Abstract
Lung cancer is the major cause of cancer-associated deaths across the
world in both men and women. Lung cancer consists of two major
clinicopathological categories, i.e., small cell lung cancer (SCLC) and
non-small cell lung cancer (NSCLC). Lack of diagnosis of NSCLC at an
early stage in addition to poor prognosis results in ineffective
treatment, thus, biomarkers for appropriate diagnosis and exact
prognosis of NSCLC need urgent attention. The proposed study aimed to
reveal essential microRNAs (miRNAs) involved in the carcinogenesis of
NSCLC that probably could act as potential biomarkers. The
NSCLC-associated expression datasets revealed 12 differentially
expressed miRNAs (DEMs). MiRNA-mRNA network identified key miRNAs and
their associated genes, for which functional enrichment analysis was
applied. Further, survival and validation analysis for key genes was
performed and consequently transcription factors (TFs) were predicted.
We obtained twelve miRNAs as common DEMs after assessment of all
datasets. Further, four key miRNAs and nine key genes were extracted
from significant modules based on the centrality approach. The key
genes and miRNAs reported in our study might provide some information
for potential biomarkers profitable to increased prognosis and
diagnosis of lung cancer.
Keywords: non-small cell lung cancer, differentially expressed miRNAs,
miRNA–mRNA network, module detection, gene term enrichment analysis,
survival analysis, transcription factor
1. Introduction
The highest number of deaths related to cancer is still associated with
lung cancer across the globe, which results in around (or more than)
1.5 million deaths annually [[34]1]. On the basis of treatment purpose,
lung cancer has been categorized into two major subgroups, i.e., small
cell cancer and non-small cell cancer. The second subgroup non-small
cell lung cancer (NSCLC) constitutes majorly three histological
subtypes, which are adenocarcinoma (40–50%), squamous cell carcinoma
(around 30%), and large cell carcinoma (around 15%), which account for
approximately 80% cases of lung cancer [[35]1,[36]2]. COPD (chronic
obstructive pulmonary disease), and other type of lung disease like
pulmonary fibrosis or pleural effusion could also be caused by NSCLC
[[37]3]. NSCLC occurrence is associated with signaling pathways (mTOR
[[38]4] and tyrosine kinase [[39]5]) and oxidative stress [[40]6] and
is also related to the changes in the cell cycle. The existing
treatment mainly involves platinum bimodal therapy (cytotoxic therapy)
[[41]7], however, in some patients, resistances to this therapy have
been reported recently. Regardless of recent improvements in treatment,
NSCLC is generally diagnosed at a highly developed (late) stage which
results in low survival rates, due to poor prognosis [[42]8]. In light
of this, the identification of appropriate treatment strategies or
novel diagnostic biomarkers is essential in controlling lung cancer.
The microRNAs (miRNAs) are small (~22 nucleotides) noncoding RNAs which
regulate more than half of the genes in human cells [[43]9]. An miRNA
is linked with diverse biological activities which include cell
differentiation, cell proliferation, disease initiation, cell
migration, disease progression, and finally apoptosis [[44]9]. The
miRNA modulates the activity of genes at the level of
posttranscription, by inhibiting their messenger RNA (mRNA) targets
translation [[45]10]. It was revealed that the expression for miRNAs is
upregulated (frequently) for oncogenic miRNAs, while the downregulated
expression of miRNA has been documented for tumor suppressor miRNA
[[46]10]. Studies have suggested that miRNAs perform a vital role in
NSCLC development by acting as potential biomarkers in its diagnosis
and prognosis [[47]8,[48]11,[49]12,[50]13]. Typically, the incidence
and advancement of NSCLC are due to multistep carcinogenesis which
involves various signal transduction pathways and change of gene
expression levels [[51]14,[52]15]. The mechanisms leading to the
promotion of carcinogenesis in NSCLC need to be exploited. Recently,
several reports have documented the role of various miRNAs expression
in different cancers (including NSCLC), probably, suggesting their
crucial roles in carcinogenesis [[53]14,[54]16,[55]17]. In particular,
some miRNAs (miRNA-224, miRNA-30d-5p) have also been demonstrated to
play important role in NSCLCs as either promoters or suppressor (cancer
promoters or as cancer suppressors) [[56]18,[57]19].
Though these aforementioned studies have documented the role of miRNAs
in lung cancer or specifically NSCLC, these reports comprised different
datasets
[[58]8,[59]9,[60]10,[61]11,[62]12,[63]13,[64]14,[65]15,[66]16,[67]17,[6
8]18,[69]19]. Therefore, in this regard, our present study performed an
integrated analysis on some of the other unexplored gene expression
profiles of NSCLC. Thus, our identified key miRNAs and related genes
show a discrepancy with the previous study results due to heterogeneity
in NSCLC cases and control subjects. In this study, we used network
analyses to show the correlation between the identified key
miRNAs/genes and NSCLC. This kind of study is envisaged to provide
useful information in exploring candidate miRNA biomarkers in human
NSCLC.
The present study reports the analysis of the identified signature
miRNAs between three distinguishing NSCLC series ([70]GSE25508,
[71]GSE19945, and [72]GSE53882) using a bioinformatics approach. Our
study revealed several promising key miRNAs/genes that have been
constantly reported in lung cancer-associated profiling studies. Their
key candidates might provide some information about miRNA’s role in
tumorigenesis and its related mechanisms. The GEO (Gene Expression
Omnibus) datasets were investigated to obtain miRNAs that were
differentially expressed between non-small cell lung cancer tissues and
normal tissue samples. A comparative analysis was undertaken to select
the differentially expressed miRNAs (DEMs) among these retrieved
datasets. To locate the DEMs associated with target genes the RNA
interactome encyclopedia was used. Further, network analysis was
applied to identify DEMs, which were then combined with mRNA to form
the mRNA–miRNA network, to elucidate key miRNAs as well as their genes.
Moreover, an enrichment analysis was performed for these key elements
(key miRNAs and key genes) was explored to reveal their potential
molecular mechanisms in NSCLC. Subsequently, the expression and
validation analysis was applied to key genes. The obtained key genes
regulated by miRNAs may provide some clue about the potential
biomarkers profitable to increased prognosis and diagnosis of lung
cancer. Therefore, these genes/miRNAs might be explored in therapeutic
interventions of NSCLC after appropriate validation.
2. Materials and Methods
The graphical illustration of the network-based integrative method used
in the current study is represented in [73]Figure 1.
Figure 1.
[74]Figure 1
[75]Open in a new tab
Illustration of the network-based integrative method used in the study.
