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Article Molecular Cluster Mining of Adrenocortical Carcinoma via Multi-Omics Data Analysis Aids Precise Clinical Therapy
Yu Guan 1,2,3,4, Shaoyu Yue 1,2,3,4, Yiding Chen 1,2,3,4, Yuetian Pan 40D, Lingxuan An 4, Hexi Du 1,2,3,* and Chaozhao Liang 1,2,3,*
1 Department of Urology, The First Affifiliated Hospital of Anhui Medical University, 218th Jixi Road, Hefei 230022, China
2 Institute of Urology, Anhui Medical University, 81th Meishan Road, Hefei 230022, China
3 Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University (AHMU), 81th Meishan Road, Hefei 230022, China
4 Department of General, Visceral, and Transplant Surgery, Ludwig-Maximilians-University Munich, D-81377 Munich, Germany
* Correspondence: duhexi1989@163.com (H.D.); liang_chaozhao@ahmu.edu.cn (C.L.); Tel .: +86-18856040979 (H.D.); +86-13505604595 (C.L.)
+ These authors contributed equally to this work.
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Citation: Guan, Y .; Yue, S .; Chen, Y .; Pan, Y .; An, L .; Du, H .; Liang, C. Molecular Cluster Mining of Adrenocortical Carcinoma via Multi-Omics Data Analysis Aids Precise Clinical Therapy. Cells 2022, 11, 3784. https://doi.org/10.3390/ cells11233784
Academic Editors: Amancio Carnero, Sandra Muñoz-Galván and José M. García-Heredia
Received: 24 October 2022
Accepted: 24 November 2022 Published: 26 November 2022
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations.
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Copyright: @ 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Abstract: Adrenocortical carcinoma (ACC) is a malignancy of the endocrine system. We collected clin- ical and pathological features, genomic mutations, DNA methylation profiles, and mRNA, lncRNA, microRNA, and somatic mutations in ACC patients from the TCGA, GSE19750, GSE33371, and GSE49278 cohorts. Based on the MOVICS algorithm, the patients were divided into ACC1-3 subtypes by comprehensive multi-omics data analysis. We found that immune-related pathways were more activated, and drug metabolism pathways were enriched in ACC1 subtype patients. Furthermore, ACC1 patients were sensitive to PD-1 immunotherapy and had the lowest sensitivity to chemother- apeutic drugs. Patients with the ACC2 subtype had the worst survival prognosis and the highest tumor-mutation rate. Meanwhile, cell-cycle-related pathways, amino-acid-synthesis pathways, and immunosuppressive cells were enriched in ACC2 patients. Steroid and cholesterol biosynthetic pathways were enriched in patients with the ACC3 subtype. DNA-repair-related pathways were enriched in subtypes ACC2 and ACC3. The sensitivity of the ACC2 subtype to cisplatin, doxorubicin, gemcitabine, and etoposide was better than that of the other two subtypes. For 5-fluorouracil, there was no significant difference in sensitivity to paclitaxel between the three groups. A comprehensive analysis of multi-omics data will provide new clues for the prognosis and treatment of patients with ACC.
Keywords: adrenocortical carcinoma; multi-omics analysis; prognosis and treatment; cell signaling pathway; sensitivity to drugs
1. Introduction
Adrenocortical carcinoma (ACC) is an aggressive endocrine malignancy that originates in the adrenal cortex, accounting for approximately 5% of adrenal tumors, with an annual incidence of 0.7-2.0 cases per million people [1,2]. The onset age of ACC has bimodal characteristics, with a high incidence in the two age stages of 40-50 years old and 1-5 years old [2,3]. The disease stage is one of the most important prognostic factors. Currently, the staging system proposed by the European Adrenal Neoplasms Research Network (ENSAT) is a commonly used international standard [4]. Stages I and II are confined to the organs and can be cured by complete resection. Stages III and IV are considered aggressive and metastatic advanced tumors, and the five-year survival rate for stage IV patients is only 6-13% [2,5]. Some researchers believe that ACC may be related to the overproduction of steroid precursors, which have the characteristics of early metastasis
and recurrence [6]. Complete surgical removal of ACC is the only chance for a long- term cure [7]. Mitotane has cytotoxic effects on steroidogenic cells in the adrenal cortex; therefore, it is recommended as a clinical adjuvant therapy, but its therapeutic effect is still unsatisfactory [2,8].
Some researchers have found that insulin-like growth factor 2 (IGF2), ß-catenin (CTNNB1), and TP53 may be potential drivers of sporadic adrenocortical tumors. The IGF system has growth-promoting and differentiation functions in the adrenal glands. IGF2 overexpression is found in most ACCs and is often associated with poor outcomes [1,9]. It regulates cell proliferation and apoptosis mainly by binding to the insulin-like growth factor 1 receptor (IGF1R), especially in pediatric patients [10,11]. As a key component of the Wnt signaling pathway, ß-catenin plays an important role in the development of the adrenal cortex [12], and is a poor prognostic factor in ACC [13]. Somatic mutations are common in ACC, and some researchers have found that somatic mutations in CTNNB1 are independent predictors of poor disease-free survival and overall survival in ACC [14]. Pan-genomic studies have found that TP53 mutations, mainly exon mutations, are common in sporadic ACC cases [15].
Multiple high-throughput detection techniques have been used in multi-omics asso- ciation studies to elaborate on a single scientific subject. Numerous variables can affect cancer development. These include a wide range of information on various facts. Infor- mation may be combined at several levels and integrated, and staging prediction accuracy can be improved by multi-omics association studies [16]. Over the past 50 years, the genetic approach to cancer has taken over the profession. However, this genome-only perspective is limited and has the propensity to present cancer as a strongly heritable illness. According to new research, cancer is a multi-omics illness and is not as heritable or exclusively hereditary as previously believed. Cancer development and manifestation are influenced by the exposome, metabolome, and genome. Cancer-specific metabolism has been genetically altered to feed and support proliferating cancer cells [17]. The etiology of tumor growth may only be partially understood using a single type of molecular dataset. Several studies have used multi-omics data to categorize cancer patients and predict prog- noses [18,19]. Therefore, a crucial stage in the machine-learning-model-based prediction of survival and recurrence is learning new characteristics from multi-omics data that help predict prognosis.
Multi-omics analysis of ACC can reveal several undiscovered oncogenic alterations and guide the exploration of new therapeutic approaches. In this study, we collected clinical and pathological characteristics, DNA methylation profiles, genomic mutations, and mRNA, lncRNA, and microRNA (miRNAs) information of patients, and somatic mutation data from four ACC datasets. Multi-omics analysis using the MOVICS algorithm provided new clues for the prognosis and treatment of patients with ACC.
2. Materials and Methods
2.1. Data Collection
Multi-omics data of ACC patients, including DNA methylation, gene mutations, mRNA, miRNA, and lncRNA, were downloaded from the TCGA-ACC dataset for molec- ular subtyping. The “TCGAbiolinks” R package (version 2.25.3) which is provided in https://github.com/BioinformaticsFMRP/TCGAbiolinks.git, was used to obtain clinical features and transcriptomic expression data. Gene symbol annotation was performed as described in our previous study [20]. The miRNA expression and DNA methylation 450 matrices were downloaded from the UCSC Xena (https://xenabrowser.net/datapages, accessed on 15 April 2022). Somatic mutation data were downloaded from the cbiopportal (https://www.cbioportal.org/, accessed on 15 April 2022). After combining all available data from the different patients, 78 eligible patients were included in the TCGA-ACC cohort. In addition, we extracted data from 89 patients with ACC from the GSE19750, GSE33371, and GSE49278 cohorts [21-24]. The batch effect is an abiotic difference between at least two datasets. To eliminate the bias caused by the batch effect, we used the combat algorithm
package “SVA” (version 3.46.0). The GEO combined cohort was used as the subsequent test cohort, and the TCGA-ACC cohort was used as the training cohort.
