Dataset Information

Osteoclasts differential-related prognostic biomarker for osteosarcoma based on single cell, bulk cell and gene expression datasets.

ABSTRACT: Osteosarcoma (OS) is one of the most common primary bone malignant tumors. Osteoclasts have been shown to have a valuable role in OS. In the present study, we analyzed the differentiation states of osteoclasts in OS and their prognostic significance based on integrated scRNA-seq and bulk RNA-seq data. Osteoclasts in distinct differentiation states were characterized, and 661 osteoclasts differentiation-related genes (ODRGs) were obtained. ORDGs in distinct differentiation states were enriched in distinct functions and pathways. TPM1, S100A13, LOXL1, PSMD10, ST3GAL4, PEF1, SERPINE2, TUBB, FAM207A, TUBA1A, and DCN were identified as the significant survival-predicting ODRGs. We successfully developed a risk score model based on these survival-predicting ODRGs. In addition, we generated a nomogram applicable for clinical with both ODRGs signatures and clinicopathological parameters, and validated in OS cohorts to predict OS patient outcome. This study proposed and verified the important roles of osteoclasts differentiation in the prognosis of patients with OS, suggesting promising therapeutic targets for OS.

SUBMITTER: Shao H

PROVIDER: S-EPMC8932072 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Osteoclasts differential-related prognostic biomarker for osteosarcoma based on single cell, bulk cell and gene expression datasets.

Shao Haiyu H Ge Meng M Zhang Jun J Zhao Tingxiao T Zhang Shuijun S

BMC cancer 20220317 1

Osteosarcoma (OS) is one of the most common primary bone malignant tumors. Osteoclasts have been shown to have a valuable role in OS. In the present study, we analyzed the differentiation states of osteoclasts in OS and their prognostic significance based on integrated scRNA-seq and bulk RNA-seq data. Osteoclasts in distinct differentiation states were characterized, and 661 osteoclasts differentiation-related genes (ODRGs) were obtained. ORDGs in distinct differentiation states were enriched in ...[more]

PMID: 35300639

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Project description:BackgroundSingle cell sequencing can provide comprehensive information about gene expression in individual tumor cells, which can allow exploration of heterogeneity of malignant melanoma cells and identification of new anticancer therapeutic targets.MethodsSingle cell sequencing of 31 melanoma patients in GSE115978 was downloaded from the Gene Expression Omniniub (GEO) database. First, the limma package in R software was used to identify the differentially expressed metastasis related genes (MRGs). Next, we developed a prognostic MRGs biomarker in the cancer genome atlas (TCGA) by combining univariate cox analysis and the least absolute shrinkage and selection operator (LASSO) method and was further validated in another two independent datasets. The efficiency of MRGs biomarker in diagnosis of melanoma was also evaluated in multiple datasets. The pattern of somatic tumor mutation, immune infiltration, and underlying pathways were further explored. Furthermore, nomograms were constructed and decision curve analyses were also performed to evaluate the clinical usefulness of the nomograms.ResultsIn total, 41 MRGs were screened out from 1958 malignant melanoma cell samples in GSE115978. Next, a 5-MRGs prognostic marker was constructed and validated, which show more effective performance for the diagnosis and prognosis of melanoma patients. The nomogram showed good accuracies in predicting 3 and 5 years survival, and the decision curve of nomogram model manifested a higher net benefit than tumor stage and clark level. In addition, melanoma patients can be divided into high and low risk subgroups, which owned differential mutation, immune infiltration, and clinical features. The low risk subgroup suffered from a higher tumor mutation burden (TMB), and higher levels of T cells infiltrating have a significantly longer survival time than the high risk subgroup. Gene Set Enrichment Analysis (GSEA) revealed that the extracellular matrix (ECM) receptor interaction and epithelial mesenchymal transition (EMT) were the most significant upregulated pathways in the high risk group.ConclusionsWe identified a robust MRGs marker based on single cell sequencing and validated in multiple independent cohort studies. Our finding provides a new clinical application for prognostic and diagnostic prediction and finds some potential targets against metastasis of melanoma.

