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 Identification of a circRNA-miRNA-mRNA regulatory network for exploring novel therapeutic options for glioma

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11894
Author(s):  
Yi He ◽  
Yihong Chen ◽  
Yuxin Tong ◽  
Wenyong Long ◽  
Qing Liu
Keyword(s):  
Regulatory Network ◽  
Cox Regression ◽  
Therapeutic Options ◽  
Functional Enrichment ◽  
Lasso Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Immune Infiltration ◽  
Cerna Network ◽  
Novel Therapeutic

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 ceRNA network of seven 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. Forty-six 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. Conclusion This study highlights the potential pathogenesis of the circRNA-miRNA-mRNA regulatory network and identifies novel therapeutic options for glioma.

scholarly journals 5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer

2021 ◽  
Vol 8 ◽  
Author(s):  
Ke Wang ◽  
Weibo Zhong ◽  
Zining Long ◽  
Yufei Guo ◽  
Chuanfan Zhong ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Regression Analysis ◽  
Biochemical Recurrence ◽  
Prognostic Model ◽  
Cox Regression ◽  
Functional Enrichment ◽  
Lasso Regression ◽  
Clinical Value ◽  
Cox Regression Analysis

The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p < 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA–protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, MAFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.

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>

scholarly journals A Five-Gene Signature for Recurrence Prediction of Hepatocellular Carcinoma Patients

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zeyu Wang ◽  
Ningning Zhang ◽  
Jiayu Lv ◽  
Cuihua Ma ◽  
Jie Gu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Early Stage ◽  
Gene Signature ◽  
Functional Enrichment ◽  
High Recurrence Rate ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Recurrence Prediction

Background. Hepatocellular carcinoma (HCC) is one of the most aggressive malignancies with poor prognosis. There are many selectable treatments with good prognosis in Barcelona Clinic Liver Cancer- (BCLC-) 0, A, and B HCC patients, but the most crucial factor affecting survival is the high recurrence rate after treatments. Therefore, it is of great significance to predict the recurrence of BCLC-0, BCLC-A, and BCLC-B HCC patients. Aim. To develop a gene signature to enhance the prediction of recurrence among HCC patients. Materials and Methods. The RNA expression data and clinical data of HCC patients were obtained from the Gene Expression Omnibus (GEO) database. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to screen primarily prognostic biomarkers in GSE14520. Multivariate Cox regression analysis was introduced to verify the prognostic role of these genes. Ultimately, 5 genes were demonstrated to be related with the recurrence of HCC patients and a gene signature was established. GSE76427 was adopted to further verify the accuracy of gene signature. Subsequently, a nomogram based on gene signature was performed to predict recurrence. Gene functional enrichment analysis was conducted to investigate the potential biological processes and pathways. Results. We identified a five-gene signature which performs a powerful predictive ability in HCC patients. In the training set of GSE14520, area under the curve (AUC) for the five-gene predictive signature of 1, 2, and 3 years were 0.813, 0.786, and 0.766. Then, the relative operating characteristic (ROC) curves of five-gene predictive signature were verified in the GSE14520 validation set, the whole GSE14520, and GSE76427, showed good performance. A nomogram comprising the five-gene signature was built so as to show a good accuracy for predicting recurrence-free survival of HCC patients. Conclusion. The novel five-gene signature showed potential feasibility of recurrence prediction for early-stage HCC.

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 Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Rujia Qin ◽  
Wen Peng ◽  
Xuemin Wang ◽  
Chunyan Li ◽  
Yan Xi ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Lasso Regression ◽  
Hub Genes ◽  
Dynamic Regulation ◽  
Cox Regression Analysis

Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein–protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify “real” hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.

scholarly journals Identification of Hub Genes Associated With Immune Infiltration and Predict Prognosis in Hepatocellular Carcinoma via Bioinformatics Approaches

2021 ◽  
Vol 11 ◽  
Author(s):  
Huaping Chen ◽  
Junrong Wu ◽  
Liuyi Lu ◽  
Zuojian Hu ◽  
Xi Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Cell Division ◽  
Immune Cell ◽  
Cox Regression ◽  
Close Association ◽  
Functional Enrichment ◽  
Microtubule Assembly ◽  
Hub Genes ◽  
Immune Infiltration

