RESEARCH
Exploring the role of the disulfidptosis-related gene SLC7A11 in adrenocortical carcinoma: implications for prognosis, immune infiltration, and therapeutic strategies
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Tonghu Liu1+, Yilin Ren1+, Qixin Wang1,4,5t, Yu Wang1,3,4, Zhiyuan Li1, Weibo Sun4,6, Dandan Fan2,3,4, Yongkun Luan1,2,3,4*, Yukui Gao4,7* and Zechen Yan1,3,4*
Abstract
Background Disulfidptosis and the disulfidptosis-related gene SLC7A11 have recently attracted significant attention for their role in tumorigenesis and tumour management. However, its association with adrenocortical carcinoma (ACC) is rarely discussed.
Methods Differential analysis, Cox regression analysis, and survival analysis were used to screen for the hub gene SLC7A11 in the TCGA and GTEx databases and disulfidptosis-related gene sets. Then, we performed an association analysis between SLC7A11 and clinically relevant factors in ACC patients. Univariate and multivariate Cox regression analyses were performed to evaluate the prognostic value of SLC7A11 and clinically relevant factors. Weighted gene coexpression analysis was used to find genes associated with SLC7A11. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses and the LinkedOmics database were used to analyse the functions of SLC7A11-associated genes. The CIBERSORT and Xcell algorithms were used to analyse the relationship between SLC7A11 and immune cell infiltration in ACC. The TISIDB database was applied to search for the correlation between SLC7A11 expression and immune chemokines. In addition, we performed a correlation analysis for SLC7A11 expression and tumour mutational burden and immune checkpoint-related genes and assessed drug sensitivity based on SLC7A11 expression. Immunohistochemistry and RT-qPCR were used to validate the upregulation of SLC7A11 in the ACC.
+Tonghu Liu, Yilin Ren and Qixin Wang contributed equally to this work.
*Correspondence:
Yongkun Luan luanyongkun99@163.com Yukui Gao gaoyukui@wnmc.edu.cn Zechen Yan yanzechen@foxmail.com
Full list of author information is available at the end of the article
☒ BMC
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Results SLC7A11 is highly expressed in multiple urological tumours, including ACC. SLC7A11 expression is strongly associated with clinically relevant factors (M-stage and MYL6 expression) in ACC. SLC7A11 and the constructed nomogram can accurately predict ACC patient outcomes. The functions of SLC7A11 and its closely related genes are tightly associated with the occurrence of disulfidptosis in ACC. SLC7A11 expression was tightly associated with various immune cell infiltration disorders in the ACC tumour microenvironment (TME). It was positively correlated with the expression of immune chemokines (CXCL8, CXCL3, and CCL20) and negatively correlated with the expression of immune chemokines (CXCL17 and CCL14). SLC7A11 expression was positively associated with the expression of immune checkpoint genes (NRP1, TNFSF4, TNFRSF9, and CD276) and tumour mutation burden. The expression level of SLC7A11 in ACC patients is closely associated withcthe drug sensitivity.
Conclusion In ACC, high expression of SLC7A11 is associated with migration, invasion, drug sensitivity, immune infiltration disorders, and poor prognosis, and its induction of disulfidptosis is a promising target for the treatment of ACC.
Keywords SLC7A11, Disulfidptosis, Adrenocortical carcinoma, Immune infiltration, Prognosis, Metabolic vulnerability, Treatment
Introduction
Adrenocortical carcinoma (ACC) is a malignant endo- crine tumour that occurs in the adrenal cortex and accounts for approximately 14% of primary adrenal tumours [1]. It generally has specific biological character- istics, such as hormone activity and high aggressiveness [2]. The estimated incidence of ACC is extremely low, at approximately 0.5 to 2 per million people per year [3]. There are two peaks in incidence: children under 4 years of age and middle-aged persons between 40 and 50 years of age [4]. In clinical practice, ACC is often difficult to diagnose at an early stage, and patients often have local- ized, distant metastases by the time they develop clinical symptoms. Thus, ACC patients lose the opportunity to surgically remove the tumour. Therefore, the prognosis of ACC patients is poor, with a 5-year survival rate of 25% for ACC patients and a 5-year survival rate of less than 13% for advanced ACC patients [5, 6].
In the treatment of ACC patients, it is now generally accepted that localized ACC can be surgically removed and assisted with mitotane. For advanced and meta- static ACC patients, first-line treatment is based on the combination of mitotane or mitotane alone with chemo- therapeutic agents such as etoposide, doxorubicin, and cisplatin [7]. Molecular targeted therapy and immuno- therapy have been proposed as promising second-line treatments for ACC by some researchers. However, the insulin-like growth factor 1 receptor (IGF1R) inhibi- tor linsitinib and immune checkpoint inhibitors such as anti-PD-1 nivolumab and pembrolizumab have not been shown to have a significant effect in ACC-related stud- ies [8-10]. Moreover, mitotane also has a long half-life, dose-limiting toxicity, and a narrow therapeutic window. Thus, there is still a lack of effective treatment for ACC patients. In this context, therapies that could induce cell death in a variety of ways, such as disulfidptosis,
ferroptosis, pyroptosis, and apoptosis, are promising therapeutic approaches to improve survival.
Disulfidptosis is a previously uncharacterized form of cell death that is different from traditional apoptosis, fer- roptosis, and cuproptosis. Disulfidptosis is a cell death pattern triggered by the accumulation of disulfides in cells, which causes disulfide stress and ultimately the col- lapse of actin cytoskeleton proteins [11]. Solute carrier family 7 member 11 (SLC7A11; also known as xCT) regu- lates cysteine uptake and intracellular disulfide synthesis [12, 13]. The accumulation of disulfides is highly toxic to cells. The reduced form of nicotinamide adenine dinucle- otide phosphate (NADPH) serves as a key factor in coun- tering this toxicity. In the absence of glucose, NADPH production by the pentose phosphate pathway is signifi- cantly reduced, resulting in the accumulation of disul- fides in cells [11]. Therefore, high expression of SLC7A11 (SLC7A11high) and glucose starvation are important conditions leading to disulfidptosis. An increasing body of evidence suggests that advances in cell death patterns will not only improve our basic understanding of cellular homeostasis but also provide new insights into the appli- cation of targeting specific death patterns in diverse dis- eases such as cancer [11]. However, the mechanism and biological significance of disulfidptosis and its associated genes in ACC are rarely reported.
In this study, bioinformatics methods were used to analyse the expression of the disulfidptosis-related gene SLC7A11 in multiple urological tumours, includ- ing ACC, as well as the association between the expres- sion of SLC7A11 and clinically relevant factors and prognostic factors in patients with ACC. Furthermore, weighted gene coexpression analysis was performed by the “WGCNA” R package to select the genes associated with SLC7A11. The functions of these genes were also predicted via GO and KEGG enrichment analysis and validated by the LinkedOmics database. We analysed the
relationship between SLC7A11 expression and immune cell infiltration through CIBERSORT and the Xcell algo- rithm. The relationship between SLC7A11 expression and partial immune chemokines in ACC was also anal- ysed using TISIDB data. Finally, the key R packages “cor- rplot”, “maftools”, and “oncoPredict” were utilized for conducting correlation analysis among SLC7A11 expres- sion levels, immune checkpoint-related genes, tumor mutation burden, and drug sensitivity.
Overall, our results suggest that SLC7A11high in ACC is strongly associated with tumour migration, invasion, immune infiltration disorders, drug sensitivity, and poor prognosis and that its induction of disulfidptosis offers new hope for the treatment of ACC patients. SLC7A11 could be a promising biomarker for the treatment and prognostic assessment of ACC.
Materials and methods
Data acquisition
The RNA sequencing data of 128 normal samples and 79 ACC samples were downloaded from the UCSC Xena platform (https://xena.ucsc.edu/) (Table S1). In addition, the clinical information of 77 patients with ACC was obtained from The Cancer Genome Atlas (TCGA) data- base (https://portal.gdc.cancer.gov/) (Table S2), and two ACC patients with incomplete clinical information were excluded from the clinically relevant study. Two sets of genes associated with disulfidptosis were obtained from previous studies (Table S3).
Analysis of differentially expressed genes (DEGs)
Differential gene analysis was performed to analyse dif- ferences in gene expression between ACC and normal adrenal tissues via the “DESeq2” R package (| log2Fold- Change) | > 1, adjusted P value<0.05) and to identify the intersection of DEGs and genes associated with disul- fidptosis (SLC7A11, INF2, CD2AP, PDLIM1, ACTN4, MYH9, MYH10, IQGAP1, FLNA, FLNB, TLN1, MYL6, ACTB, DSTN, and CAPZB).
