ORIGINAL ARTICLE
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AUNIP was a candidate marker for prognosis and immunology in pan-cancer
Xiaorong Guo11D . Ting Liu2 . Nan Li3 · Li Jin4
Received: 23 January 2025 / Accepted: 27 March 2025 / Published online: 17 May 2025 @ The Author(s) 2025
Abstract
AUNIP (Aurora kinase A[Aurora-A] and ninein-interacting protein), is a key factor regulating the end-state of DNA cleavage. It has been reported that AUNIP affects the progression of some tumors; however, the molecular functions involved in AUNIP remain unknown. We employed some databases, such as TCGA, GTEx, TIMER, GEPIA2, cBioportal, and GSCALite, to study AUNIP gene expression, prognosis, gene variation, and drug sensitivity. The relationship between AUNIP and clinicopathological information was explored using Wilcoxon test. The association between AUNIP and TMB, MSI, immunocyte infiltration, and immune checkpoints were analyzed using Spearman correlation analysis. We employed GSEA to research the functional mechanisms involved in AUNIP for pan-cancer. Moreover, we conducted immunohistochemistry (IHC) to investigate AUNIP difference expression between liver hepatocellular carcinoma (LIHC) and normal tissues. The Chisq test was used to study the correlation of AUNIP with clinical characteristics. AUNIP was highly expressed in majority of tumors and IHC analysis demonstrated that AUNIP expression was higher in LIHC than normal tissues. AUNIP overexpression had adverse outcomes in adrenocortical carcinoma (ACC), brain lower grade glioma (LGG), LIHC, mesothelioma (MESO), and sarcoma (SARC). Furthermore, high AUNIP expression led to unfavorable prognosis in LIHC. AUNIP was associated with T stage, N stage, and clinicopathological analysis in several cancers and AUNIP expression had a correlation with histologic grade in LIHC by IHC. Mutation analysis showed that AUNIP was the highest frequency of genetic changes in cholangiocarcinoma (CHOL). AUNIP was negatively associated with 30 small-molecule drugs that inhibit tumor development. AUNIP expression had association with TMB, MSI, immune cell infiltration, and immune checkpoints for various tumors. GSEA results suggested that AUNIP mainly participated in the cell cycle, DNA replication, mismatch repair, and homologous recombination.Pan-cancer study considered AUNIP as a potential prognostic marker and high latent diagnostic biomarker.
Keywords AUNIP . Pan-cancer . Prognostic . Immunity . Hepatocellular carcinoma
Abbreviations
ACC Adrenocortical carcinoma
BLCA Bladder urothelial carcinoma
BRCA Breast invasive carcinoma
CESC Cervical squamous cell carcinoma and endocer- vical adenocarcinoma
CHOL Cholangio carcinoma COAD Colon adenocarcinoma
Xiaorong Guo and Ting Liu is equal contribution to the manuscript.
☒ Li Jin jinli1089@126.com
Xiaorong Guo gxr802013@163.com
1 Department of Pathology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, Heilongjiang, China
2 Department of Pathology, Beijing Ditan Hospital, Capital Medical University, No. 8 Jing Shun East Street, Chaoyang District, Beijing 100015, China
3 Department of Pathology, The Fourth Affiliated Hospital of Harbin Medical University, Harbin 150081, Heilongjiang, China
4 Cancer Center, Department of Pathology, Zhejiang Provincial People’s Hospital, Affiliated People’s Hospital, Hangzhou Medical College, Hangzhou, Zhejiang, China
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DLBC
Lymphoid neoplasm diffuse large B-cell lymphoma
ESCA Esophageal carcinoma
GBM Glioblastoma multiforme
HNSC Head and neck squamous cell carcinoma
KICH Kidney chromophobe
KIRC
Kidney renal clear cell carcinoma
KIRP
Kidney renal papillary cell carcinoma
LAML
Acute myeloid leukemia
LGG Brain lower grade glioma
LIHC
Liver hepatocellular carcinoma
LUAD
Lung adenocarcinoma
LUSC
Lung squamous cell carcinoma
MESO
Mesothelioma
OV
Ovarian serous cystadenocarcinoma
PAAD
Pancreatic adenocarcinoma
PCPG
Pheochromocytoma and paraganglioma
PRAD Prostate adenocarcinoma
READ Rectum adenocarcinoma
SARC
Sarcoma
SKCM
Skin cutaneous melanoma
STAD
Stomach adenocarcinoma
TGCT
Testicular germ cell tumors
THCA Thyroid carcinoma
THYM
Thymoma
UCEC
Uterine corpus endometrial carcinoma
UCS
Uterine carcinosarcoma
UVM
Uveal melanoma
Introduction
Cancer is a serious disease that becomes a threat to mankind’s health. The number of deaths from cancer is increasing every year. Pan-cancer research has been prevalent in recent years, with the aim of integrating TCGA data based on different tumor types and platforms, while analyzing and interpreting these data. Our research relies on multi-omics database to explore differences between tumors, guiding tumor diagnosis, prognosis, and treatment selection (Zhang and Wang 2015; Yang et al. 2018).
AUNIP (Aurora kinase A and Ninein-interacting protein) is a centrosomal protein that interacts to promote the maintenance of Aurora-A and Ninein centrosome structures and the formation of spindles (Zhang and Wang 2015). AUNIP regulates the mitotic entry and mitotic spindle assembly by activating of Plk1 and Aurora-A. Yang et al. used bioinformatics to investigate the high expression of AUNIP in oral squamous cell carcinoma (OSCC), which is associated with tumor microenvironment, human papillomavirus infection, and cell cycle. Inhibition of AUNIP can inhibit OSCC cells’ proliferation, resulting in the G0/G1 phase arrest of OSCC cells. AUNIP overexpression
predicts bad prognosis in OSCC patients (Yang et al. 2019). However, there are few reports on pan-cancer research in AUNIP.
