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Identification of a six-gene signature predicting overall survival for Oxaliplatin and 5-Fluorouracil resistant Colon cancer and potential drug-repurposing

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Background Oxaliplatin (L-OHP) and 5-fluorouracil (5-FU) resistance in colorectal cancer (CRC) is a major medical problem. Therefore, detailed mechanisms and predictive markers are urgently needed. The aim of this study was to identify key pathways, a robust prognostic gene signature and potential drug-repurposing. Methods In order to confirm the predictive markers and detailed molecular mechanisms of L-OHP and 5-Fu chemoresistant CRC, we performed weighted correlation network analysis (WGCNA), an unsupervised analysis method, to identify the chemoresistant CRC significantly related genes. Then, the gene prognostic model was conducted by Univariate Cox regression and Lasso penalized Cox regression analysis. Subsequently, the time-dependent receiver operating characteristic (ROC) and Kaplan-Meier survival curve were performed to assess the prognostic capacity of the model. Simultaneously, pathway enrichment was done to identify the key pathways involved in chemoresistant CRC. Moreover, we explored how the hub genes interacted with key pathways and transcription factors. Then, we found the potential drug target by the subcellular location fo hub genes. Finally, we identified the potential drug-repurposing by virtual screening for chemoresistant CRC according to ZINC 15 database. Results We identified the key pathways using KEGG over-representation test and Gene Set Enrichment Analysis (GSEA): Ribosome KEGG pathway. Moreover, six hub-genes prognostic model was conducted by Univariate Cox regression and Lasso penalized Cox regression analysis. Additionally, the detailed interactions among the pathway and hub genes (RBM6, PNN, LEF1, ANO1, PAFAH1B3 and BHLHE41) were examined by protein-protein interaction (PPI) network and shortest-pathway analysis. Furthermore, ANO1 was considered the potential drug target based on the subcellular location and ZINC000018043251 was verified the potential drug by virtual screening. Conclusions Our study identified a novel six-gene resistant signature for CRC prognosis prediction and the molecular details of these interactions between hub genes (RBM6, PNN, LEF1, ANO1, PAFAH1B3 and BHLHE41) and Ribosome key pathways. Furthermore, ZINC000018043251 was verified the potential drug for ANO1 by virtual screening, which might help to improve the outcome of CRC patients.
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Identication of a six-gene signature predicting
overall survival for Oxaliplatin and 5-Fluorouracil
resistant Colon cancer and potential drug-
repurposing
Feng Yang
Sun Yat-Sen University https://orcid.org/0000-0002-7976-1640
Shaoyi Cai
Sun Yat-Sen University
Riya Su
Sun Yat-Sen University
Li Ling
Sun Yat-Sen University
Liang Tao ( taol@mail.sysu.edu.cn )
Sun Yat-Sen University
Qin Wang
Sun Yat-Sen University
Research article
Keywords: Chemoresistance, Prognosis, 5-uorouracil, Oxaliplatin, Drug-repurposing, Virtual screening
DOI: https://doi.org/10.21203/rs.3.rs-43698/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. 
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Abstract
Background
Oxaliplatin (L-OHP) and 5-uorouracil (5-FU) resistance in colorectal cancer (CRC) is a major medical
problem. Therefore, detailed mechanisms and predictive markers are urgently needed. The aim of this
study was to identify key pathways, a robust prognostic gene signature and potential drug-repurposing.
Methods
In order to conrm the predictive markers and detailed molecular mechanisms of L-OHP and 5-Fu
chemoresistant CRC, we performed weighted correlation network analysis (WGCNA), an unsupervised
analysis method, to identify the chemoresistant CRC signicantly related genes. Then, the gene
prognostic model was conducted by Univariate Cox regression and Lasso penalized Cox regression
analysis. Subsequently, the time-dependent receiver operating characteristic (ROC) and Kaplan-Meier
survival curve were performed to assess the prognostic capacity of the model. Simultaneously, pathway
enrichment was done to identify the key pathways involved in chemoresistant CRC. Moreover, we
explored how the hub genes interacted with key pathways and transcription factors. Then, we found the
potential drug target by the subcellular location fo hub genes. Finally, we identied the potential drug-
repurposing by virtual screening for chemoresistant CRC according to ZINC 15 database.
