RESEARCH
Identification of ACBD3 as a new molecular Check for updates biomarker in pan-cancers through bioinformatic analysis: a preclinical study
Xinyue Ma1,2+, Shu Huang3,4+, Huiqin Shi 1,2+, Rui Luo1,2, Bei Luo1,2, Zhenju Tan1,2, Lei Shi1,2, Wei Zhang1,2, Weixing Yang1,2, Xiaolin Zhong1,2, Muhan Lü1,2, Xia Chen5* and Xiaowei Tang1,2*
Abstract
Background Acyl-CoA-binding domain-containing 3 (ACBD3) is a multifunctional protein, that plays essential roles in cellular signaling and membrane domain organization. Although the precise roles of ACBD3 in various cancers remain unclear. Thus, we aimed to determine the diverse roles of ACBD3 in pan-cancers.
Methods Relevant clinical and RNA-sequencing data for normal tissues and 33 tumors from The Cancer Genome Atlas (TCGA) database, the Human Protein Atlas, and other databases were applied to investigate ACBD3 expression in various cancers. ACBD3-binding and ACBD3-related target genes were obtained from the STRING and GEPIA2 data- bases. The possible functions of ACBD3-binding genes were explored using Gene Ontology (GO) and Kyoto Encyclo- pedia of Genes and Genomes (KEGG) enrichment analyses. We also applied the diagnostic value and survival progno- sis analysis of ACBD3 in pan-cancers using R language. The mutational features of ACBD3 in various TCGA cancers were obtained from the cBioPortal database.
Results When compared with normal tissues, ACBD3 expression was statistically upregulated in eleven cancers and downregulated in three cancers. ACBD3 expression was remarkably different among various pathological stages of tumors, immune and molecular subtypes of cancers, cancer phosphorylation levels, and immune cell infiltration. The survival of four tumors was correlated with the expression level of ACBD3, including pancreatic adenocarcinoma, adrenocortical carcinoma, sarcoma, and glioma. The high accuracy in diagnosing multiple tumors and its correlation with prognosis indicated that ACBD3 may be a potential biomarker of pan-cancers.
Conclusion According to our pan-cancer analysis, ACBD3 may serve as a remarkable prognostic and diagnostic bio- marker of pan-cancers as well as contribute to tumor development. ACBD3 may also provide new directions for can- cer treatment targets in the future.
Keywords ACBD3, GCP60, GOCAP1, Pan-cancer, Bioinformatic analysis, Biomarker
+Xinyue Ma, Shu Huang and Huiqin Shi share the first authorship.
*Correspondence:
Xia Chen 970217858@qq.com Xiaowei Tang
Full list of author information is available at the end of the article
☒ BMC
@ The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Background
Most cancers are diagnosed at an advanced stage with a low chance of cure. The recent research on cancer bio- markers has provided a basis for cancer diagnosis and prognostic assessment, offering new opportunities for the survival of cancer patients [1-3]. Therefore, pan- cancer analyses of any possible genes are necessary to further investigate their molecular mechanisms and to determine their correlation with cancer prognosis.
Acyl-CoA binding domain-containing proteins (ACBDs) are made up of seven ACBD proteins that are essential for the transport and stabilization of acyl- CoA, cellular lipid metabolism, and organelle contact sites, and thus plays an important role in cell metabo- lism [4]. ACBDs have recently been considered key regulators in the development and progression of some cancers, including breast cancer, hepatocellular carci- noma, etc. [5-7] Acyl-CoA-binding domain-contain- ing 3 (ACBD3) is a part of the ACBD family and a 528 amino acid residue protein [8]. Its vital biological fea- ture is its interaction with different proteins [1]. ACBD3 is also known as Golgi complex-associated protein 1 (GOCAP1), Golgi phosphoprotein 1(GOLPH1), Golgi complex-associated protein of 60 kDa(GCP60), and cAMP-dependent protein kinase and peripheral-type benzodiazepine receptor-associated protein 7(PAP7) [6, 9]. The various designations for ACBD3 reflect its most prominent biological properties, such as trans- port and transfer of lipids, maintenance of Golgi integ- rity, regulation of steroidogenesis, and replication of the picornavirus family [4, 6, 10]. ACBD3 is also a cru- cial player in membrane domain organization and cel- lular signaling [3]. Existing studies have shown that ACBD3 mediates the malignant process of breast can- cer by regulating the intracellular ß-84 catenin signal- ing pathway [11]. ACBD3 also affects the replication of gastric cancer cells in an AKT-dependent manner [12]. In addition, ACBD3 may be involved in the progress of gefitinib on lung cancer cells [13].
Previous researches have demonstrated that ACBD3 played a significant role in the development and treat- ment of different cancers [11-13]. No studies have explored the association between ACBD3 and pan- cancer development until now. The aim of this study was to further explore whether ACBD3 could serve as a new pan-cancer biomarker and to further determine its molecular mechanism and prognostic relevance. This research was conducted to investigate ACBD3 expres- sion among pan-cancers, investigate the prognostic and diagnostic value of ACBD3 among various tumors, and determine the correlation between ACBD3 mutation characteristics and pan-cancer prognosis.
Methods
Gene expression analysis
We used the “TISSUE” module of Human Protein Atlas (THPA) database (https://www.proteinatlas.org) in December 2022, which works to provide informa- tion on the tissue and cellular distribution of all 24,000 human proteins, to investigate ACBD3 expression from the normal tissue atlas and tumor cell lines. And then explored ACBD3 expression in cells using the “SUB- CELL” module of THPA database. We downloaded the relevant clinical and RNA-sequencing (RNA-seq) data for normal tissues and 33 tumors from The Can- cer Genome Atlas (TCGA) database on 23 February 2023 [14]. R software (version 4.2.1) and ggplot2 pack- age (version 3.3.6) were performed to generate sta- tistical analysis and visualizations, respectively. The Wilcoxon rank-sum test and T test were performed to compare the differences in ACBD3 expression lev- els between cancer and normal tissues, and between cancer and paracancerous tissues. The Cancer Cell Line Encyclopedia (CCLE) database was used to verify gene expression in pan-cancers on 14 May 2023 [15]. Immunohistochemistry images of different cancers and normal tissues were obtained from the “TISSUE” and “PATHOLOGY” module of THPA database [16]. The data on the relationship between ACBD3 expres- sion and immune infiltrates among various tumors were obtained from the “Immune-Gene” module of TIMER2.0 database in December 2022, which used six state-of-the-art algorithms to provide more reliable estimates of immune infiltration levels for tumor pro- files [17].
