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Clinics of Oncology
Research Article ISSN: 2640-1037 Volume 6
CombinedAnalysis of Micro RNAand Proteomic Profiles and Interactions in Patients
with Primary Lung Adenocarcinoma and Lung Adenocarcinoma Brain Metastases
Zhang L1#
, Liang J2#
, Han Z3#
, Wang L4
, Tang C1*
, Liang J1*
and Zhang S5*
1
Department of Oncology, Peking University International Hospital, Beijing102206, People’s Republic of China
2
Department of Neurosurgery, Peking University International Hospital, Beijing 1 02206, People’s Republic of China
3
Department of Thoracic Surgery, Peking University International Hospital, Beijing 102206, People’s Republic of China
4
Department of Pathology, Peking University International Hospital, Beijing 102206, People’s Republic of China
5
Department of Oncology, Beijing Chest Hospital, Capital Medical University; Beijing Tuberculosis and Thoracic Tumor Research
Institute, Beijing 101149, People’s Republic of China
*
Corresponding author:
Shucai Zhang,
Department of Oncology, Beijing Chest Hospital,
Capital Medical University, Beijing Tuberculosis and
Thoracic Tumor Research Institute, Beijing 101149,
People’s Republic of China, Tel: +8613901297065,
E-mail: sczhang6304@163.com
Jun Liang,
Department of Oncology, Peking University Interna-
tional Hospital, Beijing 102206, People’s Republic of
China, Tel:+8615801353728,
E-mail: junliang@pkuih.edu.cn
Chuanhao Tang,
Department of Oncology, Peking University Interna-
tional Hospital, Beijing 102206, People’s Republic
of China, Tel: +8613910107010,
E-mail: tangchuanhao@pkuih.edu.cn
Received: 02 Mar 2022
Accepted: 15 Mar 2022
Published: 21 Mar 2022
J Short Name: COO
Copyright:
©2022 Tang C, Liang J, Zhang S. This is an open access
article distributed under the terms of the Creative Com-
mons Attribution License, which permits unrestricted use,
distribution, and build upon your work non-commercially.
Citation:
Tang C, Liang J, Zhang S, Combined Analysis of Micro
RNA and Proteomic Profiles and Interactions in Patients
with Primary Lung Adenocarcinoma and Lung Adenocar-
cinoma Brain Metastases. Clin Onco. 2022; 6(3): 1-10
Keywords:
Brain metastases; Lung adenocarcinoma; microRNAs,
Proteomics
clinicsofoncology.com 1
#
Author Contribution:
Zhang L, Liang J, Han Z. These authors have contributed
equally to this article.
1. Abstract
1.1. Objective: Brain metastasis of lung adenocarcinoma is the
main cause of death in such patients. There is an urgent need
to explore highly sensitive and specific biomarkers for the
early detection and treatment of lung adenocarcinoma brain
metastases.
1.2. Materials and Methods: We carried out an analysis of miR-
NAs expression profiles and protein spectrums of non-meta-
static primary Lung Adenocarcinoma (LP) and patients with
Brain Metastases (BM) to better explore the molecular basis
of BM. The potential roles and interactions of differential-
ly expressed miRNAs and proteins were classified by gene
ontology enrichment, Kyoto Encyclopedia of Genes and Ge-
nomes pathway analyses. The interaction between the differ-
entially expressed miRNAs and proteins was analyzed by R
soft and shown by Cytoscape.
1.3. Results: Compared with LP patients, 16 miRNAs were up-
regulated and 4 miRNAs were downregulated in the tissue
of BM patients, 53 proteins were upregulated and 35 pro-
teins were downregulated meanwhile. Enrichment pathway
analysis revealed that NF-κB signaling pathway and primary
immunodeficiency pathway played critical roles in the occur-
rence and development of brain metastasis in lung adenocar-
cinoma.
1.4. Conclusion: This study broadened the understanding of the
regulatory network of microRNA and proteomic expressions
and provided clues for the pathogenitic molecular mecha-
nism of brain metastases in lung adenocarcinoma at the miR-
NA and protein levels.
2. Introduction
Lung cancer is characterized by a high incidence of brain metas-
tases, with about 40% of patients developing brain metastases
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Volume 6 Issue 3 -2022 Research Article
during their disease course [1]. The prognosis of Non-Small Cell
Lung Cancer (NSCLC) patients with brain metastasis is very poor,
which is the main cause of death. The post-brain metastasis sur-
vival time of these patients is only 1-2 months if untreated [2].
The efficacy remains unsatisfactory even though the treatments
for brain matastasis include surgery, radiotherapy, chemotherapy
and immunotherapy nowadays. Therefore, it is an urgent task that
further study of biological and molecular mechanism underlying
BM in NSCLC needs to be done to identify new treatment targets.
MicroRNAs have been revealed as essential part of the un coding
genome, playing crucial roles through a complicated regulation in
all the most important processes and in different species [3]. Ear-
ly studies have shown that microRNA expression profiling was
associated with tumor development, progression and response to
therapy, which suggests that their possible use as diagnostic, prog-
nostic and predictive biomarkers [4]. Several miRNAs are known
to play key roles in the development of NSCLC [5]. NSCLC me-
tastasis could be inhibited through manipulation of some miRNAs
in preclinical studies [6, 7]. Increasingly evidence indicate that mi-
croRNAs are target keys involved in metastasis, which could act as
biomarkers for combating brain metastasis [8, 9].
Proteomic analysis is currently considered to be a powerful tool
for global evaluation of protein expression which has been widely
applied in fields of cancer research. Quantitative protein expres-
sion profiling is a crucial part of proteomics, which requires meth-
ods that are able to provide accurate and reproducible differential
expression values for proteins in two or more biological samples
efficiently [10].
Such profilings have been used for screening metastasis-associ-
ated proteins in recent studies [11, 12]. In this study, we analyzed
the pathogenesis of Lung Adenocarcinoma(LAC) brain metastasis
by detecting the differences in miRNA expression and proteome
expression in primary lesion of LAC and brain metastasis tissue
samples, with a view to finding biomarkers of LAC brain metasta-
sis and researching for potential therapeutic targets.
