Ferroptosis-based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression
Chen Lin1 İD Ruofei Hu2 FangFang Sun3 3 Weiwei Liang- 4
1Department of Breast Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
2Lifestyle Supporting Technologies Group, Technical University of Madrid, Madrid, Spain
3Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, The Second Affiliated Hospital, Cancer Institute, Zhejiang University School of Medicine, Hangzhou, China
4Department of Endocrinology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
Correspondence
Weiwei Liang, Department of Endocrinology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. Email: helenliangww@zju.edu.cn
Funding information
This study was funded by Zhejiang Provincial Natural Science Foundation of China [grant number LQ20H160021]
Abstract
Background: This study aimed to find ferroptosis-related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model.
Methods: We downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis-related genes were screened by univariate regression analysis, machine-learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis-related gene-based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conven- tional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival-associated ferroptosis-related genes.
Results: Seventy genes were identified as survival-associated ferroptosis-related genes. The prognostic model was constructed with 17 ferroptosis-related genes in- cluding STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was cal- culated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1-, 3-, and 5- year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems.
Conclusion: We constructed a prognosis model of ACC based on ferroptosis-related genes with better predictive value than the conventional staging system. These ef- forts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.
KEYWORDS adrenocortical carcinoma, ferroptosis, LASSO, machine learning, prognosis model
Programmed cell death has been shown to be a significant type of cell death. It acts as a natural barrier to prevent cells from developing into cancers.1,2 Dysregulation of programmed cell death signaling pathways is emerging as a key factor in tumor- igenesis.3 The most thoroughly studied aspect of programmed cell death is apoptosis.4 Research has revealed new mechanisms of programmed cell death, one of which is ferroptosis. The con- cept of ferroptosis was first proposed by Stockwell et al.5 in 2012, and it is a non-apoptotic programmed cell death process. Recent studies have focused on the role of ferroptosis in the progression, invasion, migration, and cell death of multiple types of cancers.6- 6-8 For most anti-cancer drugs, activation of programmed cell death pathways to kill tumor cells is a vital anti-tumor mechanism. Due to the acquired and intrinsic resistance of tumor cells to apop- tosis, the therapeutic efficacy of inducing apoptosis in tumor is limited.9 Therefore, the use of other forms of non-apoptotic cell death to clear tumor cells and control the proliferation of drug- resistant cell clones provides a new therapeutic possibility. The potential of targeting ferroptosis in cancer treatment has gener- ated high expectations.10-12
Adrenocortical carcinoma (ACC) is an isolated malignant tumor, which has attracted more and more attention since the end of the last century.13 It is a rare and highly aggressive malignant disease and can occur at any age. Localized tumors can be cured by sur- ery.14 Even if the tumor has been completely removed, however, recurrence is common. Unlike other tumors, treatment options after ACC recurrence are limited.14-16 The prognosis remains poor. Most studies have shown that the median survival time of ACC patients is about 12 months. It has been thought that changes in the Wnt / B-Catenin and IGF-2 signaling pathways lead to ACC, but recent studies have shown that these changes are not sufficient to cause the occurrence of malignant adrenal tumors.17,18 Therefore, the mechanism of the development and occurrence of ACC re- mains incompletely understood, and numerous genes and their functions remain to be discovered and explained.17,19 ACC shares some genetic profiles that are associated with promising thera- peutic responsiveness in other cancers.20 With the development of precision medicine, we have the opportunity to identify genes that are related to clinical outcomes and novel molecular targets for new drugs. A genomics-guided clinical care approach offers the potential for prolonging life expectancy and also improving the quality of life for ACC patients.
In this study, we aimed to find candidates ferroptosis genes, which were related to clinical outcomes of ACC. We constructed a prognosis model of ACC based on ferroptosis-related genes and then clarified the prognostic value of ferroptosis genes in ACC. These efforts may contribute to the development of better treat- ment strategies in the future.