(A) Three NSCLC series ([76]GSE25508, [77]GSE19945, and [78]GSE53882)
were used for the present analysis. (B) The DEMs were identified using
comparative approach. (C) The target genes associated with DEMs were
identified. (D) The mRNA–miRNA network was constructed. (E) The
significant modules based on centrality methods were detected. (F) The
key miRNAs and their associated genes were obtained. (G) Enrichment of
function and pathway analysis was performed for the identified key
elements (key miRNAs and key genes). (H) Survival analysis for the
obtained key genes was conducted through survival plots.
2.1. Search Strategy and Inclusion Criteria of Studies
We searched the GEO database ([79]https://www.ncbi.nlm.nih.gov/geo/,
accessed on 1 June 2021) for publicly available studies using the
following keywords: “microRNA expression or miRNA expression”, “lung
cancer or NSCLC”, “prognosis”, “non-small cell lung”, “adenocarcinoma”,
“squamous cell carcinoma”, “large cell carcinoma” and “Homo sapiens”
(organism). After a systematic and extensive review, we retrieved three
GSE series. The criteria for inclusion of miRNA series included: (1)
samples included normal tissue samples as well as diagnosed ones (NSCLC
tissue samples), (2) miRNA expression profilings, (3) the minimum limit
of the sample count in each group was 3, and (4) adequate information
was collected to perform this research. These obtained miRNA expression
profiles ([80]GSE25508, [81]GSE19945, and [82]GSE53882) were used for
the present analysis.
2.2. Acquisition of MiRNA Expression Data
The miRNA expression profiles of [83]GSE25508, [84]GSE19945, and
[85]GSE53882 were retrieved from the GEO database of the National
Centre for Biotechnology Information (NCBI) [[86]20]. These
aforementioned expression series were generated from [87]GPL7731
(Agilent 019,118 Human miRNA Microarray 2.0 G4470B), [88]GPL9948
(Agilent Human 0.6K miRNA Microarray G4471), and [89]GPL18130 (State
Key Laboratory Human microRNA array 1888) platforms respectively. The
expression dataset [90]GSE25508 consisted of 34 lung cancer and 26
normal lung tissue samples. [91]GSE19945 expression dataset consisted
of 55 lung cancer and 8 noncancer lung tissue samples. The final
expression dataset [92]GSE53882 consisted of 151 patients with NSCLC
and 397 corresponding adjacent noncancerous tissues.
2.3. Data Preprocessing and Screening of Differentially Expressed MiRNAs
(DEMs)
The GSE series were normalized and preprocessed through GEO2R,
web-based analytical tool ([93]http://www.ncbi.nlm.nih.gov/geo/geo2r/
accessed on 1 June 2021). It constitutes Linear Models for Microarray
Data (Limma) R package and GEO query. The preprocessing of datasets was
undertaken to utilize default parameters. Benjamini-Hochberg correction
method was used to correct the significant p-values obtained by the
original hypothesis test. The differentially expressed microRNAs were
extracted by applying the inclusion criteria: adjusted p-value (p <
0.05) and a |log[2] (fold-change) > 1|. Overlapped DEMs among three
miRNA series were obtained by Venny 2.1.0
([94]http://bioinfogp.cnb.csic.es/tools/venny/, accessed on 1 June
2021). It is an online tool which finds the intersection(s) of listed
elements.
2.4. Identification of the DEM Target Genes
For the prediction of the target genes (associated with DEMs), four
different databases were used. (1) TargetScan
([95]http://www.targetscan.org/vert_72/, accessed on 1 July 2021), an
algorithm, thatpredicts miRNA targets by comparing multiple genomes
[[96]21]. (2) miRmap ([97]https://mirmap.ezlab.org/, accessed on 1 July
2021), a freely available (open source) Python library, includes web
facility to predict miRNAtargets [[98]22]. (3) miRWalk
([99]http://mirwalk.umm.uni-heidelberg.de/, accessed on 1 July 2021)
(version 3.0), a computational-based approach, predicts target sites
encoded by Perl programming language [[100]23]. (4) mirDIP
([101]http://ophid.utoronto.ca/mirDIP/, accessed on 1 July 2021), a
database, provides dependable, user-friendly, and inclusive resources
to identify miRNAtargets [[102]24]. The genes that were found to be
overlapping in all the four databases were predicted as the target
gene. Venny 2.1.0 ([103]https://bioinfogp.cnb.csic.es/tools/venny/,
accessed on 1 July 2021), online visualization software, was applied
for the generation of the Venn diagram.
2.5. DEM–mRNA Network Construction
The miRNA–mRNA network was built by utilizing overlapped genes (target
genes vs. CTD (Comparative Toxicogenomics Database) NSCLC genes)
([104]Supplementary File S1) and DEMs in Cytoscape (Version 3.7.1)
software, manually using SIF files. The Cytoscape plugin cytoHubba
(version 0.1) was exploited to identify significant modules,
subnetworks, and top-ranked genes/nodes in a given network, by
employing various topological algorithms. The overlapped nodes in four
clustering methods were extracted. Finally, the obtained extracted
nodes possessed hub genes and miRNAs.
2.6. Network Analysis
In the miRNA-mRNA network, each node represented the gene/miRNA and
edges represented the connection between nodes. The following
topological properties in the constructed miRNA-mRNA network were
analyzed to find out the important behaviors of the network and hub
nodes [[105]25].
Degree distribution: In a particular network, the degree (k) of node
reflects the total number of edges (connections) by which it is
connected with other nodes [[106]26]. The degree k of a node is a local
measure of centrality of that node [[107]26]. The degree distribution
P(k) of a node n is given by the expression:
[MATH:
Pk=nkN :MATH]
where, n[k] is the number of nodes having degree k and N is the total
number of nodes in the network. P(k) indicates the importance of hubs
or modules in the network.
Betweenness centrality: In a particular network, a node’s betweenness
centrality reflects the importance of flow of information from one node
to another based on the shortest path [[108]27]. The betweenness
centrality C[B](n) of a node n is given by the expression:
[MATH:
CBn=∑s≠n<
/mi>≠tdstndsj<
/mfrac> :MATH]
where, s and t are nodes in the network other than n, d[st] is the
total number of shortest paths from s to t, and d[st] (n) is the number
of those shortest paths from s to t on which n lies
[[109]26,[110]28,[111]29].
Closeness centrality: In a particular network, closeness centrality
reflects how the information is rapidly passing from one node to
another [[112]30].