Results
We identied the key pathways using KEGG over-representation test and Gene Set Enrichment Analysis
(GSEA): Ribosome KEGG pathway. Moreover, six hub-genes prognostic model was conducted by
Univariate Cox regression and Lasso penalized Cox regression analysis. Additionally, the detailed
interactions among the pathway and hub genes (RBM6, PNN, LEF1, ANO1, PAFAH1B3 and BHLHE41)
were examined by protein-protein interaction (PPI) network and shortest-pathway analysis. Furthermore,
ANO1 was considered the potential drug target based on the subcellular location and
ZINC000018043251 was veried the potential drug by virtual screening.
Conclusions
Our study identied a novel six-gene resistant signature for CRC prognosis prediction and the molecular
details of these interactions between hub genes (RBM6, PNN, LEF1, ANO1, PAFAH1B3 and BHLHE41) and
Ribosome key pathways. Furthermore, ZINC000018043251 was veried the potential drug for ANO1 by
virtual screening, which might help to improve the outcome of CRC patients.
Background
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Colorectal cancer (CRC) is the third most frequently diagnosed cancer worldwide with over one million
cases annually, and is also the fourth commonest cause of cancer death worldwide [1]. Owing to the
chemotherapy, the mortality rate has decreased during the past decades. The chemotherapy, especially,
the combination of oxaliplatin (L-OHP) and 5-uorouracil (5-FU) was the primary treatment for CRC to
improve overall survival and quality of life for patients [2, 3].
However, despite receiving standard chemotherapy regimens (5-FU and L-OHP), most of CRC patients
eventually generate chemoresistance and cause to treatment failure [2]. This highlights the need to better
understand the mechanism of drug resistance and how can it be targeted to the patients’ advantage.
Even though great efforts have spent to explore the cause of CRC tolerance to chemotherapy, the
molecular mechanisms of L-OHP and 5-FU resistance in CRC remain unclear. Thus, the identication of
predictive biomarkers for response and the mechanism involved in chemoresistance are of great
importance.
Some studies were aimed to construct a prognosis model for predicting chemoresistance, but it has not
been well established, since most previous relevant studies constructed their prognostic models using
parameters from clinical baseline characteristics (such as blood sugar levels [4], body mass index [5] and
age [6]) and single molecular biomarkers (CDO1 [7] and MASTL [8] ). Then, a thorough understanding of
L-OHP and 5-FU mechanisms of action and interventions to overcome resistance are still relatively
needed. Recently, the development of high-throughput sequencing technology and bioinformatics showed
a great advantage for CRC prognosis prediction and the mechanism research [9, 10]. But the genetic
researches that could predict the recurrence of CRC with chemoresistance are still limited and need
further study. Therefore, the aim of this study was to identify the molecular mechanisms in CRC with
chemoresistance, and a robust prognostic gene signature, that would support the development of novel
targeted therapies.
Methods
Data collection
The clinical information and the Fragments PerKilobase Million (FPKM) values of mRNA expression data
containing 482 colon cancer and 42 normal control samples were downloaded from The Cancer Genome
Atlas (TCGA, https://cancergenome.nih.gov/). According to the clinical data, we obtained 22 CRC patients
who were only treated with L-OHP and 5-FU (Table S1). GSE17536 dataset was downloaded from the
GEO database [11]. GSE17536 was performed on Affymetrix Human Genome U133 Plus 2.0 Array
(Affymetrix, Santa Clara, CA, USA), including 177 colon cancer patients. Data were downloaded from the
publicly available database hence it was not applicable for additional ethical approval.
Weighted gene correlation network analysis (WGCNA)
WGCNA was performed to obtain the modules which was associated with the chemotherapeutic
resistance (L-OHP and 5-FU). A total of 6175 genes in the top 35 % of variance were screened from the
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data set containing 22 CRC patients who were only treated with L-OHP and 5-FU, using the R package
WGCNA [12]. In a brief, we chose the power = 6 for weighting the correlation matrix following an
approximate scale free topology. Subsequently, gene expression modules with similar patterns were
identied based on a gene cluster dendrogram and by using the dynamic tree cut method
(minModuleSize = 50, mergeCutHeight = 0.3, deepSplit = 1). The unsigned network type was used to keep
the relationships between modules and chemotherapeutic resistance. Generally, the correlation between
the phenotype and module eigengenes was considered as the module-trait associations. Therefore,
modules with
p
< 0.05 and person correlation value > 0.4 were considered signicantly related to the
chemoresistant traits. Additionally, the threshold of genes related to chemotherapeutic resistance was set
at person correlation of gene vs. module-membership value > 0.5 and person correlation of gene vs. trait-
correlation value > 0.5.
