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Retracted: Prognosis and Therapeutic Efficacy Prediction of Adrenocortical Carcinoma Based on a Necroptosis-Associated Gene Signature
BioMed Research International
Received 12 March 2024; Accepted 12 March 2024; Published 20 March 2024
Copyright @ 2024 BioMed Research International. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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References
[1] D. Ji, R. Zhong, and S. Fan, “Prognosis and Therapeutic Efficacy Prediction of Adrenocortical Carcinoma Based on a Necroptosis-Associated Gene Signature,” BioMed Research International, vol. 2022, Article ID 8740408, 21 pages, 2022.
Hindawi
Research Article Prognosis and Therapeutic Efficacy Prediction of Adrenocortical Carcinoma Based on a Necroptosis-Associated Gene Signature
Dan Ji,1 Rongfang Zhong 1,2 and Song Fan ®2
1Department of Basic Medicine, Anhui Medical College, No. 632 of Furong Road, Shushan District, Hefei, 230601 Anhui, China
2 Department of Urology, The First Affiliated Hospital of Anhui Medical University; Institute of Urology, Anhui Medical University; Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei 230032, China
Correspondence should be addressed to Song Fan; songfan1981@163.com
Received 21 February 2022; Revised 13 April 2022; Accepted 21 April 2022; Published 19 May 2022
Academic Editor: B. D. Parameshachari
Copyright @ 2022 Dan Ji et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background. Adrenocortical carcinoma (ACC) is a rare and poor prognosis malignancy. Necroptosis is a special type of cell apoptosis, which is regulated in caspase-independent pathways and mainly induced through the activation of receptor-interacting protein kinase 1, receptor-interacting protein kinase 3, and mixed lineage kinase domain-like pseudokinase. A precise predictive tool based on necroptosis is needed to improve the level of diagnosis and treatment. Method. Four ACC cohorts were enrolled in this study. The Cancer Genome Atlas ACC (TCGA-ACC) cohort was used as the training cohort; three datasets (GSE19750, GSE33371, and GSE49278) from Gene Expression Omnibus (GEO) platform were combined as the GEO testing cohort after removing of batch effect. Forty-nine necroptosis-associated genes were obtained from a prior study and further filtered by least absolute shrinkage and selection operator Cox regression analysis; corresponding coefficients were used to calculate the necroptosis-associated gene score (NAGs). Patients in the TCGA-ACC cohort were equally divided into two groups with the mean value of NAGs. We investigated the associations between NAGs groups and clinicopathological feature distribution and overall survival (OS) in ACC, the molecular mechanisms, and the value of NAGs in therapy prediction. A nomogram risk model was established to quantify risk stratification for ACC patients. Finally, the results were confirmed in the GEO-combined cohort. Result. Patients in the TCGA- ACC cohort were divided into high and low NAGs groups. The high NAGs group had more fatal cases and advanced stage patients than the low NAGs group (P < 0.001, hazard ratio (HR) = 13.97, 95% confidence interval (95% CI): 4.168-46.844; survival rate: low NAGs, 7.69% vs. high NAGs, 61.53%). NAGs were validated to be negatively correlated with OS (R = - 0.48, P < 0.001) and act as an independent factor in ACC with high discriminative efficacy (P <0.001, HR = 11.76, 95% CI: 2.86-48.42). In addition, a high predictive efficacy nomogram risk model was established combining NAGs with tumor stage. Higher mutation rates were observed in the high NAGs group, and the mutation of TP53 may lead to a high T cell infiltration level among the NAGs groups. Patients belonged to the high NAGs are more sensitive to the chemotherapy of cisplatin, gemcitabine, paclitaxel, and etoposide (all P < 0.05). Ultimately, the same statistical algorithms were conducted in the GEO-combined cohort, and the crucial role of NAGs prediction value was further validated. Conclusion. We constructed a necroptosis-associated gene signature, revealed the prognostic value between ACC and it, systematically explored the molecular alterations among patients with different NAGs, and manifested the value of drug sensitivity prediction in ACC.
1. Introduction
Adrenocortical carcinoma (ACC) is an ultrarare malignancy originating in the outer layer cortex of the adrenal gland, affecting 0.7-2.0 per million annually and leading to 0.2% of all cancer deaths in the United States [1]. ACC is a hereditary associated syndrome; many underlying genetic alterations have been found, such as TP53, ZNFR3, CTNNB1,
PRKAR1A, CCNE1, and TERF2 mutations [2]. In patients diagnosed with ACC, three main scenarios have been reported. First, approximately 40% to 60% of patients showing predominant complaints have hormone excess-related symp- toms and signs [3, 4]; hypercortisolism is frequent and often causes plethora, diabetes mellitus, muscle weakness, and oste- oporosis. Second, about 30% patients present with nonspecific symptoms due to the growth of tumor, such as abdominal or
| TCGA_ACC (n=78) | GSE19750 (n=22) | GSE33371 (n =23) | GSE49278 (n=44) | Total (n= 167) | |
|---|---|---|---|---|---|
| Gender | |||||
| Female | 47 (60.3%) | 11 (50.0%) | 16 (69.6%) | 36 (81.8%) | 110 (65.9%) |
| Male | 31 (39.7%) | 11 (50.0%) | 7 (30.4%) | 8 (18.2%) | 57 (34.1%) |
| Age | |||||
| Mean (SD) | 46.7 (15.9) | 52.5 (14) | 43 (16.8) | 45.1 (17.2) | 46.5 (16.2) |
| Median [min, max] | 49.5 [14,77] | 54.6 [23.3,72.1] | 45 [10,77] | 43.5 [15,81] | 48 [10,81] |
| Stage | |||||
| Stage I | 9 (11.5%) | 1 (4.5%) | 2 (8.7%) | 4 (9.1%) | 16 (9.6%) |
| Stage II | 37 (47.4%) | 7 (31.8%) | 10 (43.5%) | 24 (54.5%) | 78 (46.7%) |
| Stage III | 16 (20.5%) | 1 (4.5%) | 3 (13.0%) | 2 (4.5%) | 22 (13.2%) |
| Stage IV | 14 (17.9%) | 4 (18.2%) | 8 (34.8%) | 13 (29.5%) | 39 (23.4%) |
| Unknown | 2 (2.6%) | 9 (40.9%) | 1 (2.3%) | 12 (7.2%) | |
| Side | |||||
| Left | 44 (56.4%) | 10 (43.5%) | 23 (52.3%) | 77 (46.1%) | |
| Right | 34 (43.6%) | 10 (43.5%) | 21 (47.7%) | 65 (38.9%) | |
| Unknown | 22 (100.0%) | 3 (13.0%) | 25 (15.0%) |
flank pain, abdominal fullness, or early satiety [3, 5]. Third, the residual roughly 20% of ACCs are incidentally diagnosed by unrelated medical issues [6]. The European Network for the Study of Adrenal Tumors (ENSAT) staging system was rec- ommended at initial diagnosis [7]. Steroid hormone measure- ments and biochemical exclusion of a pheochromocytoma detection and imaging method are often used to screen ACC [8, 9]. Once diagnosed with ACC, clinicians would treat them with multidisciplinary therapeutic strategies, such as tumor resection [10], adjuvant therapy [11], mitotane [12], and cyto- toxic therapy and radiotherapy [13, 14].
