Key words
pancreatic cancer - differentially expressed genes - targets - bioinformatics - treatment
Introduction
Pancreatic cancer (PC), a common tumor of the gastrointestinal tract, has a poor
survival rate [1]. This is primarily because
PC is hidden on the posterior side of the right upper abdomen [2]. Patients may be unaware of the initial
symptoms such as upper abdominal discomfort, weight loss, yellowing of the skin,
fatigue, and cognitive issues, making them easily overlooked. Moreover, the lack of
precise biomarkers for PC aggravates the issue [3]. Lack of effective treatments are the second reason for poor survival
rate. Various treatments such as surgery, radiotherapy, and chemotherapy are
typically utilized. Nevertheless, those who have undergone surgery have a high
chance of relapse, and are not as responsive to radiation or chemotherapy treatments
[4]. Resultantly, more effective
biomarkers or novel treatments for PC are warranted.
Bioinformatics methods have been used in numerous diseases, including cancers [5]
[6]
[7], further providing novel
insights into cancer. A few bioinformatics studies could examine only one gene
associated with PC [8]
[9]
[10].
Tumors are not exclusively caused by a single gene, but rather are the result of
several genetic factors combined. Moreover, the above studies ignored targeted drugs
for cancer. Hence, the diagnosis and management of PC is a difficult task and its
comprehensive exploration has attracted intense curiosity.
Novel uses of earlier drugs can be a revolutionary development [11]. Drugs for non-cancerous have the potential
to treat cancer. For instance, statins used for patients undergoing heart failure
treatment have demonstrated anti-tumor activity [12]
[13]
[14]. Aspirin, an antiplatelet drug, has shown
anti-tumor effects as well [15]
[16]
[17].
Hence, it is hypothesized that some existing drugs could be useful in the treatment
of PC.
The objective of the current study was to identify target genes and drugs in PC using
several bioinformatical methods. First, three pooled datasets were selected from the
Gene Expression Omnibus (GEO) database. Second, differentially expressed genes
(DEGs) were detected between PC patients and healthy individuals. Next, these DEGs
were analyzed using several bioinformatics methods. Finally, the potential
biomarkers and drugs targeted to PC were identified. Expectedly, the present study
may offer a promising treatment for PC.
Materials and Methods
Data summary
Gene Expression Omnibus (GEO) database stores microarray and high-throughput gene
expression data [18]. Three datasets,
namely GSE62452, GSE46234, and GSE101448, were obtained from GPL6244, GPL570,
and GPL10558 platforms, respectively, in the GEO database. GSE62452 had 61
cancer and 69 normal tissues; GSE46234 comprised four cancer and four normal
samples; GSE101448 showed 19 cancer and 24 normal samples (Supplement
[Table 1S]) .
Table 1 List of the differentially expressed genes
(DEGs).
|
Term
|
Gene name
|
|
Upregulated genes
|
KIAA1324, CELA3A, CEL, EGF, AQP8, CLPS, TRHDE, CPB1, GP2,
PDK4, RBPJL, PRSS3P2, PDIA2, CTRC, IAPP, PLA2G1B, CELA3B,
ERP27, CELA2B, ERP44, CTRL, TMED6, ALB, AOX1, F11, CPA2,
REG1B, PNLIPRP2, CPA1, NR5A2, PNLIPRP1, KLK1, SERPINI2
|
|
Downregulated genes
|
SERPINB5, CEACAM6, COL1A1, FN1, LAMB3, DPCR1, SLPI, NOX4,
CDH11, ITGA2, SLC6A14, COL3A1, ANXA10, POSTN, CEACAM5, TMC5,
CTSE, GABRP, THBS2, KRT19, SULF1, LAMC2, AHNAK2, TFF1,
CLDN18, CP, AGR2
|
Ethics statement
As the data were re-analyzed from the public dataset, no ethical approval by the
local ethics committee was necessary.
DEGs identification
GEO2R, an interactive web tool, was employed to identify the DEGs between PC and
normal specimens [19]. The upregulated
DEGs are logFC >1 and p <0.05. The opposite logFC are the
downregulated DEGs. Venn diagram tool
(http://bioinformatics.psb.ugent.be/webtools/Venn/)
was applied to obtain common DEGs.
