scholarly journals Construction of competitive endogenous RNA network related to circular RNA and prognostic nomogram model in lung adenocarcinoma

2021 ◽  
Vol 18 (6) ◽  
pp. 9806-9821
Author(s):  
Pingping Song ◽  
◽  
Jing Chen ◽  
Xu Zhang ◽  
Xiaofeng Yin ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Regulatory Networks ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Circular Rna ◽  
Regulation Mechanism ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Validation Set ◽  
Competitive Endogenous Rna

<abstract> <p>Early researches have revealed that circular RNA (circRNA) had the potential of biomarkers and could affect tumor progression through regulatory networks. However, few research focused on the function of circRNA in lung adenocarcinoma and the regulation mechanism of competitive endogenous RNA. In present study, through differential expression analysis, 10 circRNAs, 98 miRNAs(microRNA) and 2497 mRNAs were screened. Based on the 10 circRNAs and related databases, a competitive endogenous RNA regulatory network (ceRNA network) containing 7 circRNAs, 13 miRNAs and 147 mRNAs was constructed. KEGG and GO analysis suggested that 147 mRNAs were obviously enriched in biological pathway related to LUAD. By constructing a PPI network, 12 hub genes were identified by MCODE. The result of survival analysis showed that 10 hub genes (BIRC5, MKI67, CENPF, RRM2, BUB1, MELK, CEP55, CDK1, NEK2, TOP2A) were significantly related to the survival of LUAD. We randomly divided 483 clinical data into two parts: train set and validation set. The train set was used for Cox regression analysis, 3 prognostic factors (stage, T, CDK1) were screened. The nomogram model was constructed based on stage, T and CDK1. The model was evaluated by ROC curve, calibration chart, Kaplan-Meier (KM) curve and validation set data. The results indicated that the model has good accuracy. Our study elucidated the regulatory mechanism of circRNA in lung adenocarcinoma, and the nomogram model also provided insight for the clinical analysis of lung adenocarcinoma.</p> </abstract>

A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Yongmei Wang ◽  
Guimin Zhang ◽  
Ruixian Wang
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Risk Score ◽  
Prognostic Model ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Functional Enrichment ◽  
Risk Scores ◽  
Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

scholarly journals Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Guomin Wu ◽  
Qihao Wang ◽  
Ting Zhu ◽  
Linhai Fu ◽  
Zhupeng Li ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Lung Adenocarcinoma ◽  
Clinical Features ◽  
Cox Regression ◽  
Differential Expression Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Immune Infiltration

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

scholarly journals A Novel Ferroptosis-Related Genes Model for Prognosis Prediction of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Fei Li ◽  
Dongcen Ge ◽  
Shu-lan Sun
Keyword(s):  
Regression Analysis ◽  
High Risk ◽  
Prediction Model ◽  
Lung Adenocarcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Low Risk ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background. Ferroptosis is a newly discovered form of cell death characterized by iron-dependent lipid peroxidation. The aim of this study is to investigate the relationship between ferroptosis and the prognosis of lung adenocarcinoma (LUAD).Methods. RNA-seq data was collected from the LUAD dataset of The Cancer Genome Altas (TCGA) database. We used ferroptosis-related genes as the basis, and identify the differential expression genes (DEGs) between cancer and paracancer. The univariate Cox regression analysis were used to screen the prognostic-related genes. We divided the patients into training and validation sets. Then, we screened out key genes and built a 5 genes prognostic prediction model by the applications of the least absolute shrinkage and selection operator (LASSO) 10-fold cross-validation and the multi-variate Cox regression analysis. We divided the cases by the median value of risk score and validated this model in the validation set. Meanwhile, we analyzed the somatic mutations, and estimated the score of immune infiltration in the high- and low-risk groups, as well as performed functional enrichment analysis of DEGs.Results. The result revealed that the high-risk score triggered the worse prognosis. The maximum area under curve (AUC) of the training set and the validation set of in this study was 0.7 and 0.69. Moreover, we integrated the age, gender, and tumor stage to construct the composite nomogram. The charts indicated that the AUC of cases with survival time of 1, 3 and 5 years are 0.698, 0.71 and 0.73. In addition, the mutation frequency of patients in the high-risk group was higher than that in the low-risk group. Simultaneously, DEGs were mainly enriched in ferroptosis-related pathways by analyzing the functional results.Conclusion. This study constructed a novel LUAD prognosis prediction model base on 5 ferroptosis-related genes, which can provide a prognostic evaluation tool for the clinical therapeutic decision.

