The Prognosis and Immunotherapy Prediction of Cuproptosis-Related Genes in Head and Neck Squamous Cell Carcinoma

doi:10.21203/rs.3.rs-2852784/v1

Background

To determine the role of cuproptosis-related genes in head and neck squamous cell carcinoma, and establish a new prognosis model and provide a novel therapeutic target.

Methods

We extracted gene expression, mutation information, and clinical data of HNSC patients through TCGA and GTEx. Then use R, GEEA, SPSS to analyze Cuproptosis-related genes, include differential gene expression, prognostic analysis, survival prediction analysis, correlation pathway analysis, and immune correlation analysis. The clinical samples were used for immunohistochemistry and PCR to verify the analytical results.

Results

We analyzed 39 normal samples and 469 HNSC to show the co-expression relationship between LncRNA and cuproptosis-related genes. A prediction model was established in predicting the survival rate and the survival period of patients. The expression of LIPT1 gradually increased with clinical grading, which was further verified by immunohistochemistry and qRT-PCR. LIPT1 is an independent prognostic factor of HNSC, and may be related to the occurrence of HNSC. Immunocyte reduction and immune escape existed in LIPT1 over-expression group.

Conclusions

Our findings demonstrate that cuproptosis-related genes can predict the risk, progression and prognosis of HNSC. LIPT1 is related to the tumor grading of HNSC, and it can be used as an independent prognostic factor and a novel target for HNSC immunotherapy.

Head and neck squamous cell carcinoma

Cuproptosis

LIPT1

Immunotherapy

Prognosis

Head and neck squamous cell carcinoma (HNSC) is a type of cancer originating in the mucosa of the oral cavity, larynx, and pharynx. Globally, HNSC account for approximately 2% of all malignancies. It is mainly related to smoking, chewing betelnut, excessive drinking and the infection of human papillomavirus (HPV) carcinogenic strain. The diagnosis of HNSC usually requires the pathological examination of biopsy of the primary tumor or secondary cervical tumor by endoscopy, percutaneous puncture or surgery. Its histopathological characteristics are cell atypia and squamous cell differentiation. The main treatment of HNSC is surgical resection, radiotherapy and chemotherapy, and systemic treatment. CT is still the main method for follow-up observation of patients, but it is unable to monitor the small progress of HNSC, such as occult metastasis, early drug resistance, and long-term prognosis of patients. Therefore, there is an urgent need for a comprehensive and effective method to evaluate the risk, treatment effect and prognosis of patients.

Cuproptosis is a newly defined form of regulatory cell death. Scientists at the Broad Institute have revealed that copper toxicity involves disruption of specific mitochondrial metabolic enzymes that trigger an unusual mechanism of cell death(1). The research results were published in Science under the title "Copper induces cell death by targeting lipoylated TCA cycle proteins". The researchers systematically tested the toxicity of copper carriers and concluded that the toxicity originated from the the assumption of copper itself. They found that many different copper-binding molecules, or copper ionophores, can induce cell death in a similar way. In addition, the researchers found that this form of cell death, which they call epidermal necrosis, differs from other well-studied forms. When the researchers blocked known cell death pathways, such as apoptosis and iron toxicity, cells treated with copper ionophores still died. At present, inducing cuproptosis of cancer cells is considered to be a promising therapeutic strategy. Therefore, cancer research related to cuproptosis has entered the vision of scientists. However, in HNSC, no therapeutic targets related to cuproptosis have been identified. Therefore, for the diagnosis, treatment and prognosis of HNSC, it is necessary to further screen cuproptosis-related genes based on clinical samples.

Long non-coding RNAs (LncRNAs) are noncoding RNAs greater than 200 nucleotides in length. Like mRNA, LncRNA is transcribed from the corresponding gene, has a 5' cap and a poly tail, and is spliced to form a mature LncRNA. In the past few decades, more and more evidence shows that LncRNAs also play a key role in regulating cancer proliferation, metastasis, cell cycle and programmed death. The abnormal expression or function of LncRNA is closely related to the occurrence of human diseases, specifically manifested in the abnormal sequence and spatial structure of LncRNA, the abnormal expression level and the abnormal interaction with binding proteins.

