See the published version of this article at BMC Cancer.
This is a preprint, 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 article
Network-based identification of biomarkers for colon adenocarcinoma
Xiaoping Liu
Corresponding Author
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Cancer.
Background: As one of the most common cancers with high mortality in the world, we are still facing a huge challenge in the prevention and treatment of colon cancer. With the rapid development of high throughput technologies, new biomarkers identification for colon cancer has been confronted with the new opportunities and challenges.
Methods: We firstly constructed functional networks for each sample of colon adenocarcinoma (COAD) by using a sample-specific network (SSN) method which can construct individual-specific networks based on gene expression profiles of a single sample. The functional genes and interactions were identified from the functional networks, respectively.
Results: Classification and subtyping were used to test the function of the functional genes and interactions. The results of classification showed that the functional genes could be used as diagnostic biomarkers. The subtypes displayed different mechanisms, which were shown by the functional and pathway enrichment analysis for the representative genes of each subtype. Besides, subtype-specific molecular patterns were also detected, such as subtype-specific clinical and mutation features. Finally, 12 functional genes and 13 functional edges could serve as prognosis biomarkers since they were associated with the survival rate of COAD.
Conclusions: In conclusion, the functional genes and interactions in the constructed functional network could be used as new biomarkers for COAD. And the subtypes could be used in realizing the personalized medicine of COAD.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
This is a list of supplementary files associated with this preprint. Click to download.
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: Published
Version 2
Posted 12 May, 2020
Published
On 17 Jul, 2020
No community comments so far
Editor assigned
On 27 Apr, 2020
Editor invited
On 26 Apr, 2020
Submission checks complete
On 14 Feb, 2020
No comments provided
Editorial decision: Major revision
On 31 Mar, 2020
Review #2 received
Received 23 Mar, 2020
Review #1 received
Received 15 Mar, 2020
Reviewer #4 agreed
On 03 Mar, 2020
Reviewer #1 agreed
On 02 Mar, 2020
Reviewer #2 agreed
On 02 Mar, 2020
Reviewer #3 agreed
On 02 Mar, 2020
Reviewers invited
Invitations sent on 29 Feb, 2020
Editor assigned
On 11 Feb, 2020
Submission checks complete
On 10 Feb, 2020
Editor invited
On 10 Feb, 2020
First submitted
On 08 Feb, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
This preprint is under consideration at BMC Cancer. 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 article
Network-based identification of biomarkers for colon adenocarcinoma
Xiaoping Liu
Corresponding Author
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: Published
Version 2
Posted 12 May, 2020
Published
On 17 Jul, 2020
No community comments so far
Editor assigned
On 27 Apr, 2020
Editor invited
On 26 Apr, 2020
Submission checks complete
On 14 Feb, 2020
No comments provided
Editorial decision: Major revision
On 31 Mar, 2020
Review #2 received
Received 23 Mar, 2020
Review #1 received
Received 15 Mar, 2020
Reviewer #4 agreed
On 03 Mar, 2020
Reviewer #1 agreed
On 02 Mar, 2020
Reviewer #2 agreed
On 02 Mar, 2020
Reviewer #3 agreed
On 02 Mar, 2020
Reviewers invited
Invitations sent on 29 Feb, 2020
Editor assigned
On 11 Feb, 2020
Submission checks complete
On 10 Feb, 2020
Editor invited
On 10 Feb, 2020
First submitted
On 08 Feb, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published version at BMC Cancer.
Background: As one of the most common cancers with high mortality in the world, we are still facing a huge challenge in the prevention and treatment of colon cancer. With the rapid development of high throughput technologies, new biomarkers identification for colon cancer has been confronted with the new opportunities and challenges.
Methods: We firstly constructed functional networks for each sample of colon adenocarcinoma (COAD) by using a sample-specific network (SSN) method which can construct individual-specific networks based on gene expression profiles of a single sample. The functional genes and interactions were identified from the functional networks, respectively.
Results: Classification and subtyping were used to test the function of the functional genes and interactions. The results of classification showed that the functional genes could be used as diagnostic biomarkers. The subtypes displayed different mechanisms, which were shown by the functional and pathway enrichment analysis for the representative genes of each subtype. Besides, subtype-specific molecular patterns were also detected, such as subtype-specific clinical and mutation features. Finally, 12 functional genes and 13 functional edges could serve as prognosis biomarkers since they were associated with the survival rate of COAD.
Conclusions: In conclusion, the functional genes and interactions in the constructed functional network could be used as new biomarkers for COAD. And the subtypes could be used in realizing the personalized medicine of COAD.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7