Survival and prognostic analysis
Univariate Cox regression analysis was performed to investigate the impact of genes (SLC7A11, MYL6, and ACTB) on the prognosis of ACC patients. ACC patients were classified into high and low-expression groups based on median tangent values. The Kaplan-Meier curve constructed between the two groups was used to analyse the impact of gene expression on the overall sur- vival of ACC patients. The TIMER database (https://cis- trome.shinyapps.io/timer/) was used to verify the results of the survival analysis.
Comparison of the SLC7A11 expression level in urological- related tumours
Differences in SLC7A11 expression in urinary system- related tumours (adrenocortical carcinoma, bladder uro- thelial carcinoma, kidney clear cell carcinoma, kidney chromophobe, kidney papillary cell carcinoma, prostate cancer, pheochromocytoma, testicular germ cell tumour) were investigated by the Wilcoxon rank sum test or X2 test in the TCGA and GTEx databases (Table S4).
Correlation analysis between SLC7A11 and other clinical characteristics
The relationships between SLC7A11 expression and clin- ically relevant factors (age, sex, clinical grade, T-, N-, and M-stage, survival time, survival status, and the expres- sion of ACTB and MYL6) were analysed by the “limma” R packages.The results were visualized using the R pack- ages “ComplexHeatmap” and “ggpubr”. The age and gene expression (MYL6 and ACTB) subgroups of ACC patients were bounded by corresponding median values (>49 and ≤49, 7.62 and ≤7.62, > 9.95 and ≤9.95).
Establishment of a signature associated with prognosis
Univariate and multivariate Cox regression analyses were performed to assess the relationships among clinically relevant factors, SLC7A11 expression, and the survival time of ACC patients. A nomogram was constructed using clinically relevant factors (age, sex, clinical grade, T stage and N stage) and the expression level of SLC7A11. The signature associated with prognosis was validated by internal validation, DCA curves, and timeROC curves based on nomoRisk and the expression of SLC7A11. The “survival”, “regplot”, “rms”, “timeRoc” and “ggDCA” pack- ages of R software were utilized in the procedure.
Weighted gene coexpression network analysis
Using the median of SLC7A11 expression as a reference standard, patients were divided into an SLC7A11 high- expression group and an SLC7A11 low-expression group. Differential expression gene analysis was conducted using the “DESeq2” R package (| log2FoldChange) | > 0, adjusted P value<0.05). Weighted gene coexpression analysis was performed using the “WGCNA” R pack- age to identify the gene segment most closely associated with SLC7A11 and analyse its association with clinical phenotypes.
Hub gene network construction and enrichment analysis of SLC7A11-interacted proteins
The STRING database (https://string-db.org/) and Cyto- scape (V3.9.0) were utilized to generate the hub gene net- work of the target template. Furthermore, the association between these hub genes and genes related to disulfidp- tosis was investigated in ACC using the GEPIA2 database
(http://gepia2.cancer-pku.cn/). The “clusterProfiler” and “org.Hs.eg.db” R packages were used for GO and KEGG enrichment analysis to assess the function of these genes.
LinkedOmics database analysis
The LinkedOmics database (http://www.linkedomics. org/login.php) is a multiomics database that includes 32 cancer types and their clinical information. We analysed and demonstrated all coexpressed genes of SLC7A11 in a volcano plot and the top 50 genes with positive and nega- tive correlations in a heatmap. In addition, we performed GO enrichment analysis using this database to validate previous results.
Correlation analysis between SLC7A11 expression and immune cell infiltration
Patients were classified into high- and low-expression groups based on the median values of SLC7A11 expres- sion. To assess the relationship between SLC7A11 expression and immune cell infiltration in ACC, the “CIBERSORT” and “Xcell” algorithms were used to cal- culate the levels of immune cell infiltration. Differences in immune cell infiltration levels between the high and low SLC7A11 expression groups were analysed, and the results are presented in a box diagram. The “reshape2”, “xCell”, “tidyr” “ggpubr”, “ggsci”, and “e1071” packages of R software were utilized in the procedure.
Correlation analysis between SLC7A11 expression and immune chemokines
TISIDB data (http://cis.hku.hk/TISIDB/) is an integrated repository portal for tumour-immune system interac- tions. The relationships between SLC7A11 expression and important immune chemokines were analysed in this database.
Correlation analysis of SLC7A11 expression and immune checkpoints and tumour mutation burden
To investigate the role of SLC7A11 in the treatment of ACC patients, we performed correlation analysis of SLC7A11 and immune checkpoint genes and tumour mutational burden. We first analysed the association between SLC7A11 and 47 immune checkpoint-related genes using the “corrplot” R package (Table S5). We then downloaded mutation data from ACC patients in the TCGA database, calculated tumour mutation burden (TMB) values using the “maftools” R package, and ulti- mately analysed the associations between SLC7A11 and ACC and TMB by Spearman correlation analysis.
Drug sensitivity analysis
We employed the gene expression data from 79 ACC samples and the gene expression and drug response val- ues from the training set to predict the sensitivity score of
198 anti-tumor drugs. This prediction was achieved using ridge regression models constructed with the R pack- age “oncoPredict” [14]. Subsequently, we divided ACC patients into two groups based on the median SLC7A11 expression value: high expression group and low expres- sion group. The differences in sensitivity scores of the anti-tumor drugs between these two groups were investi- gated using the wilcox. test. Finally, the results were visu- alized using the R package “ggplot2”. The gene expression and drug response values of the training set were down- loaded from Genomics of Drug Sensitivity in Cancer (GDSC, https://www.cancerrxgene.org/).
Sample origin
Tissue samples were taken from patients undergoing adrenal or postperitoneal occupying resection at the First Affiliated Hospital of Zhengzhou University. All patients signed an informed consent form before using clinical materials. The study was approved by the Ethics Com- mittee of the First Affiliated Hospital of Zhengzhou Uni- versity (2022-KY-1035-001).
Histopathology and immunohistochemical staining
For the histopathological assessment, paraffin sections were defatted in xylene and alcohol with gradient con- centrations (Leica ASP200S). Paraffin sections were stained with haematoxylin dye for 3 min, followed by treatment with haematoxylin differentiation solution for 45 s. Finally, after two minutes of staining with eosin dye, the sections were sealed with neutral gum (Roche HE600).
For the immunohistochemical assessment, paraffin sections of 3 microns thick were placed in a 65 ℃ incu- bator for 2 h, followed by defatting in xylene two times for 15 min each. The sections were rehydrated with 100%, 95%, 85% and 70% alcohol. After 10 min of blocking endogenous peroxidase activity with 3% hydrogen perox- ide, the slides were washed 3 times with PBS, and anti- gen repair was performed in thermal repair buffer. The slides were incubated in a closed buffer for 1 h to bind to nonspecific antibodies. Then, they were treated with SLC7A11 antibodies (Rabbit # DF-12,509, Affinity Biosci- ences, AUS) overnight and incubated with a second anti- body for 1 h. Dyeing was performed first with DAB and then again with sulforaphane (Benchmark Ultra). The sections were then sealed with neutral gum, and positive staining was observed under a microscope.
Reverse transcription-quantitative (RT-q) PCR
Total RNA was extracted from ACC samples and nor- mal adrenal tissue using a reliable Total RNA isola- tion reagent (AmoyDx, Xiamen, China), followed by reverse transcription into cDNA using a high-quality Takara RT kit (RR047A, Takara, Tokyo, Japan). The
reverse transcription reaction was conducted at 37℃ for 15 min, followed by a denaturation step at 85℃ for 5 sec, and cooling at 4℃. Subsequently, qPCR was per- formed using a trustworthy TB Green® Premix Ex Taq™ (RR820A,Takara, Tokyo, Japan) in a final volume of 20 ul, utilizing QuantStudio™ 5 (Applied Biosystems; Thermo Fisher Scientific, Inc.). The qPCR cycling condi- tions consisted of an initial denaturation at 95℃ for 30 sec, followed by 40 cycles of denaturation at 95℃ for 5 sec and annealing/elongation at 60℃ for 34 sec. Gene expression was quantified using the widely accepted 2^(- AACq) method, with GAPDH serving as the internal control. The PCR primer sequences (Azenta, lnc.) for the target gene SLC7A11 were as follows: Forward primer: 5’-GGTCCATTACCAGCTTTTGTACG-3’ and Reverse primer: 5’-AATGTAGCGTCCAAATGCCAG-3.
