Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes ASPM and KIF11 as Novel Biomarkers for Adrenocortical Carcinoma

Jia-Yin Chen1,2,#, Yu-Ting Xue1,2,#, Shi-Wei Lin3,#, Qi You1,2,#, Bin Lin1,2, Jiang-Bo Sun1,2, Qing-Shui Zheng1,2, Yong Wei1,2, Shao-Hao Chen1,2, Xue-Yi Xue1,2,4, Xiao-Dong Li1,2, Zhi-Bin Ke1,2,* and Ning Xu1,2,4,*

1Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; 2Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hos- pital, Fujian Medical University, Fuzhou 350212, China; 3Fuzhou Hospital of Traditional Chinese Medicine Affiliated to Fujian University of Traditional Chinese Medicine, Fuzhou 350001, China; 4Fujian Key Laboratory of Precision Medi- cine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China

Abstract: Introduction: Adrenocortical Carcinoma (ACC) is regarded as an aggressive endo- crine malignant tumor. The understanding of ACC tumorigenesis is still incomplete. This study aims to identify candidate tumor mutation burden (TMB)-related prognostic genes and explored the potential molecular mechanism of ACC based on comprehensive bioinformatic methods.

Methods: Single-nucleotide variations and transcriptome data were downloaded from the TCGA database. TMB scores were calculated using single-nucleotide variation data, and then, the correlation of TMB with tumor immune microenvironment, clinicopathologic characteris- tics, and PD-L1 expression level was explored. Differentially Expressed Genes (DEGs), rang- ing from high and low TMB scores, were identified. Weighted Gene Co-expression Network Analysis (WGCNA), Protein-Protein Interaction (PPI) networks, and Kaplan-Meier survival analysis were used to screen candidate TMB-related prognostic genes. Preliminary experi- mental verification of ASPM and KIF11 in ACC tumorigenesis was conducted.

ARTICLE HISTORY

Received: March 04, 2025 Revised: May 28, 2025 Accepted: June 10, 2025

DOI: 10.2174/0115680096396835251019170810

Results: Patients with high TMB had worse OS, DSS, PFS, advanced pathological stage, lower PD-L1 expression level, lower stromal score, lower immune score, and higher tumor purity score. Seven ninety-seven DEGs in all between the high and low TMB groups were identified, including 203 downregulated DEGs and 594 upregulated DEGs. Functional enrichment anal- ysis suggested that these DEGs might participate in cell division and cell cycle regulation. Furthermore, WGCNA analysis identified the turquoise module as the most significantly as- sociated module with TMB. After screening with the PPI network and validating using sur- vival analysis, a total of eight candidate TMB-related prognostic genes for ACC patients were finally identified, including ASPM, BIRC5, BUB1, CDC20, CDCA5, CEP55, KIF11, and TPX2. Preliminary experimental verification revealed that ASPM and KIF11 could promote the proliferation of ACC cells and the tumor growth of mice.

Discussion: ASPM and KIF11, identified as key TMB-related prognostic genes, promoted proliferation and inhibited apoptosis of ACC cells. This functional role revealed their signifi- cant potential as novel therapeutic targets for ACC.

Conclusion: A total of eight candidate TMB-related prognostic genes (including ASPM, BIRC5, BUB1, CDC20, CDCA5, CEP55, KIF11, and TPX2) for ACC patients were identi- fied. Preliminary experimental verification revealed that ASPM and KIF11 could promote the proliferation of ACC cells and ACC tumor growth in vivo.

Keywords: Adrenocortical carcinoma, tumor mutation burden, prognostic genes, biomarker, bioinformatics.

1. INTRODUCTION

Adrenocortical Carcinoma (ACC) is reported as an aggres- sive and rare endocrine malignant tumor [1-3]. The annual in- cidence of ACC is 0.5-2 cases per million [4], and the five- year survival rate for ACC patients is merely 16-38% [5]. Sur- gery was the first rational cancer treatment, as outlined in his- torical and contemporary oncology practices [6]. However, the majority of ACC patients were diagnosed at an advanced stage without the opportunity of surgical resection [7]. Com- bining mitotane and cytotoxic drugs is regarded as first-line treatment for cases with advanced ACC, but the prognosis is still rather poor [1]. The European Network for the Study of Adrenal Tumors (ENSAT) staging system, widely used for ACC, remains controversial [4]. Recent advances in molecu- larly targeted therapies and immune checkpoint inhibitors, which have transformed outcomes in other solid tumors, hold theoretical promise for ACC [6]. However, limited targets and sparse clinical trial data underscore the need for improved ge- nomic characterization and novel therapeutic strategies. Re- cent developments in next-generation sequencing and bioin- formatic analyses have highly facilitated the investigation of the molecular mechanism of ACC, which might contribute to the evolution of diagnosis, treatment, and risk stratification of ACC [2]. However, the understanding of ACC tumorigenesis is still incomplete.

Tumor Mutation Burden (TMB) is defined as the total number of nonsynonymous mutations per megabase and out- lines genomic mutation [8]. Numerous studies have reported the association between TMB and immunotherapy response [8,9]. Generally, higher TMB results in more new antigens, enhancing tumor immunogenicity and improving response to immunotherapy [10]. TMB is also considered as strong prog- nostic factor after immunotherapy in multiple cancer types, and patients receiving immunotherapy with a high TMB fre- quently had a better outcome compared with those with low TMB [11]. Although most studies considered immunological therapy failure in advanced ACC [12], avelumab had clinical activity and manageable safety in platinum-treated metastatic ACC patients [13]. However, currently, the relationship be- tween TMB and ACC carcinogenesis has been less investi- gated, and there are no studies exploring candidate markers associated with TMB in ACC.

This work identified candidate TMB-related prognostic genes and explored the potential molecular mechanism of ACC based on comprehensive bioinformatics methods for the first time. TMB scores were calculated using single-nucleo- tide variation data, and then the correlations of the TMB with tumor immune microenvironment, clinicopathologic charac-

teristics, and PD-L1 expression level were analyzed. Differ- ential expression analysis, Weighted Gene Co-expression Network Analysis (WGCNA), Protein-Protein Interaction (PPI) networks, and Kaplan-Meier survival analysis were used to screen candidate TMB-related prognostic genes. The Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted to ex- plore the potential mechanism. Moreover, we conducted pre- liminary experimental verification of the biological functions of ASPM and KIF11 in ACC tumorigenesis.

2. MATERIALS AND METHODS

2.1. Acquisition of Single-nucleotide Variations and Transcriptome Data

Single-nucleotide variation and transcriptome data of 92 ACC samples were downloaded from the TCGA database (https://portal.gdc.cancer.gov/) via the GDC tool. The “maftools” R package was used to perform the visualization process of the mutation profile of ACC. Besides, the transcrip- tome data of 79 ACC cases in the TCGA database were also downloaded. Considering that there were no normal adrenal samples in the TCGA database, we downloaded 128 normal adrenal samples from the Genotype Tissue Expression (GTEx) database (https://commonfund.nih.gov/GTEx/). The dataset of GSE19776 (including 22 ACC cases) from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) was obtained.

2.2. Calculation of TMB Scores and Survival Analysis

Generally, the total number of nonsynonymous mutations per megabase was defined as TMB [8]. In this study, the TMB score was calculated as total mutation frequency divided by the length of exons. Commonly, the length of exons was 38 megabase [14]. According to the median value, we divided 92 ACC samples into a low-TMB group (47 cases) and a high- TMB group (45 cases). Furthermore, we obtained the data on overall survival (OS) from the TCGA database and disease- specific survival (DSS) and progression-free survival (PFS) from TCGA-CDR [15]. The TMB score and corresponding survival information were merged. Survival analysis and log- rank test were performed to compare the OS, DSS, and PFS between low-TMB and high-TMB groups.

2.3. Correlation of TMB with Clinical Characteristics and Tumor Microenvironment

The mRNA expression level of PD-L1 between the low- TMB and high-TMB groups of ACC was compared. Clinical information from the TCGA database, including gender, pathological stage, T stage, and N stage, was also used to ex- plore whether they were correlated with TMB score. To gain insight into the association of TMB with immune cell infiltra- tion in ACC patients, the CIBERSORT algorithm was em- ployed to estimate the proportion of 22 types of immune cells [16]. The Wilcoxon test was used to compare the differential abundances of immune cell infiltration between the low-TMB and the high-TMB groups. The tumor microenvironment (TME) consists of stromal cells, immune cells, extracellular matrix, and numerous molecules, etc. It is known that the TME plays a vital role in tumor progression and anti-tumor

*Address correspondence to this author at the Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University; Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University; Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Fuzhou 350005, China; E-mail: drxun@fjmu.edu.cn (N.X); Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, 20 Chazhong Road, Fuzhou 350005, China; E-mail: kzb568030455@163.com (Z.B.K.) #These authors contributed equally to this work.

immune response [17]. In 2013, Yoshihara et al. [18] estab- lished an algorithm called ESTIMATE for estimating the pro- portion of stromal and immune components in TME using ex- pression data. Utilizing the ESTIMATE algorithm, the im- mune score, stromal score, ESTIMATE score, and tumor pu- rity score could be calculated. Then, the correlation of TMB with tumor microenvironment using the ESTIMATE algo- rithm was investigated.

