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Multi-omics profiling highlights karyopherin subunit alpha 2 as a promising biomarker for prognosis and immunotherapy respond in pediatric and adult adrenocortical carcinoma
Yihao Chena,b*, Shumin Fang ** , Chuanfan Zhongd,e, Shanshan Moª, Yongcheng Shib, Xiaohui Lingd,e, Fengping Liuf, Weide Zhongf,g, Junhong Denga, Zhong Dongb, Jiahong Chenb and Jianming Lua,g (D
aDepartment of Andrology, Guangzhou First People’s Hospital, Guangzhou Medical University, Guangzhou, China; bDepartment of Urology, Huizhou Central Hospital, Huizhou, China; “Science Research Center, Huizhou Central Hospital, Huizhou, China; dDepartment of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, China; eReproductive Medicine Center, Huizhou Central Hospital, Huizhou, China; fState Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, China; 9Department of Urology, Guangdong Key Laboratory of Clinical Molecular Medicine and Diagnostics, Guangzhou First People’s Hospital, Guangzhou Medical University, Guangzhou, China
ABSTRACT
Purpose: Adrenocortical carcinoma (ACC) afflicts both pediatric and adult populations and is characterized by dismal prognosis and elevated mortality. Given the inconsistent therapeutic benefits and significant side effects associated with the conventional chemotherapy agent, mitotane, and the nascent stage of immunotherapy and targeted treatments, there is an urgent need to identify novel prognostic biomarkers and therapeutic targets in ACC.
Methods: Utilizing multi-omic datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), we employed Weighted Gene Co-expression Network Analysis (WGCNA), Cox regression, Receiver Operating Characteristic (ROC) curves, and survival analyses to sift for potential prognostic biomarkers. We subsequently validated these findings through immunohistochemistry and cell assays, and delved into the biological role of KPNA2 in ACC through functional enrichment analysis, mutational landscape, and immune cell infiltration.
Results: A total of 77 progression-associated genes with aberrant chromosomal accessibility were discerned within the TCGA-ACC dataset. By integrating ROC and Cox regression from GEO datasets, KPNA2 emerged as an independent risk factor portending poor outcomes in ACC. ATAC-seq analysis revealed attenuated chromatin accessibility of KPNA2 in cases with unfavorable prognosis. Immunohistochemistry corroborated elevated KPNA2 expression, which was linked to enhanced proliferation and invasion. Elevated KPNA2 levels were found to activate oncogenic pathways while simultaneously suppressing immunological responses. Immune infiltration analysis further revealed a decrement in CD8+ T-cell infiltration in KPNA2-high cohorts.
Conclusion: This study demonstrates the clinical and biological significance of KPNA2 in ACC and suggests that KPNA2 could serve as a promising biomarker for predicting prognosis and immunotherapeutic responses in pediatric and adult ACC patients.
ARTICLE HISTORY
Received 9 October 2023 Revised 15 December 2023
Accepted 25 April 2024
KEYWORDS
Adrenocortical carcinoma; ATAC-seq; KPNA2; prognosis; immunotherapy
Introduction
Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with an annual incidence rate of 0.5-2 cases per million adults and 0.2-0.3 cases per million children globally [1]. The overall 5-year survival rate for ACC is a mere 35%, with stage 4 patients experiencing less than a 10% 5-year survival rate [2]. Various
therapeutic approaches for ACC encompass surgical resection, radiation therapy, chemotherapy, and tar- geted therapies [3,4]. For patients diagnosed at an early stage, surgical resection is typically the treatment of choice, especially when the tumor has not metasta- sized to other organs [5]. However, the efficacy of treatment may be compromised due to the tumor’s
CONTACT Jianming Lu louiscfc8@gmail.com Department of Andrology, Guangzhou First People’s Hospital, Guangzhou Medical University,
Guangzhou 510180, China; Jiahong Chen Hospital, Huizhou, Guangdong, 516001, China
ys_chen@163.com, Zhong Dong hzdongzhong@126.com Department of Urology, Huizhou Central
*These authors have contributed equally to this work.
+ Supplemental data for this article can be accessed online at https://doi.org/10.1080/07853890.2024.2397092.
@ 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
inherent resistance to conventional chemotherapy, particularly in advanced stages [6]. Recent years have seen intensified efforts in exploring the molecular mechanisms and biological characteristics of ACC in order to develop more therapy options, such as immu- notherapy and targeted drugs [4,5]. Despite these advancements, the application of immunotherapy in ACC is still fraught with challenges due to inconsistent therapeutic outcomes, tumor heterogeneity, immune escape mechanisms, and adverse side effects [7,8]. Therefore, the identification of a novel biomarker for ACC could facilitate improved clinical management for therapeutic response and prognosis.
The heterogeneity of ACC serves as a pivotal deter- minant for its unpredictable prognosis and limitations in treatment modalities [9]. Multi-omics, an interdisci- plinary approach amalgamating genomics, transcrip- tomics, proteomics, and metabolomics, offers an intricate exploration of ACC’s tumor heterogeneity at various molecular layers [10,11]. This promises to facil- itate the identification of novel biomarkers. Within the multi-omics framework, Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) stands as a crucial methodology. It enables the identification of cancer-related genomic regions, including chromatin changes induced by mutations, oncogenes, tumor sup- pressor genes, and transcription factor binding sites [12,13]. By leveraging ATAC-seq, we can perform differ- ential accessibility analysis to explore which protein-coding genes in ACC are influenced by the accessibility of non-coding regulatory elements. This not only aids in identifying potential biomarkers for ACC but also unveils prospective targets for immuno- therapies and pharmacological interventions
In the present study, we identified Karyopherin Subunit Alpha 2 (KPNA2) as a potential biomarker for ACC. Previous research indicates that KPNA2 functions as a nuclear import protein, primarily involved in the regu- lation of protein transport between the cell nucleus and the cytoplasm. It plays a critical role in the processes of cancer cell growth and metastasis [14]. Numerous stud- ies have demonstrated that aberrant overexpression of KPNA2 is closely associated with the progression, and therapy resistance of various cancers [15-17]. However, to date, there has been no reported research specifically investigating the role of KPNA2 in ACC.
Materials and methods
Data collection and processing
The baseline characteristics of the datasets employed in the present study are in Supplementary Table S1. To
initiate this study, we downloaded RNA-seq data, cor- responding mutation data, and clinical information for the adrenocortical carcinoma cohort from The Cancer Genome Atlas (TCGA-ACC) via the UCSC Xena platform [18]. We utilized the R packages clusterProfiler (version 4.8.1) [19] and org.Hs.eg.db (version 3.17.0) [20] to con- vert Ensembl IDs to SYMBOL IDs in the RNA-seq data. For ATAC-seq data, we employed the raw count matrix, normalized count matrix, and bigWig files acquired from the TCGA. From TCGA-ACC ATAC-seq, we selected 14 samples with matching RNA-seq data and clinical information for differential accessibility peak (DAP) analysis. As validation sets, we harvested four ACC sample datasets for both adults and pediatric cases from the GEO database [21-23]: Adults (GSE19750, GSE10927) and Pediatrics (GSE76019, GSE76021). Batch effects were corrected using the ComBat function in the sva R package (version 3.48.0) [24].
WGCNA and key module identification
Weighted Gene Co-expression Network Analysis (WGCNA) was employed for network-based gene filter- ing, aimed at detecting markers with specific attri- butes, such as progression. To delineate potential high co-expression gene clusters, we used the WGCNA package (version 1.72-1) [25] to construct a gene co-expression network for the TCGA-ACC. To obtain a more comprehensive analysis, we employed the ‘one-step’ method in WGCNA analysis, incorporating mRNA expression data (n= 19,563) from the TCGA-ACC dataset. To meet the conditions of a scale-free net- work, we determined the optimal soft-thresholding power (B=16) and transformed the adjacency matrix into a topological overlap matrix (TOM). Additionally, we calculated the corresponding dissimilarity (1-TOM) and identified PFI-associated modules using the dynamic tree cutting method.
Identification and validation of prognostic genes
To identify key genes closely related to ACC progression, we employed the timeROC R package (version 0.4) [26] to compute the Area under curve (AUC) for assessing the predictive capacity of genes in candidate modules. In the TCGA-ACC cohort, genes with AUC > 0.7 were subjected to univariate Cox regression analysis to identify prognostic genes for overall survival. Subsequently, these results were further validated in GEO cohorts. We employed the R package survminer (version 0.4.9) (https://CRAN.R-project. org/package=survminer) and set the minimum group sample size to be greater than 30%. The optimal cutoff value for KPNA2 was calculated, dividing patients into
high and low expression groups. Subsequently, we used the R package survival (version 3.5-5) (https:// CRAN.R-project.org/package=survival) to analyze prognos- tic differences between the two groups. The log-rank test was applied to assess the significance of prognostic differ- ences between samples in different groups [27].