Statistical analysis
Statistical analysis was performed using R software (ver- sion 4.2.1). The t test, Wilcoxon rank sum test, and one- way ANOVA were used to detect the differences between the groups. The survival analyses were determined by the Kaplan-Meier curve, log-rank test, and Cox propor- tional hazard regression model. The correlation analysis was evaluated using Spearman correlation analysis. In all analyses, a P value<0.05 indicated statistical significance; *, ** , and *** indicate P<0.05, P<0.01, and P<0.001, respectively.
Results
The disulfidptosis-related gene SLC7A11 was misregulated in cancers and related to prognosis in ACC
Gene expression quantification data of transcriptome profiling (HTseq-FPKM) of 207 (including 79 tumours and 128 normal) samples were downloaded from the UCSC Xena platform. In addition, the clinical informa- tion of 77 patients with ACC was obtained from the TCGA database. Two ACC patients with incomplete clinical information were excluded from the clinically relevant study. Two sets of genes associated with disul- fidptosis were derived from previous papers, and the two sets had 15 overlapping genes [15, 16] (Fig. 1A, Table S6). Three of these genes, SLC7A11, ACTB, and MYL6, were differentially expressed between ACC and normal adrenal tissues (| log2FoldChange) | > 1, adjusted P value<0.05). SLC7A11, ACTB, and MYL6 were more highly expressed in the ACC samples (Fig. 1B, Figure S1A, B). In addition, SLC7A11 and MYL6 were significantly associated with overall survival (OS) in univariate Cox regression analysis (Fig. 1 C, P<0.0001, P<0.001). To further screen the hub genes, we analysed the effect of the SLC7A11 and MYL6 genes on overall survival (OS) using Kaplan-Meier (K-M) analysis. Increased expression of the SLC7A11 and MYL6 genes resulted in shorter overall survival (Fig. 1D-E,
P<0.001, P=0.006). The TIMER database was used to verify the results of the survival analysis (Figure S1C, D). Based on its smallest P value in univariate Cox regres- sion and K-M analysis, SLC7A11 was chosen for further investigation. We further performed a pancancer analysis of SLC7A11 and found higher expression of SLC7A11 in some urology-related tumours (ACC, KIRC, KICH, KIRP, PRAD, and TGCT) than in corresponding normal tissues (Fig. 1F). Taken together, these results suggest that the disulfidptosis-related gene SLC7A11 is closely associated with ACC and warrants further investigation.
Correlation between SLC7A11 and clinically relevant factors
SLC7A11 expression in ACC patients was closely asso- ciated with M-stage and the expression of the disulfidp- tosis-related gene MYL6 (Fig. 2A). However, there was no statistical significance between SLC7A11 and some clinical factors, including age, sex, clinical grade, T-stage, N-stage, and ACTB (Fig. 2B-F, I). Myosin light chain 6 (MYL6), which plays an important role in regulating cell movement and assembling cytoskeleton structures, is bound by myosin heavy chain 14 (MYH14) and smooth muscle myosin [17, 18]. Overall, these results suggest that SLC7A11 is associated with ACC cell migration.
Construction and validation of a prognostic nomogram
Univariate and multivariate Cox regression analyses showed that SLC7A11 and T stage were independent prognostic factors for OS, with HRs of 5.595 (95% CI, 2.628-11.913) and 6.849 (95% CI, 2.331-20.120), respec- tively (Fig. 3A-B). A nomogram was established for 1-, 3-, and 5-year OS prediction in ACC patients based on the TCGA database (Fig. 3 C). The expression level of SLC7A11, age, sex, T stage, N stage, and clinical stage were eventually applied as parameters. The M-stage was excluded due to statistical uncertainty and imbalanced distribution. The calibration curves of the 1-year, 3-year, and 5-year OS rates fit well with the ideal model (Fig. 3D). The DCA curves provided insight into the range of pre- dicted risks, suggesting that the model provides high clinical value for patients (Fig. 3E). The area under the curve (AUC) for the SLC7A11 prognostic model was 0.743 for the 1-year ROC curve, 0.952 for the 3-year ROC curve, and 0.950 for the 5-year ROC curve of the discov- ery TCGA cohort (Fig. 3F). The AUCs of the SLC7A11 expression groups for OS at 1, 3, and 5 years were 0.635, 0.777 and 0.781, respectively (Fig. 3G). In summary, these results indicate that SLC7A11 is an independent prog- nostic factor in ACC patients, and the nomogram we constructed is highly predictive.
| Gene | HR | lower 95%CI | upper 95%CI | pvalue |
|---|---|---|---|---|
| ACTB | 1.475 | 0.668 | 3.255 1- | 0.336 |
| MYL6 | 3.722 | 1.846 | 7.504 | 0.000 |
| SLC7A11 | 3.848 | 2.143 | 6.909 | 0.000 |
A
B
gene list1
SLC7A11
2.0 -
The expression of SLC7A11 Log2(fpkm+1)
0
15
1
1.5 -
C
gene list2
1.0
0.5
I
μ=0.158
u=0.501
I
0.0
0.5 1.5
Normal
Tumor
Hazard ratios
D
E
SLC7A11 Low n=40
SLC7A11 High n=39
MYL6 Low n=40
MYL6 High n=39
1.00
1.00-
Survival probability (%)
0.75
Survival probability (%)
0.75
0.50
0.50
0.25
P < 0.001
0.25
P = 0.006
0.00
0.00
0
20
40
Time (Months)
60
80
100
120
0
20
40
Time (Months)
60
80
100
120
F
SLC7A11
ns
The expression of SLC7A11 Log2(fpkm+1)
6
4
Normal Tumor
**
2
ns
0
ACC
BLCA
KIRC
KICH
KIRP
PRAD
PCPG
TGCT
Screening for SLC7A11-related modules and genes in ACC A total of 2057 differentially expressed genes (DEGs) were identified between the high and low SLC7A11 expression groups (Fig. 4A). To analyse the genes
associated with SLC7A11 and further investigate the potential counteractivity of SLC7A11 in ACC and disul- fidptosis development, we performed a weighted gene coexpression network analysis. All the DEGs were
A
SLC7A11
age
gender
Clinical stage
T stage
N stage
M stage*
MYL6 **
ACTB
SLC7A11
age
gender
Clinical stage
T stage
N stage
M stage*
MYL6 **
ACTB
Low
⇐ 49
female
Stage
T1
NO
MO
⇐ 7.62
⇐ 9.95
High
>49
male
Stage II
T2
N1
M1
>7.62
>9.95
Stage III
T3
Stage IV
T4
B
C
D
E
age
⇐ 49฿
>49
gender
female
male
Clinical stage
Stage Stage II
Stage III
Stage IV
T stage
T1 12 13 E
T4
0.4
0.44
0.049
0.6
2.0
2.0
0.015
3
SLC7A11 expression
0.68
3
0.2
SLC7A11 expression
SLC7A11 expression
SLC7A11 expression
0.22
0.064
0.38
1.5
1.5
0.93
0.42
0.89
0.99
2
2
1.0
1.0
1
1
0.5
0.5
0.0
0.0
0
0
⇐ 49
>49
female
Stage I
Stage II
Stage III
Stage IV
T1
T2
T3
T4
age
gender
male
Clinical stage
T stage
F
G
H
N stage
NO
N1
M stage
MD
M1
MYL6
⇐ 7.62
>7.62
ACTB
⇐ 9.95
>9.95
0.25
0.0099
0.0044
0.47
2.0
2.0
2.0-
2.0
SLC7A11 expression
SLC7A11 expression
SLC7A11 expression
SLC7A11 expression
1.5
1.5
1.5-
1.5
1.0
1.0
1.0
1.0
0.5
0.5
0.5-
0.5
0.0
0.0
0.0
0.0
NO
N stage
N1
Mo
M stage
M1
== 7.62
MYL6
>7.62
⇐ 9.95
ACTB
>9.95
grouped into 10 modules according to average linkage hierarchical clustering (Fig. 4B,C, Figure S2A,B). There is a highly significant correlation between GS (Gene Sig- nificance) and MM (Module Membership) in this module (Fig. 4E-H). We identified hub genes in the blue module (Fig. 4D) and observed a significant correlation between these genes and SLC7A11 (P=7.8e-9, R=0.6), as well as genes related to disulfidptosis (P=0, R=0.8) in ACC (Fig S2C,D). Taken together, these results further demon- strate that SLC7A11 and its associated genes are strongly
associated with disulfidptosis, ACC cell migration and prognosis in ACC patients.