2.4. Identification of DEGs Between High and Low TMB Scores

According to the TMB level, the transcriptome data of 79 ACC samples were divided into low-TMB (41 cases) and high-TMB groups (38 cases). Then, we utilized the “limma” R package and the Wilcoxon test to identify Differentially Ex- pressed Genes (DEGs) between these two groups. |Log2 Fold Change (FC) | > 1 and False Discovery Rate (FDR) <0.05 were taken as the cutoff criterion for better significance and accuracy, as previously described [19]. A heatmap was drawn to present these significant DEGs using the “pheatmap” R package. The GO analysis and KEGG analysis for DEGs were conducted for functional annotation and pathway enrichment, respectively. The R package “org.Hs.eg.db” was exploited to obtain the Entrez ID for each DEGs, and “clusterProfler”, “en- richplot”, and “ggplot2” R packages were utilized to perform GO and KEGG analyses.

2.5. Weighted Gene Co-expression Network Analysis (WGCNA) and Construction of PPI Network

The gene co-expression networks for DEGs were con- ducted using the WGCNA package in R software and visual- ized by Cytoscape software. First of all, the WGCNA algo- rithm was used to construct a scale-free co-expression net- work for the DEGs. Further, a weighted adjacency matrix was conducted using a power function amn= |CmnlB (amn= adja- cency between gene m and gene n; Cmn= Pearson’s correlation between gene m and gene n. According to the mean connec- tivity, we selected an appropriate power of ß, a soft-threshold- ing parameter. Then, the adjacency matrix was converted to a topological overlap matrix (TOM). The dynamic tree-cut method was used to identify modules, which were named us- ing different colors. The main component for each module was defined as Module Eigengene (ME). We calculated the correlation between clinical characteristics and each ME to identify the most significant module. Finally, the module highly correlated with TMB was selected for further analysis. Search Tool for the Retrieval of Interacting Genes (STRING) (https://string-db.org/cgi/input.pl) is a vital biological database for constructing protein-protein interaction (PPI) networks. In this study, we imported TMB-related genes into the STRING database for PPI network construction. By taking the intersec- tion with 797 DEGs and screening by degree score >65, we fur- ther screened candidate TMB-related genes for ACC patients.

2.6. Validation of Prognostic Value and Differential Expression Levels between Normal and ACC Samples

Survival analysis in the TCGA database was performed to demonstrate the prognostic value of the candidate TMB- related genes for ACC patients. Besides, the dataset of

GSE19776 from the GEO database was utilized for external validation of prognostic value. We also explore the differen- tial expression levels of candidate TMB-related genes be- tween normal and ACC samples. Multivariate Cox analysis was used to reveal the independent predictive value of ASPM and KIF11 for OS. UALCAN database (http://ual- can.path.uab.edu/) was used to reveal the association of ASPM with the AJCC stage and TP53 mutant status. KEGG analysis using the gene set enrichment analysis (GSEA) method was used to reveal the functional enrichment of ASPM and KIF11.

2.7. Cell Culture

Human ACC cell lines NCI-H295R and SW-13 were pro- vided by Procell Life Science&Technology Co., Ltd. Then, the NCI-H295R cells were cultured in NCI-H295R cell-spe- cific medium (CM-0399, Procell, Wuhan, China) while the SW-13 cells were cultured in SW-13 cell-specific medium (CM-0451, Procell, Wuhan, China). All cells were cultured in a standard humidified incubator at 37°℃ in a 5% CO2 atmos- phere.

2.8. Quantitative Reverse-transcription Polymerase Chain Reaction (qRT-PCR)

Total RNAs were extracted from ACC cells with Trizon Reagent (CW0580S, CWBIO, Jiangsu, China). After measur- ing the RNA concentration, the HiScript II Q RT SuperMix for qPCR (+gDNA wiper) (R223-01, Vazyme, Nanjing, China) was used to generate cDNA. Then, 2×SYBR Green PCR Master Mix (A4004M, Lifeint, Xiamen, China) was used to perform the quantitative RT-PCR with the Fluorescence PCR instrument (CFX Connect™M, Bio-Rad Laboratories, Shanghai, China) according to the manufacturer’s protocol. Results were normalized to the expression of GADPH. Rela- tive quantification of ASPM and KIF11 expression was cal- culated by the 2-44Ct method. The primer sequences were as follows:

GAPDH: forward 5-GGTGTGAACCATGAGAAGTATGA- 3,

reverse 5-GAGTCCTTCCACGATACCAAAG-3, product size 123 bp;

ASPM: forward 5-ACACCTGTAAGGACCAGAATAGT-3, reverse 5-CCAAGCGTATCCATCACCATT-3, product size 119 bp;

KIF11: forward 5-GATGGACGTAAGGCAGCTCA-3,

reverse 5-TGTGGTGTCGTACCTGTTGG-3, product size 185 bp.

2.9. RNA Interference

The NCI-H295R and SW-13 cells were seeded in 6-well plates, and when the cell confluence reached about 80%, the cells were transfected with corresponding small-interfering RNAs (siRNAs) using Lipofectamine 2000 reagent (Thermo Fisher Scientific, Waltham, USA) according to the instruc- tions. And, the cells were subsequently cultured for about 48 hours or for the indicated periods. Next, total RNA was ex- tracted for qPCR analysis, and the total proteins were extracted for western blot analysis to confirm the expression

levels of ASPM and KIF11. The siRNAs were produced by Shanghai GenePharma Co., Ltd (Sequences were presented in Supplemental Table 1).

2.10. Western Blotting

Samples of ACC cells were washed and resuspended in RIPA lysis solution (C1053, Applygen, Beijing, China). Pro- teins were separated based on their molecular weight by so- dium dodecyl sulphate polyacrylamide gel electrophoresis (SDS-PAGE), then transferred onto polyvinylidene fluoride (PVDF) membranes (IPVH00010, Millipore, Massachusetts, USA). Next, 3% skimmed milk powder (P1622, Applygen, Beijing, China) was used to block the membranes for 1h, and the membranes were then incubated at 4℃ overnight with specific primary antibodies (anti-ß-Actin, YM3028, Immuno- way, 1/7000; anti-ASPM, DF10064, Affinify, 1/1000; anti- KIF11, YT1480, Immunoway, 1/1000). Washing the mem- branes 3 times for 10 minutes each time in 1×TBST, incubated with secondary antibodies (Rabbit Anti IgG, ZB-2301, Bei- jing, China, 1/2000) at room temperature for 2 hours, and then rinsed again in TBST. At last, the relative expression level was measured by an ultra-high sensitivity chemiluminescence imaging system (Chemi DocTM XRS+, Bio-Rad Laboratories, Shanghai, China) with SuperSignal® West Pico Chemilumi- nescent Substrate (RJ239676, Thermofisher, Shanghai, China).

2.11. CCK-8 Viability Assays

The NCI-H295R and SW-13 cells were seeded in 96-well plates at 5×103/well and incubated for the indicated times. The CCK-8 assay kit (MA0218, meilunbio) was used to measure the viability of cells. When the preset time is reached, discard the old medium and replace it with a complete medium con- taining a 10% CCK-8 assay kit. Finally, after incubating at 37℃ for one hour, the plates were measured the absorbance at 450 nm using a microplate reader.

2.12. Colony Formation Assay

The NCI-H295R and SW-13 cells, in the logarithmic growth stage, were digested by trypsin and were beaten into single cells in a complete medium. The cell suspension was diluted in gradient multiples and inoculated in 6-well plates. All cells were cultured in a standard humidified incubator at 37°℃ in a 5% CO2 atmosphere for about 2 weeks. When visi- ble clones were observed, the medium was discarded, and the plates were washed with PBS for 5 minutes. After being fixed with 0.5% paraformaldehyde for 30 minutes, the cells were stained with configured 0.1% crystal violet (G1061, Solarbio, Beijing, China) for 1 hour, and finally, the crystal violet was removed. Images were collected under the same parameters.

2.13. Flow Cytometry

The cells were cultured for a predetermined time. After collecting adequate cells, the Annexin V-FITC/PI detection kit (A211-01, Vazyme, Nanjing, China) was used to access the apoptosis according to the manufacturer’s protocol. In brief, the cell pellets were resuspended in 100uL 1xbinding buffer,

incubated with 5uL Annexin V-FITC and 5uL PI for 10 minutes, and finally added 400uL 1×binding buffer was added. NovoCyteTM Flow cytometry (NovoCyte 2060R, ACEA BIO Co., LTD, Hangzhou, China) was used for analysis.

2.14. In vivo Tumorigenesis Assay

Five-week-old male BALB/c nude mice (weight: 14-18g) were purchased from Sipeifu (Suzhou) Biotechnology Co., Ltd (Suzhou, China) and maintained in a SPF environment. A total of 30 mice were randomly divided into 6 groups (n=5) using a table of random numbers. For the ASPM in vivo func- tional assay, a total of 3×106 NCI-H295R cells were sus- pended in 200 uL medium and injected subcutaneously into the subaxillary region of each nude mouse. For the KIF11 in vivo functional assay, a total of 1×107 SW-13 cells were sim- ilarly prepared in 200 µL medium and injected subcutane- ously. Tumor growth was monitored every 3 days in a random and blinded fashion, and tumor volume was calculated as the formula: tumor volume (V) = 1/6xlong diameterx short diam- eter2. All mice were euthanized by CO2 asphyxiation after in- jection for 24 days. Tumors were surgically dissected, pre- cisely weighed, and documented by macroscopic imaging for subsequent comparative analysis. No animals or individual data points were excluded from any analysis.

