Apolipoprotein C1 and apoprotein E as potential therapeutic and prognostic targets for adrenocortical carcinoma
Cancer Biomarkers Vol. 42(1): 1-14 @ The Author(s) 2025 Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/18758592241308440 journals.sagepub.com/home/cbm
Sage
IOS Press
Shaojin Li’, Shuixiu Xiao2 and Yongli Situ3 (D
Abstract
Background: Apolipoprotein C1 (APOC1) and Apoprotein E (APOE) play important roles in lipid transport and metab- olism. In recent years, APOC1 and APOE have been shown to play key roles in the occurrence and development of various cancers. However, the expression levels, gene regulatory networks, prognostic values, and target predictions of APOC1 and APOE in adrenocortical carcinoma (ACC) remain unclear.
Methods: Various bioinformatics analysis methods were used, including gene expression profiling interactive analysis, the University of Alabama at Birmingham cancer data analysis portal, biomarker exploration of solid tumors software, the BioPortal for Cancer Genomics, search tool for the retrieval of interacting genes/proteins, gene multiple association net- work integration algorithm, Metascape, transcriptional regulatory relationships unraveled by sentence-based text-mining, LinkedOmics, and genomics of drug sensitivity in cancer analysis.
Results: APOC1 and APOE expression were strongly downregulated in patients with ACC. APOC1 and APOE expression levels were lower in male patients with ACC than those in female patients. Furthermore, APOC1 and APOE expression levels affected the prognosis of patients with ACC. The main functions of APOC1 and its altered neighboring genes (ANG) were organophosphate ester transport, rRNA processing, and positive regulation of cytokine production. Cytolysis, pro- tein ubiquitination, and histone modification were the main functions of APOE and its ANGs. The transcription factor E2F1, tumor protein p53, miR-182, miR-493, Erb-B2 receptor tyrosine kinase 2, and cyclin dependent kinase 1 were key regulatory targets of APOC1, APOE, and the ANGs. APOC1 and APOE expression in patients with ACC were positively associated with immune cell infiltration. Furthermore, anti-programmed cell death protein 1 immunotherapy strongly downregulated the expression of APOC1 in patients with ACC. Both pilaralisib and elesclomol strongly inhibited SW13 cell growth.
Conclusions: This study preliminarily clarified that APOC1 and APOE might be potential therapeutic and prognostic targets for ACC, and identified new targets and treatment strategies for ACC.
Keywords
APOC1, APOE, adrenocortical carcinoma, target prediction, gene regulation network
Received: 25 July 2023; accepted: 5 September 2024
Introduction
Adrenal cortical carcinoma (ACC), is a rare endocrine adrenal malignancy that occurs in 0.7-2 cases per million per year.1,2 The biological behavior of tumors in patients with ACC is usually aggressive, and 50%-70% of these patients have hormone overdose symptoms and signs. Glucocorticoids and/or androgens are hypersecreted by two-thirds of patients.3,4 Excess hormone secretion in patients with ACC causes increased abdominal mass, weight loss, and other constitutional symptoms.4 Females
‘Clinical laboratory, Shenzhen Longhua District Central Hospital, Shenzhen, China
2Department of Gynecology, Shenzhen Longhua District Central Hospital, Shenzhen, China
3Department of Parasitology, Guangdong Medical University, Zhanjiang, China
Corresponding author:
Yongli Situ, Department of Parasitology, Guangdong Medical University, No. 2 East Wenming Road, Zhanjiang, 524023, China. Email: styl1987@126.com
are more likely to suffer from the disease than males (1.5:1).5,6 Due to the difficult clinical presentation of ACC and the fact that most patients are diagnosed at a late metastatic stage, the prognosis for ACC is extremely poor. Patients with locoregional ACC are treated surgically; however, approximately 75% experience recurrence after surgery.7 Furthermore, for patients with advanced meta- static disease, overall survival is 12-15 months and 15% at 5 years.8,9 Compared to patients with other tumor types, patients with ACC do not benefit from improvements in overall tumor treatment. Chemotherapy and radiation therapy are ineffective in most ACC cases. Mitotane is the only drug approved by the US Food and Drug Administration for ACC and is usually only temporarily effective and has obvious side effects.10,11 Complete tumor resection remains the only curative treatment.12 Therefore, it is important to identify the target genes and regulatory targets that affect the prognosis of patients with ACC under circumstances of limited therapeutic drugs, poor therapeutic effects, and a high risk of disease recurrence.
Apolipoprotein C1 (APOC1) and Apoprotein E (APOE) are members of the apolipoprotein family. APOC1, the smallest apolipoprotein (6.6 kDa), is a triglyceride-rich and high-density lipoprotein closely related to inflamma- tion, immunity, sepsis, and diabetes.13 APOE, a 34 glyco- protein, is a high-density lipoprotein with antioxidant, anti-inflammatory, and antiatherogenic properties.14 Recently, several studies have shown that APOC1 and APOE are involved in the development and occurrence of multiple cancers. APOC1 and APOE are diagnostic and prognostic biomarkers for many cancers. Interestingly, APOC1 and APOE expression levels were different in dif- ferent tumors. Therefore, the function of APOC1 and APOE in different tumors may differ. For example, APOC1 is significantly overexpressed and its overexpres- sion is associated with lymph node metastasis, tumor-node-metastasis stage, distant metastasis, and poor prognosis in colorectal cancer, gastric cancer, and clear cell renal cell carcinoma.15-17 The overexpression level of APOE in endometrial cancer has been correlated with histo- logical grade, lymph node metastasis, and the International Federation of Gynecology and Obstetrics stage.18 However, APOC1 has significantly lower expression in esophageal cancer and hepatocellular carcinoma, which is associated with reshaping the tumor immune microenvironment and inhibiting the proliferation, migration, and invasion of cancer cells.19,20 Serum APOE levels tend to decrease in patients with laryngeal squamous cell carcinoma.21 It is cur- rently unknown if APOC1 and APOE play a role in ACC. Hence, in this study, the expression of APOC1 and APOE in patients with ACC, as well as the gene regulatory network, prognostic value, target prediction, and potential therapeutic agents were examined. These data clarify the relationships between APOC1, APOE, and ACC.
