Identification of potential gene signatures associated with osteosarcoma by integrated bioinformatics analysis

Cytochrome P450 F3 promotes colorectal cancer via inhibiting NRF2-mediated ferroptosis

Cytochrome P450 F3 (CYP4F3) is recognized as a disease-associated immune response initiator that is involved in the synthesis of cholesterol, steroids, and lipids. This study identified the upregulation of CYP4F3 expression in colorectal cancer (CRC) and its association with poor patient prognosis through a comparative analysis between CRC tumor tissues with normal tissues from public databases. The overexpression of CYP4F3 in CT26.wt and SW620, promoted cell proliferation and migration, a reduction of cellular oxidative stress, an up-regulation of the oxidative stress-related pathway NRF2, and an inhibition of cellular ferroptosis. Additionally, inhibition of NRF2 activity stimulated cellular ferroptosis when CYP4F3 was overexpressed. Ferroptosis, characterized by iron-dependent lipid peroxidation, is a non-apoptotic way of cell death with a critical role in cancer development. When given a ferroptosis agonist to CYP4F3-overexpression CRC cells, NRF2 was activated, and cell proliferation and migration were reduced. Furthermore, the mice subcutaneously injected with CYP4F3-overexpression CT26.wt cells formed significantly larger tumors compared to the CYP4F3-vector CT26.wt cell group. This study systematically identified an important role of CYP4F3 in CRC development as a regulator of CRC cells to escape ferroptosis via NRF2, highlighting the significance of CYP4F3 as a potential therapeutic target for CRC.

Distinct Gene Expression and Immune Features Between Different Neutrophil Extracellular Trap-Related Osteosarcoma Subtypes

We sought to determine neutrophil extracellular trap (NET)-related genes' potential value in improving the efficacy of diagnosis and identifying novel therapeutic targets for osteosarcoma. Data were obtained from TARGET, GEO, and CCLE database. Differentially expressed genes were identified between the subtypes based on NET-related genes. PPI network was constructed using STRING, following by ClueGO enrichment analysis. Infiltration of immune cells was calculated by ssGSEA. Risk Score model was built by LASSO Cox regression analysis. Western blot and qRT-PCR were applied to validate the expression of genes used in the model. We identified 19 NET-related genes with prognostic potential in osteosarcoma using univariate Cox regression analysis. Patients from TARGET were clustered into two subtypes with distinct prognosis and immune features. 381 DEGs were identified between the two NET subtypes. Risk Score based on BST1, SELPLG, FPR1 and TNFRSF10C was reliable to predict the prognosis of osteosarcoma patients. The four genes expressed significantly lower in osteosarcoma than normal cells. Low Risk Score individuals only existed in C1 subtype with better prognosis. Osteosarcoma were clustered into two subtypes based on NET-related genes. Risk Score model constructed by four NET-related gene was able to independently predict the prognosis of osteosarcoma.

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.

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.

Comparative Shotgun Proteomics Reveals the Characteristic Protein Signature of Osteosarcoma Subtypes

Osteosarcoma is a primary malignant bone tumor affecting adolescents and young adults. This study aimed to identify proteomic signatures that distinguish between different osteosarcoma subtypes, providing insights into their molecular heterogeneity and potential implications for personalized treatment approaches. Using advanced proteomic techniques, we analyzed FFPE tumor samples from a cohort of pediatric osteosarcoma patients representing four various subtypes. Differential expression analysis revealed a significant proteomic signature that discriminated between these subtypes, highlighting distinct molecular profiles associated with different tumor characteristics. In contrast, clinical determinants did not correlate with the proteome signature of pediatric osteosarcoma. The identified proteomics signature encompassed a diverse array of proteins involved in focal adhesion, ECM-receptor interaction, PI3K-Akt signaling pathways, and proteoglycans in cancer, among the top enriched pathways. These findings underscore the importance of considering the molecular heterogeneity of osteosarcoma during diagnosis or even when developing personalized treatment strategies. By identifying subtype-specific proteomics signatures, clinicians may be able to tailor therapy regimens to individual patients, optimizing treatment efficacy and minimizing adverse effects.