2.15. Statistical Analysis

Each experiment was performed at least three times inde- pendently. Statistical analyses were performed using SPSS 19.0 software (IBM, Armonk, NY, USA) and Prism version 8.0 (GraphPad Software, San Diego, CA, USA). Data were presented as mean ± SD unless otherwise stated. Student’s t tests, one-way ANOVA test, two-way repeated measures ANOVA test, or Wilcoxon rank sum test were used to analyze the statistical significance where appropriate. A p-value of <0.05 was considered statistically significant.

3. RESULTS

3.1. Somatic Mutation Landscape of ACC

The “Maftools” R package to analyze the data of single- nucleotide variations from the TCGA database for obtaining the somatic mutation landscape of ACC was used. Fig. (1A) revealed the mutually exclusive or coincident associations across mutated genes in ACC. Mutation information of the 30 genes with the highest mutation frequency was exhibited in the waterfall plot as well (Fig. 1B). Among all the top 9 variant classifications, missense mutations occurred most commonly in ACC (Fig. 1C). The single-nucleotide poly- morphism (SNP) accounted for the majority of variant types (Fig. 1D). The C > T transition was the most frequent single nucleotide variant (SNV) class, followed by the C > A transi- tion (Fig. 1E). Moreover, the number of mutational bases in each ACC sample was counted and presented, and several var- iant classifications were shown with different colors in the box plot (Fig. 1F-G). The top 10 mutated genes of ACC were obtained and presented, including TTN (12%), TP53 (18%), MUC4 (14%), MUC16 (14%), CTNNB1 (15%), PKHD1 (9%), NF1 (9%), CNTNAP5 (9%), PCDH15 (8%) and ASXL3 (8%) (Fig. 1H).

Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes

3.2. Correlation of TMB with Clinicopathologic Characteristics, PD-L1 Gene Expression Level, and Tumor Immune Microenvironment

After calculating the TMB score, ACC patients were clas- sified into two groups, including the low-TMB group (47 cases) and the high-TMB group (45 cases). The clinical data of these two groups are demonstrated in Table 1. Survival analysis revealed that patients in the high-TMB group were associated with significantly worse OS, DSS, and PFS (Fig. 2A-C) compared with those in the low-TMB group. Besides, high TMB was significantly associated with advanced T stage, advanced AJCC stage, and lower PD-L1 expression

level (Fig. 2D-F). However, the TMB score between male and female ACC patients was not statistically different, and there was also no significant difference in TMB score between pa- tients with and without lymph node metastasis (Fig. 2G-H). We also explored the relationship between the TMB score and the tumor immune microenvironment. In comparison to those with low TMB, samples with high TMB had significantly lower levels of naïve B cells, gamma delta T cells, and acti- vated NK cells, and higher levels of memory-activated CD4 T cells (Fig. 3A). ACC patients with high TMB have associa- tions with low stromal scores, low immune scores, low ESTI- MATE scores and high tumor purity scores (Fig. 3B-E).

Fig. (1) Contd ...

TP53 [17]

CTNNB1 [14]

MUC16 [13]

MUC4 [13]

CNTNAPS [ 8]

TIN (11)

NFt [ 8]

PKHD1 / 8}

ASXL3 [ 7]

DST [7]

HMCN1 [7]

PCDH15 [7]

PRKARTA [ 7]

ANK2 [ 6]

CCDC168 [ 6]

CMYA5/ 6}

PBN2 [ 6]

MENY [ 6]

SVEP1 | 67

FAT4 [5]

FAT4 [ 5]

SVEP1 [6]

MEN1 [6]

FBN2 [ 6]

CMYA5 [ 6]

CCDC168 [6]

ANK2 [ 6]

* P < 0.001

PRKARIA [7]

P < 0.05

PCDH15[7]

HMCN1 [7]

DST [7]

ASXL3 [ 7]

PKHOT [ 8]

NF1 [8]

CNTNAP5 [ 8]

>3 (Co-occurence)

TTN [11]

110(P-value)

MUC4 [13]

MUC16 [13]

CTNNB1 [14]

Altered in 68 (73.91%) of 92 samples.

1776

TMB

No. of samples

TP53

18%

CTNN51

15%

MUC16

14%

MUC4

14%

TTN

12%

CNTNAPS

NF1

PKHD1

ASXL3

DST

HMCHT

PCDH15

PRKARTA

ANK2

CCDC168

CMYAS

FBN2

MENS

SVEPİ

ADGRG4

AHNAK2

ATRX

EYS

FAT3

FAT4

HUWET

KMT2B

LRPT

OBSCN

WDFY4

Frame_Shift_Del

Frame_Shift_Ins

Fig. (1). The landscape of somatic mutation in ACC. The mutually exclusive or coincident associations across mutated genes in ACC (A). Mutation information of the 30 genes with the highest mutation frequency (B). The top 9 variant classifications (C). The variant type (D). The single nucleotide variants (SNV) class (E). The amounts of mutational bases in each ACC sample (F). The various variant classifications were shown with different colors in box plot (G). The top 10 mutated genes of ACC (H).

Variant Classification

Variant Type

SNV Class

Missense_Mutation

T>G

250

Nonsense_Mutation

SNP

Frame_Shift_Del

T>A

672

Splice_Site

T>C

989

Frame_Shift_Ins

INS

In_Frame_Del

C>T

3758

Nonstop_Mutation

C>G

1124

In_Frame_Ins

DEL

Translation_Start_Site

C>A

3220

1000

2000

3000

4000

5000

6000

1000

2000

3000

4000

5000

6000

7000

0.00

0.25

0.50

0.75

1.00

Variants per sample Median: 21.5

Variant Classification summary

Top 10 mutated genes

1776

TTN

12%

TP53

18%

MUC4

14%

1184

MUC16

14%

CTNNB1

15%

PKHD1

592-

NF1

CNTNAP5

PCDH15

ASXL3

ญ

Table 1. Clinical data of 92 patients with adrenocortical carcinoma from TCGA database
Characteristics	Low TMB Group (47 cases)	High TMB Group (45 cases)
Average TMB scores	0.530795	5.823392
Gender, n (%)
Female	30 (63.83%)	30 (66.67%)
Male	17 (36.17%)	15 (33.33%)
TCGA stage, n (%)
Stage I	7 (14.89%)	2 (4.45%)
Stage II	27 (57.45%)	17 (37.78%)
Stage III	9 (19.15%)	10 (22.22%)
Stage IV	3 (6.38%)	15 (33.33%)
Unknown	1 (2.13%)	1 (2.22%)

(Table 1) Contd …

Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes

Characteristics	Low TMB Group (47 cases)	High TMB Group (45 cases)
T stage, n (%)
T1	7 (14.89%)	2 (4.45%)
T2	30 (63.83%)	19 (42.22%)
T3	5 (10.64%)	6 (13.33%)
T4	4 (8.51%)	17 (37.78%)
Unknown	1 (2.13%)	1 (2.22%)
N stage, n (%)
N0	42 (89.36%)	38 (84.45%)
N1	4 (8.51%)	6 (13.33%)
Unknown	1 (2.13%)	1 (2.22%)

TMB

High

Low

TMB

High

Low

1.00-

Overall Survival

Disease-specific Survival

1.00-

0.75

0.50

0.25

P < 0.001

0.25

P < 0.001

0.00

Time (year)

TMB

High Low

000

TMB

High -

Low

Time (year)

TMB

High +

Low

TMB

low

high

Progression-free Survival

1.00-

0.75

0.50

PD-L1 expression

0.25

P < 0.001

0.00

Time (year)

TMB

High - Low

Time (year)

Abbreviation: TMB, tumor mutation burden.

Fig. (2) Contd …

Fig. (2). Correlation of TMB with clinicopathologic characteristics and PD-L1 expression level. Overall survival (A); disease- specific survival (B); progression-free survival (C); PD-L1 expression level (D); AJCC stage (E); T stage (F); gender (G); lym- phatic metastasis (H).

TMB (p<0.001)

TMB

stage

gender

p=0.700

p=0.376

(

TMB

FEMALE

MALE

Fig. (3) Contd ...

Cluster

0.6

0.4

Fraction

0.2

0.0

B cells naive

B cells memory

Plasma cells

T cells CD8

T cells CD4 naive

T cells CD4 memory resting

T cells CD4 memory activated

T cells follicular helper

T cells regulatory (Tregs)

T cells gamma delta

NK cells resting

NK cells activated

Monocytes

Macrophages MO

Macrophages M1

Macrophages M2

Dendritic cells resting

Dendritic cells activated

Mast cells resting

Mast cells activated

Eosinophils

Neutrophils

0.015

0.018

4000

0.015

2000

1.0-

1000

2000

ImmuneScore

1000

StromalScore

ESTIMATEScore

TumorPurity

0.8

0.6

-1000

-2000

-1000

low TMB

high TMB

low TMB

high TMB

low TMB

high TMB

low TMB

high TMB

Fig. (3). Correlation of TMB with tumor immune microenvironment, including immune cell infiltration (A); immune score (B); stromal score (C); ESTIMATE score (D); tumor purity (E). Identification of differentially expressed genes (DEGs) between high and low TMB ACC samples. Heatmap (F), Gene Ontology (GO) analysis (G) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis (H) of DEGs. (A higher resolution / colour version of this figure is available in the electronic copy of the article).

