Evidence map›Paper›PMID 36148318›Full record

ArticleCancer management and research2022

Prognostic Value of a Serum Panel of Inflammatory Factors in Non-Metastatic Nasopharyngeal Carcinoma Patients Undergoing Radical Radiotherapy with Adjuvant Chemotherapy.

Ting Liang, Ding Xiao, Shanshan Lu, Xu Ye, Zhiqiang Xiao

Abstract read
In one paragraph

Article in Cancer management and research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ting LiangNational Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China.
Ding XiaoResearch Center of Carcinogenesis and Targeted Therapy, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China.
Shanshan LuResearch Center of Carcinogenesis and Targeted Therapy, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China.
Xu YeDepartment of Radiation Oncology, Hunan Cancer Hospital, The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, 410008, People's Republic of China.
Zhiqiang XiaoNational Clinical Research Center of Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, People's Republic of China.ORCID 0000-0002-5127-586X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: To evaluate the prognostic value of interleukin (IL)-6, IL-8, granulocyte-macrophage colony-stimulating factor (GM-CSF), leukemia inhibitory factor (LIF), and macrophage migration inhibitory factor (MIF) in non-metastatic nasopharyngeal carcinoma (NPC) patients undergoing radical radiotherapy. Patients and Methods: A serum panel compromising the inflammatory factors was analyzed in 372 NPC patients before and after radiotherapy. Independent prognostic factors were screened out using multivariate Cox regression analysis. A prediction model was built based on the training set data and validated using the test set data. The prognostic value of these factors was evaluated using the time-dependent receiver operating characteristic (ROC) curve and an integrated time-averaged area under the curve (AUC). Results: The baseline levels of IL-6, GM-CSF, and MIF were independent factors associated with poor OS and DMFS. A predictive model base established combining the baseline levels of these factors. The AUC values for the test set were 0.9828, 0.9968, and 0.9571 at 1, 3, and 5 years, respectively, compared to 0.9978, 0.9981, and 0.9222 for the training set, respectively. The AUC values for DMFS at 1, 3, and 5-years for the training set were 0.8744, 0.8951, and 0.9358, respectively, compared to 0.9525, 0.9663, and 0.9625 for the test set, respectively. The combination of post-treatment levels of IL-6, GM-CSF, and LIF also had good predictive value for OS with an AUC value > 0.85 during follow-up. Conclusion: IL-6, GM-CSF, and MIF baseline levels are powerful prognostic factors for non-metastatic NPC patients. The combination of these factors effectively predicts OS and DMFS in non-metastatic NPC patients.

Indexed as

inflammatory factornasopharyngeal carcinomaoverall survivalprognostic valueradical radiotherapy

Identifiers

PMID36148318
PMCPMC9488185

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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.