Identification of an Anoikis-associated LncRNA Signature to Predict the Clinical Prognosis and Immune Function of Patients with Endometrial Cancer

Abstract

Background: Endometrial cancer is a highly heterogeneous malignancy in women with high mortality, and patients diagnosed with advanced endometrial cancer have a poor prognosis. Anoikis is a form of programmed cell death that is important for cancer development and metastasis. Long non-coding RNAs (lncRNAs) are considered critical regulators of gene expression and key players in cancer biology; however, the effects of anoikis-associated lncRNAs on the prognosis and treatment of patients with endometrial cancer remain unclear. Methods: Using transcriptome sequencing data and clinical information from The Cancer Genome Atlas database, we developed a novel prognostic signature for endometrial cancer based on anoikis-related lncRNAs by combining multivariate regression analysis and least absolute shrinkage and selection operator regression. The signature was validated by receiver operating characteristic (ROC) curve and Kaplan-Meier analyses. After analyzing the relationships between the seven lncRNAs in the signature and tumor progression through gene set enrichment analysis (GSEA), we further explored the differences in immune function and drug sensitivity. Additionally, to investigate the functions of these lncRNAs in the occurrence and development of endometrial cancer, we selected CFAP58-DT to conduct a series of in vitro and in vivo experiments to verify its partial functions. Results: Seven anoikis-associated lncRNAs (CFAP58-DT, AC004585.1, AC103563.9, AL121895.2, AC004596.1, AC010761.4, and AC087564.1) with prognostic value were identified for signature construction. The analysis showed excellent predictive accuracy of the signature (the largest area under the ROC curve = 0.757). GSEA indicated that these lncRNAs may regulate diverse cellular processes, including intercellular interactions, cell proliferation, differentiation, apoptosis, angiogenesis, glucose and fatty acid metabolism, immune responses, and inflammatory regulation. Furthermore, immune analysis revealed a high likelihood of immune evasion and poor immunotherapy efficacy in high-risk patients. However, there were distinct differences in the immune checkpoints and anticancer drug sensitivity between the two patient groups, which may aid in guiding treatment. Finally, our experiments showed that silencing CFAP58-DT significantly affected cell proliferation, promoted apoptosis, and reduced tumor malignancy. Conclusion: Our study highlights the significance of anoikis-associated lncRNAs in endometrial cancer progression and their potential as prognostic markers and therapeutic targets. The signature constructed using these lncRNAs may offer new avenues for endometrial cancer treatment and immunotherapy. The function of CFAP58-DT has been validated in vitro and in vivo, consistent with our previous analysis; however, further research into its upstream and downstream mechanisms is warranted.

Keywords: anoikis; endometrial cancer; lncRNAs; prognosis.

Figure 1

The flowchart of this study.

Figure 2

(A) Volcano plot identifying DE-ARGs, showing downregulated and upregulated genes. (B) Heatmap displaying the expression levels of DE-ARGs in each sample. (C) KEGG Pathway Enrichment Analysis. (D) GO Enrichment Analysis.

Figure 3

(A, B) LASSO regression analysis of 68 prognostic related DE-ARLNCRs. (C, D) The correlation between DE-ARGs and 7 DE-ARLNCRs in the signature.

Figure 4

(A-C) Risk curves showing risk score distribution. (D-F) Scatter plot of survival duration and status of patients in the high-risk and low-risk groups. (G-I) Heatmap showing the expression levels of lncRNAs in the prognostic signature. (J-L) K-M survival curves for OS in the high-risk and low-risk groups. (M-O) ROC curves for predicting OS at 1, 2, and 3 years based on DE-ARLNCRs prognostic signature. (P-S) PCA based on (P) all genes, (Q) ARGs, (R) DE-ARLNCRs and (S) the 7 lncRNAs included in the signature.

Figure 5

(A) Univariate cox regression analysis for age, grade, stage, and risk score. (B) Multivariate cox regression analysis for age, grade, stage, and risk score. (C) Distribution heatmap of 7 prognostic DE-ARLNCRs and clinicopathological variables in high-risk and low-risk groups. (D) ROC curves of clinical characteristics and risk score. (E-H) K-M survival analysis of clinical characteristics.

Figure 6

(A) Establishment of prognostic nomogram to predict survival of EC. (B) Calibration curves of the nomogram to predict 1-year and 3-year and 5-year survival.

Figure 7

(A) Bubble chart showing the correlation between the immune cell array and risk scores. (B) Immune cell, and (C) immune function differences through ssGSEA. ***, P<0.001; **, P<0.01; *, P<0.05; ns, not significant. (D) Box plots of stromal score, immune score, and ESTIMATE score for two groups. (E) Immune checkpoints analysis in two groups. ***, P<0.001; **, P<0.01; *, P<0.05.

Figure 8

(A) KM Plotter database exhibited the relationship between expression of CFAP58-DT and OS in EC patients. (B) qRT-PCR showed the CFAP59-DT expression in different EC cells. (C-H) CCK8, EdU, Calcein/PI cell staining, flow cytometry and colony formation assays showed shCFAP59-DT enhanced the apoptosis and inhibited proliferation of KLE cells. (I) Western blot showed the protein levels of BAX, Bcl2, Caspase3, Active Caspase3.β-Actin is an internal parameter.

Figure 9

(A) Images of tumors by subcutaneous injection at the end point of BALB/c nude mice. (B) Tumor volume measured at the end point. (C) Tumor weight measured at the end point. (D) Images of HE staining and immunohistochemistry (IHC) of subcutaneous xenograft tumors.