Abstract
Ovarian cancer is one of the most common gynecological malignancies,
ranking third after cervical and uterine cancer. High-grade serous
ovarian cancer (HGSOC) is one of the most aggressive subtype, and the
late onset of its symptoms leads in most cases to an unfavourable
prognosis. Current predictive algorithms used to estimate the risk of
having Ovarian Cancer fail to provide sufficient sensitivity and
specificity to be used widely in clinical practice. The use of
additional biomarkers or parameters such as age or menopausal status to
overcome these issues showed only weak improvements. It is necessary to
identify novel molecular signatures and the development of new
predictive algorithms able to support the diagnosis of HGSOC, and at
the same time, deepen the understanding of this elusive disease, with
the final goal of improving patient survival. Here, we apply a Machine
Learning-based pipeline to an open-source HGSOC Proteomic dataset to
develop a decision support system (DSS) that displayed high discerning
ability on a dataset of HGSOC biopsies. The proposed DSS consists of a
double-step feature selection and a decision tree, with the resulting
output consisting of a combination of three highly discriminating
proteins: TOP1, PDIA4, and OGN, that could be of interest for further
clinical and experimental validation. Furthermore, we took advantage of
the ranked list of proteins generated during the feature selection
steps to perform a pathway analysis to provide a snapshot of the main
deregulated pathways of HGSOC. The datasets used for this study are
available in the Clinical Proteomic Tumor Analysis Consortium (CPTAC)
data portal ([34]https://cptac-data-portal.georgetown.edu/).
Subject terms: Diagnostic markers, Biomedical engineering, Tumour
biomarkers
Introduction
Ovarian cancer is the seventh most common cancer in women and the
eighth-most common cause of cancer death overall, with five-year
survival rates below 45%. Along with the increasing life expectancy,
the number of cases diagnosed each year is also growing, with only a
minimal improvement in mortality^[35]1,[36]2.
Although once considered a single entity, ovarian cancer can be
subdivided into different histological subtypes that differ in
molecular patterns, cells of origin, and clinical features. Among these
types, high-grade serous ovarian carcinoma (HGSOC) is the most commonly
diagnosed^[37]3 and is responsible for an elevated number of deaths.
Its molecular features consist of a p53 mutation for 96% of the cases,
while BRCA1/BRCA2 accounts for 22% of cases^[38]4. One of the principal
factors influencing the elevated mortality of HGSOC patients is the
inability to perform an early diagnosis, due to the symptoms being
diverse and non-specific^[39]5. While the long-term survival of
patients with stage I and II of ovarian cancer is respectively up to
90% and 70%, 4/5 of patients with HGSOC are diagnosed during stage III,
and IV, resulting in a significantly lower survival rate of less than
20%^[40]6,[41]7. Several studies have shown the importance of an
accurate pre-operative evaluation and correct staging to enhance the
prognosis of patients with a pelvic mass suspected of HGSOC. In fact,
those treated by gynecologic oncologists had significantly lower
morbidity and overall increased survival than those treated by general
gynecologists and general surgeons^[42]5, [43]8–[44]10.
Several biomarkers, such as CA125^[45]11, HE4^[46]12 and
osteopontin^[47]13 have been used for the risk assessment of ovarian
cancer in patients with a pelvic mass. Each of the biomarkers can be
used alone or combined in multiple-biomarker algorithms (e.g.
RMI^[48]14, ROMA^[49]15, OVA1^[50]16), having received both FDA and EU
approval ^[51]17.
However, the screening methods based on these multiple-biomarker
algorithms show different limits hampering their usage in clinical
practice. All of them include CA125, a marker expressed in only 80% of
Ovarian Cancer cases, and only in the 50% in the early stage of the
disease^[52]18. The lack of expression in CA125 levels exhibited in
some ovarian cancer cases and especially in the early stages of the
disease is reflected by the sensitivity of the algorithms based on
CA125. Furthermore, other studies show that different physiological and
pathological conditions exhibit an increased expression of CA125
levels, thus limiting its specificity for the detection of this
disease^[53]19,[54]20. The use of additional biomarkers to overcome the
limits of CA125 usually improves the sensitivity of the algorithm but
always leads to a reduced specificity to detect ovarian
cancer^[55]21–[56]23. Hence, the necessity to find new molecular
distinctive features that could both improve the disease understanding
and be used as a starting point to develop new diagnostic tools, in
order to establish one of the most appropriate treatment strategies,
with the intention to improve ovarian cancer survival rates.
With this in mind, the purpose of this study was to dissect the
pathways deregulated in HGSOC and find new possible biomarkers with
high discriminating power, sensitivity and specificity that are
localized in the serum, in order to be potentially assessed without
invasive or expensive approaches. To reach this goal, we analyzed a
publicly available ovarian cancer proteomic dataset using Machine
Learning based algorithms, which can manage optimally such large scale
omic datasets. The data used in this publication were generated by the
Clinical Proteomic Tumor Analysis Consortium (NCI/NIH) ^[57]24.
Our computational approach allows us to overcome the decline in the
specificity of existing tests, maintaining both sensitivity and
specificity respectively at 98.2% and 97.2%.