Materials and methods
Gene expression profiling interactive analysis (GEPIA)
GEPIA (http://gepia.cancer-pku.cn/index.html) is an intuitive network application tool to assess the biological relationships between gene expression and prognostic information in patients with cancer.22 GEPIA was used to analyze the rela- tionships between gene expression, tumor pathological stages, and prognosis. The screening criteria were: (1) genes: APOC1 and APOE; (2) dataset: ACC; and (3) 77 patients with ACC; threshold setting conditions: P-value cutoff ≤ 0.05. Student’s t-test was used to analyze the expres- sion of APOC1 and APOE in ACC. Kaplan-Meier curves were used to analyze the prognosis of patients with ACC.23
The university of Alabama at Birmingham CANcer data analysis portal (UALCAN)
UALCAN (http://ualcan.path.uab.edu/analysis.html) facilitates tumor subgroup gene expression and survival analyses.22 UALCAN was used to analyze APOC1 and APOE expression in ACC. The “Expression Analysis” module of the UALCAN database was used to analyze The Cancer Genome Atlas (TCGA) gene expression data. The screening criteria were set as: (1) genes: APOC1 and APOE; (2) dataset: ACC; (3) 79 patients with ACC (31 male and 48 female); threshold setting conditions: p-value cutoff=0.05. Student’s t-test was used for comparative analyses.23
Biomarker exploration of solid tumors (BEST)
BEST (https://rookieutopia.com/app_direct/BEST/) pro- vides a curated database and innovative analytical pipelines to explore cancer biomarkers at high resolution. Protein expression, immune cell infiltration, candidate agents, and immunotherapy of APOC1 and APOE in ACC were ana- lyzed using the BEST software. The “Cell infiltration,” “Immunotherapy,” and “Candidate agents” modules of the BEST database were used to analyze Gene Expression Omnibus and TCGA gene expression data. The screening criteria were set as: (1) genes: APOC1 and APOE; and (2) dataset: ACC (10 datasets with 508 patients).23
Bioportal for cancer genomics (cBioPortal)
cBioPortal (http://cbioportal.org) is an online database used for tumor gene mutation analysis. cBioPortal was used to analyze gene alterations to APOC1, APOE, and the top 50 altered neighboring genes (ANGs), respectively.22 A total of 75 ACC samples were analyzed and mRNA expression Z-scores were obtained relative to all samples (log RNA Seq V2 RSEM) using a z-score threshold of +2.0.23
Search tool for the retrieval of interacting genes/ proteins (STRING) and gene multiple association network integration algorithm (GeneMANIA)
STRING (https://string-db.org/cgi/input.pl) and GeneMANIA (http://www.genemania.org) are online databases used to analyze gene-protein and protein- protein interactions.22 STRING was used to build a low- confidence level (0.150) protein-protein interaction (PPI) network to screen criteria for species defined as humans. Cytoscape software (version 3.10.2, cytoHubba plug-ins) was used to discover the core pro- teins in PPI network. GeneMANIA was used to explore the function of APOC1, APOE, and the top 50 ANGs, respectively.23
Metascape
Metascape (https://metascape.org) is an online database used to analyze the functions and signaling pathways of genes and proteins.22 In this study, Metascape was used to analyze the function and signaling pathways of APOC1, APOE, and the top 50 ANGs, respectively.23
Transcriptional regulatory relationships unraveled by sentence-based text-mining (TRRUST)
TRRUST (https://www.grnpedia.org/trrust/) is an online database used to analyze regulatory targets of gene tran- scription.22 Here, TRRUST was used to identify the key transcriptional regulators of APOC1, APOE, and the top 50 ANGs, respectively.23
Linkedomics
LinkedOmics (http://www.linkedomics.org/) is a public online platform used to analyze correlations between differ- entially expressed genes related to tumor target genes and to predict microRNA (miRNA) and kinase targets.22 LinkedOmics was used to identify kinase and miRNA targets, as well as differentially expressed genes related to APOC1 and APOE, respectively.23
Genomics of drug sensitivity in cancer analysis
The Genomics of Drug Sensitivity in Cancer database (http://www.cancerRxgene.org) is a specialized public data- base to obtain information on potential anticancer drugs.23 This database was used to identify drugs targeting APOC1 and APOE and to predict their anti-ACC activity.
Results
APOC1 and APOE expression, prognosis, and genetic alteration in ACC
As shown in Figure 1(a) to (i), APOC1 and APOE are sig- nificantly downregulated in patients with ACC (p<0.05). A significant correlation between APOC1 expression and the pathological stage was observed in patients with ACC (p<0.05) (Figure 1(j) and (k)). However, APOC1 and APOE expression were lower in male patients than that in female patients with ACC (p < 0.05) (Figure 1(l) and (m)). Patients with ACC who expressed low levels of APOC1 had longer overall survival times (p = 0.0083) (Figure 1(n)). Patients with ACC with low APOC1 and APOE expression had longer disease-free survival times than those with high APOC1 and APOE expression levels (p=0.017 and p=0.049, respectively) (Figure 1(o) and (q)). According to these results, APOC1 and APOE expression levels were altered by 9% and 15%, respectively, in patients with ACC (Figure 1(r) and (s)).
Interaction network of APOC1, APOE, and ANGs in ACC
As shown in Tables 1 and 2, APOC1 and APOE ANG alter- ation frequencies of ≥ 28.57% and ≥ 27.27% were observed in the 50 most frequent ANGs in patients with ACC, respectively. Ring finger protein 135 (42.86%), adhesion G protein-coupled receptor A2 (28.57%), and adhesion G protein-coupled receptor G2 (28.57%) were the most fre- quent ANGs of APOC1 in patients with ACC (Table 1). Furthermore, the most frequent ANGs of APOE in patients with ACC were mucin 2 (MUC2) (72.73%), MUC4 (63.64%), and outer dense fiber of sperm tails 3 like 2 (36.36%) (Table 2). A total of 38 nodes and 150 edges were identified in the PPI network (Figure 2(a)). On the basis of the PPI network, the core proteins were extracted with Cytoscape software. The results showed the core protein with high scoring were as follows (Top 5): TOMM40, PVRL2, CLPTM1, APOC1, and APOC2 (Figure 2(e)). A complex interaction network was discov- ered between APOC1 and its ANGs by co-expression, co-localization, physical interactions, shared protein domains, and genetic interactions (Figure 2(b)). Moreover, 43 nodes and 152 edges were obtained in the PPI network (Figure 2(c)). Our results showed CDC34, ODF3L2, C2CD4C, MIER2, and TPGS1 were the core protein with high scoring (Top 5) (Figure 2((f)). An intricate network of interactions existed between APOE and its ANGs, including co-expression, physical interactions, path- ways, shared protein domains, and co-localization (Figure 2(d)).
