Cuproptosis-related genes signature could predict prognosis and the response of immunotherapy in cervical cancer

Introduction

In recent years, due to progress in surgery, radiotherapy, chemotherapy and immunization, the rate of cervical cancer (CC) in developed countries has significantly decreased (1,2). However, more than half of CC cases are in the advanced stages at the time of diagnosis, and patients with stage IIB and above cannot undergo surgery (3,4). Pelvic radiotherapy combined with synchronous chemotherapy has become the standard treatment method for locally advanced CC. Randomized clinical trials have shown a typical effective rate of approximately 90%, with an average 5-year survival rate of 72% (4,5). Approximately 20% of CC patients experience pelvic recurrence and/or distant metastasis within 5 years after radiotherapy and chemotherapy, which usually leads to death (6). Therefore, finding factors that are associated with treatment efficacy will be of clinical significance.

In recent years, cuproptosis has become a research hotspot, and copper ions are essential trace elements for life (7). Recent research has found that compared to healthy patients, copper levels in the serum and tumor tissues of cancer patients are significantly increased (8). Changes in copper levels have also been shown to affect the occurrence and development of cancer (9). Research has shown that an increase in copper concentration within tumors promotes tumor growth, invasion, and treatment resistance (10). In addition, it has been reported that cuproptosis is a recently discovered cell death pattern that plays an important role in tumor growth, angiogenesis, and tumor metastasis (11,12). The prognostic risk score based on the expression of cuproptosis-related genes (CRGs) had predictive value in colon cancer, kidney cancer, esophageal cancer, bladder cancer and other cancers and may be related to the local immune infiltration of tumors (13-16). SLC25A5 has certain prognostic value in colorectal cancer (17), and high expression of SLC25A5 is a strong predictor of neuroblastoma (18). SLC6A3 is known to promote cell viability in hepatocellular carcinoma (19).

At present, research on copper ion metabolism in CC is flourishing. Previously, a risk model based on 7 CRGs was constructed, and the results suggested that CRGs can predict the prognosis of CC, and immune activity varies in different risk groups (20). Long non-coding ribonucleic acid (lncRNAs) associated with cuproptosis can be used to predict the prognosis of patients, especially those undergoing radiotherapy (21). Research has shown that cuproptosis-related angiogenesis is associated with the efficacy of immunotherapy (22). It can be seen that the copper metabolism is involved in the occurrence, development, and therapeutic efficacy of CC from multiple aspects. Thus, it is necessary to identify reliable and sensitive biomarkers of copper poisoning in CC.

This study included 25 CRGs and comprehensively analyzed their effects on the prognosis and immune infiltration of CC, as well as their relationship with radiotherapy. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-641/rc).

Methods

Acquisition of CC data

The Cancer Genome Atlas (TCGA; https://portal.gdc.Cancer.gov/) CC transcript data and clinical information were downloaded from the public database, and patients with incomplete survival information were excluded from the study. Preprocessing of the data resulted in 306 samples of CC and 3 samples of normal tissue adjacent to the cancer. The expression profiles were plotted using the R packages “ggplot2” and “pheatmap”.

Acquisition of CRGs

Based on current research, the following 25 CRGs have been identified: SLC25A5, CP, SLC23A2, NDUFB2, DLD, PDHX, DLST, LIAS, ATP7B, NDUFA1, COX7B, NDUFA2, SLC31A1, FDX1, SLC6A3, LIPT1, DLAT, PIH1D2, MITD1, ATP7A, CCS, LIPT2, ATOX1, NDUFB1, and SLC22A5 (13).

Sample collection before and after radiotherapy

In this study, we collected 20 CC patients who received radical radiotherapy at Changzhou Second People’s Hospital. The patients provided one sample before and after radical radiotherapy, totaling 20 pairs. The two sample collection times were at 0 Gy irradiation and 20–40 Gy irradiation, respectively. The specimen was soaked in formalin solution and stored at −80 ℃. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of Changzhou Second People’s Hospital [No. (2019) KY052-02]. Informed consent was taken from all the participants.

RNA sequencing

Illumina HiSeq sequencing platform was used for RNA sequencing. For messenger ribonucleic acid (mRNA) transcripts, differential expression sequencing (DESeq) software was used to screen for differentially expressed transcripts in different sample groups, and differential expression was identified using conditions |log₂FC| ≥1 and P value ≤0.05. The differentially expressed transcripts between the two groups were screened as radial-related genes. The supplementary information lists the differentially expressed genes before and after radiotherapy. The lima package in R software was used to study differentially expressed mRNA.

