Evidence map›Paper›PMID 35966316›Full record

ArticleTranslational cancer research2022

Yuan Yang, Zhaofeng Chen, Lingshan Zhou, Guozhi Wu, Xiaomei Ma, Ya Zheng, Min Liu, Yuping Wang, Rui Ji, Qinghong Guo and 1 more

Open access · diamondAbstract read
In one paragraph

Article in Translational cancer research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
1.2field-weighted citation impact, top 21% 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

10 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
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  7. Advances in the understanding of nuclear pore complexes in human diseases.Journal of cancer research and clinical oncology · 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 2 institutions in 1 country.

Yuan Yang *The First Clinical Medical College, Lanzhou University, Lanzhou, China.
Zhaofeng Chen *Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.
Lingshan ZhouDepartment of Geriatrics Ward 2, The First Hospital of Lanzhou University, Lanzhou, China.
Guozhi WuThe First Clinical Medical College, Lanzhou University, Lanzhou, China.
Xiaomei MaThe First Clinical Medical College, Lanzhou University, Lanzhou, China.
Ya ZhengDepartment of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.
Min LiuDepartment of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.
Yuping WangDepartment of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.
Rui JiDepartment of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.
Qinghong Guo *Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.
Yongning Zhou *Department of Gastroenterology, The First Hospital of Lanzhou University, Lanzhou, China.
First Hospital of Lanzhou University · CNLanzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Abnormal glucose and lipid metabolism plays a critical role in gastric carcinogenesis and development. Hence, we presented a systematic analysis of glucose and lipid metabolism-related genes to explore their function and prognostic value in gastric cancer (GC). Methods: The consensus clustering algorithm was used to identify the molecular subtypes based on glucose and lipid metabolism-related genes. Subsequently, cox regression analysis and lasso regression analysis were utilized to establish a risk prediction model. A clinical nomogram was constructed to assist prognosis assessment. In addition, ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) algorithms were performed to evaluate the immune infiltration of the metabolic model, and GSEA was used for enrichment analysis of the metabolic signature. Finally, we explored the association between the risk model and anti-cancer therapy for the purpose of clinical application for GC treatment. Results: GC samples were divided into 2 subtypes based on glucose and lipid metabolism-related genes, patients in cluster 2 had a better overall survival (OS) than those in cluster 1. Fifty-two genes were identified by univariable regression analysis. Finally, a 13-gene metabolic signature ( Conclusions: This study demonstrated that glucose and lipid metabolism-related genes were significantly associated with prognosis. Meanwhile, it will provide novel insights into exploring the immunoregulation roles of these genes.

Indexed as

gastric cancer (GC)Glucose and lipid metabolismimmune evasionprognosistumor immune microenvironment (TIME)

Identifiers

PMID35966316
PMCPMC9372230
OpenAlexW4285541462

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.