ArticleDiscover oncology2025
Identification of a novel anoikis-related gene signature to predict prognosis and tumor microenvironment in intrahepatic cholangiocarcinoma carcinoma.
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
backgroundIntrahepatic cholangiocarcinoma is a malignant tumor of hepatobiliary epithelial cells. In recent years, its incidence has gradually increased. It has a very high fatality rate and low survival rate, and the existing predictive factors for intrahepatic cholangiocarcinoma are unclear. The role of anoikis, a form of programmed cell death, in intrahepatic cholangiocarcinoma is not fully understood. This study focuses on identifying and analyzing anoikis-related differentially expressed genes in intrahepatic cholangiocarcinoma, aiming to enhance our understanding of potential treatment strategies and prognosis of intrahepatic cholangiocarcinoma.
methodsIn our study, we employed a clustering algorithm to classify samples from The Cancer Genome Atlas (TCGA) based on differentially expressed overlapping anoikis-related genes. Subsequently, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) to identify highly correlated genes and constructed a prognostic risk model based on univariate Cox proportional hazard regression. We validated the model's reliability using external datasets from the International Cancer Genome Consortium (ICGC) and the Gene Expression Omnibus (GEO). Finally, we used the CIBERSORT algorithm to investigate the correlation between risk scores and immune infiltration.
resultsThe results showed that the TCGA cohort could be divided into 2 subgroups, among which subgroup B had a lower survival probability. We identified three prognostic genes (EGF, BNIP3, TDGF1) associated with anorexia. The prognostic risk model effectively predicted overall survival and was validated in ICGC and GEO data sets. Furthermore, there were significant correlations between infiltrating immune cells and prognostic genes and risk scores.
conclusionWe identified subgroups and prognostic genes associated with ICCA dysregulation, which are important for understanding the treatment and prognosis of ICCA.
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.