3. Materials and Methods
3.1. Samples
This study was reviewed and approved by the Medical Ethics Com-
mittee of Peking University International Hospital and informed
consent forms were signed by all patients. All experiments were
carried out in accordance with relevant guidelines and regulations
or Declaration of helsinki. From September 2018 to October 2019,
Participants with histologically confirmed LAC treated in our hos-
pital were recruited. The inclusion criteria were as follows:
1) patients diagnosed as pulmonary adenocarcinoma or
LAC brain metastasis by surgery;
2) Eastern Cooperative Oncology Group performance status
(ECOG) was 0–2;
3) function of the bone marrow, heart, liver, kidney and oth-
er organs was not abnormal;
4) survival period longer than 3 months;
5) patients agreed with complete ctDNA tests for tissue
samples;
6) informed consent form was signed voluntarily by partic-
ipants.
The exclusion criteria included patients with multiple primary car-
cinomas, severe systemic diseases, active infections, and immune
diseases.
All participants were divided into the Lung Primary (LP) group
(n=5, named as L1, L2, L3, L4, and L5) and the Brain Metastasis
(BM) group (n=5, named as B1, B2, B3, B4, and B5).Tissues from
the primary tumor sites in LP group, and from the brain metastasis
tumor sites in BM group were sampled respectively, which were
placed into 10% neutral buffered formalin solution within 5 min-
utes after excision, fixed at room temperature (8–72h) and then
processed routinely into a paraffin embedded tissue block (FFPE).
All samples were stored at 4℃, until we had compiled the cohort
and were ready to begin the study. Then, the FFPE blocks were
sectioned to prepare an H&E stained slide and unstained sections
(5μm thick) on glass slides for RNA extraction. The location of
tumor tissue was determined under microscope on H&E stained
slide and sections of adjacent layers were taken.
3.2. RNA Extraction and Sequencing
Total RNA was isolated from the Formalin-Fixed Paraffin-Embed-
ded (FFPE) tumors using the Trizol reagent (Invitrogen, USA).
The purity and integrity of RNA were detected by using an Agi-
lent 2100 Bioanalyzer system (Agilent, USA). RNA samples with
RIN ≥8 and 28S/18S ≥1 were considered. The cDNA library of all
samples was sequenced using the Illumina HiSeq 2000 sequencing
platform (Illumina, USA).
3.3. Liquid Chromatography Tandem Mass Spectrometry
The Liquid Mass System(LMS) includes an Easy nLC1000 (Ther-
mo Fisher) coupled ultra-high resolution mass spectrometer Or-
bitrap Fusion Lumos (Thermo Fisher) with a Thermo Fisher elec-
trospray source. Each injection is sent to a preset column (Acclaim
PepMap C18, 100 μm x 2 cm, Thermo Scientific) for adsorption
at a flow rate of 3 L/min. The sample is then sent to the analyzer
column (Acclaim PepMap C18, 75 μm x 15 cm, Thermo Scientif-
ic) for separation.
Orbitrap Fusion Lumos was set to OT-OT mode. For a full first-lev-
el scan, the AGC Target was set to 5E5, with a scanning range of
350-1550 m/z, a resolution of 120,000 and a maximum injection
time of 50 ms. For the second scan, the cycle time was set to 3.5s,
the peptides with a charge of 2-7 were selected for fragmentation,
and the collision energy was 32%. Maxquant (1.6.2.10) was used
for data analysis. Mass tolerance was set at 20ppm and FDR at 1%.
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3.4. Data Quality Control
The raw reads obtained included low quality and other problem-
atic reads, such as those with missing insert tags, oversized in-
serts, poly(A) tags, and small tags. Data cleaning was therefore
performed on the FASTQ file to obtain the final clean reads. Data
cleaning was performed as follows: 1. Reads in which more than
50% of bases had a Qphred less than or equal to 5 were discarded;
Reads in which N accounted for more than 10% were discarded.
(N indicates that base information was indeterminable.); Reads
with 5' primer contamination were discarded; Reads lacking a 3'
primer or insert tag were discarded; The 3' primer sequence was
trimmed; Reads with poly(A), poly(T), poly(G), or poly(C) tails
were discarded.
3.5. Prediction of Novel miRNA
The small RNA reads were mapped to the genome using Bowtie
so as to analyze their expression and distribution on the genome.
The above reads on the reference sequence were compared with
the specified sequence in the miRBase to obtain details of the sR-
NAs on each sample match, including the secondary structure of
the matched miRNAs, the sequence of miRNAs in each sample,
length, the number of occurrences and other information. Then we
used mirdeep2 to predict novel miRNA [13].
3.6. GO and KEGG Enrichment Analysis of Target Gene of
Differentially Expressed Mirnas and Proteins
Gene Ontology (GO) enrichment analysis of target gene of dif-
ferentially expressed miRNAs was implemented by the topGO R
package, in which gene length bias was corrected. GO terms with
corrected P value less than 0.05 were considered significantly en-
riched by differential expressed genes.
KEGG is a database resource for understanding high-level func-
tions and utilities of the biological system, such as the cell, the
organism and the ecosystem, from molecular-level information,
especially large-scale molecular datasets generated by genome
sequencing and other high-through put experimental technologies
(http://www.genome.jp/kegg/). We used cluster Profiler R package
to test the statistical enrichment of differential expression genes in
KEGG pathways.
3.7. Statistical Analysis
Statistical differences were analysed using SPSS (version 20.0,
IBM SPSS Statistics) by independent-samples t test. Multiple
comparisons were carried out using one-way analysis of variance.
P values below .05 was defined as the significance. To control for
multiple testing the FDR was estimated [14]. To assess the rele-
vance, a linear model was applied for continuous covariates and
the ANOVA test for categorical covariates. All statistical analysis
was performed using R software v4.0.3.
4. Results
4.1. Data Quality Control
Age and gender of the patients are described in (Table 1), and all
patients had no smoking history. RNAs were extracted from FFPE
tumors of each sample, and were carried out quality detection ac-
cording to the previous experimental design. The results are shown
in (Table 2). The proportion of quality score ≥20 (Q20) was higher
than 98%, and the proportion of quality score ≥30 (Q30) was high-
er than 94%. The single base error rate was 0.001.
