Evidence map›Paper›PMID 38148838›Full record

ArticleFrontiers in oncology2023

Deciphering the Hypoxia-immune interface in esophageal squamous carcinoma: a prognostic network model.

Jie Hu, Qilong Liu, Bi Feng, Yanling Lu, Kai Chen

Open access · goldAbstract read
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Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
–field-weighted citation impact, top 56% of its field
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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 0 citations in OpenAlex.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Jie HuDepartment of Medical Oncology of The Eastern Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Qilong LiuDepartment of Gastroenterology of The Eastern Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Bi FengDepartment of Medical Oncology of The Eastern Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Yanling LuDepartment of Medical Oncology of The Eastern Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Kai ChenDepartment of Medical Oncology of The Eastern Hospital, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
Sun Yat-sen University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The rapid progress and poor prognosis of the exercise of esophageal squamous cell carcinoma (ESCA) bring great challenges to the treatment. Hypoxia in the tumor microenvironment has become a key factor in the pathogenesis of tumors. However, due to the lack of clear therapeutic targets, hypoxia targeted therapy of ESCA is still in the exploratory stage. Methods: To bridge this critical gap, we mined a large number of gene expression profiles and clinical data on ESCA from public databases. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed. We next delved into the relationship between hypoxia and apoptotic cell interactions. Meanwhile, using LASAS-Cox regression, we designed a robust prognostic risk score, which was subsequently validated in the GSE53625 cohort. In addition, we performed a comprehensive analysis of immune cell infiltration and tumor microenvironment using cutting-edge computational tools. Results: Hypoxia-related genes were identified and classified by WGCNA. Functional enrichment analysis further elucidated the mechanism by which hypoxia affected the ESCA landscape. The results of the interaction analysis of hypoxia and apoptotic cells revealed their important roles in driving tumor progression. The validation results of the prognostic risk score model in the GSE53625 cohort obtained a good area under the receiver operating characteristic (ROC) curve, and the risk score was independently verified as a significant predictor of ESCA outcome. The results of immune cell infiltration and tumor microenvironment analysis reveal the profound impact of immune cell dynamics on tumor evolution. Conclusion: Overall, our study presents a pioneering hypoxiacentered gene signature for prognostication in ESCA, providing valuable prognostic insights that could potentially revolutionize patient stratification and therapeutic management in clinical practice.

Indexed as

esophageal squamous cell carcinoma (ESCA)hypoxiaimmune infiltrationprognostic biomarkersweighted gene co-expression network analysis (WGCNA)

Identifiers

PMID38148838
PMCPMC10751000
OpenAlexW4389628201

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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.