Programmed cell death (PCD) is a genetically regulated form of cell death, and apoptosis was historically considered its only form. At present, a special type of necrosis termed necroptosis has also been proven to be a novel form of PCD [15]. Compared to apoptosis, necroptosis is regulated in caspase-independent pathways and is mainly induced by receptor-interacting protein kinase 1 (RIPK1), RIPK3, and mixed lineage kinase domain-like pseudokinase (MLKL). Necroptosis has a morphological resemblance to nonregulated necrosis caused by physical trauma manifesting as organelle swelling, plasma membrane rupture, cell lysis, depletion of energy, and local inflammation [16]. It has been proposed that necroptosis plays a key role in cancer immunity, cancer sub- types, oncogenesis, and metastasis [17]. Accumulated evi- dence showed that necroptosis is a double-edged sword in cancer. It can prevent tumor development by inducing cancer cell death. However, necroptosis-associated inflammatory reactions may also promote cancer metastasis [18, 19].
In this study, we conducted least absolute shrinkage and selection operator (LASSO) Cox regression using the data from TCGA platform to filter necroptosis-associated genes and calculate the corresponding necroptosis-associated gene score (NAGs). We revealed the correlation between NAGs and overall survival (OS) of ACC and manifested the impor- tant value in predicting prognosis and drug sensitivity for
patients with ACC. We established a high-performance nomogram risk model for convenient clinical application. Meanwhile, we revealed different molecular mechanisms and gene mutations between the high NAGs and low NAGs groups and validated that immune-related signaling path- ways played a pivotal role in ACC development.
2. Method
2.1. Summary of Cohorts. Four ACC cohorts including tran- scription profiles and clinical data were downloaded, one was from the Cancer Genome Atlas (TCGA-ACC, https:// www.cancer.gov), and three from Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/). “TCGAbiolinks” package was used to download mRNA expression profiles of the TCGA-ACC cohort and corresponding clinical informa- tion from the Genomic Data Commons (GDC) platform. The original expression data in GSE19750 and GSE33371 cohorts were downloaded and annotated via the correspond- ing GPL570 platform, while GSE49278 was annotated via the GPL16686 platform. After initial data processing, a total of 167 ACC patients were enrolled for subsequent analysis, including 78 from the TCGA-ACC cohort, 22 from the GSE19750 cohort, 23 from the GSE33371 cohort, and the remaining 44 from the GSE49278 cohort (Table 1). 12 samples from the TCGA-ACC cohort were excluded, due to the lack of clinical information, gene expression profile, or follow-up days less than 1 month. Five normal cases and 21 cases without OS information in GSE19750 were excluded; 10 normal cases, 22 adrenocortical adenoma cases, and 10 cases without OS infor- mation in GSE33371 were excluded. All the cases in GSE49278 were enrolled in the study.
2.2. Dismissal of Batch Effects. Batch effects are the nonbio- logical differences between two or more datasets. To elimi- nate the bias caused by batch effects in this study and
Raw PCA for combined expression profile
Combat PCA for combined expression profile
80
40
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Comp 2: 14.4% variance
Comp 2: 5.7% variance
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T
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Comp 1: 63.8% variance
Comp 1: 7.3% variance
GSE19750
GSE19750
GSE33371
GSE33371
GSE49278
GSE49278
(a)
(b)
ACC cohort
GSE19750 cohort
GSE33371 cohort
GSE49278 cohort
Stage
Stage
Stage
Stage
Gender
Gender
Gender
Gender
Age
Age
Age
Age
Fustat
Fustat
PANX1 DIABLO
Fustat
DNMT1
C
C
HSPA4
Fustat
2
PLKI
EGFR
HAT1
SLC39A7 SIRT3
GATA3
C
AXL BCL2
ITPK1
TRIM11
HSP90AA1 DDX58
C
TNFRSF1B
C
SPATA2 DIABLO
DDX58
IDH2
C
CYLD
PANX1
L
MIA
1
BNIP3
E
TRAF2
TRAF
HSP90AA1
ALK
BCL2L11
BRAF
OTULIN
CASP8
-
C
II
THE
L
C
SQSTM1
TNF
RNF31
GREAT
MAPK8
1
ČDKN2A
C
CFLAR
[
FLT3
KLF9
1
TERT
MLKL
HRAC9
FAS
ATRX
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ID1
TLR3
ATRX
BRAF İPMK
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SIRT1
TERT
LLC
STUB1
SIRT3
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CYLD
DIABLO BCL2 MÝCN
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IDH1
TNFRSF21
RIPK3
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MPG
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HSPA4
SQSTM1
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SIRT2
5
FASLG RIPKS
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SQSTM1
STAT3
FADD
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HDAC9
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BRAF
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ALK
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BCL2L11
STAT3
TRIM11
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CASP8
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HDAC9
ZBP1
FADD
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GATA3
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ZBP1
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CASP8
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~
CYLD
L
MAPK8
USP22
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GATA3
HAT1
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BACH2
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EGFR
BNIP3
PANX1
LEFİ
L
I
KLES
CDKN2A
MYCN
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RIPKOK
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RIPK1
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TARDBP
C
I
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DNMT1
TARDEP
BCL2
CYLD
APP
PLK1
TNFSF10
-
SIRT3
SIRTI
ZBP1
TSC1
C
TLR3
TNFRSFIA
5
RNF31
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FAS
STAT3 RIPKI
C
CDKN2A
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KLF9
C 1
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TNFRSFIB
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EGFR
C
BNIP3
SPATA2
-1
AXL
TNERSF21
C
TNFRSF21
BACH2 MYCN
MAP3K7
M
CD40
0
BG-2L11 MYC
C
RIPK1
HAT1
C
OTULIN
ALK
RNF31
FAS
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MAPSK7
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DDX58
KLF9
RIPKI
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TARDBP
TNFSF10
HSPA4
USP22
STAT3
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SPATA2
0
PANX1 RIPK3
HSP90AA1
AXL
TNFRSFIA
SOSTMI
N
I
HDAC9
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MYC
L
BCL2
I
DNMT1
HAT1
LEFI APP
C
MYC
BNIP3
FADD
SLC39A7
HL
ZBP1
LATA3
L
APP
Apr
0
STRYS
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LEF1
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1 I
RIPK3
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CD40 FADD
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SIRT2
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Stage I
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80
Alive
Stage II
Male
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Stage III
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(c)
make the transcription profiles in the three GEO cohorts more similar, the ComBat algorithms in the “sva” package was used to remove the batch effects between these three GEO-sourced cohorts, and the GEO-combined cohort was used as the testing cohort, while the TCGA-ACC cohort
was used as the training cohort in the subsequent analysis (Figures 1(a) and 1(b)).
