Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer
Prediction of novel target genes and pathways involved in bevacizumab-resistant colorectal cancer
Precious Takondwa Makondi 1 2 3
Chia-Hwa Lee 0 3
Chien-Yu Huang 3
Chi-Ming Chu 3
Yu- Ji 3
Po-Li W 3
0 School of Medical Laboratory Science and Biotechnology, College of Medical Science and Technology, Taipei Medical University , Taipei, Taiwan , 4 Department of Surgery, College of Medicine, Taipei Medical University , Taipei, Taiwan , 5 Division of General Surgery, Department of Surgery, Shuang Ho Hospital, Taipei Medical University , Taipei, Taiwan , 6 School of Public Health, National Defense Medical Center , Taipei, Taiwan , 7 Cancer Research Center and Translational Laboratory, Department of Medical Research, Taipei Medical University Hospital, Taipei Medical University , Taipei, Taiwan , 8 Division of Colorectal Surgery, Department of Surgery, Wan Fang Hospital, Taipei Medical University , Taipei, Taiwan , 9 Division of Colorectal Surgery, Department of Surgery, Taipei Medical University Hospital, Taipei Medical University , Taipei, Taiwan , 10 Graduate Institute of Cancer Biology and Drug Discovery, Taipei Medical University , Taipei , Taiwan
1 Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University , Taipei , Taiwan
2 International PhD Program in Medicine, College of Medicine, Taipei Medical University , Taipei , Taiwan
3 Editor: Irina V Lebedeva, Columbia University , UNITED STATES
Bevacizumab combined with cytotoxic chemotherapy is the backbone of metastatic colorectal cancer (mCRC) therapy; however, its treatment efficacy is hampered by therapeutic resistance. Therefore, understanding the mechanisms underlying bevacizumab resistance is crucial to increasing the therapeutic efficacy of bevacizumab. The Gene Expression Omnibus (GEO) database (dataset, GSE86525) was used to identify the key genes and pathways involved in bevacizumab-resistant mCRC. The GEO2R web tool was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analyses of the DEGs were performed using the Database for Annotation, Visualization, and Integrated Discovery(DAVID). Protein±protein interaction (PPI) networks were established using the Search Tool for the Retrieval of Interacting Genes/Proteins database(STRING) and visualized using Cytoscape software. A total of 124 DEGs were obtained, 57 of which upregulated and 67 were downregulated. PPI network analysis showed that seven upregulated genes and nine downregulated genes exhibited high PPI degrees. In the functional enrichment, the DEGs were mainly enriched in negative regulation of phosphate metabolic process and positive regulation of cell cycle process gene ontologies (GOs); the enriched pathways were the phosphoinositide 3-kinase-serine/threonine kinase signaling pathway, bladder cancer, and microRNAs in cancer. Cyclin-dependent kinase inhibitor 1A(CDKN1A), toll-like receptor 4 (TLR4), CD19 molecule (CD19), breast cancer 1, early onset (BRCA1), platelet-derived growth factor subunit A (PDGFA), and matrix metallopeptidase 1 (MMP1)
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Funding: The authors received no specific funding
for this work.
Competing interests: The authors have declared
that no competing interests exist.
were the DEGs involved in the pathways and the PPIs. The clinical validation of the DEGs in
mCRC (TNM clinical stages 3 and 4) revealed that high PDGFA expression levels were
associated with poor overall survival, whereas high BRCA1 and MMP1 expression levels
were associated with favorable progress free survival(PFS). The identified genes and
pathways can be potential targets and predictors of therapeutic resistance and prognosis in
bevacizumab-treated patients with mCRC.
Colorectal cancer (CRC) is the third most frequently diagnosed cancer and the second
leading cause of cancer deaths worldwide, accounting for 10% of the worldwide cancer incidence
and mortality [
]. Surgery is the treatment of choice for nonmetastatic CRC; however,
approximately 20% of cases present with metastatic disease at the time of diagnosis and half
of the patients experience recurrence and metastases even after complete resection of the
primary tumor, leading to a poor prognosis and median overall survival (OS) of approximately
24 months [
]. The inclusion of cytotoxic agents (irinotecan and oxaliplatin) in
fluoropyrimidine (intravenous 5-fluorouracil or oral capecitabine)-based systemic chemotherapy has
been reported to improve the associated response rates (RR) from 15%±20% to 30%±40%,
time to progression from 5±6 to 8 months, and OS from 10±12 to 20±24 months [3±7].
