OS1.2 Development and Validation of a RNA-Seq Based Prognostic Signature in Neuroblastoma

Abstract BACKGROUND Credible prognostic stratification remains a challenge for neuroblastoma (NBL) with variable clinical manifestations. RNA expression signatures might predict the outcomes; notwithstanding, independent cross-platform validation is still rare. MATERIAL AND METHODS expression data were obtained from NBL patients and then analyzed. In TARGET-NBL data, an RNA-based prognostic signature was developed and validated. Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve. Functional enrichment analysis of the RNAs was conducted using bioinformatics methods. RESULTS A total of 1,119 differentially expressed RNAs and 149 prognosis-related RNAs were identified sequentially. Then, in the training cohort, 12 RNAs were identiﬁed as signiﬁcantly associated with overall survival (OS) and were combined to develop a model that stratified NBL patients into low- and high-risk groups. Twelve RNA signature high-risk patients had poorer OS in the training cohort (n = 105, Hazard Ratios (HR)= 0.10 (0.05–0.20), P < 0.001) and in the validation cohort (n = 44, HR = 0.25 (0.09–0.69), P = 0.008). ROC curve analysis also showed that both the training and validation cohorts performed well in predicting OS (12-month AUC values of 0.852 and 0.438, 36-month AUC values of 0.824 and 0.737, and 60-month AUC values of 0.802 and 0.702, respectively). Moreover, these 12 RNAs may be involved in certain events that are known to be associated with NBL through functional enrichment analysis. CONCLUSION This study identiﬁed and validated a novel 12-RNA prognostic signature to reliably distinguish NBL patients at low and high risk of death. Further larger, multicenter prospective studies are desired to validate this model.

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Prognostic Value of a Novel Signature With Nine Hepatitis C Virus-Induced Genes in Hepatic Cancer by Mining GEO and TCGA Databases

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.648279 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jianming Wei ◽

Bo Wang ◽

Xibo Gao ◽

Daqing Sun

Keyword(s):

Hepatitis C ◽

High Risk ◽

Risk Group ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Hepatic Cancer ◽

Prognostic Signature ◽

Kegg Pathways ◽

Induced Genes

BackgroundHepatitis C virus-induced genes (HCVIGs) play a critical role in regulating tumor development in hepatic cancer. The role of HCVIGs in hepatic cancer remains unknown. This study aimed to construct a prognostic signature and assess the value of the risk model for predicting the prognosis of hepatic cancer.MethodsDifferentially expressed HCVIGs were identified in hepatic cancer data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases using the library (“limma”) package of R software. The protein–protein interaction (PPI) network was constructed using the Cytoscape software. Functional enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate and multivariate Cox proportional hazard regression analyses were applied to screen for prognostic HCVIGs. The signature of HCVIGs was constructed. Gene Set Enrichment Analysis (GSEA) compared the low-risk and high-risk groups. Finally, the International Cancer Genome Consortium (ICGC) database was used to validate this prognostic signature. Polymerase chain reaction (PCR) was performed to validate the expression of nine HCVIGs in the hepatic cancer cell lines.ResultsA total of 143 differentially expressed HCVIGs were identified in TCGA hepatic cancer dataset. Functional enrichment analysis showed that DNA replication was associated with the development of hepatic cancer. The risk score signature was constructed based on the expression of ZIC2, SLC7A11, PSRC1, TMEM106C, TRAIP, DTYMK, FAM72D, TRIP13, and CENPM. In this study, the risk score was an independent prognostic factor in the multivariate Cox regression analysis [hazard ratio (HR) = 1.433, 95% CI = 1.280–1.605, P < 0.001]. The overall survival curve revealed that the high-risk group had a poor prognosis. The Kaplan–Meier Plotter online database showed that the survival time of hepatic cancer patients with overexpression of HCVIGs in this signature was significantly shorter. The prognostic signature-associated GO and KEGG pathways were significantly enriched in the risk group. This prognostic signature was validated using external data from the ICGC databases. The expression of nine prognostic genes was validated in HepG2 and LO-2.ConclusionThis study evaluates a potential prognostic signature and provides a way to explore the mechanism of HCVIGs in hepatic cancer.