2.3. Construction of the Necroptosis Prognostic Score. A total of 67 necroptosis-associated genes were collected from a
recently published article, and after merging with the gene symbols in four enrolled cohorts, 49 necroptosis-associated genes remained for subsequent analysis (Figure 1(c)). A sta- tistical model was generated by LASSO regression analysis performed by the “glmnet” package. We used the LASSO regression to select genes and corresponding coefficients to cal- culate the NAGs. All the patients in TCGA-ACC cohort and GEO-combined cohort were divided into high and low NAGs groups with the mean NAGs. Based on the TCGA-ACC cohort, the correlation between NAGs and OS and the clinical feature distribution among NAGs groups were further estimated.
2.4. Prognostic Prediction with Multivariate Analysis and Nomogram Risk Model. Multivariate analysis was performed to investigate the independent prognostic factors, receiver operating characteristic (ROC) curves were employed, and the area under the curve (AUC) was calculated to test the stability. A nomogram risk model was established by the R package “regplot” to provide a quantitative tool for clinicians for individualized prediction of progression probability. Cal- ibration curves was drawn to assess the model goodness of fit. Decision curve analysis and clinical impact curves quan- tified the net benefits at different threshold probabilities to determine the clinical usefulness of the nomogram by using the R packages “rms” and “rmda.”
2.5. Gene Set Variation Analysis, Immune Infiltration, and Genetic Mutations. We used gene set variation analysis (GSVA) to assess the variations in the pathway activities among patients with high and low NAGs by the “GSVA” R package. The 50 hallmark gene sets were obtained from MSigDB [20, 21]. We used single-sample gene set enrich- ment analysis (ssGSEA) to investigate the infiltration of 28 immunocytes in tumors and calculate infiltration score of each type of immunocyte for each patient using [22]. A lol- lipop plot was further drawn to show the correlation between the risk score and infiltration of immunocytes with a P value less than 0.05. Anticancer immune response deter- mines the fate of tumor cells and is reflected by the cancer immunity cycle which consists of seven steps: step 1, cancer cell antigens releasing; step 2, cancer antigen presentation; step 3, immune priming and activation; step 4, immunocytes trafficking; step 5, immunocytes infiltration in tumors; step 6, tumor cells recognition by T cells; and step 7, killing of tumor cells [23]. The genetic mutations of ACC patients were also enrolled from the Genomic Data Commons (GDC) by the “TCGAbiolinks” package and further visual- ized via the “maftools” R package [24].
2.6. Precision Therapeutic Strategies. To investigate the ther- apeutic prediction ability of NAGs, we enrolled gene expres- sion profile from a melanoma cohort which contained 47 cases who received anti-CTLA4 or anti-PD1 therapy and corresponding response information [25]. The gene expres- sion distribution among NAGs groups and potential responders to anti-CTLA4 or anti-PD1 were analyzed using SubMap algorithms via GenePattern platform [26]. For che- motherapy, drug sensitivity and phenotype data from GDSC 2016 (https://www.cancerrxgene.org/) was used to predict
the chemotherapeutic response via R package “MOVICS” [27]. Estimated inhibitory concentration (IC50) was set as the index to quantificationally compare the response of each patient treated with a type of chemotherapy drug by ridge regression, and lower IC50 imply increased sensitivity to treatment, and the prediction accuracy was assessed through 10-fold cross-validation [28]. We also downloaded the RNA sequence data from GSE116439 and GSE116444 [29], which contains the data of multiple cell lines treated with or with- out cisplatin and gemcitabine for 2 hours, 6 hours, and 24 hours, to confirm the predictive value of the necroptosis value for chemotherapy sensitivity.
2.7. Statistics. All statistical analyses were performed by R (version: 4.0.2). A Fisher’s exact test was used for categorical data and a t test or Pearson’s correlation analysis was applied for continuous data. A Kaplan-Meier curve was generated by the log rank test to analyze survival rates for patients with dif- ferent detection methods, and ROC analyses were employed to examine the prediction efficiency of NAGs and performed by the R package “PROC.” A two-tailed P value < 0.05 was rec- ognized statistically significant. Hazard ratios (HRs) and 95% confidence intervals (CIs) for OS were estimated via Cox pro- portional hazard regression. The selected necroptosis- associated prognostic value of NAGs and other features was assessed by the function “ggforest” in the R package “survimi- ner” and displayed with forest plot. The selected necroptosis- associated genes and clinical features were displayed in heat- map performed by the R package “pheatmap.”
3. Results
3.1. Establishment of the Prognostic Necroptosis-Associated Gene Signature. Seven necroptosis-associated genes were eventually selected via LASSO and Cox regression analyses under the best optimal lambda value of 0.008, including LEF1, MAPK8, CYLD, TRAF2, DNMT1, PLK1, and GATA3 based on the TCGA-ACC cohort (Figures 2(a) and 2(b)). Each of these genes was proved to be related to the OS of ACC significantly (Fig. S1). NAGs was calculated with the following formula: MAPK8 expression * 0.207 + TRAF2 expression * 0.0506 + DNMT1 expression * 0.181 + PLK1 expression * 0.474 + GATA3 expression * 0.0586 + LEF1 expression * 0.103 + CYLD expression * (-0.371). We found that NAGs was negatively correlated with the OS of ACC patients, with a P value of 8e-06 (Figure 2(c), R= - 0.48). Seventy-eight patients in the TCGA-ACC cohort were equally divided into low NAGs and high NAGs groups with the median value. In the two new defined groups, different dis- tributions of clinicopathological features between low NAGs and high NAGs groups were observed, including status of survival (P=7.2e-07) and tumor stage (P=1.7e-06), but not tumor side (P=0.49) and patient age (P=0.26) or sex (P= 1) (Figure 2(d)). ROC analysis manifested a high discriminative efficiency of the necroptosis-associated gene signature, with the AUC values of 0.925 at 1 year, 0.932 at 3 years, and 0.915 at 5 years, respectively (Figure 2(e)). In addition, high and low NAGs groups exhibited clear boundaries (Figure 2(f)). K-M curves demonstrated that
500
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Optimal lambda = 0.008
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Log lambda
- ALK
HAT1
SIRT1
LEF1
DNMT1
- APP
HDAC9
SIRT2
ATRX
HSPA4
SIRT3
MAPK8
PLK1
AXL
IPMK
SPATA2
CYLD
GATA3
BCL2
ITPK1
SQSTM1
TRAF2
- BCL2L11
KLF9
STAT3
(b)
BNIP3
- LEF1
TLR3 - TNF TRAF2 MYC TRIM11 PANX1 TSC1 PLK1 USP22 RIPK1 ZBP1 - RIPK3 - RNF31 RETR
TARDBP
BRAF
MAP3K7
- TERT
150
- CDKN2A
MAPK8
R = - 0.48
P = 8e-06
CFLAR
MLKL
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- EGFR
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- GATA3
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FIGURE 2: Continued.
ACC
Status
Stage
Gender
Side
Age
High (n = 39)
0000O 0000O
Low (n = 39)
p = 7.2e-07
p = 1.7e-06
p= 1
p = 0.49
p = 0.26
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Female
Dead
Male
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Right
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⇐ 50
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patients with low NAGs had significantly longer OS than patients with high NAGs (Figure 2(g), P<0.001, HR = 13.97, 95% CI: 4.168-46.844), and NAGs also exhibited admirable prognostic value in patients with same clinico- pathological parameters (Fig. S2).