Functional enrichment
Spearman correlation analysis was employed to evaluate the association between KPNA2 expression and all other genes. The clusterProfiler R package (version 4.8.1) [19] was used for functional enrichment analysis to identify significantly enriched terms related to Gene Ontology (GO).
Immunohistochemistry (IHC)
According to the protocol outlined in our previous studies [28], a brief description is provided below. ACC and adrenal adenoma tissue samples used in this study were sourced from Huizhou Central Hospital, with ethi- cal approval granted by its ethics committee (No. KYLL2023105). All patients provided informed consent prior to the collection of their tissue samples. Samples were fixed in 4% paraformaldehyde prior to paraffin embedding. Tissue sections of 4um thickness were treated with 1% H2O2 solution, then blocked with non-immune goat serum. Sections were incubated with primary antibodies overnight at 4℃, followed by a 30-minute incubation with biotinylated secondary anti- bodies at room temperature. Final scores were calcu- lated by summing the percentages of positively stained cells and their staining intensities. Scoring was as fol- lows: 0 (0%), 1 (1%-10%), 2 (11%-50%), 3 (>50%) for cell percentages; and 0 (negative), 1 (weak), 2 (moder- ate), 3 (strong) for staining intensity [29]. An anti-KPNA2 antibody (Immunoway, YT5691) was utilized.
Cell transient transfection
According to the protocol outlined in our previous studies [28], a brief description is provided below. The present study employed two human ACC cell lines, SW13 and NCI-H295R. The cells were cultured in DMEM (BC-M-005, Bio-Channel) and DMEM/F12 medium (BC-M-002, Bio-Channel), both supplemented with 10% fetal bovine serum (BC-SE-FBS07, Bio-Channel), and maintained in a humidified incubator at 37℃ with 5% CO2. According to the manufacturer’s instructions, neg- ative control (NC) and KPNA2 siRNA (Genepharma, Suzhou, China) were transfected into ACC cells using GP-transfect-Mate (Genepharma, Suzhou, China). Plates were incubated for 48h before total protein was
harvested for Western Blot analysis. The siRNA sequences were showed in Table S2.
Western Blot
According to the protocol outlined in our previous studies [28], a brief description is provided below. Cells were harvested and lysed in RIPA buffer containing protease inhibitors. The resulting protein samples were separated by SDS-PAGE and transferred to PVDF mem- branes, which were blocked using 5% non-fat milk. Membranes were incubated with a primary anti-KPNA2 antibody (YT5691, Immunoway) and anti-ß-actin (20536-1-AP, Proteintech), both at a 1:2000 dilution, followed by incubation with a secondary antibody (SA00001-2, Proteintech) at a 1:5000 dilution. Membranes were then washed thrice with PBST for 10 min each and exposed. ß-actin served as a normal- ization control, and band intensities were quantified using Image J software.
Cell assays
According to the protocol outlined in our previous studies [28], a brief description is provided below.
For the CCK8 proliferation assay, approximately 4x103 transiently transfected cells were allocated to each well of a 96-well plate containing 100 uL of cul- ture medium. Optical density at 450nm was gauged 2h post-addition of a 1:9 CCK8 solution at time inter- vals of 2, 24, 48, and 72h using a spectrophotometer.
In the clonogenic assay, cells were plated in 6-well plates at a density of 1000 cells/well and incubated at 37℃ in a 5% CO2 atmosphere for a fortnight. Subsequent to dual PBS washes, cells were fixated with 4% paraformaldehyde for a quarter-hour and then stained with 1% crystal violet for 20min at ambi- ent temperature. The resulting colonies were enumer- ated, and the assay was performed in triplicate.
To evaluate invasive potential, a transwell assay was utilized. Around 4x104 transfected cells were seeded into the upper chamber containing serum-free medium, while the lower chamber was supplemented with complete medium. Following a 48-hour incuba- tion under standard culture conditions, cells were PBS-washed, fixated in paraformaldehyde, and stained with 0.1% crystal violet. Subsequently, the stained cells were microscopically inspected and quantified.
Landscape of ACC mutations
The ‘maftools’ R package (version 2.16) [30] was used to calculate tumor mutational burden (TMB) for each
patient. To investigate whether genomic mutations dif- fered between high and low KPNA2 expression groups, a mutation waterfall plot was generated, visualizing the top 20 significantly mutated genes (SMGs) in ACC using the maftools and ComplexHeatmap R packages (version 2.16) [31]. Copy number variation (CNV) water- fall plots of the top 10 amplified and deleted chromo- somal segments in ACC were also produced. Chi-square tests were employed to examine differences in CNV between KPNA2 expression subgroups, and Wilcoxon tests were conducted to evaluate differences in KPNA2 expression levels among mutated subgroups.
Assessment of immune cell infiltration
Immune scores of TCGA-ACC and GSE76019 were evalu- ated using the ‘IOBR’ R package (version 0.99.9) [32]. Scores for 22 types of immune cell infiltration across five algorithms were obtained. Spearman correlation analysis was employed to assess the relationship between KPNA2 expression and various immune cell scores.
Prediction of immunotherapy response and targeted drug efficacy
We employed the Subclass Mapping (Submap) algo- rithm [33] to predict responses to immune checkpoint blockade (ICB) therapy. We analyzed transcriptomic expression patterns between patient groups with dif- fering KPNA2 expression levels and divergent immuno- therapy responses. A p-value less than 0.05 was considered indicative of significant similarity between the subclasses. We then curated a collection of four immunotherapy datasets, namely Braun [34], GSE78220 [35], GSE91061 [36], and PRJNA482620 [37], from the Tiger database [38]. Anti-PD-1 immunotherapy samples were isolated and categorized based on optimal KPNA2 expression cut-off values for survival analysis, aiming to investigate the prognostic utility of KPNA2 expres- sion in anti-PD-1 immunotherapy. Additionally, we uti- lized the Connectivity Map (CMap) [39], a data-driven systemic approach for identifying relationships among genes, chemical substances, and biological conditions, to identify potential compounds targeting ACC-associated pathways. Further specificity analyses were conducted using the CMap tool to elucidate mechanisms of action (MoA) and drug targets.
Statistical analysis
Data were analyzed and visualized using R version 4.3.1. A subset of the data visualization was performed
using the Sanger Box bioinformatics analysis online tool [40]. Statistical analyses of immunohistochemistry and cellular experimental data were carried out using GraphPad Prism 8.0 software with a copyright license. The Wilcoxon rank-sum test and Kruskal-Wallis test were utilized for comparing differences between two or more groups. All p-values are two-sided, with statis- tical significance denoted as *p<0.05, ** p<0.01, and *** p<0.001.
Results
Identification of ACC progression-related gene modules through WGCNA
The workflow of the current study is depicted in Figure 1. Initially, we clustered the transcriptome sequencing data- set of 79 samples from TCGA-ACC based on median progression-free interval (PFI) times (Figure S1A). Subsequently, we performed WGCNA using 0.2 and 16 as the module merging threshold and minimum module size, respectively (Figure S1B). A heatmap was utilized to explore the relationships between the identified gene modules and PFI, resulting in six distinct gene modules (Figure 2A,B). Notably, the turquoise gene module exhib- ited a strong correlation with ACC progression (r=0.63, p=5e-08, Table S3). Additionally, in the Gene Significance vs Module Memberships plot, turquoise module genes displayed consistent results (r=0.51, p=1e-200) (Figure 2C). Consequently, after eliminating genes lacking statis- tical significance, we identified the turquoise module genes as those most highly correlated with ACC progression.
