This preprint is under consideration at Journal of Big Data. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
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Research
Seyed Shahriar Arab
Tarbiat Modares University Faculty of Biological Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2739-1617
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.
Keywords
Machine Learning Algorithms, Network Topology, Cancer, Drug Resistance, Classification, Feature Selection
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CURRENT STATUS: Under Revision
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Posted 11 Mar, 2021
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Review #4 received
Received 08 Apr, 2021
Editorial decision: Major Revision
On 08 Apr, 2021
Review #7 received
Received 06 Apr, 2021
Review #2 received
Received 06 Apr, 2021
Review #5 received
Received 06 Apr, 2021
Review #1 received
Received 03 Apr, 2021
Reviewer #7 agreed
On 24 Mar, 2021
Reviewer #6 agreed
On 22 Mar, 2021
Reviewer #5 agreed
On 22 Mar, 2021
Reviewer #4 agreed
On 21 Mar, 2021
Reviewer #3 agreed
On 21 Mar, 2021
Review #3 received
Received 21 Mar, 2021
Reviewer #2 agreed
On 20 Mar, 2021
Reviewer #1 agreed
On 20 Mar, 2021
Review #? received
Received 20 Mar, 2021
Reviewers invited
Invitations sent on 20 Mar, 2021
Editor assigned
On 05 Mar, 2021
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On 05 Mar, 2021
Editor invited
On 05 Mar, 2021
First submitted
On 28 Feb, 2021
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This preprint is under consideration at Journal of Big Data. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Seyed Shahriar Arab
Tarbiat Modares University Faculty of Biological Sciences
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2739-1617
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 11 Mar, 2021
No community comments so far
Review #4 received
Received 08 Apr, 2021
Editorial decision: Major Revision
On 08 Apr, 2021
Review #7 received
Received 06 Apr, 2021
Review #2 received
Received 06 Apr, 2021
Review #5 received
Received 06 Apr, 2021
Review #1 received
Received 03 Apr, 2021
Reviewer #7 agreed
On 24 Mar, 2021
Reviewer #6 agreed
On 22 Mar, 2021
Reviewer #5 agreed
On 22 Mar, 2021
Reviewer #4 agreed
On 21 Mar, 2021
Reviewer #3 agreed
On 21 Mar, 2021
Review #3 received
Received 21 Mar, 2021
Reviewer #2 agreed
On 20 Mar, 2021
Reviewer #1 agreed
On 20 Mar, 2021
Review #? received
Received 20 Mar, 2021
Reviewers invited
Invitations sent on 20 Mar, 2021
Editor assigned
On 05 Mar, 2021
Submission checks complete
On 05 Mar, 2021
Editor invited
On 05 Mar, 2021
First submitted
On 28 Feb, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Most of the current cancer treatment approaches are invasive along with a broad spectrum of side effects. Furthermore, cancer drug resistance known as chemoresistance is a huge obstacle during treatment. This study aims to predict the resistance of several cancer cell-lines to a drug known as Cisplatin. In this papers the NCBI GEO database was used to obtain data and then the harvested data was normalized and its batch effects were corrected by the Combat software. In order to select the appropriate features for machine learning, the feature selection/reduction was performed based on the Fisher Score method. Six different algorithms were then used as machine learning algorithms to detect Cisplatin resistant and sensitive samples in cancer cell lines. Moreover, Differentially Expressed Genes (DEGs) between all the sensitive and resistance samples were harvested. The selected genes were enriched in biological pathways by the enrichr database. Topological analysis was then performed on the constructed networks using Cytoscape software. Finally, the biological description of the output genes from the performed analyses was investigated through literature review. Among the six classifiers which were trained to distinguish between cisplatin resistance samples and the sensitive ones, the KNN and the Naïve Bayes algorithms were proposed as the most convenient machines according to some calculated measures. Furthermore, the results of the systems biology analysis determined several potential chemoresistance genes among which PTGER3, YWHAH, CTNNB1, ANKRD50, EDNRB, ACSL6, IFNG and, CTNNB1 are topologically more important than others. These predictions pave the way for further experimental researches.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
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