The potential mechanism of SLC7A11 in ACC and disulfidptosis development
According to the GO enrichment analysis of the 686 genes in the blue module, SLC7A11 showed associa- tions with key biological processes such as the execution phase of apoptosis, regulation of apoptosis (BP category). In the cellular component (CC) category, SLC7A11 was linked to focal adhesion, endoplasmic reticulum lumen.
| pvalue | Hazard ratio | |
|---|---|---|
| SLC7A11 | <0.001 | 4.020(2.237-7.226) |
| age | 0.328 | 1.012(0.988-1.038) |
| gender | 0.972 | 0.986(0.451-2.154) |
| Stage | <0.001 | 2.914(1.860-4.565) |
| T | <0.001 | 3.378(2.110-5.407) |
| N | 0.152 | 2.038(0.769-5.400) |
| M | <0.001 | 6.150(2.710-13.959) |
A
B
pvalue
Hazard ratio
SLC7A11
<0.001
5.595(2.628-11.913)
age
0.525
0.991(0.965-1.018)
gender
0.832
0.911(0.387-2.145)
Stage
0.311
0.449(0.096-2.112)
T
<0.001
6.849(2.331-20.120)
N
0.091
2.919(0.842-10.119)
M
0.971
1.033(0.181-5.909)
0
2
4
6
8
10
12
Hazard ratio
0
5
10
15
20
Hazard ratio
C
D
1.0
Points
0.8
6
10
20
30
40
50
60
70
80
90
100
Observed OS (%)
gender
0.6
0
age
70
0.4
1b
N
0
de
1-year
Stage
3-year
3
2
0.0
5-year
SLC7A11 ***
0.0
0.2
0.4
0.6
0.8
1.0
0
0.4
0.8
12
1.6
T ***
E
Nomogram-predicted OS (%)
1.5
2
2’5
3
3.5
Total points
0.4
379
240
260
280
300
320
40
360
Pr( OS time >
8.98
0.00662
0.95
0.9
0.8
0.6
0:4
0.15
0.03
0.002
Net Benefit
0.2
Pr( OS.time > 8/995
0.178
nomogram
0.985
0.97
0.94
0.85
0.7
0.5
0.3
0.06
0.005
All
0.762
None
Pr( OS.time>
b:997
0.994
0.985
0.97
0.94
0.85
0.65
0.45
0.0
0.00
0.25
0.50
Risk Threshold
0.75
1.00
F
G
1.00
1.00
0.75
0.75
Sensitivity (TPR)
Sensitivity (TPR)
0.50
0.50
0.25
0.25
1 Years(AUC = 0.743)
1 Years(AUC = 0.635)
3 Years(AUC = 0.952)
3 Years(AUC = 0.777)
5 Years(AUC = 0.950)
5 Years(AUC = 0.781)
0.00
0.00
0.00
0.25
1 - Specificity (FPR)
0.50
0.75
1.00
0.00
0.25
1 - Specificity (FPR)
0.50
0.75
1.00
A
B
Gene dendrogram and module colors
1.0
30
SLC7A11
-log10(Adjust P-value)
0.9
20
change
0.8
ISLİ
DOWN
Height
C6ORF223 .
· NOT
MYH4
UP
0.7
10
SUCNR1
ANKI & CEL
ENTPDB
. PRSS1
0.6
MTRNR2_1
0.5
0
-4
log2FoldChange
0
4
8
Dynamic Tree Cut
C
Module-trait relationships
D
MEred
MEmagenta
COPB1
COPB2
MEbrown
8
SEC23IP
ARCN1
MEyellow
STX5
MEblue
SEC24D
0
MEturquoise
SEC234
SEC24A
COPA
MEpink
F5
GOSR2
MEblack
0
YKT6
MEgreen
GRIA 1
MEgrey
CNIH 1
T
+
Os.smo
BL.CZA11
·P
gender
4
+
+
E
F
G
H
Module membership vs. gene significance cor=0.76, p=4e-130
Module membership vs. gene significance cor=0.68, p=2.9e-94
Module membership vs. gene significance cor=0.74, p=6.4e-120
Module membership vs. gene significance cor=0.69, p=4.2e-98
9
”
0
00
10
5
Gene significance for SLC7A11
8
14
Gene significance for OS
Gene significance for OS time
Gene significance for M
00
0.3
B
5
8
0
0,2
0
®
3
0
0.1
8
0
ga
0.0
AL
00
0.0
0.0
0.2
0.4
0.6
0.8
0.0
0.2
0.4
0.6
0.8
0.0
0.2
0.4
0.6
0.8
0.0
0.2
0.4
0.6
0.8
Module Membership in blue module
Module Membership in blue module
Module Membership in blue module
Module Membership in blue module
| -0.16 (0.2) | -0.053 | -0.17 | -0.17 | -0.094 | -0.16 | -0.14 | -0.077 | -0.12 | |
|---|---|---|---|---|---|---|---|---|---|
| (0.6) | (0.1) | (0.1) | (0.4) | (0.2) | (0.2) | (0.5) | (0.3) | ||
| 0.11 | 0.11 | 0.029 | -0.21 | -0.1 | 0.19 | 0.077 | 0.36 | 0.04 | |
| (0.3) | (0.4) | (0.8) | (0.07) | (0.4) | (0.1) | (0.5) | (0.001) | (0.7) | |
| 0.56 | -0.36 | 0.46 | -0.049 | -0.24 | 0.44 | 0.32 | 0.33 | 0.42 | |
| (1e-07) | (0.001) | (2e-05) | (0.7) | (0.03) | (6e-05) | (0.005) | (0.003) | (1e-04) | |
| 0.43 | -0.11 | 0.4 | -0.22 | -0.34 | 0.16 | 0.11 | 0.19 | 0.18 | |
| (8e-05) | (0.3) | (2e-04) | (0.05) | (0.002) | (0.2) | (0.3) | (0.1) | (0.1) | |
| 0.43 | -0.37 | 0.57 | -0.02 | -0.32 | 0.31 | 0.2 | 0.15 | 0.36 | |
| (8e-05) | (8e-04) | (5e-08) | (0.9) | (0.004) | (0.005) | (0.08) | (0.2) | (0.001) | |
| 0.65 | -0.54 | 0.49 | -0.044 | -0.058 | 0.52 | 0.5 | 0.21 | 0.49 | |
| (1e-10) | (4e-07) | (6e-06) | (0.7) | (0.6) | (1e-06) | (3e-06) | (0.07) | (5e-06) | |
| 0.32 | -0.25 | 0.46 | 0.14 | 0.081 | 0.21 | 0.19 | 0.0029 | 0.14 | |
| (0.005) | (0.03) | (2e-05) | (0.2) | (0.5) | (0.06) | (0.1) | (1) | (0.2) | |
| 0.27 | -0.21 | 0.35 | 0.045 | 0.1 | 0.28 | 0.27 | -0.035 | 0.34 | |
| (0.02) | (0.07) | (0.002) | (0.7) | (0.4) | (0.01) | (0.02) | (0.8) | (0.002) | |
| 0.26 | -0.23 | 0.32 | 0.038 | -0.03 | 0.19 | 0.15 | -0.062 | 0.28 | |
| (0.02) | (0.05) | (0.004) | (0.7) | (0.8) | (0.09) | (0.2) | (0.6) | (0.01) | |
| -0.049 | 0.32 | -0.13 | -0.069 | -0.073 | 0.062 | 0.024 | 0.17 | 0.05 | |
| (0.7) | (0.004) | (0.3) | (0.6) | (0.5) | (0.6) | (0.8) | (0.1) | (0.7) |
Fig. 4 Screening for SLC7A11-related modules and genes in the ACC A Volcano map revealing the differences in gene expression between the SLC7A11 upregulated and SLC7A11 downregulated expression groups. B Cluster dendrogram of ACC patients. Each coloured row represents a colour-coded module that contains a group of highly connected genes. A total of 10 modules were identified. C The correlation heatmap between the template and clinically relevant factors (OS, OS.time, SLC7A11, age, sex, clinical stage, T stage, N stage, and M stage). Each cell contains the corresponding correlation and p value. D Hub gene network in blue template. E-H Scatter plot of the blue module
Furthermore, in the molecular function (MF) category, it was associated with disulfide oxidoreductase activity and NADH dehydrogenase activity (Fig. 5A). KEGG enrich- ment analysis revealed its involvement in chemical carci- nogenesis-reactive oxygen, and protein processing in the endoplasmic reticulum (Fig. 5B). Furthermore, the results of the Gene Set Enrichment Analysis (GSEA) revealed
a significant association between these genes and “Oxi- dative phosphorylation” and “Chemical carcinogenesis - reactive oxygen species” pathways in ACC (Table S7). Another module we constructed to gain insight into the SLC7A11 coexpression genes in ACC was the LinkFinder module via the LinkedOmics database (Fig. 5 C). Heat- maps were used to show the top 50 SLC7A11-related
A
B
BP
Amyotrophic lateral sclerosis
regulation of signaling receptor activity
negative regulation of signaling receptor activity
Pathways of neurodegeneration - multiple
diseases
negative regulation of execution phase of apoptosis
Count
Alzheimer disease
regulation of execution phase of apoptosis
10
Protein processing in endoplasmic
-log10(p.adjust)
execution phase of apoptosis
20
reticulum
5
0.020
0.025
0.030
0.035
0.040
30
Prion disease
4
CC
Parkinson disease
3
focal adhesion
log10(p.adjust)
cell-substrate junction
7
Huntington disease
2
6
endoplasmic reticulum lumen
Endocytosis
5
nuclear pore
Count
4
Salmonella infection
coated vesicle membrane
3
Chemical carcinogenesis - reactive oxygen species
10
0.03
0.04
0.05
0.06
2
15
Ubiquitin mediated proteolysis
20
MF
Ontology
25
receptor antagonist activity
Nucleocytoplasmic transport
· BP
30
signaling receptor inhibitor activity
A
Cc
Small cell lung cancer
signal sequence binding
.