2 METHODS |
2.1 Data acquisition |
We downloaded the RNA-sequencing data and clinical data for 78 ACC patients from the TCGA data portal (https://tcga-data.nci. nih.gov/tcga/dataAccessMatrix.htm). Regulator genes and marker genes for ferroptosis (ferroptosis-related genes) were downloaded from the FerrDb database,21 and articles were downloaded from the PubMed database.
2.2 | Candidate gene screening and validation, I prediction model establishment
Two steps were involved in the candidate gene screening. First, we performed univariate regression analysis of every ferroptosis- related gene and overall survival. Genes with p-values < 0.05 were included in the next step. Univariate Cox regression was carried out using the “survival” R package. Then, machine-learning least absolute shrinkage and selection operator (LASSO)22 were used to select independent risk factors that affected outcomes. LASSO Cox regression was implemented using the “glmnet” R package. Correlation coefficients at lambda.min were chosen for the final model, and cross-validation was used to tune and optimize the LASSO penalty terms. K-fold cross-validation (k = 5) was used to train and test the model.
After candidate genes were selected at lambda.min, a prognostic model was then constructed using the formula below. RiskScore was then calculated for each patient.
riskScore = > candidate ferroptosis -related genes level * coresponding Coef level
2.3 | Assessing the effectiveness of prediction models
We grouped the patients into high- and low-risk groups based on the median riskScore. The KM curve for these data was used to compare the prognosis between high-risk and low-risk groups according to the riskScore. Receiver operating characteristic (ROC) curves and areas under the curve (AUCs) were calculated with the “survival- ROC”23 and “survminer” R packages to demonstrate the predictive ability of riskScore for 1-, 3-, and 5-year OS. A flow diagram of this trial is shown in Figure 1.
External validation was done using the GSE19750 cohort. Data were downloaded from the GEO database. The riskScore was calcu- lated using the formula mentioned above. The clinical data were also downloaded. We determined the ROC curve and the Kaplan-Meier curve to test the predictive value of the prognostic model.
Data collection and quality control
Candidate gene screen
Validation of prediction model
Establish prediction model
Assess the effectiveness of prediction models
Function analysis
THE CANCER GENOME ATLAS
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We generated nomogram by combining the riskScore value and clinic-pathological factors to predict survival probability at 1, 3, and 5 years. This is a quantitative and intuitive method to assess the as- sociation between variables and survival. We then measured the prognostic accuracy by calculating the Harrell’s concordance index (C-index). The larger the C-index, the more accurate the prognostic prediction proved to be.24 We compared the prediction model with conventional staging systems using the C-index. We assessed cali- bration by comparing observed and predicted survival probabilities using the KM method and applied bootstraps with 100 replicates Nomogram was undertaken using the “rms” R package.
2.4 Functional analysis |
We used Gene Ontology analysis (GO) to identify characteristic bio- logical attributes of survival-associated ferroptosis genes and per- formed Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analysis to identify functional attributes. GO and KEGG analysis was done using the following R packages: “DOSE” “org. Hs.eg.db”,25 “clusterProfiler”26 and “pathview”.27 For visualization of the data, the “ggplot2”28 package was used.
|
3 RESULTS
The RNA-sequencing data and clinical data of 78 ACC patients were downloaded from TCGA database. Two patients were excluded from
the analysis due to missing clinical information. Of those who were qualified for inclusion, 48 were female and 28 were male. The aver- age overall survival time was 3.39 + 2.69 years. Two hundred fifty- nine ferroptosis genes were downloaded from the FerrDb database and Pubmed database (123 marker genes, 109 suppressor genes, and 150 driver genes).
First, we performed univariate regression analysis of every ferroptosis-related gene and overall survival. Seventy genes were identified as survival-associated ferroptosis-related genes with p < 0.05. Figure 2A shows the HR level of each survival-associated ferroptosis-related genes.
Next, LASSO Cox regression was implemented for these 70 genes. Correlation coefficients at lambda.min were chosen for the final model (Figure 2B, C, optimal lambda.min =0.078). After fivefold cross-validation, 17 genes were included in the final model. The Coef level for each gene is shown in Table 1. RiskScore was also calculated for each patient (Table 2).