The ACBD3 expression among various tumor patho- logical stages was visualized using the “Stage Plot” mod- ule of the gene expression profiling interactive analysis (GEPIA2) database (Used on 28 February 2023). ACBD3 phosphoprotein levels in normal tissues and different cancers were obtained by searching for “PhosphoProtein” module of ACBD3 from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database in March 2023 [18]. Furthermore, we entered ACBD3 in the “Gene Sym- bol” module of the TISIDB (an integrated repository por- tal for tumor-immune system interactions) database [19], a website for analyzing interactions between tumors and the immune system, to observe the correlation between ACBD3 expression and various molecular and immune subtypes among different cancers.
ACBD3-related gene enrichment analyses
We obtained 50 available experimentally determined ACBD3-binding proteins from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING)
database in February 2023 by searching ACBD3 and Homo sapiens organism. We regulated the parameters as follows: “full STRING network”, “evidence”, “Experi- ments, Textmining, Databases”, “medium confidence (0.400)”, and “no more than 50 interactors” in the 1st shell. Subsequently, the protein - protein interaction (PPI) network was visualized using Cytoscape (version 3.9.1). Next, we retrieved the top 100 ACBD3-related tar- get genes from the “Similar Gene Detection” module of the GEPIA2 database. A Venn diagram was constructed to compare ACBD3-binding and ACBD3-related tar- get genes. Moreover, the possible functions of ACBD3- binding genes were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses by the R package “ClusterProfiler” (version 4.4.4).
Diagnostic value and survival prognosis analysis
We investigated the diagnostic and prognostic values of ACBD3 using relevant pan-cancer clinical data from TCGA database. The diagnostic performance of ACBD3 in pan-cancers was assessed using the receiver-operating characteristic (ROC) curve and the area under the ROC curve (AUC) applied by the “pROC” package (version 1.18.0). The included AUC values ranged from 0.7 to 1. As a measure of diagnostic accuracy, when the value of the AUC is closer to 1, the diagnostic value is higher. Furthermore, we constructed Kaplan-Meier (K-M) plots using the “survival” and “survminer” package (version 3.3.1) to demonstrate the relationship between ACBD3 expression level and the prognosis [OS (overall survival), DSS (disease-specific survival), and PFI (progress-free interval)] of various tumors [20], the Gene Expression Omnibus (GEO) database was used to verify this rela- tionship [21].
Genetic alteration and DNA methylation analysis
The cBioPortal website was used to investigate genetic alterations in ACBD3 on February 2023. Next, we searched out the mutation type, the alteration frequency, and the copy number alteration (CNA) of all TCGA tumors from the “Cancer Types Summary” module. Furthermore, we obtained the three-dimensional (3D) diagram of ACBD3 structure by using the “Mutations” module. In addition, we also accessed the correlation between ACBD3 alternation and clinical outcomes in dif- ferent tumors by performing the “Comparison/Survival” module. “Meth-Exp correlation” module of DNA Meth- ylation Interactive Visualization Database (DNMIVD) (http://www.unimd.org/dnmivd/) was used to assess the relationship between promoter methylation and ACBD3 expression, and “MethSurv” (https://biit.cs.ut.ee/meths urv) was used to explore the effect of DNA methylation
on tumor prognosis and the relationship between ACBD3 expression and methylation levels [22, 23].
Results
ACBD3 expression in pan-cancers
These results suggested that ACBD3 was moderately expressed in the majority of normal tissues. ACBD3 was highly expressed in the cerebral cortex, hippocam- pus, duodenum, small intestine, colon, gallbladder, pan- creas, prostate, placenta, appendix, and bone marrow. ACBD3 was expressed at low levels in the oral mucosa, liver, ovary, soft tissue, and adipose tissue (Additional file 1: Fig. S1A). The expression of ACBD3 ranked high in breast cancer, kidney cancer, and myeloma tumor cell lines (Additional file 1: Fig. S1B). Intracellular ACBD3 was mainly distributed in the Golgi apparatus (Addi- tional file 1: Fig. S1C, D).
Furthermore, we found that ACBD3 expression was sta- tistically upregulated in eleven cancer types as compared to normal tissues, including stomach adenocarcinoma (STAD), lung squamous cell carcinoma (LUSC), lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LIHC), kidney renal clear cell carcinoma (KIRC), head and neck squamous cell carcinoma (HNSC), glioblas- toma multiforme (GBM), esophageal carcinoma (ESCA), colon adenocarcinoma (COAD), cholangiocarcinoma (CHOL), and breast invasive carcinoma (BRCA), and downregulated in uterine corpus endometrial carcinoma (UCEC), kidney chromophobe (KICH), and thyroid car- cinoma (THCA) (Fig. 1A). The CCLE database revealed that ACBD3 was highly expressed in skin cutaneous mel- anoma (SKCM), BRCA, GBM, KIRC, brain lower grade glioma (LGG), and THCA (Additional file 1: Fig. S1E). In comparison with the adjacent normal tissues, ACBD3 expression was statistically upregulated in thirteen can- cers, including STAD, prostate adenocarcinoma (PRAD), pancreatic adenocarcinoma (PAAD), LUSC, LUAD, LIHC, kidney renal papillary cell carcinoma (KIRP), KIRC, HNSC, ESCA, CHOL, BRCA, and bladder urothe- lial carcinoma (BLCA), and downregulated in KICH and THCA (Fig. 1B). We also constructed a violin diagram of the relationship between various pathological stages of tumors and ACBD3 expression (Fig. 1C). Immuno- histochemistry images of normal breast tissue, liver tis- sue, lung tissue, kidney tissue, BRCA, LIHC, LUAD, and KICH were displayed (Fig. 2).