However, there are very limited studies on screening LncRNA associated with cuproptosis in HNSC. In this study, we analyzed the normal samples and tumor samples of HNSC patients to show the co-expression relationship between LncRNA and the cuproptosis-related gene, and established a prognostic model. Then, we chose LIPT1 with predictive significance for further research, and preliminarily verified the correlation between LIPT1 and clinical grading. Finally, the correlation of LIPT1 expression with immune infiltrating cells and immune checkpoints were confirmed. Our findings will help to predict the prognosis of HNSC patients and provide reference for clinical immunotherapy.

Data download and organization

Download HNSC related data: transcriptomic data, clinical data and mutational data at TCGA (https://portal.gdc.cancer.gov/). There is a total of 503 cases, 548 files and 511 mutation data.

A total of 6 HNSC patients with different grades in the First Affiliated Hospital of China Medical University were included in this study. The patients did not receive radiotherapy and chemotherapy before surgery, and the case type was head and neck squamous cell carcinoma. All patients signed informed consent, and this study was approved by the hospital ethics committee.

Cuproptosis related genes collation

Extraction of the expression levels of cuproptosis related genes. According to the expression data file (symbol.txt) and cuproptosis related genes (NFE2L2, NLRP3, ATP7B, ATP7A, SLC31A1, FDX1, LIAS, LIPT1, LIPT2, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, CDKN2A, DBT, GCSH and DLST) to calculate its expression in HNSC.

Prognosis

Construction of the prognostic model: First, the data is grouped into a Train group and a Test group. Use Train to build a prognostic model and use the Test group to verify the accuracy of the model. The formula of the model is obtained through the construction of the model. In the constructed model, the risk score is equal to the expression level of LncRNA multiplied by the coefficient to obtain the formula of the model. According to the risk score obtained from the model formula, the patients were divided into high and low risk groups according to the median value of the risk score. The Test group does the same processing steps. Draw forest graphs, losso regression graphs and cross-validation graphs.

Differential expression of cuproptosis related genes at different differentiation levels of HNSC

According to the RNAseq data of HNSC and the corresponding clinical information, the R software package ggplot2 was used to display the differential expression of cuproptosis related genes at different tumor differentiation levels. Here we used Kruskal-Wallis one-way ANOVA for comparisons of more than three groups. GTEx data from (https://gtexportal.org/home/datasets).

Select the key cuproptosis gene LIPT1 for further analysis

Immunohistochemical verification of clinical samples:

Experimental validation was performed on HNSC patients by immunohistochemical staining. As mentioned above, a total of 6 HNSC patients with different pathological grades in the First Hospital of China Medical University were included in this study. Immediately after surgical excision, tissues were fixed in 10% formalin at room temperature for 24 h and embedded in paraffin. After sectioning, dewaxing, antigen retrieval, and serum blocking, primary and secondary antibodies were added. After adding chromogenic reagent, counterstaining, dehydration and mounting, the samples were observed under a microscope.

Differences confirmed by qRT-PCR:

Total RNA was extracted from tumor tissues of different grades using TRIZOL reagent according to the manufacturer's instructions. Total RNA was amplified by RT-PCR. Primers were designed as follows: LIPT1-F: GCTCTGAATGCTGTCCAACC, LIPT1-R: GCATTGCTCCTGATCCCTTG. Data analysis was performed according to the output ct value.

The prognostic value of LIPT1 in HNSC patients.

The potential prognostic value of LIPT1 was assessed using Cox proportional hazards model and Kaplan-Meier analysis. Here we use Kaplan-Meier Plotter for analysis (https://kmplot.com/analysis)(2), and using auto select best cutoff to establish the best node value.

GSEA single gene analysis.

KEGG-related pathway analysis was performed using LIPT1 expression data and GSEA-4.3.2 software.

Spearman correlation analysis between individual genes and pathway scores. The genes contained in the corresponding pathways were collected, analyzed by the R software GSEA package, and finally the correlation between genes and pathway scores was analyzed by Spearman correlation.