Type

10 Type

KLK1

high

PTGDS

ANK1

low

IL20RB

ZGLP1

SCGN

ADH1B

DNASE1L3

HHATL

CREB3L3

GATA1

SLURP1

AL031429.2

PYDC1

KRT19

LINC01489

-5

ADH4

DIO3

UGT1A7

B3GALT2

CD244

LTF

PLAC1

MMP1

AREG

WNT10B

DLX1

SHOX2

CTRB2

HAS1

RGS20

ERFE

AC112715.1

LINC00460

AP000851.2

FEZF1

FOXA2

CCNYL2

POU4F1

HOXD13

organelle fission

nuclear division

chromosome segregation

Cell cycle

mitolie nuclear division

nudear chromosome segregation

sister chiomalid segregation

₹

Neuroactive ligand-receptor interaction-

mitotic sister chomalid segregation

spindle organization-

Oocyte meiosis

microtubule cytoskeleton organization involved in milosis-

Count

mitutic: spindle organization

Count

Cellular senescence

chromosomal region

spindle

condensed chromosome

chromosome, centromeric region

p53 signaling pathway

kinetochore

condensed chromosome, centromeric region

Gvalue

Progesterone-mediated oocyte maturation-

condensed chromosome kinetochore

qvalue

milobc spindle

C 30GG

Drug metabolism - cytochrome P450-

spindle pole

COCO

0.01

spindle midzone

.3005

2.02

tubulin binding

traction with cytokine and cytokine receptor-

5.03

ATPase activity

microtubule binding-

0.04

catalytic activity, acting on DNA-

Fanconi anemia pathway-

motor activity

microtubule motor activity

€

abolism of xenobiotics by cytochrome P450-

DNA helicase activity

single-stranded DNA helicase activity

DNA replication origin binding

DNA replication-

ATP-dependent microtubule motor acthity plus-end-directed

0.05

0.10

0.025

0.050

0.075

GeneRatio

0.100

3.3. Identification of DEGs between High and Low TMB ACC Samples

There were a total of 79 ACC samples with transcriptome data in the TCGA database, including 41 cases in the low- TMB group and 38 cases in the high-TMB group. We identi- fied 797 DEGs in all between high and low TMB groups, in- cluding 203 downregulated DEGs and 594 upregulated DEGs. Downregulated DEGs represented genes highly expressed in the low-TMB patients. Upregulated DEGs represented genes that were highly expressed in high-TMB patients. The top 40 DEGs were mapped in the heatmap (Fig. 3F). GO analysis provides three interpretations of functional annotation: bio- logical processes (BP), cellular components (CC), and molec- ular functions (MF). The top five GO results of BP included nuclear chromosome segregation, mitotic nuclear division, chromosome segregation, nuclear division, and organelle fis- sion. The top five GO results of CC included kinetochore, chromosome, centromeric region, condensed chromosome, spindle, and chromosomal region. The top five GO results of MF included motor activity, catalytic activity, acting on DNA, microtubule binding, ATPase activity, and tubulin binding.

The KEGG pathway analysis showed that DEGs were mainly enriched in the p53 signaling pathway, cellular senescence, oocyte meiosis, neuroactive ligand-receptor interaction, and cell cycle (Fig. 3G-H).

3.4. Weighted Gene co-expression Network Analysis and Construction of PPI Network

The WGCNA analysis identified a total of 15 co-expres- sion modules, which were shown using various colors (Fig. 4A-C). The module’s trait relationship results suggested that the turquoise module was the most significant module associ- ated with TMB scores. We set GS>0.5 and MM>0.8 to screen genes highly relevant to TMB in ACC patients, and finally filtered 45 genes highly relevant to TMB of ACC (Fig. 4D- E). Further univariate Cox analysis revealed that all 45 genes were associated with OS, DSS, and PFS of ACC (P<0.05, Fig. 5A-C). Next, GO analysis of these 45 genes was performed to further reveal the functional annotation using the Webgestalt database, and the results were exhibited in Fig. (5D-F). More- over, all 45 DEGs were mapped into the STRING database

Fig. (4). Weighted gene co-expression network analysis (WGCNA). Analysis of the scale-free fit signature for various soft-thresholding powers (A) and the mean connectivity for various soft-thresholding powers (B). Dependencies between modules (C); Module-trait associations (D); Scatter plot of module eigengenes in turquoise module (E). (A higher resolution / colour version of this figure is available in the electronic copy of the article).

Scale independence

Mean connectivity

Scale Free Topology Model Fit, signed R

1.0

1011121314151617181920

2000

0.8

567

0.6

Mean Connectivity

1500

0.4

1000

0.2

500

0.0

4 5 6 7 8 9 1011121314151617181920

Soft Threshold (power)

Gene dendrogram and module colors (TCGA)

1.0

0.8

Height

0.6

0.4

0.2

Dynamic Tree Cut

Module-trait relationships

Module membership vs. gene significance cor=0.75, p<1e-200

MElightcyan

-0.15 (0.2)

0.15

(0.2)

MEblack

-0.17

(0.1)

0.17

0.6

(0.1)

MEblue

-0.32

(0.004)

0.32

(0.004)

MEgreen

-0.31

(0.005)

0.31

0.5

(0.005)

MEpink

-0.30

(3e-05)

0.39

(e-04)

0.5

MEsalmon

-0,17

(0.1)

0.17

(0.1)

0.4

MEmagenta

-0.42

(10-04)

0.42

(10-04)

MEturquoise

-0.58

(20-08)

0.58

(26-06)

Gene significance

MEgrey60

0.3

-0.15

(0.2)

0.15

(0 2)

MElightgreen

0.004

-0.004

(0.4)

MEcyan

-0.015

0.015

0.2

(0.9)

(0 8)

MEbrown

0.13

(0.3)

-0.13

(0 3)

-0.5

MEtan

-0.24

(0.03)

0.24

(0.03)

0.1

MEgreenyellow

0.091

-0.091

(0,4)

(0.4)

MEpurple

0.17

(0.1)

-0.17

(0.1)

MEgrey

0.13

-0.13

-1

0.0

(0.2)

low TMB

high TMB

0.2

0.4

0.6

0.8

Module Membership in turquoise module

for PPI network construction (Fig. 5G). After taking intersec- tion with 797 DEGs and screening by degree score >65, we finally identified a total of 13 candidate TMB related genes for ACC patients, including abnormal spindle-like microceph- aly-associated protein (ASPM), baculoviral IAP repeat con- taining 5 (BIRC5), budding uninhibited by benzimidazoles 1 (BUB1), cell division cycle 20 (CDC20), cell division cycle

associated 5 (CDCA5), cell division cycle associated 8 (CDCA8), centromere protein A (CENPA), centrosomal pro- tein 55 (CEP55), kinesin family member 2C (KIF2C), kinesin family member 11 (KIF11), kinesin family member 15 (KIF15), NUF2 component of NDC80 kinetochore complex (NUF2) and microtubule nucleation factor targeting protein for Xklp2 (TPX2) (Fig. 5H).

	pvalue	Hazard ratio
DDX39A	<0.001	5.422(3.133-9.383)
CNOT9	<0.001	7.835(3.413-17.985)
B4GALT2	<0.001	2.717(1.806-4.087)
TEDC1	<0.001	11.035(4.943-24.636)
UBE2S	<0.001	4.076(2.595-6.401)
RECQL4	<0.001	2.951(2.013-4.325)
P3H1	<0.001	4.633(2.746-7.819)
TPX2	<0.001	2.889(1.958-4.263)
SUV39H2	<0.001	10.101(4.663-21.881)
AC026401.3	<0.001	2.845(1.996-4.056)
CENPO	<0.001	7.069(3.551-14.070)
TRIP13	<0.001	2.424(1.797-3.271)
MCM10	<0.001	5.934(3.409-10.330)
SPC24	<0.001	3.471(2.316-5.203)
EZH2	<0.001	4.443(2.694-7.327)
CDCA3	<0.001	4.710(2.888-7.680)
KIF15	<0.001	10.576(5.107-21.901)
HELLS	<0.001	7.641(4.004-14.579)
PRR11	<0.001	3.952(2.474-6.314)
CDCA2	<0.001	5.614(3.252-9.691)
SGO2	<0.001	4.962(2.849-8.644)
UHRF1	<0.001	4.923(2.979-8.136)
KIF11	<0.001	3.558(2.380-5.320)
TROAP	<0.001	4.497(2.779-7.275)
ASPM	<0.001	4.577(2.877-7.282)
EXO1	<0.001	4.720(2.984-7.467)
SPAG5	<0.001	5.132(3.029-8.694)
ZWINT	<0.001	3.736(2.435-5.733)
MYBL2	<0.001	2.237(1.710-2.926)
SGO1	<0.001	8.657(4.312-17.379)
KIFC1	<0.001	2.565(1.875-3.511)
CEP55	<0.001	2.734(1.974-3.786)
HJURP	<0.001	3.346(2.302-4.863)
CENPA	<0.001	3.390(2.279-5.044)
LMNB2	<0.001	6.272(3.572-11.013)
KIF2C	<0.001	2.398(1.813-3.172)
NCAPH	<0.001	3.363(2.309-4.898)
CDCA8	<0.001	2.910(2.029-4.174)
BUB1	<0.001	4.801(2.900-7.947)
SPC25	<0.001	4.110(2.639-6.402)
CKAP2L	<0.001	4.315(2.718-6.852)
NUF2	<0.001	3.618(2.404-5.444)
CDC20	<0.001	2.382(1.831-3.099)
BIRC5	<0.001	2.718(1.932-3.825)
CDCA5	<0.001	3.419(2.285-5.115)