We assumed that the different expression levels of ACBD3 might affect the immune infiltration of various tumors. Therefore, we explored the correlation between ACBD3 expression and immune infiltration in differ- ent cancers using the TIMER2.0 database. Remarkably, the expression of ACBD3 was actively correlated with cancer-associated fibroblast (CAF) infiltration in HNSC
A
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The expression of ACBD3 Log2 (TPM+1)
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BLCA
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CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
B
9
The expression levels Log2 (TPM+1)
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7
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6
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BRCA
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PCPG
PRAD
READ
SARC
SKCM
STAD
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BLCA
F= 3.27
P= 0.0392
KIRC
F= 2.92
P= 0.0337
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F= 6.25
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ACBD3 expression-log2(TPM+1)
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9
The expression of ACBD3 Log2 (TPM+1)
8
7
6
5
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4
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Expression Difference
Breast normal
BRCA
B
LIHC
7
The expression of ACBD3 Log2 (TPM+1)
6
5
4
3
2
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The expression of ACBD3 Log2 (TPM+1)
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KICH
The expression of ACBD3 Log2 (TPM+1)
6
5
4
3
2
Normal
Tumor
Expression Difference
Kidney normal
KICH
A
BLCA :: ACBD3_exp Kruskal-Wallis Test: Pv=6.46e-04 n=C1 173,C2 164,C3 21,C4 36,C6 3
B
HNSC :: ACBD3_exp Kruskal-Wallis Test: Pv=7.43e-03 n=C1 128,C2 379,C3 2,C4 2,C6 3
C
STAD :: ACBD3_exp Kruskal-Wallis Test: Pv=2.93e-06 n=C1 129,C2 210,C3 36,C4 9,C6 7
Expression (log2CPM)
8
9
7
8
申
6
6
7
9
8
6
5
4
5
4
4
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
Subtype
Subtype
Subtype
D
SKCM :: ACBD3_exp Kruskal-Wallis Test: Pv=2.42e-02 n=C1 41,C2 27,C3 14,C4 19,C6 2
E
SARC :: ACBD3_exp Kruskal-Wallis Test: Pv=2.27e-02
F
OV : ACBD3_exp Kruskal-Wallis Test: Pv=3.32e-02 n=C1 46,C2 159,C3 3,C4 61
n=C1 64,C2 38,C3 42,C4 59,C6 20
Expression (log2CPM)
8
8
7
7
7
6
6
5
5
6
4
4
5
3
3
C1
C2
C3
C4
C6
C1
C2
C3
C4
C6
C1
C2
C3
C4
Subtype
Subtype
Subtype
G
LUSC :: ACBD3_exp Kruskal-Wallis Test: Pv=3.17e-03 n=C1 275,C2 182,C3 8,C4 7,C6 14
H
LIHC : ACBD3_exp Kruskal-Wallis Test: Pv=7.75e-03 n=C1 22,C2 45,C3 135,C4 159,C6 1
GBM :: ACBD3_exp Kruskal-Wallis Test: Pv=3.86e-02 n=C1 2,C4 150,C5 1
Expression (log2CPM)
9
7
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8
6
6
7
-
-
6
4
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5
4
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(Additional file 1: Fig. S2A) and positively related to neu- trophil infiltration in BLCA, COAD, and THCA (Addi- tional file 1: Fig. S2B). ACBD3 expression also positively related to endothelial cell infiltration in COAD, HNSC, and KIRC (Additional file 1: Fig. S2C).
Figure 3 showed that ACBD3 expression was statis- tically different among the immune subtypes of nine tumors, including BLCA (Fig. 3A), HNSC (Fig. 3B), STAD (Fig. 3C), SKCM (Fig. 3D), sarcoma (SARC) (Fig. 3E), ovarian serous cystadenocarcinoma (OV)
(Fig. 3F), LUSC (Fig. 3G), LIHC (Fig. 3H), and GBM (Fig. 3I). Moreover, for immune subtype C1 (wound heal- ing), ACBD3 expressed high in BLCA, SKCM, SARC, and LUSC. For immune subtype C2 (IFN-gamma domi- nant), ACBD3 expressed high in HNSC, STAD, and OV. For immune subtype C4 (lymphocyte depleted), ACBD3 expressed high in LIHC, and GBM.
Furthermore, we identified twelve tumors with molec- ular subtypes associated with ACBD3 expression. Among the molecular subtypes of G-CIMP-high in LGG, ACBD3 showed the highest expression (Fig. 4A). Among the molecular subtypes of LunA in BRCA, ACBD3 showed the highest expression (Fig. 4B). Among the molecular subtypes of 1-ERG in PRAD, ACBD3 showed the highest expression (Fig. 4C). Among the molecular subtypes of Kinasesignaling in pheochromocytoma & paraganglioma (PCPG), ACBD3 showed the highest expression (Fig. 4D). Among the molecular subtype of CIN in COAD, ACBD3 showed the highest expression (Fig. 4E). Among the molecular subtypes of Immunoreactive and Proliferative in OV, ACBD3 showed the highest expression (Fig. 4F). Among the molecular subtypes of classical in LUSC, ACBD3 showed the highest expression (Fig. 4G). Among the molecular subtypes of iCluster:1 in LIHC, ACBD3 showed the highest expression (Fig. 4H). Among the molecular subtypes of CIN in STAD, ACBD3 showed the highest expression (Fig. 4I). Among the molecular subtypes of BRAF_Hotspot_Mutants in SKCM, ACBD3 showed the highest expression (Fig. 4J). Among the molecular subtypes of C1in KIRP, ACBD3 showed the highest expression (Fig. 4K). Among the molecular sub- types of Basal in HNSC, ACBD3 showed the highest expression (Fig. 4L).
ACBD3-related gene enrichment analyses
We identified 50 ACBD3-binding proteins from the STRING database (Fig. 5A). Next, GO|KEGG enrich- ment analysis was conducted on the 50 proteins, reveal- ing that the biological processes (BP) of the 50 proteins included Golgi organization, intermembrane lipid transfer, and Golgi vesicle budding. The cellular com- ponents (CC) of the 50 proteins were mainly involved in the Golgi apparatus subcompartment, endoplasmic
reticulum-Golgi intermediate compartment, and trans- Golgi network. The molecular function (MF) of 50 pro- teins were primarily enriched in sterol binding, protein kinase A binding, and lipid transfer activity. The KEGG pathway enrichment of the 50 proteins was primar- ily related to endocytosis and cholesterol metabolism (Fig. 5B).
In addition, the top 100 ACBD3-related target genes were retrieved, and a Venn diagram identified the four genes at the intersection of the ACBD3-binding and ACBD3-related target genes as: ARF1, BLZF1, BPNT1, and GORASP2 (Fig. 5C). The expression levels of these four genes were closely related to those of ACBD3: ARF1 (R=0.65), BLZF1 (R=0.70), BPNT1 (R=0.67), and GORASP2 (R=0.62) (Fig. 5D-G).
Diagnostic value of ACBD3 in pan-cancer
The ROC curve was drawn to explore the diagnos- tic value of ACBD3 in different cancers, and an AUC value>0.7 was considered to have a diagnostic value. As shown, ACBD3 had an accurate diagnostic value for 17 kinds of cancers, including CHOL (AUC=0.990), LIHC (AUC=0.863), STAD (AUC=0.902), PAAD (AUC=0.756), ESCA (AUC=0.883), esophagus adeno- carcinoma (ESAD) (AUC=0.901), esophagus squa- mous cell carcinoma (ESCC) (AUC=0.841), BRCA (AUC=0.805), KICH (AUC=0.895), Lung cancer (LUADLUSC) (AUC=0.726), LUSC (AUC=0.717), LUAD (AUC=0.741), SARC (AUC=0.930), PCPG (AUC=0.739), GBM (AUC=0.787), Glioma (GBM- LGG) (AUC=0.708), and Oral squamous cell carcinoma (OSCC) (AUC=0.701) (Fig. 6).
