Evidence map›Paper›PMID 35140786›Full record

ArticleJournal of oncology2022

Inflammation-Related Gene Signature: An Individualized Risk Prediction Model for Kidney Renal Clear Cell Carcinoma.

Ze Zhang, Yan-Yan Wei, Qiong-Mei Guo, Chang-Hao Zhou, Nan Li, Jin-Fang Wu, Ya-Ting Li, Wei-Wei Gao, Hui-Li Li

Open access · hybridAbstract read
In one paragraph

Article in Journal of oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.7field-weighted citation impact, top 16% 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

6 citing papers in PubMed, 11 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

9 authors at 1 institution in 1 country.

Ze ZhangDepartment of Anesthesiology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Yan-Yan WeiDepartment of Anesthesiology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Qiong-Mei GuoDepartment of Anesthesiology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Chang-Hao ZhouDepartment of Anesthesiology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Nan LiDepartment of Operating Room, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Jin-Fang WuDepartment of Operating Room, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Ya-Ting LiPharmacy Intravenous Admixture Services, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.
Wei-Wei GaoDepartment of Anesthesiology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.ORCID https://orcid.org/0000-0001-7577-7883
Hui-Li LiDepartment of Anesthesiology, The First Hospital of Hebei Medical University, Shijiazhuang, Hebei, China.ORCID https://orcid.org/0000-0002-3234-8445
Hebei Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThere is much evidence that confirms the inextricable link between inflammation and malignancy. Inflammation-related regulators were involved in the progression of kidney renal clear cell carcinoma (KIRC). However, the predictive role of single gene biomarkers is inadequate, and more accurate prognostic models are necessary. We undertook the current research to construct a robust inflammation-related gene signature that could stratify patients with KIRC.

methodsThe transcriptome sequencing data along with clinicopathologic information of KIRC were obtained from TCGA. A list of inflammation-related genes was acquired from the Molecular Signatures Database. Using the RNA-seq and survival time data from the TCGA training cohort, an inflammation-related gene signature was built using bioinformatic methods, and its performance in predicting patient prognosis was assessed by Kaplan-Meier and ROC curve analyses. Furthermore, we explored the association of risk score with immune score, stromal score, tumor immune-infiltrating cells (TIICs), immunosuppressive molecules, m6A regulators, and autophagy-related biomarkers.

resultsHerein, nine inflammation-related hub genes (ROS1, PLAUR, ACVR2A, KLF6, GABBR1, APLNR, SPHK1, PDPN, and ADORA2B) were determined and used to build a predictive model. All sets, including training set, four testing sets, and the entire TCGA group, were divided into two groups (low and high risk), and Kaplan-Meier curves all showed an adverse prognosis for patients in the high-risk group. ESTIMATE algorithm revealed a higher immune score in the high-risk subgroup. CIBERSORT algorithm illustrated that the high-risk group showed higher-level immune infiltrates. Furthermore, LAG3, TIGIT, and CTLA4 were overexpressed in the high-risk subgroup and positively associated with risk scores. Moreover, except for METTL3 and ALKBH5, the other m6A regulators decreased in the high-risk subgroup.

conclusionsIn conclusion, a novel inflammation-related gene signature comprehensively constructed in the current study may help stratify patients with KIRC.

Identifiers

PMID35140786
PMCPMC8820901
OpenAlexW4210413160

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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.