Immune-related analysis

Correlation between LIPT1 expression and immunodiafiltration level in HNSC

Based on RNAseq data from HNSCs and corresponding clinical information. Displayed via the R package ggstatsplot, uses Spearman's correlation analysis to describe correlations between quantitative variables without a normal distribution. Negative numbers represent negative correlations between two gene expressions, and positive values represent positive correlations. The closer the value is to 1 or -1, the greater the The stronger the correlation; the closer to 0, the weaker the correlation between the two.

Immune checkpoints.

Based on RNAseq data from HNSCs and corresponding clinical information. SIGLEC15, TIGIT, CD274, HAVCR2, PDCD1, CTLA4, LAG3 and PDCD1LG2 are genes related to immune checkpoints. The expression values of these 8 genes were extracted to observe the expression of genes related to immune checkpoints. Implemented through the R packages ggplot2 and pheatmap.

Statistical analysis

All statistical analyses were performed using R software (version 3.5.3) or SPSS (version 23.0). If not specified, P values less than 0.05 were considered statistically significant.

The flow chart of this study is shown in Fig.1.

Identification of Cuproptosis-related Differentially Expressed LncRNAs in HNSC

Data download and organization：A total of 39 normal samples and 469 tumor samples from HNSC patients in TCGA were included in this study. Transcriptome data can be found in symbol.txt. Clinical data can be found in clinical.xls. mRNAs and LncRNAs are found in LncRNA.txt and mRNA.txt.

The expression levels of cuproptosis related genes can be found in cuproptosisExp.txt. The coexpression results of cuproptosis related LncRNAs were visualized by drawing a Sankey diagram as shown in Fig. 2A. The upper bands represent cuproptosis-related genes, and different colors represent different genes. The lower bands represent LncRNAs, which are LncRNAs that have a coexpression relationship with cuproptosis. The lines connecting the two represent the co-expression relationship between LncRNAs and the corresponding cuproptosis gene.

Construction of the Prognostic Model Using the Expression Profiles of Cuproptosis-related LncRNAs

LASSO Cox regression analysis was used to establish a prognostic model using the expression profiles of cuproptosis-related genes. Patients were stratified into high-risk groups (n = 233) or low-risk groups (n = 233) according to the median cut-off value. In the forest plot (Fig. 2B), the high-risk ones are in red, and the low-risk ones are in green. Fig. 2C and Fig. 2D are the lasso regression graph and the cross-validation graph, respectively. The number of points with the smallest cross-validation in the cross-validation graph is the LncRNAs obtained by lasso regression. Fig. 2E shows the correlation of LncRNAs and cuproptosis related genes involved in model construction.

Table 1 shows the clinical statistical analysis of model grouping. According to the analysis results, it can be seen that the P values of clinical characteristics of patients between the Train group and the Test group are all greater than 0.05, indicating that there is no difference in clinical characteristics between the two groups. That is, there is no deviation of clinical traits between the two groups of samples.

Table 1.
Covariates	Type	Total	Test	Train	Pvalue
Age	<=65	306(65.67%)	152(65.24%)	154(66.09%)	0.9223
	>65	160(34.33%)	81(34.76%)	79(33.91%)
Gender	FEMALE	120(25.75%)	63(27.04%)	57(24.46%)	0.5963
	MALE	346(74.25%)	170(72.96%)	176(75.54%)
Grade	G1	59(12.66%)	35(15.02%)	24(10.3%)	0.2497
	G2	277(59.44%)	138(59.23%)	139(59.65%)
	G3	109(23.39%)	48(20.60%)	61(26.18%)
	G4	2(0.43%)	1(0.43%)	1(0.43%)
	unknow	19(4.08%)	11(4.72%)	8(3.43%)
Stage	Stage I	23(4.94%)	10(4.29%)	13(5.58%)	0.756
	Stage II	66(14.16%)	36(15.45%)	30(12.88%)
	Stage III	78(16.74%)	37(15.88%)	41(17.6%)
	Stage IV	236(50.64%)	120(51.5%)	116(49.79%)
	unknow	63(13.52%)	30(12.88%)	33(14.16%)
T	T1	42(9.01%)	18(7.73%)	24(10.3%)	0.6902
	T2	126(27.04%)	64(27.47%)	62(26.61%)
	T3	91(19.53%)	49(21.03%)	42(18.03%)
	T4	154(33.05%)	80(34.33%)	74(31.76%)
	unknow	53(11.37%)	22(9.44%)	31(13.3%)
M	M0	168(36.05%)	88(37.77%)	80(34.33%)	1
	M1	1(0.21%)	1(0.43%)	0(0%)
	unknow	297(63.73%)	144(61.8%)	153(65.67%)
N	N0	155(33.26%)	78(33.48%)	77(33.05%)	0.8504
	N1	65(13.95%)	29(12.45%)	36(15.45%)
	N2	151(32.4%)	77(33.05%)	74(31.76%)
	N3	6(1.29%)	3(1.29%)	3(1.29%)
	unknow	89(19.1%)	46(19.74%)	43(18.45%)