(a)
(b)
(c)
(d)
e
(f)
GSE10927
GSE12368
GSE143383
GSE33371
(g)
GSE90713
=
APOCI
*
2
APOE
Wilcoxon, p = 0.00041
Wilcoxon, p = 0.022
T-test, p = 1.9e-13
Wilcoxon, p = 0.00041
2
T-test, p = 1.2e-12
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APOC1 Expression (z-score)
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APOC1
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APOC1
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0
Normal
Tumor
Normal
Tumor
Normal
Tumor
Normal
Tumor
Normal
Turnor
(num(T)-77; num(N)=128)
ACC
(num[T]=77; num(N)=128)
ACC
Tissue
Tissue
Tissue
Tissue
Tissue
(h)
(i)
(j)
(k)
GSE143383
GSE90713
GSE76019
GSE76021
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Wilcoxon, p = 0.04
Wilcoxon, p = 0.022
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ruskal-Wallis, p = 0.044
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1
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Expression of APOC1 in ACC based on patient’s gender
Expression of APOE in ACC based on patient’s gender
1
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APOE Expression (z-score)
APOE Expression (z-score)
APOC1 Expression (z-score)
APOC1 Expression (z-score)
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2500
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Stage
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Disease Free Survival
Overall Survival
Disease Free Survival
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Low TPM
High APOC1 IPM
:
Low APOC1 TPM
2
Low APOE TPM
9
Logrank p=0.0083
High
Logrank p=0.017
High APOE TPM Logrank p=0.058
LOW APOE TPM
High APOE TPM
HR[high)=2.9
Logrank p=0.049
3
p(HR)-0.012
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HR(high)-2.3
n[high)=38
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n[high]=38
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Percent survival
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0.6
8
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2
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APOCI
8
APOCI
8
APOE
8
APOE
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
Months
(r)
Altered in 7 (9%) of 75 patients
APOC1
9%
Genetic alteration
Amplification
mRNA High
mRNA Low
No alterations
(s)
Altered in 11 (15%) of 75 patients
APOE
15%
Genetic alteration
Missense Mutation (unknown significance)
Amplification
mRNA Low
No alterations
Gene ontology function and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis of APOC1, APOE, and ANGs in ACC
As shown in Figure 3(a), molecular functions related to APOC1 and its ANGs are mainly associated with protein tyrosine kinase binding, phosphoric ester hydrolase activ- ity, DNA and transcription factor binding, and channel activity. Moreover, organophosphate ester transport, rRNA processing, cell surface receptor signaling pathways
involved in cell-cell signaling, negative regulation of phos- phate metabolic processes, and positive regulation of cyto- kine production were the main biological processes of APOC1 and its ANGs (Figure 3(b)). Cellular components of APOC1 and its ANGs included very-low-density lipo- protein particles, actin-based cell projections, and apical plasma membranes (Figure 3(c)). However, the molecular functions of APOE and its ANGs included protein tyrosine kinase binding, protein domain-specific binding, ubiquitin- protein transferase activity, and transcriptional co-regulator
| Gene | Altered group | Unaltered group | p-Value | Gene | Altered group | Unaltered group | p-Value |
|---|---|---|---|---|---|---|---|
| RNF135 | 3 (42.86%) | 1 (1.47%) | 0.00199 | MUC2 | 8 (72.73%) | 14 (21.88%) | 0.00168 |
| ADGRA2 | 2 (28.57%) | 0 (0.00%) | 0.00757 | MUC4 | 7 (63.64%) | 13 (20.31%) | 0.00613 |
| ADGRG2 | 2 (28.57%) | 0 (0.00%) | 0.00757 | ODF3L2 | 4 (36.36%) | 2 (3.13%) | 0.00345 |
| APOC1P1 | 2 (28.57%) | 0 (0.00%) | 0.00757 | PARP8 | 4 (36.36%) | 2 (3.13%) | 0.00345 |
| APOC2 | 2 (28.57%) | 0 (0.00%) | 0.00757 | BTNL9 | 4 (36.36%) | 3 (4.69%) | 0.00741 |
| APOC4 | 2 (28.57%) | 0 (0.00%) | 0.00757 | MROH2B | 4 (36.36%) | 3 (4.69%) | 0.00741 |
| BCAM | 2 (28.57%) | 0 (0.00%) | 0.00757 | ATXN7L3 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CACNA1D | 2 (28.57%) | 0 (0.00%) | 0.00757 | AXL | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CCDC54 | 2 (28.57%) | 0 (0.00%) | 0.00757 | C2CD4C | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CEACAM16 | 2 (28.57%) | 0 (0.00%) | 0.00757 | C8ORF17 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CEACAM19 | 2 (28.57%) | 0 (0.00%) | 0.00757 | CAVIN1 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CEACAM20 | 2 (28.57%) | 0 (0.00%) | 0.00757 | CBLC | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CEACAM22P | 2 (28.57%) | 0 (0.00%) | 0.00757 | CD300LG | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CENPF | 2 (28.57%) | 0 (0.00%) | 0.00757 | CDC34 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CLPTM1 | 2 (28.57%) | 0 (0.00%) | 0.00757 | CFAP97D1 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| GPA33 | 2 (28.57%) | 0 (0.00%) | 0.00757 | CNTD1 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| IGSF23 | 2 (28.57%) | 0 (0.00%) | 0.00757 | DUSP3 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| NECTIN2 | 2 (28.57%) | 0 (0.00%) | 0.00757 | ETV4 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| RN7SL48P | 2 (28.57%) | 0 (0.00%) | 0.00757 | FAM138F | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| SCML2 | 2 (28.57%) | 0 (0.00%) | 0.00757 | FAM215A | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| TMPRSS2 | 2 (28.57%) | 0 (0.00%) | 0.00757 | GJD3 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| TOMM40 | 2 (28.57%) | 0 (0.00%) | 0.00757 | GZMM | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| ZC3H13 | 2 (28.57%) | 0 (0.00%) | 0.00757 | IRGQ | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| ZNF180 | 2 (28.57%) | 0 (0.00%) | 0.00757 | MEGF8 