Transcriptomic characterization revealed that METTL7A inhibits melanoma progression via the p53 signaling pathway and immunomodulatory pathway

METTL7A is a protein-coding gene expected to be associated with methylation, and its expression disorder is associated with a range of diseases. However, few research have been carried out to explore the relationship between METTL7A and tumor malignant phenotype as well as the involvement potential mechanism. We conducted our research via a combination of silico analysis and molecular biology techniques to investigate the biological function of METTL7A in the progression of cancer. Gene expression and clinical information were extracted from the TCGA database to explore expression variation and prognostic value of METTL7A. In vitro, CCK8, transwell, wound healing and colony formation assays were conducted to explore the biological functions of METT7A in cancer cell. GSEA was performed to explore the signaling pathway involved in METTL7A and validated via western blotting. In conclusion, METTL7A was downregulated in most cancer tissues and its low expression was associated with shorter overall survival. In melanoma, METTL7A downregulation was associated with poorer clinical staging, lower levels of TIL infiltration, higher IC50 levels of chemotherapeutic agents, and poorer immunotherapy outcomes. QPCR results confirm that METTL7A is down-regulated in melanoma cells. Cell function assays showed that METTL7A knockdown promoted proliferation, invasion, migration and clone formation of melanoma cells. Mechanistic studies showed that METTL7A inhibits tumorigenicity through the p53 signaling pathway. Meanwhile, METTL7A is also a potential immune regulatory factor.

miR-137-LAPTM4B regulates cytoskeleton organization and cancer metastasis via the RhoA-LIMK-Cofilin pathway in osteosarcoma

Osteosarcoma (OS) is a rare malignant bone tumor but is one leading cause of cancer mortality in childhood and adolescence. Cancer metastasis accounts for the primary reason for treatment failure in OS patients. The dynamic organization of the cytoskeleton is fundamental for cell motility, migration, and cancer metastasis. Lysosome Associated Protein Transmembrane 4B (LAPTM4B) is an oncogene participating in various biological progress central to cancer biogenesis. However, the potential roles of LAPTM4B in OS and the related mechanisms remain unknown. Here, we established the elevated LAPTM4B expression in OS, and it is essential in regulating stress fiber organization through RhoA-LIMK-cofilin signaling pathway. In terms of mechanism, our data revealed that LAPTM4B promotes RhoA protein stability by suppressing the ubiquitin-mediated proteasome degradation pathway. Moreover, our data show that miR-137, rather than gene copy number and methylation status, contributes to the upregulation of LAPTM4B in OS. We report that miR-137 is capable of regulating stress fiber arrangement, OS cell migration, and metastasis via targeting LAPTM4B. Combining results from cells, patients' tissue samples, the animal model, and cancer databases, this study further suggests that the miR-137-LAPTM4B axis represents a clinically relevant pathway in OS progression and a viable target for novel therapeutics.

Evidence Based on an Integrative Analysis of Multi-Omics Data on METTL7A as a Molecular Marker in Pan-Cancer

Methyltransferase-like protein 7A (METTL7A), an RNA N6-methyladenosine (m6A) methyltransferase, has attracted much attention as it has been found to be closely associated with various types of tumorigenesis and progression. This study provides a comprehensive assessment of METTL7A from a pan-cancer perspective using multi-omics data. The gene ontology enrichment analysis of METTL7A-binding proteins revealed a close association with methylation and lipid metabolism. We then explored the expression of METTL7A in normal tissues, cell lines, different subtypes and cancers, and found that METTL7A was differentially expressed in various cancer species, tumor molecular subtypes and immune subtypes. Evaluation of the diagnostic and prognostic value of METTL7A in pan-cancer revealed that METTL7A had high accuracy in tumor prediction. Moreover, the low expression of METTL7A significantly correlated with the poor prognosis, including kidney renal clear cell carcinoma (KIRC), mesothelioma and sarcoma, indicating that METTL7A could be a potential biomarker for tumor diagnosis and prognosis. We focused on KIRC after pre-screening and analyzed its expression and prognostic value in various clinical subgroups. We found that METTL7A was significantly related to tumor stage, metastasis stage, pathologic stage, primary therapy outcome, histologic grade and gender, and that low METTL7A expression was associated with poorer outcomes. Finally, we analyzed the immune infiltration and co-expressed genes of METTL7A as well as the differentially expressed genes in the high and low expression groups. In conclusion, METTL7A is a better molecular marker for pan-cancer diagnosis and prognosis and has high potential as a diagnostic and prognostic biomarker for KIRC.

