ArticleCancer medicine2025
Identification of Anoikis-Related Genes in Gastric Cancer: Bioinformatics and Experimental Validation.
Article in Cancer medicine, 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
10 authors.
Funding
Abstract
introductionDistant metastasis is the main reason for the poor prognosis of gastric cancer, and anoikis refers to the cell death caused when cells detach from the extracellular matrix or adhere in incorrect locations, playing an important role in the distant metastasis of gastric cancer.
methodsDownload the TCGA-STAD dataset and the anoikis gene set, and filter out the differentially expressed anoikis genes. Perform consensus clustering of gastric cancer samples, and conduct Weighted Gene Correlation Network Analysis (WGCNA), enrichment analysis, and immune infiltration analysis for the expression characteristics of each subtype, while also filtering the genes with differential expression between subtypes. Additionally, through COX survival analysis, identify anoikis genes related to gastric cancer prognosis and establish a nomogram. Finally, validate the differentially expressed gene CYP1B1 in vivo and in vitro through clinical samples, cell culture, and the establishment of an anoikis model.
resultsThree subtypes of gastric cancer with anoikis genes were identified, each exhibiting different expression characteristics, biological pathways, and immune cell infiltration. The abundance of activated NK cells, memory B cells, and M2 macrophages showed significant differences among the three subtypes. We screened four differentially expressed gene sets and five genes (CYP1B1, EQTN, NRXN2, TBC1D3E, TCEAL5) among the three subtypes. Through survival analysis, we identified 33 independent prognostic genes and constructed a nomogram, with calibration curves indicating good consistency. Finally, we selected CYP1B1 for experimental validation, and in vivo and in vitro experiments demonstrated that CYP1B1 is highly expressed in gastric cancer, participates in the resistance to cell death in gastric cancer cells, and promotes the invasion, migration, and tumor progression of gastric cancer cells.
conclusionThe expression patterns of subtypes based on differentially expressed genes related to anoikis in gastric cancer vary, providing theoretical support for the future of personalized treatment for gastric cancer.
Indexed as
Identifiers
What Socratic 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 Socratic graph.