3.2. NAGs Is an Independent Prognostic Predictor of ACC. We enrolled five clinicopathological features and conducted multivariate Cox regression analysis to screen the indepen- dent prognostic factors of ACC. The forest plot showed that
only NAGs acted as an independent prognostic factor (P<0.001), while age (P=0.5987), gender (P=0.4944), stage (P> 0.05), and laterality (P = 0.9618) had no statistical significance (Figure 3(a)). The AUC values of different clin- icopathological features were calculated to compare their discrimination ability, and the results are shown in Figure 3(b). NAGs with an AUC value of 0.945 (95% CI: 0.887-1.00) and tumor stage with an AUC of 0.831 (95% CI: 0.729-0.933) shows a best predictive efficacy among the five clinicopathological features.
| Age | (N = 78) | 1.01 (0.9792-1.04) | 0.5987 | ||||
|---|---|---|---|---|---|---|---|
| Gender | FEMALE (N = 47) | Reference | I | ||||
| MALE (N = 31) | 1.36 (0.5631-3.28) | ||||||
| 0.4944 | |||||||
| Stage | Stage I (N=9) | Reference | |||||
| Stage II (N = 37) | 3.52 (0.3996-31.05) | ||||||
| 0.2569 | |||||||
| Stage III (N = 16) | 4.25 (0.4879-37.02) | 0.1902 | |||||
| Stage IV (N = 14) | 8.06 (0.9059-71.65) | ||||||
| 0.0613 | |||||||
| Unknown (N = 2) | 1.80 (0.0972-33.48) | ||||||
| 0.6923 | |||||||
| Laterality | Left (N = 44) | Reference | |||||
| Right (N = 34) | 0.98 (0.4246-2.26) | ||||||
| Q.9618 | |||||||
| Necroptosis score | Low-risk (N = 39) | Reference | |||||
| High-risk (N = 39) | 11.76 | ||||||
| (2.8576-48.42) | <0.001 *** | ||||||
| # Events: 27; Global p-value (Log-rank): 3.4208e-06 | I | I 1 | I | 1 | |||
Entire cohort
100%
75%
Ture negative rate
50%
25%
0%
0%
25%
50%
75%
100%
False negative rate
AUC
Nomogram 96.7 (92.8; 100.0)
Necroptosis score 94.5 (88.7; 100.0)
Gender 49.8 (36.1; 63.6)
AIC: 183.07; Concordance index: 0.83
I
0.1
0.5
1
5
10
50
100
Stage 83.1 (72.9; 93.3)
Laterality 50.3 (36.5; 64.0)
Age 55.2 (39.3; 71.2)
(a)
(b)
pbccox coxph
Points
0
10
20
30
40
50
60
70
80
90
100
Stage II
Stage I
Stage
Unknown
Stage III
Stage IV
Low-risk
High-risk
Necroptosis
score
Total points
0
20
40
60
80
100
120
140
160
180
200
220
Pr (futime < 60)
0.02
0.04
0.06
0.1
0.2
0.4
0.6
0.8
0.92
Pr (futime < 36)
0.01
0.02
0.03
0.06
0.1
0.2
0.3
0.45
0.65
(c)
FIGURE 3: Continued.
Actual survival probability
0.8
0.3
0.6
Net benefit
0.2
0.4
0.1
0.2
Hosmer-lemeshow
P = 0.865
0.0
0.0
0.2
0.4
0.6
0.8
0.0
0.2
0.4
0.6
0.8
1.0
Nomogram predicted probability
Threshold probability
Reference
Nomogram
All
Predict model
Stage
None
(d)
(e)
Number high risk (out of 1000)
1000
600
200
0
0.0
0.1
0.2
0.3
0.4
High risk threshold
1:100
1:5
1:3
1:2
Cost: benefit ratio
Number high risk
Number high risk with event
(f)
3.3. A Nomogram Incorporating the NAGs. Based on the prior results, we established a nomogram risk model com- bining NAGs and tumor stage to provide a quantitative and precise tool for clinicians to predict progression proba- bility of each patient with ACC (Figure 3(c)). For a given patient, the sum of score for each predictor represented the nomogram score, and a low number of nomogram scores indicated increased progression possibility. Likewise, we ver- ified discrimination of the nomogram risk model via ROC analysis, and the AUC value of 0.967 (95% CI: 0.928-1.00) implied its admirable predictive ability (Figure 3(b)). A P value of 0.865 in calibration analysis indicated that the pre-
diction performance of this nomogram might be equivalent to an ideal predictive model (Figure 3(d)). Decision curve analysis (DCA) and the clinical impact curves were per- formed to demonstrate high clinical net benefit almost over the entire threshold probability of the nomogram model in training cohort (Figures 3(e) and 3(f)).
3.4. Different Pathway Activation among NAGs Groups. GSVA revealed the disparities in underlying biological path- ways between the high NAGs and low NAGs groups. As the results shown (Figure 4(a)), the high NAGs group has obvi- ous cell cycle changes (including E2F targets and G2M
E2F_TARGETS
MYC_TARGETS_V1
G2M_CHECKPOINT
MYC_TARGETS_V2
DNA_REPAIR
PANCREAS_BETA_CELLS
UV_RESPONSE_UP
MTORC1_SIGNALING
UNFOLDED_PROTEIN_RESPONSE
WNT_BETA_CATENIN_SIGNALING
APICAL_SURFACE
HEDGEHOG_SIGNALING
MITOTIC_SPINDLE
TNFA_SIGNALING_VIA_NFKB
ESTROGEN_RESPONSE_EARLY
TGF_BETA_SIGNALING
EPITHELIAL_MESENCHYMAL_TRANSITION
XENOBIOTIC_METABOLISM
PI3K_AKT_MTOR_SIGNALING
CHOLESTEROL_HOMEOSTASIS
ESTROGEN_RESPONSE_LATE ADIPOGENESIS HYPOXIA KRAS_SIGNALING_DN NOTCH_SIGNALING OXIDATIVE_PHOSPHORYLATION P53_PATHWAY MYOGENESIS ANGIOGENESIS FATTY_ACID_METABOLISM GLYCOLYSIS APOPTOSIS PROTEIN_SECRETION HEME_METABOLISM UV_RESPONSE_DN ALLOGRAFT_REJECTION COMPLEMENT -4 0 RENAC
IL6_JAK_STAT3_SIGNALING
APICAL_JUNCTION
SPERMATOGENESIS
PEROXISOME
ANDROGEN_RESPONSE
COAGULATION
INFLAMMATORY_RESPONSE
REACTIVE_OXIGEN_SPECIES_PATHWAY
IL2_STAT5_SIGNALING
INTERFERON_GAMMA_RESPONSE
KRAS_SIGNALING_UP
INTERFERON_ALPHA_RESPONSE
BILE_ACID_METABOLISM
T
4
t value of GSV A score, necroptosis score High vs. Low
(a)
FIGURE 4: Continued.