scholarly journals Screening and Identification of Prognostic Tumor-Infiltrating Immune Cells and Genes of Endometrioid Endometrial Adenocarcinoma: Based on The Cancer Genome Atlas Database and Bioinformatics

2020 ◽  
Vol 10 ◽  
Author(s):  
Bingnan Chen ◽  
Di Wang ◽  
Jiapo Li ◽  
Yue Hou ◽  
Chong Qiao
Keyword(s):  
T Cells ◽  
Immune Cells ◽  
Cox Regression ◽  
Endometrial Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Regulation Mechanism ◽  
Differential Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis

BackgroundEndometrioid endometrial adenocarcinoma (EEA) is one of the most common tumors in the female reproductive system. With the further understanding of immune regulation mechanism in tumor microenvironment, immunotherapy is emerging in tumor treatment. However, there are few systematic studies on EEA immune infiltration.MethodsIn this study, prognostic tumor-infiltrating immune cells (TIICs) and related genes of EEA were comprehensively analyzed for the first time through the bioinformatics method with CIBERSORT algorithm as the core. Gene expression profile data were downloaded from the TCGA database, and the abundance ratio of TIICs was obtained. Kaplan–Meier analysis and Cox regression analysis were used to identify prognostic TIICs. EEA samples were grouped according to the risk score in Cox regression model. Differential analysis and functional enrichment analyses were performed on high- and low-risk groups to find survival-related hub genes, which were verified by Tumor Immune Estimation Resource (TIMER).ResultFour TIICs including memory CD4+ T cells, regulatory T cells, natural killer cells and dendritic cells were identified. And two hub gene modules were found, in which six hub genes including APOL1, CCL17, RBP4, KRT15, KRT71, and KRT79 were significantly related to overall survival and were closely correlated with some certain TIICs in the validation of TIMER.ConclusionIn this study, four prognostic TIICs and six hub genes were found to be closely related to EEA. These findings provided new potential targets for EEA immunotherapy.

scholarly journals Comprehensive Analysis of a Nine-Gene Signature Related to Tumor Microenvironment in Lung Adenocarcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Haihui Zhong ◽  
Jie Wang ◽  
Yaru Zhu ◽  
Yefeng Shen
Keyword(s):  
Lung Adenocarcinoma ◽  
Rna Sequencing ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Cox Regression ◽  
Survival Rates ◽  
Hub Genes ◽  
Term Survival ◽  
Cox Regression Analysis ◽  
Normal Tissues

Lung adenocarcinoma (LUAD) is the most common malignancy, leading to more than 1 million related deaths each year. Due to low long-term survival rates, the exploration of molecular mechanisms underlying LUAD progression and novel prognostic predictors is urgently needed to improve LUAD treatment. In our study, cancer-specific differentially expressed genes (DEGs) were identified using the robust rank aggregation (RRA) method between tumor and normal tissues from six Gene Expression Omnibus databases (GSE43458, GSE62949, GSE68465, GSE115002, GSE116959, and GSE118370), followed by a selection of prognostic modules using weighted gene co-expression network analysis. Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were applied to identify nine hub genes (CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6) that constructed a prognostic risk model. The RNA expressions of nine hub genes were validated in tumor and normal tissues by RNA-sequencing and single-cell RNA-sequencing, while immunohistochemistry staining from the Human Protein Atlas database showed consistent results in the protein levels. The risk model revealed that high-risk patients were associated with poor prognoses, including advanced stages and low survival rates. Furthermore, a multivariate Cox regression analysis suggested that the prognostic risk model could be an independent prognostic factor for LUAD patients. A nomogram that incorporated the signature and clinical features was additionally built for prognostic prediction. Moreover, the levels of hub genes were related to immune cell infiltration in LUAD microenvironments. A CMap analysis identified 13 small molecule drugs as potential agents based on the risk model for LUAD treatment. Thus, we identified a prognostic risk model including CBFA2T3, CR2, SEL1L3, TM6SF1, TSPAN32, ITGA6, MAPK11, RASA3, and TLR6 as novel biomarkers and validated their prognostic and predicted values for LUAD.