Our work used bioinformatics aspect to discuss the expression, prognosis, clinicopathological features, mutation, tumor mutation load (TMB), microsatellite instability (MSI), immune characteristics, and drug sensitivity of AUNIP from the viewpoint of pan-cancer, and comprehensively analyzed the characteristics and mechanism of AUNIP, providing new ideas for tumor treatment and prognosis.
Materials and methods
Differential expression of AUNIP mRNA for cancers and normal samples
TIMER2 database studied immune cells infiltration in different tumors, as well as the differential expression of 33 kinds of tumors and normal tissues from TCGA database (Li et al. 2020). Owing to the absence of normal samples in several tumors in TIMER database, we merged TCGA and GTEx to discuss the differential expression of AUNIP in 33 tumors and normal tissues.
Prognosis and diagnostic value of AUNIP
GEPIA2 database is an online platform in which survival significance maps of genes in pan-cancer can be obtained. According to the median value of AUNIP expression, AUNIP was divided into low-expression group and high- expression group and Kaplan-Meier was used to show prognostic differences of both groups (Tang et al. 2019). In addition, a receiver-operating characteristic (ROC) curve estimated the diagnose value for AUNIP using pROC in R.
Clinicopathological features
We acquired the expression data and clinicopathological parameters of 33 cancers from the TCGA database and utilized Wilcoxon test to investigate its correlation with clinicopathology, including T stage, N stage, and pathological stage.
Immunohistochemical staining
Forty-four cases of liver cancer and paracancerous tissue were collected from the Department of Pathology of the Zhejiang Provincial People’s Hospital. The study was authorized by the ethics committee of Zhejiang Provincial People’s Hospital (batch number: QT2025083), and all patients received written informed consent before surgery.
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Patients with a pathologic diagnosis of hepatocellular carcinoma who had not received any preoperative chemotherapy or radiotherapy were included in the study. Patients with other diagnosed malignancies were not included in the study. The slices were cut into 4 um thick. The sections were dewaxed, hydrated, and repaired by high-pressure antigen. The endogenous catalase activity was inactivated by 3% H2O2 at room temperature for 10 min. The non-specific antigen was blocked by 10% sheep serum after rinsed with PBS at 37 ℃ for 10 min, and rabbit anti-AUNIP polyclonal antibody (bs-15019R, 1:200, Bioss Company) was added at 4 ℃ overnight. The next day, the secondary antibody (Goat anti-Rabbit IgG, PV-6000, Beijing Zhongshan Jinqiao Biotechnology Co., Ltd.) was added, and then developed color with DAB. Finally, the slices were observed in the microscope. The standard of expression strength is: 0 points without staining; light yellow is 1 point; light brown is 2 points; dark brown is 3 points. The scoring criteria for positive cells were: 0 points for ≤ 5%; 6% ~25% is 1 score; 26% ~ 50% is 2 points; 51% ~ 75% is 3 points; > 76% is 4 points. AUNIP expression is interpreted by the percentage of positive cells multiplied by the staining intensity. The degree of positive staining was defined: ≤7 is classified as low expression, and > 7 is classified as high expression.
Mutational analysis of AUNIP
cBioPortal studied the frequency of AUNIP gene change in various tumors (Cerami et al. 2012).
Correlative analysis of AUNIP expression with TMB and MSI
TMB is the total number of genetic coding errors, base substitution, gene insertion, or deletion errors detected in somatic cells from millions of bases, and it can effectively evaluate tumor mutation and neoantigen load and is related to immunotherapy response (Zhang et al. 2019; Chan et al. 2019). MSI is due to mismatch repair gene defects. Tumors with MSl molecular characteristics increase tumor antigen load due to high-frequency gene mutation, inducing killer T lymphocyte infiltration and corresponding immunosuppressive molecule high expression, and respond well to corresponding immunotherapy (Dudley and Le 2016). The interrelation of AUNIP expression with TMB and MSI in 33 tumors was discussed by Spearman analysis.
Association of AUNIP with immune cell infiltration and immune checkpoints
Cells and molecules of the tumor microenvironment (TME) can influence the efficiency of immunotherapy, so
research on TME is of great significance in immunotherapy. Tumor immune cell infiltration is closely linked to tumor progression in the TME. The relationship of AUNIP with 23 types of immune cell infiltration in different cancers was applied using ssGSEA algorithm in R language. In addition, the stromal score, immune score, and estimate score for different tumors were investigated using ESTIMATE algorithm. Immunotherapy with immune checkpoint inhibitors has initiated a new era of tumor treatment, and finding predictable biomarkers is a necessary pathway for achieving precise tumor immunotherapy. At present, the eight commonest immune checkpoints are PD-1, PD-L1, CTLA-4, PDCD1LG2, TIGIT, HAVCR2, SIGLEC15, and LAG3. We discussed the association of AUNIP with immune checkpoints through Spearman analysis.
Correlative analysis between AUNIP expression and drug sensitivity
GSCALite is an integrated platform for genomic, pharmacogenomic, and immunogenomic gene set cancer analysis. The CTRP dataset from the GSCALite database (http://bioinfo.life.hust.edu.cn/web/GSCALite/) was employed to explore the relationship between gene expression and drug sensitivity (Liu et al. 2022).