Identification of aberrantly accessible differential peaks associated with ACC progression using ATAC-seq
Utilizing ATAC-seq as one of the multi-omics technolo- gies, we explored the tumor heterogeneity in ACC and sought to identify aberrantly accessible differential peak genes associated with the progression of ACC. We performed a differential peak analysis on the ATAC-seq data from the aforementioned TCGA-ACC samples, categorized based on their median PFI val- ues. TCGA-ACC consisted of 4 samples in the Control group and 10 in the Progression group, resulting in the identification of 3120 DAPs (Figure 2D, adjPval < 0.05, | log2 FC | > 2). Through peak-to-gene mapping, we identified 810 Differential Peak Genes (DPGs). Further annotation using the ChIPseeker package revealed that the percentage of distal elements, defined as non-promoter elements, was higher in DAPs
0
TCGA
Adrenocortical Carcinoma Cohort
4.2Mg ON
0
GSE10927
V
GEO
GSE19750
-
GSE76019
45
1RNA expression data
9
8
Gene Expression Omnibus
GSE76021
2 Survival information
Meta Cohorts
3ATAC-seq data
WGCNA & ATAC-seq
PFI
Validation
4-
SW13
NC
OD at 450nm
Si-1
2-
Si-2
1
0
0
1
2
3
Days
1IHC
2 Colony formation
Functional Enrichment
3CCK-8 4Transwell
Experimental validation
Karyopherin Subunit Alpha 2 (KPNA2)
1
10
KPNAŽ
Survival probability
as
logrank test p=4.40-10
Number at risk
1 Somatic mutation 2 Copy number variation Mutation analysis
log2(Hazard Ratio(95%CI
0
38
OS.time(Months)
1
154
152
Prognostic Value
TCGA-ACC
Cmap
0.128 0.073 0.041
High KPNA2_p
1.
KPNAR
high
0.002
-
Low KPNA2_p
8 mg
High KPNA2_b
0.016
Low KPNA2_b
logranik Best: p=0.02 lumber at risk
30
pvalue
NR
R
NR
R
Agh
CTLA4
PD-1
OS(Montha)
12
Immune landscape
Immunotherapy Response
Potential drug Predict
compared to ALL peaks. This indicates a stronger spec- ificity in distal elements’ response to ACC progression (Figure 2E). By intersecting genes from the turquoise module with DPGs, we identified 170 overlapping genes related to ACC disease progression (Figure 2F, Table S4) and conducted GO analysis (Figure 2G, Table S5). We found that these genes primarily enriched in the Wnt signaling pathway.
Identification of predictive biomarkers for ACC progression
To identify key biomarkers within the aberrantly acces- sible gene set associated with ACC progression, we performed time-dependent univariate Cox regression analysis on all genes in the TCGA-ACC transcriptome
sequencing dataset. These analyses were stratified by both median Overall Survival (OS) time and median PFI. We filtered for genes with an AUC greater than 0.7 and a Hazard Ratio (HR) greater than 1. The intersec- tion with the ACC-related gene set yielded 77 candi- date genes (Figure 3A). Subsequently, we leveraged the GEO database to obtain adult (GSE19750, GSE10927) and pediatric (GSE76019, GSE76021) ACC datasets and performed batch correction (Figure S2A-D). The adult datasets were prognostically anchored on OS, while the pediatric datasets were based on Event-Free Survival (EFS). We generated a total of six distinct vali- dation cohorts. Upon conducting univariate Cox regres- sion analyses across these cohorts for the previously identified set of 77 genes, we discerned that only KPNA2 consistently emerged as a significant
A
Module-PFI relationships
B
Cluster Dendrogram
1.0
MEgreen
0.15(0.3)
0.8
Height
0.6
MEblue
-0.26(0.04)
0.5
0.4
0.2
MEyellow
0.16(0.2)
Module colors
Merged colors
0
C
MEbrown
0.3(0.02)
Module membership vs. gene significance
D
Progression vs Control
30
AKT1
Gene significance
0.6
0
%
DAPs
0
. Up
-0.5
NS
MEturquoise
0.63(5e-08)
0.4
20
. Down
0.2
-log10 (adj.P.Val)
KDF1
STAM2
0.0
SLC12A7.
10
TMC
-0.27(0.04)
cor=0.51
GATA
MEgrey
p<1e-200
EXTL3
TEC
8
8
0
CAF2
FGF18
7
0.3
0.5
0.7
0.9
0
PFI
Module Membership in turquoise module
-5
-4
-3
-2
-1
0
1
log2 (fold change)
2
3
4
5
E
F
G
Feature Distribution
GO
WGCNA
Wnt signaling pathway
cell-cell signaling by wnt
3954
canonical Wnt signaling pathway
All peaks-
Feature
Promoter ( ⇐ 1kb)
Promoter (1-2kb)
p.adjust
regulation of Wnt signaling pathway
Promoter (2-3kb)
5’ UTR
2e-04
mesenchyme development
3’ UTR
40-04
1st Exon
170
Ge-04
regulation of canonical Wnt signaling pathway
Other Exon
1st Intron
Be-04
Other Intron
epithelial to mesenchymal transition
DAPs
Downstream ( ⇐ 300)
Count
Distal Intergenic
648
7.5
regulation of epithelial to mesenchymal transition
10.0
12.5
15.0
chondrocyte differentiation
17.5
positive regulation of epithelial to mesenchymal transition
0
25
Percentage(%)
50
75
100
0.12
0.10
0.08
DPGs
0.06
GeneRatio
prognostic risk factor across all cohorts (Figure 3B). Not only did KPNA2 display strong predictive power for adverse prognosis in ROC analysis (Figure 3C), but it also emerged as an independent prognostic factor for ACC patients in both univariate and multivariate Cox regression models after adjusting for other clinical characteristics (Figure 3D,E). Consequently, we postu- late that KPNA2 could be a promising biomarker for ACC.
Furthermore, we performed Kaplan-Meier survival analyses within these cohorts. The results indicated that patients with ACC who exhibited elevated levels of KPNA2 expression manifested a significant trend towards poorer outcomes (Figure 4A-H). Additionally, we observed that in the TCGA-ACC cohort, KPNA2 expression levels were markedly higher in the Progression group compared to the Control group (p=1.1e-07) (Figure 4I). In the ATAC-seq, the peak
A
DPGs
B
KPNA2
WGCNA
499
1
1
Cohort
AP3M1
1
21
2
42
NCAPD3
8
5
7
OS median AUC>0.7
GSE10927
COX regression
SMNDC1
STAM2
8
19
4
7
10
140
GSE19750
Risky
SRP9
URB2
1
16
16
7
21
147
250
GSE76019
Protetive
AGAP1
SMURF1
8
GSE76021
Not significant
CTNNB1
254
529
77
TRIM32
3
13
Adult Meta
None
KHNYN
424 305
1
3
PSEN1
301
Pediatric Meta
GNG12
91
1743
TLE1
2
18
C
KPNA2
110
RPS6KC1
107
1
SSR3
30
8
10
28
1.0
HDAC2
976
205
57
121
18
5
KDM5A
331
5
22
PFI HR>1
0.8
PITRM1
844
11
PFI median AUC>0.7
PTPRF
SH3BP4
520
Sensitivity
0.6
DCP2
D
OS HR>1
NR4A3
TRIP13
U.s
VAV2
Univariate COX
GSE10927:0.75
IFFO2
0
GSE19750:0.84
GSE76019:0.75
SMAD3
GSE76021:0.83
PTPRE
Adult Meta:0.69
0.0
Pediatric Meta:0.74
MED27
PTPDC1
0.0
0.2
0.4
0.6
0.8
1.0
CBFB
HPCAL1
1-Specificity
PLCL2
E
UBAC1
ANGEL2
ARHGAP1
Multivariable COX
ACTN1
PHF19
IGDCC4
ABCB4
FSCN1
AFF3
FAM171A1
ZNF711
ZIC2
BRD9
IPO4
SKA1
NFATC4
SHOC1
RPP40
RRP9
-I