scholarly journals Identification of a circRNA-miRNA-mRNA Regulatory Network for Exploring Novel Therapeutic Options for Glioma

2021 ◽  
Author(s):  
Yi He ◽  
Yihong Chen ◽  
Yuxin Tong ◽  
Wenyong Long ◽  
Qing Liu
Keyword(s):  
Regulatory Network ◽  
Cox Regression ◽  
Circular Rna ◽  
Therapeutic Options ◽  
Functional Enrichment ◽  
Lasso Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Novel Therapeutic

Abstract Background: Glioma is the most common brain neoplasm with a poor prognosis. Circular RNA (circRNA) and their associated competing endogenous RNA (ceRNA) network play critical roles in the pathogenesis of glioma. However, the alteration of the circRNA-miRNA-mRNA regulatory network and its correlation with glioma therapy haven’t been systematically analyzed.Methods: With GEO, GEPIA2, circBank, CSCD, CircInteractome, mirWalk 2.0, and mirDIP 4.1, we constructed a circRNA–miRNA–mRNA network in glioma. LASSO regression and multivariate Cox regression analysis established a hub mRNA signature to assess the prognosis. GSVA was used to estimate the immune infiltration level. Potential anti-glioma drugs were forecasted using the cMap database and evaluated with GSEA using GEO data.Results: A ceNRA network of 7 circRNAs (hsa_circ_0030788/0034182/0000227/0018086/0000229/0036592/0002765), 15 miRNAs(hsa-miR-1200/1205/1248/1303/3925-5p/5693/581/586/599/607/640/647/6867-5p/767-3p/935), and 46 mRNAs (including 11 hub genes of ARHGAP11A, DRP2, HNRNPA3, IGFBP5, IP6K2, KLF10, KPNA4, NRP2, PAIP1, RCN1, and SEMA5A) was constructed. Functional enrichment showed they influenced majority of the hallmarks of tumors. Eleven hub genes were proven to be decent prognostic signatures for glioma in both TCGA and CGGA datasets. 46 LASSO regression significant genes were closely related to immune infiltration. Finally, five compounds (fulvestrant, tanespimycin, mifepristone, tretinoin, and harman) were predicted as potential treatments for glioma. Among them, mifepristone and tretinoin were proven to inhibit the cell cycle and DNA repair in glioma.Conclusions: This study highlights the potential pathogenesis of the circRNA-miRNA-mRNA regulatory network and identifies novel therapeutic options for glioma.

scholarly journals A Prediction Model Using Alternative Splicing Events and the Immune Microenvironment Signature in Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Liping Zhu ◽  
Zhiqiang Wang ◽  
Yilan Sun ◽  
Georgios Giamas ◽  
Justin Stebbing ◽  
...  
Keyword(s):  
Alternative Splicing ◽  
Lung Adenocarcinoma ◽  
Regulatory Networks ◽  
Immune Cell ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Microenvironment ◽  
Cox Regression Analysis ◽  
Gene And Protein Expression