[MATH:
CCn=N−1∑Jdij
:MATH]
where, d[ij] is the length of the shortest path between two nodes i and
j, and N is the total number of nodes in the network which are
connected to the node n.
Stress: In a particular network, stress reflects the addition of all
nearest path of all node pairs [[113]31]. In order to compute the
stress of a node v, first calculate all shortest pathways in a graph G,
then, count the number of shortest paths passing through v. A stressed
node is the one that has large number of shortest paths passing through
it. Notably, and may be more critically, a high stress number does not
necessarily imply that node v is critical for maintaining the link
between nodes whose pathways cross through it.
2.7. Gene Term Enrichment and Pathway Analysis
The pathway enrichment analysis of hub miRNAs was implemented using
MIENTURNET (MIcroRNAENrichmentTURnedNETwork) web-tool (Mienturnet
(uniroma1.it, accessed on 1 August 2021)) [[114]32] that offers
enriched KEGG (Kyoto Encyclopedia of Genes and Genomes)
([115]https://www.genome.jp/kegg/, accessed on 1 August 2021) pathway
visualization. Further, the identified key genes associated with miRNAs
were accessed for their biological implications using gene ontology
(GO) analysis. The GO was performed in these mentioned categories, the
first one BP as biological process, second one CC as cellular component
and the third one is MF as molecular function, using the GOnet server
(a tool for interactive Gene Ontology analysis)
([116]http://tools.dice-database.org/GOnet/, accessed on 1 August
2021).
2.8. Survival Analysis and Prediction of Transcription Factors (TFs)
GEPIA (Gene expression profiling interactive analysis) allows a user to
interact with cancer and normal gene expression profiles
([117]http://gepia.cancer-pku.cn/, accessed on 1 September 2021). The
GEPIA data tool was exploited to examine the relation between
expression of selected key genes and NSCLC prognosis. The survival
analysis was undertaken by constructing the overall survival (OS) curve
of key genes. The patients on the basis of gene expression (median)
values were categorized into two classes. The OS of the key genes was
evaluated by means of the Kaplan–Meier approach (using log-rank test)
that provided survival plots. Furthermore, the key genes were validated
using box plots and pathological stages were analyzed. The TRRUST
(Transcriptional Regulatory Relationships Unraveled database was used
to predict the TFs ([118]http://www.grnpedia.org/trrust/, accessed on 1
September 2021).
3. Results
3.1. Selection of DEGs
The GSE series of NSCLC was denoted by X.
There are 3 GSE series of X (X1, X2 and X3)
Xu = (X[1]u[1], X[2]u[2], X[3]u[3]) (1)
where, u stands for upregulation.
Xd = (X[1]d[1], X[2]d[2], X[3]d[3]) (2)
where, d stands for downregulation.
To find DEMs, we compared equations, i.e., (1) with (2) as follows:
∑X[u] = X[1]u[1]∪X[2]u[2]∪X[3]u[3] (Upregulated genes in three GSE
series) (3)
∑X[d] = X[1]d[1]∪X[2]d[2]∪X[3]d[3] (Downregulated genes in three GSE
series) (4)
To find combined DEMs, we merged Equations (3) with (4) as follows:
∑Xud = ∑Xu∪∑Xd (Merge genes) (5)
Genes those showed values of p ≤ 0.05 along with log fold change of
|0.5–2.0| were chosen as statistically significant (differentially
expressed genes).
3.2. Selection of MiRNA from Datasets
According to search criteria, 3 NSCLC miRNA expression dataseries from
published literature were retrieved from public databases. The
description of datasets is provided in [119]Table 1.
Table 1.
List of datasets used in the network analysis.
Series TS N D UR DR GPL C Y
[120]GSE25508 60 26 34 39 52 7731 Finland 2011
[121]GSE19945 63 8 55 14 31 9948 Japan 2013
[122]GSE53882 548 397 151 29 7 18130 China 2017
[123]Open in a new tab
TS: Total samples; N: Normal; D: Disease; UR: Upregulated; DR:
Downregulated; Country; Y: Year.
The miRNA expression profiles in these datasets were Venny tool
compared. DEMs between cancer and normal tissue samples were identified
in each GSE dataset. [124]GSE25508 contained 39 upregulated and 52
downregulated miRNAs, [125]GSE19945 consisted of 14 upregulated and 31
downregulated miRNAs and [126]GSE53882 comprised 29 upregulated and 7
downregulated miRNAs. [127]GSE25508 dataset had the largest number of
upregulated miRNAs while [128]GSE19945 dataset possessed the least
number. Similarly, [129]GSE25508 had the maximum downregulated miRNAs
while [130]GSE53882 had the lowest number of downregulated miRNAs.
Therefore, the number of DEMs varied across the three studies. For
identification of aberrant miRNAs associated with NSCLC, three
aformentioned GEO datasets were utilized. The dataset contains 18,232
miRNAs in total, of which 172 DEMs were identified on the basis of fold
change (>1.5) and p-value (<0.05). From these datasets, the 12
overlapped DEMs were identified of which 5 were upregulated and 7 were
downregulated (Note: All miRNAs are mature miRNAs). The expression of
these selected top ranked 12 DEMs is mentioned in [131]Table 2.
Table 2.
Top 12 DEMs (7 downregulated and 5 upregulated) on the basis of log
fold change and p-value. Upregulated miRs are miR-210, miR-130b,
miR-96, miR-200b and miR-205 and downregulated miRs are miR-30a,
miR-145, miR-140 3p, miR-572, miR-144, miR-126 and miR-486-5p.
Adjusted p-Value p-Value Log FC miRNA OG OG vs. CTD
0.000488 3.6 × 10^−7 3.44103 MiR-30a 1076 1050
0.002008 4.63 × 10^−6 4.13116 MiR-145 154 149
0.002008 4.59 × 10^−6 1.95769 MiR-140-3p 387 370
0.002008 6.86 × 10^−6 2.22649 MiR-572 122 118
0.002008 7.9 × 10^−6 1.68913 MiR-144 144 137
0.004767 3.24 × 10^−5 2.08135 MiR-126 11 10
0.008815 1.93 × 10^−4 2.66117 MiR-486-5p 99 95
0.014676 1.13 × 10^−3 −1.71609 MiR-210 26 26
0.014796 1.17 × 10^−3 −1.88279 MiR-130b 592 586
0.018155 2.38 × 10^−3 −1.65166 MiR-96 290 285
0.004867 4.90 × 10^−6 −0.71609 MiR-200b 573 560
0.006767 7.90 × 10^−6 −0.65166 MiR-205 832 800
[132]Open in a new tab
Log FC: Log fold change; OG: Overlapped genes; CTD: Comparative
Toxicogenomics Database. The overlapped genes were predicted by four
databases: TargetScan, miRWalk, mirDIP, and miRmap.
