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. 2023 Mar 16;9(4):e14613. doi: 10.1016/j.heliyon.2023.e14613

A novel cuproptosis-related gene signature for overall survival prediction in uterine corpus endometrial carcinoma (UCEC)

Shitong Lin a,b,c, Yashi Xu a,b,c, Binghan Liu a,b,c, Lingling Zheng d, Canhui Cao b,c,∗∗∗∗, Peng Wu a,b,c,∗∗∗, Wencheng Ding b,c,∗∗, Fang Ren e,∗
PMCID: PMC10073764  PMID: 37035374

Abstract

Cuproptosis is a copper-dependent model of cell death involved in tumor genesis and progression. Its roles in uterine corpus endometrial carcinoma (UCEC) remains elusive. Here, we aimed to explore the expression and prognostic values of cuprotosis-related genes (CRGs) in UCEC. Expression profiles and clinical data of UCEC were downloaded from The Cancer Genome Atlas (TCGA), and randomly divided into testing or training cohort (1:1 ratio). The CRG signature was identified by LASSO regression analysis. The differentially expressed genes and their functional enrichment analysis were performed by the “limma” R package and Metascape, respectively. The immunocytes infiltration was measured by TIMER, and “GSVA” R package. In total, seven differentially expressed prognostic genes of CRGs in UCEC were identified, and four genes (GLS, CDKN2A, PC, and SUCLG1) were selected to construct a predictive model in training cohort. UCEC patients from training and testing cohorts were further divided into high- or low-risk groups according to the median risk score. High-risk group favored poor prognosis compared to low-risk group. Functional enrichment analysis revealed this CRG signature were got involved in the process of cell-cell adhesion and immune activities (e.g., IL-1 signaling pathway, cellular response to cytokine stimulus). Further analyses revealed there were significant differences between high- and low-risk patients regarding immunocytes infiltration, chemokines, and chemokine receptors. Finally, the expression and biological functions of identified CRGs were confirmed by UCEC samples and experimental methods in vitro. In summary, the CRG signature was significantly correlated with patients' overall survival, which could provide insights into the diagnosis and prognosis prediction for UCEC.

Keywords: Cuproptosis, CRG, UCEC, LASSO, Immunocytes infiltration

1. Introduction

Uterine corpus endometrial carcinoma (UCEC) remains one of the most common gynecological malignancies, with an increasing trend year by year especially in developed countries [[1], [2], [3]]. It is estimated that UCEC affects 71 100 females, and results in 17 100 deaths in China [4]. Usually, UCEC is divided into two groups: 80% type I and 20% type II based on their own clinical features [5]. Although most UCEC patients can obtain favorable prognosis with the use of radical surgery for diagnosed at early stage, there are still a proportion of patients developing recurrence or metastasis [6,7]. Therefore, more specific and effective treatments are urgently needed for these recurrent or metastatic patients.

Intracellular copper ions are get involved in key biological functional, such as reactive oxygen species degradation, energy metabolism, iron absorption and signal transduction. Abnormal distribution of copper may result in cellular dysfunction and cell death [8,9]. Cuproptosis is a new model of cell death proposed by Tsvetkov et al., in 2022, which is different from previously reported programmed cell death (e.g., apoptosis, necroptosis, pyroptosis, and ferroptosis) [10]. There are two main intracellular changes during the process of cuproptosis. Firstly, the accumulation of copper ions in cells promote abnormal oligomerization of lipoylated proteins by binding to lipoylated proteins of mitochondrial tricarboxylic acid (TCA) cycle. At the same time, accumulated copper ions reduce the levels of Fe–S cluster proteins. Together, these two changes induce a proteotoxic stress response that ultimately leads to cell death [[10], [11], [12]]. Similar to ferroptosis, cuproptosis may play pivotal roles in cancer initiation, cancer progression, remodeling microenvironment, etc. Therefore, an in-depth interrogation should be placed to explore its functional mechanism, which may provide insights to the diagnosis and treatments for malignancies.

Zheng et al. used the LASSO regression model to construct a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer, which provides us a lot of inspiration [13]. Now, whether the cuproptosis plays key roles in UCEC remains unknown. Here, we firstly comprehensively explored the expression and prognostic significance of CRGs in UCEC, and constructed a CRG signature by LASSO analysis. Then we further explored the effects of this CRG signature on prognosis, clinical characteristics, biological functions, immunocytes infiltration. Finally, we further verified the expression of candidate CRGs with clinical samples and cell lines by qRT-PCR, and verified their biological functions in UCEC cell lines by experimental methods in vitro.

2. Materials and methods

2.1. Experimental methods

2.1.1. Cell culture

Normal human endometrial stromal cell (HESC) and endometrial cancer cell (Ishikawa, HEC-1-A, HEC-1-B, and RL95-2) were obtained from the American Type Culture Collection (ATCC). HESC and Ishikawa were cultured with DMEM (11965084, Gibco, USA). HEC-1-A, HEC-1-B, and RL95-2 were cultured with McCoy's 5A (12330031, Gibco, USA), MEM (A4192201, Gibco, USA), F12 (11765054, Gibco, USA), respectively. All the culture medium were supplemented with 10% fetal bovine serum (FBS) and antibodies (1% penicillin-streptomycin). The environment of the incubator was 37 °C with 5% CO2.

