Evidence map›Paper›PMID 35136724›Full record

ArticleJournal of translational internal medicine2021

Lung adenocarcinoma-specific three-integrin signature contributes to poor outcomes by metastasis and immune escape pathways.

Yizhe Wang, Kezuo Hou, Yue Jin, Bowen Bao, Shiying Tang, Jianfei Qi, Yang Yang, Xiaofang Che, Yunpeng Liu, Xuejun Hu and 1 more

Open access · bronzeAbstract read
In one paragraph

Article in Journal of translational internal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
4.7field-weighted citation impact, top 4% 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

42 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.

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  17. MYC expression and fatty acid oxidation in EGFR-TKI acquired resistance.Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy · 2024
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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 3 institutions in 2 countries.

Yizhe WangDepartment of Respiratory and Infectious Disease of Geriatrics, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Kezuo HouDepartment of Medical Oncology, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Yue JinDepartment of Medical Oncology, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Bowen BaoDepartment of Medical Oncology, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Shiying TangDepartment of Medical Oncology, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Jianfei QiMarlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD 21201, USA.
Yang YangDepartment of Respiratory and Infectious Disease of Geriatrics, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Xiaofang CheDepartment of Medical Oncology, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Yunpeng LiuDepartment of Medical Oncology, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Xuejun HuDepartment of Respiratory and Infectious Disease of Geriatrics, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
Chunlei ZhengDepartment of Medical Oncology, The First Hospital of China Medical University, Shenyang 110001, Liaoning Province, China.
China Medical University · CNFirst Hospital of China Medical University · CNUniversity of Maryland, Baltimore · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundInhibitors targeting integrins (ITGs) are applied as a novel strategy for cancers including lung cancer; however, the heterogeneity of ITG subunits might explain why ITG-targeted inhibitors only show limited efficacy for a small group of lung cancer patients. MATERIALS AND

methodsRNA-Seq data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients were obtained from the TCGA database. Cox regression analysis was performed to construct the prognostic signature and generate the nomogram combined with pathologic stages (pStage). GEO datasets were used for verification. The related biological functions were analyzed by Gene Set Enrichment Analysis (GSEA) software and the TIMER database.

resultsBy Cox regression analysis of 30 ITG subunits, ITG subunit alpha 5 (ITGA5), ITG subunit alpha 6 (ITGA6), and ITG subunit alpha L (ITGAL) were identified as the prognostic factors in LUAD, which were included in the construction of a LUAD-specific 3-ITG signature. Following the calculation of risk score (RS) of each patient based on 3-ITG signature, patients with high RS in LUAD were found to exhibit worse prognosis, especially in early stage. Nomogram combined with RS and pStage could predict the prognosis of LUAD patients accurately. Mechanism exploration by GSEA showed that metastasis-related microenvironmental pathways were significantly enriched in the high-RS group. An elevated expression of ITGA5 was mainly associated with the promotion of cell migration and invasion, while the high expression of ITGAL had a strong positive correlation with the capability of recognizing and killing cancer cells.

conclusionsThree-ITG signature could improve the prediction ability combined with pStage in LUAD and might contribute to poor prognosis by metastasis and immune escape-related pathways.

Indexed as

biomarkersintegrinlung adenocarcinomaprognosisrisk factors.

Identifiers

PMID35136724
PMCPMC8802404
OpenAlexW4210392576

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

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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.