Table 1: Characteristics of the patients
Sample Gender Age Smoking history
BM1 Male 63 No
BM2 Male 75 No
BM3 Female 82 No
BM4 Female 52 No
BM5 Female 48 No
LP1 Female 70 No
LP2 Male 66 No
LP3 Female 60 No
LP4 Male 62 No
LP5 Female 57 No
Table 2: Results of data quality control
Sample
Raw
Reads
Bases
GC
(%)
Q20 Q30
Avg.
Quality
BM1 3839266 0.192(GB) 53 98.63% 95.68% 35.83
BM2 3735819 0.187(GB) 53 98.60% 95.61% 35.82
BM3 4123090 0.206(GB) 53 98.58% 95.50% 35.8
BM4 3340001 0.167(GB) 53 98.48% 95.38% 35.77
BM5 4210563 0.211(GB) 54 98.16% 94.84% 35.66
LP1 3679273 0.184(GB) 52 98.54% 95.32% 35.77
LP2 3834373 0.192(GB) 52 98.64% 95.60% 35.82
LP3 5482979 0.274(GB) 53 98.74% 95.82% 35.86
LP4 3604748 0.180(GB) 52 98.73% 95.88% 35.87
LP5 7561029 0.378(GB) 52 98.37% 95.49% 35.77
4.2. Identification of Differentially Expressed Mirnas and Pro-
teins
Bioinformatics analysis was performed to screen out differentially
expressed miRNAs and proteins between the LP and BM groups.
Volcano plots provided an overview of the differential expression
of miRNAs and proteins, as shown in (Figure 1). Each point in the
graph represents a miRNA or protein. The abscissa represents the
logarithmic fold change value of the multiple of difference of miR-
NA or protein expression between the two groups. The ordinate
represents the negative logarithmic P value of miRNA or protein
expression change.
An absolute Fold Change cut-off value of 2 and P < .05 were uti-
lized as the criteria to confirm significantly downregulated and
upregulated miRNAs and proteins (FDR <0.05). Red points rep-
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Volume 6 Issue 3 -2022 Research Article
resent upregulated miRNAs or proteins in BM vs LP, while green
points represent downregulated ones.
Heatmaps (Figures 2,3) show the significant changes of miRNA
and protein expression between LP and BM(FDR <0.05). The fig-
ure shows the bidirectional clustering of differentially expressed
miRNAs or proteins and groups. The depth of colour represents
the level of expression; orange represents high and purple rep-
resents low levels of miRNA and protein expression (Figure 2,3).
The above results suggested that 16 miRNAs, such as miR-31-3p,
miR-654-5p, miR-1185-1-3p, miR-487a-5p, as well as 53 pro-
teins, including VIM, RAB2A, RPLs, KRTs proteins were highly
expressed in BM patients, while miR-219a-5p, miR-1269a, miR-
615-3p, miR-196a-5p, 4 miRNAs and AHSG, HBD, SERPINC1,
COL6As, totally 35 proteins were low expressed significantly,
which may be related to the pathogenesis of brain metastasis.
The interaction network of differentially expressed miRNAs and
proteins was mapped using Cytoscape (Figure 4). The network
graph is composed of node and edge. Nodes represented genes and
proteins, and edges represented interaction relationships between
genes. The black nodes represented miRNAs, proteins with high
expression were represented by green nodes, while those with low
expression are represented by blue nodes. The lines represented
the correlation and interaction between genes and proteins.
Figure 1: Volcano plots of differentially expressed microRNAs (A) and proteins (B)
Figure 2: Heatmap of differentially expressed miRNAs
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Figure 3: Heatmap of differentially expressed proteins
Figure 4: The interaction network of differentially expressed miRNAs and proteins
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4.3. Enrichment Analysis
According to the relative level of expression between the two
groups, the differentially expressed miRNAs and proteins can be
classified as upregulated group and downregulated group. The
results of GO enrichment analysis with differentially expressed
genes and proteins are shown in (Figure 5 and Figure 6) respec-
tively. The main information of enrichment results areis shown in
the figures, and other information can be found in tables in the
original document of analysis results.
The ordinate is the classification of GO for miRNAs, while the
abscissa represents the number of enriched target genes. Differ-
entially expressed miRNAs were enriched in signalling pathways
such as binding, cell part, cell, organelle, intracellular organelle
and protein binding. For proteins, the abscissa is the classification
of GO; on the right is the number of target proteins, and on the left
is the percentage of the number of target proteins. Differentially
expressed proteins were enriched in signalling pathways such as
cellular process, cell, cell part, organelle and binding. Both en-
richment pathway analysis with differentially expressed miRNAs
and proteins revealed that cell, cell part, organelle and binding sig-
naling pathways play important roles in the development of LAC
BM.
KOBAS3.0 was used to analyze the enrichment functional areas
of the top 10 significantly differentially expressed miRNAs target
genes, shown in (Figure7), which was demonstrated that the differ-
entially expressed miRNAs were mainly enriched in extracellular
vesicles, extracellular space, extracellular organelles functional
areas.
Bubble map is a graphical representation of KEGG. KEGG Path-
way analysis was performed on the differentially expressed miR-
NAs and proteins, the results of KEGG enrichment analysis were
shown in (Figure8 and Figure9). KEGG enrichment was measured
by Gene Ratio, p value and number of genes enriched in this path-
way, respectively. Gene Ratio refers to the Ratio between the num-
ber of differentially enriched genes and the total number of differ-
entially enriched genes in this pathway. The higher Gene Ratio,
the greater the degree of enrichment; The value range of p value is
between [0,1], and the closer it is to zero, the more significant the
enrichment. The top 30 pathways with the most significant enrich-
ment of miRNAs and top 20 pathways of proteins were showed
in the item diagram. The ordinate is the description of the corre-
sponding pathways, while the abscissa is the enrichment factor, the
bubble size is the number of different genes or proteins, and the
bubble color is the P value of enrichment significance. Both
enrichment pathway analysis with differentially expressed miR-
NAs and proteins revealed that NF-κB signaling pathway and
primary immunodeficiency pathway play important roles in the
development of lung adenocarcinoma BM.