Materials and methods
Database
For this study, we used the publicly available database generated by
the Clinical Proteomic Tumor Analysis Consortium (CPTAC) ^[58]24. The
Decision Support System (DSS) was trained, tested, and validated using
the CPTAC Ovarian Cancer Confirmatory Study Proteomic Dataset, which
includes the analysis form Ovarian tissue sample from a cohort of 100
individuals with HGSOC and 25 Non-Tumor ovarian samples, performed by
the Johns Hopkins University (JHU) and Pacific Northwest National
Laboratory (PNNL) using isobaric Tags for Relative and Absolute
Quantification (iTRAQ) protein quantification method^[59]25. Clinical
features were present only for Tumor patients. The Tumor cohort was
composed of women ranging from 36 to 85 years, with an average age of
59. The 7% of the participants had an history of other malignancies.
The anatomic site of origin of tumor specimens are: ovary 52%, omentum
41%, peritoneum 3%, pelvic mass 3% and unknown origin 1%. All samples
are classified as “Serous Adenocarcinoma”. FIGO staging ranges from IIB
to IV (not specified whether A or B), with the majority of the samples
classified as stage IIIC (63.8%), followed by IV (15.2%), IIIB (7.6%),
IIIA (2.9%), IC (1.9%), IIB (1%) and a remaining 7.6% of specimens
having uncertain classification. The 80.8% of the samples are
classified as Grade 3, 5.8% as Grade 2, 0.9% as Grade 1, while for
12.5% of the samples grading was not reported. The efficacy of the DSS
was further tested on the dataset generated from the CPTAC and TCGA
Cancer Proteome Study of Ovarian Tissue, including the analysis of
samples from 174 Ovarian tumors, of which 169 from HGSOC, also
performed by JHU and PNNL using iTRAQ^[60]26. Cohort is composed of
women ranging from 35 years to 87, with an average age of 60.5. Tumor
tissue site is Ovary for 98% of the samples, Omentum in 1% of the
samples and Peritoneum ovary in 1%. All samples are classified as
“Serous Cystadenocarcinoma”. FIGO staging of the samples goes from
stage IC to IV (not specified whether A or B), where stage IIIC
accounts for 69.9% of the samples, IV for 17%, IIIB and IIC accounting
each one for 4.4%, IC for 1.5%, and IIA, IIB and IIA accounting each
one for 1%. The 81.5% of the samples are Grade 3, 16.5% are Grade 2, 1%
are Grade 1, while grading is unknown for 1% of the samples. Datasets
were subsequently processed in Python (distribution 3.9.1) using NumPy
and pandas libraries to merge JHU and PNNL datasets and remove protein
columns containing more than 10% of missing values. After that, the
data were processed and analyzed using a software tool coded in
MATLAB2020b (Mathworks Inc., MA).
Machine Learning pipeline
Here we describe the Machine Learning pipeline used to develop the
Decision Support System. Each sample from the dataset is described by
its features (i.e., the proteins). We report such pipeline in Fig.
[61]1. It includes the following steps:
Figure 1.
[62]Figure 1
[63]Open in a new tab
Machine Learning pipeline.
Feature selection based on correlation analysis
In this step, we computed for each feature the Pearson correlation
coefficient with respect to the target variable (tumor/non tumor). The
correlation coefficient between two random variables is a measure of
their linear dependency. If each feature has N scalar observations,
then the Pearson correlation coefficient of the i-th feature
[MATH: fi :MATH]
is defined as
[MATH: ρ(fi,t)=∑j=1Nfi(j)-μfiσfit(j)-μtσt :MATH]
1
where
[MATH: μfi :MATH]
,
[MATH: σfi :MATH]
,
[MATH: μt :MATH]
,
[MATH: σt :MATH]
are the mean and standard deviation of the i-th feature and the target
variable, respectively. The values of the coefficients can range from −
1 to 1, with − 1 representing a direct, negative correlation, 0
representing no correlation, and 1 representing a direct, positive
correlation. All features with an absolute value of the correlation
coefficient higher than 0.6 are then selected. In this way, we selected
all the features with a high (positive or negative) correlation with
the target variable.
Feature selection based on relief method
All the features selected from the Correlation Analysis are then
examined with a second feature selection step based on the ReliefF
algorithm^[64]27. Such an algorithm ranks the importance of the
features with respect to the target value. The importance of a feature
is represented by the weight of that feature. The values of those
weights can range from
[MATH: -1 :MATH]
to 1, with the largest positive weights assigned to the most important
features. The algorithm penalizes the features that provide different
values to k neighbors of the same class while rewarding the ones that
provide different values to k neighbors of different classes.