2.1.2. small interfering RNAs (siRNA) transfection

Specific siRNAs targeting CDKN2A, PC, GLS and SUCLG1 were obtained from the RioBio (Guangzhou, China). 1 mL mixed liquids (500 μL complete DMEM, 490 μL Opti-MEM (2363380, Gibco), 5 μL Lipofectamine3000 (L3000001, ThermoFisher), and 5 μL siRNAs) was used to down-regulate the related genes of the cells seeded in a 6-well plate. The sequences of specific siRNAs were as follows: (1) st-h-CDKN2A-1: GCTGTCGACTTCATGACAA; (2) st-h-CDKN2A-2:GCCCTAAGCGCACATTCAT; (3) st-h-CDKN2A-3: GTCGACTTCATGACAAGCA; (4) st-h-PC-1: CTGTGAAACTCGCTAAACA; (5) st-h-PC-2:GCAACTCGGACGTGTATGA; (6) st-h-PC-3: GCGCGTGTTTGACTACAGT; (7) st-h-GLS-1: CCTCAACTGGCCAAATTCA; (8) st-h-GLS-2: GGTGGTTTCTGCCCAATTA; (9) st-h-GLS-3:GGTAAATGCTGGAGCAATT; (10) st-h-SUCLG1-1:GAACGATTCTGCCACAGAA; (11) st-h-SUCLG1-2: CTGGCACCCTGACTTATGA; (12) st-h-SUCLG1-3: GCAACGGCTTCTGTCATTT.

2.1.3. Cell Counting Kit-8

Cells were evenly seeded into the 96-well plates, and cultured with complete medium for overnight. The OD450 values were measured using the CCK-8 Cell Counting Kit (A311-01, Vazyme) according to the manufactures' protocols at different times. The potent copper ionophore, Eleslomol (HY12040), was purchased from the MedChemExpress.

2.1.4. Colony formation assay

Cells were evenly seeded into the 12-well plates, and cultured with complete medium for 7 days. The complete medium was replaced every 2 days. Colonies were fixed with 4% paraformaldehyde (G1101, Servicebio) for 10 min and then stained with 0.1% crystal violet (G1014, Servicebio) for 30 min in room temperature.

2.1.5. RNA extraction, cDNA synthesis and real-time quantitative RT-PCR (qRT-PCR)

Twenty paired UCEC and adjacent normal tissues were collected. As previously described, total RNA was extracted with Trizol reagent (15596026, Thermo Fisher, USA), cDNA was synthesized with RT-PCR Kit (HF001-01, Vazyme, Nanjing), and qRT-PCR was performed with HiScript ⅡQ RT SuperMix (R223-01, Vazyme, Nanjing) according to the manufactures' instructions. The reference gene was GAPDH. The primer sequences of four candidate CRGs were displayed in Table 1.

Table 1.

Primer sequences for qRT-PCR.

Primer Sequence (5′–3′)
GLS-F AGGGTCTGTTACCTAGCTTGG
GLS-R ACGTTCGCAATCCTGTAGATTT
CDKN2A-F CTCGTGCTGATGCTACTGAGGA
CDKN2A-R GGTCGGCGCAGTTGGGCTCC
PC-F GCCATGTCATGGTAAACGGTCC
PC-R GCAGGATGTCTCTGAAACCAGC
SUCLG1-F TATGGCACCAAACTCGTTGGA
SUCLG1-R GAAGCCGTTGCTCCTGTCT
GAPDH-F GGAGCGAGATCCCTCCAAAAT
GAPDH-R GGCTGTTGTCATACTTCTCATGG
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2.1.6. Migration assay

The transwell plates (3422, Corning Incorporated Costar, 8.0 μm) were used to performed migration assay according to manufactures' protocols. Briefly, 4*10^4 cells resuspended with 200 μL FBS-free medium were seeded into the upper chamber, and 500 μL complete medium was added into the lower chamber. The upper chambers were fixed with 4% paraformaldehyde (G1101, Servicebio) for 10 min and then stained with 0.1% crystal violet (G1014, Servicebio) for 30 min in room temperature.

2.2. Bioinformatic analyses

2.2.1. Acquisition of expression profiles and clinical data of UCEC

The expression and clinical data of patients were downloaded from the TCGA database (https://portal.gdc.cancer.gov/). After data matching, a total of 550 cancer cases and 35 normal samples were used to further analyzed. The updated data of prognosis were obtained from a published study (Supplementary Tables S1 and S2) [14]. The expression and survival analyses of CRGs were performed by “ggplot2” and “survmine” R package. All the prognostic genes in UCEC were identified by “survmine” and “survival” R package.

2.2.2. Functional enrichment analyses

Correlated genes of candidate CRGs were identified through the online database GeneMINIA(http://genemania.org/) [15]. Functional enrichment analysis were performed by Metascape (https://metascape.org/gp/index.html#/main/step1) [16].

2.2.3. Identification of the CRG signature in UCEC

LASSO Cox regression model was used to construct the predictive CRG signature from seven differentially expressed prognostic CRGs by “glmnet” and “survival” R package. The lasso risk score of each patient was calculated according to the following formula: risk score = e sum (each genes' lasso coefficient * gene’s expression). The Nomogram and calibration curves were used to measure the accuracy of the prognostic model in predicting patients' prognosis by “rms” and “survival” R package, respectively.