Figure 3 showed that patients with poorer prognosis had lower riskScores. For patients who died during the follow-up, the average riskScore was -25.51 (SD = 74.47), while patients who survived follow-up had an average riskScore of 80.84 (SD = 101.83). It is clear that these groups were significantly different with regard to RiskScores (p = 1.21E-07, Figure 3).
The median riskScore was 19.68 for all patients. Patients were grouped into high- and low-risk groups based on their riskScores. The high-risk group (riskScore >19.68) had 37 patients, and the low- risk group (riskScore ≤19.68) had 39. KM curve showed that the high-risk group had poorer prognoses (p < 0.0001, Figure 4A). Then,
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| Gene | Coef |
|---|---|
| STMN1 | 0.006855766 |
| RRM2 | 0.003733332 |
| HELLS | 0.017375996 |
| FANCD2 | 0.00161208 |
| AURKA | 0.007796465 |
| GABARAPL2 | -0.0054616 |
| SLC7A11 | 0.016787224 |
| KRAS | 0.014846229 |
| ACSL4 | -0.021912674 |
| MAPK3 | -0.008147927 |
| HMGB1 | 0.013098853 |
| CXCL2 | 0.006211908 |
| ATG7 | -0.005985336 |
| DDIT4 | 0.00576449 |
| NOX1 | -0.007679209 |
| PLIN4 | 0.000928894 |
| STEAP3 | 0.002633784 |
we determined the time-dependent ROC curve to find the prog- nostic performance of riskScore for survival prediction. The AUC of the ROC curve for predicting 1-, 3-, and 5-year(s) survival time was 0.975, 0.913, and 0.915 respectively (Figure 4B-D).
Data from the GSE19750 cohort were used to perform exter- nal validation of the predictive value of the model. Consistent with the results in the TCGA cohort, patients in the high-risk group had significantly poorer survival probability than the low-risk group (p = 0.011, Figure 5A). The AUCs for 1-year, 3-year, and 5-year OS were 0.765, 0.773, and 0.805, respectively (Figure 5B-D).
We constructed the nomogram prognostic evaluation model to predict the 1-, 3-, or 5-year OS time in patients by combining riskS- cores and pathological information (Figure 5A). The predictive accu- racy of 1-, 3-, or 5-year OS is shown in Figure 5B-D. The C-index of the nomogram was 0.92 (se(C)=0.02). We also compared the predic- tion model with conventional staging systems. The C-index for the TNM staging system was 0.75 (se(C)=0.05), which was lower than that of our model. Thus, our prognostic prediction model had better predictive ability.
Figure 6 shows the GO (Figure 6A) and KEGG (Figure 6B) analy- ses of survival-associated ferroptosis genes. KEGG analysis showed that the genes were mostly enriched in central carbon metabolism in cancer, cellular senescence, and the NOD-like receptor signaling pathway.
|
4 DISCUSSION
Adrenocortical carcinoma is a highly malignant cancer with lim- ited therapeutic options. Patients usually exhibit lymph node and
| OS | Event | RiskScore | Risk | T | N | M | Stage | |
|---|---|---|---|---|---|---|---|---|
| TCGA.OR.A5J2 | 1677 | 1 | 26.65612951 | High | t3 | n0 | m1 | Stage iv |
| TCGA.OR.A5J3 | 1942 | 0 | -82.55082586 | Low | t3 | n0 | m0 | Stage iii |
| TCGA.OR.A5J5 | 365 | 1 | 220.2545248 | High | t4 | n0 | m0 | Stage iii |
| TCGA.OR.A5J6 | 2428 | 0 | -60.07161748 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5J7 | 490 | 1 | 127.9296784 | High | t3 | n0 | m0 | Stage iii |
| TCGA.OR.A5J8 | 579 | 1 | 181.7346398 | High | t3 | n0 | m0 | Stage iii |
| TCGA.OR.A5J9 | 1183 | 0 | 53.85394279 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5JA | 922 | 1 | 20.83685542 | High | t4 | n0 | m1 | Stage iv |
| TCGA.OR.A5JB | 551 | 1 | 249.1943157 | High | t4 | n0 | m1 | Stage iv |
| TCGA.OR.A5JD | 2782 | 0 | -87.26295608 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5JE | 2105 | 1 | 37.11735056 | High | t1 | n0 | m0 | Stage i |
| TCGA.OR.A5JF | 1259 | 0 | 0.159417811 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5JG | 541 | 1 | 50.49723047 | High | t4 | n1 | m1 | Stage iv |
| TCGA.OR.A5JI | 1424 | 0 | -84.30563802 | Low | t1 | n0 | m0 | Stage i |
| TCGA.OR.A5JJ | 309 | 0 | 79.28220068 | High | t4 | n1 | m1 | Stage iv |