	pvalue	Hazard ratio
DDX39A	<0.001	5.586(3,154-9.893)
CNOT9	<0.001	8.402(3.501-20.163)
B4GALT2	<0.001	2.767(1.798-4.260)
TEDC1	<0.001	11.873(5.028-28.035)
UBE2S	<0.001	4.009(2.525-6.366)
RECQLA	<0.001	3.005(1.991-4.537)
P3H1	<0.001	4.671(2.717-8.029)
TPX2	<0.001	2.834(1.899-4.228)
SUV39H2	<0.001	9,496(4.337-20.790)
AC026401.3	<0.001	5.303(2.753-10.213)
CENPO	<0.001	7.639(3.716-15.706)
TRIP13	<0.001	2.364(1.742-3.208)
MCM10	<0.001	5.618(3.217-9.811)
SPC24	<0.001	3.390(2.245-5.120)
EZH2	<0.001	4.927(2.825-8.595)
CDCA3	<0.001	4.620(2.804-7.613)
KIF 15	<0.001	10.022(4.805-20.904)
HELLS	<0.001	7.553(3.880-14.701)
PRR11	<0.001	4.074(2.490-6.665)
CDCA2	<0.001	5.371(3.091-9.331)
SGO2	<0.001	4.786(2.721-8.420)
UHRF1	<0.001	4.970(2.943-8.394)
KIF11	<0.001	3.471(2.306-5.226)
TROAP	<0.001	4.444(2.716-7.272)
ASPM	<0.001	4.448(2.779-7.118)
EXO1	<0.001	4.605(2.886-7.348)
SPAG5	<0.001	5.046(2.944-8.647)
ZWINT	<0.001	3.628(2.354-5.590)
MYBL2	<0.001	2.268(1.712-3.004)
SGO1	<0.001	8.362(4.114-16.996)
KIFC1	<0.001	2.584(1.858-3.593)
CEP55	<0.001	2.709(1.940-3.783)
HJURP	<0.001	3.624(2.379-5.521)
CENPA	<0.001	3.365(2.238-5.059)
LMNB2	<0.001	6.220(3.491-11.081)
KIF2C	<0.001	2.460(1.823-3.320)
NCAPH	<0.001	3.318(2.259-4.874)
CDCA8	<0.001	2.839(1.969-4.094)
BUB1	<0.001	4.621(2.781-7.678)
SPC25	<0.001	4.100(2.593-6.482)
CKAP2L	<0.001	4.257(2.656-6.823)
NUF2	<0.001	3.531(2.333-5.343)
CDC20	<0.001	2.694(1.941-3.739)
BIRC5	<0.001	2.721(1.906-3.885)
CDCA5	<0.001	3.444(2.267-5.234)

100

Hazard ratio

Fig. (5) Contd ...

100

Hazard ratio

	pvalue	Hazard ratio
DDX39A	<0.001	4.714(2.899-7.665)
CNOT9	<0.001	6.911(3.671-13.010)
B4GALT2	<0.001	2.605(1.845-3.678)
TEDC1	<0.001	5.467(3.103-9.633)
UBE2S	<0.001	2.537(1.824-3.529)
RECQL4	<0.001	2.257(1.676-3.038)
P3H1	<0.001	3.146(2.081-4.757)
TPX2	<0.001	2.477(1,801-3.406)
SUV39H2	<0.001	10.678(5.566-20.486)
AC026401.3	<0.001	2.019(1.556-2.620)
CENPO	<0.001	5.782(3.105-10.766)
TRIP13	<0.001	3.490(2.429-5.016)
MCM10	<0.001	4.528(2,850-7.192)
SPC24	<0.001	2.618(1.881-3.644)
EZH2	<0.001	2.853(1.989-4.091)
CDCA3	<0.001	2.957(2.078-4.210)
KIF15	<0.001	7.028(3.870-12.763)
HELLS	<0.001	3.917(2.539-6.041)
PRR11	<0.001	3.248(2.212-4.769)
CDCA2	<0.001	4.198(2.689-6.554)
SGO2	<0.001	5.463(3,323-8.981)
UHRF1	<0.001	2.933(2.045-4.206)
KIF11	<0.001	3.051(2.200-4.230)
TROAP	<0.001	2.650(1.946-3.609)
ASPM	<0.001	3.869(2.623-5.706)
EXO1	<0.001	4.438(2.824-6.976)
SPAG5	<0.001	3.699(2,494-5.485)
ZWINT	<0.001	2.921(2.088-4.086)
MYBL2	<0.001	2.168(1.697-2.770)
SGO1	<0.001	7.963(4.430-14.314)
KIFC1	<0.001	2.130(1.672-2.712)
CEP55	<0.001	2.364(1.833-3.048)
HJURP	<0.001	2,393(1.821-3.146)
CENPA	<0.001	2.911(2.097-4.041)
LMNB2	<0.001	3.838(2.580-5.708)
KIF2C	<0.001	2.067(1.633-2.616)
NCAPH	<0.001	3.454(2.376-5.021)
CDCA8	<0.001	2.341(1.772-3.092)
BUB1	<0.001	3.835(2.589-5.683)
SPC25	<0.001	3.183(2.262-4.479)
CKAP2L	<0.001	3.696(2.480-5.506)
NUF2	<0.001	2.858(2.086-3.917)
CDC20	<0.001	1.855(1.535-2.243)
BIRC5	<0.001	2.085(1.615-2.691)
CDCA5	<0.001	2.649(1.949-3.600)

100

Hazard ratio

Fig. (5) Contd ...

5 10 25 20 25

chromosomal region.

chromosome, centromeric region

kinetochore

diromasome

Log10 of FDR

condensed chromosome kinetochore

14-

chromosomal part

condensed chromosome

condensed chromosome, centromeric región

condensed nuclear chromosome kinetochore

spindhe

lag2 of enrichment ratio

-1

€

129 binding

2.2

adenyl ribonucleotide binding

microtubule binding

drug binding

tubulin binding .

microtubule matar activity

«fenyl rucestidie binding

AEPase activity

motor actbity

anaphase-promoting complex hinting

lagt nl areichmset ratio

₼

Fig. (5). Univariate Cox analysis for overall survival (OS) (A), disease-specific survival (DSS) (B) and progression-free survival (PFS) (C). Gene Ontology (GO) analysis related to biological processes (BP) (D), cellular components (CC) (E) and molecular functions (MF) (F). PPI network of TMB-related genes (G, H). (A higher resolution / colour version of this figure is available in the electronic copy of the article).

5. 10 95 20 23 39 31

mitotic cell cycle process cell division

sitter cbramacid segregation

mitatic cell cycle

NOS IS 01509

cell tytle

mitotic sister chromatid segregation

14-

organelle fissien

cell cycle process

nuclear division chromesome segregation

logi of enrichment ratio

HELLS

EZH2

TRIP13

UBEZS

CDCA2

SPAG5

SGOL2

MCMIO

NCAPH

EXO

CDC20

BIRCS

NUFZ

COCA5

MYBL2

ZWINT

ASPM

BUBT

UHRFI

KIFLI

SGOLI

RECOL

CEP55

KIFIC

SPO25

LINB2

TROAD

COCA8

DURP

CENPO

DOXISA

KIETS

CENPA

SPC24

PRRI

KIFCE

ENCKAPZL

SUV39H2

CDCA3

CDCA5

CORALD

CEP55

KIF15

KIF2C

CENPA

NUF2

KIF11

ASPM

CDCA8

CDC20

BUB1

BIRC5

TPX2

14 Current Cancer Drug Targets, XXXX, Vol. XX, No. XX

Fig. (6). Prognostic value 13 TMB-related genes in TCGA database, including ASPM (A), BIRC5 (B), BUB1 (C), CDC20 (D), CDCA5 (E), CDCA8 (F), CENPA (G), CEP55(H), KIF2C(I), KIF11(J), KIF15(K), NUF2(L) and TPX2 (M). (A higher resolution / colour version of this figure is available in the electronic copy of the article).