Relationship Between Cuproptosis-related LncRNA Signature and Clinical Characteristics in HNSC

Survival curves were constructed by survival analysis: As shown in Fig. 3A, the P values of the high- and low- risk groups in the test group and the train group were both less than 0.05, indicating that there was a difference in the survival of patients between the high- and low- risk groups, and that the constructed model could distinguish patients in these groups. It can be seen that the P value in PFS analysis was less than 0.05 which indicated that there was a difference in the progression-free survival of patients between the high- and low- risk groups. The risk curve was shown in Fig. 3B. From top to bottom are the risk curves of the whole group, the test group, and the train group. The patients were divided into two groups, high and low risk, according to the median risk. The low risk group was blue, and the high risk was red. In the Survival time curve, mortality increases as patient risk increases. The risk heat map was used to observe which LncRNAs were at high risk and which ones were at low risk. In the independent prognostic analysis, the single factor and multiple factors of the constructed model were all less than 0.05, indicating that the model could be used as an independent prognostic factor independently of other clinical traits (Fig. 3C).

The ROC curves of 1, 3 and 5 years are all greater than 0.05, indicating that the constructed model has high accuracy in predicting patient survival (Fig. 4A). The graph on the right is a combination of the constructed model and other clinical traits. It can be seen that the area under the ROC curve of the constructed model is the largest, indicating that the constructed model is better than other clinical traits in predicting survival. As shown in Fig. 4B, the model validation diagram for clinical grouping is shown. We divided patients into early (Stage I−II) and advanced (Stage III−IV) patients. It can be observed that the constructed model has a P value of less than 0.05 in both the early (left) and late (right) curves, indicating that the model is suitable for both early and late stage patients. The C-index value of the constructed model is the largest, indicating that the accuracy of predicting the survival period through the constructed model is the highest (Fig. 4C). The nomogram was used to predict the patient's survival. The score for each clinical trait can be obtained according to the individual scoring scale, and the summation value is the comprehensive score of the patient. According to the comprehensive scoring scale, the survival time of patients can be predicted according to the corresponding values at the bottom of the graph (Fig. 4D and E). Through principal component analysis, it can be observed that the difference between high- and low- risk in the Risk LncRNAs graph was the most obvious, indicating that the LncRNAs constructed by the model can distinguish patients in the high-risk group (Fig. 4F).

Analysis and Confirmation of LIPT1 Expression in Different Clinical Grading of HNSC

As mentioned above, we analyzed the expression of cuproptosis-related genes in HNSCs, but in clinical terms, it is the clinical grading that really determines the prognosis of patients. Therefore, we further analyzed the expression of all these 19 genes in different clinical stages. Among these differentially expressed genes, 9 genes (LIAS, LIPT1, LIPT2, PDHA1, PDHB, DBT, GCSH, DLST) were positively correlated with clinical grading (Fig. 5A).

We then chose LIPT1 to further explore its role in the development of HNSC. It can be seen that the expression value of LIPT1 increases as the clinical grading increases (G1-G3) by Kruskal-Wallis one-way ANOVA (Fig. 5B). G4 was not included because the sample size was too small (n=2).

Immunohistochemistry analysis showed that LIPT1 was highly expressed in HNSC. Compared with the highly and moderately differentiated samples, the expression was particularly obvious in the poorly differentiated samples (Fig. 5C).

qRT-PCR showed the same results as immunohistochemistry. The mRNA level of LIPT1 was highly expressed in the poorly differentiated samples compared to the highly and moderately differentiated samples (Fig. 5D).