3.3. Prediction of Target Genes for DEMs
MiRNA exerts its regulatory function through post-transcriptional
silencing by binding to its complementary site on the target genes. The
role of the obtained top-ranked 12 DEMs in NSCLC-associated
pathogenesis was comprehended by identifying their target genes (OG vs.
CTD) ([133]Figure 2) ([134]Supplementary File S2). We identified the
target genes for the top 12 DEMs using a combination of four databases,
i.e., mirMap, TargetScan, miRWalk and mirDIP. Each of these databases
showed different target genes for each of the input miRNAs. We selected
only those target genes which were given by at least two of these
databases and excluded those target genes which were validated by only
one of these databases. Based on this selection criterion, we obtained
a total of 4186 target genes for the top 12 DEMs. [135]Figure 2 is the
Venn diagram representation of the results given by these four
databases, for example, the value “152” shown in green represents the
number of target genes given by both mirDIP and miRWalk ([136]Figure
2).
Figure 2.
[137]Figure 2
[138]Open in a new tab
Venn diagram showing overlapping genes between four target predictive
databases, i.e., TargetScan, miRWalk, mirDIP, and miRmap. For each
miRNA, target genes were retrieved using these four databases; each
database showed some different target genes, but we extracted the
common genes that were validated in all databases. Abbreviations:
MiRNAs: MicroRNAs.
3.4. Construction of the MiRNA–mRNA Network
The construction of miRNA-mRNA network using overlapped genes (target
genes vs. CTD (Comparative Toxicogenomics Database) NSCLC genes) was
built from SIF files. The up and down regulated network were separately
built by Cytoscape as shown in the [139]additional material
(Supplementary Files S3 and S4) respectively. The upregulated
miRNA-target gene interaction network contained 1728 nodes and 1928
edges, wherein, triangles (green) represented the upregulated miRNAs
and circles (blue) represented the interacting gene partners. The
downregulated miRNA-target gene interaction network contained 1895
nodes and 2256 edges, wherein, diamonds (red) represented the
downregulated miRNAs and circles (blue) represented the interacting
gene partners. In both upregulated and downregulated miRNA-mRNA
network, the target genes were obtained from different databases and
the common ones proceeded further.
The merge interaction network constructed using upregulated and
downregulated DEMs is represented in [140]Figure 3. The merged network
was constructed using Cytoscape software. Further, this built merged
network was used for analysis of modules detection. This is the new way
to construct the miRNAs-mRNAs network by using SIF Files. If the
network was constructed using web tools like miRNet, Network Analyst
and MIENTURNET, than all key miRNAs would not have been interacted. In
this regard, to find target genes for each miRNA we utilized four
different databases (to validate our results, different databases were
used to cross-check the target genes). Thus, after obtaining all the
overlapped target genes (from four databases), these were further used
for construction of the network.
Figure 3.
[141]Figure 3
[142]Open in a new tab
MiRNA-mRNA interaction merge network. The interaction network is
constructed with the upregulated and downregulated DEMs using Cytoscape
software. The miRNA-mRNA consists of 2970 nodes and 4184 edges.
Triangle (green) represents upregulated miRNAs, diamond (red)
represents downregulated miRNAs, and circle (blue) represents the
interacting partners.
3.5. Detection of Significant Modules
Cytoscape software (version 0.1) was explored to detect significant
modules as well as top-ranked genes in the miRNA-mRNA network
([143]Figure 4). To reduce the intricacy and intrusion of the unrelated
genes from the obtained list of NSCLC genes through regulatory network,
some common elements (genes and miRNAs) were identified based on
centrality measures, i.e., degree (30 nodes (gene/miRNA) and 92 edges),
betweenness (30 nodes (gene/miRNA) and 88 edges), closeness (30 nodes
(gene/miRNA) and 86 edges) and stress (30 nodes (gene/miRNA) and 90
edges) ([144]Figure 4). The obtained 13 common elements included nine
genes: CPEB (Cytoplasmic polyadenylation element binding protein),
SAMD8 (Sterile α motif domain containing 8), FOXP1 (Forkhead box
protein P1), TRPS1 (Tricho-rhino-phalangeal syndrome 1), TCF4 (T-cell
factor 4), TBL1XR1 (Transducin (β)-like 1X related protein 1), SPRED1
(Sprouty-related, EVH1 domain-containing protein 1), CELF2 (CUGBP
Elav-like family member 2) and CDK19 (Cyclin-dependent kinase 19); and
fourmiRNAs: miR-30a-3p, miR-130b-3p, miR-200b-3p, miR-205-3p. These
common elements were referred to as key genes or key miRNAs that were
the resultant of significant modules (degree, closeness, betweenness
and stress). As an addition to this, the top ranked thirty genes top
(having highest degree, closeness, betweenness and stress) were also
scrutinized. The [145]Figure 5 is the Venn diagram depiction of the
intersection of the top 30 genes in each centrality measure.
Figure 4.
[146]Figure 4
[147]Open in a new tab
Significant modules and the top 30 ranked genes/miRNAs in the network
on the basis of (A) Degree, (B) Closeness, (C) Betweenness and (D)
Stress. The top 30 ranked genes/miRNAs in the network indicate both
gene and miRNA. Red indicates the highest rank, whereas yellow
indicates the lowest rank. Based on these modules, nine key genes and
four miRNAs were found to be common in all modules and were considered
as significant hub nodes.
Figure 5.
[148]Figure 5
[149]Open in a new tab
Venn diagram depicting the overlapped nodes in four methods used in
Cytohubba. Showing intersections of topological properties.
3.6. Analysis of Gene Term Enrichment and Pathways
Further, the identified top ranked 10 DEMs were systematically
characterized to explore their functions and pathways. The DEMs were
classified into BP, CC and MF. The GO functional annotation of the 10
candidate NSCLC DEMs biomarkers (4 upregulated and 6 downregulated
miRNAs) is represented as heat map ([150]Figure 6). The significant GO
categories related to top 10 DEMs included ion binding (MF), RNA
binding (MF), cytosol (CC), nucleoplasm (CC), transcription factor
activity (MF), biosynthetic process (BP), cell cycle (BP) and signaling
pathways (BP).
Figure 6.