Gene set enrichment analysis of AUNIP
We conducted GSEA analysis between high AUNIP expression and low AUNIP expression according to the KEGG dataset in the MSigDB database.
Statistical analysis
Wilcoxon test was employed to study the difference expression between tumors and normal tissues. The relationship of AUNIP expression with clinicopathological features was applied by Chi-square test. We investigated the association of AUNIP with TME and immune checkpoints using Spearman correlation. P < 0.05 was considered statistically significant.
Results
Overexpression of AUNIP mRNA in various tumors
The TIMER database showed that compared with normal tissue, AUNIP had high expression in BLCA, BRCA, CESC, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, PAAD, READ, STAD, THCA, and UCEC, and low expres- sion in KICH, KIRC, KIRP, and PCPG (Fig. 1A). Because of no normal tissues in some tumors in the TIMER database,
AUNIP Expression Level(log2 TPM)
ACC.Tumor (n=79)
BLCA.Tumor (n=408)
BLCA.Normal (n=19)
BRCA.Tumor (n=1093)
BRCA.Normal (n=112)
BRCA-Basal.Tumor (n=190)
BRCA-Her2.Tumor (n=82)
BRCA-LumA. Tumor (n=564) BRCA-LumB.Tumor (n=217)
CESC.Tumor (n=304)
CESC.Normal (n=3)
CHOL.Tumor (n=36)
CHOL.Normal (n=9)
COAD.Tumor (n=457)
COAD.Normal (n=41)
DLBC.Tumor (n=48)
ESCA.Tumor (n=184)
ESCA.Normal (n=11)-
GBM.Tumor (n=153)
GBM.Normal (n=5)
HNSC.Tumor (n=520)
HNSC.Normal (n=44)
HNSC-HPV+.Tumor (n=97) HNSC-HPV -. Tumor (n=421)
KICH.Tumor (n=66)
KICH.Normal (n=25)
KIRC.Tumor (n=533)
KIRC.Normal (n=72)
KIRP.Tumor (n=290)
KIRP.Normal (n=32)
LAML.Tumor (n=173) LGG.Tumor (n=516)
LIHC.Tumor (n=371) LIHC.Normal (n=50)
LUAD.Tumor (n=515)
LUAD.Normal (n=59)
LUSC.Tumor (n=501)
LUSC.Normal (n=51)
MESO.Tumor (n=87)
OV.Tumor (n=303)
PAAD.Tumor (n=178)
PAAD.Normal (n=4) PCPG.Tumor (n=179) PCPG.Normal (n=3)
PRAD.Tumor (n=497) PRAD.Normal (n=52)
READ.Tumor (n=166)
READ.Normal (n=10)
SARC.Tumor (n=259)
SKCM.Tumor (n=103)
SKCM.Metastasis (n=368)
STAD.Tumor (n=415)
STAD.Normal (n=35) TGCT.Tumor (n=150)
THCA.Tumor (n=501)
THCA.Normal (n=59)
THYM.Tumor (n=120)
UCEC.Tumor (n=545)
UCEC.Normal (n=35)
UCS.Tumor (n=57)
UVM.Tumor (n=80)
0
N
1
.
-0
1
II
*
*
A
B
0
2
1
00
4
01
ACC
ns
BLCA
BRCA
CESC
CHOL
COAD
DLBC
:
ESCA
:
HNSC
GBM
KICH
ns
KIRC
KIRP
Ps
LAML
1
A
LGG
.
LIHC
C
LUAD
LUSC
MESO
OV
PAAD
PCPG
I
PRAD
ns
READ
…
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
Tumor Normal
*P < 0.05)
Fig. 1 The expression of AUNIP in pan-cancer and normal tissues in TIMER database (A) and TCGA +GTEx (B) ( *** P < 0.001, ** P < 0.01,
TCGA was combined with GTEx to explore AUNIP expres- sion in tumors and corresponding normal samples. We dis- covered that AUNIP expression in BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, LGG, LIHC, LUAD, LUSC, OV, PAAD, READ, SKCM, STAD, THCA, THYM, UCEC, and UCS was higher than in normal tissues, but lower in KIRC, LAML, PCPG, and TGCT (Fig. 1B).
Prognosis and diagnostic value of AUNIP
in pan-cancer
We analyzed the impact of AUNIP expression on survival in 33 tumors. Prognostic index included overall survival (OS) and disease-free survival (DFS). The findings dem- onstrated that the OS of low-expression AUNIP was bet- ter than that of high-expression AUNIP for ACC, KIRP, LAML, LGG, LIHC, LUAD, MESO, PRAD, SARC, and SKCM, (Fig. 2A). Regarding DFS, the DFS for low- expression AUNIP in ACC, KIRP, LGG, LIHC, MESO,
PAAD, PRAD, and SARC was better than that of high- expression AUNIP (Fig. 2B). Among them, the OS and DFS with low AUNIP expression for ACC, KIRP, LGG, LIHC, MESO, PRAD, and SARC were higher than those with high AUNIP expression. There was no significant difference of AUNIP expression in KIRP and PRAD, compared to their corresponding normal tissues. There- fore, AUNIP was related to OS and DFS in ACC, LGG, LIHC, MESO, and SARC. Furthermore, we evaluated the diagnostic value of AUNIP in tumors. The area under the receiver-operating characteristic curve (AUC) >0.7 is
considered certain accuracy, and AUC > 0.9 is considered higher accuracy (Mishra et al. 2023a). The results demon- strated that AUNIP has high diagnostic value in BLCA, BRCA, CESC, CHOL, COAD, ESCA, HNSC, LUAD, LUSC, PCPG, READ, STAD, UCEC, and certain diag- nostic values for KICH, KIRC, LIHC, PAAD, and SARC (Figure S1).