Construction of the protein-protein interaction (PPI) network

PPI networks were drawn using the STRING online website (https://cn.string-db.org/).

Survival

Kaplan-Meier (K-M) survival analysis was performed using the R packages “survival” and “survivor”.

Functional enrichment

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the R package “clusterProfiler”. The GO terms and KEGG pathways were considered statistically significant if their P values and false discovery rates (FDR) were less than 0.05.

Building a risk prognosis model

Using least absolute shrinkage and selection operator (LASSO) regression analysis, a prognostic risk model was established based on the expression level of CRGs. Using the median as the threshold, patients were divided into high- and low-risk groups based on their risk scores. We used K-M survival curves to verify survival differences among groups with high- and low-risk. The predictive value of the CRGs prognostic signature was evaluated by receiver operating characteristic (ROC) curves of 1-, 3-, and 5-year survival rates and area under the ROC curves (AUCs). The R packages “ggrisk”, “survival”, “survivor”, and “timeROC” were used to conduct LASSO regression.

Immunological correlation analysis

To analyze the immune microenvironment, we used CIBERSORT (https://cibersort.stanford.edu/). The infiltration levels of 22 types of immune cells were quantified, and the difference in immune score between different groups was determined.

Immunohistochemistry

The cervical tissue of the research subjects was collected, and following the routine steps of formaldehyde fixation, paraffin embedding, and 4 µ after continuous sectioning, the specimen was fixed and subjected to dewaxing, hydration, antigen repair, and peroxidase blocking. Monoclonal antibodies and rabbit anti-ANT1 + ANT2 + ANT3 + ANT4 polyclonal antibody diluted with phosphate buffer salt solution was added to the samples (dilution ratio 1:100). The specific immunohistochemical procedures were carried out according to the manufacturer’s instructions (Beijing Bioss Biotechnology Co., Ltd., China).

Statistical analysis

R software (version 4.1.0) was used to process all data and all the figures. The log-rank test was used to compare differences in survival between the groups. The differences between two groups of samples were compared with the help of the Wilcox test, and the R software package “ggplot2” was used for mapping. P<0.05 was considered statistically significant.

Results

The expression of CRGs in CC

First, to reveal the correlation between CRGs, we used Spearman correlation analysis. As shown in Figure 1A, NDUFA1 and COX7B had the strongest correlation, with a correlation coefficient of 0.82 and a positive correlation. Next, we constructed a protein interaction network diagram using the STRING website and found that LIPT1 is closely related to other genes (Figure 1B). Furthermore, KEGG enrichment analysis was conducted, and the circle plot showed that the pathways enriched with NDUFA1, NDUFA2, NDUFB1, NDUFB2, and COX7B were similar and related to various diseases, such as Parkinson disease and Huntington disease. ATOX1, ATP7A, and SLC31A1 were mainly related to mineral absorption, while 11 genes, such as LIPT1 and COX7B, were related to metabolic pathways (Figure 1C). Finally, we conducted a differential expression analysis on 25 CRGs in CC tissues and normal tissues adjacent to cancer, and the results showed that there was a significant difference in the expression level of CRGs in the tumor and normal groups. The expression of COX7B, MITD1, SLC25A5, and SLC31A1 was upregulated in CC tissue, while that of LIPT1 and SLC6A3 was downregulated in tumor tissue (Figure 1D). In summary, cuproptosis is associated with various diseases, and CRGs mainly affect tumor progression by regulating metabolism. LIPT1 may be a key gene that impacts copper metabolism in CC. In addition, we found that the main CRGs that play a role in different diseases are different. We speculate that there may be different subgroups within CRGs, and that cuproptosis plays a very important role in disease.

Figure 1 Expression of cuproptosis-related genes in cervical cancer. (A) The correlation coefficient between the 25 cuproptosis-related genes; red represents a positive correlation, blue represents a negative correlation, and the darker the color is, the stronger the correlation. (B) The protein-protein interaction network shows the closest relationship between LIPT1 and the other genes. (C) KEGG pathway enrichment analysis results. (D) Differential expression of cuproptosis-related genes in cancer tissue and normal tissue; there was high expression of COX7B, MITD1, SLC25A5, and SLC31A1 in cancer tissue and low expression of LIPT1 and SLC6A3 in tumor tissue. Red represents tumor tissue, blue represents normal tissue, and P<0.05 has statistical significance. KEGG, Kyoto Encyclopedia of Genes and Genomes; TCA, tricarboxylic acid.