Furthermore, therapeutic benefits have been demonstrated to increase through the use of
targeted drugs, such as angiogenesis inhibitors (bevacizumab, ziv-aflibercept, and
ramucirumab) and antiepidermal growth factor receptor antibodies (cetuximab and panitumumab),
as the first and second lines of treatment in patients with with K-RAS-wild-type tumors
Bevacizumab is the first agent to influence OS in patients with metastatic CRC (mCRC);
when combined with irinotecan-based chemotherapy, the median OS improved from 15.6 to
20.3 months, median PFS from 6.2 to 10.6 months and RR from 34.8% to 44.8%[
addition of bevacizumab to oxaliplatin-based chemotherapy improved median PFS from 8.0 to 9.4
months though there was no significant difference in OS(19.9 to 21.3 months) [
], while in
previously treated mCRC; oxaliplatin based therapy improved both OS and PFS (10.8 to 12.9
months and 4.7 to 7.3 respectively)[
]. When compared for effectiveness, the irinotecan
based chemotherapy has shown to have an edge over oxaliplatin based chemotherapy with the
addition of bevacizumab (OS = 31.4 vs 30.1 months, PFS = 12.1 vs 10.7 months)[
results have also been echoed in the MAVERICC trial (OS = 27.5 vs 23.9 months, PFS = 12.6
vs 10.1 months)[
]. Bevacizumab is a humanized monoclonal antibody that binds to vascular
endothelial growth factor A (VEGF-A) and thus prevents interaction with its receptors,
VEGFR-1 (Flt-1) and VEGFR-2 (Flk-1/KDR), leading to the regression of existing tumor
blood vessels, normalization of the remaining blood vessels, and consequently tumor
]. However, the therapeutic effects of bevacizumab are strongly affected by the lack of
biomarkers that can facilitate selecting a population that might benefit from this medication
and can predict therapeutic resistance [18±20].
In this study, we investigated the predictive biomarkers and pathways of bevacizumab
resistance in mCRC by using microarray data from the Genetic Expression Omnibus (GEO)
database. The new biomarkers were assessed for their ability to predict OS and PFS. The
identification of predictive and prognostic biomarkers can facilitate improving the therapeutic
index of bevacizumab.
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Materials and methods
The gene expression profile of GSE86525 was obtained from the GEO (http://www.ncbi.nlm.
nih.gov/geo/) database [
], which was sequenced on the GPL16699 platform of
Agilent039494 SurePrint G3 Human GE v2 8 × 60K Microarray 039381 (Agilent Technologies, Santa
Clara, CA, USA). The GSE86525 dataset includes microarray gene expression data derived
from three bevacizumab-resistant HT29 xenograft tumors and three untreated HT29
xenograft tumors as controls. In brief, HT29 cells (1 × 107) suspended in phosphate-buffered saline
were subcutaneously injected into the flanks of BALB/c nude mice, and the tumor-bearing
mice were treated with bevacizumab (5 mg/kg, twice a week) for 3 weeks to obtain
bevacizumab-resistant tumors. MTT colorimetric assays were used to determine the 50% inhibitory
concentration for bevacizumab-resistant and untreated xenograft tumors; the tumor sizes
were compared between the two groups. The sample tissues were immediately frozen under
liquid nitrogen after isolation. Total RNAs were extracted from the samples, evaluated, labeled
and hybridized, using a SurePrint G3 Human GE 8 × 60K microarray (Agilent Technologies).
Array images were captured using a DNA microarray scanner (Agilent Technologies), and the
data were analyzed using Feature Extraction Software (Agilent Technologies) to obtain
background-corrected signal intensities. The expression data were further analyzed using
GeneSpring GX software (version 11.0, Agilent Technologies), and the differentially expressed
genes (DEGs) between the bevacizumab-resistant HT29 tumors vs untreated control were
compared using the Fisher exact test, followed by multiple corrections using the Benjamini
and Hochberg false discovery rate (FDR) method [
]. Gene sets with an FDR q-value of
<0.05 were considered statistically significant, and all experiments were performed in
Data preprocessing and DEGs screening
The data were recalculated using the GEO2R analytical tool to identify the DEGs associated
with acquired bevacizumab-resistant CRC [
]. The t test and Benjamini and Hochberg
method were used to calculate the P values and FDR, respectively . The genes were
considered to be differentially expressed for an FDR value of <0.05 and fold change (FC) of >2 or
<-2 (log2FC > 1 or < -1). The DEG expression data were extracted, and a bidirectional
hierarchical clustering plot was constructed using MultiExperiment Viewer (MeV; version 4.8)
Construction of PPI networks
Protein±protein interaction (PPI) networks were plotted using the Search Tool for the
Retrieval of Interacting Genes/Proteins (STRING; version 10.0; http://www.string-db.org/), an
online database comprising comprehensive known and predicted interactions, to determine
the interactive relationships among the DEG-encoded proteins. A combined score of >0.7
(high confidence) was used as the cutoff criterion [
]. PPI pairs were visualized using
Cytoscape software (version 3.4.0; http://www.cytoscape.org/), and the CytoNCA tool was used
to subcluster the plotted PPI networks [27±30]. Highly connected proteins with important
biological functions were identified by calculating the degree (number of line connections
between proteins) and the betweenness value (fraction of the number of shortest paths that
pass through each node; A measure of how often nodes occur on the shortest paths between
other nodes) of each node with a degree cutoff criterion of 2.
