Evidence map›Paper›PMID 32104053›Full record

ArticleJournal of pain research2020

Applying Serum Cytokine Levels to Predict Pain Severity in Cancer Patients.

Jennifer Fazzari, Jesse Sidhu, Shreya Motkur, Mark Inman, Norman Buckley, Mark Clemons, Lisa Vandermeer, Gurmit Singh

Open access · goldAbstract read
In one paragraph

Article in Journal of pain research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
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  9. Serum biomarkers for chronic pancreatitis pain patterns.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2021
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Jennifer FazzariDepartment of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario, Canada.
Jesse SidhuDepartment of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario, Canada.
Shreya MotkurDepartment of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario, Canada.ORCID 0000-0002-5593-2169
Mark InmanDepartment of Medicine, McMaster University, Hamilton, Ontario, Canada.
Norman BuckleyMichael G. DeGroote Institute for Pain Research and Care, McMaster University, Hamilton, Ontario, Canada.
Mark ClemonsDepartment of Medicine, Division of Medical Oncology, The Ottawa Hospital, Ottawa, Canada.
Lisa VandermeerCancer Research Program, Ottawa Hospital Research Institute and University of Ottawa, Ottawa, Canada.
Gurmit SinghDepartment of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario, Canada.ORCID 0000-0002-6256-5790
McMaster University · CAUniversity of Ottawa · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimCancers originating in the breast, lung and prostate often metastasize to the bone, frequently resulting in cancer-induced bone pain that can be challenging to manage despite conventional analgesic therapy. This exploratory study's aim was to identify potential biomarkers associated with cancer-induced pain by examining a sample population of breast cancer patients undergoing bisphosphonate therapy.

methodsA secondary analysis of the primary study was performed to quantify serum cytokine levels for correlation to pain scores. Cytokines with statistically significant correlations were then input into a stepwise regression analysis to generate a predictive equation for a patient's pain severity. In an effort to find additional potential biomarkers, correlation analysis was performed between these factors and a more comprehensive panel of cytokines and chemokines from breast, lung, and prostate cancer patients.

resultsStatistical analysis identified nine cytokines (GM-CSF, IFNγ, IL-1β, IL-2, IL-4, IL-5, IL-12p70, IL-17A, and IL-23) that had significant negative correlations with pain scores and they could best predict pain severity through a predictive equation generated for this specific evaluation. After performing a correlation analysis between these factors and a larger panel of cytokines and chemokines, samples from breast, lung and prostate patients showed distinct correlation profiles, highlighting the clinical challenge of applying pain-associated cytokines related to more defined nociceptive states, such as arthritis, to a cancer pain state.

conclusionExploratory analyses such as the ones presented here will be a beneficial tool to expand insights into potential cancer-specific nociceptive mechanisms and to develop novel therapeutics.

Indexed as

biomarkerscancer-induced painimmune factorsnociception

Identifiers

PMID32104053
PMCPMC7012636
OpenAlexW3005033740

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.