2738, 2022, 1, Downloaded from https://onlinelibrary. wiley.com/doi/10.1155/2022/8740408 by National Library Of Medicine, Wiley Online Library on [05/04/2026]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
Low necroptosis score group: Altered in 21 (53.85%) of 39 samples
57
0
4
0
MUC16
10%
COL5A1
8%
CTNNB1
8%
FBN2
8%
PRKAR1A
8%
TMEM247
8%
ALG10
5%
ALOX5
5%
ANK2
5%
CADPS2
5%
Status
Gender
Stage
.C>T _T>A
.C>G.T>C
C>A -T>G
High necroptosis score group: Altered in 29 (74.36%) of 39 samples
848
0
12
0
TP53
31%
CTNNB1
23%
MUC16
21%
HMCN1
15%
MEN1
15%
TTN
15%
FAT4
Missense_mutation Frame_shift_del Splice_site Status Alive Dead Gender FEMALE · MALE RET
13%
PKHD1
13%
ASXL3
10%
KMT2B
10%
Status
Gender Stage
C> TT>A
· C>G=T>C
C>A =T>G
Nonsense_mutation
In_frame_del
Multi_hit
Stage
Stage I
Stage II
Stage II
Stage IV
Unknown
(b)
FIGURE 4: Continued.
r
9
Step1. release of cancer cell antigens
Step2. cancer antigen presentation
1.0
Step3. priming and activation
Step4. T cell.recruiting
Step4. CD4 T cell.recruiting
Step4. CD8 T cell.recruiting
Step4. Th1 cell.recruiting
Step4. dendritic cell.recruiting
Step4. Th22 cell.recruiting
IFN-Gamma_signature
Step4. macrophage.recruiting
Step4. monocyte.recruiting
0.5
APM_signal
Step4. neutrophil.recruiting
Base_excision_repair
0
Necroptosis risk
Step4. NK cell.recruiting
Cell_cycle
Step4. eosinophil.recruiting
0
Step4. basophil.recruiting
DNA_replication
Step4. Th17 cell.recruiting
Fanconi_anemia_pathway
- Step4. B cell.recruiting
Homologous_recombination
Step4. Th2 cell.recruiting
MicroRNAs_in_cancer
Step4. treg cell.recruiting
0.0
Step4. MDSC.recruiting
Mismatch_repair
0
Step5. infiltration of immune cells into tumors
Nucleotide_excision_repair
- Step6. recognition of cancer cells by T cells
Oocyte_meiosis
·
Step7. killing of cancer cells
0
p53_signaling_pathway
Progesterone-mediated_oocyte_maturation
Proteasome
Pyrimidine_metabolism
Spliceosome
0
Systemic_lupus_erythematosus
0
Viral_carcinogenesis
.
rSeg
pSeg
Relations
0.25
<0.001
Positive relation
0.50
<0.01
Negative relation
0.75
Not Applicable
1.00
ns
(c)
checkpoint pathways), tumor aggression (including MYC targets V1 and MYC targets V2 pathways), and immune suppression (including WNT beta catenin signaling path- ways) related pathway activation, while the low NAGs group was with more pathway involving immune activation, including interferon alpha response, interferon gamma response, IL2 STAT5 signaling pathway, inflammatory response, and complement activation pathways [30-32]. Gene mutation is the initial event before tumorigenesis and causes different phenotypes with different manifestations, pathological features, and clinical outcomes in ACC. ACC is a heterogeneity disease with multiple molecular alterations (Fig. S3).Therefore, we further compared the genetic alter- ation landscape between the two defined groups. The muta- tion detection rate in the high NAGs group with 74.36% was higher than that in the low NAGs group with 53.85% (Figures 4(b) and 4(c)). The most frequently mutated genes in the high NAGs group included TP53 (31%), CTNNB1 (23%), MUC16 (15%), HMCN1 (15%), MEN1 (15%), and TTN (15%). High MUC16 mutation and CTNNB1 were also observed in the low NAGs group, but their mutation fre- quencies were different (10% vs. 16% and 8% vs. 23%, respectively). Other gene events, such as COL5A1 (8%), FBN2 (8%), PRKAR1A (8%), and TMEM247 (8%) muta-
tions, were also responsible for the development of low NAGs ACC. Furthermore, ACC with high NAGs has more variable mutation types than ACC with low NAGs. Missense mutation, frameshift deletion, splice site, nonsense muta- tion, and interframe deletion were all observed in the high NAGs group, while missense mutation, frameshift deletion, splice site, and nonsense mutation were observed in the low NAGs group. Interestingly, NAGs negatively correlated with T cell recruitment (including CD8 T cells and Th1 cells), dendritic cell recruitment, and macrophage and NK cell recruitment but positively correlated with CD4 T cell recruitment, Th22 cell recruitment, and Th2 and Treg cell recruitment in the cancer immunity cycle. Meanwhile, we found that NAGs was negatively correlated with the inter- feron gamma signature and APM signaling pathways but positively correlated with most signaling pathways shown in the butterfly plot (Figure 4(d)).
3.5. NAGs Links with the Response to Anti-PD-1 Immunotherapy. As mentioned above, NAGs was negatively related to immune-related pathway activation. We then employed ssGSEA to investigate the immunocyte infiltration status in the tumor microenvironment (TME). The results showed that multitudinous immunocytes gathered in the
Subtype
GSVA score
Age
Stage
Gender
Age
1
80
Status
T follicular helper cell Activated dendritic cell
Regulatory T cell
Mast cell
Activated B cell
Immature B cell
0.5
60
Macrophage
MDSC
Activated CD8 T cell
Monocyte
Effector memeory CD4 T cell
Effector memeory CD8 T cell
Natural killer cell
Type 1 T helper cell
0
40
CD56dim natural killer cell
Activated CD4 T cell
Type 2 T helper cell
Central memory CD8 T cell
Gamma delta T cell
Central memory CD4 T cell
Natural killer T cell
-0.5
20
Memory B cell
Plasmacytoid dendritic cell
CD56bright natural killer cell Immature dendritic cell Eosinophil
Neutrophil
Type 17 T helper cell
-1
0
Subtype
Gender
Low-risk
Female
High-risk
Male
Stage
Stage I
Status
Stage II
Alive
Stage III
Dead
Stage IV
Unknown
(a)
Nominal p value
Check mate immunotherapy Kruskal-Wallis, p = 0.058
1
Low-risk
0.8
3.8
High-risk
0.6
0.6
3.7
0.013
Bonferroni corrected
Necroptosis
P=
Low-risk
0.4
0.018
0.4
3.6
High-risk
p value
NR
R
NR
R
0.2
3.5
CTAL4
PD-1
CRPR
PD
SD
ORR
(b)
(c)
Estimated IC50 of cisplatinD
Estimated IC50 of gemcitabine
-1
Estimated IC50 of paclitaxel
-3.0
Estimated IC50 of etoposide
3
3
-2
-3.5
2
4.0
2
-3
1
4.5
T-test, p = 6e-04
-4
T-test, p = 7.5e-05.