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Identification of a Prognostic Six-Immune-Gene Signature and a Nomogram Model for Uveal Melanoma

10.21203/rs.3.rs-1148784/v1 ◽

2022 ◽

Author(s):

Binghua Yang ◽

Yuxia Fan ◽

Renlong Liang ◽

Yi Wu ◽

Aiping Gu

Keyword(s):

High Risk ◽

Uveal Melanoma ◽

Risk Score ◽

Risk Group ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Signature ◽

Functional Enrichment ◽

Immune Gene ◽

Prognostic Signature

Abstract Background: To identify an immune-related prognostic signature and find potential therapeutic targets for uveal melanoma. Methods: The RNA-sequencing data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. The prognostic six-immune-gene signature was constructed through least absolute shrinkage and selection operator and multi-variate Cox regression analyses. Functional enrichment analysis and single sample GSEA were carried out. In addition, a nomogram model established by integrating clinical variables and this signature risk score was also constructed and evaluated.Results: We obtained 130 prognostic immune genes, and six of them were selected to construct a prognostic signature in the TCGA uveal melanoma dataset. Patients were classified into high-risk and low-risk groups according to a median risk score of this signature. High-risk group patients had poorer overall survival in comparison to the patients in the low-risk group (p < 0.001). These findings were further validated in two external GEO datasets. A nomogram model proved to be a good classifier for uveal melanoma by combining this signature. Both functional enrichment analysis and single sample GSEA analysis verified that this signature was truly correlated with immune system. In addition, in vitro cell experiments results demonstrated the consistent trend of our computational findings.Conclusion: Our newly identified six-immune-gene signature and a nomogram model could be used as meaningful prognostic biomarkers, which might provide uveal melanoma patients with individualized clinical prognosis prediction and potential novel treatment targets.

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A prognostic miRNA based signature in early-stage HER2-positive breast cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e12600 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e12600-e12600

Author(s):

Anna Adam-Artigues ◽

Miguel Angel Beltran ◽

Juan Antonio Carbonell-Asins ◽

Sheila Zuñiga ◽

Santiago Moragon ◽

...

Keyword(s):

Systemic Treatment ◽

Early Stage ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Risk Groups ◽

Low Risk ◽

Functional Enrichment ◽

Rank Test ◽

Prognostic Signature ◽

Log Rank Test

e12600 Background: In early-stage HER2+ breast cancer (BC), escalation or de-escalation of systemic treatment is an unmet need. Integration of promising biomarkers into risk scoring will further help progressing in the field. We aim to develop a prognostic signature that integrates two miRNAs (A and B) and quantitative and qualitative clinical variables in patients diagnosed with HER2+ BC. Methods: This study was conducted in a retrospective cohort of 45 HER2+ BC patients. Patients received standard treatment for localized disease. We calculated a prognostic signature for disease-free survival (DFS) using principal components analysis for mixed data combining clinicopathological data (Ki67 and axillary lymph node [pN0, pN1, pN2, pN3]) and expression of two microRNAs (we used mir-16 as housekeeping). Multiple DFS prognostic signatures were calculated and goodness of fit was evaluated by means of Akaike’s Information Criterion (AIC) to perform Cox model selection. Signature was then dichotomized into “high risk” and “low risk” using maximally selected Log-Rank statistics by Hothorn and Lausen, as method for optimal cut-off. Kaplan-Meier curves, Log-Rank test and Breslow test were used to ascertain statistical differences in the probability of DFS between high and low risk groups. MiRNA targeted genes were selected and used to perform functional enrichment analysis with the KEGG pathway database. To select significant terms/pathways, p-values were adjusted by the Benjamini-Hochberg method (p < 0.05). Results: MiR-A and miR-B expression was higher in primary tumor of patients who relapse compared to those free of disease after treatment (p = 0.018 and 0.004, respectively). Both miRNAs were strongly correlated (r = 0.84). This signature was significantly associated with relapse of the disease (HR 1.72; CI 95%: 1.243–2.382; p < 0.01, AIC = 114.02). The optimal cut-off of this score was obtained and patients were classified into high and low risk groups. Median DFS of the high-risk was 44 months while it has been not reached yet across the low risk after a median follow-up of 67 months (HR 8.39; p = 0.005, AIC = 111.784). Significant differences in survival between both groups were found (log rank test p < 0.001; Breslow test p = 0.002). miR-A and miR-B functional enrichment analysis returned 55 significant pathways. Interestingly, P53 pathway, apoptosis and cell cycle which are closely related to tumorigenesis and treatment response, were in the top 5 enriched pathways. Conclusions: Both miRNAs included in this signature are related to important biological pathways associated to BC progression. Our new prognostic signature identifies patients with early-stage, HER2+ BC who might be candidates for escalated or de-escalated systemic treatment. This signature was able to classify patients for DFS in high or low risk groups at the moment of BC diagnosis. Further investigations to validate the value of this new signature are on-going.