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log 10(HR)
1.0
A
ENSG00000127423.10
0.5
(AUNIP)
0.0
ACC
BLCA
BRCA
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
-0.5
-1.0
Overall Survival
Overall Survival
Overall Survival
Overall Survival
Overall Survival
2
LOW AUNIP TPM
9
9
High AUNIP TPMA
LOW AUNIP TPM
High AUNIP TPM
LOW AUNIP TPM
9
HIỆN AUNIP TPM
LOW AUNIP TPM
9
LOW AUNIP TPM
Lograr pro 6e-06
ogrank p=0.0013
Lagrank p=0. 0068
High AUNIP TPM
Logrank pu0.00052
POKRY-9 de
Pipinghy-2.6
nghghjejj now(=38
D(HR)=0.0044
HRphiphi-2.1
Logrank p=6.36-05
High AUNIP TPM
=
=
DOFR)-0.011
Highghij=2.2
DO-)=9.5e-05
-
Hecaghij=1.9
Percent survival
Percent survival
níhigh)= 141 nílow)=131
Percent survival
nghighj=53 now !: 53
DOHR-48-04
Percent survival
n(high)=254
now)=252
Percent survival
níhighje 180 n(om)=179
:
06
06
4
¥
3
04
-
2
=
0
4
0
ACC
0
KIRP
0
LAML
0
LGG
0
LIHC
0
50
100
150
0
50
100
150
200
0
20
40
00
80
0
50
100
150
200
0
20
40
00
00
100
120
Months
Months
Months
Months
Months
Overall Survival
Overall Survival
Overall Survival
Overall Survival
Overall Survival
0
LOW AUNIP TPM
9
LOW AUNIP TPM
0
Low TUNP TPM
9
:
High AUNIP TPM
High AUNIP TPM
JNP TPM
LOW AUNIP TPM
Logan p=0 013
Lograna pa6. 16-07
High AUNIP TPM
LOW AUNP
HIỆN AUNIP TPM
=
Hijhighjet.5
=
Lograra p0 043
=
Hk[high)=3.6
D(FG=1.Se-06
=
Highghi=1.5
Logrark p=0.006
DO-FOTO.014 nghighi=238
B
3
Percent survival
P(HR)=0.044 n(high)=130
HRpighj=1.3
Percent survival
P(PR)-0.006
Percent survival
Con()=236
ngow)= 130
Percent survival
nghighi-229
3
ngon)=239
Percent survival
nghighi=41 n(ou)=41
AN
now)-228
08
…
0
¥
6
¥
=
2
a
6
2
2
LUAD
MESO
PRAD
SARC
0
0
SKCM
0
0
0
50
100
150
200
250
0
20
40
60
80
D
20
40
60
100
120
140
0
50
100
150
Months
Months
Months
Months
0
100
200
300
Months
log 10(HR)
B
ENSG00000127423.10
0.3
(AUNIP)
0.0
ACC
BLCA
BRCA
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
-0.3
Disease Free Survival
Disease Free Survival
Disease Free Survival
Disease Free Survival
Low AUNIP
0
LOW
9
High AUNIP TPM
LOW AUNIP TPM
0
Logrank p=9.5e-06
High AUNIP TPM
Low AUNIP
Logrank p=0.0013
High AUNIP TPM
Logrank p=0.00014
High AUNIP TPM
Logrank p=0.0038
4
HR(high)=4
6
HR(high)=2.7
08
0.8
Percent survival
P(HR)=0.00028
P(HR)=0.002
HR(high)=1.8
HR(high)=1.6
n(high)=38
Percent survival
n(high)=141
now ?- 38
Percent survival
P(HR)=0.00017
ngow)=131
n(high)=254 n(low)=252
Percent survival
P(HR)=0.004
n(high)=180
0.6
8
06
0.5
n(low)=179
2
3
2
A
6
2
0
8
ACC
0
KIRP
a
LGG
8
LIHC
0
50
100
150
0
50
100
150
200
0
50
100
150
0
20
40
60
80
100
120
Months
Months
Months
Months
Disease Free Survival
Disease Free Survival
Disease Free Survival
Disease Free Survival
3
LOW ALINIP TPM
0
2
High AUNIP TPM
LOW AUNIP TPM
9
Logrank p=0.0074
High AUNIP TPM
LOW AUNIP TPM
LOW AUNIP TPM
HR(high)=2.2
Logrank p=0.017
High AUNIP TPM
Logrank p=0.036
High AUNIP TPM
Logrark p=0.044
0
P(R)=0.0084 n(high)=41
0
HR(high)=1.7
p(HR)=0.019 nghigh)=87 now)=87
0
HR(high)=1.6
P(HR)=0.038 n(high)=244
8
HR(high)=1.4
Percent survival
P(HR)=0.042
0.6
níjon)=41
Percent survival
Percent survival
Mów)=235
Percent survival
n(high)= 130
:
0.6
n(low)=130
0.6
0
0
0.4
04
0
2
=
0
8
MESO
0
PAAD
5
PRAD
:
SARC
0
20
40
60
80
0
20
40
60
80
0
20
40
60
80
100
120
140
0
50
100
150
Months
Months
Months
Months
AUNIP was associated with clinicopathological features in some tumors
We downloaded clinicopathological data for 33 tumors. Regarding T staging, AUNIP expression was higher at T3 + T4 than at T1 + T2 in ACC, KIRP, LIHC, PRAD, and lower at T3+T4 in THCA (Fig. 3A). In ACC, HNSC, KICH, KIRC, KIRP, LUAD, LUSC, and PRAD, AUNIP was higher expressed in patients with N1&N2&N3 than in patients with N0, while in SKCM, AUNIP was higher expressed in patients with N0(Fig. 3B). In ACC, BLCA, HNSC, KIRP, LIHC, LUAD, UCEC, and UCS, AUNIP
expression increased with the increase of pathological staging, while in OV and SKCM, AUNIP expression decreased with the increase of pathological staging (Fig. 3C).
