Hypoxia-Related lncRNA Prognostic Model of Ovarian Cancer Based on Big Data Analysis

Abstract

Background: Hypoxia is regarded as a key factor in promoting the occurrence and development of ovarian cancer. In ovarian cancer, hypoxia promotes cell proliferation, epithelial to mesenchymal transformation, invasion, and metastasis. Long non-coding RNAs (lncRNAs) are extensively involved in the regulation of many cellular mechanisms, i.e., gene expression, cell growth, and cell cycle.

Materials and methods: In our study, a hypoxia-related lncRNA prediction model was established by applying LASSO-penalized Cox regression analysis in public databases. Patients with ovarian cancer were divided into two groups based on the median risk score. The survival rate was analyzed in the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets, and the mechanisms were investigated.

Results: Through the prognostic analysis of DElncRNAs (differentially expressed long non-coding RNAs), a total of 5 lncRNAs were found to be closely associated with OS (overall survival) in ovarian cancer patients. It was evaluated through Kaplan-Meier analysis that low-risk patients can live longer than high-risk patients (TCGA: p = 1.302e - 04; ICGC: 1.501e - 03). The distribution of risk scores and OS status revealed that higher risk score will lead to lower OS. It was evaluated that low-risk group had higher immune score (p = 0.0064) and lower stromal score (p = 0.00023).

Conclusion: It was concluded that a hypoxia-related lncRNA model can be used to predict the prognosis of ovarian cancer. Our designed model is more accurate in terms of age, grade, and stage when predicting the overall survival of the patients of ovarian cancer.

Figure 1

(a) The network of the hypoxia-related genes and lncRNAs. (b) DElncRNAs related to prognosis.

Figure 2

Kaplan–Meier analysis in TCGA cohort (a) and ICGC cohort (b). Kaplan–Meier analysis of the OS time in different clinical groups in TCGA cohort ((c, d) stage; (e, f) grade; (g, h) age).

Figure 3

(a, b) ROC curve analysis of 1/3/5 years (a) in TCGA cohort (b) in ICGC cohort, (c, d) ROC curve analysis of risk sore and other clinicopathological features in (c) TCGA cohort, (d) ICGC cohort, (e) distribution of risk scores, (f) survival status, in the TCGA database, distribution of (g) risk scores, and (h) survival status in the ICGC database.

Figure 4

(a) Univariate Cox regression analysis in TCGA database. (b) Regression analysis in ICGC database. Multivariate Cox regression analysis in (c) TCGA database and (d) ICGC database. PCA in (e) TCGA dataset or (f) ICGC dataset.

Figure 5

(a) Construction of nomogram including the prognostic hypoxia-related lncRNA signature and clinicopathological features. (b) The correction curve predicted the value of the nomogram in predicting prognosis.

Figure 6

GSEA of the prognostic hypoxia-related lncRNA signature.

Figure 7

(a) The immune infiltration status of the risk model. (b, c, d) TME scores in different risk groups.

Figure 8

(a) Molecular subgroups according to according to the prognostic model. (b) Kaplan–Meier survival analysis of the two clusters. (c) The relationship between clusters and risk groups. PCA (d) and tSNE2 (e) of the two clusters.

Figure 9

TME scores of the two clusters. (a) Stromal score of C1 and C2. (b) Immune score of C1 and C2. (c) ESTIMATE score of both clusters.

Figure 10

The heatmap of immune infiltration of the two clusters.