Figure 5: Gene ontology enrichment analysis of differentialy expressed miRNAs
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Volume 6 Issue 3 -2022 Research Article
Figure 6: Gene ontology enrichment analysis of differentially expressed proteins
Figure 7: Gene ontology enrichment analysis of top 10 significantly differentially expressed miRNAs target genes
Figure 8: The bubble map of Kyoto Encyclopedia of Genes and Genomes enrichment analysis of differentialy expressed miRNAs
clinicsofoncology.com 8
Volume 6 Issue 3 -2022 Research Article
Figure 9: The bubble map of Kyoto Encyclopedia of Genes and Genomes enrichment analysis of differentialy expressed proteins
5. Discussion
In this study, it was found that compared with LP group, 16 miR-
NAs and 53 proteins were highly expressed while 4 miRNAs and
35 proteins were significantly low expressed in BM group, which
may be related to the pathogenesis of LAC brain metastasis.
We found that Tissue Factor (TF) was low expressed in BM group,
which could mean that TF can inhibit brain metastasis of lung ade-
nocarcinoma patients. Previous research has reported that metasta-
sis had a predilection to organs with low levels of TF in the micro-
circulation, which enables tumor cell retention [15]. SERPINA1
(Serpin Family A Member 1) was significantly different expressed
in BM group and LP group in current study and other study [16].
Some studies have reported that Epithelial-Mesenchymal Transi-
tion (EMT) proteins and matrix metalloproteinases (MMPs) play
important roles in brain metastasis by regulating the Blood-Brain
Barrier (BBB) [17], high expression of Inner Membrane Mitochon-
drial Protein (IMMT) was significantly correlated with advanced
disease stage and poor prognosis of LAC patients [18], the NARS
levels were positively associated with LAC lymph node metastasis
[19], which were consistent with our study. We also found that
proteins like KRTs, COL6As, RAB2A, AHSG were associated to
pathogenesis of LAC brain metastasis. However, the sample size
of this study is limited, and more samples is needed in the future
for further confirmation.
MiR-31-3p are related to the occurrence and development of var-
ious malignant tumors such as colorectal cancer, as well as lymph
node metastasis and poor prognosis of lung adenocarcinoma have
reported in previous studies [20]. In current study we also demon-
strated that miR-31-3p significantly increased in BM group, sug-
gesting that miR-31-3p may promote the occurrence and develop-
ment of brain metastasis in lung adenocarcinoma patients. MiR-
493-5p is related to the pathogenesis of various malignant tumors
such as liver cancer, colorectal cancer and osteosarcoma, which
is also related to the prognosis of patients with NSCLC. It can af-
fect the malignant behavior of NSCLC cells by regulating the ex-
pression of integrin β1 (ITGB1) [21]. We found that miR-493-5p
can promote lung adenocarcinoma brain metastasis as well. These
findings indicate the possibility of miRNAs as biomarkers for the
occurrence, progression, prognosis, as well as potential therapeu-
tic targets of lung cancer BMs. Our study also found the differ-
ential expression of these miRNAs in the two groups, which is in
line with previous studies, suggesting these miRNAs are correlat-
ed with the pathogenesis of brain metastasis in lung adenocarcino-
ma. In addition, this study also found the differential expression
of miR-487a-5p, miR-654 −5p, miR-1185-1-3p, miR-376c-3p and
miR-196a-5p between BM and LP groups for the first time, which
may be associated with the brain metastasis of lung adenocarcino-
ma. Previous studies have reported that miR-654-5p can promote
the progression of colon cancer, breast cancer, and oral squamous
cell carcinoma, which is also associated with paclitaxel resistance
of ovarian cancer. We found that miR-654-3p may promote the
progression of lung adenicarcinoma brain metastasis MiR-1185-
1-3p can be used for the early diagnosis of bladder cancer. We
reported the association of miR-1185-1-3p with brain metastasis
in lung adenocarcinoma patients for the first time, no studies have
focused on the relationship between these miRNAs and lung can-
cer or brain metastasis before.
We found that the pathogenesis of brain metastasis in LAC was
related to NF-κB signaling pathway, which is in line with previ-
ous studies [22]. It was shown that primary immunodeficiency
signaling pathway also plays important role in the occurrence and
development of brain metastasis of LAC, which is a newly discov-
ered mechanism in this study. NF-κB is a key signaling pathway
in the development and metastasis of various inflammatory and
malignant tumors, and has also been found to be associated with
brain metastasis of breast cancer and lung cancer [22]. In addition,
previous studies have found that there are multiple mechanisms
involved in the occurrence and progression of lung cancer brain
clinicsofoncology.com 9
Volume 6 Issue 3 -2022 Research Article
metastases: The highly open calcium-activated potassium (KCA)
channels in lung cancer brain metastases and their microvascu-
lar endothelial cells can be used as targets for Blood-Brain Tumor
Barrier (BTB) and permeability regulation [23]. Vascular endo-
thelial growth factor C plays a role in the development and pro-
gression of lung cancer brain metastasis by enhancing the affinity
of tumor cells to specific organs and promoting the movement of
tumor cells to lymphatic vessels [24, 25]. The interaction between
neurotransmitter receptors and neurons can promote the metastasis
of lung cancer cells to the brain [25]. The abnormal expression
of tumor suppressor genes, matrix metalloproteinases, carcinoem-
bryonic antigen and other related proteins are also associated with
LAC brain metastasis.
The samples of primary and metastatic lesions from the same pa-
tient which can exclude the interference of expression differences
caused by histological differences in brain tissues and lung tissues
were not obtained for the present study. Relevant samples will be
collected in the future for further studies. In addition, a number of
studies have explored the expression of cerebrospinal fluid miR-
NAs and proteins in central nervous system malignant tumors, as
well as their diagnostic and application value. It has been found
that miRNAs differentially expressed in cerebrospinal fluid can
distinguish glioblastoma from metastatic brain malignancies and
can be used to monitor changes in disease condition and response
to treatment [26]. Due to the difficulty in obtaining BMs tissue
specimens, subsequent attempts can be made to explore the feasi-
bility of using CSF specimens of BMs and non-BMs to detect the
differentially expressed non-coding RNA and proteins.
However, a larger cohort will be required to examine and validate
the datas due to the limitation of the small size sample and cohort
selection in the present study.
In conclusion, RNA sequencing and proteomics analysis provided
a list of interesting altered transcriptomes in LAC patients with
brain metastasis. The findings of this study contribute to the eluci-
dation of the molecular mechanisms of LAC brain metastasis and
contribute to an increased understanding of the complex patho-
genetic mechanisms underlying brain metastasis, which will ulti-
mately lead to novel treatment strategies in the future.