Gene variation of AUNIP in pan-cancer
Genetic alterations are a form of epigenetics. Genetic altera- tions of AUNIP in different tumors appear in the form of mutation, structural variant, amplification, deep deletion, and multiple alterations. Among them, CHOL has the high- est frequency of gene change, which is manifested as deep
A
4
-
20-
=
B
-
=
-
4
The expression of AUNIP Log2 (TPM+1)
The expression of AUNIP Log, (TPM=1)
The expression of AUNIP
S
The expression of AUNIP Log2 (TPM+1)
4
3
The expression of AUNIP
The expression of AUNIP
The expression of AUNIP
-
The expression of ALINIP
3
2.0
Log_ (TPM+1)
Log (TPM+1)
1.5
Log2 (TPM+1)
3
Log2 (TPM+1)
4
Log (TPM+1)
N
N
1
A
1.0
0
15
1
M
4
0
8
-
1
z
3
10
I
1
0
ACC
KIRP
0
0
LIHC
PRAD
THCA
ACC
1
HNSC
0.5
KICH
0
T1872
T3&T4
·
THAT2
T3&T4
T1872
T3&T4
T stage
T stage
12
T4673
T1&12
T3414
Pathologic T stage
T stage
NO
NT
NO
NIANG
T stage
Pathologic N stage
NO
NIANIANO
Pathologic N stage
Pathologie N stage
C
-
5
5
2
=
The expression of AUNIP Log_ (TPM+1)
. 6
2.5
3
The expression of AUNIP Log_ (TPM+1)
The expression of AUNIP Log, (TPM+1)
The expression of AUNIP Logy (TPM+1)
3
The expression of AUNIP
3
The expression of AUNIP Log, (TPM+1)
4
4
Log2 (TPM+1)
The expression of AUNIP To: Log, (TPM+1)
The expression of AUNIP Log(TPM+1)
2.0
2
2
2
4
2
Z
1
-
-
1
Y
1.0
1
0
3
0
0
0
·
1
ACC
BLCA
1
HNSC
0
KIRP
0
A
LIHC
0.5
KIRC
KIRP
LUAD
Stage IAStage # Stage il&Stage IV Pathologic stage
0
Stage I&Stage Il Stage IT&Stage IV Pathologic stage
Stage I&Stage E Stage HAStage IV Clinical stage
Stage I&Stage Il Stage I&Stage IV Pathologic stage
Stage I&Stage Ii Stage B&Stage IV Pathologic stage
NO
NIAN2
NO
Pathologic N stage
NIANG
NO
Pathologic N stage
NIAN2&N3
Pathologic N stage
-
s-
1
5.01
5
=
-
The expression of AUNIP Log_ (TPM+1)
s
The expression of AUNIP Log (TPM+1)
The expression Of AUNIP Log2 (TPM+1)
6
S
The expression of AUNIP Log, (TPM+1)
4.5
3
Too. (TPM+1)
The expression of AUNIP
Log2 (TPM+1)
The expression of AUNIP
Log2 (TPM+1)
The expression of ALINIP Log, [TPM+1)
&
4.0
4
2
y
3.5
0
0
0
3
0
2
0
3.0
0
1
1
2
1
T
LUAD
”
OV
SKCM
UCEC
25
UCS
LUSC
PRAD
SKCM
0
Stage I&Sange Il Singe Ili&Stage IV Pathologic stage
Stage I&Stage Il Sage HAStage IV FIGO stage
0
0
Stage I&Stage Il Stage HISStage IV Pathologic stage
Stage ISStage Il Stage INAStage IV Clinical stage
Stage I&Stage Ii Stage II&Stage IV Clinical stage
0
NO
NTANGENS
NO
N1
Pathologic N stage
Pathologic N stage
NO
NIAN2ANS
Pathologic N stage
deletion, followed by PCPG, which is mainly manifested as deep deletion. The third genetic alteration is PAAD (muta- tion, amplification and deep deletion). No genetic alteration
2.5%-
Mutation
Structural Variant
Amplification
Deep Deletion
Multiple Alterations
Alteration Frequency
2%-
1.5%
1%-
0.5%-
Structural variant data
Mutation data
CNA data
Cholangiocarcinoma (TCGA, PanCancer Atlas)
Pheochromocytoma and Paraganglioma (TCGA, PanCancer Atlas)
Pancreatic Adenocarcinoma (TCGA, PanCancer Atlas)
Uterine Corpus Endometrial Carcinoma (TCGA, PanCancer Atlas)
Sarcoma (TCGA, PanCancer Atlas)
Bladder Urothelial Carcinoma (TCGA, PanCancer Atlas)
Stomach Adenocarcinoma (TCGA, PanCancer Atlas)
Mesothelioma (TCGA, PanCancer Atlas)
Esophageal Adenocarcinoma (TCGA, PanCancer Atlas) Adrenocortical Carcinoma (TCGA, PanCancer Atlas)
Ovarian Serous Cystadenocarcinoma (TCGA, PanCancer Atlas) Cervical Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Lung Adenocarcinoma (TCGA, PanCancer Atlas) Prostate Adenocarcinoma (TCGA, PanCancer Atlas)
Skin Cutaneous Melanoma (TCGA, PanCancer Atlas) Testicular Germ Cell Tumors (TCGA, PanCancer Atlas)
