ArticleHuman mutation2026
Exploring the Dynamic Changes of Intercellular Connections in Cervical Cancer: Insights From Transcriptomic Data Combined With Single-Cell Sequencing.
Article in Human mutation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: As a common gynecological malignancy, cervical cancer has a rising incidence rate and mortality, which has brought huge pressure to global public health. Although immunotherapy has been applied in clinical practice, its therapeutic effect is still far from satisfactory. Methods: InferCNV was used to calculate the CNV score and the ssGSE, which is an algorithm to calculate the abundance of samples. CellChat analysis and pseudotime analysis were used to observe the evolution and interaction relationships between different clusters. Establish a prognostic model for CC patients using univariate, LASSO, and Cox analysis, and evaluate copy number variation and TME in low-risk groups. Finally, ssGSEA was applied to calculate the relationship between the hallmark gene sets and immune cycle steps and to calculate drug sensitivity in different risk groups using "oncopredict." A series of experiments including CCK-8 assay, clone formation, EdU assay, and Transwell assay were performed to detect the role of COL4A1 in CC. Results: The epithelial cells were divided into nine clusters. Among them, Cluster 8 has a lower CNV score, a lower degree of variation, and a better prognosis. After that, Cluster 8 sends a signal to fibroblasts through the PTN signaling pathway. A cervical cancer-related model (CCM) was constructed based on the marker genes of Cluster 8, and it can effectively distinguish the prognosis. There is a great difference in standardized TMB, immune cell infiltration, and ESTIMATE scores between the groups. Nine drugs were identified which may achieve better therapeutic effects when applied to low-risk patients. Finally, knockdown of COL4A1 inhibits the proliferation and metastatic ability of CC cells. Conclusion: Our study revealed different interactions between subgroups in the tumor microenvironment of CC epithelial cells. We established an effective prognostic model. Ultimately, through a series of in vitro function experiments, COL4A1 was recognized as a new potential target for the therapeutic intervention of CC.
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.