Evidence map›Paper›PMID 38351697›Full record

ReviewCurrent medicinal chemistry2024

Multi-omics Combined with Machine Learning Facilitating the Diagnosis of Gastric Cancer.

Jie Li, Siyi Xu, Feng Zhu, Fei Shen, Tianyi Zhang, Xin Wan, Saisai Gong, Geyu Liang, Yonglin Zhou

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current medicinal chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
3.8field-weighted citation impact, top 7% 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

7 citing papers in PubMed, 9 citations in OpenAlex.

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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

9 authors at 2 institutions in 1 country.

Jie LiKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, Jiangsu, China.
Siyi XuJiangsu Provincial Key Laboratory of Critical Care Medicine, School of Public Health, Southeast University, Nanjing, 210009, China.
Feng ZhuPhysical and Chemical Laboratory, Jiangsu Provincial Center for Disease Control & Prevention, 172 Jiangsu Rd, Nanjing, 210009, China.
Fei ShenPhysical and Chemical Laboratory, Jiangsu Provincial Center for Disease Control & Prevention, 172 Jiangsu Rd, Nanjing, 210009, China.
Tianyi ZhangKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, Jiangsu, China.
Xin WanKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, Jiangsu, China.
Saisai GongKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, Jiangsu, China.
Geyu LiangKey Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, 210009, Jiangsu, China.
Yonglin ZhouPhysical and Chemical Laboratory, Jiangsu Provincial Center for Disease Control & Prevention, 172 Jiangsu Rd, Nanjing, 210009, China.
Southeast University · CNJiangsu Provincial Center for Disease Control and Prevention · CN

Funding

Fundamental Research Funds for the Central Universities 2242023k30020,2242023k30021,2242023k30020,2242023k30021
6 · The paper itself

Abstract

Gastric cancer (GC) is a highly intricate gastrointestinal malignancy. Early detection of gastric cancer forms the cornerstone of precision medicine. Several studies have been conducted to investigate early biomarkers of gastric cancer using genomics, transcriptomics, proteomics, and metabolomics, respectively. However, endogenous substances associated with various omics are concurrently altered during gastric cancer development. Furthermore, environmental exposures and family history can also induce modifications in endogenous substances. Therefore, in this study, we primarily investigated alterations in DNA mutation, DNA methylation, mRNA, lncRNA, miRNA, circRNA, and protein, as well as glucose, amino acid, nucleotide, and lipid metabolism levels in the context of GC development, employing genomics, transcriptomics, proteomics, and metabolomics. Additionally, we elucidate the impact of exposure factors, including HP, EBV, nitrosamines, smoking, alcohol consumption, and family history, on diagnostic biomarkers of gastric cancer. Lastly, we provide a summary of the application of machine learning in integrating multi-omics data. Thus, this review aims to elucidate: i) the biomarkers of gastric cancer related to genomics, transcriptomics, proteomics, and metabolomics; ii) the influence of environmental exposure and family history on multiomics data; iii) the integrated analysis of multi-omics data using machine learning techniques.

Indexed as

Machine LearningMetabolomicsStomach NeoplasmsBiomarkers, TumorGenomicsHumansMultiomicsProteomicsBiomarkers, TumorbiomarkersexposureGastric cancergastroscopy.machine learningmulti-omics

Identifiers

PMID38351697
OpenAlexW4391820334

What OpenQuestion holds

Textmetadata
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.