2.2. Molecular Subtypes were Identified Using Multi-Omics Analysis
Molecular subtypes were determined using multi-omics data according to recently published guidelines for the R package “MOVICS” (version 1.0) [25]. The input for “MOVICS” is multi-omics data, and the output is the recommended molecular subtypes, presenting molecular features, prognosis, treatment sensitivity, and others; the VIGNETTE of this package is provided in https://xlucpu.github.io/MOVICS/MOVICS-VIGNETTE. html (accessed on 15 April 2022), with details of how to use it. First, univariate Cox re- gression analysis was used to evaluate factors related to overall survival (OS), including relevant biological information downloaded as above (all p < 0.05). Mutated genes were those with a mutation frequency greater than 10%. Based on the evaluation of the multi- omics data, the cluster prediction index (CPI) [26] and the gap statistic [27] were utilized to determine the correct number of subtypes. The number that resulted in the maximum value of both the gap statistic and the CPI was chosen as the optimum number of clusters for the input data. Ten clustering algorithms (iClusterBayes, moCluster, CIMLR, IntNMF, ConsensusClustering, COCA, NEMO, PINSPlus, SNF, and LRA) were then used to separate patients into various subtypes, and a combined classification using a consensus set was used to identify each subtype with a high degree of robustness.
Specifically, if tmax algorithms are specified where 2 ≤ tmax ≤ 10, the package cal- culates a matrix M ___ per algorithm, where n is the number of samples and MO = 1 when samples i and j are clustered in the same subtype; otherwise, MU = 0. After obtaining all results from specified algorithms, MOVICS calculates a consensus matrix CM = Etmax M(+), and CMij€ [0, 10]. The sample similarity among the subtypes was calculated using silhouette scores.
2.3. Characteristics of Genetic Variations among Subtypes
Tumor-mutation burden (TMB) is the number of mutations per million bases, and fraction genome alteration (FGA) refers to the percentage of gene fragments with increased or lost copy numbers in the total genome. Total neoantigen and cytolytic activity (CYT) scores from previous studies were predicted by analyzing tumor-specific mutations, splic- ing, gene fusions, endogenous reverse transcription factors, and other criteria [28]. The downloaded copy number data from FireBrowse (http://firebrowse.org/, accessed on 15 April 2022) were visualized using the MafTools R package (version 2.14.0).
2.4. Comparison of Signaling Pathway Activation and Immune Infiltration
Single-sample gene-set enrichment analysis (ssGSEA) [28] package “GSVA” (version 1.1.11) was used to analyze 50 HALLMARK gene sets for each patient to reveal the ac- tivation of biological pathways. The Molecular Signatures Database (MSigDB, accessed on 15 April 2022) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analyses. The developers used a combination of automated approaches and expert curation to develop a collection of “hallmark” gene sets as part of the MSigDB. Each hallmark in this collection consists of a “refined” gene set derived from multiple “founder” sets that convey a specific biological state or process and display coherent expression [29]. The enrichment score (ES) represents the main result of gene enrichment analysis. In the ranking list, the ES-positive gene sets were at the top, and the ES-negative gene sets were at the bottom. The normalized enrichment score (NES) was the main evaluation index of the gene set enrichment results. The false discovery rate (FDR) is the rate of errors occurring in all discoveries with a set threshold of 0.05. We evaluated the immune infiltration of immunocytes and the activated status of the immune signature. Gene sets were collected from prior studies. The NES scores of different subgroups were calculated from the gene sets associated with immune and stromal features extracted from previous studies to demonstrate differences in the immune activation status [30]. We also
used ssGSEA to study the infiltration of 28 immune cells in tumors and calculated the infiltration score of each immune cell in each patient [31]. Metabolism-associated path- ways were obtained from the study of Possemato et al [32]. All of the above results were visualized using heat maps.
2.5. Prediction of Immunotherapy and Chemotherapy Treatment
To evaluate individual responses to immunotherapy, we used 795 specific gene sets found by other researchers in the melanoma cohort with anti-CTLA-4 or anti-PD-1 im- munosuppressive therapy as a reference [33]. Subclass mapping (SubMap) was used to analyze the similarity between the risk group and the immunotherapy subgroup and iden- tify patients who responded better to both immunotherapy agents [34]. The susceptibility to chemotherapeutic drugs was determined by estimating the half-maximum inhibitory concentration (IC50) of the samples using the Cancer Drug Sensitivity Genomics (GDSC) database and ridge regression analysis.
2.6. Statistical Analysis
The Kruskal-Wallis test was used to compare continuous data between the three groups. The relationship between these two factors was evaluated using Pearson’s correla- tion coefficient. The distribution of categorical variables among the groups was compared using the chi-square test. In the external validation cohort, the top 200 specific marker genes in the TCGA-ACC cohort were selected by nearest template prediction (NTP) analysis [35]. The log-rank test and K-M analysis were used to compare the survival rates of the high- and low-risk groups. Cox models were used to calculate the HR and 95% CI. The risk score relied on multivariate COX regression analysis to identify whether it had an independent prognostic effect and was bounded by p < 0.05. All analyses were performed using the R software (version 4.1.2) (http://www.r-project.org, Bell Laboratories, Windsor, WI, USA).
3. Results
3.1. Establishment of Molecular Subtypes
As shown in Figure 1a, when the number of clusters was three, the scores of the gap -statistical and CPI analyses were the highest. We then applied 10 multi-omics ensemble clustering algorithms to the three preset clusters and combined the results. Favorable consistency was observed among the three clusters using the ten algorithms (Figure 1b). Then, we evaluated the cluster quality via silhouette analysis, and the high silhouette width represented the robustness of the three clusters (0.74 vs. 0.64 vs. 0.47) (Figure 1c). Therefore, we redefined ACCs into three subtypes: ACC1, ACC2, and ACC3. Based on the multi-omics data in the TCGA-ACC cohort, we visualized diverse molecular features among the three subtypes, and the top ten items for each omics are listed in Figure 1d. In addition, we observed significantly different clinical outcomes among the three subtypes. ACC2 patients showed more advanced stages (59.1% vs. 15.6% vs. 50.0%, p = 0.022, Supplementary Table S1) and shorter overall survival, disease-specific survival, disease- free interval, and progression-free interval than ACC1 and ACC3 patients (all p < 0.001, Figure 2). ACC2 represented the poorest phenotype, ACC1 represented the best, and ACC3 was moderate.