AimsIn the cancer-related research field, there is currently a major need for a greater number of valuable biomarkers to predict the prognosis of hepatocellular carcinoma (HCC). In this study, we aimed to screen hub genes related to immune cell infiltration and explore their prognostic value for HCC.MethodsWe analyzed five datasets (GSE46408, GSE57957, GSE74656, GSE76427, and GSE87630) from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). A protein–protein interaction network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes; then, the hub genes were identified. Functional enrichment of the genes was performed on the Metascape website. Next, the expression of these hub genes was validated in several databases, including Oncomine, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Human Protein Atlas. We explored the correlations between the hub genes and infiltrated immune cells in the TIMER2.0 database. The survival curves were generated in GEPIA2, and the univariate and multivariate Cox regression analyses were performed using TIMER2.0.ResultsThe top ten hub genes [DNA topoisomerase II alpha (TOP2A), cyclin B2 (CCNB2), protein regulator of cytokinesis 1 (PRC1), Rac GTPase-activating protein 1 (RACGAP1), aurora kinase A (AURKA), cyclin-dependent kinase inhibitor 3 (CDKN3), nucleolar and spindle-associated protein 1 (NUSAP1), cell division cycle-associated 5 (CDCA5), abnormal spindle microtubule assembly (ASPM), and non-SMC condensin I complex subunit G (NCAPG)] were identified in subsequent analysis. These genes are most markedly enriched in cell division, suggesting their close association with tumorigenesis. Multi-database analyses validated that the hub genes were upregulated in HCC tissues. All hub genes positively correlated with several types of immune infiltration, including B cells, CD4+ T cells, macrophages, and dendritic cells. Furthermore, these hub genes served as independent prognostic factors, and the expression of these hub genes combing with the macrophage levels could help predict an unfavorable prognosis of HCC.ConclusionIn sum, these hub genes (TOP2A, CCNB2, PRC1, RACGAP1, AURKA, CDKN3, NUSAP1, CDCA5, ASPM, and NCAPG) may be pivotal markers for prognostic prediction as well as potentially work as targets for immune-based intervention strategies in HCC.

scholarly journals A Panel of Five-lncRNA Signature as a Potential Biomarker for Predicting Survival in Gastric and Thoracic Cancers

2021 ◽  
Vol 12 ◽  
Author(s):  
Jiayue Shao ◽  
Wei Lyu ◽  
Jiehao Zhou ◽  
Wenhui Xu ◽  
Dandan Wang ◽  
...  
Keyword(s):  
Cox Regression ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Diagnosis And Treatment ◽  
Risk Scores ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Potential Biomarker ◽  
The Relationship

Dysfunctional long non-coding RNAs (lncRNAs) have been found to have carcinogenic and/or tumor inhibitory effects in the development and progression of cancer, suggesting their potential as new independent biomarkers for cancer diagnosis and prognosis. The exploration of the relationship between lncRNAs and the overall survival (OS) of different cancers opens up new prospects for tumor diagnosis and treatment. In this study, we established a five-lncRNA signature and explored its prognostic efficiency in gastric cancer (GC) and several thoracic malignancies, including breast invasive carcinoma (BRCA), esophageal carcinoma, lung adenocarcinoma, lung squamous cell carcinoma (LUSC), and thymoma (THYM). Cox regression analysis and lasso regression were used to evaluate the relationship between lncRNA expression and survival in different cancer datasets from GEO and TCGA. Kaplan-Meier survival curves indicated that risk scores characterized by a five-lncRNA signature were significantly associated with the OS of GC, BRCA, LUSC, and THYM patients. Functional enrichment analysis showed that these five lncRNAs are involved in known biological pathways related to cancer pathology. In conclusion, the five-lncRNA signature can be used as a prognostic marker to promote the diagnosis and treatment of GC and thymic malignancies.

scholarly journals Integration Analysis of Differential Expressed Genes Regulated by Differential Methylated Regions for Predicting Prognosis in Human Colon Cancer