Identification of differentially expressed genes and pathways in BEAS-2B cells upon long-term exposure to particulate matter (PM2.5) from biomass combustion using bioinformatics analysis

BACKGROUND

Long-term exposure to PM2.5 from burning domestic substances has been linked to an increased risk of lung disease, but the underlying mechanisms are unclear. This study is to explore the hub genes and pathways involved in PM2.5 toxicity in human bronchial epithelial BEAS-2B cells.

METHODS

The GSE158954 dataset is downloaded from the GEO database. Differentially expressed genes (DEGs) were screened using the limma package in RStudio (version 4.2.1). In addition, DEGs analysis was performed by Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. A protein-protein interaction (PPI) network was constructed, MCODE plug-in and the cytoHubba plug-in in Cytoscape software was used to identify the hub genes. Finally, CytoHubba and DEGs were used to integrate the hub genes, and preliminary validation was performed by comparing the toxicology genomics database (CTD). Differential immune cell infiltration was investigated using the CIBERSORT algorithm.

RESULTS

A total of 135 DEGs were identified, of which 57 were up-regulated and 78 were down-regulated. Functional enrichment analyses in the GO and KEGG indicated the potential involvement of DEGs was mainly enriched in the regulation of endopeptidase activity and influenza A. Gene Set Enrichment Analysis revealed that Chemical Carcinogenesis - DNA adducts were remarkably enriched in PM2.5 groups. 53 nodes and 198 edges composed the PPI network. Besides, 5 direct-acting genes were filtered at the intersection of cytohubba plug-in, MCODE plug-in and CTD database. There is a decreasing trend of dendritic cells resting after BEAS-2B cells long-term exposure to PM2.5.

CONCLUSIONS

The identified DEGs, modules, pathways, and hub genes provide clues and shed light on the potential molecular mechanisms of BEAS-2B cells upon long-term exposure to PM2.5.

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

Background

Glycolysis and cholesterol synthesis are crucial in cancer metabolic reprogramming. The aim of this study was to identify a glycolysis and cholesterol synthesis-related genes (GCSRGs) signature for effective prognostic assessments of osteosarcoma patients.

Methods

Gene expression data and clinical information were obtained from GSE21257 and TARGET-OS datasets. Consistent clustering method was used to identify the GCSRGs-related subtypes. Univariate Cox regression and LASSO Cox regression analyses were used to construct the GCSRGs signature. The ssGSEA method was used to analyze the differences in immune cells infiltration. The pRRophetic R package was utilized to assess the drug sensitivity of different groups. Western blotting, cell viability assay, scratch assay and Transwell assay were used to perform cytological validation.

Results

Through bioinformatics analysis, patients diagnosed with osteosarcoma were classified into one of 4 subtypes (quiescent, glycolysis, cholesterol, and mixed subtypes), which differed significantly in terms of prognosis and tumor microenvironment. Weighted gene co-expression network analysis revealed that the modules strongly correlated with glycolysis and cholesterol synthesis were the midnight blue and the yellow modules, respectively. Both univariate and LASSO Cox regression analyses were conducted on screened module genes to identify 5 GCSRGs (RPS28, MCAM, EN1, TRAM2, and VEGFA) constituting a prognostic signature for osteosarcoma patients. The signature was an effective prognostic predictor, independent of clinical characteristics, as verified further via Kaplan-Meier analysis, ROC curve analysis, univariate and multivariate Cox regression analysis. Additionally, GCSRGs signature had strong correlation with drug sensitivity, immune checkpoints and immune cells infiltration. In cytological experiments, we selected TRAM2 as a representative gene to validate the validity of GCSRGs signature, which found that TRAM2 promoted the progression of osteosarcoma cells. Finally, at the pan-cancer level, TRAM2 had been correlated with overall survival, progression free survival, disease specific survival, tumor mutational burden, microsatellite instability, immune checkpoints and immune cells infiltration.

Conclusion

Therefore, we constructed a GCSRGs signature that efficiently predicted osteosarcoma patient prognosis and guided therapy.

