Evidence map›Paper›PMID 36923953›Full record

ArticleGenetics research2023

Comprehensive Analysis Based on the Cancer Immunotherapy and Immune Activation of Gastric Cancer Patients.

Feng Jiang, Qilong Ma

Open access · goldFull text read
In one paragraph

Article in Genetics research, 2023. 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
0.3field-weighted citation impact, top 40% 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

2 authors at 2 institutions in 1 country.

Feng JiangDepartment of Oncology, Zhongda Hospital, Southeast University, Nanjing 210009, China.ORCID 0000-0002-2308-7351
Qilong MaDepartment of General Surgery, Jining Hospital, Nanjing University, Nanjing, Jiangsu, China.
Nanjing University · CNZhongda Hospital Southeast University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

When it comes to aggressiveness and prognosis, immune cells play an important role in the microenvironment of gastric cancer (GC). Currently, there is no well-established evidence that immune status typing is reliable as a prognostic tool for gastric cancer. This study aimed to develop a genetic signature based on immune status typing for the stratification of gastric cancer risk. TCGA data were used for gene expression and clinical characteristics analysis. A ssGSEA algorithm was applied to type the gastric cancer cohorts. A multivariate and univariate Cox regression and a lasso regression were conducted to determine which genes are associated with gastric cancer prognosis. Finally, we were able to produce a 6-gene prognostic prediction model using immune-related genes. Further analysis revealed that the prognostic prediction model is closely related to the prognosis of patients with GC. Nomograms incorporating genetic signatures and risk factors produced better calibration results. The relationship between the risk score and gastric cancer T stage was also significantly correlated with multiple immune markers related to specific immune cell subsets. According to these results, patients' outcomes and tumor immune cell infiltration correlate with risk scores. In addition, immune cellular-based genetic signatures can contribute to improved risk stratification for gastric cancer. Clinical decisions regarding immunotherapy and followup can be guided by these features.

Indexed as

Stomach NeoplasmsAlgorithmsHumansImmunotherapyPrognosisTumor Microenvironment

Identifiers

PMID36923953
PMCPMC10010888
OpenAlexW4323307417

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read4
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.