Prognostic Relevance of Copy Number Losses in Ovarian Cancer

Abstract

Background/Objectives: Aneuploidy is a prevalent cancer feature that occurs in many solid tumors. For example, high-grade serous ovarian cancer shows a high level of copy number alterations and genomic rearrangements. This makes genomic variants appealing as diagnostic or prognostic biomarkers, as well as for their easy detection. In this study, we focused on copy number (CN) losses shared by ovarian cancer stem cells (CSCs) to identify chromosomal regions that may be important for CSC features and, in turn, for patients’ prognosis. Methods: Array-CGH and bioinformatic analyses on three CSCs subpopulations were performed. Results: Pathway and gene ontology analyses on genes involved in copy number loss in all CSCs revealed a significant decrease in mRNA surveillance pathway, as well as miRNA-mediated gene silencing. Then, starting from these CN losses, we validated their potential prognostic relevance by analyzing the TCGA cohort. Notably, losses of 4q34.3-q35.2, 8p21.2-p21.1, and 18q12.2-q23 were linked to increased genomic instability. Loss of 18q12.2-q23 was also related to a higher tumor stage and poor prognosis. Finally, specific genes mapping in these regions, such as PPP2R2A and TPGS2A, emerged as potential biomarkers. Conclusions: Our findings highlight the importance of genomic alterations in ovarian cancer and their impact on tumor progression and patients’ prognosis, offering advance in understanding of the application of numerical aberrations as prognostic ovarian cancer biomarkers.

Full text

Citation: Jemma, A.; Ardizzoia, A.;
Redaelli, S.; Bentivegna, A.; Lavitrano,
M.; Conconi, D. Prognostic Relevance
of Copy Number Losses in Ovarian
Cancer. Genes 2024,15, 1487. https://
doi.org/10.3390/genes15111487
Academic Editors: Albert Jeltsch and
Hilal Arnouk
Received: 3 October 2024
Revised: 29 October 2024
Accepted: 18 November 2024
Published: 19 November 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Prognostic Relevance of Copy Number Losses in Ovarian Cancer
Andrea Jemma
1
, Alessandra Ardizzoia
1,2
, Serena Redaelli
1
, Angela Bentivegna
1
, Marialuisa Lavitrano
1
and Donatella Conconi 1, *
1School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy;
a.jemma@campus.unimib.it (A.J.); a.ardizzoia@campus.unimib.it (A.A.); serena.redaelli@unimib.it (S.R.);
angela.bentivegna@unimib.it (A.B.); marialuisa.lavitrano@unimib.it (M.L.)
2Fondazione Istituto di Oncologia Molecolare ETS (IFOM), The AIRC Institute for Molecular Oncology,
20139 Milan, Italy
*Correspondence: donatella.conconi@unimib.it
Abstract: Background/Objectives: Aneuploidy is a prevalent cancer feature that occurs in many
solid tumors. For example, high-grade serous ovarian cancer shows a high level of copy number
alterations and genomic rearrangements. This makes genomic variants appealing as diagnostic or
prognostic biomarkers, as well as for their easy detection. In this study, we focused on copy number
(CN) losses shared by ovarian cancer stem cells (CSCs) to identify chromosomal regions that may
be important for CSC features and, in turn, for patients’ prognosis. Methods: Array-CGH and
bioinformatic analyses on three CSCs subpopulations were performed. Results: Pathway and gene
ontology analyses on genes involved in copy number loss in all CSCs revealed a significant decrease
in mRNA surveillance pathway, as well as miRNA-mediated gene silencing. Then, starting from these
CN losses, we validated their potential prognostic relevance by analyzing the TCGA cohort. Notably,
losses of 4q34.3-q35.2, 8p21.2-p21.1, and 18q12.2-q23 were linked to increased genomic instability.
Loss of 18q12.2-q23 was also related to a higher tumor stage and poor prognosis. Finally, specific
genes mapping in these regions, such as PPP2R2A and TPGS2A, emerged as potential biomarkers.
Conclusions: Our findings highlight the importance of genomic alterations in ovarian cancer and
their impact on tumor progression and patients’ prognosis, offering advance in understanding of the
application of numerical aberrations as prognostic ovarian cancer biomarkers.
Keywords: ovarian cancer; biomarkers; copy number loss; cancer stem cells; copy number alterations;
numerical aberrations
1. Introduction
Advances in high-throughput “omics” technologies produced a huge amount of data,
including cancer genomics data. Aneuploidy is a hallmark feature in approximately 90% of
solid tumors, reflecting widespread genomic instability [
1
]. Although its exact role in tumor
initiation and progression remains unclear, it is thought to contribute to the complexity of
chromosomal aberrations frequently observed in cancer cells. Traditionally, aneuploidy
refers to numerical changes involving whole chromosomes, but more recent studies have
expanded this concept to include segmental alterations, such as losses or gains of chromo-
some arms, which are often categorized as copy number alterations (CNAs) [
2
]. For this
reason, it can be challenging to compare different studies because numerical alterations
can involve either few genes (CNAs) or entire chromosome region (SCAs—segmental
chromosomal aberrations; copy number gains or losses affecting one chromosome arm or a
segment within a chromosome arm with at least 100 contiguous oligonucleotide probes) [
3
].