a
d
1.0-
1.0
Stage
Age
Cluster Prediction Index
Gender
0.8-
-0.8
Laterality
Stage
Gap-statistics
Subtype
Stage I
0.6-
-0.6
Stage II
Stage III
- NROB1
ERCC6L
Stage IV
ARHGAP11B
mRNA
unknown
0.4-
-0.4
KIF14
- ZWILCH
Age
BAAT
80
KCNJ14
- POLRIE
60
0.2-
-0.2
PCGF6
40
20
- DGKD
0
0.0-
0.0
Gender
2
3
4
5
6
7
8
LINC00887
TP53TG1
FEMALE
Number of Multi-Omics Clusters
EIF1B-AS1
MALE
IncRNA
- PRKCQ-AS1
Laterality
b
SNHG3
1
2
Th
Left
Subtype
Right
- AC004801.6
Sample
AL031705.1
Subtype
AC098484.2
ACC1
- FAM225A
ACC2
Similarity
- AC108463.2
ACC3
1
0.8
mRNA.FPKM
0.6
2
0.4
1
0.2
miRNA
0
-1
Subtype
MIMAT0000226
MIMATO000227
-2
ACC1
MIMAT0000691
MIMAT0004680
IncRNA.FPKM
ACC2
MIMAT0004509
ACC3
MIMAT0004672
2
MIMAT0003258
MIMAT0005951
1
MIMAT0014990
/ MIMAT0004553
0
r cg08065231
-1
og09578475
cg24189904
-2
cg07139762
cg14973588
miRNA.FPKM
Methylation
2
1
cg06391107
0
C
Silhouette plot
3 clusters Cj
og27517652
cg04175957
-1
j: n | avejecj Si
cg14385298
cg08621203
-2
M value
ACC1
1: 33 | 0.64
2
1
KMT2B
COL18A1
0
PCDH15
-1
ACC2
2: 21 | 0.74
-2
Mutation
- OBSCN
Mutated
STAB1
APOB
- MUC4
0 1
ACC3
3: 24 | 0.47
- FAT4
TLN2
DST
-0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0
Overall survival
Disease specific survival
100
100
Survival probability (%)
Survival probability (%)
75
75
50
50
25
Overall p < 0.001
25
Overall p < 0.001
ACC1
ACC2
ACC1
ACC1
ACC2
ACC2
<0.001
ACC2
ACC1
ACC3
ACC2
<0.001
ACC2
0
ACC3
0.008
<0.001
0
ACC3
0.014
<0.001
ACC3
0
12
24
36
Time (Months)
48
60
72
84
96
108
120
0
12
24
36
48
60
72
84
96
Time (Months)
108
120
Number at risk
Number at risk
33
33
30
21
17
15
11
8
6
4
2
33
33
30
21
17
15
11
8
6
4
2
21
18
8
6
1
1
0
0
0
0
0
20
17
8
6
1
1
0
0
0
0
0
24
22
20
16
12
8
5
3
2
2
2
23
21
19
15
11
8
5
3
2
2
2
0
12
24
36
Time (Months)
48
60
72
84
96
108
120
0
12
24
36
Time (Months)
48
60
72
84
96
108
120
Disease free interval
Progression free interval
100
100
Survival probability (%)
Survival probability (%)
75
75
ACC1
ACC2
ACC2
<0.001
Overall p < 0.001
50
ACC3
0.007
0.089
50
ACC1
ACC2
25
Overall p < 0.001
++ACC3
25
ACC1
ACC1
ACC2
ACC2
ACC2
<0.001
q
ACC3
q
ACC3
<0.001
0.002
0
12
24
36
48
60
72
Time (Months)
84
96
108
120
0
12
24
36
Time (Months)
48
60
72
84
96
108
120
Number at risk
Number at risk
28
27
24
17
13
13
9
7
5
4
2
33
31
28
18
14
13
10
7
5
4
2
4
4
1
0
0
0
0
0
0
0
0
21
9
1
0
0
0
0
0
0
0
0
13
11
10
7
5
5
2
1
1
1
1
24
16
12
8
6
5
2
1
1
1
1
0
12
24
36
Time (Months)
48
60
72
84
96
108
120
0
12
24
36
Time (Months)
48
60
72
84
96
108
120
3.2. Signaling Pathway Activation in ACC Subtypes
In 50 HALLMARK terms for each patient, we found that ACC1 patients had more immune activation, such as interferon alpha response [36], interferon gamma response [37] and Kras signal up [38]. ACC2 patients had more cell-cycle-related pathway activation, such as G2M checkpoint [39], E2F targets, and DNA repair (Figure 3a). After comparing 100 pathways related to metabolism of ACCs, it was observed that ACC1 patients were enriched in drug metabolism by other enzymes [40], retinol metabolism, and pentose and glucuronate interconversion pathways. ACC2 patients were enriched in the homocysteine cycle, the methionine cycle, and pyrimidine biosynthesis. Steroid biosynthesis [41], choles- terol biosynthesis [42], and terpenoid backbone biosynthesis pathways were enriched in ACC3 patients (Figure 3b). Specifically, pathways related to DNA repair [43] were enriched in ACC2 and ACC3 patients (Figure 3c).
Enrichment Score
1
Subtype
Enrichment Score
a
Subtype
Laterality
0.5
b
Laterality
1
0.5
Gender
0
Gender
0
Age
Age
-0.5
Stage
-0.5
Stage
-1
N-Glycan Biosynthesis
-1
APICAL_JUNCTION
Glycosphosphatidylinositol
EPITHELIAL_MESENCHYMAL_TRANSITION
Other Types of O-Glycan Biosynthesis
ANGIOGENESIS
Glycosphingolipid Biosynthesis
MYOGENESIS
Glycogen Biosynthesis
APICAL_SURFACE
Hexosamine Biosynthesis
INTERFERON_ALPHA_RESPONSE
Lysine Degradation
INTERFERON_GAMMA_RESPONSE
Inositol Phosphate Metabolism
KRAS_SIGNALING_UP
Shingolipid Metabolism
COMPLEMENT
Caffiene Metabolism
IL2_STAT5_SIGNALING
Retinoid Metabolism
ALLOGRAFT_REJECTION
Mucin Type O-Glycan Biosynthesis
IL6_JAK_STAT3_SIGNALING
Glycogen Degradation
INFLAMMATORY_RESPONSE
Remethylation
P53_PATHWAY
Vitamin K
COAGULATION
Glycosaminoglycan Biosynthesis
TNFA_SIGNALING_VIA_NFKB
Biotin Metabolism
APOPTOSIS
Lipoic Acid Metabolism
BILE_ACID_METABOLISM
ADP-Ribosylation
OXIDATIVE_PHOSPHORYLATION
Polyamine Biosynthesis
PEROXISOME
D-Glutamine and D-Glutamate Metabolism
ADIPOGENESIS
Thiamine Metabolism
FATTY_ACID_METABOLISM
Transsulfuration
KRAS_SIGNĀLING_DN
Taurine and Hypotaurine Metabolism
PANCREAS BETA_CELLS
Cardiolipin Biosynthesis
ESTROGEN_RESPONSE_EARLY
Sirtuin Nicotinamide Metabolism
ESTROGEN_RESPONSE LATE
Amino Sugar and Nucleotide Sugar Metabolism
XENOBIOTIC_METABOLISM
Glycosaminoglycan Degradation
REACTIVE_OXYGEN_SPECIES_PATHWAY
Other Glycan Degradation
ΗΥΡΟΧΙΑ
Porphyrin and Chlorophyll Metabolism
GLYCOLYSIS
Pentose and Glucuronate Interconversions