6. Acknowledgements
This study was supported by the Beijing CSCO Oncology Re-
search Foundation(No: Y-HS2019/2-056, Y-HR2018-315)
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Combined Analysis of Micro RNA and Proteomic Profiles and Interactions in Patients with Primary Lung Adenocarcinoma and Lung Adenocarcinoma Brain Metastases

  • 1. Clinics of Oncology Research Article ISSN: 2640-1037 Volume 6 CombinedAnalysis of Micro RNAand Proteomic Profiles and Interactions in Patients with Primary Lung Adenocarcinoma and Lung Adenocarcinoma Brain Metastases Zhang L1# , Liang J2# , Han Z3# , Wang L4 , Tang C1* , Liang J1* and Zhang S5* 1 Department of Oncology, Peking University International Hospital, Beijing102206, People’s Republic of China 2 Department of Neurosurgery, Peking University International Hospital, Beijing 1 02206, People’s Republic of China 3 Department of Thoracic Surgery, Peking University International Hospital, Beijing 102206, People’s Republic of China 4 Department of Pathology, Peking University International Hospital, Beijing 102206, People’s Republic of China 5 Department of Oncology, Beijing Chest Hospital, Capital Medical University; Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, People’s Republic of China * Corresponding author: Shucai Zhang, Department of Oncology, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Research Institute, Beijing 101149, People’s Republic of China, Tel: +8613901297065, E-mail: sczhang6304@163.com Jun Liang, Department of Oncology, Peking University Interna- tional Hospital, Beijing 102206, People’s Republic of China, Tel:+8615801353728, E-mail: junliang@pkuih.edu.cn Chuanhao Tang, Department of Oncology, Peking University Interna- tional Hospital, Beijing 102206, People’s Republic of China, Tel: +8613910107010, E-mail: tangchuanhao@pkuih.edu.cn Received: 02 Mar 2022 Accepted: 15 Mar 2022 Published: 21 Mar 2022 J Short Name: COO Copyright: ©2022 Tang C, Liang J, Zhang S. This is an open access article distributed under the terms of the Creative Com- mons Attribution License, which permits unrestricted use, distribution, and build upon your work non-commercially. Citation: Tang C, Liang J, Zhang S, Combined Analysis of Micro RNA and Proteomic Profiles and Interactions in Patients with Primary Lung Adenocarcinoma and Lung Adenocar- cinoma Brain Metastases. Clin Onco. 2022; 6(3): 1-10 Keywords: Brain metastases; Lung adenocarcinoma; microRNAs, Proteomics clinicsofoncology.com 1 # Author Contribution: Zhang L, Liang J, Han Z. These authors have contributed equally to this article. 1. Abstract 1.1. Objective: Brain metastasis of lung adenocarcinoma is the main cause of death in such patients. There is an urgent need to explore highly sensitive and specific biomarkers for the early detection and treatment of lung adenocarcinoma brain metastases. 1.2. Materials and Methods: We carried out an analysis of miR- NAs expression profiles and protein spectrums of non-meta- static primary Lung Adenocarcinoma (LP) and patients with Brain Metastases (BM) to better explore the molecular basis of BM. The potential roles and interactions of differential- ly expressed miRNAs and proteins were classified by gene ontology enrichment, Kyoto Encyclopedia of Genes and Ge- nomes pathway analyses. The interaction between the differ- entially expressed miRNAs and proteins was analyzed by R soft and shown by Cytoscape. 1.3. Results: Compared with LP patients, 16 miRNAs were up- regulated and 4 miRNAs were downregulated in the tissue of BM patients, 53 proteins were upregulated and 35 pro- teins were downregulated meanwhile. Enrichment pathway analysis revealed that NF-κB signaling pathway and primary immunodeficiency pathway played critical roles in the occur- rence and development of brain metastasis in lung adenocar- cinoma. 1.4. Conclusion: This study broadened the understanding of the regulatory network of microRNA and proteomic expressions and provided clues for the pathogenitic molecular mecha- nism of brain metastases in lung adenocarcinoma at the miR- NA and protein levels. 2. Introduction Lung cancer is characterized by a high incidence of brain metas- tases, with about 40% of patients developing brain metastases
  • 2. clinicsofoncology.com 2 Volume 6 Issue 3 -2022 Research Article during their disease course [1]. The prognosis of Non-Small Cell Lung Cancer (NSCLC) patients with brain metastasis is very poor, which is the main cause of death. The post-brain metastasis sur- vival time of these patients is only 1-2 months if untreated [2]. The efficacy remains unsatisfactory even though the treatments for brain matastasis include surgery, radiotherapy, chemotherapy and immunotherapy nowadays. Therefore, it is an urgent task that further study of biological and molecular mechanism underlying BM in NSCLC needs to be done to identify new treatment targets. MicroRNAs have been revealed as essential part of the un coding genome, playing crucial roles through a complicated regulation in all the most important processes and in different species [3]. Ear- ly studies have shown that microRNA expression profiling was associated with tumor development, progression and response to therapy, which suggests that their possible use as diagnostic, prog- nostic and predictive biomarkers [4]. Several miRNAs are known to play key roles in the development of NSCLC [5]. NSCLC me- tastasis could be inhibited through manipulation of some miRNAs in preclinical studies [6, 7]. Increasingly evidence indicate that mi- croRNAs are target keys involved in metastasis, which could act as biomarkers for combating brain metastasis [8, 9]. Proteomic analysis is currently considered to be a powerful tool for global evaluation of protein expression which has been widely applied in fields of cancer research. Quantitative protein expres- sion profiling is a crucial part of proteomics, which requires meth- ods that are able to provide accurate and reproducible differential expression values for proteins in two or more biological samples efficiently [10]. Such profilings have been used for screening metastasis-associ- ated proteins in recent studies [11, 12]. In this study, we analyzed the pathogenesis of Lung Adenocarcinoma(LAC) brain metastasis by detecting the differences in miRNA expression and proteome expression in primary lesion of LAC and brain metastasis tissue samples, with a view to finding biomarkers of LAC brain metasta- sis and researching for potential therapeutic targets. 