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| ZNF516 | 2 (28.57%) | 0 (0.00%) | 0.00757 | MEOX1 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| ARL5C | 2 (28.57%) | 1 (1.47%) | 0.0217 | MIER2 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| BCL3 | 2 (28.57%) | 1 (1.47%) | 0.0217 | MPP2 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| C19ORF12 | 2 (28.57%) | 1 (1.47%) | 0.0217 | OR4F17 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CBLC | 2 (28.57%) | 1 (1.47%) | 0.0217 | PCGF2 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CCNE1 | 2 (28.57%) | 1 (1.47%) | 0.0217 | PIP4K2B | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CD300LG | 2 (28.57%) | 1 (1.47%) | 0.0217 | PPY | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CFAP97D1 | 2 (28.57%) | 1 (1.47%) | 0.0217 | PSMB3 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| CLCN7 | 2 (28.57%) | 1 (1.47%) | 0.0217 | PYY | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| DDX5 1 | 2 (28.57%) | (1.47%) | 0.0217 | RNA5SP462 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| DUSP3 | 2 (28.57%) | 1 (1.47%) | 0.0217 | SOST | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| ENPP2 | 2 (28.57%) | 1 (1.47%) | 0.0217 | STK11 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| ETV4 | 2 (28.57%) | 1 (1.47%) | 0.0217 | THEG | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| FAM215A | 2 (28.57%) | 1 (1.47%) | 0.0217 | TPGS1 | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| G6PC3 | 2 (28.57%) | 1 (1.47%) | 0.0217 | WASH5P | 3 (27.27%) | 0 (0.00%) | 0.00244 |
| GALNT9 | 2 (28.57%) | 1 (1.47%) | 0.0217 | AHDC1 | 3 (27.27%) | 1 (1.56%) | 0.00896 |
| GSTT2 | 2 (28.57%) | 1 (1.47%) | 0.0217 | C6 | 3 (27.27%) | 1 (1.56%) | 0.00896 |
| KRT26 | 2 (28.57%) | 1 (1.47%) | 0.0217 | C7 | 3 (27.27%) | 1 (1.56%) | 0.00896 |
| MEOX1 | 2 (28.57%) | 1 (1.47%) | 0.0217 | CARD6 | 3 (27.27%) | 1 (1.56%) | 0.00896 |
| MPP2 | 2 (28.57%) | 1 (1.47%) | 0.0217 | HERC2 | 3 (27.27%) | 1 (1.56%) | 0.00896 |
| NLRP11 | 2 (28.57%) | 1 (1.47%) | 0.0217 | JUP | 3 (27.27%) | (1.56%) | 0.00896 |
| NOC4L | 2 (28.57%) | 1 (1.47%) | 0.0217 | KRT12 | 3 (27.27%) | (1.56%) | 0.00896 |
| NOL11 | 2 (28.57%) | 1 (1.47%) | 0.0217 | KRT32 | 3 (27.27%) | (1.56%) | 0.00896 |
| ODAD4 | 2 (28.57%) | 1 (1.47%) | 0.0217 | MPP3 | 3 (27.27%) | (1.56%) | 0.00896 |
| PHKA2 | 2 (28.57%) | 1 (1.47%) | 0.0217 | NLRP8 | 3 (27.27%) | 1 (1.56%) | 0.00896 |
| PPEF1 | 2 (28.57%) | 1 (1.47%) | 0.0217 | NNT | 3 (27.27%) | 1 (1.56%) | 0.00896 |
activity (Figure 3(d)). Furthermore, the main biological pro- cesses of APOE and its ANGs were cytolysis, negative regulation of kinase activity, regulation of the canonical Wnt signaling pathway, response to reactive oxygen species, protein ubiquitination, response to growth factors, cell surface receptor signaling pathways involved in
cell-cell signaling, and histone modification (Figure 3(e)). The cellular components of APOE and its ANGs included intermediate filaments, cell-cell junctions, and the extracel- lular matrix (Figure 3(f)). KEGG pathway analysis of APOE and its ANGs revealed ubiquitin-mediated proteoly- sis and regulation of the actin cytoskeleton (Figure 3(g)).
CONELG
(a)
3
EMPRE
GIMAIS
(b)
GPRS4
GP#124
NURPHÍ
GSTTAR
PPEFI
NOLIE
casotiz
DUSPE
DOXSL
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MÉGIS
CENPE
NOCAL
De3H13
CLIMA
PONAD
CACHALO
CEACANDID
APOCA
CEACAMIE
APOČ!
Networks
TMPRSSZ
Co-expression
CEACAMIS
Co-localization
PVR12
APOCZ
CLIENT
Physical Interactions
CBLG
Shared protein domains
Genetic Interactions
SCAM
APOC1
APOC1
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(d)
NLRP6
-
MUCH
CARDS
GALNIT32
VIROFOR
ETW
sost
STKIS
MƯỜNG
PYY
APOIE
RGQ
ODFILZ
TPGS1
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CHLC
HERCE
NNIT
DUSPI
MIERCE
THEG
KIRT12
UPP3
OR4FIT
UPPZ
CNTDA
Psund
GZMM
Networks
KAT32
COMDALG
GJ00
Co-expression
C17ort1.05
MEGFÉ
PCOFZ
Physical Interactions
83M.
Pathway
Shared protein domains
APOE
Co-localization
APOE
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CLCN7
VEOXI
DDX51
OSTT 28
NOL11
CEACAM
JALNTIE
PERF
PYY
BCAM
NLRP11
HORCZ
JUP
ETV4
GPA33
MUCA
KORT32
THEG
GPR124
PPEF1
8
ETW
2
C19omf12
PVRL2
APOC1
CEACAM16
POOP2
2
5523
2
MPP2
OUSP3
APOE
MERZ
COCM
NNT
2
NOCAL
EACAM2
STK11
PSMBS
B
5
S
APOCZ
OMMA
DTNLS
DUSP
CONE1
ENPP2
25
DOPSLI
₹
CARDS
APOCA
APTM
PHKA2
3
CNTDA
2
SOST
TPG41
CRL.C
ZC3H13
ARLSC
PPY
ALAPB
CBLC
KRT12
GAGNAIT
BCLJ
MPP2
SCML2
GPR64
CENPF
IMPRSS
GSF23
RGO
MPP3
MEGF
AIL
APOC1
APOE
Transcription factor, miRNA, and kinase targets of APOC1 and APOE in patients with ACC
As shown in Table 3, E2F1 is the key transcription factor involved in the network of APOC1 and its ANGs (p< 0.05). E2F1 regulates the functions of cyclin E1 and ETS variant transcription factor 4 (ETV4). Moreover, E2F1, signal transducer and activator of transcription 3
(STAT3), specificity protein 1 (SP1), and tumor protein p53 (TP53) were key transcription factors for APOE and its ANGs (p<0.05). Among these, ETV4 and MUC4 were the main regulatory genes of E2F1. STAT3 regulated MUC4 and serine/threonine kinase 11(STK11) function and APOE, MUC4, and STK11 were the main regulatory genes of SP1. TP53 regulated coactivator associated arginine
(a)
Molecular functions (APOC1)
(b)
Biological processes (APOC1)
GO:1990782: protein tyrosine kinase binding
GO:0015748: organophosphate ester transport
GO:0042578: phosphoric ester hydrolase activity
GO:0006364: rRNA processing
GO:0140297: DNA-binding transcription factor binding
GO:0044782: cilium organization
GO:0015267: channel activity
GO: 1905114: cell surface receptor signaling pathway involved in cell-cell signaling
GO:0033365: protein localization to organelle
0.0
0.5
1.0
1.5
2.0
2.5
3.0
GO:0045936: negative regulation of phosphate metabolic process
-log10(P)
GO:0001819: positive regulation of cytokine production
0.0
0.5
1.0
1.5
2.0
-log10(P)
2.5
(e)
Cellular components (APOC1)
(d)
