Evidence map›Paper›PMID 36045718›Full record

ArticleOpen life sciences2022

Identification of metabolic genes for the prediction of prognosis and tumor microenvironment infiltration in early-stage non-small cell lung cancer.

Jing Li, Yun Guan, Rongrong Zhu, Yang Wang, Huaguang Zhu, Xin Wang

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Open life sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 50% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

6 authors at 3 institutions in 1 country.

Jing LiDepartment of CyberKnife Center, Huashan Hospital, Fudan University, No. 525, Hongfeng Road, Pudong District, Shanghai 200040, China.
Yun GuanDepartment of CyberKnife Center, Huashan Hospital, Fudan University, No. 525, Hongfeng Road, Pudong District, Shanghai 200040, China.
Rongrong ZhuDepartment of Rehabilitation, Northern Jiangsu People's Hospital, Yangzhou, 225001, China.
Yang WangDepartment of CyberKnife Center, Huashan Hospital, Fudan University, No. 525, Hongfeng Road, Pudong District, Shanghai 200040, China.
Huaguang ZhuDepartment of CyberKnife Center, Huashan Hospital, Fudan University, No. 525, Hongfeng Road, Pudong District, Shanghai 200040, China.
Xin WangDepartment of CyberKnife Center, Huashan Hospital, Fudan University, No. 525, Hongfeng Road, Pudong District, Shanghai 200040, China.
Fudan University · CNHuashan Hospital · CNNorthern Jiangsu People's Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Early-stage non-small cell lung cancer (NSCLC) patients are at substantial risk of poor prognosis. We attempted to develop a reliable metabolic gene-set-based signature that can predict prognosis accurately for early-stage patients. Least absolute shrinkage and selection operator method Cox regression models were performed to filter the most useful prognostic genes, and a metabolic gene-set-based signature was constructed. Forty-two metabolism-related genes were finally identified, and with specific risk score formula, patients were classified into high-risk and low-risk groups. Overall survival was significantly different between the two groups in discovery (HR: 5.050, 95% CI: 3.368-7.574,

Indexed as

biomarkerimmune infiltrationlung cancermetabolismoverall survival

Identifiers

PMID36045718
PMCPMC9372707
OpenAlexW4290995287

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.