The Prognostic Value of LIPT1 in HNSC Patients

The potential prognostic value of LIPT1 was assessed using Cox proportional hazards model and Kaplan-Meier analysis. The HNSC patients expressed lower LIPT1 showed longer overall survival than those expressed higher LIPT1 (Fig. 6A).

GSEA Single Gene of LIPT1 in HNSC

GSEA was used to identify relevant pathways in HNSCs affected by LIPT1 expression. The results showed that LIPT1 was positively related with cell cycle, RNA degradation, DNA replication, regulation of autophagy and P53 signaling pathway (Fig. 6B), which suggested that LIPT1 may play a role in the tumorigenesis of HNSC.

Spearman Correlation Analysis Between Single Gene and Pathway Score

Spearman Correlation Analysis was further applied to verify the relationship of LIPT1 expression on HNSC related genes and pathways. The results showed that DNA replication, tumor proliferation, G2M checkpoint, MYC targets and DNA repair were positively correlated with the expression of LIPT1 (Fig. 6C), suggesting the role of LIPT1 in the tumorigenesis of HNSC. At the same time, we observed that inflammatory response and tumor inflammation signature were negatively correlated with the expression of LIPT1 (Fig. 6C), suggesting that LIPT1 may be related to the immunity of HNSC patients.

Immune-Related Analysis of LIPT1 in HNSC

Subsequently, we evaluated the correlation between LIPT1 expression and immunity in HNSC patients. Differential infiltration of some immune cells was found between LIPT1 high expression and LIPT1 low expression groups (Fig. 6D). Compared to the LIPT1 low expression group, the infiltration of T cell CD4+ Th2 cells, common lymphoid progenitor, Class-switched memory B cell, B cell native, B cell memory and B cells were higher, while the infiltration of T cell regulatory (Tregs), T cell NK, T cell CD4+ effector memory, T cell CD4+ central memory, plasmacytoid dendritic cell, neutrophil, myeloid dendritic cell activated, myeloid dendritic cell, monocyte, mast cell and endothelial cell were lower in LIPT1-high group.

The results of immune checkpoints demonstrated the high expression group of LIPT1 was positively associated with several immune recognition molecules, such as CTLA4, SIGLEC15, HAVCR2, LAG3, PDCD1 and TIGIT (Fig. 6E). The expression of immune checkpoint molecules on immune cells will inhibit the function of immune cells, so that tumors form immune escape.

HNSC is the sixth most common malignant tumor, and its mortality is relatively high, up to 40–50%(3). Although there are surgical resection, radiotherapy and chemotherapy in clinical practice, the treatment effect is still not very ideal for some patients with middle and late stage or recurrence and metastasis(4–6). Therefore, it is of great significance to make a prospective prognosis for patients and find new therapeutic targets.

Copper is an essential factor for all organisms. The active homeostasis mechanism that works across concentration gradients can keep the copper concentration in cells at a very low level(7, 8). Excess and accumulation of copper will lead to oxidative stress and cytotoxicity(9). Genetic variation of copper homeostasis can lead to fatal diseases(10), and copper ion carriers and copper chelators are considered to be anticancer agents. The researchers first used copper ion carriers to verify that only copper ions can mediate cell death, while carrying other metal ions does not have cell killing activity(1, 11). Next, it was verified that cell death induced by copper ion carrier was probably a new way of cell death, which was obviously different from the traditional cell death modes such as apoptosis, ferroptosis and necrosis(12–15). Copper does not target ETC directly, but targets a component of TCA cycle. The results showed that the knockout of seven genes could significantly alleviate the copper ion carrier mediated cytotoxicity, including FDX1, LIPT1, LIAS, DLD, DLAT, PDHA1 and PDHB. It was also detected that FDX1 was an upstream regulator of protein thiooctanylation modification. It was proved that cell death caused by copper homeostasis disorder and cell death induced by copper ion carrier were the same mechanism through various models. Some studies have pointed out that compared with normal cells, cancer cells are usually preferentially induced by copper deposition. Some copper ion carriers show advantages in this regard, and look forward to the possibility of using copper toxicity to treat cancer. The level of copper in the tumor or serum of patients with a variety of cancers including HNSC will increase abnormally. Excessive copper accumulation may endanger life, but there may be a window that allows more concentrated increase of copper in cells to selectively kill cancer cells. The newly discovered cuproptosis provides an idea for the relationship between copper and cancer. Copper ion carriers can bind copper existing in the environment and bring it into cells to induce cell death. At present, scholars have studied the relationship between copper death related genes and a variety of tumors to try to find a new method can be found to kill cancer cells by taking advantage of the unique role of this metal(16–25). Therefore, we want to know whether the biological process of cuproptosis plays a role in the progress of HNSC, and whether it can be used as a potential target for treatment of HNSC.