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Enrichment analysis of DEGs
The Database for Annotation, Visualization, and Integrated Discovery (DAVID, http://david.
abcc.ncifcrf.gov/) was used to classify the DEGs involved in the PPI networks according to
their biological processes, molecular functions, or cellular components by using the Gene
Ontology (GO) Consortium Reference (http://www.geneontology.org/) [
]. Gene sets
with a P value of <0.05 and FDR value of <0.05 were considered statistically significant. In
addition, the DAVID tool was used for pathway enrichment analysis, and the reference
pathways were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://
www.genome.jp/kegg/) database website to perform KEGG pathway enrichment analysis for
the DEGs involved in the PPI networks, with a P value of <0.05 and FDR value of <0.05 being
considered statistically significant [
Clinical validation of the DEGs
The clinical assessment of DEGs associated with bevacizumab resistance was performed using
the SurvExpress tool [
]. The colon metabase, which includes GSE12945[
], GSE31595, and GSE41258[
] with a total of 808 cases, was
used in this study. Survival profiles were compared on the basis of a high or low mRNA
expression level of a particular gene, and they were censored independently for OS and PFS in
months and stratified further according to TNM clinical stages 3 and 4. A log-rank P value of
<0.05 was considered statistically significant, and the data were analyzed using SPSS for
Macintosh (version 21, IBM Corp Armonk, NY, USA; www-01.ibm.com) for plotting Kaplan±
Meier survival curves.
Gene co-expression in colorectal cancer data
The Cancer Genome Atlas (TCGA; https://cancergenome.nih.gov/) was used to obtain CRC
data containing gene expression profiles. Level 3 RNASeq data containing gene expression
profiles of 635 CRC cases (colon adenocarcinoma, N = 463; and rectal adenocarcinoma,
N = 172) were obtained. The standard Pearson correlation coefficients (-1 to 1) and the
coefficient of variation (the ratio of standard deviation to mean) of the desired gene pairs were
calculated using SPSS for Macintosh (version 21, IBM Corp., Armonk, NY, USA;
https://www01.ibm.com). A P value of <0.05 was considered statistically significant and was used as the
DEGs screening and heat map clustering analysis
The GEO2R tool was used to identify DEGs from the data derived from the GPL16699
oligonucleotide microarray platform, comprising 62,976 probe sets. A total of 124 DEGs were
determined to be associated with bevacizumab resistance, with 57 being upregulated and 67 being
downregulated, as determined according to their log2FC and FDR values (S1 and S2 Tables).
MeV software was used to construct a heat map to obtain the bidirectional hierarchical
clustering of the DEGs and summarize the upregulated and downregulated DEGs (Fig 1).
PPI network analysis
The PPI pairs obtained using the STRING database were visualized using Cytoscape software
and analyzed using the CytoNCA plugin. The upregulated network had 88 nodes and 466
edges. Seven genes, namely cyclin-dependent kinase inhibitor 1A (CDKN1A; p21 and Cip1),
matrix metallopeptidase 1 (MMP1; interstitial collagenase), pre-B cell leukemia transcription
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Fig 1. Heat map showing up-regulated and down-regulated differentially expressed genes (DEGs) in
bevacizumab-resistant colon cancer tumors. A bidirectional hierarchical clustering heat map was constructed using
MultiExperimental Viewer(MeV). The expression values are log2 fold changes (>1 or <−1, FDR <0.05)) between
corresponding bevacizumab -resistant HT29 xenograft tumors and non-treated HT29 xenograft tumors. Black
represents no change in expression, green represents down-regulation, and red represents up-regulation.