Breast Invasive Carcinoma (TCGA, PanCancer Atlas)
Lung Squamous Cell Carcinoma (TCGA, PanCancer Atlas) Glioblastoma Multiforme (TCGA, PanCancer Atlas)
Kidney Renal Clear Cell Carcinoma (TCGA, PanCancer Atlas)
Kidney Renal Papillary Cell Carcinoma (TCGA, PanCancer Atlas)
Colorectal Adenocarcinoma (TCGA, PanCancer Atlas)
Diffuse Large B-Cell Lymphoma (TCGA, PanCancer Atlas) Liver Hepatocellular Carcinoma (TCGA, PanCancer Atlas)
Head and Neck Squamous Cell Carcinoma (TCGA, PanCancer Atlas)
Thyroid Carcinoma (TCGA, PanCancer Atlas)
Brain Lower Grade Glioma (TCGA, PanCancer Atlas)
Kidney Chromophobe (TCGA, PanCancer Atlas)
Thymoma (TCGA, PanCancer Atlas)
Acute Myeloid Leukemia (TCGA, PanCancer Atlas)
Uveal Melanoma (TCGA, PanCancer Atlas)
Uterine Carcinosarcoma (TCGA, PanCancer Atlas)
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of AUNIP was observed in DLBC, THCA, LGG, KICH, THYM, LAML, UVM, and UCS (Fig. 4).
AUNIP expression was related to TMB and MSI in some cancers
TMB and MSI are two common potential indicators for tumor immunotherapy response. The findings suggested that AUNIP expression had positive correlation with TMB in ACC, BLCA, BRCA, COAD, HNSC, LGG, LUAD, LUSC, PAAD, PRAD, SARC, and STAD, and the correlation coef- ficient with STAD is the highest (Fig. 5A). AUNIP had posi- tive interrelation with MSI in BLCA, LUSC, MESO, SARC, and STAD, and negative association with MSI in THCA (Fig. 5B).
Association of AUNIP with immune cell infiltration and immune checkpoints in pan-cancer
We applied ssGSEA to investigate the interrelation between AUNIP and 23 kinds of immune cell infiltration in pan-cancer and found that AUNIP was positively linked with Th2 cells in 30 kinds of tumors, and the positive cor- relation coefficient was the highest. Among the remaining immune cells, AUNIP had a correlation with one or more immune cells in different cancers (Fig. 6A). For the ESTI- MATE algorithm, in ACC, CESC, COAD, ESCA, GBM, HNSC, LUAD, LUSC, OV, READ, SKCM, STAD, THCA, UCEC, and UCS, AUNIP expression had negative associa- tion with stromalscore, immunescore, and estimatescore. In BLCA, CHOL, DLBC, KICH, LAML, LGG, MESO,
PAAD, PCPG, PRAD, and SARC, AUNIP was not associ- ated with stromalscore, immune score, and estimatescore, and AUNIP was related to stromalscore, immunescore, and estimate score in the remaining 7 tumors(Fig. 6B). Immune checkpoint is the target of immunotherapy at this stage, and our results showed that AUNIP was positively or negatively associated with an immune checkpoint in all tumors except ACC, CESC, CHOL, MESO, OV, and UCS (Fig. 6C).
Drug sensitivity analysis
We found that AUNIP expression was negatively linked with 50% inhibitory concentration (IC50) values of 30 drugs based on the results of the CTRP dataset in GSCA. There was a strong negative correlation with IC50 of COL-3, dinaciclib, and docetaxel (Fig. 7). These findings indicated that AUNIP was significantly associated with different drug sensitivities in various tumor cell lines and may be a latent target for cancer therapy.
Gene functional enrichment of AUNIP in pan-cancer
The results of GSEA demonstrated that AUNIP was pri- marily participated in cell cycle, DNA replication, mis- match repair, and homologous recombination in most tumors (Fig. 8). In these tumors, AUNIP was mainly involved in the development of tumors through the above pathways.
A
ACC
B
UCS
UVM
BLCA
ACC
0.6
BRCA ***
UCS
UVM
BLCA **
0.4
BRCA
UCEC
CESC
UCEC
CESC
0.3
THYM
0.4
CHOL
THYM
CHOL
0.2
THCA
COAD ***
** THCA
0.