MF
Chronic myeloid leukemia
NADH dehydrogenase activity
disulfide oxidoreductase activity
Pancreatic cancer
0.0100
0.0125
0.0150
0.0175
0.0200
0.04
0.06
0.08
0.10
GeneRatio
GeneRatio
C
D
E
SLC7A11 Association Result
A
SLC7A11
ACTAII
8
8
CO
KPNB1
ACSF2*
IRAKI
YKT6
ÍMEMI89.UTHE ZVI
PHI
5
EXOSC7’
INPP1.
COPS8
-log10(pvalue)
TBX6*
EPRS
HHEX
MMLI
៛
ENTPD6
MIL
Z-Score Group
>3
400174
Z-Score
Group
1
2
1
>3
n
n
1
2
1
(
0
0
HP2
-1
-1
-1
← 3
-2
-1
€-3
-2
2
3
3
MASFIS
45
#
-
O
IEM194A
-1.5
-1.0
0
5
0.0
0.5
1.0
1.5
2.0
TMC06
Pearson Correlation Coefficient (Pearson test)
F
G
H
FDR≤0.05
FDR>0.05
FDR<0.05
FDR>0.05
FDR≤0.05
FDR>0.05
20
-15
1.0
-0.5
0.0
05
1.0
1,5
2,0
25
20
1.5
-1.0
05
00
OS
1.0
1.5
20
20
1.5
-LO
05
0.0
05
1.0
15
20
25
microtubule cytoskeleton organization involved in mitosis
DNA replication
nucleosome binding
DNA packaging complex
chromosome segregation
ERNA binding
replication fork
spindle organi ation
helicase activity
condensed chromosome
DNA conformation change
AT Pase activity
chromosomal region
mitotic cell cycle phase transition
single-stranded DNA binding
site of DNA damage
cell cycle GI/S phase transition
protein transporter activity
midbody
cell cyle checkpoint
spindle
CENP-A containing chrumatin organization
double-stranded RNA binding
nuclear periphery
chromosome localisation
catalytic activity, acting on DNA
peptidase complex
organelle fission
heat shock protein binding
microtubule associated complex
cytokinesis
histone binding
heterochromatin
regulation of cell cycle phase transition
damaged DNA binding
kinesin binding
coated membrane
postreplication repair
double-strand break repair
metallopeptidase activity
protein-DNA complex
protein localization to chromosome
catalytic activity, acting on RNA
pigment granule
cell cycle G2/M phase transition
promoter-specific chromatin binding
Golgi-associated vesicle
meiotic cell cycle
anion transmembrane transporter activity
microtubule
Notch binding
fiolin-1-rich granule
negative regulation of cell cycle process
endoplasmic reticulum-Golgi Intermediate compartment
chromatin assembly or disassembly
sulfar compound transmembrane transporter activity
cell division site
regulation of DNA metabolic process
misfolded protein binding
preribosome coated vesicle
protein-DNA complex subuni organization
protein heterod imerization activity
negative regulation of mitotic cell cycle
neurotransmitter transporter activity
vacuolar lumen
regulation of metal ion transport
export across plasma membrane
oxidoreductase activity, acting on the CH-NH2 group of donors
transcriptional repressor complex
integrator complex
microtubule bundle formation
nucleoside-triphosphatase regulator activity
iron ion binding
mast cell grantale
trabecula morphogenesis
secondary metabolic process
phosphatidylinositol 3-kinase binding
photoreceptor inner segment
cillum or flagellum-dependent cell motility
ion channel binding
exoribonuclease complex
cellular component assembly involved in morphogenesis
insulin receptor binding
ESCRT complex
cellular component maintenance
AU-rich element binding
collagen trimer
genetic imprinting
thinlester hydrolase activity
neuromuscular junction
protein localization to cilkım
oxidoreductase activity, acting on peroxide as acceptor
non-motile cilium
glutamate receptor signaling pathway
platelet dense granule
benzene-containing compound metabolic process
pattern recognition receptor activity
oxidoreductase activity, acting on patred donors, with incorporation or
motile cilium
fatty acid derivative metabolic process
reduction of antioxidant activity
mitochondrial protein complex
fatty acid metabolic process
mitochondrial membrane part
protein localization to Golgi apparatus
structural constituent of muscle
spectrin binding
ciliary part
platelet morphogenesis
oxidoreductase activity, acting on a heme group of donors
cytochondrial inner membrane
cytochrome complex assemb ly
histone deacetylase complex
calcium-dependent protein binding
regulation of transporter activity
here-copper terminal oxidase activity
mitochondrial matrix
phagocytic cup
pinocytosis
protein activation cascade
pattern bingding
oxidoreductase complex
mitochondrial respiratory chain complex assembly
monooxygenase activity
intraciliary transport particle
regulation of neurotransmitter receptor activity
oxidoreductase activity, acting on NAD (P)HI
respiratory chain
NADH dehydrogenase complex assembly
oxidoreductase activity, acting on theCH-CH group of donors
MHC protein complex
NADH dehydrogenase complex
20
-15
-100
05
Normalized Enrichment Score
O.G
05
1.0
15
2.0
25
-20
-1.5
-10
-05
Normalized Enrichment Score
0.0
05
10
1.5
20
-20
-15
-10
-0.5
Normalized Enrichment Score
0.0
05
1.0
15
20
25
genes with positive/negative correlations (Fig. 5D-E). The GO enrichment analysis performed in the LinkedO- mics database suggested that SLC7A11 may be associ- ated with NADH dehydrogenase complex assembly, and oxidoreductase activity, acting on NAD(P)H (Fig. 5F-G). These findings collectively support the strong association of SLC7A11 and its associated genes with disulfide bond formation, disulfide metabolism, and the occurrence of disulfidptosis. SLC7A11, which is highly expressed in ACC, sets the stage for disulfidptosis.
Correlation between SLC7A11 and immune infiltration
The overall infiltration of 22 immune cells in ACC was determined based on “CIBERSORT”, with resting mem- ory CD4 T cells and macrophages accounting for a larger proportion (Fig. 6A). Differential analysis of immune cell infiltration levels in the high and low SLC7A11 expres- sion groups was performed based on the “CIBERSORT” algorithm. The infiltration levels of resting dendritic cells were positively correlated with SLC7A11, while the infil- tration levels of mast cells were negatively correlated with SLC7A11 (Fig. 6B, Figure S3A). Differences in immune cell infiltration based on the “X cell” algorithm revealed that the infiltration levels of Th2 cells and pro B cells were positively correlated with SLC7A11, while the infiltra- tion levels of B cells, cd4+Tcm cells, CD8+naive T cells, chondrocytes, class-switched memory B cells, eosino- phils and ly endothelial cells were negatively correlated with SLC7A11 (Fig. 6 C, Fig S3D). We also investigated the correlations between the expression of SLC7A11 and TME scores and immune cell infiltration (Figure S3B). Then, we further investigated the association between SLC7A11 and immune chemokines via the TISIDB data- base. The results showed that SLC7A11 had a positive relationship with the chemokines CXCL8 (rho=0.358, P=0.00128), CXCL3 (rho=0.245, P=0.0296), and CCL20 (rho=0.27, P=0.0164) and an inverse relation- ship with the chemokines CCL14 (rho =- 0.288, P=0.0103) and CXCL17 (rho =- 0.241, P=0.033) (Fig. 6D). Thus, SLC7A11 expression is strongly associated with the tumour immune microenvironment (Figure S3C). We then investigated the associations between SLC7A11 and 47 immune checkpoint-related genes. SLC7A11 was significantly associated with 4 immune checkpoint genes (P<0.05). SLC7A11 was positively associated with CD276 (cor=0.463, P<0.00001), NRP1 (cor=0.434, P<0.0001), TNFSF4 (cor=0.38, P<0.001), and TNFRSF9 (COR=0.34, P<0.05) (Fig. 6E). We also performed a cor- relation analysis for SLC7A11 and tumour mutation bur- den (TMB), which showed a positive correlation between SLC7A11 and TMB (cor=0.33, P=0.004) (Fig. 6F). Overall, these results suggest that the dysregulation of the tumour immune microenvironment in ACC was strongly associated with SLC7A11high. Furthermore, ACC
patients with SLC7A11 overexpression may have a better response to immunotherapy.