SYTL2
EFNA4
SULF2
LIFR
KIF26A
CLTA
RHBDL3
ORMDL1
BCL11A
PLPP2
LEF1
KCNK9
ATF5
CILP2
DAB2
PLD5
GATA3
WNT4
SV2C
TIGD1
SLIT2
CENPW
7
2-10123
4
5
6
7
8
9 10
log2(Hazard Ratio(95%CI))
log2(Hazard Ratio(95%CI))
FOXA2
SIM2
| Variable | pvalue | HR | ||
|---|---|---|---|---|
| TCGA-ACC-OS | ||||
| Age | 3.79E-01 | 1.011 | ||
| Gender | 9.99E-01 | 1.001 | F F | |
| Stage | 9.39E-06 | 2.628 | -1 | |
| clinical_M | 5.14E-05 | 5.300 | -- | | |
| pathologic_T | 2.48E-01 | 1.742 | { | |
| pathologic_N | 9.28E-07 | 3.040 | -1 | |
| KPNA2 | 2.41E-07 | 3.563 | 1 | |
| TCGA-ACC-PFI | ||||
| Age | 7.35E-01 | 1.004 | ||
| Gender | 2.34E-01 | 0.676 | :- 1 | |
| Stage | 2.75E-05 | 2.008 | 1 | |
| clinical_M | 9.69E-04 | 3.107 | -I | |
| pathologic_T | 1.88E-02 | 2.536 | F | |
| pathologic | 1.99E-04 | 1.775 | 1 | |
| KPNA2 | 3.99E-08 | 2.719 | I | |
| GSE19750 | ||||
| Age | 9.73E-02 | 1.035 | ||
| Gender | 6.50E-01 | 1.262 | F 1 | |
| Stage | 8.73E-01 | 1.027 | ト | |
| KPNA2 | 3.24E-02 | 2.088 | -1 | |
| GSE10927 | ||||
| Age | 5.19E-01 | 1.012 | ||
| Gender | 4.75E-01 | 1.471 | -1 | |
| Stage | 2.09E-02 | 1.818 | ||
| KPNA2 | 3.33E-02 | 16.408 | ||
| GSE76019 | ||||
| Age | 7.30E-05 | 1.019 | ||
| Gender | 4.83E-03 | 5.684 | F 1 | |
| Stage | 2.91E-03 | 3.075 | 1 | |
| KPNA2 | 4.81E-04 | 5.697 | 1 | |
| GSE76021 | ||||
| Age | 4.88E-01 | 1.004 | ||
| Gender | 6.81E-01 | 1.294 | - | |
| Stage | 3.18E-01 | 1.376 | -| | |
| KPNA2 | 2.77E-02 | 2.520 | -| | |
| Adult Meta | ||||
| Age | 1.72E-01 | 1.017 | ||
| Gender | 7.54E-01 | 1.119 | - 1 | |
| Stage | 3.73E-01 | 1.113 | ||
| KPNA2 | 1.57E-02 | 1.807 | 1 | |
| Pediatric Meta | ||||
| Age | 6.63E-05 | 1.013 | ||
| Gender | 1.08E-02 | 2.910 | -I | |
| Stage | 1.72E-03 | 2.088 | 1 | |
| KPNA2 | 7.30E-05 | 2.763 | F ト | |
| -1 0 1 2 3 4 5 6 | ||||
| Variable pvalue | HR | |||
|---|---|---|---|---|
| TCGA-ACC-OS | ||||
| Stage 9.19E-01 | 1.064 | I- -I | ||
| clinical_M 6.84E-01 | 0.732 | -I | ||
| pathologic_ 5.99E-02 | 2.299 | -| | ||
| KPNA2 8.38E-05 | 2.963 | I- -I | ||
| TCGA-ACC-PFI | ||||
| Stage 1.81E-01 | 2.110 | ト | ||
| clinical_M 2.46E-01 | 0.491 | -1 | ||
| pathologic_T 8.11E-01 | 0.906 | 1 | ||
| pathologic_I 7.49E-01 | 1.186 | - 1 | ||
| KPNA2 7.44E-06 | 2.493 | 1 | ||
| GSE19750 | ||||
| Age 8.58E-02 | 1.036 | |||
| KPNA2 2.92E-02 | 2.195 | 1 | ||
| GSE10927 | ||||
| Stage 3.96E-03 | 2.139 | 1 | ||
| KPNA2 7.91E-03 | 75.661 | I | ||
| GSE76019 | ||||
| Age 3.67E-01 | 1.008 | |||
| Gender 1.13E-02 | 5.815 | 1 | ||
| Stage 4.20E-01 | 1.763 | -- 1 | ||
| KPNA2 2.85E-02 | 4.448 | I | ||
| GSE76021 | ||||
| Stage 8.95E-02 | 1.884 | 1 | ||
| KPNA2 1.45E-02 | 3.358 | -1 | ||
| Adult Meta | ||||
| Age 2.25E-01 | 1.015 | |||
| KPNA2 2.05E-02 | 1.752 | 1 | ||
| Pediatric Meta | ||||
| Age 1.17E-01 | 1.008 | |||
| Gender 8.95E-03 | 3.171 | -/ | ||
| Stage 1.83E-01 | 1.554 | I | ||
| KPNA2 1.23E-03 | 2.338 | 1 | ||
Figure 3. Identification of KPNA2 as an ACC biomarker. (A) Venn diagram illustrating the overlap among WGCNA-derived genes, DPGs, TCGA univariate COX, and genes with AUC > 0.7. (B) Heatmap depicting univariate COX of intersecting genes in GEO data- sets. (C) AUC metrics for KPNA2 across GEO datasets. (D, E) Univariate and multivariate regression assessing KPNA2 in TCGA and GEO cohorts.
values corresponding to KPNA2 displayed variations in the Progression group. The Integrative Genomics Viewer (IGV) indicated a significant reduction in
chromatin accessibility at the Distal Intergenic region corresponding to ACC-75575 (p=0.002) (Figure 4J,K). Collectively, these findings suggest that elevated
A
C
E
G
TCGA-ACC
GSE10927
GSE19750
Adult Meta
1.0
KPNA2
1.0
KPNA2
1.0
KPNA2
1.0 -
KPNA2
L
L
L
L
Survival probability
0.8
H
Survival probability
0.8
H
Survival probability
0.8
H
Survival probability
0.8
H
0.5
0.5
0.5
0.5
0.3
0.3
0.3
0.3
0.0
logrank test p=4.4e-10
0.0
logrank test p=0.03
0.0
logrank test p=4.2e-3
0.0
logrank testp=1.5e-3
Number at risk
Number at risk
Number at risk
Number at risk
L
54
37
14
6
2
L
7
3
1
1
1
L
5
5
3
3
1
L
28
13
6
4
1
H
25
4
1
1
1
H
17
2
1
1
1
H
16
4
2
1
1
H
17
1
1
1
1
0
38
76
114
152
0
37
74
54
108
OS.time(Months)
OS.time(Months)
111
148
0
162
0
OS.time(Months)
216
54
108
162
OS.time(Months)
216
B
D
F
H
TCGA-ACC
GSE76019
GSE76021
Pediatric Meta
1.0
KPNA2
1.0
KPNA2
KPNA2
L
1.0
1.0
KPNA2
L
L
L
Survival probability
0.8
H
Survival probability
0.8
H
Survival probability
0.8
H
Survival probability
0.8
H
0.5
0.5
0.5
0.5
0.3
0.3
0.3
0.3
0.0
logrank testp=3.4e-8
0.0
logrank test p=6.0e-6
0.0
logrank test p=0.01
0.0
logrank test p=2.7e-6
Number at risk
Number at risk
Number at risk
Number at risk
L
40
20
10
4
1
L
18
15
10
3
1
L
10
5
4
1
1
L
27
13
3
1
1
H
39
4
1
1
1
H
16
6
4
1
1
H
9
1
1
1
1
H
26
6
2
1
1
0
38
76
114
152
0
21
42
63
84
0
48
96
144
192
0
48
96
EFS.time(Months)
EFS.time(Months)
144
192
PFI.time(Months)
EFS.time(Months)
K
7
Expression of KPNA2
Wilcoxon, p = 1.3e-07
6
ACC_75575:chr17:68029371-68029872
:
5
1
-
4
KPNA2H
3
2
1
Control Progression PFI
J
Wilcoxon, p = 0.002
Peaks of ACC_75575
9.0
8.5
8.0
Progression
Control
TCGA-OR-A5KX-01A
TCGA-OR-A5JZ-01A
TCGA-OR-A5J9-01A
TCGA-OR-A5J3-01A
TCGA-OR-A5J2-01A
TCGA-OR-A5J6-01A
TCGA-PK-A5H8-01A
7.5
7.0
Control Progression PFI
expression of KPNA2 portends adverse prognostic implications in multiple adult and pediatric ACC cohorts and may be associated with aberrant chroma- tin accessibility.
Functional enrichment analysis of KPNA2
To explore the biological functions of KPNA2, we employed Gene Set Enrichment Analysis (GSEA). As depicted in Figure 5, we selected the top 10 terms for both activation and inhibition based on the absolute values of the Normalized Enrichment Scores (NES). Notably, the activation set included terms related to cell proliferation such as ‘DNA Replication Initiation, ‘DNA Unwinding Involved in DNA Replication, and ‘DNA Replication Preinitiation Complex’ (Figure 5A,B,
Table S6). Conversely, the inhibition set comprised immune-related terms such as ‘T Cell Receptor Complex; ‘T Cell Receptor Binding’ ‘Antigen Binding’ and ‘MHC Protein Complex’ (Figure 5C,D, Table S6). Based on these findings, KPNA2 may contribute to the malignant progression of ACC by activating pathways involved in tumor cell proliferation and growth, while suppressing processes related to antigen presentation and T cell activation.