Decision tree
The features (i.e. the proteins) selected by the reliefF method are
used to train the CART^[65]28 algorithm for the binary
(Tumor/Non-Tumor) classification task. We chose to use a decision tree
classifier for its high interpretability and explainability, unlike
other methods of machine and deep learning. The CART tree is a binary
decision tree that is constructed by splitting a node into two child
nodes repeatedly, beginning from the root node that contains the whole
learning sample. The basic idea of the tree growth is to choose a split
among all the possible splits at each node so that the resulting child
nodes are the “purest”. The purity metric defines a node as 100% impure
when its samples evenly belong (50:50) to both the classes while
defining a node as 100% pure when all of its data belongs to a single
class. In this algorithm, only univariate splits are considered. That
is, each split depends on the value of just one feature. At node t, the
best split s is chosen to maximize a splitting criterion
[MATH: Δi(s,t) :MATH]
. When the impurity measure for a node can be defined, the splitting
criterion corresponds to a decrease in impurity. In our case, we used a
Gini criterion as the impurity measure. During the training, we chose
not to impose a control on the tree’s depth, fixing the maximum number
of splits as the size of the training set
[MATH: -1 :MATH]
and the minimum leaf size (the minimum number of samples in the leafs)
as 1. Furthermore, we fixed the cost of classifying a sample into class
j if its true class is i equal to:
*
[MATH:
Ci,j=1 :MATH]
, if
[MATH: i≠j :MATH]
*
[MATH:
Ci,j=0 :MATH]
, if
[MATH: i=j :MATH]
We decided also not to implement a pruning strategy.
Performance evaluation
To evaluate the performance of our system we computed the confusion
matrix. A confusion matrix is an N
[MATH: × :MATH]
N matrix used for evaluating the performance of a classification model,
where N is the number of target classes. In our case, the task
performed by the model is a binary classification task, thus N is equal
to 2. From the confusion matrix we calculated the classification
accuracy
[MATH: Acc=TP+TNP+N :MATH]
, the precision per class
[MATH: (PTumor=TPTP+
FP :MATH]
and
[MATH: PNonTumor=
TNTN+
FN) :MATH]
, sensitivity and specificity
[MATH: Sensitivity=
TPP,Specificity
=TNN :MATH]
. Furthermore for each class we compute the F1 score, a relevant metric
in case of unbalanced dataset,
[MATH: F1Tumor=2
∗PTumor∗Sensiti
vityP
Tumor+Se<
/mi>nsitivity :MATH]
and
[MATH: F1NonTumor=2∗PNonTumor∗
Specifi<
mi>cityPNonTumor+Specific
ity :MATH]
.
As usual, P and N denote the number of positive patients (with Tumor)
and negative patients (Non-Tumor) records, whereas TP, TN, FP and FN
stands respectively for true positive, true negative, false positive
and false negative classifications. A true positive classification
implies that the patients are correctly detected by the system as
patients without tumor, whereas a true negative classification
indicates that the system correctly recognizes the patients with HGSOC.
We developed two main performance test:
* Test 1 This test is developed to evaluate the performance of the
system only on CPTAC dataset using a 5-fold cross-validation
procedure as follows. First, we randomly shuffled the dataset and
split it into 5 groups. For each group, a single group is taken as
a hold out or test data set and the remaining groups as a training
data set. After training and test, the evaluation score is retained
and the model is discarded. This operation is then repeated for
each group. Importantly, each sample in the data set is assigned to
an individual group and stays in that group for the duration of the
procedure. This means that each sample is given the opportunity to
be used in the hold out set once and used to train the model 4
times. This procedure results in a less biased or less optimistic
estimate of the system performance than other methods, such as a
simple train/test split.
* Test 2 This test is developed to evaluate the robustness of our
system. We trained the system on CPTAC Dataset and tested it on a
different dataset called Cancer Proteome Study of Ovarian Tissue
(TCGA). This latter dataset is composed of 216 tumor patients.
Pathway enrichment analysis
We used the ranked lists of proteins resulting from the correlation
analysis, as input to perform a Pathway Enrichment Analysis using
GSEA^[66]29,[67]30 v.4.1.0 desktop software. The pathway gene set
database was:
Human_GO_AllPathways_with_GO_iea_January_13_2021_symbol.gmt release
13-01-2021, downloaded from [68]http://baderlab.org/GeneSets. This file
includes pathways from GO, Panther, NetPath, NCI, Reactome and MSigDB,
both C2 and Hallmark collection. The number of permutations was set to
1000 and the maximum size of the sets was set to 200. Visualization of
enrichment results was performed with Cytoscape^[69]31 v.3.8.2 using
EnrichmentMap Pipeline Collection apps^[70]32, setting the FDR Q value
cutoff to 0.01. In this work, we selected all the features with a
coefficient higher than the average value taken by the positive
coefficients.
Results
As the first step of feature selection, the correlation was assessed
between each feature and the tumor or non tumor variable, in order to
possibly identify the most relevant molecular features of the tumor
phenotype. The dataset after the pre-processing step consisted of 209
samples and 6223 proteins. In Table [71]1 we reported the results
obtained setting the correlation coefficient cutoff to 0.6, thus
reducing the significant features to 137 proteins. After the second
step of feature selection, the list was further reduced to 46 proteins.
Table 1.
Here are summarized the results of the correlation between proteomics
data and tumor phenotype. It appears that a vast portion of the
proteins displayed no evident correlation, and the majority of the
proteins were negatively correlated.