factor 1 (PBX1), platelet-derived growth factor alpha polypeptide (PDGFA), kin of IRRE-like
(Drosophila) (KIRREL), insulin-like growth factor binding protein 7 (IGFBP7), and dual
specificity phosphatase 5 (DUSP5), exhibited higher PPI degrees and betweenness values (Fig 2A
and Table 1). In the downregulated network, containing 88 nodes and 350 edges, nine genes,
namely breast cancer 1, early onset (BRCA1), retinoblastoma-like 1 (p107) (RBL1), toll-like
receptor 4 (TLR4), CD19, HEAT repeat-containing 1 (CD19), Fanconi anemia
complementation group D2 (FANCD2), proteasome subunit beta 11 (PSMB11), biliverdin reductase A
(BLVRA), and GINS complex subunit 4 (GINS4), showed higher PPI degrees and betweenness
values (Fig 2B, Table 2).
Functional enrichment analysis
The DAVID tool was used to classify the DEGs involved in the PPI networks according to
their common biological processes, molecular functions, or cellular components. Of the 1,454
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Fig 2. Protein±protein interaction (PPI) network of differentially expressed genes(A) up-regulated genes and (B)
down-regulated genes. The PPI pairs were imported into Cytoscape software as described in methods and materials. Pink
nodes represent up-regulated genes while green nodes represent down-regulated genes. The lines represent interaction
relationship between nodes. The highlighted DEGs represents degree = >2.
GO gene sets included from the reference database, 111 were significantly enriched (P < 0.05;
FDR < 0.05). Table 3 lists the top five gene sets, with those involved in the negative regulation
of phosphate metabolic process and positive regulation of cell cycle process being the most
significant and they include DUSP5, CDKN1A (p21 and Cip1), KIRREL, PDGFA, TLR4,
PSMB11, BRCA1, and PBX1.
KEGG pathway analysis
The DAVID tool was applied to classify the DEGs involved in the PPI networks by using
the reference pathways from KEGG. KEGG pathway analysis revealed significant results
(P < 0.05; FDR < 0.05) for three pathways: the phosphoinositide 3-kinase-serine/threonine
kinase (PI3K-AKT) signaling pathway (involving CD19, BRCA1, PDGFA, CDKN1A, and
TLR4), bladder cancer (involving CDKN1A and MMP1), and microRNAs in cancer (involving
CDKN1A, PDGFA, and BRCA1; (Fig 3, Table 4)
cyclin-dependent kinase inhibitor 1A (p21, Cip1)
matrix metallopeptidase 1 (interstitial collagenase)
pre-B-cell leukemia homeobox 1
kin of IRRE like (Drosophila)
insulin-like growth factor binding protein 7
dual specificity phosphatase 5
platelet-derived growth factor alpha polypeptide
negative regulation of phosphate metabolic process (GO:0045936)
positive regulation of cell cycle process (GO:0090068)
regulation of lipid metabolic process (GO:0019216)
positive regulation of cell cycle arrest (GO:0071158)
DNA damage response, signal transduction by p53 class mediator (GO:0030330)
As there were 111 enriched Gene-Ontologies, here we only present top 5 most significant terms according to P-value and
aFDR (False discovery rate).
Survival analysis of the enriched DEGs
The SurvExpress tool was used to assess the enriched DEGs for their ability to predict OS and
PFS in mCRC. High PDGFA expression levels were associated with poor OS, whereas high
BRCA1 and MMP1 expression levels were associated with favorable PFS. However, the
expression levels of CD19, CDKN1A, and TLR4 were neither associated with OS nor PFS (Figs 4
Mechanism of gene correlation in tumor tissues
To elucidate the mechanism underlying the gene±gene correlation of the DEGs, TCGA
RNASeq level 3 CRC data were used. BRCA1 was negatively correlated with PDGFA, CDKNA1,
CD19, and TLR4 and positively correlated with MMP1. Moreover, PDGFA was negatively
correlated with CDKNA1, BRCA1, MMP1, and TLR4. TLR4 was positively correlated with
CDKNA1 and MMP1 and negatively correlated with CD19 and BRCA1. Furthermore, CD19
was positively correlated with CDKNA1 and negatively correlated with BRCA1, MMP1, and
TLR4. However, PDGFA and CD19 were not significantly correlated (Fig 6).