ASPM

High

Low

BIRCS

High

LOW

BUB1

High

Low

1.00

1.00-

Survival probability

0.75

Survival probability

0.75

Survival probability

0.75

0.50

0.25

1.628e-10

0.25

3.177e-06

0.25

3.016e-08

0.00

Time (year)

ASPM

High Low

Y 40

BIRC5

High

LOW

BUB1

UIN

High

LOW

Time (year)

CDC20

High

Low

CDCA5

High

Low

COCA8

High

Low

1.00

1.00-

Survival probability

0.75

Survival probability

0.75

Survival probability

0.75

0.50

0.25

5.089e-07

0.25

2e-07

0.25

3.483e-07

0.00

6 £

Time (year)

2 CDC20

High Low

CDCA5

CDCA8

High Low

DON

High

Low

39 35

13 31 1

chtu

Time (year)

CENPA

High

Low

CEP55

High

Low

KIF2C

High

Low

1.00

1.00-

Survival probability

0.75

Survival probability

0.75

Survival probability

0.75

0.50

0.25

2.046e-06

0.25

4.522e-08

0.25

1.043e-05

0.00

Time (year)

CENPA

High Low

CEP55

High- Low

2 UIN

KIF2C

High-

13 8

Low

HON

Time (year)

KIF11

High

Low

KIF15

Hiện

LOW

NUF2

High

TPX2

LOW

1.00

Survival probability

0.75

Survival probability

0.75

Survival probability

0.75

Survival probability

0.75

0,50

0.50

0.60

0.50

0.26

2.353e-07

0.25

8.238e-06

0.25

1.079e-07

0.25

3.442e-05

0.00

Time (year)

Time tyear)

Time (year)

KIF11

High e

3 10

KIF15

9 13

NUF2

TPX2

Low

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B 9

Time (year)

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Time (year)

3.5. Validation of Prognostic Value and Differential Expression Levels Between Normal and ACC Samples

The survival analysis in the TCGA database demonstrated that the OS of patients with high expression of ASPM, BIRC5, BUB1, CDC20, CDCA5, CDCA8, CENPA, CEP55, KIF2C, KIF11, KIF15, NUF2, and TPX2 were significantly lower than that of patients with low expression (P < 0.05, Fig. 6).

Furthermore, we validated the prognostic value using the GEO database. Kaplan-Meier survival analysis in GEO data- base demonstrated that the OS of patients with high expres- sion of ASPM, BIRC5, BUB1, CDC20, CDCA5, CEP55, KIF11, and TPX2 were significantly lower than that of pa- tients with low expression (P < 0.05); however, the OS be- tween high and low expression of CDCA8, CENPA, KIF2C, KIF15, and NUF2 was not significantly different (P>0.05)

Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes

Fig. (7). Prognostic value of 13 TMB-related genes in GEO database, including ASPM (A), BIRC5 (B), BUB1 (C), CDC20 (D), CDCA5 (E), CDCA8 (F), CENPA (G), CEP55(H), KIF2C(I), KIF11(J), KIF15(K), NUF2(L) and TPX2 (M). (A higher resolution / colour version of this figure is available in the electronic copy of the article). (Fig. 7). The combined analysis of the GTEx database and TCGA database suggested that ASPM, BIRC5, BUB1, CDC20, CDCA5, CEP55, KIF11, and TPX2 had significantly

ASPM

High

LOW

BIRC5

High

Low

BUB1

High

Low

1.00-

1.00

1.00-

Survival probability

0.75

Survival probability

0.75

Survival probability

0.75

0.50

0.25

p = (

011

0.25

798

Be-

0.25

6.798e-04

0.00

1 12 13 1

516 16

17 7 18 192

10 11 12

13 14

18 19 20

10 1112

31 14 1

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Time (year)

161

Time (year)

19 20

ASPM

High

Low

9 6

1 1

BIRC5

High

LOW

11 7 5 2

BUB1

High

0 0 0

10 1 10

0 5

11 17 5

0 0

Low

1 0 0

7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time (year)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1920 Time (year)

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Time (year)

CDC20

High

Low

CDCAS

High

Low

CDCA8

High

Low

1.00-

1.00

1.00-

Survival probability

0.75

Survival probability

0.75

Survival probability

0.75

0.50

0.25

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0.25

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0.25

0.00

0 11 12

516

1 181

920

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8 19 20

8 9 10 11 12 13 14 1

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Time (year)

1920

Time (year)

CDC20

High Low

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CDCA5

High-

CDCA8

High

00,00

S L

1 C

101

Low

11 10

S 3

LOW

1 0 O

9 10 11 12 13 14 15 16 17 18 19 20 Time (year) 8

8 9 10 11 12 13 14 15 16 17 18 19 20 Time (year)

1 5

8 9 10 11 12 13 14 15 16 17 18 19 20 Time (year)

CENPA

High

Low

CEP55

High

LOW

KIF2C

High

LOW

1.00-

Survival probability

0.75

Survival probability

0.75

Survival probability

0.75

0.50

0.25

p = 0,439

0.25

8.83€

0.25

653

0.00

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9 10 11 12

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18 19 20

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Time (year)

CENPA

High

toto

CEP55

11 7

KIF2C

Low

ـاهـ

High

LOW 1

11 7

High

LOW

3. 1

1 0

0 11 12

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15 5

0 11 12

14 415

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Time (year)

71 19 20

KIF11

High

Low

KIF15

High

Low

NUFZ

High

Low

1PX2

High

1.00-

Survival probability

0,76

0.75

075

0.50

0.26

025

p = 0,027

0.25

p = 0.981

0.25

0.038

p =0

. 644

0.00

8 9 10 11 12 13 14 15 16 17 18 1920

8 9 10 11 12 13 14 15 16 17 18 19 20

0.00

Time (year)

10 11 12 13 14 15 16 17 18 1920

Time (year)

KIF11

High

KIF15

TPX2

Igh

11 9

10 7

1 1210 $

NUF2

118

1 2 3

D 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1920

10 11 12 13 14 15 16 17 18 19 2

0 1 2 3 4 5 6 7 8 9 1011 121314 15 1617 18 1920 Time (year)

920

5 5

Time (year)

2 3

4 5

11 12 13

8 9 101112

14 1

1920

Time (year)

higher expression levels in ACC tissue in comparison with normal adrenal gland tissues (Fig. 8).

Fig. (8). Differential expression levels between normal and ACC samples. ASPM (A), BIRC5 (B), BUB1 (C), CDC20 (D), CDCA5 (E), CEP55 (F), KIF11(G), TPX2 (H). (A higher resolution / colour version of this figure is available in the electronic copy of the article).

p=1.566e-16

p=1.47e-20

ASPM expression

BIRC5 expression

Normal

Tumor

Normal

Tumor

Type

p=1.347e-24

p=1.084e-19

BUB1 expression

CDC20 expression

Normal

Tumor

Normal

Tumor

Type

p=1.641e-24

p=1.503e-26

CDCA5 expression

CEP55 expression

Normal

Tumor

Normal

Tumor

Type

p=2.749e-32

エ

p=2.453e-32

KIF11 expression

TPX2 expression

Normal

Tumor

Normal

Tumor

Type

3.6. Inhibition of ASPM and KIF11 could Promote Apop- tosis, Inhibit the Proliferation of ACC Cells, and Suppress ACC Tumor Growth In Vivo

According to prior results and previous literature, we se- lected ASPM and KIF11 for further preliminary experimental verification. Multivariate Cox analysis revealed that the ex- pression level of ASPM or KIF11 was an independent predic- tor of OS in ACC patients (Fig. 9A-B). Using the UALCAN database, we found that the expression levels of ASPM or

KIF11 were higher in patients with AJCC stage 4 compared with stages 1-3 (Fig. 9C-D). The expression levels of ASPM or KIF11 were significantly higher in patients with mutant TP53 compared with those without mutant TP53 (Fig. 9E-F). KEGG analysis using the GSEA method revealed that ASPM or KIF11 might be mainly enriched in the P53 signaling path- way (Fig. 9G-H).

To further investigate the functions of ASPM and KIF11, qRT-PCR in the NCI-H295R and SW-13 human ACC cell

Fig. (9). Multivariate Cox analysis revealed the independently predictive value of ASPM (A) and KIF11 (B) for overall survival (OS). The expression level of ASPM (C) and KIF11 (D) among stage 1 to stage 4. The expression level of ASPM (E) and KIF11 (F) between samples with or without mutant TP53. KEGG analysis using GSEA method of ASPM (G) and KIF11 (H). The expression level of ASPM (I) and KIF11 (J) in NCI-H295R cell and SW-13 cell. The qPCR (K-L) and western blot (M-N) to verify the knockdown efficiency and screen knockdown target. (A higher resolution / colour version of this figure is available in the electronic copy of the article).