Experimental validation of KPNA2’s role in ACC
To further investigate the impact of KPNA2 on the phenotypic behavior of ACC cells, we performed a series of experimental analyses. To ascertain the expression profile of KPNA2 in ACC, clinical samples
A
B
Actived
GOBP_ATTACHMENT_OF_SPINDLE_MICROTUBULES_TO_KINETOCHORE
GOMF SINGLE STRANDED DNA
GOBP_DNA_REPLICATION_INITIATION
HELICASE ACTIVITY
- GOBP_DNA_UNWINDING_INVOLVED_IN_DNA_REPLICATION
GOBP DNA REPLICATION INITIATION
0.75
GOBP_METAPHASE_ANAPHASE_TRANSITION_OF_CELL_CYCLE
- GOBP_NEGATIVE_REGULATION_OF_METAPHASE_ANAPHASE_TRANSITION_OF_CELL_CYCLE
GOBP ATTACHMENT OF SPINDLE MICROTUBULES TO KINETOCHORE
Running Enrichment Score
GOBP_REGULATION_OF_CHROMOSOME_SEGREGATION
NES
GOBP_REGULATION_OF_CHROMOSOME_SEPARATION
2.28
0.50
GOBR_ REGULATION_OF_MITOTIC_SISTER_CHROMATID_SEGREGATION
GOBP REGULATION OF MITOTIC
SISTER CHROMATID SEGREGATION
2.32
GOCC_DNA_REPLICATION_PREINITIATION_COMPLEX
2.36
GOMF_SINGLE_STRANDED_ONA_HELICASE_ACTIVITY
GOBP METAPHASE ANAPHASE
TRANSITION OF CELL CYCLE
0.25
GOBP REGULATION OF
p.adjust
CHROMOSOME SEGREGATION
2.5e-07
2.0e-07
GOBP REGULATION OF CHROMOSOME SEPARATION
1.5e-07
0.00
1.0e-07
GOBP NEGATIVE REGULATION OF
METAPHASE ANAPHASE
5.0e-08
TRANSITION OF CELL CYCLE
GOCC DNA REPLICATION
PREINITIATION COMPLEX
Ranked List Metric
1.0
GOBP DNA UNWINDING INVOLVED IN DNA REPLICATION
0.5
0.0
-0.5
2.24
2.28
2.32
2.36
NES
5000
10000
15000
Rank in Ordered Dataset
C
Suppressed
D
GOBP_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_EXOGENOUS_PEPTIDE_ANTIGEN
GOMF T CELL RECEPTOR BINDING
0.00
GOBP_PEPTIDE_ANTIGEN_ASSEMBLY_WITH_MHC_CLASS_I_PROTEIN_COMPLEX
GOBP_PEPTIDE_ANTIGEN_ASSEMBLY_WITH_MHC_PROTEIN_COMPLEX
GOCC_MHC_CLASS_II_PROTEIN_COMPLEX
GOMF ANTIGEN BINDING
GOCC_MHC_PROTEIN_COMPLEX
GOBP ANTIGEN PROCESSING AND
Running Enrichment Score
-0.25
GOCC_T CELL_RECEPTOR_COMPLEX
PRESENTATION OF EXOGENOUS
-NES
PEPTIDE ANTIGEN
COMF_ANTIGEN_BINDING
2.75
GOMF_MHC_CLASS_II_PROTEIN COMPLEX_BINDING
GOMF MHC CLASS II PROTEIN
COMPLEX BINDING
3.00
GOMF_MHC_PROTEIN_COMPLEX_BINDING
-0.50
GOBP PEPTIDE ANTIGEN
3.25
GOMFLYCELL RECEPTOR BINDING
ASSEMBLY WITH MHC PROTEIN
COMPLEX
GOMF MHC PROTEIN COMPLEX
p.adjust
BINDING
-0.75
0.00075
GOBP PEPTIDE ANTIGEN
ASSEMBLY WITH MHC CLASS II
0.00050
PROTEIN COMPLEX
0.00025
GOCC T CELL RECEPTOR COMPLEX
II
GOCC MHC CLASS II PROTEIN
COMPLEX
Ranked List Metric
1.0
0.5
GOCC MHC PROTEIN COMPLEX-
0.0
0.5
-3.25
-3.00
-2.75
-2.50
5000
10000
15000
NES
Rank in Ordered Dataset
were collected and subjected to immunohistochemis- try. Representative images of KPNA2 immunohisto- chemical staining are presented in Figure 6A. KPNA2 expression was predominantly localized in the cell membranes and cytoplasm of adrenal cells. Notably,
the expression levels of KPNA2 protein were signifi- cantly higher in the ACC group compared to the non-cancerous group (p <0.05). Moreover, we employed loss-of-function assays to validate the role of KPNA2 in ACC cells. As demonstrated in Figure 6B, siRNA1 and
A
10X
20X
Non-cancer (n=6)
Immunoreactive Score of KPNA2
*
15
10X
20X
10
ACC (n=5)
5
0
Non-cancer
ACC
B
C
H295R
SW-13
NCI-H295R
SW13
0.4
NC
4
NC
NC si-1 si-2 si-3
NC si-1 si-2 si-3
OD at 450nm
0.3
Si-1
Si-1
Si-2
OD at 450nm
3
SI-2
KPNA2
70kDa
0.2-
2
-
Actin
0.1
1.
43kDa
0.0
0
0
1
2
3
0
1
2
3
Days
Days
D
si-NC
si-KPNA2-1
si-KPNA2-2
NCI-H295R
SW13
H295R
150
400
colony numbers
colony numbers
300
100
**
200
**
50
100
SW-13
0
0
NC
Si-1
Si-2
NC
Si-1
Si-2
E
si-NC
si-KPNA2-1
si-KPNA2-2
NCI-H295R
SW13
200
800
H295R
cell numbers
150
cell numbers
600
100
400
SW-13
50
200
0
0
NC
Si-1
Si-2
NC
Si-1
Si-2
siRNA2 effectively knocked down KPNA2 expression in both SW13 and NCI-H295R cell lines. CCK-8 growth curves indicated that the downregulation of KPNA2 significantly inhibited the proliferation of SW13 and NCI-H295R ACC cells (Figure 6C). Colony formation assays further revealed that the downregulated KPNA2 led to a marked reduction in the number of cellular colonies formed by SW13 and NCI-H295R cells (Figure 6D). Additionally, transwell assays demonstrated that knockdown of KPNA2 substantially suppressed the invasiveness of ACC cells (Figure 6E). In summary, KPNA2 is overexpressed in ACC and promotes prolifer- ation and invasion of ACC cells.
Mutational landscape in relation to KPNA2 expression in ACC
To further elucidate the role of KPNA2 in ACC from a multi-omics perspective, we examined the top 20 SMGs as well as the top 10 gain and loss CNVs at both arm-level and gene-level (Figure 7A). We then stratified these analyses by KPNA2 expression levels (Figure 7B). Firstly, ACC samples with elevated KPNA2 expression exhibited a significantly increased TMB (p=0.0054) (Figure S3A). To investigate the impact of KPNA2 expres- sion on tumor heterogeneity in ACC, we conducted inter-subgroup analyses focusing on the aforementioned SMGs and CNVs. Our results revealed that the KPNA2-high expression subgroup exhibited a higher prevalence of mutations in CTNNB1, TP53, and PKHD1 compared to the KPNA2-low expression subgroup (p<0.05).
Interestingly, in CNVs at the arm-level, the KPNA2-low expression group exhibited a higher gain in 5p13.1, 5q35.3, 5q31.2, and 5p14.1, whereas the KPNA2-high expression group showed a higher loss in 17p13.1, 4q34.3, and 9p21.3 (all p<0.05). However, no discernible differences were observed at the gene-level CNVs. Additionally, we explored KPNA2 expression dif- ferences under varying mutational statuses within SMGs and CNVs (Figure S3B-D). Overall, higher KPNA2 expression was associated with a more pronounced mutational landscape.