Numerical aberrations can readily be detected using conventional and molecular
cytogenetics techniques, routinely used in clinics, making them appealing biomarkers for
patients’ prognosis [
2
]. As a matter of fact, CNA burden or specific CNAs are associated
with poor outcomes in many cancers including neuroblastoma [
3
,
4
], breast cancer [
5
,
6
],
bladder cancer [7], glioblastoma [8,9] and ovarian cancer [10–13].
Genes 2024,15, 1487. https://doi.org/10.3390/genes15111487 https://www.mdpi.com/journal/genes

Genes 2024,15, 1487 2 of 14
Cancer stem cells (CSCs) are a subpopulation of tumor cells known for driving tu-
mor growth, treatment failure, and cancer relapse [
14
]. In this context, ovarian cancer
spheroids, found in malignant ascites, represent that subpopulation, responsible for ovar-
ian cancer dissemination, impairment of treatments effectiveness, and unfavorable patient
prognosis [15]. For these reasons, understanding the connection between CNAs and gene
expression in CSCs could be vital for revealing how genetic instability contributes to the
tumor’s aggressiveness. Moreover, several studies show that a large proportion of genomic
changes in cancer cells can be attributed to CNAs, reinforcing their role in driving key
aspects of tumor behavior [16].
In a previous work [
10
], we identified a potential prognostic role of AhRR and
PPP1R3C expression in serous ovarian cancer, starting from a detailed bioinformatics
analysis of copy number gain arising in CSCs by array comparative genomic hybridization
(array-CGH) analysis. In this work, we turn our attention to copy number losses in order
to identify in a similar way chromosomal regions or genes with a specific role in ovarian
cancer stem cells and, in turn, for patients’ prognosis.
2. Materials and Methods
2.1. Cell Lines
Ovarian cancer cell lines Caov3, Ovcar5, and Ovcar8 were purchased from ATCC
(American Type Culture Collection, Manassas, VA, USA) and Sigma-Aldrich (St. Louis, MO,
USA). Caov3 were grown in Dulbecco’s modified Eagle’s medium (DMEM); Ovcar5 and
Ovcar8 were grown in RPMI 1640. Both media were completed with the addition of 10%
fetal bovine serum (FBS) and 1% penicillin–streptomycin. All reagents were purchased from
EuroClone (Milano, Italy). All the cell lines were maintained in a humidified atmosphere at
37 ◦C with 5% CO2.
Ovarian cancer stem cells (represented by ovarian cancer spheroids) were previously
isolated and characterized [
10
]. Briefly, spheroids were generated following an anchorage-
independent growth assay starting from the three different cell lines and then characterized
by stemness markers’ expression and clonogenic nature.
2.2. Array Comparative Genomic Hybridization
Array comparative genomic hybridization (array-CGH) experiments were previously
reported [
10
]. Briefly, DNA was extracted from ovarian CSCs and the corresponding
cell lines using an QIAamp DNA Mini Kit according to the manufacturer’s instructions
(QIAGEN, Hilden, Germany). After quantification with a Nanodrop ND-2000 spectropho-
tometer (Thermo Fisher Scientific, Waltham, MA, USA), samples with a concentration over
10
µ
g/mL and an absorbance ratio A
260/280
over 1.8 and A
260/230
over 1.7 were used for
analysis. Array-CGH analysis was performed using a SurePrint G3 Human CGH Microar-
ray 8
×
60 K (Agilent Technologies, Santa Clara, CA, USA) according to the manufacturer’s
instructions. The arrays were scanned at 2
µ
m resolution and analyzed using Agilent
Feature Extraction and Agilent Cytogenomics v5.2 software (Agilent Technologies, Santa
Clara, CA, USA). The Aberration Detection Method 2 (ADM-2) algorithm was used to
compute and assist the identification of aberrations in a given sample (threshold = 5.0),
assigning a statistical score based on the average quality weighted log ratio (DLRS) of
the sample and reference channels. We applied a filtering option of a minimum of three
aberrant consecutive probes and a minimum absolute average log2 ratio that differs among
all samples and depends on DLRS values. Log2ratio values over 1 identify amplification;
values under
−
1.7 identify complete loss. Log2ratio values over 0.6 identify non-mosaic
gains; values under
−
1 identify non-mosaic losses. Accordingly, log2 ratio values for
mosaic gains range between the DLRS value and 0.6 and for mosaic losses between the
DLRS value and
−
1. As a reference genomic DNA sample, woman-matched DNA was
provided by Agilent (Agilent Technologies).

Genes 2024,15, 1487 3 of 14
2.3. Bioinformatic Analyses
2.3.1. Analysis of Genes Involved in Copy Number Losses
The Database for Annotation, Visualization and Integrated Discovery (DAVID, https:
//david.ncifcrf.gov/tools.jsp, accessed on 29 July 2024) platform was used to identify
enriched REACTOME pathways, Kyoto Encyclopedia of Genes and Genomes (KEGG)
pathways, and GO (Gene Ontology) terms (BP: biological process, and MF: molecular
function) [17]. A p-value less than 0.05 was considered statistically significant.
2.3.2. Correlation of Chromosome Aberrations and Clinical Data
To evaluate a potential prognostic role of segmental chromosome aberrations, we
analyzed the TCGA-OV (The Cancer Genome Atlas Ovarian Cancer) cohort using the
cBioPortal for Cancer Genomics web server (https://www.cbioportal.org/, accessed on
29 July 2024). Ovarian serous cystadenocarcinoma (TCGA PanCancer Atlas) was chosen.
Samples with or without loss were selected to form the different groups (4q34.3-q35.2
loss vs. no loss, 8p21.2-p21.1 loss vs. no loss, and 18q12.2-q23 loss vs. no loss), which
were compared to identify differences in clinical parameters (survival, clinical, protein,
arm-level CNA).