Identification of Survival-Related Genes in Acute Myeloid Leukemia (AML) Based on Cytogenetically Normal AML Samples Using Weighted Gene Coexpression Network Analysis

The prognosis of acute myeloid leukemia (AML) remains a challenge. In this study, we applied the weighted gene coexpression network analysis (WGCNA) to find survival-specific genes in AML based on 42 adult CN-AML samples from The Cancer Genome Atlas (TCGA) database. Eighteen hub genes (ABCA13, ANXA3, ARG1, BTNL8, C11orf42, CEACAM1, CEACAM3, CHI3L1, CRISP2, CYP4F3, GPR84, HP, LTF, MMP8, OLR1, PADI2, RGL4, and RILPL1) were found to be related to AML patient survival time. We then compared the hub gene expression levels between AML peripheral blood (PB) samples ( $n=162$ ) and control healthy whole blood samples ( $n=337$ ). Seventeen of the hub genes showed lower expression levels in AML PB samples. The gene expression analysis was also done among AML BM (bone marrow) samples of different stages: diagnosis ( $n=142$ ), posttreatment ( $n=42$ ), and recurrent ( $n=12$ ) stages. The results showed a significant increase of ANXA3, CEACM1, RGL4, RILPL1, and HP in posttreatment samples compared to diagnosis and/or recurrent samples. Transcription factor (TF) prediction of the hub genes suggested LTF as the top hit, overlapping 10 hub genes, while LTF itself is just one of the hub genes. Also, 3671 correlation links were shown between 128 mRNAs and 209 lncRNAs found in survival time-related modules. Generally, we identified candidate mRNA biomarkers based on CN-AML data which can be extensively used in AML prognosis. In addition, we mapped their potential regulatory mechanisms with correlated lncRNAs, providing new insights into potential targets for therapies in AML.

Analysis of immune related gene expression profiles and immune cell components in patients with Barrett esophagus

Barrett's esophagus (BE) is a well-known precancerous condition of esophageal adenocarcinoma. However, the immune cells and immune related genes involved in BE development and progression are not fully understood. Therefore, our study attempted to investigate the roles of immune cells and immune related genes in BE patients. The raw gene expression data were downloaded from the GEO database. The limma package in R was used to screen differentially expressed genes (DEGs). Then we performed the least absolute shrinkage and selection operator (LASSO) and random forest (RF) analyses to screen key genes. The proportion of infiltrated immune cells was evaluated using the CIBERSORT algorithm between BE and normal esophagus (NE) samples. The spearman index was used to show the correlations of immune genes and immune cells. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of key genes in BE. A total of 103 differentially expressed immune-related genes were identified between BE samples and normal samples. Then, 7 genes (CD1A, LTF, FABP4, PGC, TCF7L2, INSR,SEMA3C) were obtained after Lasso analysis and RF modeling. CIBERSORT analysis revealed that resting CD4 T memory cells and gamma delta T cells were present at significantly lower levels in BE samples. Moreover, plasma cell and regulatory T cells were present at significantly higher levels in BE samples than in NE samples. INSR had the highest AUC values in ROC analysis. We identified 7 immune related genes and 4 different immune cells in our study, that may play vital roles in the occurrence and development of BE. Our findings improve the understanding of the molecular mechanisms of BE.

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

BACKGROUND

Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis.

METHODS

In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues.

RESULTS

A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues.

CONCLUSIONS

The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

The role of SPI1-TYROBP-FCER1G network in oncogenesis and prognosis of osteosarcoma, and its association with immune infiltration

Osteosarcoma is an aggressive malignant bone sarcoma worldwide. A causal gene network with specific functions underlying both the development and progression of OS was still unclear. Here we firstly identified the differentially expressed genes (DEGs) between control and OS samples, and then defined the hub genes and top clusters in the protein–protein interaction (PPI) network of these DEGs. By focusing on the hub gene TYROBP in the top 1 cluster, a conserved TYROBP co-expression network was identified. Then the effect of the network on OS overall survival was analyzed. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and Gene Set Enrichment Analysis (GSEA) were used to explore the functions of the network. XCell platform and ssGSEA algorithm were conducted to estimate the status of immune infiltration. ChEA3 platform, GSEA enrichment analysis, and Drug Pair Seeker (DPS) were used to predict the key transcription factor and its upstream signal. We identified the downregulated SPI1-TYROBP-FCER1G network in OS, which were significantly enriched in immune-related functions. We also defined a two-gene signature (SPI1/FCER1G) that can predict poorer OS overall survival and the attenuated immune infiltration when downregulated. The SPI1-TYROBP-FCER1G network were potentially initiated by transcription factor SPI1 and would lead to the upregulated CD86, MHC-II, CCL4/CXCL10/CX3CL1 and hence increased immune infiltrations. With this study, we could better explore the mechanism of OS oncogenesis and metastasis for developing new therapies.