Correlation analysis of KPNA2 expression and immune cell infiltration
Building on our GSEA findings, which indicated a strong correlation between KPNA2 expression and tumor-immune pathways, we utilized datasets from TCGA-ACC and GSE76019 to represent adult and pedi- atric ACC populations, respectively, for the analysis of tumor immune cell infiltration (Figure 8A,B, Tables S7
and S8). Subsequently, we summarized the Spearman correlation analyses between KPNA2 expression and immune cell infiltration scores generated from TIMER, EPIC, MCPcounter, xCell, and CIBERSORT algorithms in both adult and pediatric ACC (Figure 8C,D). Specifically, in TIMER (TCGA-ACC: r =- 0.24, p=3.5x 10-2; GSE76019: r =- 0.47, p=5.4x10-3), xCell (TCGA-ACC: r =- 0.37, p=8.5x10-4; GSE76019: r =- 0.6, p=1.8x10-4), and MCPcounter (TCGA-ACC: r =- 0.26, p=2.3x 10-2; GSE76019: r =- 0.34, p=4.9x10-2) algorithms, a nega- tive correlation was observed between CD8+ T-cell infiltration and KPNA2 expression in both adult and pediatric ACC (Figure 8E-J). Given that CD8+ T-cells generally play a tumor-killing role and their increased infiltration is often considered indicative of a favorable prognosis [41], we hypothesize that elevated KPNA2 expression may lead to adverse outcomes by suppress- ing the infiltration of CD8+ T-cells in the immune microenvironment of ACC.
Immunotherapy and potential drug targets of KPNA2 in ACC
Following the discovery of the potential association between KPNA2 and the immune microenvironment in ACC, we investigated its utility as a biomarker for immunotherapy. CTLA-4 and PD-1, common targets for immunotherapy, inherently suppress autoimmunity, thereby preventing the immune system from killing cancer cells [42]. To delve deeper into the role of KPNA2 in immunotherapy for both adult and pediatric ACC, we initially employed SubMap analysis on TCGA-ACC and GSE76019 datasets. We found that adult and pediatric ACC patients with low KPNA2 expression demonstrated significant expression similar- ity to anti-PD-L1 responsive cohorts within SubMap (p<0.05; Figure 9A,B). This suggests that patients with lower KPNA2 expression may be more sensitive to anti-PD-1 therapies compared to those with higher expression, while showing no significant response to anti-CTLA-4 therapies. Subsequently, we sourced four immunotherapy datasets-Braun, GSE78220, GSE91061, PRJNA482620-from the Tiger database, and selected anti-PD-1 therapy samples for survival analysis. The results indicated that patients in the high KPNA2 expression group had significantly poorer prognoses (Figure 9D-G), suggesting limited benefits from anti-PD-1 therapy in these individuals.
Moreover, we employed the CMap database to identify compounds that could potentially target KPNA2-associated pathways in ACC. According to Normalized Connectivity Scores (NCS), we selected the
A
3
2
TMB
1
PCT
B
0
group
O
0
-
16%
MUC16
16%
CTNNB1
17%
TP53
MUC16
0.080
0.080
* CTNNB1
11%
0.030
0.130
TTN
* TP53
0.030
0.130
8%
CNTNAP5
TTN
0.030
0.080
8%
HMCN1
CNTNAP5
0.010
0.060
8%
PKHD1
HMCN1
0.050
0.030
7%
KMT2B
* PKHD1
0.000
0.080
7%
NF1
KMT2B
0.030
0.040
7%
APOB
NF1
0.010
0.050
5%
ASXL3
APOB
0.010
0.050
5%
MEN1
ASXL3
0.030
0.040
7%
PRKAR1A
MEN1
0.010
0.050
7%
SVEP1
PRKAR1A
0.030
0.040
7%
SVEP1
TUT7
0.010
0.050
TUT7
0.010
0.050
5%
FRAS1
FRAS1
0.010
0.040
5%
LRP1
LRP1
0.030
0.030
5%
STAB1
STAB1
0.030
0.030
4%
ZNRF3
ZNRF3
0.040
0.010
5%
CMYA5
CMYA5
0.030
0.030
76%
12q14.1-Amp
76%
12q14.3-Amp
76%
12q15-Amp
12q14.1-Amp
75%
5p15.33-Amp
12q14.3-Amp
75%
12q15-Amp
12q13.2-Amp
5p15.33-Amp
71%
5p13.1-Amp
12q13.2-Amp
69%
5q35.3-Amp
* 5p13.1-Amp
69%
5p13.2-Amp
5q35.3-Amp
68%
5q31.2-Amp
5p13.2-Amp
68%
5p14.1-Amp
* 5q31.2-Amp
56%
22q12.1-Del
* 5p14.1-Amp
47%
22q11.21-Del
22q12.1-Del
43%
1p36.23-Del
22q11.21-Del
41%
17p13.1-Del
1p36.23-Del
40%
** 17p13.1-Del
13q14.2-Del
13q14.2-Del
29%
4q34.3-Del
** 4q34.3-Del
29%
17q21.31-Del
17q21.31-Del
29%
4q35.1-Del
4q35.1-Del
29%
11p15.5-Del
11p15.5-Del
28%
9p21.3-Del
* 9p21.3-Del
80%
OS9
80%
AGAP2
80%
CDK4
OS9
80%
TSPAN31
AGAP2
CDK4
80%
CYP27B1
TSPAN31
80%
METTL1
CYP27B1
80%
TSFM
METTL1
80%
AVIL
TSFM
80%
CTDSP2
AVIL
80%
ATP23
CTDSP2
63%
ZNRF3
ATP23
59%
KREMEN1
ZNRF3
59%
C22orf31
KREMEN1
55%
TTC28
C22orf31
55%
TTC28
EMID1
EMID1
53%
CHEK2
CHEK2
56%
AP1B1
AP1B1
56%
RFPL1
RFPL1
56%
NEFH
NEFH
55%
RHBDD3
RHBDD3
group
CNA (arm-level) CNA (gene-level)
Frequency
Low
Gain
Gain
1.00
High
Loss
High_balanced_gain
0.75
Alterations
Loss
0.50
High_balanced_loss
0.25
Mutated
0.00
| 0.810 | 0.720 |
| 0.780 | 0.740 |
| 0.780 | 0.740 |
| 0.830 | 0.670 |
| 0.780 | 0.720 |
| 0.830 | 0.590 |
| 0.830 | 0.560 |
| 0.810 | 0.590 |
| 0.810 | 0.560 |
| 0.810 | 0.560 |
| 0.440 | 0.670 |
| 0.390 | 0.540 |
| 0.330 | 0.510 |
| 0.220 | 0.590 |
| 0.280 | 0.510 |
| 0.110 | 0.460 |
| 0.190 | 0.380 |
| 0.190 | 0.380 |
| 0.280 | 0.310 |
| 0.140 | 0.410 |
| 0.810 | 0.790 |
|---|---|
| 0.810 | 0.790 |
| 0.810 | 0.790 |
| 0.810 | 0.790 |
| 0.810 | 0.790 |
| 0.810 | 0.790 |
| 0.810 | 0.790 |
| 0.810 | 0.790 |
| 0.810 | 0.790 |
| 0.810 | 0.790 |
| 0.500 | 0.740 |
| 0.470 | 0.690 |
| 0.470 | 0.690 |
| 0.440 | 0.640 |
| 0.470 | 0.620 |
| 0.440 | 0.620 |
| 0.470 | 0.640 |
| 0.470 | 0.640 |
| 0.470 | 0.640 |
| 0.470 | 0.620 |
Figure 7. Genomic analysis related to KPNA2. (A) Integrative landscape illustrating the interplay between KPNA2 expression, TMB, SMGs, and CNV. (B) Comparative mutational analysis, highlighting variations in SMGs and CNV across distinct KPNA2 expression subgroups. * p<0.05; ** p<0.01; *** p <0.001.
