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Modular and mechanistic changes across stages of colorectal cancer

BACKGROUND: While mechanisms contributing to the progression and metastasis of colorectal cancer (CRC) are well studied, cancer stage-specific mechanisms have been less comprehensively explored. This is the focus of this manuscript. METHODS: Using previously published data for CRC (Gene Expression O...

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Autores principales: Rahiminejad, Sara, Maurya, Mano R., Mukund, Kavitha, Subramaniam, Shankar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022252/
https://www.ncbi.nlm.nih.gov/pubmed/35448980
http://dx.doi.org/10.1186/s12885-022-09479-3
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author Rahiminejad, Sara
Maurya, Mano R.
Mukund, Kavitha
Subramaniam, Shankar
author_facet Rahiminejad, Sara
Maurya, Mano R.
Mukund, Kavitha
Subramaniam, Shankar
author_sort Rahiminejad, Sara
collection PubMed
description BACKGROUND: While mechanisms contributing to the progression and metastasis of colorectal cancer (CRC) are well studied, cancer stage-specific mechanisms have been less comprehensively explored. This is the focus of this manuscript. METHODS: Using previously published data for CRC (Gene Expression Omnibus ID GSE21510), we identified differentially expressed genes (DEGs) across four stages of the disease. We then generated unweighted and weighted correlation networks for each of the stages. Communities within these networks were detected using the Louvain algorithm and topologically and functionally compared across stages using the normalized mutual information (NMI) metric and pathway enrichment analysis, respectively. We also used Short Time-series Expression Miner (STEM) algorithm to detect potential biomarkers having a role in CRC. RESULTS: Sixteen Thousand Sixty Two DEGs were identified between various stages (p-value ≤ 0.05). Comparing communities of different stages revealed that neighboring stages were more similar to each other than non-neighboring stages, at both topological and functional levels. A functional analysis of 24 cancer-related pathways indicated that several signaling pathways were enriched across all stages. However, the stage-unique networks were distinctly enriched only for a subset of these 24 pathways (e.g., MAPK signaling pathway in stages I-III and Notch signaling pathway in stages III and IV). We identified potential biomarkers, including HOXB8 and WNT2 with increasing, and MTUS1 and SFRP2 with decreasing trends from stages I to IV. Extracting subnetworks of 10 cancer-relevant genes and their interacting first neighbors (162 genes in total) revealed that the connectivity patterns for these genes were different across stages. For example, BRAF and CDK4, members of the Ser/Thr kinase, up-regulated in cancer, displayed changing connectivity patterns from stages I to IV. CONCLUSIONS: Here, we report molecular and modular networks for various stages of CRC, providing a pseudo-temporal view of the mechanistic changes associated with the disease. Our analysis highlighted similarities at both functional and topological levels, across stages. We further identified stage-specific mechanisms and biomarkers potentially contributing to the progression of CRC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09479-3.
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spelling pubmed-90222522022-04-22 Modular and mechanistic changes across stages of colorectal cancer Rahiminejad, Sara Maurya, Mano R. Mukund, Kavitha Subramaniam, Shankar BMC Cancer Research BACKGROUND: While mechanisms contributing to the progression and metastasis of colorectal cancer (CRC) are well studied, cancer stage-specific mechanisms have been less comprehensively explored. This is the focus of this manuscript. METHODS: Using previously published data for CRC (Gene Expression Omnibus ID GSE21510), we identified differentially expressed genes (DEGs) across four stages of the disease. We then generated unweighted and weighted correlation networks for each of the stages. Communities within these networks were detected using the Louvain algorithm and topologically and functionally compared across stages using the normalized mutual information (NMI) metric and pathway enrichment analysis, respectively. We also used Short Time-series Expression Miner (STEM) algorithm to detect potential biomarkers having a role in CRC. RESULTS: Sixteen Thousand Sixty Two DEGs were identified between various stages (p-value ≤ 0.05). Comparing communities of different stages revealed that neighboring stages were more similar to each other than non-neighboring stages, at both topological and functional levels. A functional analysis of 24 cancer-related pathways indicated that several signaling pathways were enriched across all stages. However, the stage-unique networks were distinctly enriched only for a subset of these 24 pathways (e.g., MAPK signaling pathway in stages I-III and Notch signaling pathway in stages III and IV). We identified potential biomarkers, including HOXB8 and WNT2 with increasing, and MTUS1 and SFRP2 with decreasing trends from stages I to IV. Extracting subnetworks of 10 cancer-relevant genes and their interacting first neighbors (162 genes in total) revealed that the connectivity patterns for these genes were different across stages. For example, BRAF and CDK4, members of the Ser/Thr kinase, up-regulated in cancer, displayed changing connectivity patterns from stages I to IV. CONCLUSIONS: Here, we report molecular and modular networks for various stages of CRC, providing a pseudo-temporal view of the mechanistic changes associated with the disease. Our analysis highlighted similarities at both functional and topological levels, across stages. We further identified stage-specific mechanisms and biomarkers potentially contributing to the progression of CRC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-022-09479-3. BioMed Central 2022-04-21 /pmc/articles/PMC9022252/ /pubmed/35448980 http://dx.doi.org/10.1186/s12885-022-09479-3 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Rahiminejad, Sara
Maurya, Mano R.
Mukund, Kavitha
Subramaniam, Shankar
Modular and mechanistic changes across stages of colorectal cancer
title Modular and mechanistic changes across stages of colorectal cancer
title_full Modular and mechanistic changes across stages of colorectal cancer
title_fullStr Modular and mechanistic changes across stages of colorectal cancer
title_full_unstemmed Modular and mechanistic changes across stages of colorectal cancer
title_short Modular and mechanistic changes across stages of colorectal cancer
title_sort modular and mechanistic changes across stages of colorectal cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022252/
https://www.ncbi.nlm.nih.gov/pubmed/35448980
http://dx.doi.org/10.1186/s12885-022-09479-3
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