2020 ◽  
Author(s):  
Xiaowen Peng ◽  
Dong Dong ◽  
Wentao Ou ◽  
Yongke Xie ◽  
Yuqi Luo
Keyword(s):  
Dna Methylation ◽  
Colon Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
Kegg Pathway ◽  
Functional Enrichment ◽  
Methylation Data ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Pathway Enrichment

Abstract Background Colon cancer is a leading cause of cancer-associated death globally, and numerous evidences show that different expressed gens (DEGs) regulated by differential methylated regions (DMRs) act an important role in tumor biology. However, the specific regulatory mechanism of DEGs related to DERs in colonic carcinogenesis is still unclear.Materials and methods RNA sequencing data and DNA methylation data of 455 colon adenocarcinoma (COAD) cases and 41 normal controls were downloaded from The Cancer Genomic Atlas (TCGA) to investigate the significant DEGs and DMRs. Gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID database. To identify the hub genes regulated by methylation, univariate cox and multivariate cox regression analyses were concluded. Furthermore, Riskscore and nomogram were built to identify the prognosis prediction power of the hub genes in colon cancer patients. Results A total of 133 DEGs regulated by DMRs were identified through analyzing RNA-seq data and DNA methylation data from TCGA; GO functional enrichment and KEGG pathway enrichment analysis showed that the genes involved in the initiation and progression of colon cancer. Univariate cox regression analysis and multivariate cox regression analysis focused on the 7 hub genes associated with overall survival, whose expression negatively correlated with their methylated level; Riskscore and nomogram model showed that the hub gens served as potential biomarker for the prognosis prediction of colon cancer patient. Conclusion Our funding suggests that the DEGs regulated by DMRs involve in the carcinogenesis and development of colon cancer, and the aberrant methylated DEGs associated with overall survival of patients may be potential diagnosis and therapeutic targets for colon cancer.

scholarly journals A Five-Immune-Related lncRNA Signature Predicts Survival of Patients With Osteosarcoma

2021 ◽  
Author(s):  
Jie Huang ◽  
Hongyi Lai ◽  
Wentao Qin ◽  
Zhandong Bo ◽  
Zhen Tan ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Data Matrix ◽  
Lasso Regression ◽  
Training Set ◽  
Cox Regression Analysis ◽  
Validation Set

Abstract Background: Osteosarcoma (OS) is the most common primary solid malignant bone tumor, and its metastasis is a prominent cause of high mortality in patients.Methods: A risk signature was constructed based on re-annotating the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) data matrix, of the lncRNAs related to OS prognosis and immunity. From the OS transcription data, which is downloaded from the TARGET, the 1126 lncRNAs those harbour co-expressions with immunity genes were selected by Pearson correlation test and later divided into the training set (n=44) and validation set (n=41) with the caret package of R. With the training set we build the model related to Osteosarcoma prognosis by the univariate and multivariate Cox, and the Lasso regression analysis, and in combination with the clinical factors we conducted the multivariate Cox regression analysis to build the 1-year, 3-year and 5-year survival rate nomograms. Afterwards, we validated the ROC and the calibration curve of the subjects with the validation set and the whole dataset. Lastly, we performed functional enrichment analysis with the GSEA, GO and KEGG to figure out the biological functions of the prognosis genes.Results: The training set was performed in univariate and multivariate Cox regression analysis, identifying 25 lncRNAs correlated with prognosis. Eleven lncRNAs were selected by the least absolute shrinkage and selection operator (LASSO) regression for multivariate cox analysis and Kaplan-Meier (KM) survival analysis. Finally, lncRNAs (RP11-69E11.4, SNHG6, MIR210HG, RP11-750H9.5 and CTD-2341M24.1) risk signature was constructed, and the validation set and the whole dataset were used to evaluate the prediction stability and accuracy of the signature. The survival times of high- and low-risk groups were significantly different in the training set, validation set and the whole dataset. Further, function enrichment and gene set enrichment analysis revealed that the lncRNAs in the signature may affect the proliferation, migration, chemotaxis and combination of Osteosarcoma-related immune cells, and involve in every pathways of OS metabolism. Conclusion: The five lncRNAs survival risk signature could potentially predict the prognosis of OS patients, additionally, may provide novel insights for future clinical diagnosis and treatment of OS.

Sign in / Sign up

Export Citation Format

Share Document