-5.0
T-test, p = 0.039
0
T-test, p = 0.00031
Low-risk
High-risk
Low-risk
High-risk
Low-risk
High-risk
Low-risk
High-risk
(d)
GSE116439 cisplatin treated vs. control
Time: 2h
Time: 6h
Time: 24h
6.50
7
⁎
Necroptosis
6.25
ns
Necroptosis
6
I
6.00
5.75
5
T .; test,.p = 0,57
T =; test,.p = 0.028
T-test, p .= 1.1e+07
5.50
DMSO
DMSO
Cisplatin
DMSO
2h
6 h
24 h
Cisplatin
Cisplatin
Time
Group
DMSO
Cisplatin
(e)
GSE116444 gemcitibine treated vs. control
Time: 2h
Time: 6h
Time: 24h
6.5
6
6.0
Necroptosis
Necroptosis
5
5.5
5.0
4
T-test, p = 0.6
T-test, p = 8.3e-09 :
T-test, p < 2.2e-16
:
4.5
ns
DMSO
Gemcitibine
DMSO
Gemcitibine
DMSO
Gemcitibine
2h
6h
24h
Time
Group
DMSO
Gemcitibine
(f)
TME of ACC with low necroptosis score but rarely in the ACC with high NAGs (Figure 5(a)). Subsequently, we con- ducted SubMap analysis with an ACC cohort containing both patients who received and did not receive anti-PD-1 or anti-CTLA4 therapy. Patients in the low NAGs group presented a potential better treatment response to anti- PD1 therapy than those in the high NAGs group (Figure 5(b), Bonferroni corrected P = 0.018). Furthermore, a checkmate cohort containing four clinical outcomes was enrolled to investigate the relevance of NAGs and different clinical endpoints. Compared to patients with progressive disease (PD), those with stable disease (SD) had obviously higher NAGs (P=0.013) as well as those with partial or
complete response (PR/CR) (P=0.4) (Figure 5(c)). Taken together, the evidence proposed above indicated that patients with low NAGs may be more suitable for anti-PD- 1 therapy and more likely to obtain clinical benefit.
3.6. NAGs Links with the Response to Cisplatin, Gemcitabine, Paclitaxel, and Etoposide Treatment. Based on the data obtained from GDSC 2016, we predicted the sensitivity of four chemotherapeutic agents to patients, including cisplatin, gemcitabine, paclitaxel, and etoposide, and revealed that patients with high NAGs were more suitable for chemotherapy (all P <0.05, Figure 5(d)). A higher IC50 was observed in the low NAGs group in the four
200
ACC-GEO
Status
Stage
Gender
Side
Age
150
Survival days
High (n = 44)
00000
100
50
Low (n = 45)
0
R =- 0.52
p = 2.3e-07
p = 0.00021
p = 0.14
p = 0.0052
P=1
P = 0.39
-50
1.4
1.6
1.8
2.0
Alive
Female
Necroptosis score
Dead
Male
Unknow
Unknow
I
Right
II
Left
III
⇐ 50
IV
> 50
(a)
(b)
1.0
200
0.8
150
Sensitivity
0.6
Futime
100
0.4
50
0.2
0
0.0
1.4
1.6
1.8
2.0
0.0
0.2
0.4
0.6
0.8
1.0
Signature
1-specificity
Necroptosis score
AUC at 1 years: 0.82
Low
AUC at 3 years: 0.826
A
High
AUC at 5 years: 0.858
(c)
(d)
FIGURE 6: Continued.
Necroptosis score
1.00
P < 0.001
Age
(N = 89)
1.01
0.3859
+
Survival probability
Hazard ratio = 4.43
Female
(0.989-1.03)
0.75
95% CI: 2.407-8.137
Gender
(N = 63)
Reference
Male
1.08
(N = 26)
(0.552-2.11)
0.8217
0.50
Stage
Stage I (N=7)
Reference
Stage II
+
3.88
(N = 41)
(0.506-29.75)
0.192
+
8.64
0.25
Stage III
(N= 6)
(0.970-76.91)
0.0533
+
+
Stage IV
22.71
+
(N = 25)
(2.929-176.16)
0.0028 **
+
0.00
Unknow
(N=10)
7.37
(0.840-64.67)
0.0715
0
50
100
150
200
Side
Left
(N = 33)
Reference
Time in months
Right (N=31)
1.23
0.5855
Necroptosis score
(0.585-2.59)
Unknow (N=25)
1.01
(0.436-2.36)
0.9746
+ Low
Necroptosis
Low-risk
+ High
score
(N = 45)
Reference
High-risk (N = 44)
4.23
(2.185-8.18)
<0.001 ***
# Events: 53; Global p-value (Log-rank): 3:4335e-09
AIC: 367.23; Concordance index: 0.8
1
0.5
1
2
5
10
20
50
100 200
(e)
(f)
100%
75%
Ture negative rate
50%
25%
RE
0%
0%
25%
50%
75%
100%
False negative rate
AUC
- Nomogram 89.9 (83.7;96.1)
Necroptosis score 83.2 (74.5;91.9)
Gender 59.5 (49.7;69.4)
Stage 77.2 (67.6;86.7)
Side 53.9 (42.1;65.7)
- Age 61.0 (48.5;73.5)
(g)
classes, suggesting that patients with high NAGs are more sensitive to chemotherapy than those with low NAGs. We analyzed drug-induced changes in NAGs across NCI-60 cell lines after exposure to cisplatin or gemcita- bine for 2, 6, and 24h. NAGs began to decrease after 2h of exposure to cisplatin (P=0.57), but there was no
statistical significance until 6 hours (P=0.028), which continuously decreased within 24h (P=1.1e -07) (Figure 5(e)). Similar to the result in cisplatin class, sig- nificantly decreased NAGs was observed at 6h (P= 8.3E- 09) and kept declining with 24h in gemcita- bine class (Figure 5(f)).