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Identification of an m6A-Related lncRNA Signature for Predicting the Prognosis in Patients With Kidney Renal Clear Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.663263 ◽

2021 ◽

Vol 11 ◽

Author(s):

JunJie Yu ◽

WeiPu Mao ◽

Si Sun ◽

Qiang Hu ◽

Can Wang ◽

...

Keyword(s):

Clear Cell ◽

Clear Cell Carcinoma ◽

Enrichment Analysis ◽

Roc Curves ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Prognostic Signature ◽

Training Set ◽

Testing Set

PurposeThis study aimed to construct an m6A-related long non-coding RNAs (lncRNAs) signature to accurately predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data obtained from The Cancer Genome Atlas (TCGA) database.MethodsThe KIRC patient data were downloaded from TCGA database and m6A-related genes were obtained from published articles. Pearson correlation analysis was implemented to identify m6A-related lncRNAs. Univariate, Lasso, and multivariate Cox regression analyses were used to identifying prognostic risk-associated lncRNAs. Five lncRNAs were identified and used to construct a prognostic signature in training set. Kaplan–Meier curves and receiver operating characteristic (ROC) curves were applied to evaluate reliability and sensitivity of the signature in testing set and overall set, respectively. A prognostic nomogram was established to predict the probable 1-, 3-, and 5-year overall survival of KIRC patients quantitatively. GSEA was performed to explore the potential biological processes and cellular pathways. Besides, the lncRNA/miRNA/mRNA ceRNA network and PPI network were constructed based on weighted gene co-expression network analysis (WGCNA). Functional Enrichment Analysis was used to identify the biological functions of m6A-related lncRNAs.ResultsWe constructed and verified an m6A-related lncRNAs prognostic signature of KIRC patients in TCGA database. We confirmed that the survival rates of KIRC patients with high-risk subgroup were significantly poorer than those with low-risk subgroup in the training set and testing set. ROC curves indicated that the prognostic signature had a reliable predictive capability in the training set (AUC = 0.802) and testing set (AUC = 0.725), respectively. Also, we established a prognostic nomogram with a high C-index and accomplished good prediction accuracy. The lncRNA/miRNA/mRNA ceRNA network and PPI network, as well as functional enrichment analysis provided us with new ways to search for potential biological functions.ConclusionsWe constructed an m6A-related lncRNAs prognostic signature which could accurately predict the prognosis of KIRC patients.

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Construction of a long noncoding RNA-based competing endogenous RNA network and prognostic signatures of left- and right-side colon cancer

Cancer Cell International ◽

10.1186/s12935-021-01901-3 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ke-zhi Li ◽

Yi-xin Yin ◽

Yan-ping Tang ◽

Long Long ◽

Ming-zhi Xie ◽

...