2.2.4. Identification of differentially expressed genes (DEGs)

DEGs between low- and high-risk group were identified by “Limma” R package. The cutoff was set as follows: (|logFC| > 0.25, Adj.P-Value < 0.05. The Volcano plots were constructed by “ggplot2” R package.

2.2.5. Gene mutation analysis

The mutations of CRGs regarding copy number variation (CNV) and single-nucleotide variation (SNV) in UCEC patients were explored by the online database Gene Set Cancer Analysis (GSCA) (http://bioinfo.life.hust.edu.cn/GSCA/#/).

2.2.6. Measurement of immunocytes infiltration

The ssGSEA algorithm was used to measure the infiltration score of 24 common immunocytes using the “GSVA” R package [17,18]. The relationships between risk score and immunocytes infiltration were measured by the Spearman coefficients.

2.2.7. Measurement of microsatellite instability (MSI) score

The estimated MSI score of UCEC patients were obtained from a published study by Li et al., who used the MSIsensor algorithm to calculate MSI score of each patient in TCGA database [19].

2.3. Statical analysis

The data was analyzed by R software (version 3.6.3). Kaplan-Meier plotter analysis was used to measure the effects of identified CRG signature on patients' prognosis. Univariate and multivariate cox regression analyses were used to measure the effects of affecting factors on patients' prognosis. The Spearman correlation analysis was used to measure the relationship between risk score and immunocytes infiltration. Statical significance was set at follows: ns: not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001, ns not significant.

3. Results

3.1. Identification of seven differentially expressed prognostic CRGs

As shown in Fig. 1A–C, 46 CRGs were identified. Most of them (39/46) were differentially expressed between tumor and normal tissues in UCEC. In summary, seven differentially expressed CRGs (GLS, CDKN2A, NNAT, MDH1, PC, SDHB, and SUCLG1) correlated with patients' prognosis were identified. Their chromosomal locations were also displayed. The heatmap displayed the expression profiles of seven identified CRGs in tumor and normal samples in UCEC form the TCGA cohort (Fig. 1D). Furthermore, most of them were positively correlated with each other (Fig. 1E). We then used the GeneMANIA database to construct the interaction network, and 19 co-expressed/physical interacted/predicted genes with seven CRGs were identified (Fig. 1F). Functional enrichment analysis revealed these genes were mainly correlated with some metabolic pathways (e.g, TCA cycle, amino acid metabolism, carbohydrate metabolic process) (Fig. 1G).

Fig. 1.

Fig. 1

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Identification of differentially expressed prognostic CRGs in UCEC from TCGA cohort. (A) The diagrams displayed the seven identified CRGs and their corresponding location on chromosomes. (B) The forest displayed the effects of seven CRGs on overall survival in UCEC. (C) The relative expression levels of seven CRGs in tumor and normal tissues. (D) The heatmap displayed the expression profiles of seven CRGs in tumor and normal samples. (E) The correlation analyses within seven CRGs. (F) The correlation network of seven CRGs identified by GeneMANIA database. (G) Functional enrichment analysis of identified genes from GeneMAMIA. CRGs: cuproptosis-related genes. * P < 0.05, *** P < 0.001.

3.2. Construction of the CRG signature predicting poor prognosis in UCEC

550 UCEC patients were randomly divided into testing or training cohort (1:1 ratio). As shown in Table 2, there were no significant differences in demographic characteristics between the training and testing cohorts, which demonstrated the consistency and comparability between the training and testing groups. We then used the Lasso Cox regression analysis to construct the prognostic model based on the seven identified CRGs in training cohort. Finally, the CRG signature containing four genes was constructed. The risk score of each patient from training cohort based on their expression profiles was calculated using the following formula: risk score = 0.3323*GLS + 0.2357*CDKN2A + 0.2723*PC + 0.2652*SUCLG1 (Fig. 2A and B, Supplementary Table S3). The risk score of each patient in testing cohort was calculated according to the same formula (Supplementary Table S4). Patients in training and testing cohorts were then classified as high- or low-risk based on the calculated median risk score. Principal component analysis (PCA) indicated that this CRG signature had discrimination power between tumor and normal samples in training or testing cohort. (Fig. 2C and D). The scatter diagrams displayed the relationships between CRGs expression, risk score and survival status in training cohort (Fig. 2E) and testing cohort, respectively (Fig. 2F). In training cohort, prognostic analyses revealed that high-risk patients had unfavorable overall survival (OS) (HR = 2.94, 95%CI = 1.60–5.43, P = 0.001), and disease-free survival (DSS) (HR = 4.00, 95%CI = 1.80–8.93, P = 0.001) compared to those in low-risk group (Fig. 2G). Similar findings were found in testing cohort regarding patients' OS (HR = 2.89, 95%CI = 1.49–5.60, P = 0.002) and DSS (HR = 6.96, 95%CI = 2.43–19.98, P < 0.001) (Fig. 2H). Further analyses revealed that patients with more advanced stage, lower differentiation, older age and insensitive to primary therapy obtained higher risk score in training and testing cohorts (Fig. 2I and J).

Table 2.

Demographic characteristics of the patient population from training and testing cohorts.

