Evidence map›Paper›PMID 35178344›Full record

ArticleFrontiers in oncology2022

The Effect of Inflammatory Markers on the Survival of Advanced Gastric Cancer Patients Who Underwent Anti-Programmed Death 1 Therapy.

Ziting Qu, Qianling Wang, Hui Wang, Yang Jiao, Min Li, Wei Wei, Yu Lei, Zhiyan Zhao, Tengteng Zhang, Yiyin Zhang and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 9 pooled it
3.2field-weighted citation impact, top 6% 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

26 citing papers in PubMed, 9 syntheses or guidelines pooled it, 32 citations in OpenAlex.

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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

11 authors at 2 institutions in 1 country.

Ziting QuDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Qianling WangDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Hui WangDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Yang JiaoDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Min LiDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Wei WeiDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Yu LeiDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Zhiyan ZhaoDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Tengteng ZhangDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Yiyin ZhangDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Kangsheng GuDepartment of Oncology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Anhui Medical University · CNFirst Affiliated Hospital of Anhui Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThis clinical study sought to determine whether the levels of inflammatory markers predicted the survival of advanced gastric cancer (AGC) patients who underwent anti-programmed death 1 (PD-1) therapy.

methodsUsing AGC patient plasma samples and baseline characteristics, we investigated the specific value of inflammatory markers in AGC from a clinical perspective in immunotherapy.

resultsOne hundred and six patients with AGC who underwent anti-PD-1 therapy were enrolled in this study between 20 July 2019 and 16 March 2021. A significant decrease in NLR, dNLR, and SII was noticed among the PR (P=0.023; P=0.036; P=0.001), SD (P=0.048; P=0.022; P=0.023), ORR (P=0.021; P=0.032; P=0.001), and DCR (P=0.003; P=0.001; P<0.001) groups after anti-PD-1 therapy. Additionally, a significant decline of PLR was also observed in PR (P=0.010), ORR (P=0.007), and DCR (P=0.005) after anti-PD-1 therapy. Only MLR levels increased significantly at the time of anti-PD-1 immunotherapy the failure compared to baseline (P=0.039). And statistically significant elevations in NLR (P=0.001), MLR (P=0.020), dNLR (P=0.002), and SII (P=0.019) were found in failure of anti-PD-1 treatment compared to optimal efficacy in AGC patients. In first-line treatment, the number of metastatic sites (P=0.001) was an independent prognostic factor for PFS, and peritoneal metastases (P=0.004) and platelet-to-lymphocyte ratio (PLR) level (P=0.014) were independent prognostic predictors of OS according to Cox regression analysis. In second-line or posterior treatment, the number of metastatic sites (P=0.007), ECOG (P=0.011), and PLR level (P=0.033) were independent prognostic factors for PFS in AGC patients, and the number of metastatic sites (P=0.003), differentiation (P=0.030), and NLR level (P<0.001) were independent prognostic factors for OS according to Cox regression analysis.

conclusionsNLR, PLR, MLR, dNLR, and SII can reflect the short-term efficacy of immunotherapy in patients who underwent anti-PD-1 therapy with AGC. PLR is an independent prognostic factor for OS in AGC patients receiving first-line immunotherapy and PFS in those receiving second-line or posterior immunotherapy. And NLR was an independent prognostic factor for OS in AGC patients receiving second-line or posterior immunotherapy. The number of metastatic sites was significantly associated with the prognosis of AGC patients who received immunotherapy.

Indexed as

immunotherapyinflammatory markersPD-1prognosisstomach neoplasm

Identifiers

PMID35178344
PMCPMC8845037
OpenAlexW4210768822

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.