COAD
0.2
TGCT
DLBC
TGCT
0
DLBC
0.1
*** STAD
ESCA
STAD
-0.2
ESCA
SKCM
GBM
SKCM
-0.3
GBM
*** SARC
HNSC *
*** SARC
HNSC
READ
KICH
READ
KICH
PRAD
KIRC
PRAD
KIRC
PCPG
KIRP
PCPG
KIRP
*** PAAD
LAML
PAAD
LAML
OV
LGG ***
OV
LGG
MEŞQ
*** LUSC
*** LUAD
LIHC
* MESQ
** LUSC
LUAD
LIHC
TMB
MSI
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A
LAG3
SIGLEC15
PDCD1LG2
C
TIGIT
HAVCR2
CTLA4
PD-1
17-Od
aDC
ACC
B cells
BLCA
CD8 T cells
BRCA
Cytotoxic cells
DC
CESC
Eosinophils
CHOL
IDC
Macrophages
COAD
Mast cells
p < 0.05
OLBC
Neutrophils
NK CD56bright cells
Cor
1.0
ESCA
NK CD56dim cells
NK cells
0.5
GBM
PDC
0.0
HNSC
T cells
T helper cells
-0.5
KICH
Tcm
-1.0
KIRC
Tem
TFH
KIRP
Tgd
LAMEL
Th1 cells
Th17 cells
LGG
Th2 cells
UHC
TReg
ACC BLCA
BRCA
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
LUAD
LUSC
B
MESO
or
PAAD
StromalScore
¥
*
* p < 0.05
PCPG
PRAD
Cor
1.0
READ
SARC
ImmuneScore
0.5
SKCM
0.0
STAD
TGCT
ESTIMATEScore
*
-0.5
THCA
-1.0
THYM
ACC
BLCA
BRCA
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
UCEC
UCS
UVM
Cor
900 > d.
-1.0
S’O-
00
0.5
1.0
Correlation between CTRP drug sensitivity and mRNA expression
Correlation
-0.4
-0.1
0.0
AUNIP
FDR
⇐ 0.05
FDR
3-CI-AHPC
AZD7762
BI-2536
BRD-K66453893
BRD-K70511574
CD-437
COL-3
CR-1-31B
FQI-2
GSK461364
GW-843682X
KX2-391
ML311
NVP-231
PHA-793887
SB-225002
SB-743921
SR-II-138A
clofarabine
cytarabine hydrochloride
dinaciclib
docetaxel
leptomycin B
nakiterpiosin
narciclasine
pevonedistat
rigosertib
tivantinib
triazolothiadiazine
vincristine
0.001
⇐ 0.0001
Drugs
Overexpression AUNIP was correlated with clinical information in LIHC and an independent prognostic gene for LIHC
IHC analysis demonstrated that AUNIP was overexpressed, compared to normal liver tissues (Fig. 9A,B). AUNIP
expression was linked with histologic grade, not corre- lated with age, gender, and pathologic stage (Table 1). Kaplan-Meier analysis suggested that the patients with high-expression group had worse prognosis (Fig. 9C).
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ACC BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
NSE
TGCT
THCA
THYM
UCEC
UCS
UVM
KEGG_HOMOLOGOUS_RECOMBINATION
2
KEGG_MISMATCH_REPAIR
KEGG_CELL_CYCLE
1
KEGG_DNA_REPLICATION
0
KEGG_RIBOSOME
-1
KEGG_ECM_RECEPTOR_INTERACTION
-2
KEGG_MATURITY_ONSET_DIABETES_OF_THE_YOUNG
-3
KEGG_P53_SIGNALING_PATHWAY
KEGG_HEDGEHOG_SIGNALING_PATHWAY
KEGG_STEROID_BIOSYNTHESIS
Discussion
The global mortality burden is predominantly attributed to malignant neoplasms resulting from dysregulated cellular proliferation. Recent decades have witnessed remarkable progress in early disease detection methodologies, encompassing the conventional approaches including radiotherapy, surgical procedures, tailored therapeutic regimens, and chemotherapeutic interventions (Mishra et al. 2023a, 2023b, 2023c). However, cancer remains a major threat to human health. Therefore, it is important to find an effective biomarker to predict the development and prognosis of cancer. DNA double-strand breakage damage is the most severe form of damage, if not repaired in time or abnormal repair occurs, it will lead to a series of changes in the cell genome, directly lead to deactivation of tumor suppressor genes or overexpression of oncogenes, and eventually lead to cell cancer (Burma et al. 2006). The most critical factor affecting the selection of DNA double-strand break repair pathways is the state of the cut end of DNA, and AUNIP is a key factor regulating the state of DNA cleavage ends. AUNIP, a binding protein of protein kinase A and Ninein proteins, also known as AIBP, is a structurally specific DNA-binding protein that is localized to the 135 open-reading framework of chromosome 1. According to reports, it is highly expressed in various tumors (Ma et al. 2020). AUNIP is highly expressed in astrocytoma and other brain tumors, suggesting that AUNIP may play a role as oncogenic genes in the development of brain tumors (Lieu et al. 2010).
Our study analyzed the expression, clinical significance, prognosis, mutation, and immunity of AUNIP from the perspective of pan-cancer using a multi-omics system. It was found that AUNIP expression was increased significantly in most tumors compared to normal tissues, suggesting
that AUNIP may be a key gene in cancer development. We conducted IHC analysis to confirm the higher expression of AUNIP in LIHC, which was consistent with TCGA database. In addition, AUNIP with high expression in ACC, LGG, LIHC, MESO, and SARC had poorer OS and DFS than those of AUNIP with low expression, suggesting that the high expression of AUNIP in some tumors influenced patients’ prognosis. Furthermore, AUNIP expression was related to the T stage, N stage, and clinicopathological stage in some cancers, indicating that AUNIP may be a promising valuable diagnostic and prognostic marker in multiple tumors. IHC analysis indicated that AUNIP was linked with histologic grade in LIHC. Moreover, AUNIP expression was an independent prognostic index by univariate and multivariate regression in LIHC. We used cBioportal to study the frequency of AUNIP gene alteration in tumors. In CHOL, the frequency of genetic changes was the highest, with all deep deletions, followed by PCPG, with all deep deletions.