G2M_CHECKPOINT
E2F_TARGETS
MYC_TARGETS_V1
KRAS_SIGNALING_UP PEROXISOME ACTED ANGIOGENESIS INFLAMMATORY_RESPONSE P53_PATHWAY MYOGENESIS
MITOTIC_SPINDLE
EPITHELIAL_MESENCHYMAL_TRANSITION
TGF_BETA_SIGNALING
UNFOLDED_PROTEIN_RESPONSE
UV_RESPONSE_DN
MYC_TARGETS_V2
MTORC1_SIGNALING
APICAL_SURFACE
DNA_REPAIR
WNT_BETA_CATENIN_SIGNALING
PANCREAS_BETA_CELLS
I
ESTROGEN_RESPONSE_EARLY
CHOLESTEROL_HOMEOSTASIS
TNFA_SIGNALING_VIA_NFKB
PI3K_AKT_MTOR_SIGNALING
NOTCH_SIGNALING
ADIPOGENESIS
KRAS_SIGNALING_DN
UV_RESPONSE_UP
HYPOXIA
FATTY_ACID_METABOLISM
HEDGEHOG_SIGNALING
SPERMATOGENESIS
APICAL_JUNCTION
ESTROGEN_RESPONSE_LATE
GLYCOLYSIS
INTERFERON_GAMMA_RESPONSE
PROTEIN_SECRETION
IL2_STAT5_SIGNALING
XENOBIOTIC_METABOLISM
OXIDATIVE_PHOSPHORYLATION
ALLOGRAFT_REJECTION
IL6_JAK_STAT3_SIGNALING
COAGULATION
ANDROGEN_RESPONSE
COMPLEMENT
REACTIVE_OXIGEN_SPECIES_PATHWAY
APOPTOSIS
HEME_METABOLISM
BILE_ACID_METABOLISM
INTERFERON_ALPHA_RESPONSE
-5
0
5
10
t value of GSVA score, necroptosis score high vs. low
(a)
Subtype
Age
Stage
Gender
1
80
Age
fustat
CD56dim natural killer cell
Activated CD4 T cell
Memory B cell
Type 2 T helper cell
Macrophage
Mast cell
0.5
60
Immature dendritic cell
MDSC
Effector memeory CD8 T cell
Type 1 T helper cell
Activated CD8 T cell
Activated B cell
Immature B cell
Eosinophil
0
40
Regulatory T cell
Natural killer cell
T follicular helper cell
Central memory CD8 T cell
Central memory CD4 T cell
Natural killer T cell
CD56bright natural killer cell
-0.5
20
Plasmacytoid dendritic cell
Gamma delta T cell
Monocyte
Effector memeory CD4 T cell
Neutrophil
Activated dendritic cell
Type 17 T helper cell
0
Subtype
Gender
-1
Low-risk
Female
High-risk
Male
Stage
Stage I
Fustat
Stage II
0
Stage III
1
Stage IV
Unknow
(b)
1
Low-risk
High-risk
0.8
0.6
P =
0.011
Low-risk
0.4
High-risk
0.2
pvalue
CTAL4-noR
CTLA4-R
PD1-noR
PD1-R
p value
Nominal p value
Bonferroni corrected
(c)
4.0
·
·
Estimated IC30 of cisplatin
Estimated IC50 of gemcitabine
0
3
3.5
Estimated IC30 of paclitaxel
-3.35
Estimated IC50 of etoposide
o
-1
-3.40
2
3.0
-2
8
-3.45
2.5
-3
1
-3.50
2.0
-4
T-test, p = 4e-07
T-test, p = 5.5e-10.
-3.55
T-test, p = 0.0059
0
T-test, p = 1.2e-07
Low-risk
High-risk
Low-risk
High-risk
Low-risk
High-risk
Low-risk
High-risk
(d)
3.7. Validation in GEO-Combined Cohort. After removing the batch effect between the GSE19750, GSE33371, and GSE49278 cohorts, we obtained a GEO-combined cohort with 89 samples. In the GEO-combined cohort, each of the selected necroptosis-related genes were also capable to pre- dict prognosis independently, and the same formula was employed to calculate the NAGs for individual patient (Fig. S4). NAGs was also found negatively associated with OS in ACC (Figure 6(a)). Similarly, 89 samples were further sepa- rated into two distinct subgroups with the mean NAGs, high NAGs group with 44 cases, and low NAGs group with 45 cases (Figure 6(d)). We also investigated the clinicopatholo- gical distribution between two groups, and the results showed that high NAGs ACC had a higher mortality rate (P=0.00021) and a predisposition of females (P = 0.0052). In contrast to the result in TCGA-ACC cohort, there was no staging difference observed among necroptosis- associated gene signature groups (P=0.14), which may be caused by multiple factors, such as the experience of a pathologist, since there was a 13% misdiagnosis rate in ACC [33]. Significant prognostic value was found in the GEO-combined cohort, with 1-year, 3-year and 5-year AUC accuracies of 0.82, 0.826 and 0.858, respectively (Figure 6(C)), and showed a statistically significant OS, with a P value less than 0.001 (95% CI: 2.407-8.13) and HR value of 4.43 (Figure 6(e)). For different patient groups with given clinicopathological features in GEO-combined cohort, NAGs was also proved as a good prognostic tool (Fig. S5). According to multivariate analysis, NAGs was reconfirmed as an independent risk factor. Interestingly, tumor stage IV was also validated as the other independent risk factor in the GEO cohort, although there was no statistical signifi- cance in the TCGA-ACC cohort (Figure 6(f)). Three param- eters, including nomogram, NAGs, and tumor stage, which were found to be high-efficiency prognostic predictors, also demonstrated high predictive ability in the GEO combined cohort, with AUC values of 0.899 (95% CI: 0.837- 0.961), 0.832 (95% CI: 0.745, 0.919), and 0.772 (95% CI: 0.676, 0.867), respectively (Figure 6(g)). Consistently, NAGs nega- tively correlated with the activation of various immune- related signaling pathways (Figure 7(a)), and more immuno- cyte infiltration (Figure 7(b)) was observed in the low NAGs
group than in the high NAGs group. Patients in the low NAGs group presented a potential better treatment response to anti-PD1 therapy than those in the high NAGs group (Figure 7(c), Bonferroni corrected P=0.011). We also observed a higher sensitivity of cisplatin, gemcitabine, pacli- taxel, and etoposide among patients in the high NAGs sub- group, which is similar with the prediction results in TCGA-ACC cohort (all P<0.05, Figure 7(d)).
4. Discussion
Adrenal tumors are very common, with an incidence rate of 3%-10% and are mainly diagnosed as small benign nonfunc- tional adrenocortical adenomas [34]. However, ACC is a rare but aggressive type among adrenal tumors. The overall prognosis of ACC is poor but heterogeneous, with a 5-year survival ranging from 13% to 81%. The challenge faced by clinicians in managing ACC after resection is to select a suit- able chemotherapeutic scheme for different patients, while limited parameters can be used for efficacy prediction, and only an ENSAT staging system is available to date. However, survival differences were still reported at a given ENSAT stage in ACC due to genetic heterogeneity [35]. Therefore, it is imperative to uncover the underlying molecular mecha- nisms in ACC development and provide a complementary method for risk stratification and therapeutic prediction.
The value of molecular markers in distinguishing patients with different risks has been demonstrated in many cancers, such as prostate-specific antigen (PSA) in prostate cancer. Immunohistochemistry of Ki67 in the ACC tumor is a standard to assess the cell proliferation status; several studies already reported the prognostic value of Ki67 in ACC. Beuschlein et al. [36] reported that Ki67 in a powerful prognostic factor to the disease-free survival of ACC patients after surgery, grade 1 ACC with the positive staining of Ki67 less than 10%, grade 2 with positive area between 10 and19%, and Ki67-positive area higher than 20% links with grade 3 ACC tumors. Duregon et al. proved Ki-67 to be the best prognostic indicator of overall survival, being supe- rior to the mitotic index [37]. However, Libé et al. [38] revealed that the prognostic value of Ki67 did not show a good performance in the OS prediction of ACC patients
with advanced stage III and IV tumors. Martins-Filho et al. [39] also demonstrated that the prognostic value of Ki67- positive rate is not consistent among adults and pediatrics; in adults, Ki67 ≥ 10% showed the highest HR for recurrence, and this value raised to ≥15% in pediatric ACC tumors.