The expression of CRGs predicts the prognosis of CC patients

Survival analysis was performed on 25 CRGs in the TCGA queue, and K-M curves were plotted. The results showed that COX7B, NDUFA1, NDUFA2, NDUFB1 and PIH1D2 were statistically significant for overall survival, the P values are 0.008, 0.01, 0.02, 0.03, 0.04 respectively, and high expression predicted a good prognosis (Figure 2A-2E). No statistically significant results were obtained in the analysis of the prognostic value of the other 20 genes (P>0.05).

Figure 2 Survival analysis of cervical cancer based on cuproptosis-related genes. (A) COX7B, (B) NDUFA1, (C) NDUFA2, (D) NDUFB1 and (E) PIH1D2 were positively correlated with prognosis. CESC, cervical squamous cell carcinoma.

Building a risk prognostic model

To better evaluate the predictive value of CRGs for CC prognosis, we constructed a LASSO regression model. The coefficients of selected features are shown by lambda parameters. The abscissa reports the value of lambda, and the corresponding reports the coefficients of the independent variable (Figure 3A,3B). By analyzing the relationship between overall survival, death, and expression changes in various genes in CC patients in the TCGA database, the SLC25A5, COX7B, NDUFA2, and PIH1D2 genes were shown to be protective factors, and their expression decreased with increasing risk scores. The SLC23A2 and DLAT genes were risk factors, and their expression increased with increasing risk scores (Figure 3C). According to the expression of these risk genes, the samples were divided into high- and low-risk groups. The K-M curve was drawn to show that the low-risk group had a survival advantage (Figure 3D, P<0.001). In addition, we plotted receiver operating characteristic (ROC) curves to further demonstrate the role of CRGs in the prognosis of CC patients, with AUC values of 0.645, 0.681, and 0.709 at 1, 3, and 5 years, respectively (Figure 3E). In summary, CRGs can predict the prognosis of CC patients.

Figure 3 Establishing a LASSO prognostic risk model. (A) The corresponding abscissa is the independent variable lambda value, and the ordinate is the coefficient of the independent variable. (B) The LASSO regression model was used to plot the relationship between the partial likelihood bias and the log (λ). (C) Integrating survival time, survival status, and gene expression data, the LASSO regression analysis results identified SLC25A5, COX7B, NDUFA2, PIH1D2, SLC23A2 and DLAT as risk factors. Light blue represents the low-risk group, red represents the high-risk group; 0 represents death, and 1 represents survival. Risk score = (−0.1757) * SLC25A5 + (0.1113) * SLC23A2 + (−0.069) * COX7B + (0.2048) * NDUFA2 + (0.0972) * DLAT + (−0.2049) * PIH1D2. (D) Patients in the low-risk group had a better prognosis. Light red represents the low-risk group, while blue represents the high-risk group. (E) Plot of the ROC curve with AUC values of 0.645, 0.681, and 0.709 for 1, 3, and 5 years, respectively. HR, hazard ratio; CI, confidence interval; LASSO, least absolute shrinkage and selection operator; ROC, receiver operating characteristic; AUC, area under the curve.

Analysis of differences between the high- and low-risk groups

To explore the potential biological processes that were different in the high- and low-risk groups, we conducted a functional enrichment analysis, and Figure 4A shows the pathways with significant differences in the high- and low-risk groups (P<0.001). The low-risk group was enriched in tumor promotion signature and the deoxyribonucleic acid (DNA) repair pathway, meanwhile, the inflammatory response, the phosphatidylinositol 3-kinase protein kinase b mammalian target of rapamycin (PI3K AKT mTOR) pathway, the P53 pathway, transforming growth factor-beta 1, and the interleukin (IL)-10 anti-inflammatory signaling pathway were enriched in the high-risk group. The high-risk group impacted the occurrence and development of tumors through multiple pathways. Subsequently, we used CIBERSORT to evaluate the infiltration of 22 immune cells in the high- and low-risk groups. The results showed that resting natural killer (NK) cells and M0 macrophages were the main cells in the high-risk group, while naive B cells, CD8 T cells, regulatory T cells (Tregs), gamma delta T cells, M2 macrophages, and neutrophils were the main cells in the low-risk group (Figure 4B). In addition, we also analyzed the correlation between the high- and low-risk groups and programmed cell death-ligand 1 (PD-L1), and the high-risk group had a strong correlation with PD-L1 (Figure 4C). These results indirectly indicate that the immune landscapes of the two groups are different.

