Evidence map›Paper›PMID 39252063›Full record

ArticleJournal of translational medicine2024

Overall survival prediction of gastric cancer using the gene signature of CT-detected extramural venous invasion combined with M2 macrophages infiltration.

Hao Yang, Xinyi Gou, Caizhen Feng, Yuanyuan Zhang, Boshi Sun, Peng Peng, Yi Wang, Nan Hong, Yingjiang Ye, Jin Cheng and 1 more

Abstract read
In one paragraph

Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Imaging genomics of cancer: a bibliometric analysis and review.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025
    Review
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.

Hao Yang *Department of Oncology Surgery, Harbin Medical University Cancer Hospital, Harbin, China.
Xinyi Gou *Department of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China.
Caizhen FengDepartment of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China.
Yuanyuan ZhangDepartment of Pathology, Peking University People's Hospital, Beijing, China.
Boshi SunDepartment of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Peng PengDepartment of Hernia and Abdominal Wall Surgery, Peking University People's Hospital, Beijing, 100044, China.
Yi WangDepartment of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China.
Nan HongDepartment of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China.
Yingjiang YeDepartment of Gastrointestinal Surgery, Peking University People's Hospital, Beijing, China.
Jin ChengDepartment of Radiology, Peking University People's Hospital, 11 Xizhimen South St, Beijing, 100044, China. chengjinpkuph@outlook.com.
Bo GaoDepartment of Hernia and Abdominal Wall Surgery, Peking University People's Hospital, Beijing, 100044, China. bo.gao@bjmu.edu.cn.

Funding

Peking University People's Hospital Research and Development Funds RS2021-08the National Natural Science Foundation of China 81901819
6 · The paper itself

Abstract

backgroundCT-detected Extramural venous invasion (EMVI) is known as an independent risk factor for distant metastasis in patients with advanced gastric cancer (GC). However, the molecular basis is not clear. In colorectal cancer, M2 macrophages plays a vital role in determining EMVI. This study aimed to investigate the relationship between CT-detected EMVI and the M2 macrophages as well as prognosis predictionusing a radiogenomic approach.

methodWe utilized EMVI-related genes (from mRNA sequencing of 13 GC samples correlated with EMVI score by spearman analysis, P < 0.01) to overlap the co-expression genes of WGCNA module and M2 macrophages related genes (from mRNA data of 371 GC patients in TCGA database), generating a total of 136 genes. An EMVI-M2-prognosis-related hub gene signature was constructed by COX and least absolute shrinkage and selection operator (LASSO) analysis from a training cohort TCGA database (n = 371) and validated it in a validation cohort from GEO database (n = 357). High- and low-risk groups were divided by hub gene (EGFLAM and GNG11) signature-derived risk scores. We assessed its predictive ability through Kaplan-Meier (K-M) curve and COX analysis. Furthermore, we utilized ESTIMATE to detect tumor mutation burden (TMB) and evaluate sensitivity to immune checkpoint inhibitors (ICIs). Expression of hub genes was tested using western blotting and immunohistochemistry (IHC) analysis.

resultsThe overall survival (OS) was significantly reduced in the high-risk group (Training/Validation: AUC = 0.701/0.620; P < 0.001/0.003). Furthermore, the risk score was identified as an independent predictor of OS in multivariate COX regression analyses (Training/Validation: HR = 1.909/1.928; 95% CI: 1.225-2.974/1.308-2.844). The low-risk group exhibited significantly higher TMB levels (P = 1.6e

conclusionOur study demonstrated EMVI-M2 gene signature could effectively predict the prognosis of GC tissue, reflecting the relationship between EMVI and M2 macrophages.

Indexed as

Gene Expression Regulation, NeoplasticMacrophagesNeoplasm InvasivenessStomach NeoplasmsAgedAnimalsCell Line, TumorFemaleGene Expression ProfilingHumansKaplan-Meier EstimateMaleMiddle AgedPrognosisReproducibility of ResultsSurvival AnalysisExtramural venous invasionGastric cancerImmune microenvironmentM2 macrophages infiltrationRadiogenomics

Identifiers

PMID39252063
PMCPMC11382430

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.