GSE76019(Pediatric)
B
Spearman R
P value
- T_cells_CD8_CIBERSORT
T_cells_regulatory_(Tregs)_CIBERSORT
0.25
<0.001
NK_cells_resting_CIBERSORT
NK_cells_activated_CIBERSORT
0.50
<0.01
Macrophages_MO_CIBERSORT
Macrophages_M1_CIBERSORT
1.00
<0.05
Macrophages_M2_CIBERSORT
A
Not Applicable
- Dendritic_cells_resting_CIBERSORT
Dendritic_cells_activated_CIBERSORT
CD8_T_cells_MCPcounter
ns
-NK_cells_MCPcounter
T_cells_CD8_CIBERSORT -
-Myeloid_dendritic_cells_MCPcounter
T_cells_regulatory_(Tregs)_CIBERSORT-
Fibroblasts_MCPcounter
NK_cells_resting_CIBERSORT-
CD8+_Tcm_xCell
NK_cells_activated_CIBERSORT-
CD8+_Tem_xCell
Macrophages_MO_CIBERSORT -
DC_xCell
Macrophages_M1_CIBERSORT-
Fibroblasts_xCell
Macrophages_M2_CIBERSORT-
Macrophages_M1_xCell
Dendritic_cells_resting_CIBERSORT-
- Macrophages_M2_xCell
Dendritic_cells_activated_CIBERSORT-
-NK_cells_xCell
CD8_T_cells_MCPcounter-
- Th1_cells_xCell
NK_cells_MCPcounter-
Tregs_xCell
Myeloid_dendritic_cells_MCPcounter-
Fibroblasts_MCPcounter-
KPNA2
CD8_Tcells_EPIC
Macrophages_EPIC
CD8+_Tem_xCell-
NKcells_EPIC
CD8+_Tem_xCell-
T_cell_CD8_TIMER
DC_xCell-
Macrophage_TIMER
Fibroblasts_xCell-
-DC_TIMER
Macrophages_M1_xCell-
KPNA2
Macrophages_M2_xCell-
NK_cells_xCell-
Spearman R
Relations
Th1_cells_xCell-
1.0
Tregs_xCell-
pos
CD8_Tcells_EPIC-
Macrophages_EPIC-
0.5
neg
NKcells_EPIC-
T_cell_CD8_TIMER-
Macrophage_TIMER-
0.0
DC_TIMER-
KPNA2-
-0.5
C
TCGA(Adult)
D
E TIMER
F
G MCPcounter
TCGA
TCGA
TCGA
xCell
CD8+ T Cell infiltration level
0.23
R = - 0.24, p = 0.035
CD8+ Tcm infiltration I evel
0.15
R = - 0.37, p = 0.00085
CD8+ T Cell infiltration level
R = - 0.26, p = 0.023
0.22
0.21
0.10
2
0.20
0.05
1
0.19
0.18
0.00
0
2
4
6
2
4
6
2
4
6
Expression of KPNA2
Expression of KPNA2
Expression of KPNA2
H
J
GSE76019
GSE76019
GSE76019
TIMER
xCell
MCPcounter
CD8+ T Cell infiltration level
R = - 0.47, p = 0.0054
CD8+ Tcm infiltration I evel
0.05
R = - 0.6, p = 0.00018
CD8+ T Cell infiltration level
4.0
R = - 0.34, p = 0.049
0.24
0.04
0.03
3.6
0.02
0.23
0.01
3.2
0.00
0.22
-0.01
2.8
9
10
11
9
10
11
9
10
11
Expression of KPNA2
Expression of KPNA2
Expression of KPNA2
| TCGA | TIMER | EPIC | MCPcounter | xCell | CIBERSORT |
|---|---|---|---|---|---|
| CD8 T cell | cor =- 0.24 * | cor =- 0.16 p=0.17 | cor =- 0.26 * | Tcm: cor =- 0.37 *** Tem: cor =- 0.11 p=0.33 | cor =- 0.33 ** |
| NK cell | NULL | cor =- 0.13 p=0.24 | cor =- 0.03 p=0.77 | cor=0.003 p=0.98 | resting: cor=0.24 * actived: cor =- 0.31 ** |
| Tregs | NULL | NULL | NULL | cor =- 0.44 *** | cor=0.10 p=0.40 |
| Macrophage | cor =- 0.19 p=0.1 | cor =- 0.38 *** | NULL | M1: cor =- 0.15 p=0.18 M2: cor =- 0.4 *** | MO: cor=0.40 *** M1: cor =- 0.21 p=0.06 M2: cor =- 0.35 ** |
| Fibroblast Th1 cell Dendritic cell | NULL | NULL | cor=0.45 *** | cor =- 0.4 *** | NULL |
| NULL | NULL | NULL | cor=0.37 *** | NULL | |
| cor=0.43 *** | NULL | cor =- 0.20 p=0.07 | cor =- 0.31 ** | resting: cor =- 0.06 p=0.61 actived: cor=0.37 *** |
| GSE76019 | TIMER | EPIC | MCPcounter | xCell | CIBERSORT |
|---|---|---|---|---|---|
| CD8 T cell | cor =- 0.47 ** | cor =- 0.3 p=0.09 | cor =- 0.34 * | Tcm: cor =- 0.6 *** Tem: cor=0.22 p=0.22 | cor=0.06 p=0.72 |
| NK cell | NULL | cor =- 0.23 p=0.2 | cor =- 0.54 ** | cor =- 0.03 p=0.85 | resting: cor =- 0.04 p=0.82 actived: cor =- 0.32 p=0.06 |
| Tregs | NULL | NULL | NULL | cor =- 0.02 p=0.93 | cor=0.5 ** |
| Macrophage Fibroblast Th1 cell Dendritic cell | cor =- 0.52 ** | cor =- 0.57 *** | NULL | M1: cor =- 0.37 * M2: cor =- 0.21 p=0.24 | MO: cor=0.6 *** M1: cor =- 0.46 ** M2: cor =- 0.5 ** |
| NULL | NULL | cor=0.45 *** | cor =- 0.49 ** | NULL | |
| NULL | NULL | NULL | cor=0.37 * | NULL | |
| cor =- 0.13 p=0.47 | NULL | cor =- 0.64 *** | cor =- 0.31 p=0.08 | resting: cor =- 0.13 p=0.46 actived: cor=0.19 p=0.28 |
Figure 8. Correlation between KPNA2 and immune cell infiltration. (A and B) Correlation graphs of KPNA2 with immune (C and D) comprehensive summary detailing the statistical significance of the association between KPNA2 and immune cell infiltration levels in TCGA-ACC and GSE76019. (E-J) Scatter plots generated through TIMER, xCell, and MCPcounter algorithms to elucidate the correlation between KPNA2 expression and CD8+ T-cell infiltration scores in TCGA-ACC and GSE76019.
A
B
TCGA-ACC(Adult)
GSE76019(Pediatric)
1
0.128
0.073
0.041
High KPNA2_p
0.180
High KPNA2_p
0.8
0.002
Low KPNA2_p
0.001
Low KPNA2_p
0.6
High KPNA2_b
High KPNA2_b
0.4
0.016
Low KPNA2_b
0.008
Low KPNA2_b
0.2
pvalue
NR
R
NR
R
pvalue
NR
R
NR
R
CTLA4
PD-1
CTLA4
PD-1
Nominal p
Bonferroni corrected
D
E
F
G
Braun_PD1
GSE78220_PD1
GSE91061_PD1
PRJNA482620_PD1
1.0
KPNA2
1.0
KPNA2
1.0
KPNA2
1.0
KPNA2
low
high
high
low
Survival probability
high
0.8
high
Survival probability
low
Survival probability
0.8
low
0.8
Survival probability
0.8
0.5
0.5
0.5
0.5
0.3
0.3
0.3
0.3
0.0
logrank test p=0.02
0.0
logrank test p=0.03
0.0
logrank test p=0.01
0.0
logrank test p=0.04
Number at risk
Number at risk
Number at risk
Number at risk
low
7.1
44
3.4
18
high
18
15
5
2
1
high
32
20
13
7
2
low
18
18
1,3
9
2
high
101
58
30
19
1
low
8
7
7
$
2
low
1:7
14
12
9
1
high
16
14
7
4
1
0
18
36
54
72
0
8
16
24
32
0
9
18
27
OS(Months)
OS(Months)
OS(Months)
36
0
14
OS(Months)
28
42
56
H
TCGA-ACC(Adult)
GSE76019(Pediatric)
cobimetinib
naproxol
TCGA(Adult)
ibrutinib
sunitinib
amsacrine
nutlin-3
buparlisib
palbociclib
refametinib
tipifarnib
tas
Ro-4987655
DMBI
14
0.7
palbociclib
pitavastatin
0.6
progesterone
valrubicin
naproxol
0.5
dacinostat
taselisib
ibrutinib
0.4
devazepide
torin-2
lapatinib
rociletinib
6
palbociclib
-NCS
Ro-4987655
tamoxifen
ellagic-acid
buparlisib
1.95
7b-cis
mepacrine
2.00
2.05
fulvestrant
dacomitinib
vandetanib
2.10
naproxol
buparlisib
trametinib
.
ibrutinib
14
mitoxantrone
golvatinib
vandetanib
vandetanib
afatinib
pralatrexate
etoposide
Ro-4987655
2.10
2.05
2.00
1.95
1.90 1.90
1.95
2.00
2.05
2.10
GSE76019(Pediatric)
-NCS
-NCS
top 20 compounds from both TCGA-ACC and GSE76019 datasets (Figure 9H). Mechanism-of-action (MOA) anal- ysis (Figure S4A,B) revealed six potential ACC thera- peutic agents-naproxol, ibrutinib, palbociclib,
Ro-4987655, buparlisib, and vandetanib-that could potentially target KPNA2. Collectively, our findings indicate that KPNA2 serves as a potential biomarker for immunotherapy and as a drug target in ACC.