UV_RESPONSE_UP
Ascorbate and Aldrate Metabolism
CHOLESTEROL_HOMEOSTASIS
Retinol Metabolism
MTORC1_SIGNALING
Drug Metabolism by other enzymes
SPERMATOGENESIS
Riboflavin Metabolism
G2M_CHECKPOINT
Nicotinamide Adenine Dinucleotide Biosynthesis
E2F TARGETS
Nicotinate and Nicotinamide Metabolism
UNFOLDED_PROTEIN_RESPONSE
Metabolism of Xenobiotics by Cytochrome P450
DNA_REPAIR
Drug Metabolism by Cytochrome P450
MYC_TARGETS_V1
Metabolism
MYC_TARGETS_V2
Steroid Hormone Biosynthesis
WNT_BETA_CATENIN_SIGNALING
Tyrosine Metabolism
HEDGEHOG_SIGNALING
Phenylalanine Metabolism
Cardiolipin Metabolism
NOTCH_SIGNALING
TGF BETA SIGNALING
Oxidative Phosphorylation
UV_RESPONSE_DN
Nitrogen Metabolism
MITOTIC_SPINDLE
Glycerophospholipid Metabolism
Primary Bile Acid Biosynthesis
PI3K_AKT_MTOR_SIGNALING
ANDROGEN_RESPONSE
Pantothenate and CoA Biosynthesis
PROTEIN_SECRETION
Dopamine Biosynthesis
Epinephrine Biosynthesis
HEME_METABOLISM
Norepinephrine Biosynthesis
Ether Lipid Metabolism
C
Linoleic Acid Metabolism
alpha-Linoleic Acid Metabolism
Cyclooxygenase Arachidonic Acid Metabolism
Prostanoid Biosynthesis
Neomycin, Kanamysin and Gentamicin Biosynthesis
Galactose Metabolism
Starch and Suctose Metabolism
Kynurenine Metabolism
Arachidonic Acid Metabolism
Retinoic Acid Metabolism
Prostaglandin Biosynthesis
Glutathione Metabolism
KEGG_P53_SIGNALING_PATHWAY
Fructose and Mannose Metabolism
Gluconeogenesis
BIOCARTA_RB_PATHWAY
Glycolysis
BIOCARTA_P53_PATHWAY
Alanine, Aspartate and Glutamate Metabolism
Urea Cycle
ST_WNT_BETA_CATENIN_PATHWAY
Arginine Biosynthesis
KEGG_WNT_SIGNALING_PATHWAY
Fatty Acid Elongation
WNT_SIGNALING
Biosynthesis of Unsaturated Fatty Acids
Propanoate Metabolism
GO_CHROMATIN_REMODELING
Valine, Leucine and Isoleucine Degradation
REACTOME_CHROMATIN_MODIFYING_ENZYMES
Citric Acid Cycle
Glyoxylate and Dicarboxylate Metabolism
GO_NOTCH_SIGNALING_PATHWAY
Nicotinamide Adenine Metabolism
REACTOME_SIGNALING_BY_NOTCH
Cysteine and Methionine Metabolism
Glycerolipid Metabolism
PID_NOTCH_PATHWAY
Ketone Biosynthesis and Metabolism
Samples in TCGA-ACC cohort
Pyruvate Metabolism
Sulfur Metabolism
Selenocompound Metabolism
Vitamin B6 Metabolism
Subtype
Heme Biosynthesis
Stage
Glycine, Serine and Threonine Metabolism
Valine, Leucine and Isoleucine Biosynthesis
ACC1
Pentose Phosphate
Stage
Ubiquinone and other Terpenoid-Quinone Biosynthesis
ACC2
Fatty Acid Biosynthesis
Phenylalanine, Tyrosine and Tryptophan Biosynthesis
ACC3
Stage II
Beta-Alanine Metabolism
Stage III
Fatty Acid Degradation
Butanoate Metabolism
Stage IV
Arginine and Proline Metabolism
Histidine Metabolism
Age
unknown
Tryptophan Metabolism Homocysteine Biosynthesis
80
Methionine Cycle
Purine Biosynthesis
Folate One Carbon Metabolism
60
Pyrimidine Biosynthesis
Purine Metabolism
40
Laterality
Gender
Pyrimidine Metabolism
Terpenoid Backbone Biosynthesis
20
Left
FEMALE
Cholesterol Biosynthesis Steroid Biosynthesis
Right
MALE
Folate biosynthesis
0
Aldosterone Biosynthesis
Cortisol Biosynthesis
Estradiol Biosynthesis
Testosterone Biosynthesis
Samples in TCGA-ACC cohort
| Samples in TCGA-ACC cohort | Enrichment Score | ||
|---|---|---|---|
| Subtype | 1 | ||
| Laterality | 0.5 | ||
| Gender | |||
| Age | 0 | ||
| Stage | -0.5 | ||
| WP_DNA_MISMATCH_REPAIR | |||
| REACTOME_MISMATCH_REPAIR | -1 | ||
| KEGG_MISMATCH_REPAIR | |||
Figure 3. Differential activity of tumor-associated pathways across three ACCs subtypes in TCGA- ACC cohort. (a) Heatmap of 50 differentially activated HALLMARK pathways. (b) Heatmap of 100 pathways related to metabolism of ACCs. (c) Heatmap of DNA repair pathways.
According to the TMB data, we selected six genes (DST, FAT4, KMT2B, APOB, OBSCN, and ZCCHC6) with the highest mutation rates for demonstration. ACC2 patients had the highest rates of tumor mutation (Figure 4a). We then compared the base mutations of all patients; ACC2 also had the most mutations. C > T and T > C mutations were the most common (Figure 4b). However, ACCS had no obvious difference in genome copy numbers (Figure 4c), and the patients were divided into altered and unaltered groups according to the mutation data to compare their survival outcomes. The results showed that the mutation group had worse OS (Figure 4d). In addition, protein-protein interaction
(PPI) enrichment analysis was performed using Metascape online tools based on the BioGrid, InWeb_IM, and OmniPath databases. The molecular complex detection (MCODE) algorithm was used to identify densely connected network components, and each MCODE component was independently enriched in different pathways and biological processes. Finally, seven pathways with the best scores were retained to describe the function of DEGs in ACCs, including cell-cycle function, mitotic nuclear division, and the PID PLK1 pathway (Figure 4e). Finally, we also found that these mutated genes were related to transcription factors, such as E2F1, TP53, E2F4, and YBX1 (Figure 4f).
