Evidence map›Paper›PMID 35592543›Full record

ArticleInternational journal of general medicine2022

Inflammation-Related Gene Signature for Predicting the Prognosis of Head and Neck Squamous Cell Carcinoma.

Yilong Lu, Zengrong Jia

Abstract read
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Article in International journal of general medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

2 authors.

Yilong LuSchool of Stomatology, Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
Zengrong JiaThyroid Surgery, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: The inflammatory response was associated with the prognosis of head and neck squamous cell carcinoma (HNSCC). This study aimed to perform a novel prognostic signature based on inflammation-related genes (IRGs) for a better understanding of the prognosis of HNSCC. Patients and Methods: IRGs were obtained from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Functional enrichment analysis was performed to explore potential pathways. Univariate and multivariate Cox regression as well as the Least Absolute Shrinkage and Selection Operator (LASSO) were utilized to construct an IRGs-based prognostic model on TCGA database and the GEO database was utilized for outcome validation. The nomogram model was constructed based on independent prognostic factors after univariate and multivariate Cox regression. The immune cell infiltration level was analyzed via the Tumor Immune Estimation Resource (TIMER) database. Results: In this study, we confirmed that 60% IRGs were abnormally expressed in HNSCC samples, and these were associated with important oncobiology. Then, a prognostic signature comprising 7 hub genes was generated based on TCGA database. The results were validated in 97 patients from GSE41613. A nomogram comprising risk score, age, M stage and N stage was generated to improve the accuracy of prognosis evaluation. The immune cell infiltration analysis suggested that 5 hub genes (ADGRE1, OLR1, TIMP1, GPR132 and CCR7) were negatively correlated with tumor purity and positively correlated with the infiltration of immune cells. Conclusion: Our study established a novel signature consisting of 7 hub genes for the prognostic prediction in patients with HNSCC.

Indexed as

HNSCCinflammation-related geneprognosisTCGA

Identifiers

PMID35592543
PMCPMC9113041

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