ArticleEuropean journal of medical research2026
KNTC1 and PRC1 define an immunosuppressive microenvironment and poor prognosis in liposarcoma.
Article in European journal of medical research, 2026. 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
backgroundLiposarcomas (LPS) is a highly heterogeneous malignant soft tissue tumor. Tumor microenvironment immune traits critically affect cancer progression and treatment efficacy. However, immune-related biomarkers for prognostic assessment, reflecting tumor immune microenvironment features and with diagnostic potential, remain insufficiently explored in LPS.
methodsThe RNA-seq data and clinical information of patients with liposarcoma were downloaded from the GEO and TCGA database. The "limma" package performed the differential expression genes (DEGs) analysis, and the weighted gene co-expression network analysis (WGCNA) method was used to identify the liposarcoma-related module. We performed the single sample gene set enrichment analysis (ssGSEA) to calculate the enrichment scores for 28 tumor-infiltrating lymphocyte (TIL) subpopulations based on previously established immune signatures. Then, the consensus clustering was conducted using the "ConsensusClusterPlus" package. After that, the lasso and multivariate Cox regression analysis was applied for the risk model construction. The ESTIMATE algorithm for immune infiltration, "clusterProfiler" for function enrichment, "survival" for prognostic difference and "timeROC" for receiver operator characteristic curve (ROC) analysis were performed. The wound healing and transwell assay were conducted.
resultsAfter differential expression analysis, 852 DEGs between liposarcoma and para-cancer samples were obtained, and the turquoise was the liposarcoma-related gene module through WGCNA analysis. Consensus clustering based on immune signatures stratified patients into immunity-high (H) and immunity-low (L) subtypes with significant survival differences. Integration of these findings led to a robust 2-gene prognostic signature (KNTC1 and PRC1) via LASSO and Cox regression. Both model genes exhibited outstanding diagnostic performance (AUC > 0.9). High RiskScore was significantly associated with aggressive pathological subtypes, metastasis, and an immunosuppressive microenvironment characterized by lower overall immune infiltration. Conversely, low-risk patients showed enhanced immune cell abundance and activity. In addition, functional validation confirmed that KNTC1 silencing significantly impaired LPS cell migration and invasion, underscoring its role in tumor progression.
conclusionsWe constructed a robust two-gene prognostic model that effectively predicts survival, reflects tumor immune microenvironment heterogeneity, and distinguishes pathological subtypes in LPS. Our findings provided valuable insights for prognostic stratification and personalized treatment strategies.
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.