The overall mortality of CRC has remained unchanged over the past decades, despite advances
in surgical and medical therapy [
]. This is due to the difficulties associated with early
detection of the disease and the development of acquired therapeutic resistance, leading to
ineffective treatment in patients with metastatic diseases [42±44]. Therefore, the etiological
factors and mechanisms of acquired therapeutic resistance must be explored to improve
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Fig 3. Significant KEGG pathways and the genes involved. Gene enrichment analysis showing KEGG pathways
significantly enriched in bevacizumab resistant HT29 xenograft tumors and the genes involved in the pathways (the
pathways are in order of their enrichment from left to right) (FDR <0.05, p-value <0.05).
survival rates and prevent disease recurrence . Microarray technology has been widely
used in the identification of therapeutic targets for diagnosis and prognosis of cancers [
Our study performed a systematic bioinformatic analysis of the microarray data of HT29
xenograft tumor models with acquired bevacizumab resistance and identified 124 DEGs, 57 of
which were upregulated and 67 were downregulated. CD19, BRCA1, PDGFA, CDKNA1,
MMP1, and TLR4 exhibited high PPI degrees and were enriched in the PI3K-AKT signaling
aKEGG: Kyoto Encyclopedia of Genes and Genome
bFDR:False discovery rate
CDKN1A, CD19, PDGFA, BRCA1, TLR4
CDKN1A, PDGFA, BRCA1
Fig 4. Kaplan-Meier survival curves presenting the prognostic relationship between high and low expression of
specific genes involved in bevacizumab resistance to overall survival (OS) in TNM clinical stage 3 and 4(A)
PDGFA, (B) BRCA1, (C) TLR4, (D) MMP1 (E) CD19 and (F) CDKN1A expression. The survival curves were
plotted using the survExpress online tool. The specific DEGs expression levels were dichotomized by median value and
stratified for TNM clinical stage. The results presented visually by Kaplan-Meier survival plots. p-values were
calculated using log-rank statistics. Patient number(N) = 270, p = Logrank p-value.
pathway, bladder cancer, and microRNAs in cancer; however, only high PDGFA expression
levels were associated with poor OS, whereas high BRCA1 and MMP1 expression levels were
associated with favorable PFS. These discrepancies may be because the study cohort was not
specifically on bevacizumab treatment, thus suggesting that biomarkers that predict OS do not
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Fig 5. Kaplan-Meier survival curves presenting the relationship between high and low expression of specific genes
involved in bevacizumab resistance to Progress Free survival in TNM clinical stage 3 and 4 (A) PDGFA, (B)
BRCA1, (C) TLR4, (D) MMP1 (E) CDKN1A and (F) CD19 expression. The survival curves were plotted using the
survExpress online tool. The specific DEGs expression levels were dichotomized by median value and stratified for
TNM clinical stage. The results presented visually by Kaplan-Meier survival plots. P-values were calculated using
logrank statistics. Patient number(N) = 242, p = Logrank p-value.
PLOS ONE | https://doi.org/10.1371/journal.pone.0189582
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Fig 6. Gene expression correlation of the DEGs involved in the pathways in the colorectal carcinoma tumor
samples from TCGA data base. TCGA RNASeq Level 3 data was used and Pearsons correlation coefficients (-1 to 1)
were calculated. Number of patients = 653. The percentage presents the coefficient of variation of the genes, the lines
presents negative correlation and the arrow presents positive correlation (p-value <0.05).
specifically predict PFS. Therefore, to confidently interpret the study results, these biomarkers
require further assessment in patients specifically treated with bevacizumab.
