ArticleBMC cancer2020
Sepsis-associated pathways segregate cancer groups.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Role of MLIP in burn-induced sepsis and insights into sepsis-associated cancer progression.Frontiers in immunology · 2025Article
- Elucidating immune-related gene transcriptional programs via factorization of large-scale RNA-profiles.iScience · 2024Article
- Subtyping colorectal cancer based on septic shock-associated genes: prognosis and immune characteristics.Frontiers in genetics · 2024Article
- Association between site of infection and mortality in patients with cancer with sepsis or septic shock: A retrospective cohort study.Experimental and therapeutic medicine · 2023Article
- Transcriptome profiling of colorectal tumors from patients with sepsis reveals an ethnic basis for viral infection risk and sepsis progression.Scientific reports · 2022Article
- Behavioral Differences Between Subject Groups Identified Using Smart Homes and Change Point Detection.IEEE journal of biomedical and health informatics · 2021Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundSepsis 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.
methodsGene 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.
resultsA 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.
conclusionTranscriptome-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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