Characteristic
 Training
 Testing
 P
No. 275 275
Grade, n (%) 0.519
G1 47 (48%) 51 (52%)
G2 67 (55.8%) 53 (44.2%)
G3 155 (48.3%) 166 (51.7%)
G4 6 (54.5%) 5 (45.5%)
Stage, n (%) 0.536
Stage I 163 (47.8%) 178 (52.2%)
Stage II 27 (52.9%) 24 (47.1%)
Stage III 71 (55%) 58 (45%)
Stage IV 14 (48.3%) 15 (51.7%)
Primary therapy, n (%) 0.363
Non-response 39 (57.4%) 29 (42.6%)
Response 196 (50.5%) 192 (49.5%)
MSI status, n (%) 0.419
MSI-H 89 (53%) 79 (47%)
MSS/MSI-L 180 (48.8%) 189 (51.2%)
Age, median (IQR) 63 (57, 71) 65 (58, 72) 0.127
BMI, median (IQR) 32.02 (25.83, 38.19) 31.14 (24.92, 38.28) 0.305
GLS, median (IQR) 2.08 (1.61, 2.55) 2.14 (1.65, 2.72) 0.145
CDKN2A, median (IQR) 2.35 (1.42, 4.13) 2.65 (1.38, 4.68) 0.200
PC, median (IQR) 2.03 (1.63, 2.42) 2.07 (1.64, 2.52) 0.523
SUCLG1, mean ± SD 4.15 ± 0.42 4.2 ± 0.43 0.141
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MSI-H: Microsatellite-Instability High; MSI-L: Microsatellite-Instability Low; MSS: microsatellite stable; IQR: Interquartile range; SD: standard deviations.

Fig. 2.

Fig. 2

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Establishment of the CRG signature in UCEC from TCGA cohort. (A) The partial likelihood deviance of candidate CRGs regarding OS in training cohort. (B)The coefficients of candidate CRGs in training cohort. (C) and (D) PCA of the CRG signature in training and testing cohorts, respectively. (E) and (F) The risk score diagrams displayed the relationships between four CRGs expression, risk score, and survival status in training and testing cohorts, respectively. (G) and (H) The effects of risk score on UCEC patients’ survival (overall survival, disease-specific survival) in training and testing cohorts, respectively. (I) The relationships between risk score and clinic-parameters in training cohort. (J) The relationships between risk score and clinic-parameters in testing cohort. CRGs: cuproptosis-related genes; PCA: principal component analysis. *** P < 0.001.

3.3. Independent prognostic value of the four-gene signature in UCEC

To identify the independent prognostic factors regarding OS and PFS, we further performed univariate and multivariate Cox regression analyses based on the clinical data and risk score of UCEC from TCGA cohort. As shown in Table 3, univariate Cox regression analysis revealed that risk score was significantly correlated with patients' OS in training cohort (HR = 3.899, 95%CI = 2.386–6.372, P < 0.001) and testing cohort (HR = 2.211, 95%CI = 1.385–3.529, P < 0.001). Multivariate Cox regression analysis also revealed that risk score was significantly correlated with patients' OS in training cohort (HR = 2.533, 95%CI = 1.469–4.368, P < 0.001) and testing cohort (HR = 1.686, 95%CI = 1.029–2.762, P = 0.038) (Table 4). These findings suggested that the CRG signature could be applied as an independent prognostic indicator in UCEC.

Table 3.

Univariate Cox analysis for overall survival in training and testing cohorts from the TCGA UCEC cohort.

Characteristics No. Training cohort
 N Testing cohort
Hazard ratio (95% CI) P-value Hazard ratio (95% CI) P-value
Age 274 0.076 274 0.061
>60 159 Reference 183 Reference
≤60 115 0.567 (0.303–1.060) 0.076 91 0.509 (0.252–1.030) 0.061
BMI 275 1.004 (0.979–1.030) 0.733 275 0.996 (0.977–1.016) 0.715
Stage 275 <0.001 275 <0.001
Stage I 163 Reference 178 Reference
Stage II 27 1.847 (0.680–5.016) 0.228 24 1.503 (0.503–4.490) 0.466
Stage III 71 2.943 (1.465–5.912) 0.002 58 3.516 (1.791–6.902) <0.001
Stage IV 14 10.124 (4.551–22.523) <0.001 15 6.496 (2.679–15.753) <0.001
Risk Score 275 3.899 (2.386–6.372) <0.001 275 2.211 (1.385–3.529) <0.001
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UCEC: Uterine Corpus Endometrial Carcinoma; 95%CI: 95% confidence interval; No.: number of patients; ns: not significant.

Table 4.

Multivariate Cox analysis for overall survival in training and testing cohorts from the TCGA UCEC cohort.

Characteristics Total(N) Training cohort
 Testing cohort
Hazard ratio (95% CI) P-value Hazard ratio (95% CI) P-value
Stage 275 ns 275 ns
Stage I 163 Reference 178 Reference
Stage II 27 1.497 (0.544–4.116) 0.435 24 1.433 (0.479–4.285) 0.520
Stage III 71 2.144 (1.034–4.444) 0.040 58 2.991 (1.498–5.973) 0.002
Stage IV 14 4.763 (1.903–11.921) <0.001 15 4.718 (1.859–11.974) 0.001
Risk Score 275 2.533 (1.469–4.368) <0.001 275 1.686 (1.029–2.762) 0.038
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UCEC: Uterine Corpus Endometrial Carcinoma; 95%CI: 95% confidence interval; No.: number of patients; ns: not significant.