BackgroundAlternative splicing (AS) is a gene regulatory mechanism that drives protein diversity. Dysregulation of AS is thought to play an essential role in cancer initiation and development. This study aimed to construct a prognostic signature based on AS and explore the role in the tumor immune microenvironment (TIME) in lung adenocarcinoma.MethodsWe analyzed transcriptome profiling and clinical lung adenocarcinoma data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq. Prognosis-related AS events were analyzed by univariate Cox regression analysis. Gene set enrichment analyses (GSEA) were performed for functional annotation. Prognostic signatures were identified and validated using univariate and multivariate Cox regression, LASSO regression, Kaplan–Meier survival analyses, and proportional hazards model. The context of TIME in lung adenocarcinoma was also analyzed. Gene and protein expression data of Cyclin-Dependent Kinase Inhibitor 2A (CDKN2A) were obtained from ONCOMINE and Human Protein Atlas. Splicing factor (SF) regulatory networks were visualized.ResultsA total of 19,054 survival-related AS events in lung adenocarcinoma were screened in 1,323 genes. Exon skip (ES) and mutually exclusive exons (ME) exhibited the most and fewest AS events, respectively. Based on AS subtypes, eight AS prognostic signatures were constructed. Patients with high-risk scores were associated with poor overall survival. A nomogram with good validity in prognostic prediction was generated. AUCs of risk scores at 1, 2, and 3 years were 0.775, 0.736, and 0.759, respectively. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. Low-risk patients had a higher StromalScore, ImmuneScore, and ESTIMATEScore. AS-based risk score signature was positively associated with CD8+ T cells. CDKN2A was also found to be a prognostic factor in lung adenocarcinoma. Finally, potential functions of SFs were determined by regulatory networks.ConclusionTaken together, our findings show a clear association between AS and immune cell infiltration events and patient outcome, which could provide a basis for the identification of novel markers and therapeutic targets for lung adenocarcinoma. SF networks provide information of regulatory mechanisms.

scholarly journals The Prognostic Value of Bone Morphogenetic Proteins and Their Receptors in Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Wangyang Meng ◽  
Han Xiao ◽  
Rong Zhao ◽  
Dong Li ◽  
Kuo Li ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Differential Expression ◽  
Bone Morphogenetic Proteins ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Significant Differential Expression ◽  
Cox Regression Analysis

BackgroundBone morphogenetic proteins (BMPs) regulate tumor progression via binding to their receptors (BMPRs). However, the expression and clinical significance of BMPs/BMPRs in lung adenocarcinoma remain unclear due to a lack of systematic studies.MethodsThis study screened differentially expressed BMPs/BMPRs (deBMPs/BMPRs) in a training dataset combining TCGA-LUAD and GTEx-LUNG and verified them in four GEO datasets. Their prognostic value was evaluated via univariate and multivariate Cox regression analyses. LASSO was performed to construct an initial risk model. Subsequently, after weighted gene co-expression network analysis (WGCNA), differential expression analysis, and univariate Cox regression analysis, hub genes co-expressed with differentially expressed BMPs/BMPRs were filtered out to improve the risk model and explore potential mechanisms. The improved risk model was re-established via LASSO combining hub genes with differentially expressed BMPs/BMPRs as the core. In the testing cohort including 93 lung adenocarcinoma patients, immunohistochemistry (IHC) was performed to verify BMP5 protein expression and its association with prognosis.ResultsBMP2, BMP5, BMP6, GDF10, and ACVRL1 were verified as downregulated in lung adenocarcinoma. Survival analysis identified BMP5 as an independent protective prognostic factor. We also found that BMP5 was significantly correlated with EGFR expression and mutations, suggesting that BMP5 may play a role in targeted therapy. The initial risk model containing only BMP5 showed a significant correlation (HR: 1.71, 95% CI: 1.28−2.28, p: 3e-04) but low prognostic accuracy (AUC of 1-year survival: 0.6, 3-year survival: 0.6, 5-year survival: 0.63). Seventy-nine hub genes co-expressed with BMP5 were identified, and their functions were enriched in cell migration and tumor metastasis. The re-established risk model showed greater prognostic correlation (HR: 2.58, 95% CI: 1.92–3.46, p: 0) and value (AUC of 1-year survival: 0.72, 3-year survival: 0.69, and 5-year survival: 0.68). IHC results revealed that BMP5 protein was also downregulated in lung adenocarcinoma and higher expression was markedly associated with better prognosis (HR: 0.44, 95% CI: 0.23–0.85, p: 0.0145).ConclusionBMP5 is a potential crucial target for lung adenocarcinoma treatment based on significant differential expression and superior prognostic value.

