Inflammation-Related LncRNAs Signature for Prognosis and Immune Response Evaluation in Uterine Corpus Endometrial Carcinoma

Abstract

Backgrounds: Uterine corpus endometrial carcinoma (UCEC) is one of the greatest threats on the female reproductive system. The aim of this study is to explore the inflammation-related LncRNA (IRLs) signature predicting the clinical outcomes and response of UCEC patients to immunotherapy and chemotherapy.

Methods: Consensus clustering analysis was employed to determine inflammation-related subtype. Cox regression methods were used to unearth potential prognostic IRLs and set up a risk model. The prognostic value of the prognostic model was calculated by the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. Differential abundance of immune cell infiltration, expression levels of immunomodulators, the status of tumor mutation burden (TMB), the response to immune checkpoint inhibitors (ICIs), drug sensitivity, and functional enrichment in different risk groups were also explored. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the expression patterns of model IRLs in clinical specimens.

Results: All UCEC cases were divided into two clusters (C1 = 454) and (C2 = 57) which had significant differences in prognosis and immune status. Five hub IRLs were selected to develop an IRL prognostic signature (IRLPS) which had value in forecasting the clinical outcome of UCEC patients. Biological processes related to tumor and immune response were screened. Function enrichment algorithm showed tumor signaling pathways (ERBB signaling, TGF-β signaling, and Wnt signaling) were remarkably activated in high-risk group scores. In addition, the high-risk group had a higher infiltration level of M2 macrophages and lower TMB value, suggesting patients with high risk were prone to a immunosuppressive status. Furthermore, we determined several potential molecular drugs for UCEC.

Conclusion: We successfully identified a novel molecular subtype and inflammation-related prognostic model for UCEC. Our constructed risk signature can be employed to assess the survival of UCEC patients and offer a valuable reference for clinical treatment regimens.

Keywords: TCGA; UCEC; immunotherapy; inflammation; prognostic signature; tumor microenvironment.

Figure 1

An outline of this research is depicted in this plot.

Figure 2

The association between the transcription level of IRLs and clinicopathological and prognostic features of the UCEC patients. (A, B) The transcription levels of 27 differentially expressed IRLs between the tumor and normal samples were visualized by heatmap and boxplot. (C) The overall survival of UCEC patients in the two clusters was calculated by Kaplan-Meier curves. (D) The transcription levels of 27 differentially expressed IRLs between the two clusters with clinical features were shown in heatmap. (E-H) The ratio of different age (E), grade (F), histological type (G), and stage (H) in the groups. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 3

Differential expression profile of immune checkpoint related genes and TME components between clusters. (A) The expression of PD-1 in normal and UCEC tissues. (B) The expression of CTLA-4 in normal and UCEC samples. (C) The expressionion level of PD-1 in the clusters. (D) The expression level of CTLA-4 in the clusters. (E) The correlation of the transcription levels of IRLs and PD-L1, red circle means positive relationship. (F) The correlation of the transcription levels of IRLs and CTLA-4, red circle means positive correlation. (G) The infiltrating levels of 21 immune cell types in two clusters. (H–K) The (H) Immunescore, (I) Stromalscore, (J) Tumor purity score, and (K) ESTIMATEscore in cluster 1 and cluster 2. *P < 0.05, ***P < 0.001.

Figure 4

Construction and validation of IRLPS. (A-C) Survival analysis for patients in the (A) training, (B) testing, and (C) entire cohort. (D-F) ROC curves measuring the predictability of the signature in the (D) training set, (E) testing set, and (F) entire cohort. (G-I) Distribution of risk score, survival status, and heatmap of the transcription levels of five prognostic signatures in the (G) training set, (H) testing set, and (I) entire cohort.

Figure 5

The prognostic value of IRLPS in stratified patient groups, and correlation of IRLPS with clinicopathological features and immunescore. IRLPS showed satisfactory prediction performance in patients regardless of (A, B) age and (C, D) stage, (E, F) histological type and (G, H) grade. (I) Heatmap and clinical features of the groups. (J–P) Distribution of IRLPS stratified by (J) age, (K) grade, (L) histological type, (N) immunological subtype, (M) Immunescore, (O) tumor stage and (P) cluster. *P < 0.05, **P < 0.01 ***P < 0.001.