Establishment of the prognostic gene signature
Only CRC patients with a follow-up period longer than 1 months were included for survival analysis.
Univariate Cox regression analysis was performed by R package survival (https://CRAN.R-
project.org/package=survival) to identify prognostic genes, and genes were considered signicant with a
cut-off of
p
< 0.05. Then, Lasso-penalized Cox regression analysis was performed to further select
prognostic genes for overall survival in patients with CRC. Then a prognostic gene signature was
constructed based on a linear combination of the regression coecient derived from the Lasso Cox
regression model coecients (β) multiplied with its mRNA expression level.
The risk score=
The optimal cut-off value was investigated by the R package survminer
(http://www.sthda.com/english/rpkgs/survminer/) and two-sided log-rank test. Patients were classied
into a high-risk and low-risk according to the threshold (medium of risk score). The time-dependent
receiver operating characteristic (ROC) curve was drawn to evaluate the predictive value of the prognostic
gene signature for overall survival using the R package survivalROC [13]. The Kaplan-Meier survival curve
combined with a log-rank test was used to compare the survival difference in the high- and low-risk group
using the R package survival. As the prognostic genes could predict the prognosis of CRC, so they were
considered as the hub genes among the genes related to the chemotherapeutic resistance. Then the
predictive value of the prognostic hub-gene signature was further investigated in the GSE17536 testing
cohort.
Independent prognostic role of the gene signature
To investigate whether the prognostic hub-gene signature could be independent of other clinical
parameters (including gender, age and stage), univariate and multivariate Cox analyses were performed
by R package survival using the Cox regression model method with forwarding stepwise procedure with
the cutoff of
p
< 0.01.
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Pathway enrichment analysis
Kyoto Encyclopedia of Genes and Genomes (KEGG) is a data base for systematic analysis of gene
function which links genomic information with higher-level systemic function [14]. After obtaining the
genes related to the chemotherapeutic resistance by WGCNA, the R package clusterProler [15] was used
to perform a KEGG over-representation test, at a cutoff of
p
< 0.05. Simultaneously, Gene Set Enrichment
Analysis (GSEA) were performed [16] to explore the potential molecular mechanisms, with a cutoff of
FDR < 0.05 and
p
< 0.05. Then, combining the two results to get the key pathway involved in the
chemoresistance.
Protein-protein interaction
The Human protein-protein interaction (PPI) network used in this analysis was retrieved from the STRING
database (medium condence: 0.2) [17]. The PPI network was constructed with the genes related to the
chemotherapeutic resistance and core genes involved in the key pathways were visualized using
Cytoscape 3.7.1 [18].
Shortest pathway analysis
To nd possible connections between hub genes and the signaling pathways of interest, we performed a
shortest-pathway analysis. The shortest pathway is dened as the minimum number of edges required to
travel from one node in the PPI network to another. The Python package NetworkX
(http://networkx.github.io) was applied to compute all the shortest paths between hub genes and the key
pathway genes.
Identing of Potential Drug-repurposing
The 3D structure of ANO1 was download from SWISS-MODEL (Q5XXA6, https://swissmodel.expasy.org/)
and its biding site was found by Schrodinger maestro 2019-1 [19]. Then, we built a library of 2106 FDA
approved drugs obtained from ZINC15 database [20]. Finally, we performed virtual screening and
molecular docking by Schrodinger maestro 2019-1 to nd the potential drug-repurposing.
Statistical analysis
Statistical analysis was performed using R 3.5.3 (R Foundation for Statistical Computing, Vienna,
Austria). Qualitative variables were analyzed using the Pearson test or Fisher’s exact test; quantitative
variables were analyzed using a t-test for paired samples. Multiple groups of normalized data were
analyzed using one-way ANOVA. If not specied above,
p
< 0.05 was considered statistically signicant.
