Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer

Zheng, Jianfeng; Cao, Benben; Zhang, Xia; Niu, Zhenbin; Tong, Jinyi

doi:10.1155/2020/3641231

Cited by 21 publications

(23 citation statements)

References 59 publications

(37 reference statements)

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“…For example, Zheng and his partners analyzed TCGA and GSE6791 databases and constructed a 4 lncRNA signature which was related to survival rate of CC patients. 32 8-lncRNA signature of CC has also been identified by integrating DNA methylation, copy number variation and transcriptome data. 33 In this study, a prognostic model of immune-related lncRNAs was established with multiple regression analysis.…”

Section: Discussionmentioning

confidence: 99%

Identification and Experimental Validation of Immune-Associate lncRNAs for Predicting Prognosis in Cervical Cancer

Ye¹,

Chen²,

Lü³

2021

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Purpose: Cervical cancer (CC) is a major risk for health of modern women. Immune-related long non-coding RNAs (lncRNAs) can also serve as prognostic markers of overall survival (OS) in patients with CC. This study aimed to identify an immune-related lncRNA signature for the prospective assessment of prognosis in CC patients. Methods: We first calculated immune scores of CC patients in the Cancer Genome Atlas (TCGA) database. Univariate Cox, Lasso Cox and multivariate Cox regression analyses were perfumed to establish an immune-relative lncRNA signature. In addition, we processed pathway enrichment analysis and immune infiltration analysis between patients with higher or lower risk. Finally, T-cell Chemotaxis assays were processed to verify the function of 2 key lncRNAs. Results: Our results suggested that patients with higher immune scores had longer survival time and some lncRNAs expressed differentially between two groups. Eight lncRNAs (LINC02802, LINC01877, RBAKDN, LINC02480, WWC2-AS2, LINC01281, ZBTB20-AS1, IFNG-AS1) were identified as prognostic signatures for CC. The immune-related lncRNA signature was correlated with disease progression and worse prognosis. Immune infiltration analysis indicated that the expression of 8-lncRNA signatures were corrected with infiltration level of immune cell subtypes. In addition, T-cell Chemotaxis assay validated that 2 key lncRNAs (ZBTB20-AS1 and LINC01281) could significantly promote the migration ability of T cells to CC cells. Conclusion: Our finding demonstrated the value of lncRNAs in evaluating the immune infiltrate of the tumor. The 8-lncRNA signature could predict the prognosis of CC and contribute to decisions regarding the immunotherapeutic strategy.

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Section: Discussionmentioning

confidence: 99%

Identification and Experimental Validation of Immune-Associate lncRNAs for Predicting Prognosis in Cervical Cancer

Ye¹,

Chen²,

Lü³

2021

OTT

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“…Studies have found that lncRNA is an important regulator of cancer immune response. Previous studies had shown that four immunerelated lncRNAs including BZRAP1-AS1, EMX2OS, ZNF667-AS1, and CTC-429P9.1, significantly correlated with immune cell infiltration and had important prognostic value for cervical cancer patients (Zheng J. et al, 2020). Another study found that five immune-related lncRNAs containing AP001007.1, LBX-AS1, MIR155HG, MAPT-AS1, and LINC00515, regulate the tumor immune microenvironment through the interaction of cytokines and cytokine receptors, complement and coagulation cascades to affect the prognosis of glioma (Wang et al, 2020).…”

Section: Discussionmentioning

confidence: 95%

A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer

Qin

Wei

et al. 2021

Front. Genet.