Figure 4 Comparison of the high- and low-risk groups. (A) The pathways enriched in the high- and low-risk groups were different, and the figure shows the significant results (P<0.05). (B) The infiltration of 22 types of immune cells varied in the high- and low-risk groups. (C) The expression of PD-L1 was different between the high- and low-risk groups, with PD-L1 expression being upregulated in the high-risk group. Red represents the low-risk group, and light blue represents the high-risk group. PD-L1, programmed cell death-ligand 1; PI3K, phosphatidylinositol 3-kinase; AKT, protein kinase b; mTOR, mammalian target of rapamycin; IL, interleukin; NK, natural killer; TGFB, transforming growth factor beta.

Identification of differentially expressed genes before and after radiotherapy

We collected samples from 20 CC patients who needed radical radiotherapy in clinical practice and performed RNA sequencing on cancer and adjacent specimens (23). We identified 4,675 differentially expressed genes before and after radiotherapy, of which 3,275 were upregulated and 1,400 were downregulated after radiotherapy (available online: https://cdn.amegroups.cn/static/public/tcr-24-641-1.xlsx). Radiotherapy affected the expression of CRGs; the expression of SLC6A3 was upregulated after radiotherapy and that of SLC25A5 was downregulated after radiotherapy (Figure 5). Radiotherapy could alter cuproptosis in CC, thereby affecting tumor metabolism.

Figure 5 The differentially expressed cuproptosis-related genes before and after radiotherapy; there was high expression of SLC6A3 after radiotherapy and low expression of SLC25A5 after radiotherapy.

Immunohistochemistry (IHC)

SLC25A5 expression was upregulated in CC tissue, downregulated after radiotherapy, and decreased as the risk score increased. To confirm the role of SLC25A5 in the development of CC, we measured the level of SLC25A5 in CC tissue before and after radiotherapy using IHC staining. The results showed that ANTs in CC were mainly expressed in the nucleus, with strong expression before radiotherapy and weak expression after radiotherapy. The expression in adjacent tissues was even lower, and in the adjacent tissues, ANTs were mainly expressed in the basal cell layer (Figure 6A-6E). SLC25A5 seems to be related to the degree of differentiation of CC, with poorer differentiation leading to higher expression (dilution ratio 1:100).

Figure 6 ANTs are mainly expressed in the nucleus of cervical cancer cells, and they had high expression before radiotherapy and low expression after radiotherapy (hematoxylin-eosin staining, ×100). (A) Nuclear staining of adjacent tissues. (B,C) Nuclear staining of cervical cancer tissue before radiotherapy. (D,E) Nuclear staining of cervical cancer tissue after radiotherapy.

Discussion

Cell death caused by the excessive accumulation of copper ions in cells is called cuproptosis (24,25). Copper ions, as cofactors of various key metabolic enzymes that drive physiological processes, are widely involved in cell metabolism. It is necessary to regulate the level of copper ions in the internal environment and maintain a lower level to ensure normal cell function (26). Recently, there is research showing that cuproptosis is associated with various diseases (27). Globally, the incidence of CC is highest among all tumors of the female reproductive system (28). The relationship between CRGs and cancer has recently become a research topic of interest; however, there are few studies related to CC, and it is even rarer to find studies that include the 25 CRGs in the analysis.

The concentration of intracellular copper ions is regulated by a complex network of copper-dependent genes. In our study, LIPT1 was most closely associated with other CRGs, and LIPT1 may serve as a key factor in copper metabolism that affects the occurrence and development of CC. Recent research has shown that knocking down the expression of LIPT1 inhibits the proliferation and invasion of liver cancer cells (29); LIPT1 is associated with the prognosis of melanoma (30), breast cancer (31) and other cancer patients. The role of LIPT1 in tumors cannot be ignored, and its correlation with CC and mechanism of action deserves further exploration. By plotting the K-M survival curve, we found that COX7B, PIH1D2, NDUFA1, NDUFA2, and NDUFB1 were positively correlated with patient prognosis. COX7B gene expression is upregulated in CC, and high expression indicated a good prognosis. As mentioned above, COX7B plays an important role in CC, and the specific mechanism of COX7B deserves further exploration. There is relatively little research on NDUFA1, NDUFA2, and NDUFB1. However, in the literature, we found that they are associated with the COX7 gene family and that they are key redox factors that regulate the expression of genes related to energy metabolism in tumor cells (32). This suggests that NDUFA1, NDUFA2, and NDUFB1 may further influence the occurrence and development of CC by intervening in cellular redox. In summary, copper ions are widely involved in various processes in cellular metabolism, and their level plays a crucial role in disease occurrence, disease development, and patient prognosis. Identifying the genetic loci associated with cuproptosis and intervening will provide new insights into disease prevention and treatment.