Correlation between SLC7A11 and drug sensitivity in ACC
Differential analysis of the drug senstivity score of 198 common chemotherapy drugs between the high and low SLC7A11 expression groups was conducted. We identi- fied 55 statistically significant chemotherapy drugs, most of which had significantly lower drug senstivity score in the high SLC7A11 expression group, such as YK-4-279, tozasertib, docetaxel, vinblastine, bortezomib, paclitaxel, MN-64, and KU-55,933. Only a few drugs exhibited higher senstivity score in the group with high SLC7A11 expression, including SB505124 (Fig. 7A-I; Figure S4A- I). In conclusion, these results suggest a close association between the expression level of SLC7A11 and the sen- sitivity of ACC patients to anti-tumor drugs, implying a potentially significant role of SLC7A11 in the treatment of ACC.
The expression validation of SLC7A11 in ACC
Histopathology and immunohistochemical staining revealed that SLC7A11 expression was significantly increased in ACC tissue compared to ACC-adjacent tis- sue and normal adrenal tissue (Fig. 8A-D, Figure S5A-D). The results of RT-qPCR clearly demonstrate a signifi- cantly higher expression of SLC7A11 in ACC compared to normal adrenal gland samples (p<0.01) (Fig. 8E, Table S8). Taken together, these results validate our analysis of SLC7A11 expression.
Discussion
ACC is a malignant tumour with a poor prognosis and is usually associated with hormone secretion, such as that for glucocorticoids and androgens [19, 20]. Although significant advances have been made in immunotherapy and targeted therapies for ACC, effective therapies for ACC are still lacking in clinical practice [2]. Recent stud- ies have revealed that killing cancer cells by inducing programmed cell death processes, such as disulfidptosis, ferroptosis, and apoptosis, can be a promising treatment method.
We obtained gene sets related to disulfidptosis from previous related studies and screened them for SLC7A11 [15, 16], the gene most closely associated with ACC. SLC7A11 is a cysteine transporter that exports intracel- lular glutamate and imports extracellular cystine at a 1:1 ratio [21]. Cysteine uptake in cells is mainly dependent on System Xc -. This transport system consists of two subunits, the light-chain transport subunit SLC7A11 and the heavy-chain regulatory subunit SLC3A2; SLC7A11 is a 12-pass transmembrane protein that primarily medi- ates cysteine/glutamate reverse transport activity [22]. SLC3A2 is primarily responsible for maintaining the
A
B
SLC7A1
OP
high
100%
B collo naive
0.6-
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
#$
ns
-
ns
ns
ns
B cols memory
Plasma cells
80%-
cols CDB
T colis CD4 naive
T cels CD4 memory net
Toils CD4 memory sc
Relative percentage
cells follicular helper
0.4
60%
colis rogulanory [Tregs
T cols gamma delta
Proportion
NK colls nosting
NK colis activated
40%-
Monocytos
Macrophages MO
Macrophages M1
0.2
Macrophages M2
Dendrtic cells moting
20%
Dendritic cells activated
Mast colis rooting
Mast colls activated
Eos nochis
0%-
Noutrophils
0.0
B cells naive
B cells memory
Plasma cells
T cells CD6
T cells CD4 naive
T cells CD4 memory resting
T cells CD4 memory activated
T cells follicular helper
T cells regulatory (Tregs)
T cells gamma delta
NK cells resting
NK cells adivated
Monocytes
Macrophages MO
Macrophages MT
Macrophages MZ
Dendritic cells resting Dendritic cells activated
Mast cells resting
Mast cells activated
Eosinophils
Neutrophils
C
CIBERSORT
SLC7A11
LOW
High
0.3
Expression
0.2
0.1
:
C
i
0.0
The cells
pro B-coiff
8-C689
CD4+ Tom
CD8+ naive T-cells
Chondrocyte
Clase-switched memory B-cells
Eosinophins
ly Endothešal cets
D
xCell
ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
ACC (79 samples)
rho=0.358 p=0.001.28
5.0
rho=0.245 p=0.0296
rho=0.270 p=0.0164
9
rho =- 0.288 p=0.0103
.
rho =- 0.241 p=0.033
5.0
2.5-
2.5
2.5
6
CXCL8_exp
2.5
CXCL3_exp
CCL20_exp
0.0
CCL14_exp
CXCL17_exp
0.0
0.0
3
0.0
-2.5
2.5-
-2.5
0
-2.5
-5.0
-5.0
-5.0
-2.5
0.0
SLC7A11_exp
2.5
5.0
-2.5
0,0
SLC7A11_exp
2.5
5.0
-2.5
0.0
SLC7A11_cxp
2.5
5.0
-3
-2.5
0.0
SLC7A11_exp
2.5
5.0
-2.5
0.0
SLC7A11_exp
2.5
5.0
E
SLC7A11
TNFSF4
TNFRSF9
F
NRP1
CD276
1
SLC7A11
1
0.43
0.38
0.46
0.34
0.8
1
R = 0.33, p = 0.004
0.6
NRP1
0.43
1
0.39
0.42
0.16
0.4
0
0.2
Tumor mutation burden
TNFSF4
0.38
0.39
1
0.35
0.16
0
-1
-0.2
CD276
-2
0.46
0.42
0.35
1
0.14
-0.4
-0.6
-3
TNFRSF9
0.34
0.16
0.16
0.14
1
-0.8
-1
0.0
0.5
1.0
1.5
2.0
SLC7A11 expression
protein stability and membrane localization of SLC7A11 as a chaperone. In other words, SLC7A11 is essential for regulating cysteine transport. Moreover, SLC7A11 is strongly associated with the development of multiple
malignancies and often predicts poor prognosis. We found that SLC7A11 is differentially expressed in most urinary system-related tumours and is often highly expressed in tumour tissues. In ACC, SLC7A11high is an
A
B
C
SLC7A11
Low
High
SLC7A11
Low
High
SLC7A11
Low
High
1.1e-05
0.00025
0.0003
6
YK-4-279 senstivity Score
6
Tozasertib senstivity Score
SB505124 senstivity Score
4.0
5
4
3.6
4
2
3.2
3
Low
High
Low
High
Low
SLC7A11
SLC7A11
SLC7A11
High
D
E
F
SLC7A11
Low
High
SLC7A11
Low
High
SLC7A11
Low
High
0.15
0.001
0.0012
0.0013
0.5
0.05
Docetaxel senstivity Score
Vinblastine senstivity Score
0.4
Bortezomib senstivity Score
0.04
0.10
0.3
0.03
0.05
0.2
0.02
0.1
0.01
0.00
0.0
Low
Low
High
Low
SLC7A11
High
SLC7A11
SLC7A11
High
G
H
SLC7A11
Low
High
SLC7A11
Low
High
SLC7A11
Low
High
0.0018
8.0-
0.0017
7.5-
0.0021
2.0
Paclitaxel senstivity Score
7.5
KU-55933 senstivity Score
7.0-
MN-64 senstivity Score
1.5
6.5
7.0
1.0
6.0
0.5
6.5
5.5
0.0
6.0
5.0
Low
High
Low
High
SLC7A11
Low
SLC7A11
SLC7A11
High
independent prognostic factor, and the nomogram based on SLC7A11 and other clinically relevant factors can accurately predict prognostic relevance in ACC patients.