For the cBioportal web server, copy number data from the GDC analysis pipelines
were provided in ASCAT format and converted to discrete GISTIC data using the following
thresholds:
−
2 or Deep Deletion indicates a homozygous deletion;
−
1 or Shallow Deletion
indicates a heterozygous deletion; 0 is diploid; 1 or Gain indicates a low-level gain (a few
additional copies); 2 or Amplification indicates a high-level amplification (more copies)
(https://www.cbioportal.org/).
The Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/,
accessed on 29 July 2024) provides access to the subset of TCGA data that has been har-
monized against GRCh38 (hg38) using GDC Bioinformatics Pipelines, which provides
methods to the standardization of biospecimen and clinical data and the generation of
derived data [
18
]. The CNA pipeline uses either NGS or Affymetrix SNP 6.0 (SNP6) array
data to identify genomic regions that are repeated and infer the copy number of these
repeats. Three sets of pipelines have been used for CNV inferences: ASCAT, ABSOLUTE,
and DNAcopy.
We chose the TCGA-OV project, divided the patients into cohorts according to the
tumor stage or age at diagnosis (under or over 63 years), and then we compared the
frequency of copy number gain and loss in each cohort.
2.3.3. Analysis of FHOD3, TPGS2, and KIAA1328 Expression in Ovarian Cancer
GEPIA2 (http://gepia2.cancer-pku.cn/, accessed on 29 July 2024) and Kaplan–Meier
plotter (https://kmplot.com/analysis/, accessed on 29 July 2024) web servers were used
to analyze the correlation between the selected genes’ expressions and patients’ overall
survival and disease-free survival in ovarian cancer. The selected cut-off value was “median
cut-off” (GEPIA2, OV Dataset) and “auto selected best cut-off” (Kaplan–Meier plotter
mRNA Chip). p-value < 0.05 was considered statistically significant. OncoDB web server
(https://oncodb.org/, accessed on 29 July 2024) was consulted for the analysis of correlation
between expression of genes and clinical stage of the tumor. p-value < 0.05 was considered
statistically significant.
3. Results
3.1. Lost Genes Were Involved in mRNA Processing
Ovarian cancer spheroids were obtained from three ovarian cancer cell lines (Caov3,
Ovcar5, and Ovcar8) belonging to high-grade serous histotype [
19
–
23
]. In our previous
study, we checked the expression of ovarian cancer stemness markers ALDH, CD44, ABCG2,
and NANOG and spheroids’ clonogenic nature through PKH staining [
10
]. All ovarian
cancer spheroids showed an increased expression of stemness markers compared to the

Genes 2024,15, 1487 4 of 14
corresponding cell line and spheroids’ clonogenic nature. These results validate our model
as ovarian cancer stem cells [10].
Firstly, based on the copy number losses shared by all three CSC subpopulations,
we identified a list of 1253 genes, which underwent enrichment analyses in GO terms,
REACTOME pathways, and KEGG pathways through the DAVID platform (Table S1).
KEGG pathway enrichment analysis revealed a significant decrease in the mRNA
surveillance pathway. This pathway serves as a quality control mechanism that identifies
and degrades abnormal mRNAs, and it includes nonsense-mediated mRNA decay (NMD),
nonstop mRNA decay (NSD), and no-go decay (NGD) [
24
]. In particular, lost genes
involved in this pathway were almost exclusively located on chromosome X, except for the
PPP2R2A gene located in 8p21.1: GSPT2 (Xp11.22), UPF3B (Xq24), CSTF2,NXF2,NXF2B,
NXF3 (all Xq22.1), NCBP2L (Xq22.3), NXT2 (Xq23), PABPC1L2A and PABPC1L2B (Xq13.2),
and PABPC5 (Xq21.31). Instead, REACTOME pathway enrichment analysis highlighted a
loss of downregulation of SMAD2/3:SMAD4 transcriptional activity.
GO enrichment analysis identified a strong downregulation of gene silencing by
miRNA (BP: biological process), with 60 downregulated miRNAs in CSCs, confirmed
by a decrease of mRNA binding involved in posttranscriptional gene silencing GO MF
(molecular function), with 31 downregulated miRNAs in CSCs.
Interestingly, some GO terms associated with mRNA processing (such as negative
regulation of transcription by RNA polymerase II, poly(A)+ mRNA export from nucleus,
mRNA 3
′
-UTR binding, poly(A) binding, and mRNA export from nucleus) were underrep-
resented in our CSC subpopulations. Moreover, CSCs showed upregulation of angiogenesis,
autophagy, G0 to G1 transition, and ERK1 and ERK2 cascade.
Taken together, these results explain some characteristics of our CSCs models and
confirm the importance of copy number losses in tumorigenesis and cancer progression.
3.2. Copy Number Losses Correlated with Clinical Parameters in the cBioPortal Web Server
Analyzing CNAs and SCAs shared by all the three CSCs subpopulations, we identified
three autosomal regions: 4q34.3-q35.2 (nt 178341937-191133609, 206 probes), 8p21.2-p21.1
(nt 25774629-28393917, 61 probes), and 18q12.2-q23 (nt 32483714-75486904, 686 probes)
(
Figure 1
). Moreover, loss of the entire chrX was observed (nt 4239811-152009316,
2467 probes).