Functional and pathway enrichment analysis
Database for Annotation, Visualization, and Integrated Discovery (DAVID), an
online bioinformatics tool, was used for Gene ontology (GO) function and Kyoto
Encyclopedia of Genes and Genomes (KEGG) pathway analyses [19].
Protein-protein interaction (PPI) network
To establish an association among the DEGs and construct the PPI network, the
Search Tool for the Retrieval of Interacting Genes (STRING,
http://string-db.org) was applied [20]. Subsequently, Cytoscape version 3.7.2
was used to visualize the PPI network. The MCODE (Molecular Complex Detection)
plugin from Cytoscape analyzed the hub genes [21].
Drug screening
The Drug Gene Interaction Database (DGIdb)
(https://www.dgidb.org) was used to search for drugs associated
with hub genes.
Statistics analysis
Fisher’s exact test was employed to evaluate functional enrichments. The
t-test was applied to screen DEGs. A value of p <0.05
indicated statistical significance.
Results
DEGs Identification
Venn diagram depicts 60 genes, including 33 upregulated ([Fig. 1a]) and 27 downregulated genes
([Fig. 1b]) overlapping among three
datasets. [Table 1] lists the names of
DEGs.
Fig. 1 The common differentially expressed genes (DEGs) in
GSE62452, GSE46234, and GSE101448. a: The common 33 upregulated
DEGs. b: The common 27 downregulated DEGs.
Functional enrichment analysis
For functional enrichment, biological process (BP) terms were clustered in the
“multicellular organismal process”, “biological
regulation”, “cell communication”, “response to
stimulus”, and “metabolic process”. Besides, cellular
component (CC) terms were associated with “endomembrane system”,
“extracellular space”, “vesicle”,
“membrane” and “protein-containing complex”. In
Molecular Function (MF) annotation, functional enrichment was associated with
“hydrolase activity”, “structural molecule”,
“protein binding”, “ion binding,” and
“enzyme regulator” ([Fig.
2]). KEGG pathway revealed enrichment in “small cell lung
cancer”, “glycolipid metabolism”, “ECM-receptor
interaction”, “pathways in cancer”, and “focal
adhesion” ([Table 2]).
Fig. 2 Functional analysis for differentially expressed genes.
Table 2 Kyoto Encyclopedia of Genes and Genomes (KEGG)
analysis of the differentially expressed genes (DEGs).
|
Gene
|
Description
|
p-Value
|
|
hsa04512
|
ECM-receptor interaction
|
7.55E-07
|
|
hsa04510
|
Focal adhesion
|
9.85E-06
|
|
hsa05222
|
Small cell lung cancer
|
0.004666
|
|
hsa00561
|
Glycerolipid metabolism
|
0.014453
|
|
hsa05200
|
Pathways in cancer
|
0.0425
|
The construction of PPI
Forty-eight genes and 145 edges were clustered in the PPI network ([Fig. 3a]). Top genes were selected via the
MCODE plugin. [Fig. 3b] shows nine top
genes (CLPS, CELA3B, CPA2, CELA3A, CPA1, CPB1, CTRC, CTRL, and PRSS3P2).
Fig. 3 The protein-protein interaction (PPI) network and hub genes
analysis. a: The PPI networks for differentially expressed genes.
b: The top 9 genes in the PPI networks. Red nodes indicate
upregulated genes; blue nodes indicate downregulated genes.
Screening the drugs
The top nine genes were employed to find drugs. CPA1 and CLPS genes matched with
10 drugs ([Table 3]). In the KEGG
pathway, these genes were associated with the “fat digestion and
absorption pathway”, “pancreatic secretion” and
“protein digestion and absorption” ([Fig. 4]
).
Fig. 4 Screening drugs for hub genes. a: The potential
drugs targeted the CPA1 and CLPS. b: The pathway associated with
CPA1 and CLPS genes.