Tumor
Positive correlation 20
Negative correlation 117
Noncorrelation 6086
[72]Open in a new tab
We then used the entire set of proteins and their respective
correlation coefficient as a ranked list to perform a GSEA pathway
enrichment analysis. The output was subsequently visualized and
interpreted using the Cytoscape add-on EnrichmentMap. Resulting
Normalized Enrichment Scores (NESs) ranged from -3.3251 to 3.4016. A
subnetwork (Fig. [73]3) was generated from the main enrichment map
selecting the most enriched pathways, setting the cutoff of NES to
[MATH: +- :MATH]
2.5, in order to drive the attention only on the most represented
pathways. As in Fig. [74]3A, B the over-represented pathways are
related to three main categories: RNA maturation and export,
Translation and DNA Repair. By contrast, under-represented pathways
(Fig. [75]3C) include: immune response, cell-matrix adhesion and
extracellular matrix adhesion, protease activities, G-Protein coupled
receptors signalling, myogenesis, muscular contraction, wound healing
and blood coagulation.
Figure 3.
[76]Figure 3
[77]Open in a new tab
A Subnetwork was created from the main network to increase the
interpretability. Red and blue nodes represent pathways that are
upregulated (A, B) and downregulated (C). The diameter of each node is
proportional to the number of proteins included. Pathways sharing
proteins are connected with blue edges, with the thickness of the edges
proportional to the number of protein shared. Clusters of nodes were
manually annotated.
Explainable decision support system for tumor/non-tumor classification and
biomarker discovery
With respect to test 1, we evaluated our method on the dataset
presented in “[78]Database” section. So, we started with a full dataset
consisting of 209 samples and 6223 proteins. After the first step of
Feature Selection based on Correlation Analysis, 137 features were
left. Then, after the ReliefF-based Feature Selection step, we obtained
46 proteins. Finally, the dataset comprising 209 samples of 46 features
was used to train the decision tree classifier. The model and the
biomarkers achieved are shown in Fig. [79]2. The model is characterized
by a graph with split conditions on three proteins: TOP1, PDIA4 and
OGN. Furthermore, in Table [80]2 we report the classification confusion
matrix that was computed collecting the prediction at the end of each
iteration of the 5-fold cross-validation. All computed metrics from the
confusion matrix are equal to 98.1% for accuracy, 98.2% for the
sensitivity, 97.6% for specificity, 93% for precision of Non-Tumor
class and 99.4% for precision of Tumor class, and 95.3% and 98.8% for
F1-score of Non-Tumor and Tumor classes, respectively. With respect to
test 2 we analyze the robustness of our system: for this reason we
trained it on a dataset (CPTAC) and tested on a different one (TCGA).
This latter dataset is composed of 216 tumor patients. In Table [81]3
we report the confusion matrix achieved. Furthermore, we calculate the
accuracy of the system and the precision, sensitivity and F1-score per
Tumor class that are equal to 98.2%, 100%, 97.2%, and 98.6%
respectively. We did not computed metrics regarding the Non-Tumor class
since the TCGA dataset does not present samples of this class.
Figure 2.
Figure 2
[82]Open in a new tab
Final decision tree, with focus on the biomarkers.
Table 2.
This Confusion Matrix is achieved in fivefold-cross-validation on CPTAC
Ovarian Cancer Confirmatory Study Proteomic Dataset (209 samples). The
matrix compares the actual target values (Truth) with those predicted
(Pred.) by our model. On first diagonal are reported the samples
correctly classified, whereas on second diagonal are reported the
misclassified samples.
Pred. Truth
Non-tumor Tumor
Non-tumor 40 3
Tumor 1 165
[83]Open in a new tab
Table 3.
This Confusion Matrix reports the performance of our system trained on
CPTAC Ovarian Cancer Confirmatory Study Proteomic Dataset and tested on
TCGA Cancer Proteome Study of Ovarian Tissue (216 samples). The matrix
compares the actual target values (Truth) with those predicted (Pred.)
by our model. On first diagonal are reported the samples correctly
classified, whereas on second diagonal are reported the misclassified
samples. The TCGA dataset only presents samples from the Tumor class.
Pred. Truth
Non-tumor Tumor
Non-tumor 0 6
Tumor 0 210
[84]Open in a new tab
Discussion
Given the impact and the high mortality rate of HGSOC, numerous studies
from the past few years took advantage of ’-omic’ scale expression data
to characterize its underlying molecular features and to discover novel
biomarkers. Nevertheless, the vast majority of existing studies makes
use of RNA expression rather than protein expression. The main reason
is the advantage of transcriptomics being a robust and cost-effective
high-throughput technology. However, mRNA levels do not always
correlate to protein abundance, given the number of regulatory
processes occurring after mRNA transcription^[85]33,[86]34. Hence, to
find novel biomarkers suitable for cost-effective and non-invasive
diagnostic methods such as blood or serum testing, we choose to base
our analysis on Proteomics data.