To evaluate the potential prognostic role of these copy number losses, we analyzed the
correlation between loss in these regions and clinical parameters from the TCGA-OV (The
Cancer Genome Atlas Ovarian Cancer) cohort using the cBioPortal for Cancer Genomics
web server. Patients were divided into two groups based on the presence or absence of
18q12.2-q23 loss in tumor biopsies. The clinical data comparison between these two groups
displayed a statistically significant difference in disease-free survival (DFS, Figure 2A).
Moreover, an increased aneuploidy score and a higher fraction of genome altered in
ovarian cancer samples carrying 18q12.2-q23 loss were observed (Table 1,
Figure S1A,B
).
Thirteen proteins showed differential expression across the two groups of samples when
protein levels were analyzed using mass spectrometry (Figure 2B). GO and pathway
enrichment analyses on proteins overexpressed in the “no-loss” group of samples showed
only one statistically significant GO term (negative regulation of transcription, DNA-
templated). Enrichment analyses on two proteins overexpressed in the 18q “loss” group of
samples identified a statistically significant increase in cysteine and methionine metabolism
(KEGG pathway).
On the other hand, samples carrying 4q34.3-q35.2 loss showed an increase in the
fraction of genome altered (p< 0.01), as well as in the mutational count (p< 0.01) (Table 1,
Figure S1C,D). Finally, loss of 8p21.2-p21.1 was associated with a higher mutational count
(p< 0.01) and increased tumor mutational burden (TMB) nonsynonymous (p< 0.01) (Table 1,
Figure S1E,F). Taken together, these data suggest that the identified aberrations could
correlate with generalized genetic–genomic instability.

Genes 2024,15, 1487 5 of 14
Genes 2024, 15, x FOR PEER REVIEW 5 of 14
samples identified a statistically significant increase in cysteine and methionine metabo-
lism (KEGG pathway).
Figure 1. Copy number losses shared by all three CSCs subpopulations: 4q34.3-q35.2 (206 probes),
8p21.2-p21.1 (61 probes), 18q12.2-q23 (686 probes), and the entire chrX (2467 probes). Red: CN loss;
blue: CN gain; red cycles: chromosome number.
On the other hand, samples carrying 4q34.3-q35.2 loss showed an increase in the frac-
tion of genome altered (p < 0.01), as well as in the mutational count (p < 0.01) (Table 1,
Figure S1C,D). Finally, loss of 8p21.2-p21.1 was associated with a higher mutational count
(p < 0.01) and increased tumor mutational burden (TMB) nonsynonymous (p < 0.01) (Table
1, Figure S1E,F). Taken together, these data suggest that the identified aberrations could
correlate with generalized genetic–genomic instability.
As a result, we searched for potential variations in arm-level CNAs of other chromo-
somes in samples with the identified CN losses. We found statistically significant differ-
ences in the frequency of in arm-level CNAs in samples with CN losses with respect to
samples without losses, especially in samples with 8p21.2-p21.1 loss (Table 1). This result,
in addition to the previously reported higher tumor mutational burden, underlines that
these samples are characterized by a great genetic instability.
Given the small number of genes in CN losses of chr8 and chr4, we carried out en-
richment analyses in GO terms, REACTOME pathways, and KEGG pathways in order to
uncover a possible explanation for the observed data. A statistically significant GO term
(positive regulation of the apoptotic process, p < 0.01, BP: biological process) was found
Figure 1. Copy number losses shared by all three CSCs subpopulations: 4q34.3-q35.2 (206 probes),
8p21.2-p21.1 (61 probes), 18q12.2-q23 (686 probes), and the entire chrX (2467 probes). Red: CN loss;
blue: CN gain; red cycles: chromosome number.
As a result, we searched for potential variations in arm-level CNAs of other chro-
mosomes in samples with the identified CN losses. We found statistically significant
differences in the frequency of in arm-level CNAs in samples with CN losses with respect
to samples without losses, especially in samples with 8p21.2-p21.1 loss (Table 1). This result,
in addition to the previously reported higher tumor mutational burden, underlines that
these samples are characterized by a great genetic instability.
Given the small number of genes in CN losses of chr8 and chr4, we carried out
enrichment analyses in GO terms, REACTOME pathways, and KEGG pathways in order to
uncover a possible explanation for the observed data. A statistically significant GO term
(positive regulation of the apoptotic process, p< 0.01, BP: biological process) was found
by DAVID analysis of the genes involved in 8p21.2-p21.1 CN, indicating that the loss of
these genes may result in the loss of apoptosis. No interesting cluster was identified for
4q34.3-q35.2 genes.
Table 1. Variations in clinical parameters and chromosome arm-level CNAs in samples carrying
4q34.3-q35.2, 8p21.2-p21.1, or 18q12.2-q23 loss. Only chromosome arms with statistically significant
differences are reported.
4q34.3-q35.2 Loss vs.
No Loss
8p21.2-p21.1 Loss vs.
No Loss
18q12.2-q23 Loss vs.
No Loss
Aneuploidy score unvaried unvaried ↑
Fraction of genome altered ↑unvaried ↑
Mutational count ↑ ↑ unvaried
TMB (nonsynonymous) unvaried ↑unvaried
2p unvaried unvaried ↓CN gain
2q unvaried ↑CN gain ↓CN gain
3q unvaried ↑CN gain unvaried

Genes 2024,15, 1487 6 of 14
Table 1. Cont.
4q34.3-q35.2 Loss vs.
