Unveiling the Significance of NCAP Family Genes in Adrenocortical Carcinoma and Adenoma Pathogenesis: A Molecular Bioinformatics Exploration
Mahshid Arastonejad1, Daniyal Arab2, Somayeh Fatemi3 and Pezhman Golshanrad4
1Department of Human and Molecular Genetics, Virginia Commonwealth University, Richmond,
VA, USA. 2Department of Human Genetics, Science and Research Branch, Islamic Azad University, Tehran, Iran. 3Department of Medical Genetics, School of Medicine, Babol University of Medical Sciences, Babol, Iran. 4Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
Article reuse guidelines: sagepub.com/journals-permissions DOI: 10.1177/11769351241262211
S Sage
ABSTRACT
OBJECTIVES: Adrenocortical carcinoma (ACC), a rare and aggressive adrenal cortex cancer, poses significant challenges due to high mortality, poor prognosis, and early post-surgery recurrence. Variability in survival across ACC stages emphasizes the need to uncover its molecular underpinnings. Adrenocortical adenoma, a benign tumor, adds to diagnostic challenges, highlighting the necessity for molecular insights. The Non-SMC Associated Condensin Complex (NCAP) gene family, recognized for roles in chromosomal structure and cell cycle control. This study focuses on evaluating NCAP gene functions and importance in ACC through gene expression profiling to identify diag- nostic and therapeutic targets.
METHODS: Microarray datasets from ACC patients, obtained from the Gene Expression Omnibus database, were normalized to eliminate batch effects. Differential expression analysis of NCAP family genes, facilitated by the GEPIA2 database, included survival and pathological stage evaluations. A Protein-Protein Interaction network was constructed using GeneMANIA, and additional insights were gained through Gene Ontology enrichment analysis, correlation analysis, and ROC curve analysis.
RESULTS: ACC samples exhibited elevated levels of NCAPG, NCAPG2, and NCAPH compared to normal and adenoma samples. Increased expression of these genes correlated with poor overall survival, particularly in advanced disease stages. The Protein-Protein Interaction net- work highlighted interactions with related proteins, and Gene Ontology enrichment analysis demonstrated their involvement in chromosomal structure and control. Differentially expressed NCAP genes showed positive associations, and ROC curve analysis indicated their high dis- criminatory power in identifying ACC from adenoma and normal samples.
CONCLUSION: The study emphasizes the potential importance of NCAPG, NCAPG2, and NCAPH in ACC, suggesting roles in tumor aggressiveness and diagnostic relevance. These genes could serve as therapeutic targets and markers for ACC, but further exploration into their molecular activities and validation studies is imperative to fully harness their diagnostic and therapeutic potential, advancing precision medicine approaches against this rare but lethal malignancy.
KEYWORDS: NCAPG, NCAPG2, NCAPH, bioinformatics analysis, GEO, survival, PPI network, stage analysis
RECEIVED: January 15, 2024. ACCEPTED: May 9, 2024.
TYPE:Original Research
FUNDING: The author(s) received no financial support for the research, authorship, and/or publication of this article.
DECLARATION OF CONFLICTING INTERESTS: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
CORRESPONDING AUTHOR: Pezhman Golshanrad, Sharif University of Technology, Tehran, Azadi Ave, P932+FM4 Sharif University of Technology, Tehran 11365-8639, Iran. Email: parsamkaka@yahoo.com
Introduction
Adrenocortical carcinoma (ACC) is a rare and aggressive malignancy that begins in the adrenal cortex and has an annual incidence rate of 0.7 to 2.0 cases per 1 000 000 people.1 ACC is distinguished by high mortality, a poor prognosis, and vulner- ability to recurrence, with the majority of recurrences occurring within the first 2 years after surgical resection.2,3
The dramatic discrepancy in survival rates among different phases of the disease is one of the major features of ACC.4 Patients with localized ACC have a median 5-year survival rate of 74%, which drops to 56% in the presence of regional metas- tases and even lowers to 37% in the presence of distant
metastases.5 At the moment, the major therapy approach for ACC is complete surgical resection. Nonetheless, a significant proportion of ACC patients have metastases at the time of diagnosis, posing a significant treatment challenge.1,6 As a result, a thorough investigation into the molecular underpin- nings of ACC becomes critical, as understanding the processes driving its progression is thought critical for the development of novel diagnostic and treatment strategies.