Time-dependent receiver operating characteristic curves (ROC) revealed the CRG signature could well predict the OS in UCEC, whose cumulative survival rates of 1-,3-,5-year were 0.953,0.807, and 0.765 in training cohort (Fig. 3A), and the cumulative survival rates of 1-,3-,5-year were 0.956, 0.833, 0.755 in testing cohort (Fig. 3B). We further drew nomograms based on the above findings, and the C-index were 0.762 (95%CI = 0.728–0.795) and 0.732 (95%CI = 0.686–0.778) in training and testing cohorts, respectively (Fig. 3C and D). The predicted survival probability at 1-,3-,5-year in the calibration curves were also roughly consistent with the actual survival rates in training and testing cohorts (Fig. 3E and F). Collectively, these findings indicated that the identified CRG signature could be a valuable prognostic model for UCEC.

Fig. 3.

Fig. 3

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Independent prognostic analysis of the constructed CRG signature in UCEC. (A) and (B) Time-dependent ROC of the CRG signature for overall survival in training and testing cohorts, respectively. (C) and (D) The Nomograms were used to predict 1, 3, and 5-year survival rates of UCEC patients in training and testing cohorts, respectively. (E) and (F) The Calibration curves of the nomogram at 1, 3, and 5 years in training and testing cohorts, respectively. CRG: cuproptosis-related gene; ROC: receiver operating characteristic curve.

3.4. CNV and SNV analysis of four CRGs in UCEC

Firstly, we explore the gene mutation characteristics of the four CRGs in UCEC using the GSCA. The mutation types and their corresponding proportions regarding CNV in UCEC were displayed in the pie charts (Fig. 4A). Correlation analyses revealed that CNV was positively correlated with the expression of GLS, SUCLG1, and PC (Fig. 4B). Kaplan-Meier survival analyses indicated that the patients with wild type favored better OS (P = 0.0021) and progression-free survival (PFS) (P = 0.045) compared to those with copy number amplification or deletion (Fig. 4C).

Fig. 4.

Fig. 4

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Gene mutation analysis of the identified CRG signature in UCEC. (A)The mutation types and their corresponding proportions of four CRGs regarding CNV in UCEC. (B) Correlation analyses between CNV and mRNA expression. (C) The effects of gene set CNV on prognosis in UCEC. (D) The mutation types and their corresponding proportions of four CRGs regarding SNV in UCEC. (E) The effects of gene set SNV on prognosis in UCEC. CRG: cuproptosis-related gene; CNV: copy number variation; SNV: single-nucleotide variation; OS: overall survival; PFS: progression-free survival; DSS: disease-specific survival.

Then, we explored the characteristics of SNV of this CRG signature in UCEC. As shown in Fig. 4D, the somatic mutation rates of GLS, CDKN2A, PC and SUCLG1 were 4.14%, 1.51%, 6.59%, and 3.77%, respectively. Survival analysis revealed that patients with mutant SNV obtained better OS (P = 0.007), PFS (P = 0.0022), and disease-specific survival (P = 0.027) compared to those patients with wild type (Fig. 4E).

3.5. Functional enrichment analysis

To further explore the functional differences between high- and low-risk groups, we firstly identified the DEGs between low-risk versus high-risk groups. In total, 283 and 310 DEGs were identified (|logFC| > 0.25, Adj.P-Value < 0.05) in training and testing cohorts, respectively (Fig. 5A and B). Functional enrichment analyses of these DEGs revealed that they mainly got involved in the pathway of cell-cell adhesion and immune activities (e.g., IL-1 signaling pathway, cellular response to cytokine stimulus) in training and testing cohorts (Fig. 5C and D).

Fig. 5.

Fig. 5

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Functional differences between high- and low-risk groups. (A) and (B) DEGs between low- and high-risk groups in training and testing cohorts, respectively. (C) and (D) Functional enrichment analyses of identified DEGs between low- and high-risk groups in training and testing cohorts, respectively. DEGs: differentially expressed genes.

3.6. Correlations of the CRG signature with the dMMR (deficient mismatch repair) status and their prognostic significance in UCEC

Microsatellite instability (MSI) which may affect immunocytes infiltration by generating novel antigens frequently occurred in UCEC, we further explored the differences of MSI in different groups. Here, we measured the MSI score of each patient from the training and testing cohorts. Negative correlations were found between risk score and MSI score in training (R = −0.128, P = 0.034) and testing cohorts (R = −0.281, P < 0.001) (Fig. 6A and D). The Sankey diagrams displayed the relationships between risk groups and MSI status in two different cohorts (Fig. 6B and E). Further histograms displayed the different proportions of each MSI status in two different cohorts (Fig. 6C and F). Overall, the proportion of MSI-H was much lower in high-risk patients compared to low-risk patients either in training (27.01 vs 37.96%) or testing cohorts (18.25% vs 39.42%).

Fig. 6.