No Loss
8p21.2-p21.1 Loss vs.
No Loss
18q12.2-q23 Loss vs.
No Loss
4p ↑CN loss unvaried ↑CN loss
4q arm involved in CNA unvaried ↑CN loss
5p unvaried ↑CN gain unvaried
5q unvaried ↑CN loss unvaried
6p unvaried ↑CN gain ↑CN loss
7p unvaried unvaried ↑CN loss
7q unvaried unvaried ↑CN loss
8p unvaried arm involved in CNA unvaried
9p unvaried unvaried ↑CN loss
9q ↑CN loss ↑CN loss unvaried
10p unvaried ↑CN gain unvaried
10q unvaried ↑CN loss unvaried
11q unvaried ↑CN gain ↑CN loss
12q unvaried unvaried ↑CN loss
13q unvaried ↑CN loss unvaried
14q unvaried ↑CN loss unvaried
15q unvaried ↑CN loss unvaried
16p ↑CN loss unvaried ↑CN loss
16q unvaried ↑CN loss ↑CN loss
17p unvaried ↑CN gain unvaried
18p unvaried ↑CN gain ↑CN loss
18q ↑CN loss unvaried arm involved in CNA
19p unvaried unvaried ↑CN loss
20p ↑CN gain unvaried unvaried
20q ↑CN gain ↑CN gain unvaried
↑: increase; ↓: decrease.
Genes 2024, 15, x FOR PEER REVIEW 6 of 14
by DAVID analysis of the genes involved in 8p21.2-p21.1 CN, indicating that the loss of
these genes may result in the loss of apoptosis. No interesting cluster was identified for
4q34.3-q35.2 genes.
Figure 2. (A) Disease-free survival data of TCGA-OV patients divided into group A (samples with
18q12.2-q23 loss) and group B (samples without 18q12.2-q23 loss). (B) Differential expressed protein
between samples of group A and group B analyzed using mass spectrometry (cBioPortal for Cancer
Genomics web server). Blue dots: significant differentially expressed proteins between the two
groups. Grey dots: not significant differentially expressed proteins between the two groups.
Table 1. Variations in clinical parameters and chromosome arm-level CNAs in samples carrying
4q34.3-q35.2, 8p21.2-p21.1, or 18q12.2-q23 loss. Only chromosome arms with statistically significant
differences are reported.
4q34.3-q35.2 Loss vs.
no Loss
8p21.2-p21.1 Loss vs.
no Loss
18q12.2-q23 Loss vs.
no Loss
Aneuploidy score
unvaried
unvaried
↑
Fraction of genome altered
↑
unvaried
↑
Mutational count
↑
↑
unvaried
TMB (nonsynonymous)
unvaried
↑
unvaried
2p
unvaried
unvaried
↓ CN gain
2q
unvaried
↑ CN gain
↓ CN gain
3q
unvaried
↑ CN gain
unvaried
4p
↑ CN loss
unvaried
↑ CN loss
4q
arm involved in CNA
unvaried
↑ CN loss
Figure 2. (A) Disease-free survival data of TCGA-OV patients divided into group A (samples with
18q12.2-q23 loss) and group B (samples without 18q12.2-q23 loss). (B) Differential expressed protein
between samples of group A and group B analyzed using mass spectrometry (cBioPortal for Cancer
Genomics web server). Blue dots: significant differentially expressed proteins between the two
groups. Grey dots: not significant differentially expressed proteins between the two groups.

Genes 2024,15, 1487 7 of 14
3.3. Copy Number Losses Correlated with Clinical Parameters in the GDC Data Portal
We additionally investigated the potential correlations between clinical parameters
and CN loss in 4q34.3-q35.2, 8p21.2-p21.1, and 18q12.2-q23 through the GDC Data Portal be-
cause the two repositories process the TCGA data differently, using different bioinformatics
methods to analyze copy number alterations.
First, we divided the patients into two groups according to the median age at diagnosis
(63 years) and checked the percentage of gains and losses in both groups (Figure 3A). A
statistically significant difference (p< 0.05) in 8p21.2-p21.1 CN percentage was found; in
particular, a lower percentage of 8p21.2-p21.1 gain was observed in samples of the youngest
group (age at diagnosis under 63 years), while the percentage of CN loss was similar in
both groups (Figure 3A).
Genes 2024, 15, x FOR PEER REVIEW 8 of 14
Figure 3. (A) 4q34.3-q35.2, 8p21.2-p21.1, and 18q12.2-q23 loss and gain percentages in samples of
patients categorized by age at diagnosis. (B) Distribution of 18q copy number in samples categorized
by clinical stage. #: statistically significant difference between stage IV samples and samples of all
other stages, p < 0.05; §: statistically significant difference between stage IV and stage II/III samples,
p < 0.05; *: statistically significant difference between stage IV and stage III samples, p < 0.05. (C)
Localization of the 18q sub-regions represented in B (red boxes).
3.4. 18.q12.2 Region Correlated with Clinical Stage, Overall Survival, and Progression-Free
Survival
Considering the previous results, we focused our aention on lost genes within the
18q12.2 region that showed a significant correlation (p < 0.05) with stage IV: FHOD3,
TPGS2, and KIAA1328 (Table S2). Using the GEPIA2 web server, we identified a
statistically significant association between decreased expression of these genes and
reduced disease-free survival (DFS) in ovarian cancer patients (p < 0.05), suggesting a
prognostic relevance for this region. Moreover, we also found a link between gene
expression and overall survival (OS) (Figure 4A, p < 0.05). The OncoDB web server further
confirmed a correlation between the expression of these three genes and tumor stage,
consistent with the observed CNA (Figure S2). Finally, to evaluate the role of each single
gene, we consulted the Kaplan–Meier Ploer web server, selecting the serous histotype.