Molecular functions (APOE)
GO:0034361: very-low-density lipoprotein particle
GO:1990782: protein tyrosine kinase binding
GO:0098858: actin-based cell projection
GO:0019904: protein domain specific binding
GO:0016324: apical plasma membrane
GO:0004842: ubiquitin-protein transferase activity
GO:0003712: transcription coregulator activity
0
1
2
3
4
5
-log10[P)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
-log10(P)
(f)
Biological processes (APOE)
(f)
GO:0007283: spermatogenesis
Cellular components (APOE)
GO:0019835: cytolysis
GO:0033673: negative regulation of kinase activity
GO:0005882: intermediate filament
GO:0060828: regulation of canonical Wnt signaling pathway
GO:0005911: cell-cell junction
GO:0000302: response to reactive oxygen species
GO:0016567: protein ubiquitination
GO:0031012: extracellular matrix
GO:0001894: tissue homeostasis
GO:0070848: response to growth factor
0.0
0.5
1.0
1.5
2.0
GO:0007610: behavior
-log10[P)
GO:0043009: chordate embryonic development
GO:1905114: cell surface receptor signaling pathway involved in cell-cell signaling GO:0016570: histone modification
GO:0032940: secretion by cell
0
1
2
3
4
5
-log10[P)
(g)
KEGG pathway (APOE)
hsa04120: Ubiquitin mediated proteolysis
hsa04810: Regulation of actin cytoskeleton
0.0
0.5
1.0
1.5
2.0
2.5
-log10(P)
| Gene | Key TF | Description | Regulated gene | P-value |
|---|---|---|---|---|
| APOC1 | E2F1 | E2F transcription factor 1 | CCNE1, ETV4 | 0.0374 |
| APOE | E2F1 | E2F transcription factor 1 | ETV4, MUC4 | 0.039 |
| APOE | STAT3 | signal transducer and activator of transcription 3 (acute-phase response factor) | MUC4, STK11 | 0.0433 |
| APOE | SP1 | Sp1 transcription factor | APOE, MUC4, STK11 | 0.0472 |
| APOE | TP53 | tumor protein p53 | CARM1, DNMT1 | 0.0451 |
methyltransferase 1 and DNA methyltransferase 1 function. miRNA targets of APOC1 and APOE were identified using LinkedOmics software (Table 4). MiR-19A, miR-19B, miR-182, and miR-302C were the miRNA targets of APOC1 in ACC (p<0.001) (Table 4). However, the miRNA targets of APOE in ACC were miR-19A, miR-19B, miR-181A, miR-181B, miR-181C, miR-181D, and miR-493 (p<0.001) (Table 4). Erb-B2 receptor tyro- sine kinase 2 (ERBB2), transforming growth factor beta receptor 1 (TGFBR1), and cyclin dependent kinase 1 (CDK1) were kinase targets of APOC1 in patients with ACC (p<0.01) (Table 5). Moreover, the kinase targets of APOE were mitogen-activated protein kinase kinase 7 (MAP2K7), CDK1, and CDK2 in patients with ACC (p<0.01) (Table 5).
Correlation of differentially expressed genes and expression of APOC1 and APOE in patients with ACC
As shown in Figure 4(a) and (d), 4471 and 4021 genes are closely related to APOC1 and APOE, respectively, in patients with ACC. Among them, 2115 and 1696 genes showed positive correlations whereas 2356 and 2325 genes showed negative correlations with APOC1 and APOE expression, respectively (Figure 4(a) and (d)). In addition, 50 genes were significantly positively and nega- tively correlated with APOC1 and APOE expression in patients with ACC (Figure 4(b), (c), (e), and (f)). Among them, APOC1 expression was strongly positively associated with APOE (Pearson correlation coefficient (PCC) =
| Gene | Gene set | Leading edge number | P-value | FDR |
|---|---|---|---|---|
| APOC1 | TTTGCAC, | 162 | <2.2e-16 | <2.2e-16 |
| miR-19A, | ||||
| miR-19B | ||||
| TAGAACC, | 15 | <2.2e-16 | <2.2e-16 | |
| miR-182 | ||||
| ATGTTAA, | 84 | <2.2e-16 | <2.2e-16 | |
| miR-302C | ||||
| APOE | TTTGCAC, | 200 | <2.2e-16 | <2.2e-16 |
| miR-19A, | ||||
| miR-19B | ||||
| TGAATGT, | 161 | <2.2e-16 | <2.2e-16 | |
| miR-181A, | ||||
| miR-181B, | ||||
| miR-181C, | ||||
| miR-181D | ||||
| ATGTACA, | 117 | <2.2e-16 | <2.2e-16 | |
| miR-493 |
0.7713, p=9.06e-17) (Figure 4(g)), apolipoprotein C1 pseudogene 1 (APOC1P1) (PCC=0.6812, p=4.872e-12) (Figure 4(h)), and negatively associated with sparc/osteo- nectin, cwcv and kazal-like domains proteoglycan (testican) 1 (SPOCK1) (PCC =- 0.5938, p= 8.008e-9) (Figure 4(i)). APOE expression was positively associated with APOC1 (PCC=0.7713, p=9.06e-17) (Figure 4(j)), family with sequence similarity 196 member B (FAM196B) (PCC= -0.5897, p=1.076e-8) (Figure 4(k)), and calcium/cal- modulin dependent protein kinase 1D (CAMK1D) (PCC = -0.5669, p=5.122e-8) (Figure 4(1)) expression.
Correlation of immune cell infiltration and APOC1 and APOE expression and anti-programmed cell death protein 1 (PD1)/programmed death-ligand 1 (pd-L1) immunotherapy in ACC
As shown in Figure 5(a) to (h), APOC1 and APOE expres- sion in patients with ACC is positively associated with immune cell infiltration (p<0.05). In addition, APOC1 was significantly downregulated in patients with ACC treated with anti-PD1 (p=0.01) (Figure 5(i)). However, APOE expression in patients with ACC treated with anti-PD1/PD-L1 was significantly upregulated (p=0.05) (Figure 5(j)).
Therapeutic drugs of APOC1 and APOE in ACC
The BEST database was used to evaluate APOC1 and APOE expression, where high expression indicated drug
resistance; pilaralisib and elesclomol were identified as first-line drugs, respectively (Figure 6(a) and (f)). Next, the Genomics of Drug Sensitivity in Cancer database was used to evaluate the inhibitory effects of pilaralisib and ele- sclomol on ACC cell lines. Pilaralisib inhibited 919 cell lines with area under the curve (AUC) values greater than 0.374 (Figure 6(c)). On these cell lines, pilaralisib had a good inhibitory effect (0.573≤IC50 (uM) ≤740) (Figure 6(b)). Moreover, pilaralisib strongly inhibited the ACC cell line SW13 (AUC values =0.857985, IC50 (µM)= 16.488967) (Figure 6(d) and (e)). In contrast, ele- sclomol inhibited 921 cell lines, with AUC values greater than 0.0209 (Figure 6(h)). On these cell lines, elesclomol had a good inhibitory effect (0.000231 ≤IC50 (UM)≤ 10.3) (Figure 6(g)). Furthermore, elesclomol also strongly inhibited SW13 (AUC values =0.421688, IC50 (M)= 0.007628) (Figure 6(i) and (j)).