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BackgroundColorectal cancer (CRC) is one of the most common malignant tumors with a poor prognosis. At present, the pathogenesis is not completely clear. Therefore, finding reliable prognostic indicators for CRC is of important clinical significance. In this study, bioinformatics methods were used to screen the prognostic immune-related lncRNAs of CRC, and a prognostic risk scoring model based on immune-related lncRNAs signatures were constructed to provide a basis for prognostic evaluation and immunotherapy of CRC patients.MethodsThe clinical information and RNA-seq data of CRC patients were obtained from The Cancer Genome Atlas (TCGA) database. The information of immune-related lncRNA was downloaded from the immunology database and analysis portal. The differentially expressed immune-related lncRNAs (IRLs) were screened by the edgeR package of R software. The prognostic value of IRLs was studied. Based on Cox regression analysis, a prognostic index (IRLPI) based on IRLs was established, and the relationship between the risk score and the clinicopathological characteristics of CRC was analyzed to determine the effectiveness of the risk score model as an independent prognostic factor.ResultsA total of 240 differentially expressed IRLs were identified between normal colorectal cancer tissues and normal colorectal cancer tissues, in which 8 were significantly associated with the survival of CRC patients (P < 0.05), including LINC00461, LINC01055, ELFN1-AS1, LMO7-AS1, CYP4A22-AS1, AC079612.1, LINC01351, and MIR31HG. And most of the lncRNAs related to survival were risk factors for the prognosis of CRC. The index established based on the 7 survival-related IRLs found to be highly accurate in monitoring CRC prognosis. Besides, IRLPI was significantly correlated with a variety of pathological factors and immune cell infiltration.ConclusionEight immune-related lncRNAs closely related to the prognosis of CRC patients were identified from the TCGA database. At the same time, an independent IRLPI was constructed, which may be helpful for clinicians to assess the prognosis of patients with CRC and to formulate individualized treatment plans.

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“…Using the data from TCGA database, Zheng et al ( 23 ) observed EMX2OS was significantly downregulated in the CC tissues compared with normal controls. K-M survival curve analysis showed CC patients with high expression of EMX2OS had better OS compared with the low-expression group.…”

Section: Discussionmentioning

confidence: 99%

A novel lncRNA-mRNA-miRNA signature predicts recurrence and disease-free survival in cervical cancer

Tian

Guo

et al. 2021

Braz J Med Biol Res

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Cervical cancer (CC) patients have a poor prognosis due to the high recurrence rate. However, there are still no effective molecular signatures to predict the recurrence and survival rates for CC patients. Here, we aimed to identify a novel signature based on three types of RNAs [messenger RNA (mRNAs), microRNA (miRNAs), and long non-coding RNAs (lncRNAs)]. A total of 763 differentially expressed mRNAs (DEMs), 46 lncRNAs (DELs), and 22 miRNAs (DEMis) were identified between recurrent and non-recurrent CC patients using the datasets collected from the Gene Expression Omnibus (GSE44001; training) and The Cancer Genome Atlas (RNA- and miRNA-sequencing; testing) databases. A competing endogenous RNA network was constructed based on 23 DELs, 15 DEMis, and 426 DEMs, in which 15 DELs, 13 DEMis, and 390 DEMs were significantly associated with disease-free survival (DFS). A prognostic signature, containing two DELs (CD27-AS1, LINC00683), three DEMis (hsa-miR-146b, hsa-miR-1238, hsa-miR-4648), and seven DEMs (ARMC7, ATRX, FBLN5, GHR, MYLIP, OXCT1, RAB39A), was developed after LASSO analysis. The built risk score could effectively separate the recurrence rate and DFS of patients in the high- and low-risk groups. The accuracy of this risk score model for DFS prediction was better than that of the FIGO (International Federation of Gynecology and Obstetrics) staging (the area under receiver operating characteristic curve: training, 0.954 vs 0.501; testing, 0.882 vs 0.656; and C-index: training, 0.855 vs 0.539; testing, 0.711 vs 0.508). In conclusion, the high predictive accuracy of our signature for DFS indicated its potential clinical application value for CC patients.

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Immune-Related Four-lncRNA Signature for Patients with Cervical Cancer

Cited by 21 publications

References 59 publications

Identification and Experimental Validation of Immune-Associate lncRNAs for Predicting Prognosis in Cervical Cancer

Identification and Experimental Validation of Immune-Associate lncRNAs for Predicting Prognosis in Cervical Cancer

A Prognostic Model Based on the Immune-Related lncRNAs in Colorectal Cancer

A novel lncRNA-mRNA-miRNA signature predicts recurrence and disease-free survival in cervical cancer

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