Prognostic value of ACBD3 in pan-cancer
Figure 7 showed that the OS, DSS, and PFI of four tumors were closely correlated with the expression levels of ACBD3, including PAAD, adrenocortical carcinoma (ACC), SARC, and GBMLGG. As for PAAD, the progno- sis was negatively related to ACBD3 expression, includ- ing OS [hazard ratio (HR) = 1.63, 95% confidence interval (CI): 1.07-2.46, p=0.022], DSS (HR=1.67, 95% CI: 1.05- 2.67, p=0.032), and PFI (HR=1.32, 95% CI: 0.90-1.94, p=0.155). As for ACC, the prognosis was negatively
(See figure on next page.)
Fig. 4 ACBD3 expression in different molecular subtypes of various TCGA cancers. brain lower grade glioma (LGG), breast invasive carcinoma (BRCA), prostate adenocarcinoma (PRAD), pheochromocytoma and paraganglioma (PCPG), colon adenocarcinoma (COAD), ovarian serous cystadenocarcinoma (OV), lung squamous cell carcinoma (LUSC), liver hepatocellular carcinoma (LIHC), stomach adenocarcinoma (STAD), skin cutaneous melanoma (SKCM), kidney renal papillary cell carcinoma (KIRP), head and neck squamous cell carcinoma (HNSC). ACBD3 expressed the highest in the molecular subtype of A G-CIMP-high in LGG, B LunA in BRCA, C 1-ERG in PRAD, D Kinasesignaling in PCPG, E CIN in COAD, F Immunoreactive and Proliferative in OV, G classical in LUSC, H iCluster:1 in LIHC, I CIN in STAD, J BRAF_Hotspot_Mutants in SKCM, K C1in KIRP, and L Basal in HNSC
A
Expression (log2CPM)
LGG Kruskal-Wallis Test: P=3.59e-15
B
BRCA Kruskal-Wallis Test: P=1.78e-03
C
PRAD Kruskal-Wallis Test: P=4.20e-05
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Normal
1-ERG
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3-ETV4
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PCPG Kruskal-Wallis Test: P=4.36e-03
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COAD
F
OV Kruskal-Wallis Test: P=1.34e-03
Kruskal-Wallis Test: P=2.29e-05
Expression (log2CPM)
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LUSC Kruskal-Wallis Test: P=5.83e-03
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LIHC Kruskal-Wallis Test: P=3.94e-02
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SKCM Kruskal-Wallis Test: P=3.88e-02
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BPNT1
VR 1
TSPO
A
RNPEP
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B
Golgi organization
MF 12
EHD4
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intermembrane lipid transfer
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GBF1
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Golgi apparatus subcompartment
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trans-Golgi network
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0.075
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endoplasmic reticulum-Golgi intermediate compartment
0.025
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sterol binding
4
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RTN4
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OSBP
protein kinase A binding
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MEF 2A
TGOLN2
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46
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p-value< 0.001
p-value< 0.001
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p-value< 0.001
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R = 0.62
0
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log2(ARF1 TPM)
log2(BLZF1 TPM)
5
log2(BPNT1 TPM)
log2(GORASP2 TPM)
0
00
៛
4
”
+
៛
2
2
₪
2
-
0
0
0
.
0
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
0
2
4
6
8
log2(ACBD3 TPM)
log2(ACBD3 TPM)
log2(ACBD3 TPM)
log2(ACBD3 TPM)
(See figure on next page.)
Fig. 6 Correlations between ACBD3 expression and receiver operating characteristic (ROC) curve across TCGA tumors, area under the ROC curve (AUC) value >0.7 was considered to have a diagnostic value. As a measure of diagnostic accuracy, the value of AUC is closer to 1, the diagnostic value is higher. A cholangiocarcinoma (CHOL) (AUC=0.990), B liver hepatocellular carcinoma (LIHC) (AUC=0.863), C stomach adenocarcinoma (STAD) (AUC=0.902), D pancreatic adenocarcinoma (PAAD) (AUC=0.756), E esophageal carcinoma (ESCA) (AUC=0.883), F esophagus adenocarcinoma (ESAD) (AUC=0.901), G esophagus squamous cell carcinoma (ESCC) (AUC=0.841), H breast invasive carcinoma (BRCA) (AUC=0.805), I kidney chromophobe (KICH) (AUC=0.895), J Lung cancer (LUADLUSC) (AUC=0.726), K lung squamous cell carcinoma (LUSC) (AUC=0.717), L lung adenocarcinoma (LUAD) (AUC=0.741), M sarcoma (SARC) (AUC=0.930), N pheochromocytoma and paraganglioma (PCPG) (AUC=0.739), O glioblastoma multiforme (GBM) (AUC=0.787), P Glioma (GBMLGG) (AUC=0.708), Q Oral squamous cell carcinoma (OSCC) (AUC=0.701)
A
B
C
D
CHOL
LIHC
1.0
STAD
PAAD
1.0
1.0
1.0
0.8
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
ACBD3
0.2
ACBD3
0.2
ACBD3
0.2
ACBD3
AUC: 0.990
AUC: 0.863
AUC: 0.902
AUC: 0.756
0.0
CI: 0.971-1.000
0.0
CI: 0.824-0.901
0.0
CI: 0.848-0.956
0.0
CI: 0.594-0.917
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
E
ESCA
F
G
ESCC
H
ESAD
BRCA
1.0
1.0
1.0
1.0
0.8
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
ACBD3
0.2
ACBD3
0.2
ACBD3
0.2
ACBD3
AUC: 0.883
AUC: 0.901
AUC: 0.841
AUC: 0.805
0.0
CI: 0.770-0.997
0.0
CI: 0.780-1.000
0.0
CI: NA-NA
0.0
CI: 0.773-0.836
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
I
J
K
KICH
LUADLUSC
LUSC
L
LUAD
1.0
1.0
1.0
1.0
0.8
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
ACBD3
0.2
ACBD3
0.2
ACBD3
0.2
ACBD3
AUC: 0.895
AUC: 0.726
AUC: 0.717
AUC: 0.741
0.0
CI: 0.830-0.961
0.0
CI: 0.692-0.761
0.0
CI: 0.662-0.771
0.0
CI: 0.697-0.785
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
M
SARC
N
PCPG
O
P
GBM
GBMLGG
1.0
1.0
1.0
1.0
0.8
0.8
0.8
0.8
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
Sensitivity (TPR)
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
ACBD3
0.2
ACBD3
0.2
ACBD3
0.2
ACBD3
AUC: 0.930
AUC: 0.739
AUC: 0.787
AUC: 0.708
0.0
CI: 0.874-0.986
0.0
CI: 0.532-0.946
0.0
CI: 0.674-0.900
0.0
CI: 0.580-0.836
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
0.00
0.25
0.50
0.75
1.00
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
1-Specificity (FPR)
Q
OSCC
1.0
0.8
Sensitivity (TPR)
0.6
0.4
0.2
ACBD3
AUC: 0.701
0.0
CI: 0.604-0.797
0.00
0.25
0.50
0.75
1.00
1-Specificity (FPR)
related to ACBD3 expression, including OS (HR=2.56, 95% CI: 1.17-5.61, p=0.018), DSS (HR=2.60, 95% CI: 1.15-5.88, p=0.022), and PFI (HR=4.02, 95% CI: 2.02-8.03, p<0.001). Also, for SARC, the prognosis was negatively related to ACBD3 expression, including OS (HR=1.66, 95% CI: 1.10-2.48, p=0.015), DSS (HR= 1.86, 95% CI: 1.18-2.91, p=0.007), and PFI (HR=1.60, 95% CI: 1.14-2.24, p=0.006). And for GBMLGG, the prog- nosis was negatively related to ACBD3 expression, including OS (HR=1.47, 95% CI: 1.15-1.87, p=0.002), DSS (HR=1.47, 95% CI: 1.14-1.90, p=0.003), and PFI (HR=1.30, 95% CI: 1.05-1.61, p=0.014).