Keyword(s):

Colon Cancer ◽

Prognostic Value ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Molecular Characteristics ◽

Prognostic Signature ◽

Cerna Network ◽

Colon Cancers ◽

Cancer Tissues

Abstract Background Cancers located on the right and left sides of the colon have distinct clinical and molecular characteristics. This study aimed to explore the regulatory mechanisms of location-specific long noncoding RNAs (lncRNAs) as competing endogenous RNAs (ceRNAs) in colon cancer and identify potential prognostic biomarkers. Method Differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs) between right- and left-side colon cancers were identified by comparing RNA sequencing profiles. Functional enrichment analysis was performed for the DEGs, and a ceRNA network was constructed. Associations between DELs and patient survival were examined, and a DEL-based signature was constructed to examine the prognostic value of these differences. Clinical colon cancer tissues and Gene Expression Omnibus (GEO) datasets were used to validate the results. Results We identified 376 DELs, 35 DEMs, and 805 DEGs between right- and left-side colon cancers. The functional enrichment analysis revealed the functions and pathway involvement of DEGs. A ceRNA network was constructed based on 95 DEL–DEM–DEG interactions. Three DELs (LINC01555, AC015712, and FZD10-AS1) were associated with the overall survival of patients with colon cancer, and a prognostic signature was established based on these three DELs. High risk scores for this signature indicated poor survival, suggesting that the signature has prognostic value for colon cancer. Examination of clinical colon cancer tissues and GEO dataset analysis confirmed the results. Conclusion The ceRNA regulatory network suggests roles for location-specific lncRNAs in colon cancer and allowed the development of an lncRNA-based prognostic signature, which could be used to assess prognosis and determine treatment strategies in patients with colon cancer.

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Identification of an epithelial-mesenchymal transition-related lncRNA prognostic signature for patients with glioblastoma

Scientific Reports ◽

10.1038/s41598-021-03213-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

XinJie Yang ◽

Sha Niu ◽

JiaQiang Liu ◽

Jincheng Fang ◽

ZeYu Wu ◽

...

Keyword(s):

Immune Cell ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Prognostic Signature ◽

Mesenchymal Transition

AbstractGlioblastoma (GBM) is a strikingly heterogeneous and lethal brain tumor with very poor prognosis. LncRNAs play critical roles in the tumorigenesis of GBM through regulation of various cancer-related genes and signaling pathways. Here, we focused on the essential role of EMT and identified 78 upregulated EMT-related genes in GBM through differential expression analysis and Gene set enrichment analysis (GSEA). A total of 301 EMT-related lncRNAs were confirmed in GBM through Spearman correlation analysis and a prognostic signature consisting of seven EMT-related lncRNAs (AC012615.1, H19, LINC00609, LINC00634, POM121L9P, SNHG11, and USP32P3) was established by univariate and multivariate Cox regression analyses. Significantly, Kaplan–Meier analysis and receiver-operating-characteristic (ROC) curve validated the accuracy and efficiency of the signature to be satisfactory. Quantitative real-time (qRT)-PCR assay demonstrated the expression alterations of the seven lncRNAs between normal glial and glioma cell lines. Functional enrichment analysis revealed multiple EMT and metastasis-related pathways were associated with the EMT-related lncRNA prognostic signature. In addition, we observed the degree of immune cell infiltration and immune responses were significantly increased in high-risk subgroup compared with low-risk subgroup. In conclusion, we established an effective and robust EMT-related lncRNA signature which was expected to predict the prognosis and immunotherapy response for GBM patients.

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Co-Expression Network Revealed the Potential Regulatory Mechanism of Lncrnas in Relapse Hepatocellular Carcinoma.