Discussion
ACC is a highly malignant tumor, characterized by its propensity for metastasis and resistance to standard therapies. Many patients are diagnosed at advanced stages, resulting in poor prognosis [2,43]. Mitotane is currently the only approved chemotherapeutic agent for treating ACC, primarily used in cases where surgical resection is not feasible or when recurrence or metas- tasis occurs post-surgery. However, its therapeutic efficacy is generally slow, varies among individuals, and is accompanied by significant side effects [4]. Immunosuppressive agents and targeted therapies, as emerging directions for ACC treatment, are still in clin- ical trials and face challenges such as inconsistent effi- cacy, intense side effects, and drug resistance [2,8]. Therefore, the identification of a biomarker that can predict the prognosis and immunotherapeutic response in ACC patients is of paramount importance. In this study, using bioinformatics, immunohistochemistry, and in vitro experiments, we identified KPNA2 as a gene associated with ACC progression and found that it has robust predictive power for immunotherapeutic responses.
After identifying a gene set associated with ACC progression through WGCNA and ATAC-seq, we per- formed GO analysis and discovered a significant enrichment of the Wnt/B-catenin signaling pathway within this gene set. Aberrant activation of the Wnt/B-catenin pathway can drive tumorigenesis by promoting cellular proliferation, survival, and migration [44]. Numerous studies have shown that inhibiting the Wnt/B-catenin pathway can suppress the proliferation and growth of ACC cells, consequently slowing tumor development. Research by Morgan K Penny et al. found that targeting the oncogenic Wnt/ß-catenin sig- naling pathway could disrupt ECM expression and impact ACC tumor growth [45]. Rottlerin, a natural plant polyphenol, has been shown to inhibit cell pro- liferation and induce apoptosis in ACC cell lines and xenograft models [46]. Additionally, Niclosamide can downregulate the expression of ß-catenin and inhibit the levels of epithelial-mesenchymal transition media- tors [47].
Moreover, we filtered out KPNA2 from this gene set as the most predictive biomarker for ACC prognosis and as a potential drug target. KPNA2 is a nuclear transport protein belonging to the karyopherin protein family. It plays a crucial role in the molecular transport process between the cell nucleus and the cytoplasm [48,49]. Its primary function is to shuttle proteins con- taining nuclear localization signals from the cytoplasm to the nucleus to participate in nuclear biological
processes such as gene transcription, DNA repair, and cell cycle regulation [50] Through GO analysis, we found that KPNA2 significantly activates pathways related to cell replication and cell cycle progression. In subsequent experiments, we also discovered that KPNA2 promotes ACC cell proliferation and metastasis. Some research indicates that KPNA2 is overexpressed in multiple types of cancer and promotes tumor pro- gression both in vitro and in vivo, correlating with poor patient prognosis. For example, studies have shown that KPNA2 is associated with shorter overall survival in lung adenocarcinoma and that its overexpression enhances the migratory ability of lung adenocarci- noma cells [51,52]. Similarly, Altan et al. found that KPNA2 promotes gastric cancer progression and poor patient prognosis through the activation of the Wnt/ß-catenin signaling pathway [53]. Additionally, in ovarian and colorectal cancers, KPNA2 facilitates tumor progression by participating in the AKT signaling path- way [54,55]. Hence, it is evident that the roles and mechanisms of KPNA2 vary across different types of cancer.
Tumor heterogeneity serves as a critical determi- nant of both prognosis and therapy response in ACC. Variations in mutations across different cells can lead to disparities in cell growth, proliferation, and signal- ing pathways, thereby contributing to heterogeneity. Genomic mutational analysis can elucidate the land- scape of gene mutations within the tumor [56]. In the present study, we found significant differences in TP53 and CTNNB1 mutations among the KPNA2 expression subgroups, both of which have been confirmed to be associated with the occurrence and progression of ACC [57-59]. Our findings indicate that the expression levels of KPNA2 in ACC are significantly correlated to various degrees with TMB, SMGs, and CNV, suggesting that KPNA2 is a predictor of higher TMB, with poten- tial implications for immunotherapeutic responsive- ness [60].
The tumor immune microenvironment, comprising immune cells, cytokines, chemokines, and immune checkpoint molecules, plays a pivotal role in cancer onset, progression, metastasis, and therapy response. It dictates how the immune system interacts with cancer cells, thus affecting their survival, proliferation, and migration [61]. In our analysis, we observed that KPNA2 significantly inhibits immune response-related pathways. Research has demonstrated that increased nuclear transporter KPNA2 contributes to tumor immune evasion by enhancing PD-L1 expression in pancreatic ductal adenocarcinoma (PDAC) [16]. In addition, multiple algorithms indicate that KPNA2 expression negatively correlates with CD8+ T cells in
both adult and pediatric datasets. Submap analysis revealed that low expression of KPNA2 is significantly correlated with a potential PD-L1 immune response, while high expression of KPNA2 in the immunotherapy cohort suggests a poor prognosis. The results indicate that patients with low KPNA2 expression, coupled with upregulated immune checkpoints and increased infil- tration of CD8+ T cells, are most likely to benefit from immunotherapy. Consequently, KPNA2 possesses potential prognostic value for immunotherapeutic interventions.
In addition to existing treatments, exploring the combination of Mitotane and KPNA2 inhibitors pres- ents a promising direction for research. Mitotane, a specific anticancer drug used for treating ACC, is an isomer of dichlorodiphenyltrichloroethane (DDT) and demonstrates direct cytotoxic effects on adrenal tis- sues, though its exact mechanism of action is not fully understood [62]. However, the efficacy of Mitotane as a monotherapy in ACC is hampered by its variable out- comes and significant side effects [63,64]. Combining Mitotane with other drugs is a critical avenue for ACC treatment, but recent clinical trial results have been less than satisfactory [65,66]. KPNA2 inhibitors have already shown some pre-clinical promise in breast can- cer and colorectal cancer [15,17]. In our future research, we plan to investigate the combined effect of KPNA2 inhibitors and Mitotane in ACC.
While the role of KPNA2 has been explored in various other cancers, its specific impact on ACC has been largely uncharted until now, thereby filling a critical knowledge gap in the existing literature. It is important, however, to acknowledge certain limita- tions inherent in our research. Firstly, although we have validated our findings through publicly avail- able databases, the sample size of ACC specimens obtained for this study remains limited, necessitat- ing further validation from a more expansive data- set. Secondly, while our data analysis has identified potential agents for targeting KPNA2, ongoing work involves a more exhaustive series of cellular and other experimental assays aimed at confirming the efficacy and mechanistic pathways of these candi- date compounds.
Conclusion
In conclusion, our study introduces KPNA2 as a novel biomarker for ACC, offering positive implications for prognostic risk assessments and shaping future direc- tions in the development of targeted therapeutics and immunomodulatory interventions for ACC patients.
Acknowledgment
We would like to express our gratitude to all the contribu- tors of the public datasets.
Authors contributions
Jianming Lu, Jiahong Chen, and Zhong Dong played instru- mental roles in the conceptualization and design of the study. Bioinformatics analysis was conducted by Jianming Lu, Yihao Chen, Yongcheng Shi, and Fengping Liu. The collection of clinical samples was undertaken by Jiahong Chen and Zhong Dong. Supervision of the research was carried out by Jianming Lu, Zhong Dong, Xiaohui Ling, Junhong Deng and Weide Zhong. Manuscript preparation was done by Jiahong Chen, Yihao Chen and Xiaohui Ling, while experimental vali- dation was achieved by Chuanfan Zhong, Shumin Fang, Shanshan Mo and Yihao Chen. All authors made substantial contributions to the article and granted approval for its submission.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statements
The public data used in this study has been described in the Materials and Methods.
Funding
This research was supported by grants from the National Natural Science Foundation of China (Grant no. 82003271). The Guangzhou Planned Project of Science and Technology (Grant no. 2023A04J1269). Medical Science and Technology Research Fund of Guangdong Province (Grant no. B2021317). Huizhou High Level Hospital Construction Science and Technology Special Project (Grant No. 2022CZ010004).
ORCID
Jianming Lu ID http://orcid.org/0000-0002-3794-641X
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