Results
Construction of weighted expression network to identify chemotherapeutic resistant genes
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A total of 22 samples who were only treated with L-OHP and 5-FU were include in WGCNA. We selected β
= 6 as the appropriate soft-thresholding value to ensure a scale-free network and 20 modules were
identied. These modules were shown in distinct colors (Figure 1A). Then, the correlations between
module eigengenes and clinical traits were determined (Figure 1B, Table S2). The cutoff to screen the
chemoresistant-related modules was
p
< 0.05 and person correlation value > 0.4. Based on the cutoff, ve
modules signicantly related to chemotherapeutic resistance were brown module, magenta module, tan
module, blue module and greenyellow module (Figure 1B). A total of 262 genes (Table S3) related to
chemotherapeutic resistance were identied at the cutoff of person correlation of gene vs. module-
membership value > 0.5 and person correlation of gene vs. trait-correlation value > 0.5.
Establishment of the six-hub -gene-based prognostic gene signature
22 patients with a follow-up period longer than 1 months and 262 chemoresistant genes were included in
the TCGA cohort to train the prognostic model. Univariate Cox regression model and Lasso-penalized Cox
analysis were performed to construct the prognostic model. The six hub genes in the prognostic model
were RBM6, PNN, LEF1, ANO1, PAFAH1B3 and BHLHE41. The risk score = 0.0.3576 × expression level of
ANO1 + 0.00846 × expression level of BHLHE41 + 0.03693 × expression level of LEF1 – 0.00260 ×
expression level of PAFAH1B3 + 0.00357 × expression level of PNN + 0.04727 × expression level of
RBM6. Patients were divided into a high- and low-risk group. The area under the ROC curve (AUC) for
overall survival was 0.730 (Figure 2A). The overall survival was signicantly poorer in the high-risk group
than the low-risk group (p = 2.35e-3 < 0.05, Figure 2B). To validate the predictive value of the six-gene
signature, we calculated risk score with the same formula for patients in GSE17536. The AUC for overall
survival was 0.720 (Figure 2C). What’ more, consistent with the results in the TCGA cohort, patients in the
high-risk group shown signicantly poorer overall survival than patients in the low-risk group (P = 3.862e-3
< 0.05, Figure 2D).
Independent prognostic role of the gene signature
22 patients with complete information including gender, age and stage were included for further analysis.
Univariate and multivariate Cox regression analysis indicated that stage and our prognostic model were
both an independent prognostic factor for overall survival (
p
< 0.01, Figure 3A and B). Moreover,
according to the Hazard ratio, our prognostic was more sensitive than stage. Then, the results were
valeted in the GSE17536 datasets. Stage and our prognostic model were also found to be an independent
prognostic factor for overall survival (
p
< 0.01) and our prognostic model was more sensitive (Figure 3C
and D), which was consistent with the results from TCGA cohort.
Identication the key pathways through KEGG over-representation test and GSEA
To identify the key pathways involved in CRC with chemotherapeutic resistance. KEGG over-expression
pathway analyses were conducted by clusterProler to examine the function of previously identied
chemoresistant genes through WGCNA. Based on the KEGG database, chemoresistant genes were highly
enriched in eight pathways (Figure 4A). As another method for pathway analysis, GSEA targets the whole
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genes expression other than chemoresistant genes, which was obtained by Articial threshold. GSEA
analysis produced a total of 3 pathways which were enriched in resistant group and 19 pathways were in
the sensitive groups (Table S4). Based on the KEGG over-representation test and GSEA results (Figure
4B), one overlapping pathway Ribosome KEGG pathway was identied.
Protein- protein interaction (PPI) network and shortest-pathway analysis
To explore the interaction of hub genes and core genes involved in the Ribosome KEGG pathway, we
subjected the genes to a search for the retrieval of string databases. The PPI network comprised 277
nodes and 4503 edges. Then, we applied a shortest-path analysis on the PPI network. We found that
RBM6 could directly interact with PNN (Figure 4C). What’s more, as showed in the Figure 4D, PNN, LEF1,
RBM6 and PAFAH1B3 could directly interact with genes involved in Ribosome pathway. ANO1 and
BHLHE41 should through other genes to crosstalk with the Ribosome pathway.