Correlation-based overview on the most deregulated pathways
We first performed a correlation analysis. In this way, we reduced the
number of features in the dataset, and at the same time, removed the
“background noise” represented by the proteins that had a random
correlation with the Tumor phenotype^[87]35. We then used the gene set
enrichment analysis to extract biological insight from the ranked list
of proteins that emerged from the correlation analysis. Among the
over-represented pathways, displayed in Fig. [88]3 and summarized in
Table [89]4, we found established and well-known cancer signatures,
such as the increase of MYC and E2F downstream genes and DNA-Repair
related genes such as MCMs and RAD21^[90]36–[91]39. Interestingly, as
shown in Fig. [92]3B, pathways related to mRNA splicing, export,
metabolism, and translation were strikingly abundant and predominant
among all the over-represented pathways. Given the crucial role of
splicing as a source of biological complexity and plasticity, this same
mechanism can be exploited by cancer cells to adapt and thrive in
tumor-induced pathological conditions such as hypoxia^[93]40 and,
favoring tumor progression, by contributing to the reprogramming of the
cellular processes^[94]41. In accordance with this, a study shows that
the spliceosome inhibitory drug Sudemycin is able to induce selective
cytotoxicity in chronic lymphocytic leukemia (CLL) cells by targeting
SF3B1, a component of U2 snRNP, which is also found in 13 nodes of our
network. At the level of RNA export, there are several forms of cancer
associated with dysregulation of some nucleoporins (Nup98, Nup214),
components of the transcription-export complex TREX (THOC1), and
exportines (XPO1, XPO5) that are also included in several nodes of our
network and may be worth investigating further for their involvement in
HGSOC^[95]42–[96]44. As shown in Fig. [97]3A a large portion of
pathways involved in the assembly of the initiation complex and
ribosome biogenesis were significantly over-represented. Increasing
evidence links deregulation of translational control to cancer
insurgence and progression. Indeed, one of the most regulated steps
during translation is its initiation, given its role in the decision of
the rate of production of every protein, or if it is produced at
all^[98]45. It is therefore not surprising that initiation factor
encoding genes (eIFs) are overexpressed in a variety of cancers, such
as breast, prostate and pancreatic cancer^[99]46, [100]47. Altered
ribosome biogenesis also concurs to the altered translational activity
of cancer cells; for example, it has been observed that in the
aggressive breast cancer cell line MA-, 43S pre-rRNA was abnormal,
resulting in an impaired ability to initiate p53 cap-independent
translation via IRES^[101]48. Another cluster of pathways that stood
out from our analysis involves nonsense-mediated decay (NMD) activity.
NMD is a mechanism of post-transcriptional gene regulation, whose main
purpose is exerting quality control on the mRNA through the recognition
of premature termination codons (PTC), that may be introduced because
of genetic mutations, or errors occurring during transcription or
splicing. Beyond quality control, NMD emerged also as a mechanism for
fine-tuning the amount of certain proteins^[102]49. An example is
represented by the regulation of selenocysteine-containing proteins
(SePs), such as glutathione peroxidase 1 (Se-GPx1) abundance in
response to a decrease in selenium (Se) concentrations via NMD
recognition of a Sec TGA codon^[103]50. Indeed, among the pathways
present in this highly interconnected cluster, two groups of proteins
are involved in selenocysteine synthesis^[104]51. SePs are known to be
oxidoreductases, using selenocysteine in their active site. Their role
in malignancy progression may vary according to the stage: on one hand
they can inhibit tumor development by dampening oxidative insults that
could induce mutagenesis and genomic instability while, on the other,
they could offer tumor cells a competitive advantage to oxidative
stress and chemotherapeutics, at an advanced stage^[105]52. This may
indicate that in the context of HGSOC, they could favor tumor
progression. The last members of this supercluster are proteins
involved in the Slit/Robo pathway. Slits are a family of secreted
proteins, as they bind to the transmembrane Robo receptors, they
activate a signalling pathway that regulates various physiological
processes, such as neural axon guidance, angiogenesis, cellular
proliferation and motility, thus making it worthwhile to lead future
research toward investigating their role as new druggable targets for
HGSOC^[106]53, [107]54. Conversely, Fig. [108]3C shows the pathways
that are significantly less represented in tumor cells than expected in
physiological conditions. The first recognizable cluster involves the
immune response. The avoidance of immune destruction is one of the
hallmarks of cancer and has always represented a hot topic for research
since the discovery of immunotherapy focused on targeting immune
checkpoints^[109]55. In particular, the central nodes are involved in
the regulation of complement activation, suggesting that HGSOC cells
counteract the complement activation also by downregulating proteins
involved in its activation such as CR2^[110]56. The second cluster of
Fig. [111]3C involves cell-substrate adhesion and extracellular matrix
(ECM) organization. Under-representation of pathways related to
adhesion is a characteristic of cancer cells, in fact, adhesion
molecules not only maintain contact with other cells or the substrate
but also play a role as signalling molecules for a variety of cellular
functions, such as growth regulation and gene expression, moreover,
loss of adhesion is related to the Epithelial-Mesenchymal Transition
(EMT), which leads to cell migration and invasiveness^[112]57,[113]58.