Adrenocortical adenoma, a benign neoplasm that originates in the adrenal cortex, is a kind of adrenal tumor. While not as dangerous as ACC, adrenocortical adenomas are important in the setting of adrenal tumors. They appear as non-cancerous
İ $ BY NC
GEO Database
GSE33371
GSE12368
|log2FC| ≥1 adj.P.Val < 0.01
Analysis by R (limma package)
DEGs
Evaluation of NCAP Family Genes
Survival Analysis
Pathological Stage Analysis
Protein-protein Interaction network
Gene Ontology Enrichment Analysis
Correlation Analysis
ROC Curve Analysis
growths in the adrenal cortex, and distinguishing between ACC and adrenocortical adenoma is crucial for accurate diag- nosis and patient management.1,7
The Non-SMC Associated Condensin Complex (NCAP) gene family has lately sparked interest in cancer biology. Condensins, known for their critical functions in cellular mitosis and chromosome organization, have been discovered to be involved in the regulation of a wide range of cellular processes.8 Their participation in chromosomal aggregation and segregation during the cell cycle makes them fascinating targets for investigation in a condition characterized by uncontrolled cellular proliferation.9,10 Nonetheless, the precise roles and regulatory mechanisms of NCAP genes in the context of ACC are mainly unknown. Understanding the role of NCAP family genes in ACC may
lead to the discovery of novel diagnostic and treatment mechanisms.
In recent years, gene expression profiling, made possible by microarray technology, has emerged as a powerful tool for exam- ining the molecular foundations of ACC. This work uses gene expression microarrays to investigate the roles of the NCAP gene family in the context of ACC. This paper takes a comprehensive approach to elucidating the roles and significance of the NCAP gene family in the context of ACC, including gene expression data analysis, survival and stage analysis, the construction of pro- tein-protein interaction networks, gene ontology enrichment analysis, correlation analysis, and ROC curve analysis. This extensive investigation has the potential to shed light on novel diagnostic and therapeutic targets for ACC, ultimately improving our understanding of this rare but deadly cancer (Figure 1).
Material and Methods
Obtaining microarray data
Two microarray datasets comprising mRNA expression pro- files of ACC patients were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/ geo/) for the identification of differentially expressed genes (DEGs) associated with ACC.11 To retrieve the functional genomic data, the GEOquery package (version 2.68.0) in R language software (version 4.3.1) was used.12 GSE33371 and GSE12368 were the 2 datasets used in this analysis. These datasets share the same microarray platform, GPL570 (HG- U133_Plus_2), based on the Affymetrix Human Genome U133 Plus 2.0 Array. GSE33371 contains samples from 33 ACC patients, 22 Adrenocortical Adenomas, and 10 normal adrenal cortex samples, all from different people. There are 12 ACC samples, 16 Adrenocortical Adenoma samples, and 6 normal adrenal cortex samples in GSE12368. All raw expres- sion values were normalized and log2 transformed to ensure data comparability. To eliminate the possibility of batch effects, the sva package (version 3.48.0) in R was used to combine data from the 2 separate datasets.13
Evaluation of NCAP family genes expression
The NCAP family genes’ differential expression was examined using the R limma package (version 3.56.2), which is available on the Bioconductor platform.14 DEGs were identified using the |log2FoldChange| ≥1 criterion and an adjusted P-value of <0.01. Given the existence of ACC, adenoma, and normal samples, 3 distinct analyses were carried out: 1. carcinoma ver- sus normal samples; 2. carcinoma versus adenoma samples; and 3. adenoma samples versus normal samples.