Only TPGS2A showed a strong correlation between expression and both DFS and OS in
ovarian cancer patients (Figure 4B), suggesting its pivotal role in ovarian cancer.
Figure 3. (A) 4q34.3-q35.2, 8p21.2-p21.1, and 18q12.2-q23 loss and gain percentages in samples of
patients categorized by age at diagnosis. (B) Distribution of 18q copy number in samples categorized
by clinical stage. #: statistically significant difference between stage IV samples and samples of
all other stages, p< 0.05; §: statistically significant difference between stage IV and stage II/III
samples, p< 0.05; *: statistically significant difference between stage IV and stage III samples, p< 0.05.
(C) Localization of the 18q sub-regions represented in B (red boxes).
On the other hand, considering the 18q12.2-q23, we evidenced a statistically significant
enrichment in the percentage of stage IV samples with loss of this region (p< 0.05,
Table S2
).
We checked the distribution of gain and loss also within the 18q12.2-q23 sub-regions
(Figure 3B,C). Different percentages of CNAs among stages were identified (Table S2). In
particular, stage IV showed a higher percentage of samples with loss of 18q12.2, as well
as samples with loss of 18q12.3 and 18q12.3-q21.1 compared to other stages (Figure 3B,C).
These findings could validate the role of this CN loss and demonstrate the involvement of

Genes 2024,15, 1487 8 of 14
18q loss in ovarian cancer progression. No correlation between 8p or 4q losses and tumor
stage was evidenced.
3.4. 18.q12.2 Region Correlated with Clinical Stage, Overall Survival, and
Progression-Free Survival
Considering the previous results, we focused our attention on lost genes within the
18q12.2 region that showed a significant correlation (p< 0.05) with stage IV: FHOD3,TPGS2,
and KIAA1328 (Table S2). Using the GEPIA2 web server, we identified a statistically
significant association between decreased expression of these genes and reduced disease-
free survival (DFS) in ovarian cancer patients (p< 0.05), suggesting a prognostic relevance
for this region. Moreover, we also found a link between gene expression and overall
survival (OS) (Figure 4A, p< 0.05). The OncoDB web server further confirmed a correlation
between the expression of these three genes and tumor stage, consistent with the observed
CNA (Figure S2). Finally, to evaluate the role of each single gene, we consulted the Kaplan–
Meier Plotter web server, selecting the serous histotype. Only TPGS2A showed a strong
correlation between expression and both DFS and OS in ovarian cancer patients (Figure 4B),
suggesting its pivotal role in ovarian cancer.
Genes 2024, 15, x FOR PEER REVIEW 9 of 14
Figure 4. (A) GEPIA2 correlation between ovarian cancer patients’ DFS or OS and 18q12.2 genes
(FHOD3, TPGS2, and KIAA1328, 3 signatures) expression (p-value < 0.05). Red line: high expression
group; blue line: low expression group. (B) Kaplan–Meier Ploer correlation between patients’ DFS
or OS and TPGS2 expression (p-value < 0.01). Red line: high expression group; black line: low ex-
pression group.
4. Discussion
Molecular biomarkers are defined as any measurable molecular indicator of risk dis-
ease or patient outcome. This category includes germline or somatic genetic variants, ep-
igenetic signatures, transcriptional changes, and proteomic signatures [25]. Despite the
vast number of investigations, the identification of novel biomarkers in ovarian cancer is
still an urgent need [26].
The principal defining features of high-grade serous ovarian carcinoma (HGSOC),
the most prevalent form of epithelial ovarian cancer, are copy number alterations and ge-
nomic rearrangements [27]. This makes genomic variants appealing as diagnostic or prog-
nostic biomarkers, as well as for their easy detection [13].
Cancer stem cells (CSCs) are a subpopulation of cancer cells that are capable of self-
renewal, differentiation, proliferation, and drug resistance. CSCs are also important in in-
vasiveness and metastatic capability of tumors. For these reasons, it is understandable that
a worse prognosis of the patient correlates with higher expression of the molecular signa-
tures related to CSCs [28].
In a previous study, we isolated and characterized the CSC subpopulation from
Caov3, Ovcar5, and Ovcar8 ovarian cancer cell lines. Caov3 and Ovcar8 are known to be
representatives of HGSOC [19,20,22,29]. Concerning the Ovcar5 cell line, despite its origin
Figure 4. (A) GEPIA2 correlation between ovarian cancer patients’ DFS or OS and 18q12.2 genes
(FHOD3,TPGS2, and KIAA1328, 3 signatures) expression (p-value < 0.05). Red line: high expression
group; blue line: low expression group. (B) Kaplan–Meier Plotter correlation between patients’ DFS
or OS and TPGS2 expression (p-value < 0.01). Red line: high expression group; black line: low
expression group.

Genes 2024,15, 1487 9 of 14
4. Discussion
Molecular biomarkers are defined as any measurable molecular indicator of risk
disease or patient outcome. This category includes germline or somatic genetic variants,
epigenetic signatures, transcriptional changes, and proteomic signatures [
25
]. Despite the
vast number of investigations, the identification of novel biomarkers in ovarian cancer is
still an urgent need [26].