3. Materials and Methods 3.1. Samples This study was reviewed and approved by the Medical Ethics Com- mittee of Peking University International Hospital and informed consent forms were signed by all patients. All experiments were carried out in accordance with relevant guidelines and regulations or Declaration of helsinki. From September 2018 to October 2019, Participants with histologically confirmed LAC treated in our hos- pital were recruited. The inclusion criteria were as follows: 1) patients diagnosed as pulmonary adenocarcinoma or LAC brain metastasis by surgery; 2) Eastern Cooperative Oncology Group performance status (ECOG) was 0–2; 3) function of the bone marrow, heart, liver, kidney and oth- er organs was not abnormal; 4) survival period longer than 3 months; 5) patients agreed with complete ctDNA tests for tissue samples; 6) informed consent form was signed voluntarily by partic- ipants. The exclusion criteria included patients with multiple primary car- cinomas, severe systemic diseases, active infections, and immune diseases. All participants were divided into the Lung Primary (LP) group (n=5, named as L1, L2, L3, L4, and L5) and the Brain Metastasis (BM) group (n=5, named as B1, B2, B3, B4, and B5).Tissues from the primary tumor sites in LP group, and from the brain metastasis tumor sites in BM group were sampled respectively, which were placed into 10% neutral buffered formalin solution within 5 min- utes after excision, fixed at room temperature (8–72h) and then processed routinely into a paraffin embedded tissue block (FFPE). All samples were stored at 4℃, until we had compiled the cohort and were ready to begin the study. Then, the FFPE blocks were sectioned to prepare an H&E stained slide and unstained sections (5μm thick) on glass slides for RNA extraction. The location of tumor tissue was determined under microscope on H&E stained slide and sections of adjacent layers were taken. 3.2. RNA Extraction and Sequencing Total RNA was isolated from the Formalin-Fixed Paraffin-Embed- ded (FFPE) tumors using the Trizol reagent (Invitrogen, USA). The purity and integrity of RNA were detected by using an Agi- lent 2100 Bioanalyzer system (Agilent, USA). RNA samples with RIN ≥8 and 28S/18S ≥1 were considered. The cDNA library of all samples was sequenced using the Illumina HiSeq 2000 sequencing platform (Illumina, USA). 3.3. Liquid Chromatography Tandem Mass Spectrometry The Liquid Mass System(LMS) includes an Easy nLC1000 (Ther- mo Fisher) coupled ultra-high resolution mass spectrometer Or- bitrap Fusion Lumos (Thermo Fisher) with a Thermo Fisher elec- trospray source. Each injection is sent to a preset column (Acclaim PepMap C18, 100 μm x 2 cm, Thermo Scientific) for adsorption at a flow rate of 3 L/min. The sample is then sent to the analyzer column (Acclaim PepMap C18, 75 μm x 15 cm, Thermo Scientif- ic) for separation. Orbitrap Fusion Lumos was set to OT-OT mode. For a full first-lev- el scan, the AGC Target was set to 5E5, with a scanning range of 350-1550 m/z, a resolution of 120,000 and a maximum injection time of 50 ms. For the second scan, the cycle time was set to 3.5s, the peptides with a charge of 2-7 were selected for fragmentation, and the collision energy was 32%. Maxquant (1.6.2.10) was used for data analysis. Mass tolerance was set at 20ppm and FDR at 1%.
  • 3. clinicsofoncology.com 3 Volume 6 Issue 3 -2022 Research Article 3.4. Data Quality Control The raw reads obtained included low quality and other problem- atic reads, such as those with missing insert tags, oversized in- serts, poly(A) tags, and small tags. Data cleaning was therefore performed on the FASTQ file to obtain the final clean reads. Data cleaning was performed as follows: 1. Reads in which more than 50% of bases had a Qphred less than or equal to 5 were discarded; Reads in which N accounted for more than 10% were discarded. (N indicates that base information was indeterminable.); Reads with 5' primer contamination were discarded; Reads lacking a 3' primer or insert tag were discarded; The 3' primer sequence was trimmed; Reads with poly(A), poly(T), poly(G), or poly(C) tails were discarded. 3.5. Prediction of Novel miRNA The small RNA reads were mapped to the genome using Bowtie so as to analyze their expression and distribution on the genome. The above reads on the reference sequence were compared with the specified sequence in the miRBase to obtain details of the sR- NAs on each sample match, including the secondary structure of the matched miRNAs, the sequence of miRNAs in each sample, length, the number of occurrences and other information. Then we used mirdeep2 to predict novel miRNA [13]. 3.6. GO and KEGG Enrichment Analysis of Target Gene of Differentially Expressed Mirnas and Proteins Gene Ontology (GO) enrichment analysis of target gene of dif- ferentially expressed miRNAs was implemented by the topGO R package, in which gene length bias was corrected. GO terms with corrected P value less than 0.05 were considered significantly en- riched by differential expressed genes. KEGG is a database resource for understanding high-level func- tions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-through put experimental technologies (http://www.genome.jp/kegg/). We used cluster Profiler R package to test the statistical enrichment of differential expression genes in KEGG pathways. 3.7. Statistical Analysis Statistical differences were analysed using SPSS (version 20.0, IBM SPSS Statistics) by independent-samples t test. Multiple comparisons were carried out using one-way analysis of variance. P values below .05 was defined as the significance. To control for multiple testing the FDR was estimated [14]. To assess the rele- vance, a linear model was applied for continuous covariates and the ANOVA test for categorical covariates. All statistical analysis was performed using R software v4.0.3. 4. Results 4.1. Data Quality Control Age and gender of the patients are described in (Table 1), and all patients had no smoking history. RNAs were extracted from FFPE tumors of each sample, and were carried out quality detection ac- cording to the previous experimental design. The results are shown in (Table 2). The proportion of quality score ≥20 (Q20) was higher than 98%, and the proportion of quality score ≥30 (Q30) was high- er than 94%. The single base error rate was 0.001. Table 1: Characteristics of the patients Sample Gender Age Smoking history BM1 Male 63 No BM2 Male 75 No BM3 Female 82 No BM4 Female 52 No BM5 Female 48 No LP1 Female 70 No LP2 Male 66 No LP3 Female 60 No LP4 Male 62 No LP5 Female 57 No Table 2: Results of data quality control Sample Raw Reads Bases GC (%) Q20 Q30 Avg. Quality BM1 3839266 0.192(GB) 53 98.63% 95.68% 35.83 BM2 3735819 0.187(GB) 53 98.60% 95.61% 35.82 BM3 4123090 0.206(GB) 53 98.58% 95.50% 35.8 BM4 3340001 0.167(GB) 53 98.48% 95.38% 35.77 BM5 4210563 0.211(GB) 54 98.16% 94.84% 35.66 LP1 3679273 0.184(GB) 52 98.54% 95.32% 35.77 LP2 3834373 0.192(GB) 52 98.64% 95.60% 35.82 LP3 5482979 0.274(GB) 53 98.74% 95.82% 35.86 LP4 3604748 0.180(GB) 52 98.73% 95.88% 35.87 LP5 7561029 0.378(GB) 52 98.37% 95.49% 35.77 4.2. Identification of Differentially Expressed Mirnas and Pro- teins Bioinformatics analysis was performed to screen out differentially expressed miRNAs and proteins between the LP and BM groups. Volcano plots provided an overview of the differential expression of miRNAs and proteins, as shown in (Figure 1). Each point in the graph represents a miRNA or protein. The abscissa represents the logarithmic fold change value of the multiple of difference of miR- NA or protein expression between the two groups. The ordinate represents the negative logarithmic P value of miRNA or protein expression change. An absolute Fold Change cut-off value of 2 and P < .05 were uti- lized as the criteria to confirm significantly downregulated and upregulated miRNAs and proteins (FDR <0.05). Red points rep-