SLC7A11high promotes cysteine transport in cancer cells and increases intracellular cysteine and extracellu- lar glutamate accumulation. Cystine is converted to cys- teine, a rate-limiting precursor for glutathione (GSH), in the cytosol through an NADPH-consuming reduction reaction [21]. GSH is a very important reducing agent in cells and is essential for maintaining the reducing
environment in cells. Cancer cells have more reactive oxygen species (ROS) than normal cells. ROS are nor- mally used to stimulate tumorigenesis and progression but can induce cell death when they exceed safe limits [12, 23, 24]. Increased intracellular GSH alleviates this problem well, allowing cancer cells to live in a comfort- able environment. In other words, SLC7A11high in cancer cells contributes to the development of cancer cells.
In addition, previous studies indicated that GSH may be associated with tumour drug resistance and that elevated
A
B
ACC
normal
ACC
adjacent tissue
M
0
400um
0
400um
0
400um
!
0
400pm
2
C
ACC
normal
D
ACC
normal
0
400gm
0
400um
0
400 pm
0
400um
E
Relative expression of SLC7A11
15
*
10
5
T
T
Normal
ACC
GSH may increase drug resistance in cancer cells. Okuno et al. [25] revealed that SLC7A11-mediated upregula- tion of GSH promotes cisplatin resistance in ovarian cancer cells. Lo et al. [26] found that GSH upregulation was associated with increased resistance to gemcitabine in pancreatic cancer cells. Our results on drug sensitiv- ity analysis demonstrate a strong correlation between the expression of the SLC7A11 gene and the sensitivity of ACC patients to anti-tumor drugs. ACC patients with high expression of SLC7A11 exhibit increased sensitiv- ity to certain anti-tumor drugs (YK-4-279, tozasertib, docetaxel, vinblastine and so on), suggesting an impor- tant role for SLC7A11 in the treatment of ACC patients.
In addition, previous studies have shown that SLC7A11-mediated extracellular glutamate accumu- lation not only serves as a raw material for cancer cell growth but also plays an essential role in tumour migra- tion and invasion [27, 28]. Susan et al. [29] found that system Xc-released glutamate acts on Ca2+-permeable a-amino-3-hydroxy-5-methylisoxazole-4-propionic acid receptors (AMPA-R) in glioma cells, inducing intracellu- lar Ca2+ oscillations that affect tumour cell migration and invasion ability. The researchers found that glutamate transferred by system Xc- to the extracellular space drives epithelial morphology destruction and promotes lumen filling and basement membrane disruption, the key characteristics of the invasive phenotype of cancer cells [30]. Similarly, when we performed a clinically relevant analysis of SLC7A11, we found that SLC7A11 was asso- ciated with M-stage and MYL6. The M1 stage was more prevalent in ACC patients with SLC7A11high, and MYL6 played an important role in cancer cell migration. This suggests that SLC7A11 may contribute to distant tumour migration. Furthermore, when we performed weighted gene coexpression analysis using the “WGCNA” R pack- age, we found that the blue template genes most closely associated with SLC7A11 were strongly associated with M-stage. Further enrichment analysis of these genes revealed a strong association with cellular component focal adhesions. Focal adhesions are made up of more than 150 different proteins that bind the cytoskeleton to the extracellular matrix and are involved in the migration, proliferation, and differentiation of cancer cells [31-33]. Dynamic turnover of focal adhesions is key to cell migra- tion [34]. Thus, in ACC, SLC7A11high is strongly associ- ated with the invasion and migration of cancer cells.
In addition, glutamate regulates the immune micro- environment of cancer cells by binding to glutamate receptors on cancer cells [35]. Long et al. [36] found that glutamate promotes the proliferation, activation, and immune suppression of Tregs in gliomas. In our study, we also found that SLC7A11 expression is strongly asso- ciated with the dysregulation of immune cell infiltration in ACC patients. In ACC patients with SLC7A11high, the
number of Th2 cells and pro B cells was increased, but the number of B cells, cd4+Tcms, CD8+naive T cells, chondrocytes, class-switched memory B cells, eosino- phils and ly endothelial cells was decreased.
Furthermore, SLC7A11 was significantly associ- ated with 4 immune checkpoint genes, CD276, NRP1, TNFSF4, and TNFRSF9. Through the TISIDB database, we also learned that SLC7A11 expression in ACC is posi- tively correlated with the immune chemokines CXCL8, CXCL3, and CCL20 and negatively correlated with the immune chemokines CCL14 and CXCL17. CXCL8, also known as interleukin-8 (IL8), and its associated pathway CXCL8-CXCR1/2 play an important role in tumour pro- liferation, invasion, and migration [37, 38]. In Ras-driven cancers, CXCL8 attracts tumour-associated neutrophils (TANs) to the tumour immune microenvironment, and TANs secrete arginase 1 to favour immune suppression [39, 40]. The CXCL8-CXCR1/2 pathways also play a con- firmed role in resistance to chemotherapy in breast, pros- tate, and colorectal cancers [37]. Previous studies have also shown that the immune chemokines CXCL3 and CCL20 also play an important role in tumour growth, invasion, and migration [41, 42]. Thus, the immune chemokines CXCL8, CXCL3, and CCL20, which are positively associated with SLC7A11, have SLC7A11-like functions in the development of tumours and provide evidence of association at the tumour immune microen- vironment level for poor prognosis in ACC patients with SLC7A11high
In summary, SLC7A11 can influence tumour progres- sion, drug sensitivity, immune infiltration, and the abil- ity to migrate and invade distant sites by modulating cysteine/glutamate transportation in the ACC. It has the potential to be an essential molecular marker for assess- ing prognosis in ACC patients. Given the range of roles that SLC7A11 plays in tumour development, the devel- opment of oncology therapies targeting SLC7A11 is clini- cally significant.
Current applications of SLC7A11 in the treatment of tumours can be broadly classified into two categories: direct targeting of SLC7A11 transporter activity, which inhibits tumour progression by inducing intracellular ROS accumulation and ferroptosis, and targeting meta- bolic vulnerabilities exposed by SLC7A11-overexpressing cancer cells, such as glucose or glutamine dependency [21]. SLC7A11high in cancer cells was associated with increased consumption of NADPH and glutamate. Sta- tistically, approximately 30-50% of glutamate is exported by SLC7A11 in exchange for cysteine [43]. The deficiency of NADPH and glutamate is compensated by glucose and glutamine metabolism. Thus, SLC7A11high cancer cells are highly dependent on glucose and glutamine, which is thought to be the metabolic vulnerability of exposed can- cer cells [44].
Recent studies have found that when glucose is scarce, SLC7A11high cancer cells significantly reduce NADPH, which is produced by the glucone-gluconate pentachlo- rophenate pathway. This leads to a substantial buildup of disulfides in the cells, which in turn triggers disulfide stress and the collapse of actin cytoskeleton proteins that ultimately leads to rapid cell death. This form of death differs from ferroptosis and apoptosis and is called disul- fidptosis. Cysteine starvation is known to lead to ferrop- tosis in cancer cells [45]. Previous studies have found that cancer cells with SLC7A11high have higher levels of cyste- ine accumulation [46]. When cysteine starvation occurs, cancer cells do not experience significant ferroptosis but prevent cell death caused by glucose deficiency [47]. In other words, in cancer cells with SLC7A11high, glucose starvation may carry more cytotoxicity than cysteine deficiency does [21]. This also suggests that targeting SLC7A11high tumours exposes metabolic vulnerability and induces disulfidptosis in cancer cells as a potential therapeutic approach. This seems to be a treatment that works on a wide range of tumours. However, in previous experiments, researchers found that glucose transporter inhibitors were not effective in inducing disulfidptosis in some cancer cells [21]. This may be because some can- cer cells do not have the conditions for disulfidptosis or because of their strong resistance to glucose starvation, the mechanisms of which need to be further investigated.
Disulfidptosis and treatments targeting metabolic vul- nerability have not been reported in ACC studies, and our analysis of a public database revealed that ACC has the underlying conditions for disulfidptosis. Thus, treat- ments targeting metabolic vulnerability are promising for ACC treatment.
We performed differential analysis using the TCGA and GTEx databases and found that SLC7A11 was sig- nificantly more highly expressed in ACC than in normal adrenal tissue [48]. Increased expression of SLC7A11 mediates increased intracellular cysteine and NADPH depletion, laying the groundwork for disulfidptosis in ACC.