Figure 6

Establishing the IRLPS based on risk score and clinical factors, and validating it in calibration plot. (A) ROC plot indicates that IRLPS is superior in predicting the prognosis in UCEC patients than previous works. (B) IRLPS is also more superior in prediction accuracy than histological type or tumor stage alone. (C) Combining IRLPS with clinical factors is better yet. (D) A nomogram to illustrate the IRLPS, a risk model to predict endometrial carcinoma patient prognosis basing on aforementioned IRLPS, and clinical factors. (E–G) Calibration curves showing the favorable performance of nomogram. **P < 0.01 ***P < 0.001.

Figure 7

Differentially activated pathways and immune infiltration between the groups. (A, B) Multiple GSEA analysis was conducted to predict the potential functions and pathways involved in (A) high-risk and (B) low-risk groups. (C, D) Stromal score does not differ significantly between the groups. However, correlation analysis implies significant relationship between stromal score and IRLPS. (E, F) ESTIMATE score differs significantly between the groups, and correlation analysis implies a significant relationship between ESTIMATE score and IRLPS. (G, H) Immunescore differs significantly between the groups, and correlation analysis implies a significant relationship between Immunescore and IRLPS risk score.

Figure 8

Relationships between IRLPS and different aspects in the immune microenvironment, including infiltration abundances and activation status of immune cells and cell stemness. (A) Violin plot depicts 21 immune cell types that is differently distributed in high and low risk IRLPS risk score groups. (B) The correlation of IRLs expression and infiltration abundance of immune cells, visualized by heatmap. (C–H) The correlation of 6 immune cell types with the 5 IRLs in our risk signature. (I, J) ssGSEA reveals significant difference in (I) immune cell abundance and (J) activation of immune processes between the groups. (K) Correlation analysis implies significant relationship in cancer cell stemness represented by methylation of RNA (RNAss) with risk score. (L) No significant difference in epiregulin mRNA stemlike indices (EREG mRNAsi) between the groups. (M) The mRNA based stemlike indices (mRNAsi) is significantly different in the groups. *P < 0.05, **P < 0.01 ***P < 0.001, ns indicates no statistical difference.

Figure 9

Difference in ICGs expression in the two IRLPS groups. (A) The boxplot shows the correlation of ICGs and risk score. (B) Correlation between expression of ICGs and IRLPS. (C-E) Correlation analysis reveals expression levels of ICGs (C) CTLA-4, (D) HAVCR-2, and (E) PDCD1 are negatively related to IRLPS risk score. (F) Boxplot illustrates significantly higher expression of ICG PD-1 in the IRLPS low-risk group than in the high-risk group. (G–J) IPS scoring reveals (G) IPS, (H) IPS-CTLA4, (I) IPS-CTLA-4/PD-L1/PD-1/PD-L2, and (J) IPS-PD-L1/PD-1/PD-L2 scores were all significantly higher in the low-risk group. *P < 0.05; **P < 0.01; ***P < 0.001.

Figure 10

TMB and microsatellite instability are negatively correlated to IRLPS risk score, and can contribute to more significant prognostic discrimination combined with risk score. (A) Boxplot shows higher TMB in the low-risk group. (B) Correlation analysis implies TMB is potentially negatively related to IRLPS risk score. (C) Kaplan-Meier analysis indicates unfavorable outcome for low TMB patients. (D) Patients with lower TMB and higher risk score have significantly more pessimistic outcomes. (E, F) Mutation profile in (E) low and (F) high risk score groups. (G) IRLPS high-risk group has higher proportion of MSS and lower proportion of MSI-H. (H) Divided by microsatellite status, the MSI-H group has significantly lower risk score.

Figure 11

High-risk group is generally less sensitive to chemotherapy and several potential small molecule therapeutical agents targeting the IRLs. (A-C) Correlation of risk score clustering and chemotherapy response. Response to (A) etoposide, (B) cisplatin, and (C) doxorubicin is generally less significant in high-risk patients. (D) Several small molecular agents are found to be able to counter the expression of these IRLs.