  • 4. clinicsofoncology.com 4 Volume 6 Issue 3 -2022 Research Article resent upregulated miRNAs or proteins in BM vs LP, while green points represent downregulated ones. Heatmaps (Figures 2,3) show the significant changes of miRNA and protein expression between LP and BM(FDR <0.05). The fig- ure shows the bidirectional clustering of differentially expressed miRNAs or proteins and groups. The depth of colour represents the level of expression; orange represents high and purple rep- resents low levels of miRNA and protein expression (Figure 2,3). The above results suggested that 16 miRNAs, such as miR-31-3p, miR-654-5p, miR-1185-1-3p, miR-487a-5p, as well as 53 pro- teins, including VIM, RAB2A, RPLs, KRTs proteins were highly expressed in BM patients, while miR-219a-5p, miR-1269a, miR- 615-3p, miR-196a-5p, 4 miRNAs and AHSG, HBD, SERPINC1, COL6As, totally 35 proteins were low expressed significantly, which may be related to the pathogenesis of brain metastasis. The interaction network of differentially expressed miRNAs and proteins was mapped using Cytoscape (Figure 4). The network graph is composed of node and edge. Nodes represented genes and proteins, and edges represented interaction relationships between genes. The black nodes represented miRNAs, proteins with high expression were represented by green nodes, while those with low expression are represented by blue nodes. The lines represented the correlation and interaction between genes and proteins. Figure 1: Volcano plots of differentially expressed microRNAs (A) and proteins (B) Figure 2: Heatmap of differentially expressed miRNAs
  • 5. clinicsofoncology.com 5 Volume 6 Issue 3 -2022 Research Article Figure 3: Heatmap of differentially expressed proteins Figure 4: The interaction network of differentially expressed miRNAs and proteins
  • 6. clinicsofoncology.com 6 Volume 6 Issue 3 -2022 Research Article 4.3. Enrichment Analysis According to the relative level of expression between the two groups, the differentially expressed miRNAs and proteins can be classified as upregulated group and downregulated group. The results of GO enrichment analysis with differentially expressed genes and proteins are shown in (Figure 5 and Figure 6) respec- tively. The main information of enrichment results areis shown in the figures, and other information can be found in tables in the original document of analysis results. The ordinate is the classification of GO for miRNAs, while the abscissa represents the number of enriched target genes. Differ- entially expressed miRNAs were enriched in signalling pathways such as binding, cell part, cell, organelle, intracellular organelle and protein binding. For proteins, the abscissa is the classification of GO; on the right is the number of target proteins, and on the left is the percentage of the number of target proteins. Differentially expressed proteins were enriched in signalling pathways such as cellular process, cell, cell part, organelle and binding. Both en- richment pathway analysis with differentially expressed miRNAs and proteins revealed that cell, cell part, organelle and binding sig- naling pathways play important roles in the development of LAC BM. KOBAS3.0 was used to analyze the enrichment functional areas of the top 10 significantly differentially expressed miRNAs target genes, shown in (Figure7), which was demonstrated that the differ- entially expressed miRNAs were mainly enriched in extracellular vesicles, extracellular space, extracellular organelles functional areas. Bubble map is a graphical representation of KEGG. KEGG Path- way analysis was performed on the differentially expressed miR- NAs and proteins, the results of KEGG enrichment analysis were shown in (Figure8 and Figure9). KEGG enrichment was measured by Gene Ratio, p value and number of genes enriched in this path- way, respectively. Gene Ratio refers to the Ratio between the num- ber of differentially enriched genes and the total number of differ- entially enriched genes in this pathway. The higher Gene Ratio, the greater the degree of enrichment; The value range of p value is between [0,1], and the closer it is to zero, the more significant the enrichment. The top 30 pathways with the most significant enrich- ment of miRNAs and top 20 pathways of proteins were showed in the item diagram. The ordinate is the description of the corre- sponding pathways, while the abscissa is the enrichment factor, the bubble size is the number of different genes or proteins, and the bubble color is the P value of enrichment significance. Both enrichment pathway analysis with differentially expressed miR- NAs and proteins revealed that NF-κB signaling pathway and primary immunodeficiency pathway play important roles in the development of lung adenocarcinoma BM. Figure 5: Gene ontology enrichment analysis of differentialy expressed miRNAs
  • 7. clinicsofoncology.com 7 Volume 6 Issue 3 -2022 Research Article Figure 6: Gene ontology enrichment analysis of differentially expressed proteins Figure 7: Gene ontology enrichment analysis of top 10 significantly differentially expressed miRNAs target genes Figure 8: The bubble map of Kyoto Encyclopedia of Genes and Genomes enrichment analysis of differentialy expressed miRNAs
