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BMC Cancer

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01-12-2020 | Septicemia | Research article

Sepsis-associated pathways segregate cancer groups

Authors: Himanshu Tripathi, Samanwoy Mukhopadhyay, Saroj Kant Mohapatra

Published in: BMC Cancer | Issue 1/2020

Abstract

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.

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Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the global burden of disease study. Lancet. 2020;395:200–11.CrossRefPubMedPubMedCentral

Liu Z, Mahale P, Engels EA. Sepsis and risk of cancer among elderly adults in the United States. Clin Infect Dis. 2019;68:717–24.CrossRefPubMed

Moore JX, Akinyemiju T, Bartolucci A, Wang HE, Waterbor J, Griffin R. A prospective study of cancer survivors and risk of sepsis within the REGARDS cohort. Cancer Epidemiol. 2018;55:30–8.

Abou Dagher G, El Khuri C, Chehadeh AAH, Chami A, Bachir R, Zebian D, et al. Are patients with cancer with sepsis and bacteraemia at a higher risk of mortality? A retrospective chart review of patients presenting to a tertiary care Centre in Lebanon. BMJ Open. 2017;7:1–8.CrossRef

Torres VBL, Azevedo LCP, Silva UVA, Caruso P, Torelly AP, Silva E, et al. Sepsis-associated outcomes in critically ill patients with malignancies. Ann Am Thorac Soc. 2015;12:1185–92.PubMed

Rosolem MM, Rabello LSCF, Lisboa T, Caruso P, Costa RT, Leal JVR, et al. Critically ill patients with cancer and sepsis: clinical course and prognostic factors. J Crit Care. 2012;27:301–7.CrossRefPubMed

Aygencel G, Turkoglu M, Turkoz Sucak G, Benekli M. Prognostic factors in critically ill cancer patients admitted to the intensive care unit. J Crit Care. 2014;29:618–26.CrossRefPubMed

Wang Y, Zhou J, Wu K. High 28-day mortality in critically ill patients with sepsis and concomitant active cancer. J Int Med Res. 2018;46:5030–9.CrossRefPubMedPubMedCentral

Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74.PubMed

10.

Vincent J-L, Opal SM, Marshall JC, Tracey KJ. Sepsis definitions: time for change. Lancet. 2013;381:774–5.CrossRefPubMedPubMedCentral

11.

Angus DC, van der Poll T. Severe Sepsis and septic shock. N Engl J Med. 2013;369:840–51.CrossRefPubMed

12.

Mukhopadhyay S, Thatoi PK, Pandey AD, Das BK, Ravindran B, Bhattacharjee S, et al. Transcriptomic meta-analysis reveals upregulation of gene expression functional in osteoclast differentiation in human septic shock. PLoS One. 2017;12:1–17.

13.

Bergenfelz C, Medrek C, Ekström E, Jirström K, Janols H, Wullt M, et al. Wnt5a induces a Tolerogenic phenotype of macrophages in Sepsis and breast Cancer patients. J Immunol. 2012;188:5448–58.CrossRefPubMed

14.

Diener KR, Al-Dasooqi N, Lousberg EL, Hayball JD. The multifunctional alarmin HMGB1 with roles in the pathophysiology of sepsis and cancer. Immunol Cell Biol. 2013;91:443–50.CrossRefPubMed

15.

Aversa Z, Alamdari N, Hasselgren PO. Molecules modulating gene transcription during muscle wasting in cancer, sepsis, and other critical illness. Crit Rev Clin Lab Sci. 2011;48:71–86.CrossRefPubMed

16.

Jiang Z, Gentleman R. Extensions to gene set enrichment. Bioinformatics. 2007;23:306–13.CrossRefPubMed

17.

Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999;27:29–34.CrossRefPubMedPubMedCentral

18.

Gentleman R. Category: Category Analysis. R package version 2018; 2.50.0. 2000.

19.

Warnes GR, Bolker B, Bonebakker L, Gentleman R, Liaw WHA, Lumley T et al.. gplots: Various R Programming Tools for Plotting Data. 2019. https://CRAN.R-project.org/package=gplots.

20.

Csardi G NT. The igraph software package for complex network research. 2006. http://igraph.org.

21.

Carey V, Gentleman. Carey V, Gentleman R, Mar J, Vertrees J, Gatto L. MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers. R package version. 2018; 1.58.1.

22.

Grambsch PM, Terry M. Therneau. Modeling survival data: extending the cox model. New York: Springer; 2000. ISBN 0-387-98784-3.

23.

Alboukadel Kassambara, Marcin Kosinski, Przemyslaw Biecek. survminer: Drawing Survival Curves using “ggplot2”. 2019. https://CRAN.R-project.org/package=survminer.

24.

Team RC, et al.. R: A language and environment for statistical computing. 2013.

25.

Tang KW, Alaei-Mahabadi B, Samuelsson T, Lindh M, Larsson E. The landscape of viral expression and host gene fusion and adaptation in human cancer. Nat Commun. 2013;4:1–9.

26.

Cao S, Wendl MC, Wyczalkowski MA, Wylie K, Ye K, Jayasinghe R, et al. Divergent viral presentation among human tumors and adjacent normal tissues. Sci Rep. 2016;6:28294.

27.

Kazemian M, Ren M, Lin J-X, Liao W, Spolski R, Leonard WJ. Possible human papillomavirus 38 contamination of endometrial Cancer RNA sequencing samples in the Cancer genome atlas database. J Virol. 2015;89:8967–73.CrossRefPubMedPubMedCentral

28.

Cantalupo PG, Katz JP, Pipas JM. Viral sequences in human cancer. Virology. 2018;513:208–16.CrossRefPubMed

29.

Salyakina D, Tsinoremas NF. Viral expression associated with gastrointestinal adenocarcinomas in TCGA high-throughput sequencing data. Human Genomics. 2013;7:1–12.CrossRef

30.

Chaturvedi AK, Engels EA, Pfeiffer RM, Hernandez BY, Xiao W, Kim E, et al. Human papillomavirus and rising Oropharyngeal Cancer incidence in the United States. JCO. 2011;29:4294–301.CrossRef

31.

Chao JS, Zhao SL, Ou-Yang SW, Qian YB, Liu AQ, Tang HM, et al. Post-transplant infection improves outcome of hepatocellular carcinoma patients after orthotopic liver transplantation. World J Gastroenterol. 2019;25:5630–40.CrossRefPubMedPubMedCentral

32.

Chen J, Lv Y, Mu F, Xu K. Long-term response of metastatic renal clear cell carcinoma following a subcutaneous injection of mixed bacterial vaccine: a case report. Onco Targets Ther. 2019;12:2531–8.CrossRefPubMedPubMedCentral

33.

Bindels LB, Neyrinck AM, Loumaye A, Catry E, Walgrave H, Cherbuy C, et al. Increased gut permeability in cancer cachexia: mechanisms and clinical relevance. Oncotarget. 2018;9:18224–38.CrossRefPubMedPubMedCentral

Title: Sepsis-associated pathways segregate cancer groups
Authors: Himanshu Tripathi
Samanwoy Mukhopadhyay
Saroj Kant Mohapatra
Publication date: 01-12-2020
Publisher: BioMed Central
Keywords: Septicemia
Septicemia
Septic Shock
Published in: BMC Cancer / Issue 1/2020
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-020-06774-9

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Abstract

Background

Methods

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Conclusion

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