NCAP family genes survival and stage analysis
The Kaplan-Meier technique was used to analyze the survival of NCAP family genes using the GEPIA2 (Gene Expression Profiling Interactive Analysis, version 2) database.15 For large- scale expression profiling and interactive analysis, this online resource makes use of data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx).16,17 In addition, pathological stage analysis for ACC was performed in GEPIA2 using the “Expression Analysis-Pathological Stage Plot” module. This resulted in violin plots depicting the expres- sion levels of NCAP family genes at various stages of ACC.
Protein-protein interaction (PPI) network construction
A Protein-Protein Interaction (PPI) network was created using the GeneMANIA Cytoscape plugin.18,19 GeneMANIA uses a comprehensive collection of functional relationships to identify genes related with a set of input genes, including protein and
genetic interactions, pathways, co-expression, co-localization, and protein domain similarity. The interaction network was shown using Cytoscape, an open-source bioinformatics software.
NCAP PPI network gene ontology enrichment analysis
A functional enrichment analysis was performed to discover functions that were overrepresented among a set of genes that were likely related to experimental circumstances.20 For this analysis, the clusterProfiler package (version 4.8.3) in R was utilized, with an emphasis on Gene Ontology (GO).21 GO is a bioinformatics resource that uses standardized vocabulary to describe gene products’ biological processes, cellular compo- nents, and molecular functions. Significantly enriched proteins were found using groupGo, and enrichGO using a q-value of <. 01 as the significance criterion.
Analysis of correlation
Pearson correlation analysis, a statistical method for determin- ing the strength and direction of linear correlations between continuous variables, was used to examine the link between NCAP family members. Significant correlations were deter- mined using a P-value ≤.05 and a correlation coefficient≥.5.
ROC curve evaluation
Receiver Operating Characteristic (ROC) curve analysis was performed in R using the pROC package (version 1.18.4) to assess the predictive potential of the NCAP gene family in the context of ACC.22 ROC curves are useful for determining the sensitivity and specificity of diagnostic tests or biomarkers. The Area Under the Curve (AUC) was calculated to measure the capacity of NCAP family gene expression to differentiate between distinct sample types (Carcinoma vs Adenoma and Normal) in this study. An AUC of 0.5 indicates that there is no discriminatory power (equivalent to random chance), whereas an AUC of 1.0 indicates complete discrimination. In general, a greater AUC indicates higher efficiency.
Results
Data gathering
GSE33371 and GSE12368 microarray datasets containing expression data from Adrenocortical Carcinoma (ACC) patients, Adrenocortical Adenoma, and Normal samples were acquired and processed for the study. The first stages in data preprocessing were to convert raw expression values into log2 values using R software, followed by normalization using the Normalize-Quantiles function. These datasets were combined, and any batch effects were removed (Figure 2), yielding a total of 99 samples, which were allocated among the Carcinoma, Adenoma, and Normal sample groups, as shown in Table 1.
Log2 Transformed Expression
0
0
10
15
A)
GSM825367
GSM825369
GSM825371
GSM825373
GSM825375
GSM825377
GSM825379
GSM825381
GSM825383
GSM825385
GSM825387
GSM825389
GSM825391
GSM825393
GSM825395
GSM825397
GSM825399
GSM825401
GSM825403
GSM825405
GSM825407
GSM825409
GSM825411
GSM825413
GSM825415
GSM825417
GSM825419
GSM825421
