ArticlePloS one2024
Predicting the immunity landscape and prognosis with an NCLs signature in liver hepatocellular carcinoma.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 5 citations in OpenAlex.
- PSMD4 promotes malignant phenotypes and is associated with angiogenesis-related signaling and immune remodeling in hepatocellular carcinoma.Discover oncology · 2026Article
- Analysing cell death patterns to predict outcomes and treatment options in patients with high-grade serous ovarian carcinoma.Scientific reports · 2026Article
- The Molecular Network of Neutrophil Extracellular Traps in Hepatocellular Carcinoma: Biogenesis, Function, and Therapeutic Implications.Molecules (Basel, Switzerland) · 2026Review
- Interplay between NETosis and the lncRNA-microRNA regulatory axis in the immunopathogenesis of cancer.Journal of physiology and biochemistry · 2025Review
- Mitochondrial Gene Scoring System: Predicting Prognosis and Precision Therapy for TACE Non-Response in Hepatocellular Carcinoma Patients.Journal of Cancer · 2025Article
- Prognostic Signature of NETs-Related Genes in Hepatocellular Carcinoma Based on Bulk and Single-Cell Transcriptomics.Journal of hepatocellular carcinoma · 2025Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundActivated neutrophils release depolymerized chromatin and protein particles into the extracellular space, forming reticular Neutrophil Extracellular Traps (NETs). This process is accompanied by programmed inflammatory cell death of neutrophils, known as NETosis. Previous reports have demonstrated that NETosis plays a significant role in immune resistance and microenvironmental regulation in cancer. This study sought to characterize the function and molecular mechanism of NETosis-correlated long non-coding RNAs (NCLs) in the prognostic treatment of liver hepatocellular carcinoma (LIHC).
methodsWe obtained the transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and evaluated the expression of NCLs in LIHC. A prognostic signature of NCLs was constructed using Cox and Last Absolute Shrinkage and Selection Operator (Lasso) regression, while the accuracy of model was validated by the ROC curves and nomogram, etc. In addition, we analyzed the associations between NCLs and oncogenic mutation, immune infiltration and evasion. Finally, LIHC patients were classified into four subgroups based on consensus cluster analysis, and drug sensitivity was predicted.
resultsAfter screening, we established a risk model combining 5 hub-NCLs and demonstrated its reliability. Independence checks suggest that the model may serve as an independent predictor of LIHC prognosis. Enrichment analysis revealed a concentration of immune-related pathways in the high-risk group. Immune infiltration indicates that immunotherapy could be more effective in the low-risk group. Upon consistent cluster analysis, cluster subgroup 4 presented a better prognosis. Sensitivity tests showed the distinctions in therapeutic effectiveness among various drugs in different subgroups.
conclusionOverall, we have developed a prognostic signature that can discriminate different LIHC subgroups through the 5 selected NCLs, with the objective of providing LIHC patients a more precise, personalized treatment regimen.
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