  • 8. clinicsofoncology.com 8 Volume 6 Issue 3 -2022 Research Article Figure 9: The bubble map of Kyoto Encyclopedia of Genes and Genomes enrichment analysis of differentialy expressed proteins 5. Discussion In this study, it was found that compared with LP group, 16 miR- NAs and 53 proteins were highly expressed while 4 miRNAs and 35 proteins were significantly low expressed in BM group, which may be related to the pathogenesis of LAC brain metastasis. We found that Tissue Factor (TF) was low expressed in BM group, which could mean that TF can inhibit brain metastasis of lung ade- nocarcinoma patients. Previous research has reported that metasta- sis had a predilection to organs with low levels of TF in the micro- circulation, which enables tumor cell retention [15]. SERPINA1 (Serpin Family A Member 1) was significantly different expressed in BM group and LP group in current study and other study [16]. Some studies have reported that Epithelial-Mesenchymal Transi- tion (EMT) proteins and matrix metalloproteinases (MMPs) play important roles in brain metastasis by regulating the Blood-Brain Barrier (BBB) [17], high expression of Inner Membrane Mitochon- drial Protein (IMMT) was significantly correlated with advanced disease stage and poor prognosis of LAC patients [18], the NARS levels were positively associated with LAC lymph node metastasis [19], which were consistent with our study. We also found that proteins like KRTs, COL6As, RAB2A, AHSG were associated to pathogenesis of LAC brain metastasis. However, the sample size of this study is limited, and more samples is needed in the future for further confirmation. MiR-31-3p are related to the occurrence and development of var- ious malignant tumors such as colorectal cancer, as well as lymph node metastasis and poor prognosis of lung adenocarcinoma have reported in previous studies [20]. In current study we also demon- strated that miR-31-3p significantly increased in BM group, sug- gesting that miR-31-3p may promote the occurrence and develop- ment of brain metastasis in lung adenocarcinoma patients. MiR- 493-5p is related to the pathogenesis of various malignant tumors such as liver cancer, colorectal cancer and osteosarcoma, which is also related to the prognosis of patients with NSCLC. It can af- fect the malignant behavior of NSCLC cells by regulating the ex- pression of integrin β1 (ITGB1) [21]. We found that miR-493-5p can promote lung adenocarcinoma brain metastasis as well. These findings indicate the possibility of miRNAs as biomarkers for the occurrence, progression, prognosis, as well as potential therapeu- tic targets of lung cancer BMs. Our study also found the differ- ential expression of these miRNAs in the two groups, which is in line with previous studies, suggesting these miRNAs are correlat- ed with the pathogenesis of brain metastasis in lung adenocarcino- ma. In addition, this study also found the differential expression of miR-487a-5p, miR-654 −5p, miR-1185-1-3p, miR-376c-3p and miR-196a-5p between BM and LP groups for the first time, which may be associated with the brain metastasis of lung adenocarcino- ma. Previous studies have reported that miR-654-5p can promote the progression of colon cancer, breast cancer, and oral squamous cell carcinoma, which is also associated with paclitaxel resistance of ovarian cancer. We found that miR-654-3p may promote the progression of lung adenicarcinoma brain metastasis MiR-1185- 1-3p can be used for the early diagnosis of bladder cancer. We reported the association of miR-1185-1-3p with brain metastasis in lung adenocarcinoma patients for the first time, no studies have focused on the relationship between these miRNAs and lung can- cer or brain metastasis before. We found that the pathogenesis of brain metastasis in LAC was related to NF-κB signaling pathway, which is in line with previ- ous studies [22]. It was shown that primary immunodeficiency signaling pathway also plays important role in the occurrence and development of brain metastasis of LAC, which is a newly discov- ered mechanism in this study. NF-κB is a key signaling pathway in the development and metastasis of various inflammatory and malignant tumors, and has also been found to be associated with brain metastasis of breast cancer and lung cancer [22]. In addition, previous studies have found that there are multiple mechanisms involved in the occurrence and progression of lung cancer brain
  • 9. clinicsofoncology.com 9 Volume 6 Issue 3 -2022 Research Article metastases: The highly open calcium-activated potassium (KCA) channels in lung cancer brain metastases and their microvascu- lar endothelial cells can be used as targets for Blood-Brain Tumor Barrier (BTB) and permeability regulation [23]. Vascular endo- thelial growth factor C plays a role in the development and pro- gression of lung cancer brain metastasis by enhancing the affinity of tumor cells to specific organs and promoting the movement of tumor cells to lymphatic vessels [24, 25]. The interaction between neurotransmitter receptors and neurons can promote the metastasis of lung cancer cells to the brain [25]. The abnormal expression of tumor suppressor genes, matrix metalloproteinases, carcinoem- bryonic antigen and other related proteins are also associated with LAC brain metastasis. The samples of primary and metastatic lesions from the same pa- tient which can exclude the interference of expression differences caused by histological differences in brain tissues and lung tissues were not obtained for the present study. Relevant samples will be collected in the future for further studies. In addition, a number of studies have explored the expression of cerebrospinal fluid miR- NAs and proteins in central nervous system malignant tumors, as well as their diagnostic and application value. It has been found that miRNAs differentially expressed in cerebrospinal fluid can distinguish glioblastoma from metastatic brain malignancies and can be used to monitor changes in disease condition and response to treatment [26]. Due to the difficulty in obtaining BMs tissue specimens, subsequent attempts can be made to explore the feasi- bility of using CSF specimens of BMs and non-BMs to detect the differentially expressed non-coding RNA and proteins. However, a larger cohort will be required to examine and validate the datas due to the limitation of the small size sample and cohort selection in the present study. In conclusion, RNA sequencing and proteomics analysis provided a list of interesting altered transcriptomes in LAC patients with brain metastasis. The findings of this study contribute to the eluci- dation of the molecular mechanisms of LAC brain metastasis and contribute to an increased understanding of the complex patho- genetic mechanisms underlying brain metastasis, which will ulti- mately lead to novel treatment strategies in the future. 6. Acknowledgements This study was supported by the Beijing CSCO Oncology Re- search Foundation(No: Y-HS2019/2-056, Y-HR2018-315) References 1. Zimmermann S, Dziadziuszko R, Peters S. Indications and limita- tions of chemotherapy and targeted agents in non-small cell lung cancer brain metastases. Cancer Treat Rev. 2014; 40: 716-22. 2. 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