Development of a novel immune-related lncRNA signature as a prognostic classifier for endometrial carcinoma

Abstract

Endometrial carcinoma (EnCa) is one of the deadliest gynecological malignancies. The purpose of the current study was to develop an immune-related lncRNA prognostic signature for EnCa. In the current research, a series of systematic bioinformatics analyses were conducted to develop a novel immune-related lncRNA prognostic signature to predict disease-free survival (DFS) and response to immunotherapy and chemotherapy in EnCa. Based on the newly developed signature, immune status and mutational loading between high‑ and low‑risk groups were also compared. A novel 13-lncRNA signature associated with DFS of EnCa patients was ultimately developed using systematic bioinformatics analyses. The prognostic signature allowed us to distinguish samples with different risks with relatively high accuracy. In addition, univariate and multivariate Cox regression analyses confirmed that the signature was an independent factor for predicting DFS in EnCa. Moreover, a predictive nomogram combined with the risk signature and clinical stage was constructed to accurately predict 1-, 2-, 3-, and 5-year DFS of EnCa patients. Additionally, EnCa patients with different levels of risk had markedly different immune statuses and mutational loadings. Our findings indicate that the immune-related 13-lncRNA signature is a promising classifier for prognosis and response to immunotherapy and chemotherapy for EnCa.

Keywords: bioinformatics; endometrial carcinoma; immune-related lncRNA; signature.

Figure 1

Identification of the immune-related lncRNA signature in the training cohort. (A) Survival status and risk score distribution in the high- and low-risk groups. Green dots: surviving patients; red dots: dead patients. (B) Expression patterns of 13 lncRNAs in high- and low-risk groups. (C) Kaplan-Meier curve analysis of DFS of EnCa patients in high- and low-risk groups. (D) Time-dependent ROC curve analysis.

Figure 2

Validation of the immune-related lncRNA signature in the testing cohort. (A) Survival status and risk score distribution in the high- and low-risk groups. Green dots: surviving patients; red dots: dead patients. (B) Expression patterns of 13 lncRNAs in high- and low-risk groups. (C) Kaplan-Meier curve analysis of DFS of EnCa patients in high- and low-risk groups. (D) Time-dependent ROC curve analysis.

Figure 3

Validation of the immune-related lncRNA signature in the entire cohort. (A) Survival status and risk score distribution in the high- and low-risk groups. Green dots: surviving patients; red dots: dead patients. (B) Expression patterns of 13 lncRNAs in high- and low-risk groups. (C) Kaplan-Meier curve analysis of DFS of EnCa patients in high- and low-risk groups. (D) Time-dependent ROC curve analysis.

Figure 4

ROC analysis of clinical factors and the risk score. Calculated AUCs for risk score, age, stage, histological type, and grade of the total survival risk score according to the ROC curve at (A) one year, (B) three years, and (C) five years. Calculated AUCs for risk score and combined clinical factors of the total survival risk score according to the ROC curve at (D) one year, (E) three years, and (F) five years.

Figure 5

Cox regression analysis was used to assess the independent prognostic value of the risk score. Univariate Cox regression analysis of age, stage, histological type, grade, and risk score in the (A) training cohort, (B) testing cohort, and (C) entire cohort. The multivariate Cox regression analysis of age, stage, histological type, grade, and risk score in the (D) training cohort, (E) testing cohort, and (F) entire cohort.

Figure 6

Construction of a nomogram for survival prediction. (A) The nomogram combining the signature with clinicopathological features. (B) Calibration plot showing that nomogram-predicted survival probabilities corresponded closely to the actual observed proportions.

Figure 7

Association between immune cell infiltration and the immune-related risk signature. (A) The violin plot represents immune cell infiltration in the high- and low-risk groups. (B) The Bubble plot represents the correlation between immune cell infiltration and the immune-related risk score. Positive correlation between (C) activated dendritic cells, (D) resting dendritic cells, or (E) M0 macrophage infiltration and the immune-related risk score. Negative correlation between (F) activated CD4+ memory T cells, (G) CD8+ T cells, or (H) regulatory T cell infiltration and the immune-related risk score.

Figure 8

Alteration landscape for high- and low-risk EnCa samples in the TCGA cohort. (A, B) The rates of PTEN mutation, PIK3CA mutation, ARID1A mutation, TTN mutation, and PIK3R1 mutation in EnCa with a high-risk score were lower than those with EnCa with a low-risk score. (C) EnCa patients with high-risk scores had a heavier tumor mutation burden than with those with low-risk scores. (D) Negative correlation between tumor mutation burden and the immune-related risk score. (E) Kaplan-Meier curve analysis of DFS of EnCa patients with different tumor mutation burdens.