Table 3 The known drugs associated with CAP1 and CLPS
genes.
|
Drug ID
|
Drug name
|
p-Value
|
|
DB04058
|
d-[(Amino)carbonyl]phenylalanine
|
0.001036
|
|
DB03441
|
2-Benzyl-3-iodopropanoic Acid
|
0.001142
|
|
DB04316
|
d-(N-Hydroxyamino)carbonyl]phenylalanine
|
0.001628
|
|
DB08222
|
Methoxyundecylphosphinic Acid
|
0.001753
|
|
DB04233
|
(Hydroxyethyloxy)tri(ethyloxy)octane
|
0.002442
|
|
DB06924
|
(2 R)-2-Benzyl-3-nitropropanoic acid
|
0.002850
|
|
DB03012
|
Phenylalanine-N-sulfonamide
|
0.003800
|
|
DB02451
|
B-Nonylglucoside
|
0.00455
|
|
DB03201
|
d-Cysteine
|
0.00570
|
|
DB02494
|
α-Hydroxy-β-phenylpropionic Acid
|
0.00995
|
Discussion
PC has the highest mortality and lowest survival rates of all cancers due to its
difficulty to be detected in the early stages and the lack of effective treatments.
Therefore, identifying biomarkers to diagnose or treat PC becomes urgent. Data
sequencing can reveal the underlying diagnostic and prognostic mechanisms of
different diseases, especially cancer. The development of related medications has
opened up a new way to examine cancer and hypothesize about its molecular
causes.
In this study, GSE62452, GSE46234, and GSE101448 datasets were analyzed for DEGs
between abnormal and normal tissues. Sixty DEGs were screened. BP terms were
clustered in the “multicellular organismal process”,
“biological regulation”, “cell communication”,
“response to stimulus”, and “metabolic process”.
Further, CC terms were associated with “endomembrane system”,
“extracellular space”, “vesicle”,
“membrane”, and “protein-containing complex”. MF
annotation revealed an association with “hydrolase activity”,
“structural molecule”, “protein binding”,
“ion binding” and “enzyme regulator”. In the KEGG
pathway, PC was enriched in “small cell lung cancer”,
“glycolipid metabolism”, “ECM-receptor interaction”,
“pathways in cancer”, and “focal adhesion”. These
results revealed an association of abnormal lipid metabolism with PC. Numerous
research papers have established a link between lipid metabolism disorders and PC,
in agreement with our results [22]
[23]
[24].
A total of 48 genes with 145 edges were included in the PPI part. Thereafter, hub
genes were selected by the MCODE algorithm. Nine top genes, namely CLPS, CELA3B,
CPA2, CELA3A, CPA1, CPB1, CTRC, CTRL, and PRSS3P2 were employed to identify drugs.
CPA1 and CLPS genes matched with 10 drugs. In the KEGG pathway, these genes showed
association with "pancreatic secretion”, “protein digestion
and absorption”, and “fat digestion and absorption
pathway”.
The protein encoded by the co-enzyme colipase (CLPS), a cofactor for efficient
dietary lipid hydrolysis, performs tissue-specific regulation of expression in
pancreatic alveolar cells [25]
[26]. CLPS is key to the development and
progression of PC and is a likely target for treatment [27]. Furthermore, CLPS has been reported to
contribute to type 2 diabetes development [28].
Carboxypeptidase A1 (CPA1), a zinc metalloprotease produced by pancreatic alveolar
cells, plays a vital role in the cleavage of C-terminal branched chains from dietary
proteins [29]. When comparing the
differentiating marker between normal and neoplastic pancreatic alveolar cells, CPA1
displays high sensitivity [29]
[30]. Besides, the CPA1 variant aggravates the
risk of chronic pancreatitis [31]. Hence, CLPS
and CPA1 genes were associated with PC. We found 10 medications that have been given
the green light by the FDA, which could potentially be useful in treating PC, and
are specifically targeted at CLPS and CPA1 genes.
Conclusion
Overall, CPA1 and CLPS genes as well as candidate drugs were identified by
bioinformatics methods in this study. This study may offer a novel idea for the
diagnosis and treatment of PC.
Author Contributions
(I) Conception and design: Zhang Hongjian; (II) Administrative support: Wan Zheng;
(III) Provision of study materials: Xiao Xiaojie; (IV) Collection and assembly of
data: Xiao Xiaojie, Liu Xinmei, Chen Huaying, and Zhao Xiaoyan; (V) Data analysis
and interpretation: All authors; (VI) Manuscript writing: All authors; (VII) Final
approval of manuscript: All authors.