| TCGA.OR.A5JK | 1255 | 0 | -13.39745347 | Low | t4 | n0 | m1 | Stage iv |
| TCGA.OR.A5JL | 670 | 0 | -124.2939039 | Low | t1 | n0 | m0 | Stage i |
| TCGA.OR.A5JM | 562 | 1 | 46.32584829 | High | t4 | n0 | m1 | Stage iv |
| TCGA.OR.A5JO | 889 | 0 | 30.20226513 | High | t1 | n0 | m0 | Stage i |
| TCGA.OR.A5JP | 149 | 0 | 94.40884423 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5JQ | 674 | 0 | -77.31526038 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5JR | 3688 | 0 | -130.3421379 | Low | t1 | n0 | m0 | Stage i |
| TCGA.OR.A5JS | 383 | 0 | 29.70127434 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5JT | 488 | 0 | -61.35190065 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5JV | 1541 | 0 | -95.23738127 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5JW | 1924 | 0 | 8.031815994 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5JX | 950 | 0 | 98.19251924 | High | t3 | n0 | m0 | Stage iii |
| TCGA.OR.A5JY | 552 | 1 | 63.85014088 | High | t4 | n1 | m1 | Stage iv |
| TCGA.OR.A5JZ | 211 | 0 | -76.63893854 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5K0 | 1029 | 0 | 30.6211023 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5K1 | 2723 | 0 | -41.34566511 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5K2 | 994 | 1 | 94.27576045 | High | t4 | n0 | m0 | Stage iii |
| TCGA.OR.A5K3 | 2842 | 0 | -69.84016404 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5K4 | 528 | 0 | -52.32932887 | Low | t4 | n0 | m0 | Stage iii |
| TCGA.OR.A5K5 | 253 | 0 | 27.93880869 | High | t3 | n0 | m0 | Stage iii |
| TCGA.OR.A5K6 | 1130 | 0 | 1.900433368 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5K8 | 504 | 0 | 40.08479735 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5K9 | 344 | 1 | 100.4672018 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5KO | 1414 | 0 | 20.61064176 | Low | t4 | n0 | m1 | Stage iv |
| TCGA.OR.A5KT | 2673 | 0 | 10.91838006 | Low | t1 | n0 | m0 | Stage i |
| TCGA.OR.A5KU | 4673 | 0 | 18.75358502 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5KV | 3659 | 0 | 41.91500616 | High | t2 | n1 | m0 | Stage iii |
| TCGA.OR.A5KW | 1525 | 0 | -23.1429177 | Low | t2 | n1 | m0 | Stage iii |
| TCGA.OR.A5KX | 1091 | 0 | 115.6707949 | High | t2 | n1 | m0 | Stage iii |
| TCGA.OR.A5KY | 391 | 1 | 130.5067414 | High | t4 | n1 | m1 | Stage iv |
| OS | Event | RiskScore | Risk | T | N | M | Stage | |
|---|---|---|---|---|---|---|---|---|
| TCGA.OR.A5KZ | 125 | 1 | 218.2568428 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5L3 | 3897 | 0 | -10.86225829 | Low | t1 | n0 | m0 | Stage i |
| TCGA.OR.A5L4 | 724 | 0 | -262.5860277 | Low | t4 | n0 | m0 | Stage iii |
| TCGA.OR.A5L5 | 840 | 0 | -51.65798978 | Low | t1 | n0 | m0 | Stage i |
| TCGA.OR.A5L6 | 628 | 0 | 33.8583362 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5L8 | 555 | 0 | 29.68040353 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5L9 | 645 | 0 | -38.37036871 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LA | 487 | 0 | -75.732104 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LB | 1204 | 1 | 80.98199258 | High | t4 | n0 | m1 | Stage iv |
| TCGA.OR.A5LC | 159 | 1 | 198.3267946 | High | t4 | n0 | m1 | Stage iv |
| TCGA.OR.A5LD | 1197 | 1 | 68.83890176 | High | t4 | n0 | m0 | Stage iii |
| TCGA.OR.A5LE | 662 | 1 | 64.07674666 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LG | 1589 | 0 | 27.02398016 | High | t3 | n0 | m0 | Stage iii |
| TCGA.OR.A5LH | 2385 | 1 | -58.9701129 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LJ | 1105 | 1 | 49.85160852 | High | t2 | n1 | m1 | Stage iv |
| TCGA.OR.A5LK | 2222 | 0 | -44.29088098 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LL | 1613 | 1 | 24.1344422 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LM | 1858 | 0 | -14.74304601 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LN | 1916 | 0 | -93.96683194 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LO | 1949 | 0 | 147.1147267 | High | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LP | 1583 | 0 | -175.3190167 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LR | 639 | 0 | -87.18232022 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LS | 882 | 0 | -8.787969716 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.OR.A5LT | 365 | 0 | -31.61654757 | Low | t3 | n0 | m0 | Stage iii |