[151]Figure 6
[152]Figure 6
[153]Open in a new tab
Gene Ontology of the 10 candidate sepsis DEM biomarkers. Representation
of functional enrichment of miRNAs showing (A) miRNAs versus Molecular
Functions, (B) miRNAs versus Biological Processes, and (C) miRNAs
versus Cellular components. Abbreviations: DEMs: Differentially
expressed miRNAs.
Additionally, [154]Figure 7 illustrates the enriched pathways of top 10
DEMs (4 upregulated and 6 downregulated miRNAs) associated with NSCLC
in the form of generated heat map. The significant signal pathways of
DEMs were mainly enriched with Hepatitis B, cell cycle, FoxO signaling
pathway and Hippo signaling pathway.
Figure 7.
[155]Figure 7
[156]Open in a new tab
Heatmap of the 10 DEMs (4 upregulated and 6 downregulated miRNAs)
showing the top 10 enriched functional pathways (X₋axis represents the
name of the pathways and Y₋axis represents the miRNAs).
Further, KEGG pathways of the key miRNAs (miR-30a-3p, miR-130b-3p,
miR-200b-3p, miR-205-3p) were explored from mieunturnet ([157]Figure
8). The disease ontology of the key miRNAs is shown in the lower panel
in [158]Figure 8.
Figure 8.
[159]Figure 8
[160]Open in a new tab
MiRNA enrichment analysis. (Upper Panel) Mieunturnet used to explore
the KEGG pathways of miRNAs. (Lower Panel) Disease ontology of miRNAs.
Abbreviations: KEGG: Kyoto Encyclopedia of Genes and Genomes.
Furthermore, the gene term enrichment analysis was performed to explore
the functions and pathways of key genes regulated by DEMs. In the
molecular function group, the key genes associated with DEMs were
principally involved in DNA binding, ion binding, DNA-binding
transcription factor activity, transcription factor binding, RNA
binding, and mRNA binding ([161]Figure 9). In the biological process
group, the key genes associated with DEMs were linked with anatomical
structure development, protein-containing complex assembly, cell
differentiation, biosynthetic process, cellular nitrogen compound
metabolic process, cellular component assembly, and cellular protein
modification process, and lipid metabolic process ([162]Figure 9). In
the cellular component group, the key genes associated with DEMs were
mainly related to the nucleus, protein-containing complex, cytoplasm,
nucleoplasm, plasma membrane, and cytosol ([163]Figure 9).
Figure 9.
[164]Figure 9
[165]Open in a new tab
GO of the NSCLC associated key genes (CPEB3, SAMD8, FOXP1, TRPS1, TCF4,
TBL1XR1, SPRED1, CELF2, and CDK19) from the miRNA-mRNA network as a
result of four significant modules. Abbreviations: CPEB: Cytoplasmic
polyadenylation element binding protein; SAMD8: Sterile α motif domain
containing 8; FOXP1: Forkhead box protein P1; TRPS1:
Tricho-rhino-phalangeal syndrome 1; TCF4: T-cell factor 4; TBL1XR1:
Transducin (β)-like 1X related protein 1; SPRED1: Sprouty-related, EVH1
domain-containing protein 1; CELF2: CUGBP Elav-like family member 2;
CDK19: Cyclin-dependent kinase 19.
3.7. Survival Plot Analysis of the Key Genes
Survival analysis of obtained key genes was undertaken using GEPIA. The
overall survival analysis of the obtained key genes (CPEB3, SAMD8,
FOXP1, TRPS1, TCF4, TBL1XR1, SPRED1, CELF2, and CDK19) was examined to
link their correlation with the prognosis of NSCLC ([166]Figure 10).
Survival curves are used to show the survival ability with time and
survival rate (using p-value 0.05).Moreover, the GEPIA tool was
utilized to validate the expression of key genes between control and
lung cancer tissues (in LUSC cohort). It was determined that the miRNA
expression of genes CDK19, SAMD8, TBL1XR1, and TRPS1 were significantly
upregulated in the LUSC dataset between lung cancer patients and
controls ([167]Figure 11). Moreover, the relation between key gene
expression and pathological/tumor stage in NSCLC patients was estimated
that revealed the association of key genes with tumor stage NSCLC
patients ([168]Figure 12).
Figure 10.
[169]Figure 10
[170]Open in a new tab
Survival analysis of key genes CPEB3, SAMD8, FOXP1, TRPS1, TCF4,
TBL1XR1, SPRED1, CELF2, and CDK19. The survival curves of key genes in
patients with NSCLC were obtained from GEPIA. Survival plots were used
to show the survival ability with time and survival rate.
Figure 11.
[171]Figure 11
[172]Open in a new tab
Expression analysis of selected key genes CPEB3, SAMD8, FOXP1, TRPS1,
TCF4, TBL1XR1, SPRED1, CELF2, and CDK19. Box plots obtained from GEPIA
showing expression profiles of key genes in tumor (red) and normal
(green) samples of LUSC datasets (p < 0.05) in patients with NSCLC.
‘Outlier’ represents the statistical difference in gene expression
between two boxplots.
Figure 12.
[173]Figure 12
[174]Open in a new tab
Pathological analysis of selected key genes CPEB3, SAMD8, FOXP1, TRPS1,
TCF4, TBL1XR1, SPRED1, CELF2, and CDK19 in NSCLC. Violin plots obtained
from GEPIA showing pathological stage condition for key genes using
LUSC datasets in patients with NSCLC. The X₋axis represents the major
pathological stages while the Y₋axis represents the log scale
transformed expression data.
3.8. Identification of the TFs
The aforementioned key genes were further explored to obtain their TFs.
The Enrichr database used inbuild source TRUST, which identified
potential TF. Initially, the extracted TF belonged to two organisms,
i.e., human and mouse. However, the mouse-associated TFs were excluded
from the analysis, and the human-associated TFs are mentioned in the
table ([175]Table 3). It was revealed that TP63 (transformation-related
protein 63), VHL (von Hippel-Lindau tumor suppressor, LEF1 (Lymphoid
enhancer-binding factor 1), RUNX3 (Runt-related transcription factor
3), ESR1 (Estrogen receptor 1), EGR1 (Early growth response protein 1)
and AR (Androgen receptor) possibly played significant roles in NSCLC.
Table 3.
Transcription factors prediction through TRUST.