Identify Potential drug-repurposing
According to the subcellular location information of UniPort database, we found that RBM6, PNN, LEF1,
and BHLHE1 were located in the nucleus, PAFAH1B3 was in the cytoplasm, and ANO1 belonged to a
transmembrane protein. So, we speculated that ANO1 was the potential drug target. Then, we utilized the
virtual screening technique by Schrodinger maestro 2019-1 to identify potential drug-repurposing for
ANO1. The 3D protein structure of ANO1 was downloaded from SWISS (Q5XXA6,) and the active site was
found by Schrodinger maestro 2019-1 (Figure S1). According to the glide scores (Table S5), the
ZINC000018043251 was the top one hit from output of structure-based virtual screening process. And
there were 6 H-bond and 1 Pi-pi interaction in the ligand -protein complex (Figure 5).
Discussion
Although advances in diagnosis and cancer therapy were encouraging in the past decade, CRC remains a
high-risk digestive tract tumor with the low overall survival rate due to CRC chemoresistance [21]. So, L-
OHP and 5-FU resistance in CRC is a major medical problem. Therefore, there is currently an urgent need
to explore the molecular mechanisms of chemoresistance in CRC and identify predictors of therapy in
CRC.
The aim of the present study was to identify the molecular mechanisms in CRC with chemoresistance,
and a robust prognostic gene signature, that would support the development of novel targeted therapies.
Our initial genome-wide screening, conducted in a small panel (n = 22) of CRC patients treated with 1st
line L-OHP and 5-FU chemotherapy, pointed to a list of 262 candidate mRNA associated with
chemoresistance by WGCNA. Subsequently, six hub genes were identied by the prognostic signature
module based on the TCGA data set (n = 524). And its eciency was validated in GSE17536 (n = 177).
Simultaneously, KEGG over-representation test and GSEA were performed to identify the key pathway.
Furthermore, the molecular details of the interactions between hub genes and key pathways were
explored by the PPI network and shortest pathway analysis. Moreover, ANO1 was considered the potential
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drug target based on the subcellular location and ZINC000018043251 was veried the potential drug by
virtual screening Taking together, in this study, we explored the detailed mechanisms involved in the
chemotherapeutic (L-OHP and 5-FU) resistance and constructed a six-gene prognostic model to predict
the outcome of CRC.
Unfortunately, most of CRC patients eventually generate chemoresistance [22] and approximately 50% of
treated patients do not obtain any benet [23]. Thus, the identication of predictive biomarkers for
response is of great importance. Considering the multi-faceted role of mRNAs in the development and
prognosis of CRC [24], we screened the genes related to the L-OHP and 5-FU resistance by WGCNA and
then constructed a prognostic risk assessment model based on multiple mRNAs to serve as the basis for
clinical treatment. Unlike previous studies on CRC prognosis, the present study used the genes related to
chemoresistance to construct the prognosis model, so it might help to improve the outcome of the CRC
who suffered from chemoresistance. In terms of the validity and stability assessment of the prognostic
model, we not only performed the standard ROC analysis used in most studies [25], but also validated the
six-gene prognosis risk assessment model using the additional date sets. Combing the results of Fig.3
and Fig.5, the six-gene prognostic model could signicantly distinct the outcome of CRC. This suggested
that our analysis was accurate and robust and supported the development of novel targeted therapies.
Moreover, six hub genes (RBM6, PNN, LEF1, ANO1, PAFAH1B3 and BHLHE41) were identied by WGCNA
and prognostic model, were consistent with previous studies. Such as, knockdown ANO1 expression
could increase the apoptosis effects induced by L-OHP and 5-FU [26]. Moreover, RNA sequencing revealed
PNN could be a predictive biomarker of clinical outcome in stage colorectal cancer patients treated with
L-OHP and 5-Fu chemotherapy [27]. Additionally, it was reported that LEF1 was associated with 5-FU [28]
and L-OHP caused bending of DNA produced targets for LEF1-binding [29]. And a study showed that
LEF1 could regulate MACC1 to promote the drug resistance [30]. What’ more, BHLE41 could be used to
identify patients who would actually receive benet from oxaliplatin treatment [31]. Though, there were no
evidence that PAFAH1B3 was associated with L-OHP and 5-FU resistance in CRC, PAFAH1B3 was related
to prognosis and played an important role in multiple type of cancers including Lung, breast, ovarian,
melanoma [32, 33]. In the present study, we also explored the potential drug-repurposing by virtual
screening targeted ANO1, which might expand potential therapeutic strategies for L-OHP and 5-FU
resistant CRC treatment. We found the ZINC000018043251and was the potential drugs for ANO1.