| TCGA.OU.A5PI | 709 | 0 | 12.48413456 | Low | t2 | n1 | m1 | Stage iv |
| TCGA.P6.A5OF | 207 | 1 | 227.6880918 | High | t4 | n0 | m0 | Stage iii |
| TCGA.P6.A5OG | 383 | 1 | 119.2763823 | High | t4 | n0 | m1 | Stage iv |
| TCGA.PA.A5YG | 470 | 0 | -99.68999465 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.PK.A5H8 | 3240 | 0 | -72.91560771 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.PK.A5H9 | 307 | 0 | -85.04336783 | Low | t2 | n0 | m0 | Stage ii |
| TCGA.PK.A5HA | 830 | 0 | -72.43647323 | Low | t1 | n0 | m0 | Stage i |
Note: OS, overall survival in days. Events indicate survival status. 1 represents patient was dead. 0 represents patient was alive. The patients were classified into low-risk group and high-risk group according to the median value of the risk scores.
distant metastases by the time of diagnosis. Surgery is the pri- mary treatment strategy, while adjuvant therapies are frequently needed. Mitotane is currently the only agent approved.16 For ad- vanced ACC, a combination of mitotane with a cytotoxic regimen of etoposide, doxorubicin, and cisplatin (EDP-M) is recommended. However, a narrow therapeutic window and endocrine side ef- fects restrict the clinical use of these drugs.29,30 Thus, there is an urgent need to identify drug targets and develop new therapeutic strategies to treat ACC.
High-throughput biotechnology such as genomics provides a good entry point for basic medicine to clinical medicine. Prognostic and predictive biomarkers selected from high-throughput genomic data are of critical importance in cancer management.31 The ques- tion of how to mine valuable information efficiently from vast
biological sequences is crucial to researchers. Meanwhile, tradi- tional variable-selecting methods such as multivariate regression analysis are insufficient when facing big data. LASSO, a regular- ization method, is a promising solution. LASSO is particularly at- tractive in prognostic studies due to its capabilities of regression coefficients shrinkage and automatic variable selection.32 LASSO has been successfully applied in prognostic model studies.33,34 In this study, we focused on candidate ferroptosis genes related to prognosis of ACC for the first time. We constructed a prognosis model based on 17 survival-associated ferroptosis-related genes using the machine-learning method. These efforts may contribute to the development of better treatment strategies in the future. We found that the predictive value of our model is better than that of the conventional staging system. Our study provided a handful of
FIGURE 3 RiskScores of patients with different survival statuses during follow-up. 0 representing death and 1 representing survival
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3-year OS
0.0
5-year OS
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.2
0.4
0.6
0.8
1.0
FP
FP
AUC = 0.773
AUC = 0.805
(A)
(B)
response to oxidative stress
VEGF signaling pathway
protein serine/threonine/tyrosine kinase activity
phagophore assembly site membrane
NOD-like receptor signaling pathway
phagophore assembly site
-log2(PVaule)
peptidyl-serine phosphorylation
35
Mitophagy - animal -
30
membrane raft
25
20
FoxO signaling pathway
-log10(pvalue)
GO Terms
glucose transmembrane transporter activity
15
10
ficolin-1-rich granule
Ferroptosis
9
coenzyme binding
Count
8
3
7
cellular response to nutrient levels
6
Endocrine resistance -
cellular response to extracellular stimulus
9
12
Central carbon metabolism in cancer -
autophagosome
15
aging
Cellular senescence
NADP binding
MAP kinase activity
Autophagy - other -
BP
CC
MF
GO Category
0.0
2.5
5.0
7.5
10.0
Count
candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.