a
0
2
4
6
L
8%
DST
L
Stage
Age
Laterality
8%
FAT4
Stage I
80
Left
8%
KMT2B
APOB
Stage II
60
Right
6%
6%
OBSCN
Stage III
Stage IV
40
6%
ZCCHC6
20
Subtype
Stage
unknown
0
ACC1
Age
ACC2
Gender
ACC3
Gender
FEMALE
Laterality
MALE
Subtype
b
C
n = 32
n = 22
n = 24
Copy number-altered genome
Copy number-lost genome
Copy number-gained genome
2
kruskal.test p = 4.0 x 10-6
log10 (TMB + 1)
ACC3
H
H
1
ACC2
4
H
0
ACC1
ACC2
ACC3
C>T
ACC1
H
H
4
T>C
C>A
C>G
T>A
T>G
0.6
0.4
0.2
0.0
-0.4
-0.2
0.0
0.2
FGA (Fraction of Genome Altered)
FGL or FGG (Fraction of Genome Lost or Gained)
d
e
100%
Logrank Test p-Value: 3.001 x 10
-3
mitotic nuclear division
Probability of Overall Survival
Cell Cycle
90%
Altered group
PID PLK1 PATHWAY
80%-
Unaltered group
cellular response to DNA damage stimulus
70%
meiotic cell cycle
60%
Mitotic G1 phase and G1/S transition
50%
APC/C-mediated degradation of cell cycle proteins
40%
30%
20%
10%
0%
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
150
f
Overall Survival (Months)
Regulated by: E2F1
Regulated by: TP53
Regulated by: E2F4
Regulated by: YBX1
Regulated by: E2F3
Regulated by: TFDP1
Regulated by: KAT2B
Regulated by: MYC
Regulated by: RB1
Regulated by: SP1
Regulated by: MYCN
Regulated by: ATM
0
2
4
6
8
10
12
14
-log10(P)
3.3. ACC1 Patients May Benefit More from Anti-PD-1 Therapy, and Chemotherapy Is More Suitable for ACC2 Patients
To further evaluate the immune status of the three subtypes, we compared their esti- mated immune scores, immune cell subsets, and immune signaling molecules, which have been reported to serve as biomarkers for immunotherapy [31,44]. As shown in Figure 5a, the ACC1 subtype exhibited the highest estimated immune score (p = 11.1 x 10-8), im- mune cell subsets (p =6.9 x 10-8), and immune signaling molecules (p = 1.6 × 10-6), which was consistent with the results of the pathway analysis. We investigated the infiltration landscape of different immune cells among the three subtypes. Compared to ACC2 and ACC3 subtypes, the ACC1 subtype had higher filtration of immunocytes such as central- memory CD4 T cells, plasmacytoid dendritic cells, mast cells, macrophages, regulatory T cells, activated CD8 T cells, and T helper cells. Based on 18 immune-related signatures that have been published [45], the ACC1 subtype exhibited higher immune scores in most signatures, including cytotoxic cells, T.NK. meta, CYT, treg cells, T cells, 13 T-cell signatures, TLS, WNTTGFB signatures, B cell cluster, 6 gene IFN signatures, macrophages, and MDSC (Figure 5b). PD-L1 is a special protein in tumor cells that can bind to PD1 on effector T cells to induce T cell exhaustion, which is a pivotal factor implicated in tumor immune escape [46]. We found that the ACC1 subtype expressed more PD-L1 and PD-1 than the ACC2 and ACC3 subtypes (all p < 0.05, Figure 5c). Therefore, the ACC1 subtype repre- sented the high immune-infiltration phenotype, which was also called “hot tumor” and indicated a potential response to anti-PD-1 therapy [47]. Furthermore, SubMap analysis showed that patients with the ACC1 subtype would benefit more from anti-PD-1 therapy (Bonferroni p < 0.05, Figure 5d). In addition, we evaluated the susceptibility of the three subtypes to chemotherapy. The results showed that ACC2 patients had better sensitivity to cisplatin (p=3.9 × 10-6), doxorubicin (p=3.4 × 10-6), gemcitabine (p=1.7×10-7), and etoposide (p = 3.3 x 10-6) (Figure 6). Collectively, this novel ACC molecular classification may facilitate the selection of appropriate treatments for different patients. The treatment of ACC1 patients with anti-PD-1 therapy and ACC2 patients with cisplatin, doxorubicin, gemcitabine, or etoposide appears to confer more clinical benefits.
3.4. Extra Validation for Molecular Subtypes in GEO Cohorts
To further validate the results in the TCGA-ACC cohort, three GEO cohorts were enrolled: GSE19750, GSE33371, and GSE49278. A combat algorithm was first conducted to eliminate the batch effect of the three GEO cohorts to make the data more comparable (Figure 7a). To distinguish the three subtypes, the top 300 specific genes for each subtype were selected to represent the separation of the three subtypes (Figure 7b). Consistently, we compared the OS of different subtypes and found that ACC2 patients had the worst prognosis (p < 0.001, Figure 7c), which was consistent with the results in the TCGA-PRAD cohort. ACC2 patients had worse survival than ACC1 and ACC3 (17.6% vs. 62.5% vs. 43.5%, p < 0.01) and a worse proportion of advanced-stage disease (55.9% vs. 18.7% vs. 26.0%, p = 0.031, Supplementary Table S2). In the pathway enrichment analysis, ACC1 patients had more activation of immune and drug-metabolism pathways, while ACC2 patients had more cell-cycle-related pathway activation, and pathways related to DNA repair were enriched in ACC2 and ACC3 patients. These functional results were similar to those of the TCGA-ACC cohort (Figure 8). Among the 18 immune-related signatures, ACC1 patients also scored highest for most items, including cytotoxic cells, T.NK. meta, CYT, treg cells, T cells, 13 T-cell signatures, TLS, B cell cluster, 6 gene IFN signatures, macrophages, and MDSC. The results showed that ACC1 represented a high immune activation phenotype and was potentially susceptible to targeted immunotherapy. Furthermore, it is worth mentioning that the ACC3 subtype also exhibited relatively higher immune scores, despite being well below ACC1 (Figure 9a). SubMap analysis showed that the patients with ACC1 were sensitive to anti-PD-1 therapy. We observed that ACC3 patients were sensitive to anti-CTLA-4 therapy (Figure 9b). Moreover, drug-sensitivity tests showed that ACC2 patients had the highest drug sensitivity to cisplatin, doxorubicin, gemcitabine, etoposide,
and paclitaxel (Figure 9c) (all p < 0.01). In addition, our system remained an independent prognostic factor in the four ACC patient cohorts after adjustment for other major clinical characteristics (all p < 0.05, Table 1). This shows the reliability of our classification.
A
Subtype ACC1 ACC2 E ACC3
Subtype
ACC1
ACC2
ACC3
Subtype E ACC1 ACC2 E ACC3
0.8+
Anova, p = 1.9x 10-6
Anova, p = 1.2 x 10”
5
Anova, p = 6.5 x 10-6
Estimate Immune Score
Immune Cell Subsets
Immune Signaling Molecules
0.4
0.5
0.4
0.0
0.0
0.0
-0.4
-0.5
-0.4
ACC1
ACC2
ACC3
ACC1
ACC2
ACC3
-0.8
ACC1
ACC2
ACC3
Subtype
Subtype
Subtype
B
Subtype
1
Subtype
1
Laterality
0.5
Laterality
0.5
Gender
0
Gender
0
Subtype
Age
Stage
-0.5
Age
ACC1
-1
Stage
-0.5
-1
ACC2
Central memory CD4 T cell
Cytotoxic cells
ACC3
Plasmacytoid dendritic cell
Mast cell
T.NK. meta
Laterality
Activated dendritic cell
CYT
Left
Macrophage MDSC
Treg cells
Right
Regulatory T cell
T cells
Gender
T follicular helper cell
Activated CD8 T cell
13 T-cell signature
FEMALE MALE
Monocyte
Effector memeory CD4 T cell
TLS
Natural killer cell
WNTTGFB signature
Age
Effector memeory CD8 T cell
80
Type 1 T helper cell
B cell cluster
60
Activated B cell
6 gene IFN signature
Immature B cell Neutrophil
40
Macrophages
20
Type 17 T helper cell
MDSC
0
CD56bright natural killer cell
CD56dim natural killer cell Eosinophil
B.P. meta
Stage
TGFB1 activated
unknown
Immature dendritic cell
Central memory CD8 T cell
TITR
Stage I
Memory B cell
C ECM
Stage II
Activated CD4 T cell
Stage III
Type 2 T helper cell
CD8 T cells
Stage IV
Gamma delta T cell
Natural killer T cell
Th17 cells
samples in TCGA-ACC cohort
samples in TCGA-ACC cohort
C
1
type
ACC1
ACC2
ACC3
D
ACC1
5
ACC2
0.8
Immunocyte Infiltration
ACC3
0.6
4
3
ACC1
0.4
ACC2
2
0.2
ACC3
1
pvalue
CTAL4-noR
CTLA4-R
PD1-noR
PD1-R
pvalue
0
Nominal p value
PD-L1
PD-1
PD-L2
CTLA4
Bonferroni corrected
5.0-
9
o
A
Estimated IC50 of Doxorubicin
·
S
O
P
Estimated IC50 of Cisplatin
Estimated IC50 of 5-Fluorouracil
-1
3
UP
3
6
·
4.5
9
O
D
8
S
0
0
8
· D
Q
6
el
0
O
o
0
8
Ba
S
0
D.
