Screening and Identification of an Immune-Associated lncRNA Prognostic Signature in Ovarian Carcinoma: Evidence from Bioinformatic Analysis

Abstract

Backgrounds: The dysregulated long noncoding RNAs (lncRNAs) have been described to be crucial regulators in the progression of ovarian carcinoma. The infiltration status of immune cells is also related to the clinical outcomes in ovarian carcinoma. The present research is aimed at constructing an immune-associated lncRNA signature with potential prognostic value for ovarian carcinoma patients.

Methods: We obtained 379 ovarian carcinoma cases with available clinical data and transcriptome data from The Cancer Genome Atlas database to evaluate the infiltration status of immune cells, thereby generating high and low immune cell infiltration groups. According to the expression of the immune-associated lncRNA signature, the risk score of each case was calculated. The high- and low-risk groups were classified using the median risk score as threshold.

Results: A total of 169 immune-associated lncRNAs that differentially expressed in ovarian carcinoma were included. According to the Lasso regression analysis and Cox univariate and multivariate analyses, 5 immune-associated lncRNAs, including AC134312.1, AL133467.1, CHRM3-AS2, LINC01722, and LINC02207, were identified as a predictive signature with significant prognostic value in ovarian carcinoma. The following Kaplan-Meier analysis, ROC analysis, and Cox univariate and multivariate analyses further suggested that the predicted signature may be an independent prognosticator for patients with ovarian carcinoma. The following gene set enrichment analysis showed that this 5 immune-associated lncRNAs signature was significantly related to the hedgehog pathway, basal cell carcinoma, Wnt signaling pathway, cytokine receptor interaction, antigen processing and presentation, and T cell receptor pathway.

Conclusion: : This study suggested a predictive model with 5 immune-associated lncRNAs that has an independent prognostic value for ovarian carcinoma patients.

Figure 1

Construction of high and low immune infiltration groups in ovarian carcinoma. (a) Unsupervised clustering of ovarian carcinoma patients from the TCGA cohort using the ssGSEA method from immune cell types. The whole cohort was clustered into the high immune infiltration group (N = 193, immunity_H) and low immune infiltration group (N = 186, immunity_L). (b) The tumor purity, stromal score, immune score, and ESTIMATE score were assessed using the ESTIMATE algorithm between high and low immune infiltration groups. ^∗∗∗p < 0.001. (c, d) The expression levels of HLA family members (c) and CD274 (d) were evaluated between high and low immune infiltration groups in the TCGA cohort. ^∗∗∗p < 0.001. (e) The proportion difference of several immune cells in the two groups was assessed by the CIBERSORT method. TCGA: The Cancer Genome Atlas; HLA: human leukocyte antigen; ssGSEA: single-sample gene set enrichment analysis; N: number; CIBERSORT: Cell-type Identification by Estimating Relative Subsets of RNA Transcripts.

Figure 2

Analysis of differentially expressed lncRNAs. (a) The volcano plot of differentially expressed lncRNAs between ovarian carcinoma cases and adjacent normal cases. (b) The volcano plot of differentially expressed lncRNAs between high and low immune infiltration groups. (c) Venn diagram of (a) and (b). IMM_H represents high immune infiltration group; IMM_L represents low immune infiltration group.

Figure 3

Construction of an immune-related lncRNA signature associated with the prognosis of ovarian carcinoma. (a, b) 12 immune-related lncRNAs were identified by a Lasso regression analysis. (c) Cox multivariate regression analysis of 5 immune-related lncRNAs for construction of a prognostic model in ovarian carcinoma. (d) Kaplan-Meier survival curve between high- and low-risk groups in the TCGA cohort. (e) Time-dependent ROC curve analysis for evaluating the reliability of the prognostic model. (f) The risk curve of each case reordered by risk score. (g) Scatter plot of the cases. Green dots presented survival cases, and red dots presented death cases. ROC: receiver operating characteristic.

Figure 4

Independent prognostic values of the prognostic model. (a) Cox univariate analysis of the risk score and clinical features for overall survival in the TCGA cohort. (b) Cox multivariate analysis of the risk score and clinical features for overall survival in the TCGA cohort. (c) Calculation of the AUC for the risk score, age, grade, and stage using the ROC curve. TCGA: The Cancer Genome Atlas; AUC: area under the curve; ROC: receiver operating characteristic.

Figure 5

Results of GSEA in the TCGA cohort. (a) Three significantly enriched signaling pathways in the high-risk group. (b) Three significantly enriched signaling pathways in the low-risk group. GSEA: gene set enrichment analysis; TCGA: The Cancer Genome Atlas.