ArticleJournal of hepatocellular carcinoma2026
PTM-Related Signatures Predict Lymph Node Metastasis and Shape Microenvironment in Hepatocellular Carcinoma: A Single-Cell and Bulk Integrated Analysis.
Article in Journal of hepatocellular carcinoma, 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Hepatocellular carcinoma (HCC) has a poor prognosis with lymph node metastasis (LNM), severely impacting survival. Protein post-translational modifications (PTMs) regulate tumor microenvironment (TME) dynamics, but their role in HCC LNM is unknown. Methods: This study systematically analyzed single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic data from TCGA-LIHC and validation cohorts. Key cellular subpopulations and differentially expressed genes (DEGs) were identified. PTM-related candidate genes were identified through intersection analysis. Prognostic genes were determined via Cox regression and integrated into a random survival forest (RSF) model. Patients were stratified to assess associations with TME remodeling, genomic alterations, immune checkpoints, and drug sensitivity. Pseudotime trajectory and transcription factor activity analyses elucidated molecular dynamics. Results: Hepatocyte populations were significantly expanded in both LNM and non-LNM HCC groups. The intersection of scRNA-seq DEGs, bulk DEGs, and PTM-related genes identified five candidate genes, among which PJA1 and RFPL4B identified as potential prognostic risk factors and were incorporated into a robust RSF model. High-risk patients exhibited poor survival outcomes, increased tumor mutational burden, and pronounced tumor microenvironment alterations. PJA1 expression was negatively correlated with hepatocyte infiltration. Pseudotime analysis revealed that LNM-associated hepatocytes were arrested in early differentiation states, coinciding with dysregulated transcription factor activity. Conclusion: This study computationally identified PJA1 and RFPL4B as post-translational modification-related candidate prognostic genes potentially associated with HCC LNM. The proposed risk model stratified patients into subgroups characterized by distinct tumor microenvironment features and genomic instability. Computational analysis suggested that arrest of hepatocyte differentiation and dysregulation of transcription factor networks may be implicated in metastatic progression, thereby offering novel therapeutic insights.
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.