Immune cell infiltration is closely linked to cancer progression (Marcas and Walzer 2018). Recent studies have suggested that tumor progression is caused by an imbalance between the tumor’s immune state and the host’s immune response (Nabbi et al. 2019). We studied the relationship between AUNIP and immunocyte infiltration and observed that AUNIP expression was positively related to Th2 for most tumors, indicating that with the increase of AUNIP expression, Th2 concentration was up-regulated. Th2 cells are not conducive to the anti-tumor effect of cellular immunity. Th1/Th2 drift will protect the tumor from immune surveillance and immune attack, thus promoting the development and progression of tumors (Sharma et al. 2007). Furthermore, we applied the ESTIMATE algorithm to discuss the correlation between AUNIP and stromalscore, immunescore, and estimatescore in different tumors. In most
A
Normal tissues
Hepatocellular carcinoma
B
C
p = 2.8e-05
12
1.0
Low groups
High groups
10
0.9
IHC score
Survival probability
8
0.8
6
0.7
4
7
0.6
2
HR = 3.16 (1.59 - 6.28)
0.5
P = 0.001
Normal
LIHC
0
1
2
3
4
5
6
Time
tumors, AUNIP was negatively correlated with these three scores. The application in immune checkpoint inhibitors has elevated immunotherapy to a new level. Immunotherapy has been considered as an effective therapy for various advanced and invasive cancers (Morse et al. 2005; Zhou and Zhong 2004). At present, immunotherapy has been applied to a variety of tumors. Common immune checkpoints include PD-1, PD-L1, CTLA-4, PDCD1LG2, TIGIT, HAVCR2, SIGLEC15, and LAG3. In some tumors, AUNIP expression was positively related to immune checkpoint expression, suggesting that these patients with high AUNIP expression may benefit from immunotherapy. TMB and MSI are effective markers to predict the effect of immunotherapy. MSI-H’s tumor gene repair system is abnormal, and there may be more gene mutations, which are easily recognized by
T cells and may respond better to immunotherapy (Bateman 2021). The higher the TMB, the greater the probability that neoantigens expressed by the tumor will be identified by the immune system. Therefore, tumors with high TMB are more sensitive to immune therapy (Liu et al. 2019). Our study demonstrated that AUNIP had positive association with TMB and MSI in BLCA, SARC, and STAD, and patients with high AUNIP expression in these three types of tumors were more susceptible to immunotherapy.
The results of gene enrichment analysis showed that AUNIP caused tumors progression through the cell cycle, DNA replication, mismatch repair, and homologous recombination in most tumors. This is consistent with the literature reports (Lou et al. 2017) In addition, we also performed a correlative analysis between AUNIP and drug
| Characteristics | AUNIP | X | P | |
|---|---|---|---|---|
| Low expression | High expression | |||
| Age | 0.25 | 0.72 | ||
| ≤ 55 | 14 | 20 | ||
| > 55 | 5 | 5 | ||
| Gender | 0.24 | 0.63 | ||
| Female | 10 | 15 | ||
| Male | 9 | 10 | ||
| Pathologic stage | 0.09 | 0.76 | ||
| Stage I+ Stage II | 9 | 13 | ||
| Stage III + Stage IV | 10 | 12 | ||
| Histologic grade | 4.54 | 0.03 | ||
| G1+G2 | 13 | 9 | ||
| G3 | 6 | 16 | ||
sensitivity, and we used a public database to predict sev- eral candidate targeted small-molecule drugs. We found that AUNIP was negatively related to IC50 values of 30 drugs, indicating that these drugs stop the progression of the tumor. This provides a novel insight into expanding the therapeutic selection of these targeted small-molecule drugs and developing new drugs specifically targeting AUNIP.
We performed IHC analysis to discuss AUNIP expression in LIHC and the findings demonstrated that AUNIP expression was up-regulated in LIHC. The patients with high-expression group had unfavorable prognosis. These findings suggested that overexpression AUNIP was correlated with the progress of LIHC development and prognosis.
In our work, the expression, prognosis, and characteristics of AUNIP were elucidated by pan-cancer analysis. However, there are some shortcomings in this study. The characteristics of AUNIP were analyzed through bioinformatics and only conducted IHC to verify the overexpression of AUNIP in LIHC. However, there was no biological experiment to verify it. Therefore, in the following studies, it needs more experiment to further validate the mechanism of effect of AUNIP in inducing tumors.
Acknowledgements The authors express our gratitude for the contribu- tions of TCGA, GTEx, TIMER, GEPIA2, cBioportal, and GSCALite databases.
Author contributions All authors participated in the design, methodology, data analysis, and manuscript review of the study; the contributions of XRG and TL are equal. NL provided experimental concepts and designs. XRG, TL, and LJ have made contributions in conceptualization, project management, writing review, and editing. All authors have read and approved the final manuscript.
Funding This study was granted from Zhejiang Province Traditional Chinese Medicine Technology Project (2023ZL249).
Availability of data and materials The data included in the research report are included in the article. Further inquiries can be made directly to the corresponding author.
Declarations
Conflict of interest The authors declare no competing interests.
Ethics approval and consent to participate The study has been per- formed in accordance with the Declaration of Helsinki and was approved by Institutional Research Ethics Committee of the Zheji- ang Provincial People’s Hospital, and written informed consent was obtained from all patients.
Consent for publication.
Not applicable.
Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, 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/.
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