Here we found that proteases inhibitor-related pathways are
significantly underrepresented. Proteases are enzymes that catalyze the
hydrolysis of proteins, they take part in a plethora of physiological
functions and their deregulation is associated with as many pathologies
such as neurodegenerative disorders, inflammatory diseases,
cardiovascular diseases and cancer^[114]59. Serpins, in particular, are
serine protease inhibitors, regulating several biological activities,
including coagulation, regulation of blood pressure, angiogenesis and
hormone transport. Among the Serpins present in the nodes of our
networks, Serpin B1, Serpin B5 and Serpin B9 have been found to be
associated to tumor suppression and increased overall survival in
Colorectal Cancer, suggesting that they could exert the same role also
in HGSOC^[115]60–[116]62. The next cluster examined in Fig. [117]3C
belongs to the pathways involved in the negative regulation of
coagulation. Activated Protein C (APC) is One of the most recurrent
proteins among the nodes, along with its interactors Thrombodulin (TM)
and Endothelial Cell Protein C Receptor (EPCR). APC is a serine
protease that acts as an anticoagulant by inhibiting thrombin formation
when the latter is bound to TM. This function is enhanced by EPCR,
which binds APC and presents it to the TM-Thrombin complex^[118]63. The
role of these three proteins in tumorigenesis is supported by the
observation that the decrease or loss in their expression is related to
tumor progression and poor prognosis^[119]64. It is accepted that
enhanced coagulation represents a risk factor for the development of
metastasis, possibly due to the fact that thrombin may favor the
adherence of cancer cells either to platelets and to endothelial
cells^[120]65. Interestingly, pathways related to myogenesis and
muscular contraction were also found significantly under-represented.
Among the nodes, Dystrophin (DMD) and other muscular
distrophy-associated proteins: dysferlin and calpain-3 are found
ubiquitously. These proteins are well-known for their role in the
Duchenne muscular dystrophy, however, a role in cancer pathogenesis is
slowly emerging. In this respect, it has been observed that Duchenne
muscular dystrophy mdx mouse model was prone to develop skeletal
muscle-associated tumors and that the dystrophic muscle presented
genomic instability in a tumor-like fashion both in the mouse model and
in humans^[121]66. Furthermore, DMD has been found to be downregulated
in several tumors affecting the nervous system, hematological
malignancies, melanoma and carcinomas, including lung adenocarcinoma,
prostate, colon and breast cancer^[122]67. Our results show that DMD
has a strong negative correlation to the tumor phenotype (
[MATH: -0.75 :MATH]
), thus suggesting that an altered DMD expression may play a relevant
role in the pathogenesis of HGSOC. The last underrepresented pathway is
the G Protein-coupled receptor (GPCR) signalling pathway. GPCRs are the
largest family of transmembrane signal transduction proteins, involved
in a variety of biological processes, ranging from neurotransmission to
hormone release, tissue development and homeostasis. It is not
surprising that their dysfunction leads to numerous diseases^[123]68.
Among the GCPRs present in the nodes of our network, the most relevant
are GNA13, GNAS, SHH, FZD3 and SMO. These proteins exhibit loss of
function mutations in cancers such as diffused B-cell lymphoma,
Burkitt’s Lymphoma and basal cell carcinoma^[124]69, suggesting a
possible role as oncosuppressors also in HGSOC. Overall, this analysis
offers a plausible overview of the relevantly deregulated pathways in
HGSOC, with most the pathways already known to be related to tumor
progression, and some that could represent new paths to explore, in
order to dissect the mechanisms underlying this gynecological
malignancy. Given these premises, it may be worth lead future
researches on the emerged proteins and their link to HGSOC.
Table 4.
Summary of the 100 top-most deregulated pathways, ranked by their NES
values, selected from the pathways composing the Subnetwork in Fig.
[125]3. Pathways are named according to their Gene Ontology name or
their standard name. In the left column are listed the 50 pathways that
are found to be less represented in HGSOC tumor biopsies, a lower NES
score corresponds to a lower representation. The right column displays
the 50 pathways that appear to be the most over represented. A higher
NES score correspond to a higher over representation.