We used the GSE57495, GSE83300, and GSE19750 in the GEO database for validation and found that the OS prognosis was negatively related to ACBD3 expression in GBMLGG (HR=2.30, 95% CI: 1.19-4.43, p=0.013). However, there was no statistically significant relation- ship between ACBD3 expression and prognosis in PAAD and ACC (Additional file 1: Fig. S3).
Genetic alteration and DNA methylation analysis of ACBD3 in pan-cancers
The gene alteration characteristics of ACBD3 in TCGA pan-cancer atlas were obtained. We found that among the many alteration types of ACBD3, “Amplification” was the most common type in patients with BRCA (9.22% alteration frequency) and LIHC (5.38% alteration fre- quency) (Fig. 8A). It is worth noting that, “Amplification” was the only type of genetic alteration in ovarian serous cystadenocarcinoma, pheochromocytoma, and paragan- glioma (Fig. 8A). “Mutation” is the only type of genetic alteration in kidney chromophobe and adrenocortical carcinoma cases (Fig. 8A). “Deep Deletion” is the only type of genetic alteration in diffuse large B-cell lym- phoma and prostate adenocarcinoma (Fig. 8A). Figure 8B displayed the 3D structure of ACBD3 protein. Figure 8C displayed the number, sites, and types of the ACBD3 genetic alteration. As shown in Fig. 8C, “Missense Muta- tion” was the most dominant type of mutation. Alteration in R484Q/ * and R516W in GOLD-2 could have led to the missense mutations in ACBD3.
In addition, we determined the relationship between ACBD3 alterations and clinical outcomes in BRCA and
LIHC. For BRCA, patients with ACBD3 alterations had a worse prognosis in terms of progression-free survival (Fig. 8D) and disease-specific survival (Fig. 8E) than those patients without ACBD3 alterations. Patients with LIHC and ACBD3 alteration had a better prognosis in overall survival than those patients without ACBD3 alterations (Fig. 8F).
Furthermore, we accessed the relationship between DNA methylation and ACBD3 expression and found that promoter methylation was positively correlated with ACBD3 expression in PAAD (Pearson r=0.2) (Fig. 9A). In addition, higher methylation ß values of ACBD3-Body- N_Shelf-cg15084160 led to a worse OS prognosis in PAAD [HR=1.52, CI(1.014;2.281), P<0.05] (Fig. 9B and Table 1).
Protein phosphorylation analysis
Figure 10A summarized the correlation between vari- ous cancers and ACBD3 phosphorylation sites. The S316 locus showed an increased phosphorylation level in BRCA when compared with normal tissues (Fig. 10B). Compared with normal tissues, the S20 locus exhibited a lower phosphorylation level in HNSC (Fig. 10C), with LUAD (Fig. 10D) exhibited the opposite trend. The phos- phorylation levels at S43 were higher in KIRC (Fig. 10E), LUAD (Fig. 10D), and GBM (Fig. 10F) and decreased in HNSC (Fig. 10C).
Discussion
Known as GCP60, ACBD3 majored in maintaining the structure and function of the Golgi apparatus. Changes in Golgi structure and function are closely related to can- cer development, and Golgi-associated proteins may help diagnose cancer and guide treatment [24-26]. Since the Golgi apparatus is mainly involved in the synthesis and redistribution of new proteins, we speculate that ACBD3 promotes protein binding and thus plays significant roles in the occurrence and development of many tumors with different characteristics. Previous studies demonstrated that ACBD3 was involved in the development and treat- ment of various types of cancers [11-13]. Neverthe- less, no studies have explored the function of ACBD3 in
(See figure on next page.)