.
0
&
·
4.0
8
8
a
4
0
O
4
®
8
-2-
·
·
o
®
·
2-
8
LO
3.5.
·
0
3.0-
Kruskal-Wallis, p = 3.9 x 106
-3-
Kruskal-Wallis, p = 3.4 x 106
Kruskal-Wallis, p = 0.068
ACC1
ACC2
ACC3
ACC1
ACC2
ACC3
ACC1
ACC2
ACC3
Estimated IC50 of Gemcitabine
0
3-
Estimated IC50 of Etoposide
-3.0-
O
O
1
8
o ·
·
0
Estimated IC50 of Paclitaxel
9
O
5
8
8
·
D
8
3.5
5
6
2-
188
-2
9
P
o
9
Ht
%
0 4
0 00%
O
8
.
0
.
0
A
.
0
0
8
O
.
a
0
D
O
O
·
:+
C
4.0-
·
9
9
-3
o
0
8
b
1-
P
O
00
0
o
0
.
·
-4.5-
-4-
Krusk
-Wallis, p = 1.7 x 107
0-
Krusk
-Wallis, p = 3.3 x 10-6
-5.0-
Krusk
-Wallis, p = 0.056
ACC1
ACC2
ACC3
ACC1
ACC2
ACC3
ACC1
ACC2
ACC3
a
Raw PCA for combined expression profile
Combat PCA for combined expression profile
8
8
Comp 2: 14.4% variance
3
Comp 2: 5.7% variance
40
40
2
20
GSE19750
GSE33371
GSE49278
0
-
0
-20
-20
-40
GSE19750
GSE33371
GSE49278
-150
-100
-50
0
50
-60
-40
-20
0
20
40
b
Comp 1: 63.8% variance
Comp 1: 7.3% variance
C
ACC1
ACC2- ACC3
100
Overall p < 0.001
75
template features
Survival probability (%)
50
25
I
ACC1
ACC2
ACC2
<0.001
-
0
ACC3
0.011
0.011
0
12
24
36
48
60
72
84
96
108
Time (Months)
120
Number at risk
32
31
29
26
26
23
21
18
16
12
10
1
34
18
10
5
5
3
3
3
3
3
2
p - value
ACC2
ACC3
0
23
19
17
14
8
7
7
5
4
4
2
ACC1
0
12
24
36
48
60
72
84
96
108
120
class predictions
Time (Months)
Enrichment Score
Enrichment Score
a
1
b
Subtype
1
Side
0.5
Subtype
Side
0.5
Gender
0
Gender
0
Age
-0.5
Age
-0.5
Stage
-1
Stage
INTERFERON ALPHA_RESPONSE
INTERFERON GAMMA_RESPONSE IL6_JAK_STAT3_SIGNALING
Aldosterone Biosynthesis
-1
Cortisol Biosynthesis
Estradiol Biosynthesis
INFLAMMATORY_RESPONSE
Testosterone Biosynthesis
ALLOGRAFT_REJECTION
Glycerolipid Metabolism
TNFA_SIGNALING_VIA_NFKB
Pyruvate Metabolism
APOPTOSIS
Propanoate Metabolism
KRAS_SIGNALING_UP
Biosynthesis
COAGULATION
Fatty Acid Degradation
COMPLEMENT
Valine, Leucine and Isoleucine Degradation
IL2_STAT5_SIGNALING
Terpenoid Backbone Biosynthesis
MITOTIC_SPINDLE
Cholesterol Biosynthesis
Biosynthesis
G2M_CHECKPOINT
Steroid Hormone Metabolism
E2F TARGETS
Steroid Hormone Biosynthesis
DNA REPAIR
Fatty Acid Elongation
MYC_TARGETS_V1
Biosynthesis of Unsaturated Fatty Acids
MYC_TARGETS_V2
Ketone Biosynthesis and Metabolism
ESTROGEN_RESPONSE_EARLY
Butanoate Metabolism
ESTROGEN RESPONSE_LATE
Glutathione Metabolism
APICAL_SURFACE
Metabolism of Xenobiotics by Cytochrome P450
MYOGENESIS
Drug Metabolism by Cytochrome P450
Ascorbate and Aldrate Metabolism
APICAL JUNCTION
Histidine Metabolism
EPITHELIAL_MESENCHYMAL_TRANSITION ANGIOGENESIS
Thiamine Metabolism
Oxidative Phosphorylation
TGF_BETA_SIGNALING
Arginine and Proline Metabolism
UV_RESPONSE_DN
Beta-Alanine Metabolism Purine Metabolism
HEDGEHOG_SIGNALING
WNT_BETA_CATENIN_SIGNALING
Pyrimidine Metabolism
Pentose and Glucuronate Interconversions
NOTCH_SIGNALING
Glyoxylate and Dicarboxylate Metabolism Glycine, Serine and Threonine Metabolism Folate biosynthesis Urea Cycle
BILE ACID_METABOLISM
OXIDATIVE_PHOSPHORYLATION
ADIPOGENESIS
FATTY ACID_METABOLISM
Primary Bile Acid Biosynthesis
P53_PATHWAY
Nicotinamide Adenine Dinucleotide Biosynthesis
XENOBIOTIC_METABOLISM
Nicotinate and Nicotinamide Metabolism
REACTIVE_OXYGEN SPECIES_PATHWAY
Selenocompound Metabolism
Lipoic Acid Metabolism
PROTEIN_SECRETION
Nicotinamide Adenine Metabolism
HEME METABOLISM
Polyamine Biosynthesis
SPERMATOGENESIS
D-Glutamine and D-Glutamate Metabolism
KRAS_SIGNALING_DN
Nitrogen Metabolism
PANCREAS_BETA CELLS
Alanine, Aspartate and Glutamate Metabolism
ANDROGEN_RESPONSE
Arginine Biosynthesis
MTORC1_SIGNALING
Folate One Carbon Metabolism
CHOLESTEROL_HOMEOSTASIS
Vitamin K
PEROXISOME
Ubiquinone and other Terpenoid-Quinone Biosynthesis
PI3K_AKT_MTOR_SIGNALING
Gluconeogenesis Glycolysis
HYPOXIA
Starch and Suctose Metabolism
GLYCOLYSIS
Galactose Metabolism
UNFOLDED_PROTEIN_RESPONSE
Neomycin, Kanamysin and Gentamicin Biosynthesis
UV_RESPONSE_UP
Glycogen Biosynthesis
Samples in GEO cohort
Glycogen Degradation
Hexosamine Biosynthesis
C
Enrichment Score
1
Biotin Metabolism
Arachidonic Acid Metabolism
Cyclooxygenase Arachidonic Acid Metabolism
Prostanoid Biosynthesis
Prostaglandin Biosynthesis
Transsulfuration
Retinoic Acid Metabolism
Phenylalanine, Tyrosine and Tryptophan Biosynthesis
WP_DNA_MISMATCH_REPAIR
Ether Lipid Metabolism
Linoleic Acid Metabolism Heme Biosynthesis
REACTOME_MISMATCH_REPAIR
KEGG_MISMATCH_REPAIR
Porphyrin and Chlorophyll Metabolism
N-Glycan Biosynthesis
GO_CHROMATIN_REMODELING
Amino Sugar and Nucleotide Sugar Metabolism
REACTOME_CHROMATIN_MODIFYING_ENZYMES
Fructose and Mannose Metabolism
Glycerophospholipid Metabolism
KEGG_P53_SIGNALING_PATHWAY
Caffiene Metabolism
BIOCARTA_RB_PATHWAY
Drug Metabolism by other enzymes
Pantothenate and CoA Biosynthesis
BIOCARTA_P53_PATHWAY
Citric Acid Cycle
GO_NOTCH_SIGNALING_PATHWAY
Pentose Phosphate Riboflavin Metabolism Remethylation
REACTOME_SIGNALING_BY_NOTCH
PID_NOTCH_PATHWAY
Sirtuin Nicotinamide Metabolism
Cardiolipin Metabolism
ST_WNT_BETA_CATENIN_PATHWAY
Pyrimidine Biosynthesis
KEGG_WNT_SIGNALING_PATHWAY