Recent studies have demonstrated that ACC is sensitive to fer- roptosis, indicating that induction of ferroptosis could be a prom- ising treatment approach. Therefore, we constructed a prognostic model including 17 survival-associated ferroptosis-related genes. Belavgeni’s study showed direct inhibition of glutathione peroxidase 4, a key factor in the initiation of ferroptosis, in human ACC NCI- H295R cells leading to high necrotic populations.35 High STMN1 ex- pression has been observed in aggressive ACC patients.36,37 Ikeya’s recent study shows that overexpression of AURKA, a gene identified in our study, can cause atypical mitosis in adrenocortical carcinoma with the p53 somatic variant.38 The p53 protein, an important reg- ulator of ferroptosis, is frequently mutated in ACC.39 ACSL4, which has been reported to dictate ferroptosis sensitivity by shaping cel- lular lipid composition,40 is demonstrated to be highly expressed in mouse adrenal glands. 41
In our study, ferroptosis gene riskScores showed good predictive value. Nomograms have been well developed as a prognostic assess- ment tool and proven to be more accurate than conventional stag- ing systems in several cancers.42-44 We constructed a nomogram by combining ferroptosis gene riskScores and clinic-pathological factors. Our model showed better predictive value than the con- ventional staging system, a finding supported by C-index (0.92) and calibration curve. In terms of precision medicine, our model has po- tential clinical applications.
There are some possible weaknesses in this study. We performed internal validation using k-fold cross-validation and bootstrap resa- mpling methods. External and multicenter prospective cohorts with large sample sizes are still needed to validate the clinical application of our model, and basic research needs to be done to clarify the un- derlying mechanism.
In conclusion, our study identified candidate ferroptosis genes, which were related to clinical outcomes of ACC. We constructed a prognosis prediction model of ACC based on ferroptosis-related genes. Our model showed better predictive value than the conven- tional staging system. These efforts provided a handful of underly- ing targets for revealing the molecular basis of ACC, as well as for drug development.
ACKNOWLEDGMENTS
The authors would like to thank Prof. Kimberly J. Bussey who up- loaded the raw data of GSE19750. The authors also would like to thank Dr. Wenxuan Peng for helping to polish the text.
CONFLICT OF INTEREST
The authors declare that they have no conflict of interest.
AUTHOR CONTRIBUTIONS
Lin, Liang, and Hu contributed to the literature search and the design of the study. Lin and Liang analyzed and interpreted the data. Lin and Liang wrote the study Lin and Sun formatted the figures and
tables. Sun revised the article Hu helped perform the analysis with constructive discussions. The final study was approved by all the authors.
DATA AVAILABILITY STATEMENT
All data generated or analyzed in this study are available from TCGA data portal (https://tcga-data.nci.nih.gov/tcga/) and GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi).
ORCID Chen Lin İD https://orcid.org/0000-0001-6783-215X
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How to cite this article: Lin C, Hu R, Sun F, Liang W. Ferroptosis-based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression. J Clin Lab Anal. 2022;36:e24465. doi:10.1002/jcla.24465