Fig. 6

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The relationships between risk score and the dMMR status in UCEC. (A) and (D) The relationships between risk score and MSI score in training and testing cohorts, respectively. (B) and (C) The Sankey diagram and histogram displayed the relationships between risk score and the dMMR status in training cohort. (E) and (F) The Sankey diagram and histogram displayed the relationships between risk score and the dMMR status in testing cohort. (G) and (H) The prognostic significances of different dMMR status and CRG signature on patients’ survival in training and testing cohorts, respectively. MSS: microsatellite stable; MSI-L: low microsatellite instability; MSI-H: high microsatellite instability; NA: not applicable; dMMR: deficient mismatch repair; OS: overall survival; DSS: disease-specific survival.

We combined microsatellite stable (MSS) and MSI-L patients for follow-up analysis, and patients were further divided into four subgroups (MSS/MSI-L-High, MSS/MSI-L-Low, MSI-H-High, MSI-H-Low) according to the dMMR status and risk score. As shown in Fig. 6G, prognostic analyses revealed that patients with MSS/MSI-L and High-risk score (MSS/MSI-L-H) favored worst OS (Log-rank P = 0.002), and DSS (Log-rank P = 0.001) compared to those in other three subgroups in training cohort. Similar findings were found in testing cohort (Fig. 6H). In summary, the identified CRG signature could affect patients' dMMR status and prognosis in UCEC.

3.7. Relationships between the CRG signature and immune infiltration levels in UCEC

Based on the above findings, we further explored the effects of this CRG signature on immunocytes infiltration in UCEC. As shown in Supplementary Fig. S1, we firstly measured the relationships between four identified genes (GLS, CDKN2A, PC, and SUCLG1) and immunocytes infiltration using the TIMER. We then further measured the immunocytes infiltration score of each patient using the “GSVA” R package. As shown in Fig. 7A and B, the risk score was negatively correlated with most immunocytes, except Th2 cells, Th1 cells, Tcm, Macrophages, B cells, and aDC in training and testing cohorts. Low-risk patients obtained more abundant eosinophils, iDC, mast cells, NK CD56bright cells, NK cells, pDC, T cells and Th17 cells compared to high-risk patients either in training or testing cohorts, while aDC showed the opposite trend (Fig. 7C and D).

Fig. 7.

Fig. 7

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The relationships between risk score and immunocytes infiltration in UCEC. (A) and (B) The relationships between risk score and different immunocytes infiltration in training and testing cohorts, respectively. (C) and (D) Comparisons of different immunocytes infiltration between high- and low-risk groups in training and testing cohorts, respectively. * P < 0.05, ** P < 0.01, *** P < 0.001.

We also further explored the relationships between risk score and the expression of chemokines, chemokine receptors, and antigen-presenting molecules. As shown in Supplementary Fig. S2A, the expression of some chemokines (CCL22, CCL21, CXCL2, CXCL3, CXCL14 and CXCL17) were higher in low-risk patients compared to high-risk patients in two cohorts, while CCL7, CCL8, CCL28, CXCL10, CXCL11, CXCL16 displayed the opposite trend. High- and low-risk patients also displayed different patterns of chemokine receptors and antigen-presenting expression in training and testing cohorts (Supplementary Figs. S2B and S2C). Collectively, these findings indicated that immune-environment varied greatly in patients with different risk score in two cohorts.

3.8. Verification of the identified CRGs in UCEC samples and cell lines by experimental methods in vitro

Finally, we used the collected UCEC samples and cell lines to verify the expression profiles of identified CRGs using qRT-PCR. As shown in Fig. 8A, GLS was significantly up-regulated in normal HESC compared to four UCEC cell lines (Ishikawa, HEC-1-A, HEC-1-B, and RL95-2). CDKN2A, PC and SUCLG1 were significantly up-regulated in some UCEC cell lines compared to HESC. We also confirmed the above findings with 20 paired UCEC samples (Fig. 8B–E). These results were basically consistent with the findings from TCGA cohorts.

Fig. 8.

Fig. 8

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The relative expression levels of four identified CRGs in cell lines and 20 paired UCEC samples. (A) The expression of GLS, CDKN2A, PC and SUCLG1 in HESC, Ishikawa, HEC-1-A, HEC-1-B, and RL95-2. (B), (C), (D), (E) The expression of GLS, CDKN2A, PC, and SUCLG1 in 20 pairs of UCEC samples. * P < 0.05, ** P < 0.01, *** P < 0.001, ns not significant.

Elesclomol is an effective copper ion carrier, which can promote cuproptosis. Elesclomol inhibits FDX1 mediated Fe–S cluster biosynthesis by specifically binding α2/α3 Helix and β5 chains of ferredoxin 1 (FDX1) [10,[20], [21], [22]]. We found different concentrations of Elesclomol could significantly inhibit the abilities of cell proliferation (Fig. 9A) and clonal formation (Fig. 9B and C) in UCEC cell lines (Ishikawa and HEC-1-B). The mRNA levels of GLS, CDKN2A, and PC were also significantly downregulated after treated with Elesclomol in Ishikawa (Fig. 9D). We further measured the biological functions of four identified CRGs in UCEC cell lines. As shown in Fig. 9E–H, different siRNAs targeting GLS, CDKN2A, PC, and SUCLG1 were designed and synthesized. Down-regulated GLS and CDKN2A could significantly reduce the capacities of cell proliferation and colony formation in HEC-1-B (Fig. 9I–K). Similar findings were observed in Ishikawa after SUCLG1 was down-regulated, while down-regulated PC has no obvious effects on cell proliferation and colony formation in Ishikawa (Fig. 9K–L). All these findings confirmed that the cuproptosis inducers (Elesclomol) and four identified CRGs could play pivotal roles in UCEC.