The principal defining features of high-grade serous ovarian carcinoma (HGSOC), the
most prevalent form of epithelial ovarian cancer, are copy number alterations and genomic
rearrangements [
27
]. This makes genomic variants appealing as diagnostic or prognostic
biomarkers, as well as for their easy detection [13].
Cancer stem cells (CSCs) are a subpopulation of cancer cells that are capable of self-
renewal, differentiation, proliferation, and drug resistance. CSCs are also important in
invasiveness and metastatic capability of tumors. For these reasons, it is understandable
that a worse prognosis of the patient correlates with higher expression of the molecular
signatures related to CSCs [28].
In a previous study, we isolated and characterized the CSC subpopulation from
Caov3, Ovcar5, and Ovcar8 ovarian cancer cell lines. Caov3 and Ovcar8 are known to be
representatives of HGSOC [
19
,
20
,
22
,
29
]. Concerning the Ovcar5 cell line, despite its origin
still being debated, it is currently used as an HGSOC model [
21
,
22
], and for this reason, we
decided to utilize three cell lines for our experiment. Remarkably, we identified a potential
prognostic role of AhRR and PPP1R3C in serous ovarian cancer by bioinformatics analyses
of array-CGH data performed on these CSCs [
10
]. In this work, we started from copy
number losses shared by those CSC subpopulations in order to find chromosomal regions
or genes that may be informative about patients’ prognosis.
First, we looked at the enriched pathways and GO terms of the 1253 genes that were
found to be involved in copy number loss in all CSCs subpopulations. KEGG pathway
analysis revealed a significant decrease in the mRNA surveillance pathway, including
nonsense-mediated mRNA decay (NMD), nonstop mRNA decay (NSD), and no-go decay
(NGD). NMD recognizes and degrades transcripts with a premature translation-termination
codon, preventing the production of C-terminally truncated proteins that can have a delete-
rious effect in the cell [30]. Evidence has identified NMD as a key driver of tumorigenesis
in a tumor-specific manner. In some cancers, NMD is enhanced to degrade certain mR-
NAs, including those encoding tumor suppressors. Conversely, in other tumors, NMD is
inhibited, promoting the expression of oncoproteins or other proteins that support tumor
growth and progression [31].
An interesting gene in this pathway was the PPP2R2A gene (the only one located
at 8p21.1), which is deleted at high frequencies in luminal type B breast cancer and non-
small cell lung cancer, as well as being one of the most common breakpoints in prostate
cancer [
32
]. Loss of PPP2R2A inhibits homologous recombination DNA repair, suggesting
it as a potential marker for PARP inhibitor responses in clinics [33].
REACTOME pathway enrichment analysis showed loss of downregulation of SMAD2/
3:SMAD4 transcriptional activity. In the nucleus, the SMAD2/3:SMAD4 heterotrimer
complex acts as a transcriptional regulator. SMAD2, SMAD3, and SMAD4 are considered
to be key mediators of TGF-
β
signaling [
34
]. Ovarian tumors are significantly influenced by
the TGF-
β
pathway and SMAD proteins [
35
]. In particular, upregulation of this pathway
promotes the EMT process and enhances tumor cell resistance to paclitaxel [
36
]. Our results
confirmed the important role of this pathway in ovarian CSCs.
GO enrichment analyses showed a statistically significant downregulation of gene
silencing by miRNA in CSCs, together with a decrease of some GO terms related to mRNA
processing. The maintenance of CSCs’ stemness and malignancy depends on mRNA
modifications [
37
], and miRNA plays a significant role in this process. In fact, aberrant
expression of miRNA, often due to genetic modifications, is essential for the initiation and
progression of human cancers as they act as both tumor suppressors and oncogenes [38].

Genes 2024,15, 1487 10 of 14
Taken together, these results validated our CSCs models, underlined some CSC char-
acteristics, and confirmed the importance of copy number losses in tumorigenesis and
cancer progression.
Subsequently, we identified only three CN losses and the loss of whole chromosome X
shared by all spheroids and investigated a possible correlation with patients’ prognosis
suggested by their presence in all CSCs models. Loss of chromosome X was abundantly
reported in cancer as a potential mechanism of X-linked tumor suppressor gene inactivation,
so we focused our attention on the other three CN losses [39,40].
Loss of 4q34.3-q35.2 correlated with an increase in the fraction of genome altered, as
well as in the mutational count in these samples, suggesting an increased genomic instability.
Terminal 4q loss has been found in colorectal cancer as a marker of advanced stage [
41
], in
hepatoblastoma as a poor prognostic factor [
42
], and in intrahepatic cholangiocarcinoma
associated with a high histological grade [
43
]. Moreover, deletion of 4q34.3 predicted
early relapse after adjuvant chemotherapy in lung adenocarcinoma [
44
]. Frequent LOH
(loss of heterozygosity) in the 4q terminal region in hepatocellular carcinoma, head and
neck squamous cell carcinoma, and oral carcinoma has also been reported [
42
]. With
regard to ovarian cancer, a loss of 4q34.3 in mucinous and clear cell ovarian cancer cell
lines [
12
] and a potential correlation between 4q35.2 loss and chemoresistance [
45
] were
previously reported.
Loss of 8p21.2-p21.1 in ovarian cancer biopsies was associated with a higher mutational
count and increased TMB (nonsynonymous) together with a statistically significant increase
in arm-level CNAs, indicating a great genetic instability of these samples. Bioinformatics
analyses of the genes involved in 8p21.2-p21.1 CNA revealed a statistically significant
cluster in the positive regulation of the apoptotic process, suggesting that the loss of these
genes may lead to the loss of apoptosis.
