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Sepsis-associated pathways segregate cancer groups
BACKGROUND: Sepsis and cancer are both leading causes of death, and occurrence of any one, increases the likelihood of the other. While cancer patients are susceptible to sepsis, survivors of sepsis are also susceptible to develop certain cancers. This mutual dependence for susceptibility suggests s...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160985/ https://www.ncbi.nlm.nih.gov/pubmed/32293345 http://dx.doi.org/10.1186/s12885-020-06774-9 |
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author | Tripathi, Himanshu Mukhopadhyay, Samanwoy Mohapatra, Saroj Kant |
author_facet | Tripathi, Himanshu Mukhopadhyay, Samanwoy Mohapatra, Saroj Kant |
author_sort | Tripathi, Himanshu |
collection | PubMed |
description | BACKGROUND: Sepsis and cancer are both leading causes of death, and occurrence of any one, increases the likelihood of the other. While cancer patients are susceptible to sepsis, survivors of sepsis are also susceptible to develop certain cancers. This mutual dependence for susceptibility suggests shared biology between the two disease categories. Earlier analysis had revealed a cancer-related pathway to be up-regulated in Septic Shock (SS), an advanced stage of sepsis. This has motivated a more comprehensive comparison of the transcriptomes of SS and cancer. METHODS: Gene Set Enrichment Analysis was performed to detect the pathways enriched in SS and cancer. Thereafter, hierarchical clustering was applied to identify relative segregation of 17 cancer types into two groups vis-a-vis SS. Biological significance of the selected pathways was explored by network analysis. Clinical significance of the pathways was tested by survival analysis. A robust classifier of cancer groups was developed based on machine learning. RESULTS: A total of 66 pathways were observed to be enriched in both SS and cancer. However, clustering segregated cancer types into two categories based on the direction of transcriptomic change. In general, there was up-regulation in SS and one group of cancer (termed Sepsis-Like Cancer, or SLC), but not in other cancers (termed Cancer Alone, or CA). The SLC group mainly consisted of malignancies of the gastrointestinal tract (head and neck, oesophagus, stomach, liver and biliary system) often associated with infection. Machine learning classifier successfully segregated the two cancer groups with high accuracy (> 98%). Additionally, pathway up-regulation was observed to be associated with survival in the SLC group of cancers. CONCLUSION: Transcriptome-based systems biology approach segregates cancer into two groups (SLC and CA) based on similarity with SS. Host response to infection plays a key role in pathogenesis of SS and SLC. However, we hypothesize that some component of the host response is protective in both SS and SLC. |
format | Online Article Text |
id | pubmed-7160985 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-71609852020-04-22 Sepsis-associated pathways segregate cancer groups Tripathi, Himanshu Mukhopadhyay, Samanwoy Mohapatra, Saroj Kant BMC Cancer Research Article BACKGROUND: Sepsis and cancer are both leading causes of death, and occurrence of any one, increases the likelihood of the other. While cancer patients are susceptible to sepsis, survivors of sepsis are also susceptible to develop certain cancers. This mutual dependence for susceptibility suggests shared biology between the two disease categories. Earlier analysis had revealed a cancer-related pathway to be up-regulated in Septic Shock (SS), an advanced stage of sepsis. This has motivated a more comprehensive comparison of the transcriptomes of SS and cancer. METHODS: Gene Set Enrichment Analysis was performed to detect the pathways enriched in SS and cancer. Thereafter, hierarchical clustering was applied to identify relative segregation of 17 cancer types into two groups vis-a-vis SS. Biological significance of the selected pathways was explored by network analysis. Clinical significance of the pathways was tested by survival analysis. A robust classifier of cancer groups was developed based on machine learning. RESULTS: A total of 66 pathways were observed to be enriched in both SS and cancer. However, clustering segregated cancer types into two categories based on the direction of transcriptomic change. In general, there was up-regulation in SS and one group of cancer (termed Sepsis-Like Cancer, or SLC), but not in other cancers (termed Cancer Alone, or CA). The SLC group mainly consisted of malignancies of the gastrointestinal tract (head and neck, oesophagus, stomach, liver and biliary system) often associated with infection. Machine learning classifier successfully segregated the two cancer groups with high accuracy (> 98%). Additionally, pathway up-regulation was observed to be associated with survival in the SLC group of cancers. CONCLUSION: Transcriptome-based systems biology approach segregates cancer into two groups (SLC and CA) based on similarity with SS. Host response to infection plays a key role in pathogenesis of SS and SLC. However, we hypothesize that some component of the host response is protective in both SS and SLC. BioMed Central 2020-04-15 /pmc/articles/PMC7160985/ /pubmed/32293345 http://dx.doi.org/10.1186/s12885-020-06774-9 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article Tripathi, Himanshu Mukhopadhyay, Samanwoy Mohapatra, Saroj Kant Sepsis-associated pathways segregate cancer groups |
title | Sepsis-associated pathways segregate cancer groups |
title_full | Sepsis-associated pathways segregate cancer groups |
title_fullStr | Sepsis-associated pathways segregate cancer groups |
title_full_unstemmed | Sepsis-associated pathways segregate cancer groups |
title_short | Sepsis-associated pathways segregate cancer groups |
title_sort | sepsis-associated pathways segregate cancer groups |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7160985/ https://www.ncbi.nlm.nih.gov/pubmed/32293345 http://dx.doi.org/10.1186/s12885-020-06774-9 |
work_keys_str_mv | AT tripathihimanshu sepsisassociatedpathwayssegregatecancergroups AT mukhopadhyaysamanwoy sepsisassociatedpathwayssegregatecancergroups AT mohapatrasarojkant sepsisassociatedpathwayssegregatecancergroups |