Less represented pathways Over-represented pathways
Pathway description NES Pathway description NES
Regulation of vascular smooth muscle cell proliferation − 1.8195
Pre-mRNA splicing 3.4016
Positive regulation of phospholipid metabolic process − 1.818 mRNA
Splicing 3.3727
Neutrophil chemotaxis − 1.8175 Regulation of mRNA processing 3.3537
Positive regulation of lipid transport − 1.8168 Cap-dependent
translation initiation 3.2584
Positive regulation of protein kinase B signaling − 1.8157 rRNA
processing 3.2518
IGF1R signaling cascade − 1.8154 rRNA processing in the nucleus and
cytosol 3.2488
Allograft rejection − 1.8151 Influenza viral RNA transcription and
replication 3.2475
Positive regulation of transporter activity − 1.8148 Influenza
infection 3.2379
PID_IFNG_PATHWAY − 1.8141 Major pathway of rRNA processing in the
nucleolus and cytosol 3.2266
BIOCARTA_BIOPEPTIDES_PATHWAY − 1.8141 L13a-mediated translational
silencing of ceruloplasmin expression 3.2208
Regulation of heart rate − 1.8134 Spliceosomal complex 3.2119
Tertiary granule lumen − 1.8111 Viral gene expression 3.2043
PID_CXCR4_PATHWAY − 1.8088 Eukaryotic translation initiation 3.1978
Negative regulation of small molecule metabolic process − 1.8082 GTP
hydrolysis and joining of the 60S ribosomal subunit 3.1919
Negative regulation of cell-substrate adhesion − 1.8075 Regulation of
mRNA splicing, via spliceosome 3.1886
Regulation of glucose transmembrane transport − 1.8065 Cytosolic
ribosome 3.1753
Monocarboxylic acid transport − 1.8039 Ribosome 3.1709
Positive regulation of cholesterol transport − 1.8038 Formation of a
pool of free 40S subunits 3.1677
Gastrin signaling pathway − 1.8037 Viral transcription 3.1615
Activation of MAPKK activity − 1.8037 Ribosomal subunit 3.1467
Cortical cytoskeleton − 1.8036 Structural constituent of ribosome
3.1382
Amine metabolic process − 1.8035 Eukaryotic translation elongation
3.137
Negative regulation of cell projection organization − 1.8027
Translational initiation 3.1285
PID_ERBB1_DOWNSTREAM_PATHWAY − 1.8018 Peptide chain elongation 3.1255
Negative regulation of neuron projection development − 1.8012
Regulation of RNA splicing 3.122
IRS-related events triggered by IGF1R − 1.8001 SRP-dependent
cotranslational protein targeting to membrane 3.1111
Growth factor receptor binding − 1.7996 Nonsense mediated decay (NMD)
independent of the exon junction complex (EJC) 3.1079
Regulation of reactive oxygen species biosynthetic process − 1.799
Viral mRNA translation 3.1065
Neuronal system − 1.7989 Eukaryotic translation termination 3.0998
Negative regulation of axonogenesis − 1.7965 HALLMARK_MYC_TARGETS_V1
3.0941
Opioid signalling − 1.7963 Response of EIF2AK4 (GCN2) to amino acid
deficiency 3.0812
Cell–cell adhesion via plasma-membrane adhesion molecules − 1.7957
Protein targeting to ER 3.0792
BIOCARTA_HER2_PATHWAY − 1.7956 Nonsense mediated decay (NMD) enhanced
by the exon junction complex (EJC) 3.0764
PID_ERBB1_RECEPTOR_PROXIMAL_PATHWAY − 1.795 Nonsense-mediated decay
(NMD) 3.0737
Phosphatidylinositol binding − 1.7946 Catalytic step 2 spliceosome
3.072
Phosphatidic acid biosynthetic process − 1.7934 Selenocysteine
synthesis 3.0554
Granulocyte chemotaxis − 1.7913 SRP-dependent cotranslational protein
targeting to membrane 3.0479
Regulation of blood vessel endothelial cell migration − 1.791
Establishment of protein localization to endoplasmic reticulum 3.0463
B cell receptor signaling pathway − 1.7905 Regulation of expression of
SLITs and ROBOs 3.0373
Monocarboxylic acid binding − 1.7896 Cotranslational protein targeting
to membrane 3.0326
Toll-like receptor cascades − 1.7875 Nuclear-transcribed mRNA catabolic
process, nonsense-mediated decay 3.0094
Regulation of calcium-mediated signaling − 1.7874 Regulation of
alternative mRNA splicing, via spliceosome 3.0092
Triglyceride metabolism − 1.7864 Selenoamino acid metabolism 2.972
Multicellular organismal movement − 1.7857 Protein localization to
endoplasmic reticulum 2.9604
Hydrogen peroxide catabolic process − 1.7848 Ribonucleoprotein complex
assembly 2.9379
Negative regulation of cellular response to growth factor stimulus
− 1.7846 Ribonucleoprotein complex subunit organization 2.9292
Gamma carboxylation, hypusine formation and arylsulfatase activation
− 1.7846 Activation of the mRNA upon binding of the cap-binding complex
and eIFs, and subsequent binding to 43S 2.9227
Regulation of sodium ion transport − 1.7843 rRNA processing 2.9199
Detection of external stimulus − 1.7843 mRNA Processing 2.9151
Regulation of Rho protein signal transduction − 1.7842 Translation
initiation complex formation 2.8601
[126]Open in a new tab
Decision support system based on three discriminating biomarkers
As shown in Fig. [127]1, the step following Correlation Analysis
consisted in a second feature selection method based on Relief
algorithm. This allowed a further reduction and a list of the most
important features ordered by importance score. The topmost 46 features
were used as input to train and develop the highly discriminating
Decision Support System, which is able to distinguish a tumor from a
Non-Tumor patient based on the differential expression of three
proteins: Topoisomerase 1 (TOP1), Protein Disulfide Isomerase Family A
Member 4 (PDIA4) and Osteoglycin (OGN) ,as displayed in Fig. [128]2.