In this study, we analyzed 39 normal samples of HNSC patients in TCGA and 469 tumor samples to show the co expression relationship between LncRNA and corresponding cuproptosis-related genes. At the same time, by constructing a LASSO Cox regression analysis, a prediction model was established using the expression profile of cuproptosis-related genes. Survival analysis showed that the survival rates of patients in high- and low- risk groups were different. The model built in the study could distinguish patients in high- and low- risk groups, and the mortality rate increased with the increase of patient risk through risk curve analysis. The independent prognostic analysis showed that the model could be used as an independent prognostic factor independent of other clinical features. The results of ROC curve show that the model has high accuracy in predicting the survival rate of patients, and is superior to other clinical features. The C-index curve showed that the model built had the highest accuracy in predicting the survival period. The principal component analysis showed that the LncRNA constructed by the model could distinguish the patients in the high-risk population.

Next, considering that the clinical treatment and prognosis are usually related to the grading of tumor differentiation, we further analyzed the cuproptosis-related genes that may be related to tumor differentiation, and selected LIPT1 for the next step of research. LIPT1 (Lipoyltransferase 1) is a protein coding gene involved in the metabolism of lipoic acid(26, 27). Some studies have shown that LIPT1 gene mutation can lead to some hereditary diseases, including lipoyltransferase 1 deficiency and Leigh syndrome with leukodystrophy(28, 29). Recently, studies have confirmed that LIPT1 has a good prognosis in patients with urothelial carcinoma, liver hepatocellular carcinoma or melanoma(30–32). However, the role of LIPT1 in HNSC tumorigenesis and progression is still unclear. In this study, we found that the expression of LIPT1 gradually increased with the increase of clinical grading. We further verified by immunohistochemistry and qRT PCR, thus confirming that LIPT1 plays a role in HNSC tumor differentiation. At the same time, we found that LIPT1 was up-regulated in HNSC and was an independent prognostic factor for poor prognosis of HNSC. GSEA Single Gene and Spearman Correlation Analysis showed that LIPT1 may be related to the occurrence of HNSC tumor, and may affect the patient's immune system, causing immune escape. As we all know, immunotherapy is gradually showing its importance in the treatment of HNSC(6, 33–35). Therefore, we further studied the immune cell infiltration and analyzed the immune checkpoints. The results showed that the LIPT1 overexpression group did indeed have the reduction of immune cells and immune escape. Therefore, LIPT1 may be one of the targets for HNSC immunotherapy.

This research was a bioinformatics research based on big data. It analyzed the relationship between cuproptosis and HNSC, and confirms the significance of LIPT1 in the occurrence and development of HNSC, as well as its potential as a therapeutic target. It was an innovation and frontier in this research field and provides a theoretical basis for clinical practice.

In conclusion, by analyzing the relationship between cuproptosis-related gene and LncRNA, this study showed that cuproptosis-related gene can predict the risk, progression and prognosis of HNSC. LIPT1 was further confirmed that it could be used as an independent prognostic factor, and was related to tumor grading of HNSC. The results also suggested that LIPT1 could be used as a novel target for immunotherapy of HNSC.

Ethical Approval

All patients signed informed consent, and this study was approved by the ethics committee of The First Hospital of China Medical University.

Competing interests

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

Authors’ contributions

Study concept and design (JG), data acquisition (XS), in vitro experiment (XS and JZ), data analysis (XS and NY), statistical analysis (XS, NY and JZ), and manuscript drafting (XS, NY, JZ and JG). All authors contributed to the article and approved the submitted version.