Fig. 7 Kaplan-Meier plots [Overall Survival (OS), Disease Specific Survival (DSS), and Progress Free Interval (PFI)]for ACBD3 expression in pan-cancers. A For pancreatic adenocarcinoma (PAAD), the prognosis was negatively related to ACBD3 expression, including OS [hazard ratio (HR)= 1.63, 95% confidence interval (CI): 1.07-2.46, p=0.022], DSS (HR= 1.67, 95% CI: 1.05-2.67, p=0.032), and PFI (HR= 1.32, 95% CI: 0.90 -1.94, p=0.155). B For adrenocortical carcinoma (ACC), the prognosis was negatively related to ACBD3 expression, including OS (HR=2.56, 95% CI: 1.17- 5.61, p=0.018), DSS (HR=2.60, 95% CI: 1.15-5.88, p=0.022), and PFI (HR=4.02, 95% CI: 2.02-8.03, p <0.001). C For sarcoma (SARC), the prognosis was negatively related to ACBD3 expression, including OS (HR= 1.66, 95% CI: 1.10-2.48, p=0.015), DSS (HR= 1.86, 95% CI: 1.18-2.91, p=0.007), and PFI (HR= 1.60, 95% CI: 1.14-2.24, p=0.006). D For glioma (GBMLGG), the prognosis was negatively related to ACBD3 expression, including OS (HR=1.47, 95% CI: 1.15-1.87, p=0.002), DSS (HR= 1.47, 95% CI: 1.14-1.90, p=0.003), and PFI (HR= 1.30, 95% CI: 1.05-1.61, p=0.014
A
PAAD
PAAD
PAAD
1.00
ACBD3
1.00
ACBD3
1.00
ACBD3
Low
Low
Low
Survival probability
High
High
0.75
High
0.75
Survival probability
Survival probability
0.75
0.50
0.50
0.50
0.25
Overall Survival HR = 1.63 (1.07 - 2.46)
0.25
0.25
Disease Specific Survival HR = 1.67 (1.05 - 2.67) P = 0.032
+
Progress Free Interval HR = 1.32 (0.90 - 1.94)
0.00
P = 0.022
0.00
P= 0.155
0
1000
2000
0
1000
2000
0
1000
2000
Time (days)
Time (days)
Time (days)
Low
89
12
3
Low
85
12
3
Low
89
11
2
High
90
11
3
High
88
10
3
High
90
8
1
B
ACC
ACC
ACC
1.0
ACBD3
1.0
ACBD3
1.00
ACBD3
Low
Low
Low
Survival probability
0.8
High
Survival probability
0.8
High
Survival probability
0.75
High
0.6
0.6
0.50
0.4
0.4
Overall Survival HR = 2.56 (1.17 - 5.61)
Disease Specific Survival HR = 2.60 (1.15 - 5.88)
0.25
Progress Free Interval HR = 4.02 (2.02 - 8.03
0.2
P = 0.018
0.2
P = 0.022
0.00
P < 0.001
0
1000
2000 Time (days)
3000
4000
0
1000
2000
3000 Time (days)
4000
0
1000
2000
3000
4000
Time (days)
Low
39
25
15
6
1
Low
38
24
15
6
1
Low
39
19
12
5
1
High
40
21
7
2
1
High
39
21
7
2
1
High
40
9
4
1
1
C
SARC
SARC
SARC
1.0
ACBD3
1.0
ACBD3
1.00
ACBD3
Low
Low
Low
Survival probability
0.8
High
Survival probability
0.8
High
Survival probability
High
0.75
0.6
0.6
0.50
0.4
0.4
Overall Survival HR = 1.66 (1.10 - 2.48)
Disease Specific Survival HR = 1.86 (1.18 - 2.91)
0.25
Progress Free Interval++ + HR = 1.60 (1.14 - 2.24)
0.2
P = 0.015
0.2
P = 0.007
P = 0.006
0
1000
2000
3000 Time (days)
4000
5000
0
1000
2000
3000 Time (days)
4000
5000
0
1000
2000
3000
4000
Time (days)
Low
131
64
29
12
5
1
Low
128
62
28
12
5
1
Low
131
46
19
7
3
High
132
61
19
6
1
1
High
129
60
19
6
1
1
High
132
30
11
2
0
D
GBMLGG
GBMLGG
GBMLGG
1.00
ACBD3
1.00
ACBD3
1.00
ACBD3
Low
Low
Low
Survival probability
0.75
High
Survival probability
0.75
High
Survival probability
0.75
High
0.50
0.50
0.50
0.25
Overall Survival HR = 1.47 (1.15 - 1.87)
0.25
Disease Specific Survival HR = 1.47 (1.14 - 1.90)
0.25
Progress Free Interval HR = 1.30 (1.05 - 1.61)
0.00
P = 0.002
0.00
P = 0.003
0.00
P = 0.014
0
2000
4000
6000
0
2000
4000
6000
0
1000
2000
3000
4000
5000
Time (days)
Time (days)
Time (days)
Low
349
34
6
0
Low
341
33
6
0
Low
349
70
18
6
1
0
High
349
29
9
1
High
336
28
9
1
High
349
60
16
6
3
1
A
· Mutation
· Structural Variant
B
8%
· Amplification
·Deep Deletion
Alteration Frequency
6%
· Multiple Alterations
4%
2%
Structural variant data
Mutation data
CNA data
Breast Invasive Carcinoma
Cholangiocarcinoma
Uterine Carcinosarcoma
Uterine Corpus Endometrial Carcinoma
Liver Hepatocellular Carcinoma
Thymoma
Ovarian Serous Cystadenocarcinoma
Skin Cutaneous Melanoma Stomach Adenocarcinoma
Lung Adenocarcinoma
Pancreatic Adenocarcinoma
Lung Squamous Cell Carcinoma
Pheochromocytoma and Paraganglioma
Esophageal Adenocarcinoma
Diffuse Large B-Cell Lymphoma
Cervical Squamous Cell Carcinoma
Bladder Urothelial Carcinoma
Head and Neck Squamous Cell Carcinoma
Sarcoma
Kidney Chromophobe
Colorectal Adenocarcinoma
Testicular Germ Cell Tumors
Adrenocortical Carcinoma
Prostate Adenocarcinoma
Glioblastoma Multiforme
Brain Lower Grade Glioma Thyroid Carcinoma
Kidney Renal Clear Cell Carcinoma
Acute Myeloid Leukemia
Kidney Renal Papillary Cell Carcinoma
Mesothelioma
Uveal Melanoma
C
R233*/Q
5
R484Q/ R516W
0
ACBP
GOLD_2
0
100
200
300
400
528aa
D
E
F
100%
BRCA
100%
BRCA
100%
Progression Free
Disease-Specific
Probability of Overall Survival
LIHC
80%
80%
80%
60%
60%
60%
40%
Logrank Test P-Value: 0.0420
40%
Logrank Test P-Value: 0.0458
40%
Logrank Test P-Value: 0.0188
20%
Altered group
20%
Altered group
20%
Altered group
Unaltered group
Unaltered group
Unaltered group
0%
0
40
80
120
160
200
240
280
0%
40
80
120
160
200
240
280
0%
0
0
20
40
60
80
100
120
Months of Progress Free Survival
Months of Disease-Specific Survival
Months of Overall Survival
pan-cancers systematically. In order to gain a more com- prehensive understanding of ACBD3, we are the first to explore its function and expression of ACBD3 in pan- cancers from the perspective of bioinformatic analysis.