Discussion
Abnormal APOC1 and APOE expression have been observed in various tumors. However, APOC1 and APOE expression in patients with ACC remains unknown. Strong downregulation of APOC1 and APOE expression has been observed in patients with ACC. APOC1 and APOE expression levels were lower in male patients with ACC than those in female patients with ACC. Low APOC1 and APOE expression levels were associated with longer survival times in patients with ACC. The number of cases in the female population generally outnumbers that of the male population (1.5:1),5,6 suggesting that sex differences in APOC1 and APOE expression may be an important factor affecting the prognosis of patients with ACC. However, this hypothesis requires further investiga- tion. These findings suggest that APOC1 and APOE are potential targets for ACC therapy. Next, the abnormal expression of APOC1 and APOE in patients with ACC was explained through genetic alterations. These results revealed that APOC1 and APOE expression were altered by 9% and 15%, respectively, in patients with ACC, with the type of genetic alteration mainly including amplification and high and low RNA levels. In addition, genetic altera- tions may lead to decreased expression of APOC1 and APOE. However, further investigation is required to address this issue. Finally, the prognostic value of APOC1 and APOE expression in patients with ACC was evaluated. According to these results, patients with ACC with low APOC1 and APOE expression had longer survival times than those with high APOC1 and APOE expression. Thus, APOC1 and APOE may serve as potential prognostic markers for patients with ACC.
The potential interactions between APOC1, APOE, and the ANGs were evaluated. As a result of co-expression, co-localization, and shared protein domains, APOC1, APOE, and the ANGs were linked to a complex interaction
| Gene | Kinase target | Description | Leading edge number | p-value |
|---|---|---|---|---|
| APOC1 | Kinase_ERBB2 | erb-b2 receptor tyrosine kinase 2 | 6 | 0.0036900 |
| Kinase_TGFBR1 | transforming growth factor beta receptor 1 | 6 | 0.0045662 | |
| Kinase_CDK1 | cyclin dependent kinase 1 | 60 | 0.0068729 | |
| APOE | Kinase_MAP2K7 | mitogen-activated protein kinase kinase 7 | 3 | <2.2e-16 |
| Kinase_CDK1 | cyclin dependent kinase 1 | 57 | <2.2e-16 | |
| Kinase_CDK2 | cyclin dependent kinase 2 | 77 | 0.0024752 |
network. The functions of APOC1, APOE, and the ANGs were also evaluated. The molecular functions related to APOC1 and its ANGs mainly included protein tyrosine kinase binding, phosphoric ester hydrolase activity, DNA and transcription factor binding, and channel activity. Protein tyrosine kinases play important roles in various cel- lular processes, including growth, motility, differentiation, and metabolism. Abnormal expression of protein tyrosine kinases usually leads to cell proliferation disorders and is closely related to tumor invasion, metastasis, acquired resistance, and tumor angiogenesis.24 DNA- and RNA-binding proteins are a broad class of molecules that regulate numerous cellular processes in all living organ- isms, and create intricate dynamic multilevel networks to control nucleotide metabolism and gene expression. These interactions are highly regulated, and their dysregulation contributes to the development of various diseases, includ- ing cancer.25 Targeting transcription factors and DNA-binding proteins is an important strategy in cancer treatment.26,27 The molecular functions of APOE and its ANGs included protein tyrosine kinase binding, ubiquitin- protein transferase activity, and transcriptional co-regulatory activity. Ubiquitination, one of the most important post-translational modifications, plays a versatile role in cancer-related pathways and is involved in protein metabolism, cell cycle progression, apoptosis, and tran- scription.28 The regulation of ubiquitination is an important cancer treatment strategy. The current results showed that the biological processes related to APOC1 and its ANGs mainly included rRNA processing, negative regulation of phosphate metabolic processes, and positive regulation of cytokine production. The biological processes affecting tumor proliferation, invasion, metastasis, and metabolism have been reported.29-31 The biological processes related to APOE and its ANGs mainly included cytolysis, negative regulation of kinase activity, protein ubiquitination, response to growth factors, and histone modification. These biological processes affect tumor proliferation, inva- sion, metastasis, and angiogenesis.32-34 KEGG pathway analysis of APOE and its ANGs showed that ubiquitin- mediated proteolysis and actin cytoskeleton regulation were enriched. Actin filaments are major components of the cytoskeleton in eukaryotic cells. Mutations and abnor- mal expression of cytoskeletal and cytoskeleton-associated proteins play important roles in cancer cell resistance to
chemotherapy and metastasis.35 Taken together, APOC1, APOE, and the ANGs may contribute to the development and progression of ACC. Thus, ACC might be treated by regulating these genes and signaling pathways.
Transcription factors and miRNA and kinase targets of APOC1 and APOE were examined in patients with ACC. These results showed that E2F1, STAT3, SP1, and TP53 were key transcription factors of APOC1, APOE, and the ANGs in patients with ACC. E2F1 cooperates with enhan- cer of zeste 2 polycomb repressive complex 2 subunit to stimulate the expression of genes involved in ACC aggres- siveness.36 STAT3 promotes angiogenesis in patients with ACC, thereby making STAT3 a selective target for molecular-targeted therapy of ACC.37 SP1 is a well-known member of the transcription factor family that plays import- ant roles in cell growth, differentiation, apoptosis, and car- cinogenesis.38 However, its role in ACC has not yet been reported. ACC is a rare tumor type associated with TP53 mutations. Genetic susceptibility caused by mutations in TP53 is associated with approximately 50% of childhood ACC cases, but only 3%-6% of adult ACC cases.39 Moreover, miR-19A, miR-19B, miR-182, miR-302C, miR-181A, miR-181B, miR-181C, and miR-181D were targets of APOC1 and APOE in patients with ACC. These miRNAs are involved in the proliferation, migration, inva- sion, and drug resistance of tumor cells, making them prom- ising targets for cancer treatment.40-42 However, these miRNAs have not been reported to be associated with ACC. ERBB2, TGFBR1, CDK1, MAP2K7, and CDK2 were kinase targets of APOC1 and APOE in patients with ACC. CDK1 serves as a therapeutic target for ACC by regu- lating the epithelial-mesenchymal transition, G2/M phase transition, and PANoptosis.43 CDK2 mRNA expression was strongly upregulated in ACC and CDK inhibitors showed dose-dependent antiproliferative effects. CDK2 mRNA expression is strongly upregulated in ACC and the CDK inhibitor flavopiridol has a dose-dependent anti- proliferative effect.44 ERBB2, TGFBR1, and MAP2K7 might be promising targets for cancer therapy. However, their relationship to ACC remains unexplored. Thus, these transcription factors, miRNAs, and kinases of APOC1, APOE, and the ANGs may be able to treat ACC.