10.21203/rs.3.rs-387093/v1 ◽

2021 ◽

Author(s):

Yuan Fang ◽

Yang Yang ◽

XiaoLi Zhang ◽

Na Li ◽

Bo Yuan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Clinical Samples ◽

Survival Prediction ◽

Cancer Type ◽

Gene Modules ◽

Prediction Functions

Abstract Background: The mechanistic basis for the relapse of hepatocellular carcinoma (HCC) remains poorly understood. Recent research has highlighted the important roles of long non-coding RNAs (lncRNAs) in HCC. However, there are only a few studies on lncRNAs associated with the relapse of HCC.Methods:We analyzed lncRNA and mRNA profiles in the GSE101432 dataset associated with HCC relapse. The differentially expressed lncRNAs and mRNAs were used to construct an lncRNA-mRNA co-expression network. Weighted gene co-expression network analysis followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted on the database. Furthermore, correlation and survival analyses were performed using The Cancer Genome Atlas database, and the clinical samples were verified by qRT-PCR.Results:In this study, lncRNAs and mRNAs associated with HCC recurrence were identified. Two gene modules were found to be closely linked to HCC relapse. The functional enrichment analysis results of lncRNAs and co-expression mRNAs indicated that they were closely related to the recurrence of HCC. In addition, we verified that the overall survival and recurrence-free survival of these genes in HCC have survival prediction functions. In total, we identified and validated two lncRNAs (LINC00941 and LINC00668) and six mRNAs (LOX, MICB, OTX1, BAIAP2L2, KCTD17, NDUFA4L2) associated with HCC relapse.Conclusion: In summary, we identified the key gene modules and central genes associated with recurrent HCC, and constructed lncRNA-mRNA networks related to this cancer type. These results provide a foundation for future basic research on the mechanism of recurrent liver cancer.

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Co-Expression Network Revealed The Potential Regulatory Mechanism of lncRNAs In Relapse Hepatocellular Carcinoma.

10.21203/rs.3.rs-387093/v2 ◽

2021 ◽

Author(s):

Yuan Fang ◽

Yang Yang ◽

XiaoLi Zhang ◽

Na Li ◽

Bo Yuan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Clinical Samples ◽

Survival Prediction ◽

Cancer Type ◽

Gene Modules ◽

Prediction Functions

Abstract Background: The mechanistic basis for the relapse of hepatocellular carcinoma (HCC) remains poorly understood. Recent research has highlighted the important roles of long non-coding RNAs (lncRNAs) in HCC. However, there are only a few studies on lncRNAs associated with the relapse of HCC.Methods:We analyzed lncRNA and mRNA profiles in the GSE101432 dataset associated with HCC relapse. The differentially expressed lncRNAs and mRNAs were used to construct a lncRNA-mRNA co-expression network. Weighted gene co-expression network analysis followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted on the database. Furthermore, correlation and survival analyses were performed using The Cancer Genome Atlas database, and the clinical samples were verified by qRT-PCR.Results:In this study, lncRNAs and mRNAs associated with HCC relapse were identified. Two gene modules were found to be closely linked to HCC relapse. The functional enrichment analysis results of lncRNAs and co-expression mRNAs indicated that they were closely related to the relapse of HCC. In addition, we verified that the overall survival and recurrence-free survival of these genes in HCC have survival prediction functions. In total, we identified and validated two lncRNAs (LINC00941 and LINC00668) and six mRNAs (LOX, MICB, OTX1, BAIAP2L2, KCTD17, NDUFA4L2) associated with HCC relapse.Conclusion: In summary, we identified the key gene modules and central genes associated with relapse of HCC, and constructed lncRNA-mRNA networks related to this cancer type. These results provide a foundation for future basic research on the mechanism of relapse of HCC.

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LncRNA SNHG8 is identified as a key regulator of acute myocardial infarction by RNA-seq analysis

Lipids in Health and Disease ◽

10.1186/s12944-019-1142-0 ◽

2019 ◽

Vol 18 (1) ◽

Cited By ~ 1

Author(s):

Liu-An Zhuo ◽

Yi-Tao Wen ◽

Yong Wang ◽

Zhi-Fang Liang ◽

Gang Wu ◽

...

