells. Moreover, MG63 and U2OS cells treated with convallatoxin were transfected with Ov-PTHR1 or sh-DKK1, aiming to explore whether convallatoxin impeded the malignant progression of OS by modulating PTHR1 and Wnt/β-catenin pathway. CCK-8, wound healing and transwell assays were employed to assess the proliferation, migration, and invasion of OS cells. Differentiation markers (collagen 1, osteopontin, RANKL, Runx2, osteocalcin) were measured to evaluate OS cell differentiation. Results illuminated that convallatoxin suppressed proliferation, migration, and invasion as well as promoted osteogenic differentiation of OS cells. Besides, convallatoxin inhibited PTHR1 expression and inactivated Wnt/β-catenin pathway and PTHR1 overexpression activated Wnt/β-catenin pathway. Furthermore, PTHR1 overexpression or DKK1 knockdown reversed the suppressing effects of convallatoxin on OS cell proliferation, migration, and invasion, as well as the enhancing effect of convallatoxin on OS cell osteogenic differentiation. Collectively, convallatoxin may repress the malignant progression of OS by blocking PTHR1 and Wnt/β-catenin pathway.

A Genomic Signature Reflecting Fibroblast Infiltration Into Gastric Cancer Is Associated With Prognosis and Treatment Outcomes of Immune Checkpoint Inhibitors

Background: The immunotherapy efficacy in gastric cancer (GC) is limited. Cancer-associated fibroblasts (CAFs) induce primary resistance to immunotherapy. However, CAF infiltration in tumors is difficult to evaluate due to the lack of validated and standardized quantified methods. This study aimed to investigate the impact of infiltrating CAFs alternatively using fibroblast-associated mutation scoring (FAMscore). Methods: In a GC cohort from Affiliated Hospital of Jiangsu University (AHJU), whole exon sequencing of genomic mutations, whole transcriptome sequencing of mRNA expression profiles, and immunofluorescence staining of tumor-infiltrating immune cells were performed. GC data from The Cancer Genome Atlas were used to identify genetic mutations which were associated with overall survival (OS) and impacted infiltrating CAF abundance determined by transcriptome-based estimation. FAMscore was then constructed through a least absolute shrinkage and selection operator Cox regression model and further validated in AHJU. The predictive role of FAMscore for immunotherapy outcomes was tested in 1 GC, one melanoma, and two non-small-cell lung cancer (NSCLC-1 and -2) cohorts wherein participants were treated by immune checkpoint inhibitors. Results: FAMscore was calculated based on a mutation signature consisting of 16 genes. In both TCGA and AHJU, a high FAMscore was an independent predictor for poor OS of GC patients. FAMscore was associated with immune-associated genome biomarkers, immune cell infiltration, and signaling pathways of abnormal immunity. Importantly, patients with high FAMscore presented inferiority in the objective response rate of immunotherapy compared to those with low FAMscore, with 14.6% vs. 66.7% (p<0.001) in GC, 19.6% vs. 68.2% (p<0.001) in NSCLC-1, 23.1% vs 75% (p = 0.007) in NSCLC-2, and 40.9% vs 75% (p = 0.037) in melanoma. For available survival data, a high FAMscore was also an independent predictor of poor progression-free survival in NSCLC-1 (HR = 2.55, 95% CI: 1.16-5.62, p = 0.02) and NSCLC-2 (HR = 5.0, 95% CI: 1.13-22.19, p = 0.034) and poor OS in melanoma (HR = 3.48, 95% CI: 1.27-9.55, p = 0.015). Conclusions: Alternative evaluation of CAF infiltration in GC by determining the FAMscore could independently predict prognosis and immunotherapy outcomes. The FAMscore may be used to optimize patient selection for immunotherapy.

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

Simple Summary

The standard method for the diagnosis and monitoring of osteosarcoma is biopsy and tumor imaging, which causes discomfort to patients and is difficult to repeat. A blood sample can be used as a non-invasive method for monitoring tumor material. Vimentin and ezrin show clinical significance in samples obtained from OS patients but need circulating tumor cell purification, since they are expressed in leukocytes. Due to the low-temperature storage of the samples, it proved impossible to perform purification to remove the contamination. We propose that novel or OS-specific biomarkers using differential gene expression from the Gene Expression Omnibus (GEO) database is a promising approach for developing diagnostic and tumor progression strategies. Seven genes from the database showed significant expression in OS cell lines/primary cells compared to a normal blood donor, together with ezrin and VIM. The expression of the five candidate genes together with ezrin and vimentin were quantified by qRT-PCR and analyzed using a mathematical model with high efficiency to discriminate between OS patients and normal samples, resulting in the selection of three candidate genes: COL5A2 (one of the five from the database) as well as ezrin and VIM. Our study demonstrates that these genes in retrospective samples could serve as tools of OS detection and predictors of disease progression.

Abstract

A liquid biopsy is currently an interesting tool for measuring tumor material with the advantage of being non-invasive. The overexpression of vimentin and ezrin genes was associated with epithelial-mesenchymal transition (EMT), a key process in metastasis and progression in osteosarcoma (OS). In this study, we identified other OS-specific genes by calculating differential gene expression using the Gene Expression Omnibus (GEO) database, confirmed by using quantitative reverse transcription-PCR (qRT-PCR) to detect OS-specific genes, including VIM and ezrin in the buffy coat, which were obtained from the whole blood of OS patients and healthy donors. Furthermore, the diagnostic model for OS detection was generated by utilizing binary logistic regression with a multivariable fractional polynomial (MFP) algorithm. The model incorporating VIM, ezrin, and COL5A2 genes exhibited outstanding discriminative ability, as determined by the receiver operating characteristic curve (AUC = 0.9805, 95% CI 0.9603, 1.000). At the probability cut-off value of 0.3366, the sensitivity and the specificity of the model for detecting OS were 98.63% (95% CI 90.5, 99.7) and 94.94% (95% CI 87.5, 98.6), respectively. Bioinformatic analysis and qRT-PCR, in our study, identified three candidate genes that are potential diagnostic and prognostic genes for OS.