Construction and validation of a novel gene signature for predicting the prognosis of osteosarcoma

Osteosarcoma (OS) is the most common type of primary malignant bone tumor. The high-throughput sequencing technology has shown potential abilities to illuminate the pathogenic genes in OS. This study was designed to find a powerful gene signature that can predict clinical outcomes. We selected OS cases with gene expression and survival data in the TARGET-OS dataset and GSE21257 datasets as training cohort and validation cohort, respectively. The univariate Cox regression and Kaplan–Meier analysis were conducted to determine potential prognostic genes from the training cohort. These potential prognostic genes underwent a LASSO regression, which then generated a gene signature. The harvested signature’s predictive ability was further examined by the Kaplan–Meier analysis, Cox analysis, and receiver operating characteristic (ROC curve). More importantly, we listed similar studies in the most recent year and compared theirs with ours. Finally, we performed functional annotation, immune relevant signature correlation identification, and immune infiltrating analysis to better study he functional mechanism of the signature and the immune cells’ roles in the gene signature’s prognosis ability. A seventeen-gene signature (UBE2L3, PLD3, SLC45A4, CLTC, CTNNBIP1, FBXL5, MKL2, SELPLG, C3orf14, WDR53, ZFP90, UHRF2, ARX, CORT, DDX26B, MYC, and SLC16A3) was generated from the LASSO regression. The signature was then confirmed having strong and stable prognostic capacity in all studied cohorts by several statistical methods. We revealed the superiority of our signature after comparing it to our predecessors, and the GO and KEGG annotations uncovered the specifically mechanism of action related to the gene signature. Six immune signatures, including PRF1, CD8A, HAVCR2, LAG3, CD274, and GZMA were identified associating with our signature. The immune-infiltrating analysis recognized the vital roles of T cells CD8 and Mast cells activated, which potentially support the seventeen-gene signature’s prognosis ability. We identified a robust seventeen-gene signature that can accurately predict OS prognosis. We identified potential immunotherapy targets to the gene signature. The T cells CD8 and Mast cells activated were identified linked with the seventeen-gene signature predictive power.

miR-6807-5p Inhibited the Odontogenic Differentiation of Human Dental Pulp Stem Cells Through Directly Targeting METTL7A

Background: Tooth tissue regeneration mediated by mesenchymal stem cells (MSCs) has become the most ideal treatment. Although the known regulatory mechanism and some achievements have been discovered, directional differentiation cannot effectively induce regeneration of tooth tissue. In this study, we intended to explore the function and mechanism of miR-6807-5p and its target gene METTL7A in odontogenic differentiation.

Methods: In this study, human dental pulp stem cells (DPSCs) were used. Alkaline phosphatase (ALP), Alizarin red staining (ARS), and calcium ion quantification were used to detect the odontogenic differentiation of miR-6807-5p and METTL7A. Real-time RT-PCR, western blot, dual-luciferase reporter assay, and pull-down assay with biotinylated miRNA were used to confirm that METTL7A was the downstream gene of miR-6807-5p. Protein mass spectrometry and co-immunoprecipitation (Co-IP) were used to detect that SNRNP200 was the co-binding protein of METTL7A.

Results: After mineralized induction, the odontogenic differentiation was enhanced in the miR-6807-5p-knockdown group and weakened in the miR-6807-5p-overexpressed group compared with the control group. METTL7A was the downstream target of miR-6807-5p. After mineralized induction, the odontogenic differentiation was weakened in the METTL7A-knockdown group and enhanced in the METTL7A-overexpressed group compared with the control group. SNRNP200 was the co-binding protein of METTL7A. The knockdown of SNRNP200 inhibited the odontogenic differentiation of DPSCs.

Conclusion: This study verified that miR-6807-5p inhibited the odontogenic differentiation of DPSCs. The binding site of miR-6807-5p was the 3′UTR region of METTL7A, which was silenced by miR-6807-5p. METTL7A promoted the odontogenic differentiation of DPSCs. SNRNP200, a co-binding protein of METTL7A, promoted the odontogenic differentiation of DPSCs.