Term p-Value Adjusted p-Value Key Genes
TP63 human 0.006731046 0.029080631 TCF4
VHL human 0.008965773 0.029080631 TCF4
LEF1 human 0.012977027 0.029080631 TCF4
RUNX3 human 0.013421829 0.029080631 TCF4
ESR1 human 0.033691182 0.044511811 FOXP1
EGR1 human 0.038917584 0.044511811 TCF4
AR human 0.041087826 0.044511811 TRPS1
[176]Open in a new tab
Abbreviations: TP63: Transformation-related protein 63; VHL: Von
Hippel-Lindau tumor suppressor; LEF1: Lymphoid enhancer-binding factor
1; RUNX3: Runt-related transcription factor 3; ESR1: Estrogen receptor
1; EGR1: Early growth response protein 1; AR: Androgen receptor.
4. Discussion
Lung cancer has the highest mortality rate among all forms of cancers
across the globe. The underlying molecular mechanisms leading to the
occurrence and development of NSCLC remain unexplored. Delayed
diagnosis and poor prognosis of NSCLC is still a concern, which
requires urgent attention. In this context, an in-depth investigation
into the mechanisms and factors leading to NSCLC progression is
necessary for effective treatment. Common genetic alterations in the
development of a particular disease can easily be determined through
well-developed microarray technology. It allows the identification of
gene targets for appropriate diagnosis, therapy, and prognosis of
tumors. Microarray data analyzed using bioinformatics methods, could be
utilized for screening cancer biomarkers and therapeutic targets
[[177]33]. Kentaro Inamura and Yuichi Ishikawa reported the two main
characteristics attributed to miRNAs, due to which they are employed in
diagnostics and prognostics as well as the targeted therapeutics in
tumors [[178]8], which includes; easy accessibility of miRNAs towards a
non-invasive liquid biopsy and the stability of miRNAs in FFPE
(formalin-fixed paraffin-embedded) samples. These two features make
miRNAs promising biomarkers in cancer diagnosis which could be applied
to histological classification or genetic alterations [[179]8].
Though many studies have focused on the relationship between miRNAs and
NSCLC, however, a study showing a comparative analysis of the
aforementioned miRNA expression profiles ([180]GSE25508, [181]GSE19945,
and [182]GSE53882) has not been conducted. Thus, our identified hub
miRNAs and their associated genes show a discrepancy with the results
obtained in previous studies due to heterogeneity in NSCLC cases and
control subjects. For instance, researchers have shown miR-582-5p
[[183]34] and miR-107 [[184]35] as prognostic biomarkers which included
a total of 30 [[185]34] and 137 [[186]35] matched NSCLC tissue samples
and adjacent normal (noncancerous) tissue samples respectively. A
recent investigation has also been conducted on eight NSCLC-associated
datasets ([187]GSE19188, [188]GSE118370, [189]GSE10072, [190]GSE101929,
[191]GSE7670, [192]GSE33532, [193]GSE31547, and [194]GSE31210),
however, it identified DEGs and its related target genes [[195]36].
Further, some of the earlier reports have also shown the correlation of
miRs to lung cancer as well as specifically NSCLC but these studies
comprised different datasets
[[196]8,[197]9,[198]10,[199]11,[200]12,[201]13,[202]14,[203]15,[204]16,
[205]17,[206]18,[207]19]. Moreover, a bioinformatics integrative
analysis carried out on NSCLC by Shao and colleagues, in the year 2017,
included only two datasets [208]GSE63459 and [209]GSE36681, which
identified some novel miRs as potential biomarkers [[210]37].
Furthermore, some studies have shown the analysis on NSCLC by
constructing the circRNA–miRNA–mRNA network (circRNA: circular RNA)
[[211]38,[212]39] or lncRNA–miRNA–mRNA network (lncRNA: long non-coding
RNA) [[213]40], LINC00973-miRNA-mRNA ceRNA (ceRNA: competing endogenous
RNA) [[214]36] but included different datasets altogether
([215]GSE101684 andGSE112214 [[216]38]; [217]GSE102286, [218]GSE112214
and [219]GSE101929 [[220]39]; [221]GSE193628 [[222]40]; [223]GSE27262,
[224]GSE89039, [225]GSE101929, [226]GSE40791 and [227]GSE33532
[[228]41]), besides the given datasets used in our present
investigation. Some more published literatures on NSCLC included data
patient samples like [[229]42,[230]43]. Taking together these findings,
it is noteworthy to mention that the present study performed an
integrated analysis on some of the other unexplored miRNA expression
profiles of NSCLC. In this context, our identified hub miRNAs and their
related genes could be implicated in the development and progression of
NSCLC. Moreover, since our study performed the bioinformatics analysis
of the unexplored miRNA expression profiles, thus, from the initial
long lists of miRNAs, we have provided only a few important key miRNAs
that can be targeted as therapeutic targets as all miRNAs and their
associated target genes cannot form therapeutic targets for the cure of
NSCLC.
The study involved three distinguishing microarray expression profiles
([231]GSE25508, [232]GSE19945, and [233]GSE53882). These expression
series consisting of tissue samples (NSCLC and normal) were analyzed
for the selection of DEMs. Our analysis revealed a total of 172
identified DEMs in NSCLC, which included 82 upregulated and 90
downregulated miRNAs. As a result, 12 overlapping differentially
expressed miRNAs were obtained which included 5 upregulated (miR-130b,
miR-96, miR-210, miR-200b, and miR-205) and 7 downregulated (miR-30a,
miR-145, miR-140-3p, miR-572, miR-144, miR-126, and miR-486-5p) miRNAs.
After a literature search, it was found that these obtained DEMs have
been involved in lung cancer expression profiling studies [[234]11].
The upregulated miRNAs revealed in our study (miR-130b, miR-96,
miR-210, miR-200b and miR-205) show consistency with the previous
profiling studies (between normal tissue samples and lung cancer
samples), that has reported the 26 consistently up-regulated miRNAs
(miR-210, miR-21, miR-182, miR-31, miR-205, miR-200b, miR-183, miR-203,
miR-196a, miR-708, miR-92b, miR-193b, miR-106a, miR-135b, miR-96,
miR-17-5p, miR-20b, miR-18a, miR-200a, miR-93, miR-130b, miR-200c,
miR-375, miR-20a, miR-18b) [[235]11]. MiR-130b, was found to be the
miRNA that showed the largest (most significant) deviation in lung
cancer patients in those that developed metastases from controls (p =
0.0004) as compared to those patients that did not develop metastases
[[236]44]. MiR-130b (onco-miRNA) is straightaway regulated by NF-κB and
assists in NF-κB activation by lessening cylindromatosis expression.