GSM825423
GSM825425
GSM825427
GSM825429
GSM825431
GSM310460
GSM310462
GSM310464
GSM310466
GSM310468
GSM310470
GSM310472
GSM310474 GSM310476 GSM310478
GSM310480
GSM310482
GSM310484
GSM310486
GSM310488
GSM310490
GSE33371
Evaluation of NCAP family gene expression
Using the limma software, differential expression analysis revealed that 3 of the 6 NCAP family genes (NCAPG,
NCAPG2, and NCAPH) were statistically significant (Figure 3). These genes showed differential expression in Carcinoma samples when compared to Normal samples and in Carcinoma samples when compared to Adenoma sam- ples, but no significant changes were seen when Adenoma samples were compared to Normal samples (Table 2). In
GSE33371
| GSM825367 GSM825369 | |||||
|---|---|---|---|---|---|
| GSM825371 | |||||
| GSM825373 | |||||
| GSM825375 | |||||
| GSM825377 | |||||
| GSM825379 GSM825381 | Normal Carcinoma Adenoma | ||||
| GSM825383 | |||||
| GSM825385 | |||||
| GSM825387 | |||||
| GSM825389 | |||||
| GSM825391 | |||||
| GSM825393 | |||||
| GSM825395 | samples samples samples | ||||
| GSM825397 | |||||
| GSM825399 | |||||
| GSM825401 | |||||
| GSM825403 | |||||
| GSM825405 | |||||
| GSM825407 | |||||
| GSM825409 | |||||
| GSM825411 | |||||
| GSM825413 | |||||
| GSM825415 | |||||
| GSM825417 | |||||
| GSM825419 GSM825421 | |||||
| GSM825423 | |||||
| GSM825425 | |||||
| GSM825427 | |||||
| GSM825429 | |||||
| GSM825431 | |||||
| GSM310460 | |||||
| GSM310462 | |||||
| GSM310464 | |||||
| GSM310466 | |||||
| GSM310468 | |||||
| GSM310470 GSM310472 | |||||
| GSM310474 | |||||
| GSM310476 | |||||
| GSM310478 GSM310480 | |||||
| GSM310482 | |||||
| GSM310484 | |||||
| GSM310486 | |||||
| GSM310488 GSM310490 |
Log2 Transformed Expression
2
A
9
co
B)
Figure 2. A visual representation of the elimination of batch effects using ComBat for Affymetrix platforms. Boxplot (A) displays the distributions of gene
expression and the samples of microarray datasets prior to the elimination of batch effects. Boxplot (B) shows the corresponding concepts after batch
removal. The boxplots illustrate the normalization and reduction in technical diversities among datasets.
addition, there were no significant differences in the expres- sion patterns of NCAPH2, NCAPD2, and NCAPD3 when Carcinoma samples were compared to Adenoma and Normal samples (Figure 4).
NCAP family gene survival and stage analysis
Using the GEPIA database, we evaluated the clinical prognos- tic significance of NCAPG, NCAPG2, and NCAPH in ACC. According to the GEPIA database, increased expression of
| NUMBER OF SAMPLES | GSE33371 | GSE12368 | TOTAL |
|---|---|---|---|
| Adrenocortical carcinoma | 33 | 12 | 45 |
| Adrenocortical adenoma | 22 | 16 | 38 |
| Normal samples | 10 | 6 | 16 |
| Total | 65 | 34 | 99 |
| Male | 24 | 10 | 34 |
| Female | 41 | 24 | 65 |
Carcinoma vs Adenoma
A)
Downregulate
No Significance
Upregulate
30
-Log,, Adj.P.Val
20
NCAPG
INCAPG2
10
INCAPH
NCAPD2
NCAPD3
0
NCAPH2
-5.0
-2.5
0.0
2.5
5.0
Log, fold change
B) Carcinoma vs Normal
total = 23521 variables
30
20
-Log., Adj.P.Val
10
INCAPG2
NCAPD2
NCAPD3
NCAPH
0
NCAPH2
-5.0
-2.5
0.0
2.5
5.0
Log, fold change
C) Adenoma vs Normal
total = 23521 variables
15
-Log., Adj.P.Val
10
5
NCAPH2 NCAPD2
NCAPHNCAPD3]
0
NCAPG NCAPG2
-5.0
-2.5
0.0
2.5
5.0
Log, fold change
total = 23521 variables
NCAPG, NCAPG2, and NCAPH was associated with poor overall survival in ACC patients (P≤.05). Also, NCAPG, NCAPG2, and NCAPH had very significant and high overall survival hazard ratios of 6 (P =. 00015), 6.2 (P =. 00011), and 6.5 (P=9.3e-05) respectively (Figure 5). Furthermore, these genes were shown to be significantly overexpressed in advanced ACC stages (stages III and IV) compared to primary stages (stages I and II) (Figure 6).