Next, the correlation between APOC1 and APOE expres- sion and the differentially expressed genes in patients with ACC were examined. A correlation was observed between
(a)
APOC1 Association Result
(b)
(c)
APOC1
”
2
15
-log10(pvalue)
-
10
Z-Score Group 3 f
Z-Score Group 3
0
2
4
a
0
-1
€-3
2
4
-1
2
43
-4
5
0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
APOC1
APOC1
Pearson Correlation Coefficient (Pearson test)
(d)
APOE Association Result
(e)
(f)
0
APOE
1
-log10(pvalue)
10
Z-Score Group
-3
Z-Score Group 3
H
E
W 4
N
Jag
2
-1
0
0-3
4
e-3
5
0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
APOE
APOE
Pearson Correlation Coefficient (Pearson test)
(g)
Pearson-Correlation:0.7713 P-value:9.06e-17 Sample Size:(N=79)
(h)
Pearson-Correlation:0.6812 P-value:4.872e-12 Sample Size:(N=79)
(i)
Pearson-Correlation :- 0.5938 P-value:8.008e-09 Sample Size:(N=79)
&
6
0
00
APOE
15
APOC1P1
$
SPOCK1
10
2
10
0
5
0
4
6
8
10
12
14
16
4
6
8
10
12
14
16
4
6
8
10
12
14
16
APOC1
APOC1
APOC1
(j)
Pearson-Correlation:0.7713 P-value:9.06e-17 Sample Size:(N=79)
(k)
Pearson-Correlation :- 0.5897 P-value:1.076e-08 Sample Size:(N=79)
(I)
Pearson-Correlation :- 0.5669 P-value:5.122e-08 Sample Size:(N=79)
2
10
15
00
0
00
00
APOC1
FAM196B
6
CAMK1D
0
6
10
+
4
₪
2
5
0
0
8
10
12
14
16
18
8
10
12
14
16
18
8
10
12
14
16
18
APOE
APOE
APOE
(a)
APOC1
(b)
APOE
(c)
GSE10927
1
Monocytic_lineage
Cor - 0.452; Pval - 60-04
Neutrophil
Myeloid_cendrift_clab
Macrophago
Endotheaalco
T_col_Coa
2.
APOC1
FIbr DCLASES
T_cell_CD4
ImmuneScore_estimate
T_cols
Cytotoxic :_ lymphocytes
Cod_T_cells B_lineage
Myeloid_dendritic_cels
Neutrophils
Endothelial cells
Macrophage
CD8_T_cels
Neutrophil DC
B_lineage NK_cells
T_cell_COM
T_cols
T_col_CD8
Cytotoxic_lymphocytes
B_cell
Fibroblasts
Monocytes
CAF$
Macrontome 09 Macrophages_M2
CD4_Ters
APOCI Expression (z-score)
Macrophages Ml
Fangen
Pericytes
1- 20h
CD8_Tcels
(d)
CD4+_naive_T-cells
Macrophages
GSE12368
Sebocytes
Macrophages_M2
Cor = 0.506; Pval = 6.60-03
Mast_cells
DOC
DC
aDc
CD4+_naive_T-cells
APOC1
NIKT
Neutrophils
POC
NKT
CUP
ImmuneScore_estimate
CD4+_Tcm
pro_8-cels
Neutrophils
Mast_cells
Eosinophi
CD4+ T-CUB
Fibroblasts
Class-switched_memory_B-cells
Myocytes CDC
ly_Endothelial cels
Endothelial_cols
Memory_B-cells
naive_B-cells
Sebocytes
Fibroblasts
CMP
MSC
ly_Endothelial_cells
my_Endothelial_cells
mv_Endothelial_cells
MEP
Erythrocytes
GMP
NK_cells
Endothelial_cells
Memory_8-cels
APOCI Expression (z-score)
Hepatocytes
CD4+_memo-
CD4+_memory_T-cells
NES
MSC
Astrocytes
Bascohits
Olenlast
Eosinoohls
(e)
GSE33371
HSC
Adipocytens
Mesancial cels
CD8. Tom
CO4+_Tem
Cor = 0.452: Pval = fe-04
CDC
COB+_Tem
OC
naive_8-cels
2
APOC1
CD8+_T-cells
Chondrocytes
CD8+_T-ceils
CD4+_Tem Platelets
CD8+_naive T-cels
ImmuneScore_estimate
CDB ._ naive_T-cells
Freadi pocylons
Vodafone
CD4. Tom
Keratinocules
CD4+_T-cels
Epithelial cells
Melanocytes
Neurons
Algorithmus
Mendeyters
Chondrocytes
Macrophages
Algorithms
Mesangial cells
Plasma_cells
CIBERSORT ARS CIBERSORT_ABS
Macrophages Mit
FPIC
Smooth_muscle
ESTIMATE
Osteoblast
Thi_cels
ESTIMATE
Neurons
Tgd_cells
Pericytes
pro_B-ccils
Basophils
B-cels
APOC1 Expression (z-score)
1-556 MEP
Correlation
CDB ._ Tom
Preadipocytos
Plasma ces
Correlation
CD8+
3
2
(f)
Skeletal_muscle
Myceyos
Mocyles
0.5
GSE76021
-1
Cor = 0.417; Pval = 2.50-02
Macrophages
Mega Tregs
CD8_Tcells Bcells
Epithelial cells
APOC1
NKcells
CAFS
Adipocytes
Endothelial
Class-switched_memory_8-cells
ImmuneScore_estimate
CD4_Tcells
Smooth_muscle Skeletal_muscle
Monocytes
T_cols_CD8
Macrophages_M2
Dendrioc_cels,resting
Macrophase
NK_cells_activated
Macrophages_MI
T_cells_CD8
POPIS
picanha
B cells naive
Dendritic_cels
Macrophages, M1
T cells samma direita T_cells_folicular_helper
B_cels. THERE
T_cells_CD4
Dendritic_cells_activated
T_cells_regulatory_(Tregs)
NK_cells_resting
APOC1 Expression (z-score)
Neutrophils
B_cells_naive
Mast_cells_resting
Mast_cells_activated
NK_cells_resting
Eosinophils
T_cells_CD4_memory actual
T_cells_CDB
_cells_CD4_naive
T_cells_CD4_memory-activated
T-cells_CD4_nave
(g)
coils memory
T_cells_CD4 members Mo
Macro
GSE90713
Cor - 0.318; Pval = 1.50-02
Mast_cells_activated
Mast cells resting
Macrophages Mi
Dendres see
Monocytes
Macrophages M2
T_cells follicular_helper
APOE
Dendritic_cells_resting
T_cells_regulatory_(Tregs)
T_cells_gamma_delta
ImmuneScore_estimate
Eosinophils
T_cells_CO8
Dendritic_cells_activated
Monocytes
NK_cells_activated
T_cells_CD4_memory_resting
Plasma_cells
B_cells_naive
Macrophages_MO
Plasma cells
T_cells gamma carta
Ces Folicular_helper
B_cols_memory
Tensemanales med
T_cells_regulatory-Mapy
AR CHIS Testing
Mas este
ocels_nave
T_cells_CD4 naive
@ cells memory
Macrophages Mi NK_cells resting
Masz_cols_resting
Mast_cells_activated
T_cells_CD4_memory_activated
Macrophages_M1
Eosinophils
APOE Expression (z-score)
T_cells_CD4_memory_resting
T_calls_CD8
Mast_cells_activated
Macrophages_MO
T_cells_CD4_memory_activated
T_cells_CD4_naive
(h)
Macrophages_M1
Monocytes
T_cells_CD4_memory_resting
GSE143383
Comment_COB
Cor = 0.307; Pval = 20-02
T cells Che T_cells_CD8
cophages 0
B cells mummery
Dendro mie
NOCH
T cells folictre beleer
APOE
T_cels_regulatory_(Tregs)
T ents aumma dela
ImmuneScore_estimate
Plasma_cols
T_cells_CD4
Dendritic_cells_activoned
ImmuneScore
ESTIMATE Score
GSE90713
GSE 143383
StromalScore ESTIMATEScore ImmuneScore