Compared with previous studies, which were focused on the relationship between the expression of ANO1
and chemoresistance, we identied not only ANO1 was the therapeutic target but also its potential drugs.
In this way, we could accelerate drug development to improve the outcome of L-OHP and 5-FU resistant
CRC patients. Taking together, previous studies support our nding that the hub genes (PNN, ANO1, LEF1
and BHLHE41) was associated with 5-Fu or L-OHP resistance, which suggested the accuracy and robust
of our analysis.
CRC remains a high-risk digestive tract tumor with low overall survival rate due to CRC chemoresistance
[21]. Therefore, a thorough understanding of L-OHP and 5-FU mechanisms of action and interventions to
overcome resistance are still relatively required. In this study, the key pathway Ribosome KEGG pathway
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involved in chemoresistance, was identied by employing a KEGG over-representation test and GSEA. And
the result was consistent with the previous studies. As was reported that L-OHP killed cells by inducing
ribosome biogenesis stress, so the imbalance of Ribosome pathway might inuence the L-OHP
resistance in CRC [34]. And Eukaryotic initiation factor 4A2 (EIF4A2) was required for mRNA binding to
ribosome and promoted experimental metastasis and L-OHP resistance in CRC [35]. Generally speaking,
5-FU used in chemotherapy worked by hindering the process of ribosome biogenesis [36, 37]. And
emerging evidence supports targeting the Ribosome biogenesis could avoid chemoresistance [38]. Taken
together, the previous studies suggested that Ribosome pathway might involve in the L-OHP and 5-FU
resistance in CRC. However, the previous researches didn’t show the detailed mechanisms of CRC
chemoresistance to L-OHP and 5-FU. So, in order to solve the problems, based on the PPI of the hub
genes and core genes involved in Ribosome pathway, we found the molecular details of these
interactions by short pathway analysis. That suggested one most possible molecular mechanism
involved in chemoresistance, which might help overcome the chemoresistance.
Conclusion
Taken together, our study identied a novel six-gene resistant signature for CRC prognosis prediction
based on TCGA and GEO data set. That might help patients to obtain an objective response to rst-line
treatment. What’s more, the molecular details of these interactions between hub genes (RBM6, PNN, LEF1,
ANO1, PAFAH1B3 and BHLHE41) and Ribosome key pathways might help a thorough understanding of
its mechanisms of action and interventions to overcome resistance. Furthermore, ANO1 was considered
the potential drug target based on the subcellular location and ZINC000018043251 was veried the
potential drug by virtual screening, which might help to improve the outcome of CRC patients.
Declarations
Acknowledgements
Not applicable.
Funding
This work was supported by the National Natural Science Foundation of China (grant No. 81473234), the
Joint Fund of the National Natural Science Foundation of China (grant No. U1303221), the Fundamental
Research Funds for the Central Universities (grant No.16ykjc01) and the grant from Department of
Science and Technology of Guangdong Province (grant No.20160908).
Availability of data and materials
Publicly available datasets were analyzed in this study. The data can be found here:
TCGA, https://portal.gdc.cancer.gov.
Page 10/18
GSE17536, http://www.ncbi.nlm.nih.gov/geo/.
The Human Protein Atlas, http://www.proteinatlas.org/.
ZINC 15 database, https://zinc15.docking.org/.
UniPort, https://www.uniprot.org/.
Contributions
Liang Tao and Qin Wang conceptualized and developed an outline for the manuscript and revised the
manuscript. Feng Yang and Shaoyi Cai conceived, designed, analyzed the data, and write the manuscript.
Li Ling and Riya Su generated the gures and tables. All authors read and approved the nal manuscript.
Corresponding author
Correspondence to Liang Tao and Qin Wang.
Ethics declarations
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
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Figures
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Figure 1
Identication of modules and genes associated with the chemotherapeutic resistance. (A) Dendrogram of
6175 genes in top 35% of variance clustered based on a dissimilarity measure (1 - TOM). (B) Heatmap of
the correlation between module eigengenes and the clinical trait (resistance). Each cell contains the
corresponding correlation coecient and p-value.
Page 15/18
Figure 2
Time-dependent ROC analysis and Kaplan-Meier analysis for the six-gene signature in CRC. (A) Time-
dependent ROC analysis of six-gene signature in TCGA cohort. (B) Kaplan-Meier curve of the six-gene
signature in TCGA cohort. (C) Time-dependent ROC analysis of six-gene signature in GSE17536 cohort.