Protein-protein interaction (PPI) network construction
A PPI network was built utilizing NCAPG, NCAPG2, and NCAPH genes using the GeneMania database to obtain the top 20 linked proteins based on Physical Interaction, Co-expression, Co-localization, Pathway, Predicted, and Shared protein domains. The finished PPI network contained 23 nodes and 275 edges. These 20 proteins from highest degree to lowest degree include SMC2, NCAPD2,SMC4,AURKB,PRIM1,CCNA2,TOP2A, LMNB1, NDC80, CENPF, PRIM2, BRCA2, NCAPD3, MKI67,NEK6,NCAPH2,PLEC,HSF2,HECTD3,SLC15A4 (Figure 7).
NCAP PPI network gene ontology enrichment analysis
Gene Ontology (GO) enrichment analysis was used to deter- mine the functional roles and signaling processes of genes in the NCAP family’s PPI network. This study discovered links between biological processes such as “regulation of chromosome segrega- tion,” “regulation of chromosome separation,” and “chromosome separation,” among others. These genes were found in cell com- ponents such as “condensed chromosome,” “nuclear chromo- some,” and “condensed nuclear chromosome.” In terms of molecular function, enrichment was observed in “single-stranded DNA binding,” “magnesium ion binding,” and “DNA-directed 5’-3” RNA polymerase activity (Figure 8A).”
The NCAP family genes and their linked proteins are shown in the Cnetplot to be involved in the regulation of chro- mosomal shape and dynamics. These genes play a role in chro- mosome segregation, chromosome condensation, chromosome separation, and chromosome organization, all of which are required for successful cell division (Figure 8B).
Analysis of correlation
The present study used Pearson correlation analysis in R to evalu- ate the relationship between the expression of NCAPG, NCAPH, and NCAPG2. The study discovered positive correlations between all 3 NCAP genes that were differentially expressed in ACC, NCAPG and NCAPG2 (Correlation =. 85, P-value=6.21e-29), NCAPG and NCAPH (Correlation =. 85, P-value=8.79e-29), and NCAPH and NCAPG2 (Correlation =. 78, P-value=4.03e- 21) (Figure 9).
| CARCINOMA | CARCINOMA | ADENOMA VS NORMAL | ||||
|---|---|---|---|---|---|---|
| VS NORMAL | VS ADENOMA | |||||
| Gene symbol | adj.P.Val | logFC | adj.P.Val | logFC | adj.P.Val | logFC |
| NCAPG | 6.41E-12 | 1.41 | 2.86E-18 | 1.45 | 9.75E-01 | -0.03 |
| NCAPG2 | 1.69E-11 | 1.13 | 6.49E-17 | 1.12 | 9.91E-01 | 0.01 |
| NCAPH | 4.35E-06 | 1.38 | 5.09E-10 | 1.43 | 9.75E-01 | -0.05 |
| NCAPH2 | 7.59E-01 | -0.03 | 6.98E-01 | 0.03 | 8.15E-01 | -0.05 |
| NCAPD2 | 6.00E-08 | 0.92 | 7.84E-10 | 0.79 | 8.08E-01 | 0.13 |
| NCAPD3 | 7.10E-03 | 0.27 | 3.88E-05 | 0.30 | 9.52E-01 | -0.03 |
type
E
Adenoma
- Carcinoma
= Normal
A)
B)
C)
6
6
S
Expression level (NCAPG)
Expression level (NCAPG2)
Expression level (NCAPH)
+
S
.4
4
3
3
3
Adaroma n=38
Normal
3
Carcinoma
Adenoma a-38
Carcinoma
Normal 0-16
Adenoma 1-35
Carcinoma 1-45
Normal 116
n=45
n-16
n-45
D)
E)
F)
6.0
4.8
6
Expression level (NCAPD3)
Expression level (NCAPD2)
Expression level (NCAPH2)
6.5
4.5
$
13
5.0
9
1.5
3
Adenoma 1-38
Carcinoma
Normal
Adenoma
n=45
0-16
5-35
Carcinoma nº45
Normal
3.6
1-16
Ačchema
1-38
Carcinoma n=45
Normal 116
A)
Overall Survival
B)
Overall Survival
C)
Overall Survival
2.