By exploring the TCGA database, we found that ACBD3 expression was remarkably upregulated in eleven cancers, and downregulated in three cancers. This find- ing suggests that ACBD3 regulates the formation and replication of tumors and facilitates the development of
A
C
ethnicity
ethnicity
race
0.8 [Not Available]
PAAD - ACBD3
age
[Not Evaluated]
event
[Unknown]
5
0.6
HISPANIC OR LATINO
cg15084160
NOT HISPANIC OR LATINO
0.4
race
ACBD3 (FPKM)
4
[Not Evaluated]
cg25844216
0.2
[Unknown]
ASIAN
BLACK OR AFRICAN AMERICAN
3
WHITE
cg18385299
age
(35,57]
(57,65]
2
cg15863254
(65,73]
(73,88]
event
Alive
1
cg12446722
Pearson r=0.2
Dead
P-value=5.90e-03
Relation_to_UCSC_CpG_Island
0
cg00396407
Island
L
N_Shelf
0.01
0.02
0.03
0.04
0.05
N_Shore
Open_Sea
ACBD3 (mean beta value of promoter)
cg25746764
UCSC_RefGene_Group
1stExon
3’UTR
B
cg25894045
5’UTR;1stExon
Body
TSS1500
TSS200
Survival Probability
0.
ACBD3-Body-N_Shelf-cg15084160
LR test p-value=0.042
cg06314646
0.8
HR=1.52
0.6
cg19029205
0.4
cg20786939
0.2
8
Lower (n=92) Higher (n=92)
cg04210991
0
500
1000
1500
2000
2500
Survival Time (days)
cg14971781
Relation_to_UCSC_COG Island
UCSC RefGene Relation to farine Group
| CpG site | Cancer | Gene | Group | CpG Island | HR | CI | P |
|---|---|---|---|---|---|---|---|
| cg20786939 | KIRC | ACBD3 | TSS200 | Island | 0.362 | (0.208;0.632) | <0.001 |
| cg18385299 | LGG | ACBD3 | Body | N_Shore | 0.382 | (0.264;0.551) | <0.001 |
| cg25844216 | LGG | ACBD3 | 3'UTR | Open_Sea | 0.573 | (0.394;0.834) | 0.004 |
| cg15084160 | PAAD | ACBD3 | Body | N_Shelf | 1.52 | (1.014;2.281) | 0.043 |
HR hazard ratio, CI confidence interval, KIRC kidney renal clear cell carcinoma, LGG brain lower grade glioma, PAAD pancreatic adenocarcinoma
most cancers by acting as an oncogenic gene. We inves- tigated the correlation between ACBD3 expression and the molecular and immune subtypes of TCGA tumors and found that the molecular and immune subtypes of HNSC, STAD, SKCM, OV, LUSC, and LIHC were related to ACBD3 expression. We have assumed that ACBD3 might play a potential role in the occurrence of tumor subtypes, and more experimental results are needed to support this theory. In addition, analysis of the molecu- lar and immune subtypes of various malignant tumors provides a research direction for new tumor therapeutic targets.
We identified ten proteins that interacted most closely with ACBD3: GOLGA3, PI4KB, ARF1, GOLPH3, OSBP, TGOLN2, GORASP2, GOLGB1, GORASP1, and GBF1. The GO|KEGG pathway enrichment analysis suggested that “Golgi organization” and “protein kinase A bind- ing” is the main function of ACBD3, which confirms our hypothesis about the function of the ACBD3-bind- ing proteins. Previous studies had revealed that protein kinase A (PKA) is involved in cancer transformation [27]. The occurrence and development of LIHC, OV, GBM, and ESCC are closely related to PKA [28-31], which
A
B
. Head and neck squamous carcinoma Į
· Lung adenocarcinoma 1
3
BRCA
S20
2
. Head and neck squamous carcinoma Į
· Glioblastoma multiforme 1
1
S43 . Clear cell renal cell carcinoma 1
S316 · · Breast cancer f
Z-value
· Lung adenocarcinoma f
0
ACBP
GOLD_2
S316 P=9.4E-07
-2
0
100
400
528aa
-3
Normal (n=18)
Primary tumor (n=125)
C
E
3
HNSC
3
HNSC
3
KIRC
2
2
2
Z-value
1
1
Z-value
1
0
0
0
S20
-1
P=6.9E-03
S43 P=3.4E-08
-1
-1
S43
-2
-2
P=2.7E-09
-2
Normal (n=70)
Primary tumor (n=108)
-3
Normal (n=70)
Primary tumor (n=108)
-3
Normal (n=83)
Primary tumor (n=110)
D
F
3
LUAD
4
LUAD
3
GBM
2
2
2
Z-value
1
0
Z-value
1
0
0
-1
-2
S43
-1
-2
S20
-4
S43
P=1.5E-03
P=2.8E-16
-2
P=3.2E-02
-3
Normal (n=102)
Primary tumor (n=111)
-6
Normal (n=102)
Primary tumor (n=111)
-3
Normal (n=10)
Primary tumor (n=99)
reflects the potential association between ACBD3 and various tumors.
What’s more, exploration of the relationship between ACBD3 expression and the diagnostic value of vari- ous malignant tumors with different biological charac- teristics showed that ACBD3 can be used to diagnose a variety of cancers, including CHOL, LIHC, STAD, PAAD, ESCA, ESAD, ESCC, BRCA, KICH, LUAD- LUSC, LUSC, LUAD, SARC, PCPG, GBM, GBMLGG, and OSCC. Notably, ACBD3 had high diagnostic value (AUC>0.9) for CHOL, STAD, ESAD, and SARC. In addi- tion, the K-M survival curve for various cancers revealed that ACBD3 was closely associated with the progno- sis in PAAD, ACC, SARC, and GBMLGG. Because of the difficulty of integrating all sarcoma-related data,
we verified the remaining three results using the GEO database and found that only the prognosis of GBM- LGG was correlated with ACBD3 expression. We can- not rule out that this negative result is due to the small amount of data in the GEO database. Furthermore, we found that higher methylation ß values of ACBD3- Body-N_Shelf-cg15084160 led to worse OS prognosis in PAAD. These discoveries indicate that ACBD3 has a very important diagnostic and prognostic significance in most cancers, and is expected to be a new biomarker for pan-carcinoma.
In addition, ACBD3 gene mutation analysis has shown that ACBD3 mutations exist in a variety of tumor cells, with missense mutations being the most common. The missense mutations of R484Q/ * and R516W in GOLD-2
can lead to missense mutations in ACBD3. In BRCA, the ACBD3 altered group had poor prognosis, whereas the reverse was true for LIHC. These results provide a new direction for evaluating the prognosis.
