ArticleDiscover oncology2025
Machine learning identifies INHBA DPT ADH7 FBP2 and GPR155 as diagnostic biomarkers for gastric cancer.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Gastric cancer still is a severe threat to human health, often presenting with a poor prognosis, effective biomarkers for early detection and targeted treatment are urgently needed. This study performed a comprehensive bioinformatics and machine learning approach to identify key protein biomarkers for gastric cancer and elucidate their potential functions. Gastric cancer-related datasets were obtained from the NCBI Gene Expression Omnibus database. Differential expression analysis identified 171 genes with noticeable differences between control and tumor samples. Utilizing LASSO, SVM-RFE, and RF algorithms, five genes—INHBA, DPT, ADH7, FBP2 and GPR155—were identified as potential biomarkers. A logistic regression model demonstrated the highest performance among ten machine learning models constructed using these five genes. Shapley additive explanations (SHAP) were employed to illustrate the detailed contribution of the pivotal genes to the logstics model. Gene set enrichment analysis and gene set variation analysis were then used to find out the functional roles of these genes in gastric cancer cells. At length, we revealed the distinctive effects of signature genes on immune cell infiltration and patient diagnosis. In conclusion, the identified proteins have the potential to serve as diagnostic biomarkers and provide treatment value for gastric cancer. This study offers a comprehensive, data-driven approach to uncover critical molecular targets for improved detection and management of this deadly disease.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.