Low NCAPG Group
9
Low NCAPG2 Group
:
High NCAPG Group
High NGAPG2 Group
Low NCAPH Group
High NCAPH Group
Logrank p=2e-05
Logrank p=1.3e-05
Logrank p=1.le-05
0.8
HR(high)=6
HR(high)=6.5
p(HR)=0.00015
0.8
HR(high)=6.2
p(HR)=0.00011
0.8
p(HR)=9.3c-05
Percent survival
nthigh) 38
n(high)=38
n(high) 38
0.6
n(low)=38
Percent survival
0.6
p(low) =38
Percent survival
0.6
n(low)=38
0.4
0.4
0.4
0,2
3
3
0,0
8
8
0
50
100
150
0
50
100
150
0
50
100
150
Months
Months
Months
ROC curve evaluation
ROC curve studies revealed that NCAPG, NCAPG2, and NCAPH have great discriminatory strength in distinguishing between distinct sample types, as evidenced by high Area Under the Curve (AUC) values. The AUC for NCAPG was
0.963 (P-value: 2.6e-15), for NCAPG2 0.943 (P-value: 4.1e- 14), and 0.857 (P-value: 1.1e-09) for NCAPH (Figure 10).
Discussion
Adrenocortical carcinoma (ACC) is a rare and deadly malig- nancy, with most patients diagnosed at an advanced stage,
NCAPG
NCAPG2
NCAPH
F value = 7.61
F value = 7.43
0
F value = 5.38
Pr(>F) = 0.000176
Pr(>F) = 0.000214
Pr(>F) = 0.000215
5
+
+
-
-
£
2
2
”
-
-
-
℮
0
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
Stage I
Stage II
Stage III
Stage IV
SLE15A4
SMC2
TOP2A
BRCA2
HECTD3
CCNA2
NEK6
PRIM2
CENPF
MKI67
NCAPG2
NCAPH
SMC4
NCAPD3
NCAPG
EMNB1
PLEC
NDC80
Physical Interaction
Co-expression
Co-localization
AURKB
NCAPH2
Pathway
PRIMI
HSF2
Predicted
NCAPD2
Shared protein domains
contributing to its poor prognosis and high mortality rates. This study aimed to comprehensively investigate the potential roles of Non-SMC Associated Condensin Complex (NCAP) family genes in ACC. By employing a multifaceted approach, including microarray data analysis, survival and stage assess- ments, construction of protein-protein interaction networks, enrichment analysis, correlation, and the ROC curve analysis, we sought to shed light on the involvement of these genes in ACC and their clinical implications.
The complexity of tumorigenesis involves intricate genetic and molecular changes. Among the 6 NCAP family genes, including NCAPG, NCAPG2,NCAPH,NCAPH2,NCAPD2, and NCAPD3, recent studies have highlighted their possible sig- nificance in various clinical disorders.23-25
NCAPG and NCAPG2: NCAPG and NCAPG2 are inte- gral components of the condensin complex, crucial for chromo- somal condensation and segregation during mitosis. Research has indicated that higher expression of NCAPG is associated
with tumor growth and poor prognosis in breast cancer.25 Similarly, elevated expression of NCAPG2 is identified as a potential prognostic marker in lung cancer, with increased levels linked to worse survival outcomes.26
NCAPH and NCAPH2: NCAPH and NCAPH2, compo- nents of the condensin complex involved in chromosomal con- densation and segregation, have been associated with the development and progression of several cancers. NCAPH has been found to be upregulated in hepatocellular carcinoma (HCC) and linked to tumor growth, vascular invasion, and a poor prognosis.27 Likewise, overexpression of NCAPH2 in ovarian cancer is connected with advanced tumor stage and reduced overall survival.28