StromalScore
GSE33371
CSE10927
GSE19775
GSE90713
TCLA
GSE143383
(i)
(j)
APOE Expression (z-score)
Amato cohort 2020
Kim cohort 2019
Anti-PD-1
Anti-PD-1/PD-L1
T-test, p = 0.01
APOC1
2
T-test, p = 0.05
APOE
1
APOC1 Expression (z-score)
APOE Expression (z-score)
0
0
-1
$1
NR
R
NR
R
Response
Response
(a)
(b)
(c)
Cell line IC50 values (Pilaralisib)
Cell line AUC values (Pilaralisib)
10ª
Number of cell lines screened : 919
1.0
Number of cell lines screened : 919
Pilaralisib_372
Maximum IC50 (uM) : 740
Maximum AUC : 0.988
High expression indicates resistance
Kobe2602_563
Geometric mean (uM) : 26.3
Minimum AUC : 0.374
Minimum IC50 (uM) : 0.573
SB52334_304
102
Min screening concentration (uM) : 0.0781
0.8
Motesanib_1029
Max screening concentration (uM) : 20 0
C-75_435
GSK1904529A_202
IC50 (micromolar)
-max conc
LFM-A13_192
10’
CD532_449
0.6
Capivasertib_1136
AUC
ARRY-520_474
Elesclomol_1031
10°
TW 37_1149
Correlation
0.4
JNJ38877605_284
SB505124_476
0.5
CI-1033_362
0
BMS-754807_184
-0.5
10-1
-1
-min cone
0.2
GSE12368
GSE143383
GSE90713
GSE76019
GSE33371
GSE10927
GSE76021
GSE19750
TCGA_ACC
102
ranked by sensitivity
0.0
ranked by sensitivity
(d)
(e)
Cell line (SW13) IC50 values (Pilaralisib)
Cell line (SW13) AUC values (Pilaralisib)
103
1.0
IC50 (µM) : 16.488967
AUC : 0.857985
102
0.8
IC50 (micromolar)
max conc
10
0.6
AUC
10°
0.4
10
-min cone
0.2
102
0.0
ranked by sensitivity
ranked by sensitivity
(f)
(g)
(h)
Cell line IC50 values (Elesclomol)
Cell line AUC values (Elesclomol)
102
1.0
Number of cell lines screened : 921
Number of cell lines screened : 921
High expression indicates resistance
Elesclomol_1031
Maximum IC50 (uM) : 10.3
Geometric mean (uM) : 0.0486
Maximum AUC : 0.997
IGFR_3801_1430
10
Minimum IC50 (uM) : 0.000231
Minimum AUC : 0.0209
ARRY-520_474
Min screening concentration (uM) : 0.000781
0.8
FGFR_3831_1422
Max screening concentration (uM) : 0.200
Talazoparib_1259
IC50 (micromolar)
10°
PARP_9482_1460
0.6
PARP_0108_1459
- max cone
10
AUC
PI-103_302
PLK_6522_1451
AZD4877_1409
Correlation
0.4
10-2
AZD8186_1444
0.5
AZD6482_156
0
EphB4_9721_1413
-0.5
10-
-min conc
0.2
-1
GSE143383
GSE90713 TCGA_ACC
GSE19775
10-
ranked by sensitivity
0.0
ranked by sensitivity
(i)
(j)
Cell line (SW13) IC50 values (Elesclomol)
Cell line (SW13) AUC values (Elesclomol)
102
1.0
IC50 (u.M) :0.007628
AUC : 0.421688
10”
0.8
IC50 (micromolar)
10°
0.6
max conc
10-
AUC
102
0.4
104
min cone
0.2
10-
ranked by sensitivity
0.0
ranked by sensitivity
APOC1 and APOE expression and 4471 and 4021 genes, respectively. Among them, APOC1P1 was positively and SPOCK1, FAM196B, CAMK1D were negatively correlated with the expression of APOC1 and APOE. Thus, targeting these genes may provide additional therapies for ACC. Immune infiltration of tumors plays a key role in tumor pro- gression and outcome.45 Cancer immunotherapy has led to significant advances in the treatment of multiple cancers. As expected, immune cell infiltration was positively correlated with APOC1 and APOE expression levels in patients with ACC. Expression levels of APOC1 and APOE in tumors are closely related to the infiltration of various immune cells. In addition, APOC1 and APOE were significantly co-expressed with immunomodulatory genes. APOC1 and APOE are immunological biomarkers that are associated with the infiltration of various immune cells.13,46 Furthermore, APOC1 and APOE were strongly downregu- lated and upregulated, respectively, in patients with ACC that were treated with anti-PD1/PD-L1. In patients with ACC, targeting APOC1, APOE, or the related regulatory targets may improve the immune microenvironment. Patients with ACC that are treated with anti-PD1 may have a better prognosis. Furthermore, the inhibitory effects of pilaralisib and elesclomol on ACC lines were investigated. Pilaralisib and elesclomol inhibited the expression in 919 and 921 cancer cell lines, respectively. Among them, elesclomol had a stronger inhibitory effect on the SW13 cell line than that of pilaralisib. In summary, pilaralisib and elesclomol inhibited cancer cells on a broad spectrum. The phosphoinositide 3-kinase inhibi- tor pilaralisib and the HSP90 inhibitor elesclomol have good antitumor effects and are safe. However, the effect of pilaralisib and elesclomol on ACC remains unclear. Thus, pilaralisib, elesclomol, and inhibitors of APOC1 and APOE or their regulatory targets, may be important strategies for the treatment of ACC. We identified the roles of APOC1 and APOE in ACC using bioinformatics methods. However, further validation through in vitro and ex vivo experiments is necessary to confirm their relationship.
In conclusion, this study preliminarily clarified that APOC1 and APOE might be potential therapeutic and prog- nostic targets for ACC and provided insights into the mech- anism and treatment of ACC.
ORCID iD
Yongli Situ ID https://orcid.org/0000-0003-2244-115X
Statements and declarations
Author contributions
INTERPRETATION OR ANALYSIS OF DATA: SL and YS.
PREPARATION OF THE MANUSCRIPT: SL and SX.
REVISION FOR IMPORTANT INTELLECTUAL CONTENT:
SL, SX and YS. SUPERVISION: YS.
Funding
This research was funded by postdoctoral Foundation of Guangdong Medical University (4SG22292G).
Declaration of conflicting interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
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