scholarly journals Comprehensive analysis of competitive endogenous RNA associated with immune infiltration in lung adenocarcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenjie Chen ◽  
Wen Li ◽  
Zhenkun Liu ◽  
Guangzhi Ma ◽  
Yunfu Deng ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Mast Cells ◽  
Correlation Analysis ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cerna Network ◽  
Monocytes And Macrophages ◽  
Competitive Endogenous Rna

AbstractTo identify the prognostic biomarker of the competitive endogenous RNA (ceRNA) and explore the tumor infiltrating immune cells (TIICs) which might be the potential prognostic factors in lung adenocarcinoma. In addition, we also try to explain the crosstalk between the ceRNA and TIICs to explore the molecular mechanisms involved in lung adenocarcinoma. The transcriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) database, and the hypergeometric correlation of the differently expressed miRNA-lncRNA and miRNA-mRNA were analyzed based on the starBase. In addition, the Kaplan–Meier survival and Cox regression model analysis were used to identify the prognostic ceRNA network and TIICs. Correlation analysis was performed to analysis the correlation between the ceRNA network and TIICs. In the differently expressed RNAs between tumor and normal tissue, a total of 190 miRNAs, 224 lncRNAs and 3024 mRNAs were detected, and the constructed ceRNA network contained 5 lncRNAs, 92 mRNAs and 10 miRNAs. Then, six prognostic RNAs (FKBP3, GPI, LOXL2, IL22RA1, GPR37, and has-miR-148a-3p) were viewed as the key members for constructing the prognostic prediction model in the ceRNA network, and three kinds of TIICs (Monocytes, Macrophages M1, activated mast cells) were identified to be significantly related with the prognosis in lung adenocarcinoma. Correlation analysis suggested that the FKBP3 was associated with Monocytes and Macrophages M1, and the GPI was obviously related with Monocytes and Macrophages M1. Besides, the LOXL2 was associated with Monocytes and Activated mast cells, and the IL22RA1 was significantly associated with Monocytes and Macrophages M1, while the GPR37 and Macrophages M1 was closely related. The constructed ceRNA network and identified Monocytes, Macrophages M1 and activated Mast cells are all prognostic factors for lung adenocarcinoma. Moreover, the crosstalk between the ceRNA network and TIICs might be a potential molecular mechanism involved.

scholarly journals Identification of Prognostic RBPs in Osteosarcoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110049
Author(s):  
Bei Li ◽  
Long Fang ◽  
Baolong Wang ◽  
Zengkun Yang ◽  
Tingbao Zhao
Keyword(s):  
Regression Analysis ◽  
Prognostic Factors ◽  
Differential Expression ◽  
Ribosome Biogenesis ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Malignant Tumors ◽  
Differential Expression Analysis ◽  
Cox Regression Analysis

Osteosarcoma often occurs in children and adolescents and causes poor prognosis. The role of RNA-binding proteins (RBPs) in malignant tumors has been elucidated in recent years. Our study aims to identify key RBPs in osteosarcoma that could be prognostic factors and treatment targets. GSE33382 dataset was downloaded from Gene Expression Omnibus (GEO) database. RBPs extraction and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological function of differential expression RBPs. Moreover, we constructed Protein-protein interaction (PPI) network and obtained key modules. Key RBPs were identified by univariate Cox regression analysis and multiple stepwise Cox regression analysis combined with the clinical information from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Risk score model was generated and validated by GSE16091 dataset. A total of 38 differential expression RBPs was identified. Go and KEGG results indicated these RBPs were significantly involved in ribosome biogenesis and mRNA surveillance pathway. COX regression analysis showed DDX24, DDX21, WARS and IGF2BP2 could be prognostic factors in osteosarcoma. Spearman’s correlation analysis suggested that WARS might be important in osteosarcoma immune infiltration. In conclusion, DDX24, DDX21, WARS and IGF2BP2 might play key role in osteosarcoma, which could be therapuetic targets for osteosarcoma treatment.

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