NCAPD2 and NCAPD3: As key components of the con- densin complex, NCAPD2 and NCAPD3 play roles in chromo- somal condensation and segregation. Recent research suggests their roles in carcinomas and tumors. NCAPD2 has been dem- onstrated to be upregulated in lung adenocarcinoma and associ- ated with immune infiltration and Tumor mutational burden (TMB).29 Additionally, NCAPD3 has been identified as a potential prognostic marker in colorectal cancer which enhances glucose metabolism reprograming and promotes the Warburg effect in colorectal tumorigenesis and CRC progression.30
These observations imply that NCAP family genes may indeed participate in carcinogenesis, tumor growth, and aggres- siveness, making them potential candidates for therapeutic targeting.31,32
In our study, the initial step involved the acquisition of 2 microarray datasets, GSE33371 and GSE12368, from the GEO database. These datasets contained mRNA expression patterns from ACC patients, adrenocortical adenomas, and healthy tis- sues. Merging these datasets after appropriate normalization was essential to ensure data comparability and eliminate potential batch effects. Among the 6 NCAP family genes, NCAPG, NCAPG2, and NCAPH exhibited statistically significant over- expression when comparing ACC samples to normal and ade- noma samples. In contrast, no significant changes were detected when comparing adenoma and normal tissues. These findings suggest that NCAPG, NCAPG2, and NCAPH may play vital
A)
Gene Ontology
regulation of chromosome
segregation
☒
regulation of chromosome
separation
☒
chromosome separation
☒
positive regulation of
☒
OS
chromosome segregation
positive regulation of
chromosome separation
☒
regulation of chromosome
condensation
☒
p.adjust
condensed chromosome
☒
0.01
0.02
nuclear chromosome
0.03
condensed nuclear chromosome
☒
0.04
chromosomal region
☒
DNA packaging complex
☒
Count
☒
☐ 5
chromosome, centromeric region
☐
10
single-stranded DNA binding
☒
magnesium ion binding
☒
DNA-directed 5’-3’ RNA
polymerase activity
O
MF
5’-3’ RNA polymerase activity
O
RNA polymerase activity
0.2
0.4
0.6
GeneRatio
B)
TOP2A
NCAPH2
regulation of chromosome segregation
regulation of chromosome condensation
NEK6
regulation of chromosome separation
MKI67
chromosome separation
category
chromosome separation
positive regulation of chromosome segregation
positive regulation of chromosome segregation
positive regulation of chromosome separation
NCAPD3
regulation of chromosome condensation
regulation of chromosome organization
regulation of chromosome segregation
CENPF
positive regulation of chromosome separation
regulation of chromosome separation
sister chromatid segregation
size
5.0
NDC80
sister chromatid segregation
☒
7.5
☒
10.0
☒
12.5
NCAPH
regulation of chromosome organization
AURKB
SMC2
NCAPD2
NCAPG2
NCAPG
SMC4
A)
B)
C)
6
6
6
5
5
1
NCAPG2
5
Sample Type
NCAPH
4
NCAPH
4
☒ Adenoma
☒
Carcinoma
4
3
3
☒
Normal
Correlation: 0.85
2
Correlation: 0.85
2
Correlation: 0.78 p-value: 4.03e-21
p-value: 6.21e-29
p-value: 8.790-29
3
3
4 NCAPG
5
6
3
4 NCAPG
5
6
3
4
NCAPG2
5
6
A)
B)
C)
1.0
1.0
1.0
0.8
870 (0 981, 0.867)
0.8
T391 (0 981, 0.867)
0.8
Sensitivity
0.6
Sensitivity
0.6
Sensitivity
0.6
3 002 (0 963, 0 644)
0.4
0.4
0.4
0.2
0.2
0.2
P-Value: 2.6e-15
P-Value: 4.1e-14
P-Value: 1.1e-09
AUC: 0.963
AUC: 0.857
0.0
0.0
AUC: 0.943
0.0
1.0
0.8
0.6
Specificity
0.4
0.2
0.0
1.0
0.8
0.6
Specificity
0.4
0.2
0.0
1.0
0.8
0.6
Specificity
0.4
0.2
0.0
roles in ACC, possibly acting as diagnostic markers or therapeu- tic targets. Evaluating the clinical relevance of these gene expres- sion changes is of utmost importance.