Strikingly, as assessed in Test 1, the system showed 97.6% of
specificity, 98.2% of sensitivity on the CPATC Ovarian Cancer
Confirmatory Study Proteomic Dataset,with an F1 score of 98.8% for the
tumor class and 93% for the fewer cases belonging to Non-Tumor class,
while once tested on the second dataset (Test 2), it showed 97.2%
sensitivity and 98.6% F1 score, thus eliminating the risk that the good
performance was due to overfitting. Furthermore, these three proteins
also appear to have a serum localization, thus making them ideal
candidates, after clinical validation, for the development of
non-invasive tests. The first biomarker is TOP1, one of the six human
topoisomerases, whose function is to unwind negative DNA supercoilings
occurring during the events of replication ^[129]70. TOP1 is also known
to play a role in the maintenance of genomic integrity, in fact, a
decrease in TOP1 activity, due to low expression or lack of recruitment
to chromatin by SMARCA4, may result in DNA damage and genomic
breaks^[130]71,[131]72. This is reflected by the upregulation of TOP1
in cancer cells, which undergo through replicative and transcriptional
stress^[132]73. Given this crucial role, there are several FDA-approved
drugs targeting TOP1. The most famous are the camptothecin alkaloid
derivatives, which act by binding at the interface between the DNA and
the topoisomerase^[133]74. The second biomarker, PDIA4, is one of the
largest member of the Protein Disulfide Isomerases family (PDIs), which
are known to mediate protein folding via either the formation or the
breakage of disulfide bonds^[134]75. Other than its protein folding
function, exerted when located in the endoplasmic reticulum, PDIA4 can
also be present on the surface of the platelet, where it participates
in thrombus formation^[135]76. It has been observed to be
over-expressed in a cohort of Epithelial Ovarian Cancer (EOC) patients,
where it was associated with disease progression and poor
prognosis^[136]77, potential mechanisms involve the inhibition of
apoptosis emerged in another study, where the over-expression of PDIA4
in tumor cells reduced caspase 3 and 7 activity favoring cell
growth^[137]78, thus potentially enabling tumor resistance to
therapy^[138]79. Lastly, OGN, a small leucine-rich proteoglycan (SLRP)
protein. Its function is different in different cell types: in the
extracellular compartment it is involved in collagen cross-linking,
while in vascular smooth cells (VSMCs) and fibroblasts, a reduced
expression leads to cellular proliferation. Its implications in tumor
progression are quite recent but evident. For instance, OGN appears to
be under the control of p53, and several studies show a reduction or
lack of OGN expression in a variety of cancers, among which breast,
colon, lung, ovarian and pancreatic cancer^[139]80. It has been
observed in bladder cancer that ECRG4 promotes OGN expression by
upregulating NFIC, preventing the activation of NF-KB downstream
pathways, thus inhibiting cell proliferation and migration^[140]81.
Furthermore, in breast cancer, OGN seems to reverse epithelial to
mesenchymal transition by repressing the PI3K/Akt/mTOR axis^[141]82.
Overall, the DSS managed to identify, among the HGSOC proteome, three
proteins that are known to be linked to tumorigenesis. In addition, the
high sensitivity and specificity of these biomarkers for the
distinction between tumor and Non-Tumor patients, coupled with the fact
that they also appear to be localized in the serum, is promising for
their possible clinical use for the diagnosis of HGSOC. It’s worth
noting that in our analysis seral biomarkers CA125 and HE4 were found
to not correlate with Tumor phenotype, and were consequently dropped at
the fist step of the pipeline. This prevented us from performing a
proper comparison, since the lack of correlation implies that if we
build a classifier using only these two proteins, this will be with any
probability unable to distinguish Tumor from Non Tumor samples if
applied to our datasets.
Conclusions
To summarize, we provided a reliable overview of the most relevant
deregulated pathways in HGSOC, focusing mainly on those genes that were
not related directly to HGSOC before, thus providing novel associations
and new starting points for future researches. Furthermore, we
developed a Decision Support System able to find three possible
Biomarkers for the diagnosis of HGSOC. These three proteins are
ubiquitous and exert their primary function in physiological
conditions. However, a role for TOP1 as an oncogene has been already
strongly suggested, being found upregulated in different types of
tumors, including breast, liver and colorectal
cancers ^[142]83–[143]86. Indeed, several TOP1-targeting drugs have
received FDA approval ^[144]74,[145]87,[146]88. The connection of PDIA4
and OGN with tumor progression is relatively recent, PDIA4 has been
found overexpressed in a cohort of EOC patients, and associated with
poor prognosis, cell gowth and resistance. On the other hand, a
decrease in OGN expression was found in different types of cancers.
This is coherent with the results of our dataset analysis, in which we
found they showed a strong correlation with the tumor phenotype, with
TOP1 and PDIA4 positively correlating and OGN being negatively
correlated. Furthermore, the predictive efficiency of this system in
considerably high in both of the tested datasets. Notwithstanding,
further validation is crucial to support this in silico results, and,
for a possible clinical use, further studies are needed to assess if
the proportions of these biomarkers are maintained in the serum as they
are in HGSOC biopsies. Finally, once clinically and experimentally
validated, this pipeline could be easily applied to other tumor
datasets for the purpose of discovering novel biomarkers and clinical
predictors.
Acknowledgements