Hazard ratio

Expression of ASPM in ACC based on individual cancer stages

15-

Transcript per million

Stage2

Stage3

TCGA samples

Hazard ratio

Expression of KIF11 in ACC based on individual cancer stages

60-

Transcript per million

Stope3

TCGA samples

Expression of ASPM in ACC based on TP53 mutation status

Expression of KIF11 in ACC based on TP53 mutation status

Enrichment plat: KEGG_P53_SIGNALING_PATHWAY

Enrichment plot: KEGG_PS3_SIGNALING_PATHWAY

Transcript per million

10-

Transcript per million

6 -

TOGA samples

Ctrl

SiNC

Relative ASPM expression

1.5

SW-13

1.5

siASPM #1

NCI-H295R

Relative ASPM expression

siASPM #2

siASPM #3

siNC

1.0

siASPM #1

1.0-

siASPM #2

siASPM #3

0.5

0.5-

ASPM

0.0

ß-actin

Ctrl

SiNC

Relative KIF11 expression

SW-13

NCI-H295R

Relative KIF11 expression

1.5

siKIF11 #1

siKIF11 #2

siKIF11 #3

siNC

siKIF11 #1

1.0-

siKIF11 #2

siKIF11 #3

0.5-

KIF11

0.0

ß-actin

lines was conducted. The results showed that the expression level of ASPM was higher in the NCI-H295R cell compared with the SW-13 cell, while the expression level of KIF11 was higher in the SW-13 cell compared with the NCI-H295R cell (Fig. 9I-J). Hence, we chose NCI-H295R for the further ex- perimentations of ASPM, and SW-13 for the further experi- mentations of KIF11. According to the results of qRT-PCR and Western blot, we selected the two interfered targets with the highest interference efficiencies for further experiments

(Fig. 9K-N). CCK-8 assays and colony formation assays demonstrated that the inhibition of ASPM or KIF11 could al- leviate the proliferation and cloning ability of ACC cells (Fig. 10A-F). Flow cytometric assay revealed that the depletion of ASPM or KIF11 could significantly promote apoptosis of ACC cells (Fig. 10G-J). Furthermore, in vivo assays were performed to detect the effect of ASPM and KIF11 on tumor- igenesis. It was discovered that the knockdown of ASPM or KIF11 significantly inhibited tumor growth (Fig. 10K-P).

Fig. (10). Inhibition of ASPM and KIF11 could promote apoptosis, inhibit proliferation of ACC cells and suppress ACC tumor growth in vivo. CCK-8 and colony formation assay to reveal the proliferation ability and cloning ability (A-F). Flow cytometry to analyze cell apoptosis (G- J). The tumor growth curves and weight of BALB/c mice implanted with NCI-H295R cells (K-M) or SW-13 cells (N-P). (A higher resolution / colour version of this figure is available in the electronic copy of the article).

2.0-

Ctrl

SiNC

Ctrl

SiNC

siASPM #1 siASPM #3

SiNC

siASPM #1

siASPM #3

200

siASPM #1

OD (450nm)

1.5-

the number of colonies

siASPM #3

150

1.0-

100-

0.5-

50-

0.0

Time (h)

Ctrl

SiNC

2.0-

siNC

Ctrl

SiNC

siKIF11 #2 siKIF11 #3

siKIF11 #2

OD (450nm)

the number of colonies

500-

siKIF11 #3

1.5

siKIF11 #3

400-

300-

1.0

200-

0.5

100-

0.0

Time (h)

Ctrl

SiNC

siASPM #1 siASPM #3

20-

siASPM #1

siASPM #3

Apoptosis(%)

15-

4.40

80.7

5.08

91.9

7.22

Os 140

Ctrl

SiNC

siKIF11 #2 siKIF11 #3

siNC

15-

siKIF11 #2

siKIF11 #3

1.71

Apoptosis(%)

0.79

1.42

036

2:28

”

942

10.#

11.1

400

建医科大学泌尿外科研究所 stitute of Urology, Fujian Medical University

500

shNC

Tumor weight (mg)

300

Tumor volume (mm3)

400

shASPM #1

300

shASPM #2

200

shNC

200

100

shASPM #1

100

shASPM #2

shNC

shASPM #1

400

福建医科大学泌尿外科研究所 Institute of Urology, Fujian Medical University

150

Tumor volume (mm3)

shNC

Tumor weight (mg)

300

shKIF11 #1

shNC

100

shKIF11 #2

200

shKIF11 #1

100

shKIF11 #2

shNC

shKIF11 #1

shKIF11 #2

4. DISCUSSION

It is known that higher TMB was associated with favorable outcomes in patients receiving PD-1 or PD-L1 immune check- point inhibitors across various malignancies [20], including melanoma [21], non-small cell lung cancer [22], urothelial carcinoma and renal cell cancer [23], etc. However, there were fewer studies investigating the relationship of TMB with ACC carcinogenesis and immunological therapy. This is the first at- tempt to identify candidate prognostic markers of ACC based on tumor mutation burden analysis. In this study, we calcu- lated the TMB scores of each sample using the TCGA data of single nucleotide variations. According to the median value, ACC samples were classified into the low TMB group and the high TMB group. The present study revealed that ACC pa- tients with high TMB had worse OS, DSS, PFS, advanced stage, and lower PD-L1 expression. Whereas, TMB scores did not differ between ACC patients with or without lymph node metastasis; Gender did not affect TMB in patients with ACC. Moreover, we also demonstrated the relationship between the TMB score and tumor immune microenvironment. Interest- ingly, Patients with high TMB had significantly lower levels of naïve B cells, gamma delta T cells, and activated NK cells, and higher levels of memory-activated CD4 T cells in com- parison to those with low TMB. The proportion of tumor cells in tissue was defined as tumor purity. Frequently, patients with high stomal cells or immune cells harbor low tumor pu- rity according to tumor microenvironment theory [24]. Rhee et al. [25] have reported that tumor purity is a vital factor in evaluating the correlation of gene expression and TMB across multiple carcinomas. Mao et al. [26] also demonstrated that low tumor purity could predict a worse prognosis for colon cancer, which might be attributed to higher mutation fre- quency. However, the connection between tumor purity with TMB of ACC has not been thoroughly assessed. We found that high TMB of ACC was associated with low stromal score, low immune score, low ESTIMATE score, and high tumor purity for the first time.

After screening by the PPI network and validating using survival analysis, a total of eight candidate TMB-related prog- nostic genes for ACC patients were finally identified: ASPM, BIRC5, BUB1, CDC20, CDCA5, CEP55, KIF11, and TPX2. Zhang et al. [27] reported that BIRC5 was an immune-autoph- agy-related gene with independent prognostic value in renal clear cell carcinoma. Tian et al. [28] revealed that elevated expression of BUB1 was significantly correlated with the worse prognosis of patients with ACC. Guo et al. [29] also indicated that ACC patients with a high expression of BUB1 were associated with worse OS. Xiong et al. [30] showed that the overexpression of CDC20 predicted a poor prognosis of hepatocellular carcinoma (HCC) and might be an independent risk factor for HCC. Anurag et al. [31] found that CDC20 may serve as a potential biomarker and therapeutic target of pedi- atric ACC. Luo et al. [32] demonstrated that CDCA5 deple- tion inhibited the proliferation of prostate cancer cells and in- terrupted cancer cell behavior via the AKT pathway. Li et al. [33] confirmed that miR-144-3p could inhibit CEP55 expres- sion to suppress non-small cell lung cancer development. Li et al. [33, 34] reported that miR-148a-3p acted as a repressor via binding to CEP55 in esophageal carcinoma. Zhang et al. [35] verified that the knockdown of TPX2 could significantly

inhibit prostate cancer cells’ migration and epithelial-mesen- chymal transition. However, currently, fewer studies explore the definite relationship between ASPM, BIRC5, BUB1, CDC20, CDCA5, CEP55, KIF11, and TPX2 and the develop- ment and progression of ACC. This study suggested these eight TMB-related hub genes might serve as novel markers and potential therapeutic targets of ACC. Further research is therefore needed to confirm these findings.

According to prior results and previous literature, we se- lected ASPM and KIF11 for further preliminary experimental verification. It has been reported that ASPM and KIF11 were vital in tumorigenesis in various cancers. Chen et al. [36] re- vealed that the cell cycle of glioblastoma cells could be ar- rested at the G0/G1 phase by down-regulating ASPM. Wang et al. [37] confirmed that ASPM could effectively induce col- orectal cancer cell migration and invasion. Wu et al. [38] sug- gested that silencing of ASPM could suppress diffuse large B- cell lymphoma cell growth, induce cell apoptosis, and arrest the cell cycle via the Wnt/B-catenin pathway. Wu et al. [39] indicated that ASPM interacted with KIF11 to promote HCC progression via the Wnt/B-catenin signaling pathway. Piao et al. [40] found that the expression level of KIF11 could predict the aggressiveness and prognosis of prostate cancer patients. Wang et al. [41] revealed that the downregulation of KIF11 could increase the effect of adriamycin on breast cancer cells. Wei et al. [34] also demonstrated that high expression of KIF11 could facilitate the proliferation of gallbladder cancer cells through the ERBB2/PI3K/AKT signaling pathway. In this study, we explored the potential function of ASPM and KIF11. Multivariate Cox analysis revealed that the expression levels of ASPM or KIF11 were the independent predictors of OS in ACC patients. Using the UALCAN database, the ex- pression levels of ASPM and KIF11 were found to be signif- icantly higher in patients with mutant TP53 compared with those without mutant TP53. Functional analysis demonstrated that ASPM and KIF11 might function via the P53 signaling pathway. Moreover, preliminary experiments demonstrated that inhibiting ASPM or KIF11 could promote apoptosis and reduce the proliferation of ACC cells.