MiR-130b-3p also downregulates PTEN expression and promotes the
proliferation, invasion, migration, and cytoskeletal rearrangement by
activating PI3K and integrin β1signaling pathways. Moreover, the
inhibitors of miR-130b-3p have been shown to induce apoptosis
[[237]45,[238]46,[239]47]. Mir-96 (member of family miR-183)
up-regulation has been reported in breast cancer [[240]48]. Reports
have suggested that miR-96 performs both oncosuppressor and oncogene
functions by lessening or promoting cell growth in different cancer
types [[241]49,[242]50,[243]51,[244]52]. The significant increase of
miR-96 expression in our NSCLC miRNA profiling, as compared with normal
tissue, is in accordance with published reports
[[245]53,[246]54,[247]55]. A study has reported the up-regulation of
miR-96 by targeting FOXO3 as its major gene [[248]56]. The expression
of RAD51 and REV1 (related to DNA repair and homologous recombination)
is downregulated by miR-96 that perhaps has a vital role in DNA repair
inhibition and chemosensitivity [[249]57]. MiR-210 has been previously
linked to lung cancer through the modulation of the JAK2/STAT3 pathway
[[250]58]. A recent study (on lung cancer) has demonstrated the role of
miR200b as a possible biomarker for PD-L1 expression [[251]59]. The
expression of miR200b is inversely proportional to PD-L1 expression,
i.e., low miR200b expression was related to High PD-L1 expression,
whereas high miR200b expression was linked to low PD-L1 expression in
human lung cancer patients [[252]59]. MiR-205 has been recognized as an
extremely accurate biomarker for lung cancer (squamous) [[253]60].
MiR-205 has been revealed as a biomarker in lung squamous cell
carcinoma specifically [[254]61].
Addtionally, our identified six downregulated miRNAs (miR 30a, miR 145,
miR 140 3p, miR 572, miR 144, miR 126 and miR 486 5p) are also in
agreement with the previous investigation, that has reported
consistently (a total of 28) down-regulated miRNAs in profiling studies
(miR-126, miR-30a, miR-451, miR-486-5p, miR-30d, miR-145, miR-143,
miR-139-5p, miR-126, miR-140-3p, miR-138, miR-30b, miR-486, miR-101,
miR-125a, miR-198, miR-144, miR-140, miR-218, miR-32, miR-338-3p,
miR-99a, miR-195, miR-497, miR-30c, miR-130a, miR-16, miR-139)
[[255]11]. The downregulation of miR-30a has been recently reported in
a recent investigation carried out in NSCLC using a computational
approach [[256]51]. It has been suggested that mir-30a expression could
have an effect on the NSCLC patient’s survival rates [[257]62]. MiR-126
and miR-145 have been documented to perform roles in NSCLC by
inhibiting the growth of tumor growth cells [[258]63]. MiR-126 has been
shown to enhance the sensitivity of NSCLC cells to an anticancer agent
by targeting VEGFA (vascular endothelial growth factor A) [[259]64].
MiR-140-3p has been suggested as a biomarker in squamous cell carcinoma
[[260]65]. Lung cancer-related microarray analysis has shown miR-144 as
one of the most significantly down-regulated miRNAs [[261]66]. The
downregulated expression of miR-486-5p is in accordance with a recently
published study, which has shown the inhibition of NSCLC through mTOR
signaling pathway repression via targeting ribosomal proteins
[[262]67]. Moreover, the study demonstrated miR-486-5p as a promising
biomarker during the early stages of NSCLC [[263]67]. The consistently
reported upregulated miRNA miR-210 has been reported in nine studies
(average FC: 2.65) while miR-21 has been reported in seven studies
(average FC: 4.39). Also, consistently reported downregulated miRNA
miR-126 has been reported in ten studies (average FC: 0.33) while
miR-30a has been reported in eight studies (average FC: 0.36). Also,
studies have shown miR-210 as the most frequently reported upregulated
miRNA in both squamous carcinoma-based analysis
[[264]65,[265]68,[266]69,[267]70,[268]71,[269]72] and adenocarcinoma
[[270]73,[271]74,[272]75]. These investigations are in line with our
present hypothesis.
By incorporating the centrality concept [[273]76] and its associated
algorithms we constructed the miRNA–mRNA interaction network through
analyzing the topological properties (degree, betweenness, closeness,
and stress) to expose the key miRNAs regulating the network.
MiR-30a-3p, miR-130b-3p, miR-200b-3p and miR-205-3pwere identified as 4
key miRNAs [[274]76]. Among these aforementioned key miRNAs
miR-130b-3p, miR-200b-four and miR-205-3p were found to be upregulated
while miR-30a-3p showed downregulated expression in NSCLC. As already
mentioned, these miRNAs have been previously reported, thus showing
consistency with earlier published literatures. This suggested a
significant role of these miRNAs played in the regulation of NSCLC.
However, it is interesting to mention that the reported downregulated
expression of miR-572 is exclusive to our present study.
Further, key genes regulating the network were identified from the
constructed miRNA–mRNA network that included CPEB3, SAMD8, FOXP1,
TRPS1, TCF4, TBL1XR1, SPRED1, CELF2, and CDK19. CPEB (cytoplasmic
polyadenylation element-binding protein) is an RNA-binding protein
which interacts with CPE (cytoplasmic polyadenylation element) in the
3′UTRs of specific mRNAs to repress or activate translation [[275]77].
The role of CPEB3 (as a tumor suppressor) has recently been
demonstrated in colorectal cancer through regulation
(post-transcriptional) of the JAK/STAT signaling pathway [[276]78]. Its
downregulated expression has also been reported in cervical cancer
[[277]79] and human HCC [[278]80]. A study has examined the role of
FOXP1 (Forkhead box protein P1) in lung cancer which suggested its
essentiality in preventing the development of lung adenocarcinoma via
suppression of chemokine signaling pathways [[279]81]. It is also
considered a potential therapeutic target in cancer [[280]82,[281]83].