The results of this study reveal PDGFA overexpression to be associated with bevacizumab
resistance and the prognosis of patients with mCRC. These results are consistent with those of
a previous study, which identified PDGFA as a potential predictor of therapeutic resistance
and an individual prognostic marker for bevacizumab treatment, because PDGFA expression
was observed to be decreased after single-dose bevacizumab treatment in responders but
remained unchanged in nonresponders [
]. PDGFA targeting with the PDGF receptor has
been reported to increase chemotherapeutic sensitivity in different cancers [47±50]. Therefore,
our study supports the current understanding that PDGFA acts not only as a predictor of
treatment response but also as a prognostic factor, because PDGFA upregulation not only limited
the response to bevacizumab but also affected the prognosis of patients with mCRC in this
study. Notably, PDGF overexpression has been implicated in bevacizumab resistance and poor
prognosis in bevacizumab-treated patients because the PDGF pathway is considered an
alternative pathway in the development of bevacizumab resistance [
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The expression levels of MMP1 and BRCA1 were associated with PFS in patients with
mCRC. Although this study is the first to demonstrate the aforementioned relationship in
mCRC, MMPs have received attention in terms of their role in the mechanism underlying
resistance to antiangiogenic therapy, because increased MMP2 and MMP9 expression levels
have been associated with resistance to the anti-VEGF and antiplacental growth factor drug
aflibercept and with poor OS [
]. Furthermore, MMP1 expression has been strongly
associated with tumor metastasis and adverse outcomes in mCRC and has been suggested as a
potential prognostic and therapeutic target [55±59]. A previous study reported that BRCA1 is
associated with early onset CRC and functions as a DNA repair gene to cytotoxic drugs [
BRCA1 has been considered as a predictor of treatment response and prognosis in breast,
ovarian, and lung cancers [61±66]; however, its role in mCRC and bevacizumab resistance is
yet to be explored. The present results suggest that BRCA1 may exert protective effects in
mCRC; therefore, BRCA1 should be thoroughly studied because BRCA1 targeting might not
only increase the prognostic and therapeutic effects of bevacizumab but also affect the
expression levels of its associated genes, namely PDGFA, CDKN1A, TLR4, and MMP1.
CD19, CDKN1A, and TLR4 have also been reported to influence therapeutic resistance or
overall prognosis in cancer. CD19 has been associated with chemotherapy and multidrug
resistance in many hematological tumors, and plays a central role in targeted therapeutics against
B-cell malignancies (because of its expression patterns throughout the B-cell lineage), and
against most B-cell malignancies with successful preclinical experiments and first-generation
clinical trials [67±72]. CDKN1A has been implicated in cell cycle regulation, cell death, DNA
repair, and cell motility [
]. Studies have demonstrated CDKN1A overexpression to be
associated with poor prognosis in gastric and esophageal carcinomas [
]. Furthermore, studies
have reported that TLR4 plays a role in CRC; polymorphisms increasing TLR4 signaling led to
a highly aggressive CRC, whereas those reducing TLR4 signaling exerted protective effects [
]. In addition, high TLR4 expression levels have been associated with highly advanced grades
of colonic neoplasia and with lower OS, a high probability of CRC relapse, and the presence of
liver metastases in humans [78±81]. Studies have also suggested TLR4 to promote angiogenesis
in different cancers by activating the PI3K-AKT signaling pathway to induce VEGF
expression. In addition, TLR4 inhibition is associated with VEGF inhibition [82±84]. This finding
can explain TLR4 downregulation in the bevacizumab-resistant tumors in this study; however,
in vitro validation of this finding is required.
Notably, five of the six genes that were commonly enriched as well as associated with
bevacizumab resistance belonged to the PI3K-AKT signaling pathway. Therefore, we suggest that
the PI3K-AKT signaling pathway is responsible for restraining the therapeutic efficacy of
bevacizumab in mCRC. This observation is in accordance with the results of previous studies,
which have suggested that modifications in the PI3K-AKT signaling pathway increase
bevacizumab resistance as an alternative pathway to VEGF inhibition [85±87]. Moreover, the
occurrence of mutations in the PI3K-AKT signaling pathway remains the main challenge for mCRC
treatment with new biological agents [
86, 88, 89
The present findings provide novel data that could predict bevacizumab treatment response
and the emergence of resistance. Furthermore, this approach can predict patient prognosis;
however, additional studies are required to validate the study findings and determine their
S1 Table. Down-regulated bevacizumab-resistant genes.
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Conceptualization: Chien-Yu Huang, Chi-Ming Chu, Po-Li Wei.
Data curation: Precious Takondwa Makondi, Chia-Hwa Lee, Yu-Jia Chang.
Investigation: Precious Takondwa Makondi, Chi-Ming Chu, Po-Li Wei.
S2 Table. Up-regulated genes.
Formal analysis: Chi-Ming Chu.
Funding acquisition: Po-Li Wei.
Methodology: Chi-Ming Chu.
Project administration: Yu-Jia Chang.
Resources: Yu-Jia Chang, Po-Li Wei.
Software: Precious Takondwa Makondi.
Supervision: Yu-Jia Chang, Po-Li Wei.
Validation: Chia-Hwa Lee, Po-Li Wei.
Visualization: Yu-Jia Chang.
Writing ± original draft: Precious Takondwa Makondi, Yu-Jia Chang, Po-Li Wei.
Writing ± review & editing: Chia-Hwa Lee, Chien-Yu Huang, Yu-Jia Chang, Po-Li Wei.
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