Our findings shed light on the molecular mechanisms un- derlying ACC progression. However, the complexity of tumor heterogeneity and the dynamic nature of cancer evolution ne- cessitate advanced analytical approaches. Single-cell analysis has emerged as a useful tool to dissect tumor heterogeneity at an unprecedented resolution [42]. Liquid biopsy-based detec- tion of circulating tumor DNA (ctDNA) can dynamically monitor tumor heterogeneity and evolutionary dynamics, thereby providing a non-invasive approach to predict thera- peutic response and assess recurrence risk [43]. Ohyama et al. [44] revealed that molecular barcoding (MB) notably im- proved the sensitivity and specificity of ctDNA analysis in de- tecting tumor-derived mutations and treatment targets for pan- creaticobiliary cancers compared to conventional next-gener- ation sequencing, offering enhanced clinical utility for ge- nomic profiling and treatment guidance. In addition, DNA methylation, as a pivotal epigenetic hallmark, serves as a highly specific biomarker for non-invasive early detection, molecular subtyping, and therapeutic stratification through liquid biopsy applications [45]. It is worth of our attention whether these non-invasive technologies can be applied to

ACC to more effectively identify prognostic biomarkers. Na- zha et al. [46] found that blood-based ctDNA analysis effec- tively identifies actionable genomic alterations in advanced ACC patients, providing a non-invasive strategy to guide per- sonalized therapies and clinical trial enrollment. However, re- search on identifying ACC-specific liquid biopsy biomarkers remains scarce, which may be associated with the rarity of ACC and molecular heterogeneity. Future integration of ctDNA mutation profiling, methylation signatures, and high- resolution sequencing can provide a multi-dimensional view of ACC biology, achieving earlier detection of molecular re- lapse and real-time tracking of clonal evolution.

Several limitations should be noted in this study. Firstly, the retrospective design with a limited sample size may reduce statistical power and lead to selection bias. Expanding the co- hort size via multicenter collaborations and incorporating pro- spective real data would enhance the statistical reliability. Secondly, the absence of external cohorts for cross-validation raises concerns about the robustness of the gene signature. Thirdly, although genomic and transcriptomic analyses pro- vided insights into TMB-related prognostic genes, the lack of multi-omics integration, including metabolomics or prote- omics, limits the comprehensive identification of actionable targets for precision oncology. Additionally, the reliance on in vitro models without overexpression systems restricts mecha- nistic validation. Finally, the absence of patient-derived xen- ograft (PDX) models precludes validation of ACC-specific tu- mor-stroma interactions and endocrine microenvironmental dependencies. Future studies should construct PDX platforms to achieve clinical translatability.

CONCLUSION

This study firstly identified a total of eight candidate TMB-related prognostic genes (including ASPM, BIRC5, BUB1, CDC20, CDCA5, CEP55, KIF11, and TPX2) for ACC patients, which might be of great importance for ACC devel- opment and progression. Preliminary experimental verifica- tion revealed that ASPM and KIF11 could promote the prolif- eration of ACC cells and ACC tumor growth in vivo. The re- sults of the current work might help us gain insight into the molecular mechanism of ACC.

AUTHORS’ CONTRIBUTIONS

The authors confirm their contribution to the paper as fol- lows: study conception and design: Zhi-Bin Ke, Ning Xu; data collection: Bin Lin, Jiang-Bo Sun, Shao-Hao Chen; analysis and interpretation of results: Qing-Shui Zheng, Yong Wei, Xue-Yi Xue, Xiao-Dong Li; draft manuscript: Jia-Yin Chen, Yu-Ting Xue, Shi-Wei Lin, Qi You. All authors reviewed the results and approved the final version of the manuscript.

LIST OF ABBREVIATIONS

ACC

= Adrenocortical Carcinoma

ASPM = Abnormal Spindle-like Microcephaly-asso- ciated Protein

BIRC5 = Baculoviral IAP Repeat Containing 5

BP = Biological processes

BUB1 =	Budding Uninhibited by Benzimidazoles 1
CC =	Cellular Components
CCK-8 =	Cell Counting Kit-8
CDCA5 =	Cell Division Cycle Associated 5
CDCA8 =	Cell Division Cycle Associated 8
CDC20 =	Cell division Cycle 20
CENPA =	Centromere Protein A
CEP55 =	Centrosomal Protein 55
ctDNA =	Circulating Tumor DNA
DEGs =	Differentially Expressed Genes
DSS =	Disease-specific Survival
ENSAT =	European Network for the Study of Adrenal Tumors
GEO =	Gene Expression Omnibus
GO =	Gene Ontology
GSEA =	Gene Set Enrichment Analysis
GTEx =	Genotype Tissue Expression
KEGG =	Kyoto Encyclopedia of Genes and Ge- nomes
KIF2C =	Kinesin Family Member 2C
KIF11 =	Kinesin Family Member 11
KIF15 =	Kinesin Family Member 15
ME =	Module Eigengene
MF =	Molecular Functions
NUF2 =	NUF2 Component of NDC80 Kinetochore Complex
OS =	Overall Survival
PDX =	Patient-derived Xenograft
PFS =	Progression-free Survival
PPI =	Protein-protein Interaction
PVDF =	Polyvinylidene Fluoride
qRT-PCR =	Quantitative Reverse-transcription Poly- merase Chain Reaction
SDS-PAGE	Sodium Dodecyl Sulphate Polyacrylamide Gel Electrophoresis
siRNA =	Small-interfering RNAs
SNP =	Single Nucleotide Polymorphism
SNV =	Single Nucleotide Variants
STING =	Search Tool for the Retrieval of Interacting Genes
TMB =	Tumor Mutation Burden
TME =	Tumor Microenvironment

Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes

TOM = Topological Overlap Matrix

TPX2 = Targeting protein for Xklp2

WGCNA = Weighted Gene Co-expression Network Analysis

All animal experiments were approved by the Experi- mental Animal Ethics Committee of Fujian Medical Univer- sity (Approved No. of ethics committee: IACUC FJMU 2025- 0204).

HUMAN AND ANIMAL RIGHTS

The experiments were conducted in compliance with Chi- na’s national standard GB/T 35892-2018 (Laboratory Ani- mal-Guideline for Ethical Review of Animal Welfare), which is an internationally recognized framework for laboratory an- imal ethics.

This study adheres to internationally accepted standards for animal research, following the 3Rs principle. The AR- RIVE guidelines were employed for reporting experiments in- volving live animals, promoting ethical research practices.

Not applicable.

AVAILABILITY OF DATA AND MATERIALS

The datasets supporting the findings of this study are avail- able from public repositories and the corresponding author. Specifically, the data for Figures 1-8 and 9A-H were sourced from publicly available databases, including The Cancer Ge- nome Atlas (TCGA, https://portal.gdc.cancer.gov), the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/ geo/), and the Genotype-Tissue Expression (GTEx, https://gtexportal.org/)t. The original experimental data in- cluding Figures 9I-N and Figure 10 are available from the cor- responding author upon reasonable request.

FUNDING

This study was supported by the “Eyas Plan” Youth Top- notch Talent Project of Fujian Province (Grant number: SCYJHBJRC-XN2021).

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or other- wise.

ACKNOWLEDGEMENTS

Declared none.

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Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes

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DISCLAIMER: The above article has been published, as is, ahead-of-print, to provide early visibility but is not the final version. Major publication processes like copyediting, proofing, typesetting and further review are still to be done and may lead to changes in the final published version, if it is eventually published. All legal disclaimers that apply to the final published article also apply to this ahead-of-print version.

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Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes ASPM and KIF11 as Novel Biomarkers for Adrenocortical Carcinoma

1. INTRODUCTION

2. MATERIALS AND METHODS

2.1. Acquisition of Single-nucleotide Variations and Transcriptome Data

2.2. Calculation of TMB Scores and Survival Analysis

2.3. Correlation of TMB with Clinical Characteristics and Tumor Microenvironment

2.4. Identification of DEGs Between High and Low TMB Scores

2.5. Weighted Gene Co-expression Network Analysis (WGCNA) and Construction of PPI Network

2.6. Validation of Prognostic Value and Differential Expression Levels between Normal and ACC Samples

2.7. Cell Culture

2.8. Quantitative Reverse-transcription Polymerase Chain Reaction (qRT-PCR)

2.9. RNA Interference

2.10. Western Blotting

2.11. CCK-8 Viability Assays

2.12. Colony Formation Assay

2.13. Flow Cytometry

2.14. In vivo Tumorigenesis Assay

2.15. Statistical Analysis

3. RESULTS

3.1. Somatic Mutation Landscape of ACC

Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes

3.2. Correlation of TMB with Clinicopathologic Characteristics, PD-L1 Gene Expression Level, and Tumor Immune Microenvironment

3.3. Identification of DEGs between High and Low TMB ACC Samples

3.4. Weighted Gene co-expression Network Analysis and Construction of PPI Network

14 Current Cancer Drug Targets, XXXX, Vol. XX, No. XX

3.5. Validation of Prognostic Value and Differential Expression Levels Between Normal and ACC Samples

3.6. Inhibition of ASPM and KIF11 could Promote Apop- tosis, Inhibit the Proliferation of ACC Cells, and Suppress ACC Tumor Growth In Vivo

4. DISCUSSION

CONCLUSION

AUTHORS’ CONTRIBUTIONS

LIST OF ABBREVIATIONS

Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes

ETHICS APPROVAL AND CONSENT TO PARTICI- PATE

HUMAN AND ANIMAL RIGHTS

CONSENT FOR PUBLICATION

AVAILABILITY OF DATA AND MATERIALS

FUNDING

CONFLICT OF INTEREST

ACKNOWLEDGEMENTS

REFERENCES

Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes

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