The report has shown the association of TRPS1 (Tricho-rhino-phalangeal
syndrome 1) with MDR (multidrug resistance) in lung cancer
[[282]84,[283]85,[284]86]. Trps1 (GATA protein) has been shown as a
potential tumor marker (cytologic) in a variety of cancers
(osteosarcoma, malignant tumor, prostatic carcinoma, and breast cancer)
[[285]87,[286]88,[287]89] and plays an imperative role in the
differentiation and enlargement of mammals [[288]90,[289]91]. TCF-4 (T
cell factor-4) carries out important roles in the development and
carcinogenesis. High expression of TCF-4 was revealed in NSCLC samples
as compared to normal tissues [[290]92]. However, later a report showed
an association between NLK (Nemo-like kinase), a member of the protein
kinase (serine/threonine) superfamily, and TCF4 (T-cell factor 4), a
transcription factor substrate for NLK in case of lung cancer that
revealed that NLK expression was found to be negatively correlated with
the expression of TCF4 in lung cancer advancement [[291]93]. TCF-4, as
a component of the Wnt pathway, also works as a tumor suppressor in
colon cancer [[292]94] and is involved in papillary thyroid carcinoma
via regulation of HCP5 [[293]95]. TBL1XR1 (Transducin (β)-like 1X
related protein 1) is a subunit of SMRT/NCoR repressor complexes and is
essentially required for activating signaling pathways [[294]96].
TBL1XR1 is recognized as the prognostic marker of NSCLC and is found to
be related to gastric, breast, and stomach cancers [[295]97]. SPRED1
(Sprouty-related, EVH1 domain-containing protein 1) has been reported
as a tumor repressor in paediatric acute myeloblastic leukaemia
[[296]98]. CELF2 (CUGBP Elav-like family member 2), an RNA binding
protein isoform of CELF, performs important functions in the
development and activation of T cells [[297]99] CELF2 acts as a tumor
suppressor for a variety of cancers, (ovarian cancer, breast cancer,
gastric cancer, glioma, hepatocellular carcinoma, including lung cancer
and thus is considered as a biomarker in lung squamous cell carcinoma
and breast cancer [[298]100,[299]101]. It has been documented that the
growth of NSCLC cells could be suppressed by CELF2 via inhibition of
the PREX2-PTEN interaction [[300]102].) CDKs (Cyclin-dependent kinases)
play role in many critical processes, such as cell cycle,
communication, transcription, metabolism, and apoptosis [[301]103]. Not
much is known about the CDK19 mechanism regarding their mediator kinase
functions [[302]104]. These key genes were proceeded for enrichment
analysis. Furthermore, the GO analysis was performed to explore the
biological function of genes regulated by key miRNAs. The GO analysis
revealed that hub genes mainly participated in biosynthetic process
(BP), anatomical structure development (BP) lipid metabolic process
(BP), cell differentiation (BP), nucleoplasm (CC), plasma membrane
(CC), nucleus (CC), cytoplasm (CC), DNA binding, ion binding,
DNA-binding transcription factor activity (MF), transcription factor
binding (MF), RNA binding (MF) and mRNA binding (MF). Thus, these
predicted functions further substantiate our findings.
Moreover, the expression plots of the identified key genes showed a
significant correlation with NSCLC prognosis. On combining these
results, it could be interpreted that the key genes played a
significant role in the regulation of NSCLC. For further elucidation,
the key genes were predicted for their TFs. TP63, a member of tumor
suppressor protein p53 [[303]105], is known to associate with the
development and tumorigenesis of cancers, in particular with cancer
metastasis [[304]106]. VHL is a product of the tumor suppressor gene
VHL. Recently, a study on human kidney cells has shown its function in
cell growth regulation and differentiation [[305]107]. LEF1, a protein
encoded by the LEF1 gene in humans, has shown its expression in several
cancers [[306]108]. This protein belongs to TCF (T-cell Factor) family,
thus is involved in the Wnt signaling pathway and is vital for stem
cell maintenance and organ development [[307]109]. RUNX3, a protein
encoded by the RUNX3 gene in humans and a component of TGF-β
(transforming growth factor-β), has shown tumor-suppressive effects in
several cancers [[308]110,[309]111]. ESR1, a protein encoded by the
ESRI gene, has shown its association with many kinds of cancers
(endometrial, breast, and prostate) [[310]112]. EGR1 is chiefly
involved in tissue injury, fibrosis, and immune response processes.
Recent reports have shown the involvement of EGR1 in the initiation and
succession of cancer. Nevertheless, the precise mechanism of EGR1
modulation remains unexplored [[311]113]. AR (Androgen receptor), a
ligand-dependent transcription factor, has been shown to involve in
prostate cancer [[312]114]. Thus, it could be interpreted that these
identified TFs may play a significant role in the pathogenesis of
NSCLC.
Interestingly, our observations have shown that besides NSCLC other
literature have also documented the significance of these
aforementioned key genes/miRNAs in other types of cancers. Therefore,
these signature genes/miRNAs can largely offer benefits as biomarkers
to other cancers as well, besides NSCLC, in diagnosis. Similar reports
have also demonstrated the significance of key miRNAs and associated
target genes in various syndromes [[313]115,[314]116,[315]117]. It is
noteworthy to mention that SAMD8 is exclusive to our study like
miR-572. The utilization of the aforementioned NSCLC biomarkers
(identified in our study) could lead to earlier diagnosis resulting in
efficient treatment of lung cancer and reduction in disease occurrence,
and overall better chances of survival and ultimately a better life for
patients with lung cancer. The revealed TFs have also been shown in
other cancers. The study presented here also comprises some limitations
such as, the inclusion of a comparatively smaller sample size and lack
of experimental validations. To validate our findings the identified
key miRNAs/gene should be further investigated in a larger number of
patients through experiments.
5. Conclusions
This study used a bioinformatics approach to analyze the miRNA
expression profiles consisting of NSCLC tissue samples and adjacent
normal tissue samples. Though previous reports have shown the
correlation of signature miRNAs with NSCLC
[[316]12,[317]13,[318]14,[319]15], however, a comparative meta-analysis
on the retrieved miRNA profiles has not been conducted on NSCLC
specifically. Our results identified 12 overlapping miRNAs that were
differentially expressed among three expression series, out of which 5
were upregulated and 7 were downregulated. The centrality-based method
was employed which revealed four key miRNAs and nine genes. We
performed the GO analysis of the key genes/miRNAs which were shown to
participate in the biosynthetic process, nucleoplasm, DNA binding, ion
binding, RNA binding, and signaling pathways. Further, we carried out
the survival, expression, and pathological analyses of the identified
hub genes using GEPIA. Subsequently, the TFs were predicted for the key
genes in humans. These identified genes/miRNAs can serve as a potential
prognostic predictor of patients with NSCLC. However, these signature
genes/miRNAs warrant further investigations due to lack of experimental
evidence. Therefore, these obtained results from this bioinformatics
analysis require validation through experimental research, such as
qRT-PCR and Western Blot, to understand the molecular mechanisms of
NSCLC.
Acknowledgments