Our analysis using the GEPIA database revealed a significant correlation between high expression levels of NCAPG, NCAPG2, and NCAPH and poor overall survival in ACC patients. The associated hazard ratios were notably high, under- scoring their potential prognostic significance. Additionally, these genes were found to be substantially overexpressed in advanced ACC stages (stages III and IV) compared to primary stages (stages I and II). This highlights the potential of NCAP family genes as disease progression, critical for tailoring effective treatment regimens based on the stage of the disease.
We further constructed a PPI network for NCAPG, NCAPG2, and NCAPH using the GeneMANIA database. This network provided insights into potential functional associations among these genes. Subsequently, a Gene Ontology (GO) enrichment analysis indicated that these genes are closely associ- ated with the regulation of chromosomal dynamics, segregation, separation, and condensation. These findings imply that the NCAP family genes play significant roles in controlling chromo- somal dynamics and organization, which will guide future research into their precise mechanisms in ACC. The chromo- somal regulatory pathway in cancer refers to the processes and mechanisms that maintain chromosome stability and integrity in normal cells, as well as how these processes become dysregulated in cancer cells. Chromosomes are structures in the nucleus of cells that carry DNA, an organism’s genetic material. They are critical in preserving the genome’s integrity and appropriate functioning. Any changes in the structure or number of chromosomes can cause genomic instability, which is a hallmark of cancer.
Correlation analysis among the differentially expressed NCAP family genes showed positive and significant relation- ships, suggesting potential co-regulation or interplay among these genes in ACC. Understanding these interrelationships may provide deeper insights into their combined roles in ACC development. Moreover, the ROC curve analysis demonstrated the strong discriminatory power of NCAPG, NCAPG2, and
NCAPH in distinguishing between different sample types, signified by the high Area Under the Curve (AUC) values. These findings suggest their potential utility as reliable diag- nostic markers for distinguishing ACC from adenoma and normal samples. However, further validation studies are neces- sary to confirm their diagnostic accuracy.
Limitation
Functional enrichment analysis and PPI network construction provide valuable insights into the potential roles of NCAP family genes, these analyses are based on computational predic- tions and database annotations, which may not fully capture the complexity of gene functions in specific biological contexts. Experimental validation of gene functions is necessary to con- firm the findings. Also, the association between NCAP family gene expression levels and clinical outcomes, such as overall survival and disease stage, provides important prognostic insights. However, these findings are based on retrospective analyses and correlation studies, which do not establish causal- ity. Prospective studies and experimental validations in clinical settings are needed to confirm the clinical relevance of these genes as prognostic markers or therapeutic targets.
While this study highlights the potential significance of NCAP family genes in ACC, the precise biological mecha- nisms underlying their roles remain elusive. Functional studies, including gene knockout or overexpression experiments, path- way analysis, and mechanistic investigations, are essential to elucidate the molecular pathways and cellular processes involv- ing these genes in ACC pathogenesis. Addressing these limita- tions and conducting further research to validate the findings could strengthen the scientific evidence supporting the involve- ment of NCAP family genes in ACC and facilitate the devel- opment of targeted diagnostic and therapeutic strategies for this aggressive cancer.
Conclusion
In conclusion, this study provides substantial evidence for the significance of 3 NCAP family genes, namely NCAPG,
NCAPG2, and NCAPH, in adrenocortical carcinoma. These genes exhibit higher expression levels, prognostic implications, stage-dependent expression, and involvement in critical bio- logical processes. To fully harness the diagnostic and therapeu- tic potential of NCAP family genes in ACC, further research into the molecular mechanisms governing their activities is essential. This study lays the foundation for developing tailored treatments and precision medicine approaches to combat this aggressive cancer.
Acknowledgements
Not Applicable.
Authors contribution
All authors contributed to the study conception and design. Original draft preparation and data analysis was conducted by MA and DA. Writing, reviewing and editing of the draft man- uscript was completed by MA, DA, and SF. PG supervised the writing, analyses, and revision of the manuscript. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not Applicable.
Consent for publication
Not Applicable.
Availability of data and materials
The authors declare that the data supporting the findings of this study are addressed within the article.
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