Fig. 9.

Fig. 9

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Functional verification of the identified CRG signature by experimental methods in vitro. (A) The changes of cell proliferation after Ishikawa and HEC-1-B were treated with different concentrations of Elesclomol, respectively. (B) and (C) The changes of clonal formation after Ishikawa and HEC-1-B were treated with different concentrations of Elesclomol, respectively. (D) The changes of identified CRGs expression in Ishikawa after treated with different concentrations of Elesclomol. (E) Different siRNAs targeting GLS in HEC-1-B. (F) Different siRNAs targeting CDKN2A in HEC-1-B. (G) Different siRNAs targeting PC in Ishikawa. (H) Different siRNAs targeting SUCLG1 in Ishikawa. (I) The changes of cell proliferation in HEC-1-B after GLS and CDKN2A were knockdown. (J) The changes of cell proliferation in Ishikawa after SUCLG1 was knockdown. (K) The changes of clonal formation in HEC-1-B after GLS and CDKN2A were knockdown. (L) The changes of clonal formation in Ishikawa after PC and SUCLG1 were knockdown. CRG: Cuproptosis-related gene. * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001, ns not significant.

4. Discussion

Recent years, the morbidity of UCEC is rapidly increasing year by year for the following reasons: population aging, obesity, infertility, estrogen use, genetic susceptibility, etc. [23,24]. The mortality rate of UCEC has increased faster than incidence rate for more patients are diagnosed with older age, advanced stage or special pathological type [24,25]. Radical hysterectomy remains the standard treatment for early diagnosed UCEC, and adjuvant chemotherapy or radiotherapy are needed for advanced or high-risk patients [26,27]. Indeed, the therapeutic strategies are limited for advanced or recurrent UCEC, and their five-year survival rates are far from satisfactory [28,29]. Therefore, more in-depth exploration of the mechanism of pathogenesis and metastasis for UCEC are needed to prolong patients' survival time. In recent years, bioinformatics is a useful tool to explore tumor pathogenesis and tumor microenvironment in gynecological malignancies [[30], [31], [32], [33]]. Here, we firstly explored the expression and prognostic significance of CRGs in UCEC, and constructed the CRG signature containing four CRGs (GLS, CDKN2A, PC and SUCLG1). Patients were then classified as high- or low-risk group according to the median risk score. Cox regression analyses indicated that the CRG signature could be a good model for assessing patients' prognosis in UCEC. Further analyses also revealed that the CRG signature was significantly correlated with patients' clinical parameters, dMMR status, and immunocytes infiltration. Finally, we also verified the expression levels of candidate CRGs in UCEC samples and cell lines. All these findings confirmed that the CRG signature may play pivotal roles in predicting patients' prognosis and selecting optimal treatments for UCEC.

Cuproptosis is another form of metal ion-dependent cell death since the discovery of ferroptosis in 2012. Unlike ferroptosis, which mainly catalyzes lipid peroxidation of highly expressed unsaturated fatty acids on cell membranes by ferrous iron or ester oxygenase, cuproptosis is closely related to the regulation of mitochondrial respiration, especially the TCA cycle [10,34]. Tsvetkov et al. revealed that copper ions could bind to the sulfoyl moiety induced by FDX1 and leaded to the oligomerization of fatty acylated DLAT, resulting in cell death. Here, we first identified the gene set that may be related to coproptosis, including copper ion homeostasis, protein lipoacylation, lipoacylation substrate and TCA cycle. Further LASSO analysis revealed that the CRG signature containing four CRGs (GLS, CDKN2A, PC and SUCLG1) had excellent performance in predicting patients' prognosis in UCEC. GLS encodes type K mitochondrial glutaminase, which can catalyze the glutamine into glutamate and ammonia, the former can be further converted into α-ketoglutarate, and then participate in the TCA cycle [35,36]. Although CDKN2A has been proved to be a key tumor suppressor gene, it is significantly up-regulated and correlated with poor survival prognosis in UCEC [37,38]. PC and SUCLG1 are get involved in the TCA cycle, which encode pyruvate carboxylase and the alpha subunit of the heterodimeric enzyme succinate coenzyme A ligase, respectively. PC has been proved to play pivotal roles in enhancing cell proliferation and invasion in a variety of malignancies [39,40].

MSI results from dysfunction of DNA mismatch repaired-related genes (hMSH2, hMLH1, hMSH3, hMSH6, hPMSH1 and hPMSH2), and it is estimated that the overall positive rate of MSI in UCEC is 31.37% [41,42]. In our study, prognostic analyses revealed that patients with high-risk score and MSS/MSI-L status (MSS/MSI-L-H) obtained the worst prognosis compared to those in other subgroups. The advent of immunotherapy has brought hope for improving the prognosis of advanced or recurrent UCEC, and FDA has approved the pembrolizumab (anti-PD-1) for use in solid malignancies with DNA mismatch repair dysfunction based on the findings of KEYNOTE-028/158studies [43,44]. Our study revealed that the rate of MSI-H in high-risk group was lower than that in low-risk group in training and testing cohorts, which suggested that patients in low-risk group were more likely to benefit from immunotherapy. All these findings indicated the CRG signature combined with dMMR status was a good prognostic predictor in UCEC. Of course, how the CRG signature affect the dMMR status still needs further exploration.