Phosphorylation is one of the most extensive post- translational modifications and plays an important role in regulating cell growth, differentiation, apoptosis, and cell signaling [32]. Kinase inhibitors are also con- sidered valuable for the treatment of tumors [33]. Thus, we investigated the phosphorylation levels of ACBD3 in BRCA, HNSC, KIRC, LUAD, and GBM. We found that the phosphorylation levels of ACBD3 at various phos- phorylation sites decreased in HNSC, but increased in BRCA, KIRC, LUAD, and GBM. This discovery could lead to further research on the molecular mechanisms and potential therapeutic targets for tumors. However, we cannot get rid of the possibility that the difference in phosphorylation levels is a by-product of meaning- less signal dysregulation. Therefore, further experimen- tal verification is required.
Previous research had revealed that CAF was involved in various cancers developing [34]. We discov- ered that ACBD3 expression positively related to CAF infiltration in HNSC. Besides, ACBD3 expression was also different in various immune subtypes of HNSC, which may indicate a correlation between the occur- rence and development of HNSC and the infiltration of CAF.
The advantage of this study is that we reflected the expression and clinical value of ACBD3 in pan-cancers using a variety of databases in a comprehensive and systematic manner. Secondly, this was the first study to analyze the biological significance of ACBD3 in pan- cancers and obtain relatively comprehensive results.
However, our study has several limitations. First of all, we only used the existing RNA-seq and clinical data of cancers in online databases for analysis but lacked actual clinical data. Secondly, there is need to conduct further biological experiments to verify our conclu- sions. Currently, various bioinformatic analysis meth- ods are available. In future studies, we plan to combine various learning methods, such as machine learning, to further understand the function of ACBD3 in various cancers.
In summary, we found a statistically significant rela- tionship between ACBD3 expression and immune sub- types, molecular subtypes, diagnosis, prognosis, tumor mutation burden, protein phosphorylation levels, and immune cell infiltration in pan-cancers. This compre- hensive and systematic pan-cancer analysis of ACBD3 supports further explorations into the critical role of ACBD3 during the development of tumors and offer a
comprehensive analytical basis for further molecular, biological, and experimental verification in future clinical decisions.
Conclusion
According to our pan-cancer analysis of ACBD3, ACBD3 may serve as a novel prognostic and diagnostic biomarker for pan-cancers as it contributes to tumor development. As such, ACBD3 may also provide new directions for cancer treatment targets in the future.
Supplementary Information
The online version contains supplementary material available at https://doi. org/10.1186/s40001-023-01576-8.
Additional file 1: Figure S1 The expression level of ACBD3. A In normal tissues, ACBD3 expressed highly in cerebral cortex, hippocampus, duo- denum, small intestine, colon, gallbladder, pancreas, prostate, placenta, appendix, and bone marrow. Moreover, ACBD3 expressed lowly in oral mucosa, liver, ovary, soft tissue, and adipose tissue; B In tumor cell lines, The expression of ACBD3 ranks high in breast cancer, kidney cancer, and myeloma in tumor cell lines; C, D Intracellular ACBD3 is mainly distributed in Golgi; E ACBD3 expressed high in SKCM, BRCA, GBM, KIRC, LGG, and THCA in Cancer Cell Line Encyclopedia (CCLE) database. Small cell lung cancer (SCLC), colon and rectal cancer (COADREAD), large b-cell lym- phoma (DLBC), sarcoma (SARC), multiple myeloma (MM), acute myeloid leukemia (LAML), skin cutaneous melanoma (SKCM), breast cancer (BRCA), liver hepatocellular carcinoma (LIHC), mesothelioma (MESO), ovarian cancer (OV), esophageal cancer (ESCA), endometrioid cancer (UCEC), glioblastoma multiforme (GBM), pancreatic adenocarcinoma (PAAD), neuroblastoma (NB), lung adenocarcinoma (LUAD), stomach adenocar- cinoma (STAD), kidney clear cell carcinoma (KIRC), non-small cell lung carcinoma (NSC), acute lymphoeytic leukemia (ALL), lung squamous cell carcinoma (LUSC), brain lower grade glioma (LGG), head and neck cancer (HNSC), thyroid cancer (THCA), chronic myelocytic leukemia (LCML), Human medulloblastoma cells (MB), prostate cancer (PRAD), cervical cancer (CESC), chronic lymphocytic leukemia (CLL). Figure S2 Correlation between immune infiltrates and ACBD3 expression in different tumors. head and neck cancer (HNSC), bladder cancer (BLCA), colon cancer (COAD), thyroid cancer (THCA), kidney clear cell carcinoma (KIRC). A The expression of ACBD3 was actively correlated with the cancer-associated fibroblast (CAF) infiltration for HNSC. B The expression of ACBD3 was positively related to neutrophil infiltration for BLCA, COAD, and THCA. C ACBD3 expression was positively related to endothelial cell infiltration for COAD, HNSC, and KIRC. Figure S3 Kaplan-Meier plots ([Overall Survival (OS)]) for ACBD3 expression in pan-cancers. A Pancreatic For pancreatic adenocarcinoma (PAAD), there was no statistically significant relationship between ACBD3 expression and prognosis. B For Glioma (GBMLGG), the OS prognosis was negatively related to ACBD3 expression, HR = 2.30, 95% CI: 1.19-4.43, p=0.013. C For adrenocortical carcinoma (ACC), there was no statistically significant relationship between ACBD3 expression and prognosis.
Acknowledgements
This work was supported by the following grants: Natural Science Foundation of Sichuan Province (No. 2022NSFSC1378). Xiaowei Tang has received the research support.
Author contributions
All authors contributed to the study conception and design. Material prepara- tion, data collection and analysis were performed by XM, SH and HS. The first draft of the manuscript was written by XM and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Funding
This work was supported by the following grants: Natural Science Foundation of Sichuan Province (No. 2022NSFSC1378). Xiao-Wei Tang has received the research support.
Availability of data and materials
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1Department of Gastroenterology, The Affiliated Hospital of Southwest Medical University, Street Taiping No. 25, Region Jiangyang, Luzhou 646099, Sichuan, China. 2Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China. 3Department of Gastroenterology, Lian- shui County People’s Hospital, Huaian, China. 4Department of Gastroenterol- ogy, Lianshui People’s Hospital of Kangda College Affiliated to Nanjing Medical University, Huaian, China. 5Department of Gastroenterology, Clinical Medical College and the First Affiliated Hospital of Chengdu Medical College, Street Baoguang No.278, Region Xindu, Chengdu 610500, Sichuan, China.
Received: 6 April 2023 Accepted: 7 December 2023 Published online: 14 December 2023
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