Many cell death mechanisms have been revealed to date, and necroptosis is a novel form of PCD that induces cell death via a caspase-independent pathway and an alternative method for apoptosis. Walz et al. [40] reported that the mor- phological feature of confluent necrosis in ACC is universal, while the benign adrenocortical adenomas (ACN) completely lack this kind of highly reproducible feature; what is more, the Ki67 levels above 10% were found in more than 96.8% ACC samples and never in ACNs. Pearlstein et al. [41] reported the similar results; necrosis was more fre- quent in ACCs (93.3%; 14 of 15) compared with benign ACNs (8.7%; 2 of 23). Necrosis is morphologically charac- terized by rounding of the cell, cytoplasmic swelling, pres- ence of dilated organelles, and absence of chromatin condensation, while necrotic cell death is carried out by complex signal transduction pathways and execution mech- anisms [42, 43]. The results from prior studies showed that some necroptosis-associated genes, such as RIPK1, RIPK3, and MLKL, can be used as prognostic markers in tumors. For instance, Feng et al. found that low expression of RIPK3 was related to a short OS and disease-free survival in colo- rectal cancer [44]. McCormick et al. demonstrated that low expression of RIPK1 promoted the progression of neck squamous carcinoma [45]. In this study, we conducted a necroptosis-associated gene signature for ACC using LASSO Cox regression analyses based on the TCGA-ACC cohort. We revealed that the necroptosis-associated gene signature was negatively correlated with OS in ACC. Further survival analysis proved significantly different OS among necroptosis- associated gene signature groups. Multivariate Cox regression analysis validated that the signature could act as an indepen- dent risk factor in ACC.
To screen clinicopathological parameters with high predic- tive value, ROC analysis was performed to calculate the corre- sponding AUC, and tumor stage was eventually enrolled. Combining NAGs with tumor stage, we established a nomo- gram risk model to quantify the probabilities of tumor progres- sion. Compared to the ENSAT staging system, this novel nomogram model included extra molecular parameters, showed higher clinical net benefit, and presented excellent accuracy of prediction. We believe that it could be a novel diag- nostic complement tool for orthodox management of ACC.
An up to 40%-70% recurrence rate has been reported in ACC even after surgical resection, which is higher than most malignancies. Adjuvant therapy is often used as a means of preventing postoperative recurrence. A randomized con- trolled phase 3 clinical trial proposed that combination adju- vant therapy with etoposide, doxorubicin, cisplatin, and mitotane is the frontline treatment in advanced ACC, but progression-free survival (PFS) and OS, with 5.6 months and 14.8 months, respectively, were still short [46]. Gemcit- abine is a salvage regimen once preliminary chemotherapy fails, and the disease control rate is as low as 30% [47]. Meanwhile, there are no ideal tools that can be used to select
suitable patients for chemotherapy. We revealed that NAGs can predict the efficacy of etoposide, cisplatin, gemcitabine, and paclitaxel in the treatment of ACC. In addition, we observed that NAGs declined after receiving gemcitabine treatment or cisplatin treatment in NCI-60 cell lines. Since NAGs is negatively associated with the OS of ACC, the sig- nificance of chemotherapy in improving prognosis has been reconfirmed. Although patients with low NAGs are not suit- able for immunotherapy, chemotherapy can bring new hope to these patients.
At present, limited treatment options are available for advanced ACC [7]. Immunotherapy provides new options for altering the routine strategies of advanced ACC, and the value of immune checkpoint inhibitor (ICI) therapy has been proposed in many studies. However, the results of a multicenter study of four ICI drugs hint at a poor overall response rate and progression-free survival in treating ACC [48]. Several pathway alterations and molecular alter- ations may be responsible for ICI therapy resistance, and further immunological markers of response might solve this dilemma. According to a published review, immunocyte infiltration status is the main influencing factor affecting the effectiveness of immunotherapy [49]. High immunocyte infiltration, especially T cells, is generally related to a high response. In ACC, WNT-ß catenin pathway activation was related to the decreased recruitment of the specific lineage basic leucine zipper transcription factor ATF-like 3 lineage (BATF3) of dendritic cells, which is associated with the pro- duction of chemokines such as CXCL9 and CXCL10, leading to the downregulation of T cell infiltration. In addition, upregulation of TP53 inactivating mutations leads to the lack of production of pivotal chemokines in T cells and nat- ural killer recruitment, which contribute to the exclusion of T cytotoxicity from the tumor microenvironment and reduced activation of cytotoxicity-related chemokines. In our study, WNT pathway activation and a high TP53 muta- tion rate were observed in ACC with high NS, and on the other hand, ACC with low NAGs had substantive immune-related pathways and immunocyte recruiting path- way activation. All these results indicated higher immuno- cyte infiltration and a good response in low NAGs group than in the high NAGs group. As expected, further ssGSEA and SubMap analyses validated them, and patients with PD tended to have a higher NAGs than those with SD, PR, or CR. The evidence mentioned above indicates that the necroptosis-associated gene signature may be a promising predictor for ICI therapy. We constructed a necroptosis- associated gene signature associated with the prognosis of ACC, revealed molecular mechanisms of high-risk ACC, and found the value of drug sensitivity prediction. We estab- lished a nomogram risk model to quantify the risk stratifica- tion of ACC and wish to provide a high-efficacy predictive tool for clinicians. But there are still some limitations. For instance, more expression products of the seven genes that can be detected by a cheap means are needed to concern, and the sample size is small as there are only 167 cased enrolled in our study. In conclusion, we generated a novel multigene predictor which can make a contribution to the OS prediction of ACC.
Data Availability
The data used to support the findings of this study are avail- able from the corresponding author upon request.
Ethical Approval
The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).
Conflicts of Interest
All authors have completed the ICMJE uniform disclosure form. The authors have no conflicts of interest to declare.
Authors’ Contributions
JD and ZRF conceived the presented idea. JD developed the theory and performed the computations. ZRF and FS veri- fied the analytical methods. JD took the lead in writing the manuscript. FS supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.
Acknowledgments
The current study is supported by the 2019 Anhui Provincial Department of Education, University Excellent Talents Training Funding Project: “Overseas Visiting and Training Program for Outstanding Young Backbone Talents in Col- leges” (gxgnfx2019102) and Summit Disciplines Plan of The First Affiliated Hospital of Anhui Medical University (GFXK03).
Supplementary Materials
Figure S1: K-M plot showing the prognostic value of seven selected genes in TCGA-ACC cohort. Figure S2: K-M plot showing the prognostic value of NAGs among different clinical subgroups in TCGA-ACC cohort. Figure S3: summary of the mutations in ACC patients from TCGA-ACC cohort. Figure S4: K-M plot showing the prognostic value of seven selected genes in combined GEO cohort. Figure S5: K-M plot showing the prognostic value of NAGs among different clinical sub- groups in combined GEO cohort. (Supplementary Materials)
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