Keyword(s):

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Roc Curve ◽

Regulatory Networks ◽

Pairwise Comparison ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Diagnostic Value ◽

Functional Enrichment ◽

Rna Seq

Abstract Background Long noncoding RNAs (lncRNAs) are involved in numerous physiological functions. However, their mechanisms in acute myocardial infarction (AMI) are not well understood. Methods We performed an RNA-seq analysis to explore the molecular mechanism of AMI by constructing a lncRNA-miRNA-mRNA axis based on the ceRNA hypothesis. The target microRNA data were used to design a global AMI triple network. Thereafter, a functional enrichment analysis and clustering topological analyses were conducted by using the triple network. The expression of lncRNA SNHG8, SOCS3 and ICAM1 was measured by qRT-PCR. The prognostic values of lncRNA SNHG8, SOCS3 and ICAM1 were evaluated using a receiver operating characteristic (ROC) curve. Results An AMI lncRNA-miRNA-mRNA network was constructed that included two mRNAs, one miRNA and one lncRNA. After RT-PCR validation of lncRNA SNHG8, SOCS3 and ICAM1 between the AMI and normal samples, only lncRNA SNHG8 had significant diagnostic value for further analysis. The ROC curve showed that SNHG8 presented an AUC of 0.850, while the AUC of SOCS3 was 0.633 and that of ICAM1 was 0.594. After a pairwise comparison, we found that SNHG8 was statistically significant (PSNHG8-ICAM1 = 0.002; PSNHG8-SOCS3 = 0.031). The results of a functional enrichment analysis of the interacting genes and microRNAs showed that the shared lncRNA SNHG8 may be a new factor in AMI. Conclusions Our investigation of the lncRNA-miRNA-mRNA regulatory networks in AMI revealed a novel lncRNA, lncRNA SNHG8, as a risk factor for AMI and expanded our understanding of the mechanisms involved in the pathogenesis of AMI.

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Identification of An Epithelial-Mesenchymal Transition-Related lncRNA Prognostic Signature For Patients With Glioblastoma

10.21203/rs.3.rs-820534/v1 ◽

2021 ◽

Author(s):

XinJie Yang ◽

Sha Niu ◽

JiaQiang Liu ◽

ZeYu Wu ◽

Shizhang Ling ◽

...

Keyword(s):

High Risk ◽

Signaling Pathways ◽

Epithelial Mesenchymal Transition ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Prognostic Signature ◽

Mesenchymal Transition ◽

Underlying Mechanisms

Abstract Purpose: Glioblastoma (GBM) is a class of strikingly heterogeneous and lethal brain tumor with very poor prognosis. LncRNAs play critical roles in the tumorigenesis and progression of GBM through regulation of various cancer-related genes and signaling pathways. Here, we aimed to establish an epithelial-mesenchymal transition (EMT)-related lncRNA signature for GBM and explore its underlying mechanisms. Methods: Differential expression analysis and Gene set enrichment analysis (GSEA) were performed to explore key genes and signaling pathways associated with GBM. Spearman correlation analysis, Univariate and multivariate Cox regression analyses were used to construct a lncRNA prognostic signature for GBM patients. Kaplan-Meier analysis and receiver-operating-characteristic (ROC) analysis were applied to assess the performance of the prognostic signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analyses were performed to explore the underlying mechanisms of the signature. Single-sample GSEA (ssGSEA) was employed to explore the relationship of the signature and immune activities in GBM.Results: We focused on the essential role of EMT in GBM and identified 78 upregulated EMT-related genes in GBM. A total of 301 EMT-related lncRNAs were confirmed in GBM and a prognostic signature consisting of seven EMT-related lncRNAs (AC012615.1, H19, LINC00609, LINC00634, POM121L9P, SNHG11, and USP32P3) was established, which could divide GBM patients into low- and high-risk subgroups. The accuracy and efficiency of the signature were validated to be satisfactory. Functional enrichment analysis revealed multiple EMT and metastasis-related pathways were associated with the EMT-related lncRNA prognostic signature. In addition, we found the degree of immune cell infiltration and immune responses were significantly increased in high-risk subgroup compared with low-risk subgroup. Conclusion: we established an effective and robust EMT-related lncRNA signature which is expected to predict the prognosis and immunotherapy response for GBM patients.

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