Funding

The authors have no funding to disclose.

Availability of data and materials

All data and materials in this research could be obtained from the authors.

Tsvetkov P, Coy S, Petrova B, Dreishpoon M, Verma A, Abdusamad M et al (2022) Copper induces cell death by targeting lipoylated TCA cycle proteins. Science 375(6586):1254–1261
Lanczky A, Gyorffy B (2021) Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot): Development and Implementation. J Med Internet Res 23(7):e27633
Magnes T, Wagner S, Kiem D, Weiss L, Rinnerthaler G, Greil R et al (2021) Prognostic and Predictive Factors in Advanced Head and Neck Squamous Cell Carcinoma.Int J Mol Sci. ; 22(9)
Bozec A, Culie D, Poissonnet G, Dassonville O (2019) Current role of primary surgical treatment in patients with head and neck squamous cell carcinoma. Curr Opin Oncol 31(3):138–145
Madhukar G, Subbarao N (2021) Current and Future Therapeutic Targets: A Review on Treating Head and Neck Squamous Cell Carcinoma. Curr Cancer Drug Targets 21(5):386–400
Plavc G, Strojan P (2020) Combining radiotherapy and immunotherapy in definitive treatment of head and neck squamous cell carcinoma: review of current clinical trials. Radiol Oncol 54(4):377–393
Lutsenko S (2010) Human copper homeostasis: a network of interconnected pathways. Curr Opin Chem Biol 14(2):211–217
Kim BE, Nevitt T, Thiele DJ (2008) Mechanisms for copper acquisition, distribution and regulation. Nat Chem Biol 4(3):176–185
Atakul T, Altinkaya SO, Abas BI, Yenisey C (2020) Serum Copper and Zinc Levels in Patients with Endometrial Cancer. Biol Trace Elem Res 195(1):46–54
Ge EJ, Bush AI, Casini A, Cobine PA, Cross JR, DeNicola GM et al (2022) Connecting copper and cancer: from transition metal signalling to metalloplasia. Nat Rev Cancer 22(2):102–113
Tsvetkov P, Detappe A, Cai K, Keys HR, Brune Z, Ying W et al (2019) Mitochondrial metabolism promotes adaptation to proteotoxic stress. Nat Chem Biol 15(7):681–689
Carneiro BA, El-Deiry WS (2020) Targeting apoptosis in cancer therapy. Nat Rev Clin Oncol 17(7):395–417
Shin D, Lee J, You JH, Kim D, Roh JL (2020) Dihydrolipoamide dehydrogenase regulates cystine deprivation-induced ferroptosis in head and neck cancer. Redox Biol 30:101418
Tang D, Kang R, Berghe TV, Vandenabeele P, Kroemer G (2019) The molecular machinery of regulated cell death. Cell Res 29(5):347–364
Tang Y, Li C, Zhang YJ, Wu ZH (2021) Ferroptosis-Related Long Non-Coding RNA signature predicts the prognosis of Head and neck squamous cell carcinoma. Int J Biol Sci 17(3):702–711
Feng A, He L, Chen T, Xu M (2022) A novel cuproptosis-related lncRNA nomogram to improve the prognosis prediction of gastric cancer. Front Oncol 12:957966
Hou D, Tan JN, Zhou SN, Yang X, Zhang ZH, Zhong GY et al (2022) A novel prognostic signature based on cuproptosis-related lncRNA mining in colorectal cancer. Front Genet 13:969845
Mo X, Hu D, Yang P, Li Y, Bashir S, Nai A et al (2022) A novel cuproptosis-related prognostic lncRNA signature and lncRNA MIR31HG/miR-193a-3p/TNFRSF21 regulatory axis in lung adenocarcinoma. Front Oncol 12:927706
Wang Y, Huang X, Chen S, Jiang H, Rao H, Lu L et al (2022) In Silico Identification and Validation of Cuproptosis-Related LncRNA Signature as a Novel Prognostic Model and Immune Function Analysis in Colon Adenocarcinoma. Curr Oncol 29(9):6573–6593
