Back to search

Article

Integrated bulk and single-cell transcriptomic analyses identify MAD2L1 as a candidate diagnostic and prognostic marker of a lineage-agnostic cell- cycle program in hepatocellular carcinoma

2026-08-28

Abstract excerpt

<title>Abstract</title> <p> <bold>Objective</bold> Aberrant cell-cycle regulation is a central hallmark of hepatocellular carcinoma (HCC), yet the cellular distribution of this proliferative program and its association with patient prognosis and tumor-microenvironmental heterogeneity remain incompletely characterized. We aimed to identify a reproducible cell-cycle-associated transcriptional program in HCC, prio...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
58042b17-b43d-5756-9276-0cce05a974bf
DOI
10.21203/rs.3.rs-10486204/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Integrated bulk and single-cell transcriptomic analyses identify MAD2L1 as a candidate diagnostic and prognostic marker of a lineage-agnostic cell- cycle program in hepatocellular carcinomaDOI 10.21203/rs.3.rs-10486204/v1
Understanding the heterogeneity in liver hepatocellular carcinoma with a special focus on malignant cell through single-cell analysisSemantic proximity
Development and validation of a six-gene T cell-derived prognostic signature based on single-cell transcriptomics for hepatocellular carcinomaSemantic proximity
Deconvolution of B-cell heterogeneity identifies PDIA6 as a prognostic driver in hepatocellular carcinomaSemantic proximity
Unraveling the significance of cuproptosis in hepatocellular carcinoma heterogeneity and tumor microenvironment through integrated single-cell sequencing and machine learning approachesSemantic proximity
Integrating single-cell and bulk RNA-seq to reveal cholesterol homeostasis-related genes via machine learning to predict prognosis and therapeutic response in hepatocellular carcinomaSemantic proximity
Identification of Anoikis-Related Prognostic Genes and Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Single-Cell and Bulk Transcriptomic AnalysisSemantic proximity
Integration of single-cell sequencing and drug sensitivity profiling reveals an 11-gene prognostic model for liver cancer.Semantic proximity
HCCDB v2.0: Decompose the Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCCSemantic proximity
HCCDB v2.0: Decompose Expression Variations by Single-cell RNA-seq and Spatial Transcriptomics in HCCSemantic proximity
Integrating a Hundred Machine Learning Combinations and Single- Cell Transcriptomics to Define a KIAA0408-Driven B-Cell Prognostic Signature in Ovarian CancerSemantic proximity
Single-cell analysis reveals the intra-tumor heterogeneity and identifies MLXIPL as a biomarker in the cellular trajectory of hepatocellular carcinomaSemantic proximity
Multimodal single-cell analysis reveals the prognostic role of myeloid subpopulation–driven PI3K/AKT signaling in hepatocellular carcinomaSemantic proximity
Select a neighboring publication to make it the new centre.