Lysine Degradation
C
Kynurenine Metabolism
WNT_SIGNALING
Tryptophan Metabolism
Samples in GEO cohort
Homocysteine Biosynthesis
Methionine Cycle
Subtype
Stage
ADP-Ribosylation
Glycosphosphatidylinositol
ACC1
Stage I
Cardiolipin Biosynthesis Purine Biosynthesis
ACC2
Other Types of O-Glycan Biosynthesis
Stage II
Dopamine Biosynthesis
ACC3
Epinephrine Biosynthesis
Stage III
Norepinephrine Biosynthesis
Glycosphingolipid Biosynthesis
Stage IV
Glycosaminoglycan Degradation
Other Glycan Degradation
Age
unknown
Shingolipid Metabolism
Valine, Leucine and Isoleucine Biosynthesis
80
Inositol Phosphate Metabolism
Glycosaminoglycan Biosynthesis
60
alpha-Linoleic Acid Metabolism
Sulfur Metabolism
Gender
Vitamin B6 Metabolism
40
Laterality
Taurine and Hypotaurine Metabolism
Left
FEMALE
Tyrosine Metabolism
20
Phenylalanine Metabolism
0
Right
MALE
Retinoid Metabolism
Retinol Metabolism
Cysteine and Methionine Metabolism
Mucin Type O-Glycan Biosynthesis
Samples in GEO cohort
| Subtype | 0.5 0 | ||||
|---|---|---|---|---|---|
| Side | |||||
| Gender | -0.5 | ||||
| Age | -1 | ||||
| Stage | |||||
Figure 8. Differential activity of tumor-associated pathways across three ACCs subtypes in GEO cohort. (a) Heatmap of 50 differentially activated HALLMARK pathways. (b) Heatmap of 100 pathways related to metabolism of ACCs. (c) Heatmap of DNA repair pathways.
a
Subtype
1
b
Side
0.5
Nominal p value
Gender
0
ACC1
1
Age
Stage
-0.5
Subtype
-1
ACC1
WNTTGFB signature
ACC2
ACC2
0.8
TITR
ACC3
TGFB1 activated
Laterality
0.6
C ECM
Left
ACC3
CD8 T cells
Right
Cytotoxic cells
Gender
Bonferroni corrected
0.4
T cells
FEMALE
ACC1
CYT
MALE
B.P. meta
Age
0.2
B cell cluster
80
ACC2
Th17 cells
60
6 gene IFN signature
40
20
13 T-cell signature
ACC3
0
TLS
Stage
Treg cells
pvalue
CTAL4-noR
CTLA4-R
PD1-noR
PD1-R
unknown
Macrophages
Stage
T.NK. meta
Stage II
MDSC
Stage III
Stage IV
C
Samples in GEO cohort Kruskal-Wallis, p = 1.3 x 10-6
4.0-
-0.5-
Kruskal-Wallis, p = 1.4 x 10-8
4.5
Kruskal-Wallis, p = 0.78
Estimated IC50 of Cisplatin
Estimated IC50 of Doxorubicin
Estimated IC50 of 5-Fluorouracil
3.5
-1.0-
4.0
3.0
-1.5
3.5
-2.0-
2.5-
3.0
-2.5-
2.0-
2.5
-3.0
ACC1
ACC2
ACC3
ACC1
ACC2
ACC3
ACC1
ACC2
ACC3
Kruskal-Wallis, p = 3.2 x 10-9
Kruskal-Wallis, p = 7.4 x 109
Kruskal-Wallis, p = 0.0091
0-
3
-3.35-
Estimated IC50 of Gemcitabine
Estimated IC50 of Etoposide
Estimated IC50 of Paclitaxel
-1
2-
-3.40
-2-
-3.45
1.
-3
-3.50
-4-
0-
-3.55
ACC1
ACC2
ACC3
ACC1
ACC2
ACC3
ACC1
ACC2
ACC3
| HR | 95% CI | p Value | |
|---|---|---|---|
| TCGA-ACC Cohort | |||
| Age | 1.013 | (0.986-1.041) | 0.351 |
| Gender, male vs. female | 1.455 | (0.614-3.451) | 0.394 |
| Laterality, right vs. left | 1.533 | (0.664-3.54) | 0.317 |
| Stage | |||
| Stage II vs. stage I | 2.883 | (0.301-27.628) | 0.358 |
| Stage III vs. stage I | 6.28 | (0.641-61.55) | 0.115 |
| Stage IV vs. stage I | 16.164 | (1.49-175.301) | 0.022 |
| Stage unknow vs. stage I | 2.451 | (0.117-51.349) | 0.564 |
| ACC subtype | |||
| ACC2 vs. ACC1 | 45.146 | (7.393-275.694) | 0 |
| ACC3 vs. ACC1 | 4.661 | (0.877-24.779) | 0.071 |
| GEO cohort | |||
| Age | 1.01 | (0.99-1.031) | 0.31 |
| Gender, male vs. female | 1.236 | (0.649-2.354) | 0.518 |
| Laterality | |||
| Right vs. left | 1.15 | (0.54-2.45) | 0.717 |
| Unknow vs. left | 1.2 | (0.51-2.822) | 0.677 |
| Stage | |||
| Stage II vs. stage I | 2.765 | (0.351-21.81) | 0.334 |
| Stage III vs. stage I | 8.223 | (0.909-74.351) | 0.061 |
| Stage IV vs. stage I | 11.723 | (1.504-91.399) | 0.019 |
| Stage unknow vs. stage I | 4.067 | (0.453-36.548) | 0.21 |
| ACC subtype | |||
| ACC2 vs. ACC1 | 4.959 | (2.241-10.97) | 0 |
| ACC3 vs. ACC1 | 2.578 | (1.048-6.341) | 0.039 |
4. Discussion
The common age of onset for patients with ACC is between 50 and 70 years. Al- though most ACC is considered sporadic and the cause is unknown, a small number of cases are thought to be associated with genetic predisposition, including Lynch syn- drome, Li-Fraumeni syndrome, multiple endocrine neoplasia type 1, and familial ade- nomatous polyposis [48-51]. Ripley et al. [52] found that the first-line treatment for re- current or metastatic ACC depends on the patient’s underlying state and tumor charac- teristics. For patients who tolerate systemic chemotherapy, etoposide, doxorubicin, and cisplatin combined with mitotane (EDP-M) is superior to streptomycin-mitotane. ACC is an aggressive form of cancer, with an overall 5-year survival rate of 16-47%. The 5-year survival rates from stages I to IV were 81%, 61%, 50%, and 13%, respectively [4,53,54]. Through targeted gene analysis, mutations in TP53 or CTNNB1 have been found to be associated with molecular alterations in major ACC signaling pathways, higher tumor stage, and poorer disease-free survival (DFS). Activation of the Wnt/CTNNB1 pathway is associated with a high mitotic rate and a low survival rate. However, these markers did not show independent prognostic value in multivariate analyses, including tumor grade [55].