Frequent deletion (23%) of 8p21.2 was identified in TCGA tumors and reported in
previous studies for ovarian cancer, particularly for serous histology and high-grade and
chemoresistant samples [
46
]. Another study identified loss on 13q32.1 and 8p21.1 as the
most reliable combination for detecting chemoresistant disease, with EXTL3 as a potential
gene linked to antineoplastic drug-resistance [45].
Kaveh et al. analyzed copy number data from breast, ovarian, endometrial, and
cervical cancers and identified 8p21.2 loss in cancers of the reproductive system, indicating
BNIP3L (a proapoptotic gene) and PPP2R2A as interesting tumor suppressor genes [
47
].
Our results support PPP2R2A’s role in ovarian CSCs also.
Moreover, we identified for the first time a statistically significant difference in 8p21.2-
p21.1 gain between early- and late-onset ovarian cancer (cut-off 63 years), suggesting a
potential favorable prognostic role of this gain, in accordance with data observed in samples
with loss.
As shown for loss of 4q34.3-q35.2 and 8p21.2-p21.1, 18q12.2-q23 loss correlated with
clinical parameters related to genomic instability, such as an increased aneuploidy score
and arm-level CNAs, as well as a higher fraction of altered genome. Intriguingly, a
statistically significant difference in disease-free survival of patients with or without loss
was found. Pathway enrichment analyses of differentially expressed proteins in the two
groups revealed a statistically significant increase in cysteine and methionine metabolism
in the 18q loss group (PSAT1 and LDHB proteins).
Phosphoserine aminotransferase 1 (PSAT1) catalyzes the second step of the serine-
glycine biosynthesis pathway, and its overexpression was reported in ovarian cancer [
48
],
lung adenocarcinoma [
49
], and breast cancer [
50
]. Lactate dehydrogenase (LDH) plays key
roles in cancer metabolism reprogramming [
51
]. LDHA directly catalyzes the conversion
of pyruvate to lactate; on the contrary, LDH-B converts lactate to pyruvate. Upregulation
of LDH-B in tumors has been reported and correlated with disease progression and poor
prognosis [50–52]. These data could explain the reduced DFS of patients with 18q loss.
Additionally, a link between 18q12.2-q23 and the stage of the tumor was found; in
fact, a statistically significant increase of samples with CN loss in this region was found in

Genes 2024,15, 1487 11 of 14
stage IV. Interestingly, within the 18q12.2-q23 region, different sub-regions with different
percentages of alterations among stages were present. In particular, the percentage of CN
loss of the 18q12.2 region (containing FHOD3,TPGS2, and KIAA1328) was significantly
increased in stage IV samples with respect to all other samples. Genomic observation
was supported by mRNA data from the OncoDB web server that confirmed a correlation
between the expression of these three genes and the clinical stage of the tumor. Moreover,
a strong correlation between TPGS2A expression and both DFS and OS in ovarian cancer
patients could suggest for the first time its prognostic role in ovarian cancer.
5. Conclusions
In conclusion, we analyzed copy number losses shared by our previously isolated and
characterized ovarian CSC subpopulations. Pathway and gene ontology further validated
our CSC models, underlining some CSC characteristics and confirming the importance of
copy number losses in tumorigenesis and cancer progression.
Then, starting from these CN losses, we validated their potential prognostic relevance
by analyzing the TCGA cohort. Our analysis of copy number alterations in ovarian CSCs
revealed novel insights by identifying three specific copy number losses associated with
higher genetic instability and patients’ prognosis. The identified potential candidate genes
not only enhance our understanding of the role of numerical aberrations as biomarkers but
also suggest new avenues for targeted therapies. By elucidating the pathways influenced
by these genetic alterations, our findings could help the development of personalized
treatment strategies, ultimately aiming to improve patient outcomes and more effectively
manage cancer progression.
Supplementary Materials: The following supporting information can be downloaded at https://www.
mdpi.com/article/10.3390/genes15111487/s1. Table S1: KEGG pathway, REACTOME pathway,
and GO (BP: biological process, and MF: molecular function) enrichment analyses of 1253 genes
involved in copy number losses. Figure S1: Correlation between copy number losses and clinical
parameters. Table S2: 18q12.2-q23 loss in different tumor stages. Figure S2: OncoDB correlation
between expressions of FHOD3, TPGS2, and KIAA1328 and the clinical stage of a tumor.
Author Contributions: Conceptualization: D.C.; methodology: A.A., A.J. and D.C.; formal analysis:
A.A., A.J., S.R. and D.C.; investigation: A.A., A.J., S.R. and D.C.; writing—original draft preparation:
A.J. and D.C.; writing—review and editing: A.A., A.J., S.R., A.B., M.L. and D.C.; funding acquisition:
M.L. and D.C.; resources: A.B. and M.L.; supervision: D.C. All authors have read and agreed to the
published version of the manuscript.
Funding: This research was funded by the University of Milano-Bicocca [2021-ATE-0167 and 2023-
ATE-0402 to D.C. and 2023-ATE-0605 to M.L.] and Instand-NGS4P H2020 Project ID: 874719 [to
M.L.].
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data that support the findings of this study are available from the
corresponding author [D.C.] upon reasonable request.
Conflicts of Interest: The authors declare no conflicts of interest.
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