(D) Kaplan-Meier curve of the six-gene signature in GSE17536 cohort. CRC, colorectal cancer; ROC,
receiver operating characteristic; TCGA, The Cancer Genome Atlas
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Figure 3
Forrest plot of the Univariate and multivariate Cox regression analysis in TCGA cohort (A univariate, B
multivariate) and GSE17536 cohort (C univariate, D multivariate).
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Figure 4
Pathway enrichment and shortest-pathway analysis. (A) KEGG over-representation test pathway analysis
of all chemoresitant genes by clusterProler (p < 0.05). (B) The result of GSEA between chemoresistance
and chemosensitivity groups (FDR < 0.05 and p < 0.05). (C) The shortest pathway among six hub genes
based on the PPI network. (D) The molecular details interactions between six hub genes and key pathway
(Ribosome). KEGG, Kyoto Encyclopedia of Genes and Genomes; GSEA, Gene Set Enrichment Analysis;
PPI, protein-protein interaction.
Page 18/18
Figure 5
The potential drugs based on the structure of ANO1. The yellow represented Hydrogen bonds; The green
represented Pi interactions; The purple represented salt bridges.
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Background Inherent or acquired chemo resistance in cancer patients has been a perpetual limitation in cancer treatment. Expanding knowledge on essential cellular processes opens a new window for therapeutic targeting. Ribosome biogenesis is a process that shows potential due to its fundamental role in cell development and contribution to tumorigenesis as a result of its upregulation. Inhibiting components of ribosome biogenesis has been explored and has shown interesting results. Yet, an important key component, methyltransferase Fibrillarin (FBL), which influences both the abundance and composition of ribosomes, has not been exploited thus far. Methods In this literature review, we describe relevant aspects of ribosome biogenesis in cancer to emphasize the potential of FBL as a therapeutic target, in order to lower the genotoxic effects of anti-cancer treatment. Results Remarkably, the amplification of the 19q13 cytogenetic band, including the gene coding for FBL, correlated to cell viability and resistance in pancreatic cells as well as to a trend toward a shorter survival in pancreatic cancer patients. Targeting ribosome biogenesis, more specifically compared to the secondary effects of chemotherapeutics such as 5-fluorouracil or oxaliplatin, has been achieved by compound CX-5461. The cell dependent activity of this Pol I inhibitor has been reported in ovarian cancer, melanoma and leukemia models with active or mutated p53 status, presenting a promising mechanism to evade p53 resistance. Conclusion Targeting critical ribosome biogenesis components in order to decrease the genotoxic activity in cancer cell looks promising. Hence, we believe that targeting key protein rRNA methyltransferase FBL shows great potential, due to its pivotal role in ribosome biogenesis, its correlation to an improved survival rate at low expression in breast cancer patients and its association with p53.
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Background Cysteine dioxygenase type 1 (CDO1) acts as a tumor suppressor gene, and its expression is regulated by promoter DNA methylation in human cancer. The metabolic product mediated by CDO1 enzyme increases mitochondrial membrane potential (MMP), putatively representing chemoresistance. The aim of this study is to investigate the functional relevance of CDO1 gene in colon cancer with chemotherapy. Patients and Methods We investigated 170 stage III colon cancer patients for CDO1 methylation by using quantitative methylation-specific polymerase chain reaction (PCR). To elucidate the functional role of CDO1 gene in colorectal cancer (CRC) biology, we established cell lines that stably express CDO1 gene and evaluated chemosensitivity, MMP, and tolerability assay including anaerobic environment. Results Hypermethylation of CDO1 gene was an independent prognostic factor for stage III colon cancer on multivariate prognostic analysis. Surprisingly, patients with CDO1 hypermethylation exhibited better prognosis than those with CDO1 hypomethylation in stage III colon cancer with postoperative chemotherapy (P = 0.03); however, a similar finding was not seen in those without postoperative chemotherapy. In some CRC cell lines, forced expression of CDO1 gene increased MMP accompanied by chemoresistance and/or tolerance under hypoxia. Conclusion CDO1 methylation may be a useful biomarker to increase the number of stage III colon cancer patients who can be saved by adjuvant therapy. Such clinical relevance may represent the functionally oncogenic property of CDO1 gene through MMP activity.