There are abundant immune cells and other components (fibroblasts, extracellular matrix, blood vessels or lymphatic vessels) in the complex tumor immune microenvironment of endometrial cancer, which play key roles in tumorigenesis and progression [45]. To our knowledge, our study for the first time explored the relationships between the CRG signature and immunocytes infiltration in UCEC. Patients in low-risk group also obtained more abundant immunocytes infiltration compared to those in high-risk group. Furthermore, the pattern of some chemokines, chemokine receptors, and antigen-presenting molecules expression varied greatly in patients with high- or low-risk score in two cohorts. Our study provided a new perspective for the immune microenvironment regulation and immunotherapy for UCEC, although the mechanism of differences in immunocytes infiltration between high- and low-risk groups and its impacts on prognosis or immunotherapy still need to be explored in the future.

With no doubt, there are some limitations in our study. First, current findings mainly from the TCGA cohorts. We should further verify our bioinformatic findings with more external datasets. Second, only 20 paired UCEC samples were used to confirm the expression levels of candidate CRGs, which could be the reason why SUCLG1 did not been confirmed to be differentially expressed in tumor and normal tissues. We should confirm their expression profiles with more collected samples in future. Last, we should further explore these genes' functions in vitro and vivo. Whether some drugs of copper ionophores (e.g., Elesclomol, Disulfiram) or the potential small-molecule drugs are effective in UCEC also needs to be further explored.

5. Conclusions

In summary, we measured the expression and prognostic significance of CRGs in UCEC, and a novel prognostic mode of four CRGs (GLS, CDKN2A, PC, SUCLG1) was constructed. Our study confirmed this CRG signature was an independent prognostic factor regarding OS in UCEC, which in turn could provide new perspectives for predicting the prognosis of UCEC, and provide some clinical guidance for individual treatments in UCEC. The underlying effects and mechanism of four identifies CRGs on malignant transformation and immunocytes infiltration in UCEC remain further exploration in future.

Ethics approval

The UCEC specimens were obtained from the Clinical Database and Biobank of Patients With Gynecologic Neoplasms (ClinicalTrials.gov Identifier: NCT01267851). The ethical information can be found at http://clinicaltrials.gov./ct2/show/study/NCT01267851.

Author contribution statement

Shitong Lin: Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Yashi Xu, Binghan Liu, Lingling Zheng: Performed the experiments; Contributed reagents, materials, analysis tools or data.

Canhui Cao, Peng Wu: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data.

Wencheng Ding: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data.

Fang Ren: Conceived and designed the experiments; Contributed reagents, materials, analysis tools or data; Wrote the paper.

Funding statement

This work was supported by Innovative Research Group Project of the National Natural Science Foundation of China [82072895, 82141106, 82103134, 81903114 and 2021YFC2701201].

Fang Ren was supported by Health Commission of Henan Province [222300420091].

Canhui Cao was supported by Postdoctoral Research Foundation of China [2021M702223]; Shenzhen Science and Technology Innovation Program [JCYJ20210324105808022].

Data availability statement

The detailed information of UCEC was obtained from The Cancer Genome Atlas (https://portal.gdc.cancer.gov/). Other databases used in our study can be found at the following websites: GeneMINIA (http://genemania.org/), Metascape (https://metascape.org/gp/index.html#/main/step1), GSCA (http://bioinfo.life.hust.edu.cn/GSCA/#/).

Declaration of interest's statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2023.e14613.

Contributor Information

Canhui Cao, Email: canhuicao@foxmail.com.

Peng Wu, Email: pengwu8626@tjh.tjmu.edu.cn.

Wencheng Ding, Email: dingwencheng326@163.com.

Fang Ren, Email: renfang@foxmail.com.

Abbreviations

UCEC

uterine corpus endometrial carcinoma

RG

cuprotosis-related gene

TCGA

The Cancer Genome Atlas

LASSO

least absolute shrinkage and selection operator

TCA

tricarboxylic acid

CNV

copy number variation

SNV

single-nucleotide variation

GSCA

gene set cancer analysis

PCA

principal component analysis

ROC

receiver operating characteristic curve

dMMR

deficient mismatch repair

OS

overall survival

DSS

disease-specific survival

PFS

progression-free survival

MSI

microsatellite instability

MSS

microsatellite stable

dMMR

deficient mismatch repair

Appendix A. Supplementary data

The following are the Supplementary data to this article.

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figs2
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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.csv (229.2MB, csv)
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mmc2.xlsx (63.5KB, xlsx)
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mmc3.xlsx (49.7KB, xlsx)
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mmc4.xlsx (51.4KB, xlsx)
figs1
mmcfigs1.jpg (3MB, jpg)
figs2
mmcfigs2.jpg (2.3MB, jpg)

Data Availability Statement

The detailed information of UCEC was obtained from The Cancer Genome Atlas (https://portal.gdc.cancer.gov/). Other databases used in our study can be found at the following websites: GeneMINIA (http://genemania.org/), Metascape (https://metascape.org/gp/index.html#/main/step1), GSCA (http://bioinfo.life.hust.edu.cn/GSCA/#/).

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