Xu M, Mu J, Wang J, Zhou Q, Wang J (2022) Construction and validation of a cuproptosis-related lncRNA signature as a novel and robust prognostic model for colon adenocarcinoma. Front Oncol 12:961213
Xu S, Liu D, Chang T, Wen X, Ma S, Sun G et al (2022) Cuproptosis-Associated lncRNA Establishes New Prognostic Profile and Predicts Immunotherapy Response in Clear Cell Renal Cell Carcinoma. Front Genet 13:938259
Zhang C, Zeng Y, Guo X, Shen H, Zhang J, Wang K et al (2022) Pan-cancer analyses confirmed the cuproptosis-related gene FDX1 as an immunotherapy predictor and prognostic biomarker. Front Genet 13:923737
Zhang G, Sun J, Zhang X (2022) A novel Cuproptosis-related LncRNA signature to predict prognosis in hepatocellular carcinoma. Sci Rep 12(1):11325
Zhang Z, Zeng X, Wu Y, Liu Y, Zhang X, Song Z (2022) Cuproptosis-Related Risk Score Predicts Prognosis and Characterizes the Tumor Microenvironment in Hepatocellular Carcinoma. Front Immunol 13:925618
Yang M, Zheng H, Xu K, Yuan Q, Aihaiti Y, Cai Y et al (2022) A novel signature to guide osteosarcoma prognosis and immune microenvironment: Cuproptosis-related lncRNA. Front Immunol 13:919231
Cronan JE (2020) Progress in the Enzymology of the Mitochondrial Diseases of Lipoic Acid Requiring Enzymes. Front Genet 11:510
Ni M, Solmonson A, Pan C, Yang C, Li D, Notzon A et al (2019) Functional Assessment of Lipoyltransferase-1 Deficiency in Cells, Mice, and Humans. Cell Rep 27(5):1376–86e6
Soreze Y, Boutron A, Habarou F, Barnerias C, Nonnenmacher L, Delpech H et al (2013) Mutations in human lipoyltransferase gene LIPT1 cause a Leigh disease with secondary deficiency for pyruvate and alpha-ketoglutarate dehydrogenase. Orphanet J Rare Dis 8:192
Tort F, Ferrer-Cortes X, Thio M, Navarro-Sastre A, Matalonga L, Quintana E et al (2014) Mutations in the lipoyltransferase LIPT1 gene cause a fatal disease associated with a specific lipoylation defect of the 2-ketoacid dehydrogenase complexes. Hum Mol Genet 23(7):1907–1915
Lv H, Liu X, Zeng X, Liu Y, Zhang C, Zhang Q et al (2022) Comprehensive Analysis of Cuproptosis-Related Genes in Immune Infiltration and Prognosis in Melanoma. Front Pharmacol 13:930041
Yan C, Niu Y, Ma L, Tian L, Ma J (2022) System analysis based on the cuproptosis-related genes identifies LIPT1 as a novel therapy target for liver hepatocellular carcinoma. J Transl Med 20(1):452
Chen Y, Xu T, Xie F, Wang L, Liang Z, Li D et al (2021) Evaluating the biological functions of the prognostic genes identified by the Pathology Atlas in bladder cancer. Oncol Rep 45(1):191–201
Wang X, Wu S, Liu F, Ke D, Wang X, Pan D et al (2021) An Immunogenic Cell Death-Related Classification Predicts Prognosis and Response to Immunotherapy in Head and Neck Squamous Cell Carcinoma. Front Immunol 12:781466
Wang X, Cao K, Guo E, Mao X, Guo L, Zhang C et al (2021) Identification of Immune-Related LncRNA Pairs for Predicting Prognosis and Immunotherapeutic Response in Head and Neck Squamous Cell Carcinoma. Front Immunol 12:658631
Solomon B, Young RJ, Rischin D (2018) Head and neck squamous cell carcinoma: Genomics and emerging biomarkers for immunomodulatory cancer treatments. Semin Cancer Biol 52(Pt 2):228–240

No competing interests reported.

The Prognosis and Immunotherapy Prediction of Cuproptosis-Related Genes in Head and Neck Squamous Cell Carcinoma

Status:

Version 1

Abstract

Figures

Background

Materials and Methods

Results

Discussion

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1