The immune system plays an important role in the surveillance and elimination of cancer cells, and immune evasion through various mechanisms is considered one of the characteristics of cancer [56]. Priming and activation of peripheral immune cells lead
to a T-cell inflammatory phenotype, including expansion of CD8+ cytotoxic T cells, in- terferon signaling, and local production of chemokines [57]. Rooney et al. [28] demon- strated that cytolytic immune activity, measured by industrial expression of perforin 1 and granzyme B genes, was associated with higher mutation counts. Their prediction of antigenic epitopes in a range of solid tumor malignancies supports the idea that tumor types with a high mutational burden are more susceptible to immunotherapy strategies. Several researchers have demonstrated that tumor-infiltrating lymphocytes (TILs) are as- sociated with improved clinical outcomes in ovarian-cancer patients [58-60]. Importantly, blocking PD-1, LAG-3, or CTLA-4 with gene ablation or blocking antibodies alone leads to compensatory upregulation of other checkpoint pathways, enhancing their ability to locally suppress T cells, which in turn can be overcome by a combination of blocking strategies [61].
According to Jouinot et al., the Ki67 index and targeted methylation measures of MS-MLPA can be utilized in conjunction with ENSAT staging and clinically common prog- nostic indicators [62]. Nevertheless, CpG islands are infrequently methylated, especially those connected to gene promoters. Further research is required to ascertain the extent to which DNA methylation of CpG islands controls gene expression [63]. The genetic changes identified in the targetable pathway indicate a potential route for novel treatments aimed at common chemotherapy-resistant cancers [64]. Dysregulation of miRNA subsets in ACC may contribute to the development of this malignancy. Additionally, it has been demonstrated that ACC patients with high expression of miRNA-related subsets have poorer survival rates, indicating the potential prognostic utility of these subsets [65]. Ac- cording to certain researchers, the ACC-related genes TP53 (8 of 41 tumors, 19.5%) and CTNNB1 (4 of 41 tumors, 9.8%) both exhibited somatic mutations. Somatic mutations in recurrent ZNRF3 and TERT sites and genes created by ACC are mutually exclusive. Additionally, according to gene ontology, Wnt signaling is the most often altered pathway in ACC [66].
Researchers have discovered that lncRNA SNHG3 is associated with miR-577/SMURF1 in prostate cancer and miR-139-5p/TOP2A in renal cell carcinoma. It is also associated with CDK6, Bax, Bcl-2, N-cadherin, E-cadherin, and vimentin [67,68]. When Xp21 is lost, NR0B1, which causes X-linked AHC, GK, which causes glycerol kinase deficiency, and in certain circumstances, DMD are also lost (resulting in Duchenne muscular dystrophy). When the Xp21 deletion expanded proximally to encompass DMD or when a larger loss extended distally to include IL1RAPL1 and DMD, developmental abnormalities were observed in men with Xp21 deletion [69]. DNA methylation collaborates with histone changes and miRNAs to control transcription. Additionally, research has shown that DNA methylation controls miRNA expression [63]. Some scientists have discovered that there is no connection between transcription expression and its target factors in E. coli. Furthermore, the static gene regulatory networks (GRNs) currently in use are insufficient to explain transcriptional regulation. This suggests that, when examining the cell at a systemic level, one cannot expect to observe a causal link between the expression of tran- scription factors and their targets [70]. The expression of hundreds of transcripts is often cataloged by RNA-seq measurements, but most are redundant (i.e., strongly correlated) or noisy. In addition, the number of samples available is less than the number of fea- tures owing to the expenses associated with conducting experiments, which makes it simple for conventional machine learning and statistical algorithms to overfit the biological data [71].
The biological processes of a tumor are extremely complex, and different types of features are associated with each other. Therefore, it is crucial to interpret the hetero- geneity of tumors using multi-omics analysis. We used 10 algorithms to determine the ACC multi-omics system by consensus clustering, which made the system more stable and convincing. We found that immune-related pathways were more activated, and drug metabolism pathways were enriched in ACC1 subtype patients. In addition, ACC1 patients are sensitive to PD-1 immunotherapy and have the lowest sensitivity to chemotherapeu-
tic drugs. Patients with the ACC2 subtype had the worst survival prognosis and the highest tumor-mutation rate. Meanwhile, cell-cycle-related pathways, amino acid syn- thesis pathways, and immunosuppressive cells were enriched in ACC2 patients, steroid and cholesterol biosynthetic pathways were enriched in patients with the ACC3 subtype, and DNA-repair-related pathways were enriched in subtypes ACC2 and ACC3. We as- sumed that between ACC1 and ACC2, ACC3 is the transition type. According to the results, even though ACC2 and ACC3 have a comparable distribution of tumor stage, patients who belonged to ACC2 had much worse prognoses than those who belonged to ACC3. In comparison to other subtypes, ACC3 exhibits more WNT pathway activation, greater steroid and cholesterol production, greater copy number change, and a lack of the OBSCN and ZCCHC6 mutations that were found in ACC1 and ACC2. The lowest level of immune pathway activation is then met by the ACC3 subtype. The sensitivity of the ACC2 subtype to cisplatin, doxorubicin, gemcitabine, and etoposide was better than that of the other two subtypes. For 5-fluorouracil, there was no significant difference in the sensitivity to paclitaxel between the three groups. We believe that multi-omics analysis in ACC can provide patients with more accurate clinical treatment and better prognosis prediction.
Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/cells11233784/s1. Table S1: Summarization of clinical features of ACC subtypes in TCGA-ACC cohort; Table S2: Summarization of clinical features of ACC subtypes in GEO cohort.
Author Contributions: Conceptualization, Y.G., S.Y. and C.L .; methodology, Y.G. and Y.C .; software, Y.P. and L.A .; validation, Y.G., H.D. and C.L .; formal analysis, Y.G .; data curation, S.Y .; writing, Y.G. and S.Y .; review and editing, H.D .; supervision, C.L .; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.
Funding: This work is supported by the National Natural Science Foundation of China (81802827 and 81630019), the Anhui Natural Science Foundation of China (2108085QH315) and the scientific research project of the Education Department of Anhui Province (YJS20210270).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Multi-omics data of ACC patients including DNA methylation, gene mutations, mRNA, miRNA, and LncRNA were downloaded from TCGA-ACC dataset (https://portal. gdc.cancer.gov, accessed on 15 April 2022) and GEO dataset (https://www.ncbinlm.nih.gov/geo/, accessed on 15 April 2022); the miRNA expression and DNA methylation 450 matrix were download from the UCSC Xena (https://xenabrowser.net/datapages, accessed on 15 April 2022). Download the somatic mutation data from cbiopportal (https://www.cbioportal.org/, accessed on 15 April 2022). Further enquiries can be directed to the corresponding author. We declare that the data and materials in this study will be provided free of charge to scientists for noncommercial purposes.
Conflicts of Interest: The authors declare no conflict of interest.
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