By constructing a risk model, the correlation between a certain influencing factor and disease prognosis can be predicted. Recent research has shown that in constructing lncRNA signatures related to cuproptosis, lncRNAs related to cuproptosis were closely related to CC patient prognosis, immune score, and chemotherapy sensitivity (33). The impact of cuproptosis on CC is widespread and important, but currently, there are few risk models constructed using CRGs as risk factors. In our study, a LASSO regression model was constructed for 25 CRGs to differentiate the high- and low-risk groups, which had significant differences in prognosis, immune infiltration, and response to immunotherapy. The high-risk group was enriched in multiple pathways, and there was significant infiltration of resting NK cells and M0 macrophages in the high-risk group, indicating a poor prognosis. NK cell resetting is an effective process that kills tumor cells in the tumor microenvironment. However, due to the limits of metabolism on effector function, NK cell resetting function was inhibited (34), which is consistent with the poor prognosis of the high-risk group with significant infiltration. According to our previous research, macrophages can promote tumor progression, and M0 macrophages are associated with the efficacy of radiotherapy (35), which may be one of the reasons for the poor prognosis in the high-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group, with naive B cells, CD8 T cells, Tregs, gamma delta T cells, M2 macrophages, and neutrophils enriched in the low-risk group. In our grouping model, high CD8⁺ T-cell infiltration indicates a good prognosis, which is consistent with recent research (16). We speculate that changes in CRGs may improve prognosis by improving the immune microenvironment. Changes in the tumor microenvironment can affect the therapeutic efficacy of programmed cell death 1 (PD-1)/PD-L1 monoclonal antibodies. Research shows that radiotherapy combined with PD-1/PD-L1 monoclonal antibodies can effectively increase the number of patients who benefit from immunotherapy (36). In our analysis, the high-risk group was strongly correlated with PD-L1. This not only indicates that cuproptosis is associated with the immune response but also suggests that high-risk populations may benefit from immunotherapy; these findings can guide clinical treatment and improve patient prognosis. In summary, this model has potential predictive power and provides a reference for further exploring the mechanisms and functions of its prognostic value.

In our previous research, we showed that radiotherapy can increase immune infiltration and enhance the ability to kill tumors (35). Our results revealed that CRGs can improve the immune microenvironment and improve patient prognosis. To clarify the relationship between CRGs and radiotherapy, we collected 20 samples from CC patients before and after radiotherapy for RNA sequencing. RNA sequencing (RNASeq) can be a valuable tool for CC patients (37). The differentially expressed genes before and after radiotherapy were compared with CRGs, and we identified the following 2 CRGs related to CC radiotherapy: SLC25A5 and SLC6A3. SLC25A5 is a protective factor that has upregulated expression in CC tissue; SLC25A5 was also evaluated by immunohistochemistry. The results confirmed that SLC25A5 expression is downregulated after radiotherapy. First, we showed that CRGs can be affected by radiation, and second, we speculate that cuproptosis may affect the efficacy of radiotherapy. Recently, there have been data analyses indicating a potential correlation between copper poisoning and radiation sensitivity (21). However, validating this hypothesis will require deeper experimental verification.

Conclusions

In summary, we analyzed the expression and prognostic value of 25 CRGs in CC, established a risk model to explore the differences and clinical significance of CRGs in the high- and low-risk groups, and noted that SLC25A5 is a CRG affected by radiation, which may affect the efficacy of radiotherapy in CC patients. These results will be beneficial for the precise treatment of CC patients.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-641/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-641/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-641/prf

Funding: This work was supported by Talent Start-up Funds of Tongji Hospital (No. RCQD2407), Sustainable Development Science and Technology Innovation Plan of Chongming District (No. CK Y2024-19), and Science and Technology Development Foundation of Nanjing Medical University (No. NMUB20230045).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-24-641/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by the Ethics Committee of Changzhou Second People’s Hospital [No. (2019) KY052-02]. Informed consent was taken from all the participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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