Studies have shown that glucose is usually metabolized in two major ways: glycolysis and the pentose phosphate pathway [49]. The pentose phosphate pathway is a ubiq- uitous glucose metabolism pathway in plants, animals, and microorganisms. The pentose phosphate pathway varies from tissue to tissue and is less common in skel- etal muscle tissue and more common in tissues with high lipid content, such as the adrenal gland, breast, and adi- pose tissue [50, 51]. In addition, the pentose phosphate pathway is an important source of NADPH, which is used to synthesize steroids produced in the adrenal cor- tex [50, 52]. Thus, the adrenal gland is highly dependent on NADPH, suggesting that blocking glucose uptake may be effective in inducing disulfidptosis in ACC cells.
The enrichment analysis of SLC7A11 and its closely related genes indicated that these genes are closely asso- ciated with endoplasmic reticulum lumen, focal adhe- sion, and disulfide oxidoreductase activity in ACC. The endoplasmic reticulum is a reticular organelle that regu- lates the folding and posttranslational maturation of most membrane proteins and secretory proteins [53]. Proteins synthesized in the endoplasmic reticulum, often require a stable three-dimensional structure through the pro- duction of disulfide bonds, which are the basis of protein biological functions [54]. With the formation of disulfide bonds, H2O2, a byproduct, is produced in the endoplas- mic reticulum, and ROS levels in the endoplasmic reticu- lum are subsequently upregulated [54]. Under normal conditions, the reductive environment in the cytoplasm and endoplasmic reticulum prevents the production of disulfide bonds by cytoplasmic proteins [11], suggesting that ROS produced during protein folding and matura- tion in the endoplasmic reticulum contribute to disulfide bonds and disulfide production. In conclusion, the endo- plasmic reticulum is an important site for the formation of disulfide bonds in cells and is closely associated with the occurrence of disulfidptosis.
The production of disulfide bonds mainly depends on the protein disulfide isomerase (PDI) family and other oxidoreductases [54]. In our enrichment analysis of SLC7A11 and its closely related genes, we found that these genes appear to regulate the activity of disulfide oxidoreductase, which regulates disulfide bonds and disulfide synthesis in ACC cells [55]. We also noted that these genes are closely associated with oxidoreductase activity, acting on NADP (H) and NADP dehydrogenase activity when validated using the LinkedOmics database. Thus, SLC7A11 is highly expressed in ACC cells, which not only influences the formation of disulfide bonds in cells but also regulates glucose and NADPH metabolism, leading to the potential for disulfidptosis in ACC cells.
If attempting to target the metabolic vulnerability dis- played by ACC cells with high expression of SLC7A11, such as inhibiting glucose transporters which would impede glucose uptake and subsequently lead to a sig- nificant reduction in NADPH production through the phosphogluconate pathway, it is very likely to induce the accumulation of intracellular disulfide bonds and disulfides, thereby triggering disulfidptosis. Therefore, when employing this approach for ACC treatment, the induction of disulfidptosis in ACC cells can potentially improve patient prognosis. In addition, previous stud- ies have shown that glucose transporter inhibitors can inhibit the development of multiple tumours. Addition- ally, they can overcome tumour cell resistance to chemo- therapeutics, radiotherapy, and immunotherapies and enhance the anticancer efficacy of antitumour agents [56-58]. In summary, targeting metabolic vulnerability in
cancer is fraught with limitless possibilities in the era of precision oncology, and targeting metabolic vulnerability to induce disulfidptosis in ACC cells holds promise for the treatment of ACC.
Although our study demonstrates the significance of SLC7A11 for the prognostic prediction of ACC and the occurrence of disulfidptosis, it still has limitations. First, gene expression and clinical information were derived from public databases, and our findings need to be vali- dated through other clinical data we collected. Second, the specific mechanism of SLC7A11 and disulfidptosis in ACC needs to be further investigated.
In summary, this study demonstrates that the highly expressed disulfidptosis-related gene SLC7A11 influ- ences glucose and NADPH metabolism and regulates disulfide bonds and disulfide formation in ACC. These results suggest that ACC cells with SLC7A11high have the potential for disulfidptosis and that targeting their meta- bolic vulnerability to induce disulfidptosis has the poten- tial to improve overall patient survival.
Conclusion
In summary, our study found that high expression of disulfidptosis-related SLC7A11 was associated with migration, invasion, drug sensitivity, immune infiltration disorders, and poor prognosis in ACC, and targeting its metabolic vulnerability to induce disulfidptosis has the potential to improve overall patient survival.
List of abbreviations
| ACC | Adrenocortical carcinoma |
| GO | Gene Ontology |
| KEGG | Kyoto Encyclopedia of Genes and Genomes |
| NADPH | Nicotinamide adenine dinucleotide phosphate |
| SLC7A11 | Solute carrier family 7 member 11 |
| TCGA | The Cancer Genome Atlas |
| GTEx | Genotype-Tissue Expression Project |
| GSH | Glutathione |
| ROS | Reactive oxygen species |
| ER | Endoplasmic reticulum |
Supplementary Information
The online version contains supplementary material available at https://doi. org/10.1186/s12935-023-03091-6.
Additional file 1: Figure S1 Expression of disulfidptosis-related genes and their effect on the prognosis of ACC patients A, B Expres- sion of the genes MYL6 and ACTB in ACC and normal adrenal tissues. C, D Kaplan-Meier survival analysis of SLC7A11 and MYL6 in the TIMER database.Additional file 2: Figure S2 Sample dendrogram and trait heatmap A ACC sample dendrogram and trait heatmap.B Calculation of the scale-free fit index of various soft-thresholding powers (B) and analysis of the mean connectivity of various soft-thresholding powers (B). C,D The relationship between hub genes in the blue module and SLC7A11, as well as genes related to disulfidptosis.Additional file 3 : Figure S3 Relation- ships between SLC7A11 expression and immune cell infiltration in ACC A, B Relationships between SLC7A11 expression and immune cell infiltration. C Relationships between SLC7A11 expression and chemokines. D The Xcell algorithm revealed that the infiltration levels of some immune cells in ACC patients.Additional file 4: Figure S4 Relationship between SLC7A11 expression and drug sensitivity in ACC A-I SLC7A11 expres-
sion correlates with the sensitivity of anticancer drugs in ACC patients. Ad- ditional file 5: figure S5 haematoxylin-eosin staining of tissue samples A-D HE staining in four ACC samples. Additional file 6: Table S1 The TCGA iden- tifier numbers of ACC samples; Table S2 The clinical information for the 77 ACC patients; Table S3 Two sets of genes associated with disulfidptosis; Table S4 The expression profiles of SLC7A11 in urogenital system-related tumors; Table S5 The 47 immune checkpoint-related genes; Table S6 The 15 overlapping genes of the two sets of genes associated with disulfidpto- sis; Table S7 The results of the Gene Set Enrichment Analysis (GSEA); Table S8 The q-pcr raw data.
Acknowledgements
We acknowledge our use of R Software, TCGA, GTEx, TIMER, TISIDB, and LinkedOmics databases, and we appreciate the platforms and the authors who uploaded their data.
Authors’ contributions
Tonghu Liu, Yilin Ren, Qixin Wang: manuscript, bioinformatics, experimental design and concept. Yu Wang, Zhiyuan Li, Weibo Sun, Dandan Fan: data consolidation, software, statistical analysis. Yongkun Luan, Yukui Gao, Zechen Yan: supervision, design, funding, conceptualization.
Funding
Young Elite Scientists Sponsorship Program by Henan Association for Science and Technology (HAST) (2022HYTP043).
Data Availability
The publicly available datasets used in this study can be found in the Materials and Methods.
Declarations
Ethics approval and consent to participate
The study was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (2022-KY-1035-001).
Consent for publication
All the authors have read and approved the final article.
Competing interests
The authors declare no competing interests.
Author details
1Department of Surgery, The First Affiliated Hospital of Zhengzhou University, 450001 Zhengzhou, Henan, China
2BGI College & Henan Institute of Medical and Pharmaceutical Sciences,
Zhengzhou University, 450001 Zhengzhou, Henan, China
3Henan Engineering Research Center of Tumour Molecular Diagnosis and Treatment, 450001 Zhengzhou, Henan, China
4Institute of Molecular Cancer Surgery of Zhengzhou University,
450001 Zhengzhou, Henan, China
5Department of Surgery, Nanyang Central Hospital, 473005 Nanyang, Henan, China
6Department of Radiation Oncology and Oncology, Henan Provincial
People’s Hospital & the People’s Hospital of Zhengzhou University, 450003 Zhengzhou, Henan, China
7Department of Urology, The First Affiliated Hospital of Wannan Medical College, Yijishan Hospital, 241001 Wuhu, Anhui, China
Received: 22 July 2023 / Accepted: 4 October 2023 Published online: 02 November 2023
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