Project description:BackgroundDespite the comparatively low prevalence of osteosarcoma (OS) compared to other cancer types, metastatic OS has a poor overall survival rate of fewer than 30%. Accumulating data has shown the crucial functions of immunogenic cell death (ICD) in various cancers; nevertheless, the relationship between ICD and OS was not previously well understood. This research aims to determine the function of ICD in OS and construct an ICD-based prognostic panel.MethodsSingle cell RNA sequencing data from GSE162454 dataset distinguished malignant cells from normal cells in OS. The discrepancy in ICD scores and corresponding gene expression was intensively explored between malignant cells and normal cells. Using the RNA sequencing data of the TARGET-OS, GSE16091, GSE21257, and GSE39058 datasets, the molecular subtype of OS was determined by clustering seventeen ICD-related genes obtained from the literature. Differentially expressed genes (DEGs) between different molecular subtypes were identified to develop a novel ICD-associated prognostic panel.ResultsThe malignant cells had a remarkable decrease in the ICD scores and corresponding gene expression compared with normal cells. A total of 212 OS patients were successfully stratified into two subtypes: C1 and C2. C1-like OS patients were characterized by better prognostic outcomes, overexpression of ICD genes, activation of the ICD pathway, high inflitration abundance of immunocytes, and low expression levels of immune checkpoint genes (ICGs); however, the reverse is true in C2-like OS patients. Utilizing the limma programme in R, the DEGs between two subtypes were determined, and a 5-gene risk panel consisting of BAMBI, TMCC2, NOX4, DKK1, and CBS was developed through LASSO-Cox regression analysis. The internal- and external-verification cohorts were employed to verify the efficacy and precision of the risk panel. The AUC values of ROC curves indicated excellent prognostic prediction values of our risk panel.ConclusionsOverall, ICD represented a protective factor against OS, and our 5-gene risk panel serving as a biomarker could effectively evaluate the prognostic risk in patients with OS.

Project description: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.

Project description:BackgroundThe 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.MethodsThe 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.ResultsFCER1G 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.ConclusionsOur 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.

Project description:Background: Currently, there is few literature comprehensively analyzing landscape of cuproptosis-related genes (CRGs) in liver hepatocellular carcinoma (LIHC) with multiple omics approaches. Aims: Using comprehensive analysis, we aim to find out how CRGs works on LIHC. Method: With data from The Cancer Genome Atlas (TCGA) database, we constructed a prognostic prediction model for CGRs using LASSO regression analysis and performed immune infiltration analysis using the same dataset. To validate findings, we utilized RNA expression data from the International Cancer Genome Consortium (ICGC). Furthermore, we analyzed the enrichment and features of CRGs in epithelial cells using single-cell RNA sequencing (scRNA-seq) data. To validate the reliability of findings, we performed several experiments including RT-PCR, cloning formation assay, scratch assay, and Transwell assay. Result: We have constructed a high-precision risk scoring model composed of CRGs for predicting prognosis in TCGA-LIHC. Reliability of the risk prognosis model was confirmed through Kaplan-Meier curve analysis, time-dependent ROC analysis, and multivariate regression analysis. Furthermore, we found knocking down PDSS1 increased sensitivity of LIHC cells to copper ions, and both proliferation and migration abilities were significantly reduced. Finally, we comprehensively characterized the features of CRGs in LIHC through scRNA-seq. Conclusion: In this study, we introduce PDSS1 as a novel CRG in HCC. Utilizing scRNA-seq, we provide a comprehensive landscape of cuproptosis across various cell subtypes within the HCC tumor microenvironment. Furthermore, we detailed the characteristics of high PDSS1-expressing tumor cells, including their distinctive transcription factors, metabolic profiles, and interactions with different subtypes within the tumor microenvironment. This work not only elucidated the role of PDSS1 in HCC but also enhanced our understanding of cuproptosis dynamics during tumor progression.

Project description:Osteosarcoma is one of the most common malignant bone tumours in children. In this study, we aimed to construct a cuproptosis-related lncRNAs signature to predict the prognosis and immune landscape of osteosarcoma patients. Databases from TARGET were used to acquire osteosarcoma patient datasets, which included clinical information and RNA sequencing data. Cuproptosis-related lncRNAs was obtained by correlation analysis. Through univariate Cox regression analysis, prognosis-related lncRNAs were obtained. We used nonnegative matrix factorization clustering to identify potential molecular subgroups with different cuproptosis-related lncRNA expression patterns. The least absolute shrinkage and selection operator algorithm and multivariate Cox regression analysis were used to construct the prognostic signature. The ESTIMATE algorithm, Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes were applied to explore the underlying mechanisms in the immune landscape of osteosarcoma. We used gene set enrichment analysis to compare the different enrichments in the high-risk group and the low-risk group. Furthermore, we predicted the response to targeted drugs in patients with different risk groups. Using multivariable analysis, we developed a risk scoring model based on 7 long noncoding RNAs and calculated two molecular subgroups from osteosarcoma patients from the database. There is a better immune microenvironment in the low-risk group compared to the high-risk group. At the same time, the gene functional enrichment analysis based on the differently expressed genes obtained by grouping showed they were mainly related to immunity, indicating that cuproptosis-related lncRNAs may affect the prognosis of osteosarcoma by regulating immunity. Moreover, these patients in high-risk group were more susceptible to targeted drugs than the low-risk group. We identified a cuproptosis-related lncRNA prognostic signature for osteosarcoma and showed a close connection in terms of immunity. Moreover, we provided some potential targeted drugs for the treatment of osteosarcoma.

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Dataset Information

Osteoclasts differential-related prognostic biomarker for osteosarcoma based on single cell, bulk cell and gene expression datasets.

Publications

Osteoclasts differential-related prognostic biomarker for osteosarcoma based on single cell, bulk cell and gene expression datasets.

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