Al-Bzour Noor N, Al-Bzour Ayah N, Qasaymeh Abdelrahman, Saeed Azhar, Chen Lujia, Saeed Anwaar
Department of Medicine, Division of Hematology & Oncology, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA.
Department of Pathology and Laboratory Medicine, University of Vermont Medical Center, Burlington, VT, USA.
Sci Rep. 2024 Dec 5;14(1):30328. doi: 10.1038/s41598-024-81395-x.
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, often linked to chronic inflammation. Our study aimed to probe inflammation pathways at the genetic level and pinpoint biomarkers linked to HCC patient survival.
We analyzed gene transcriptome data from 246 resectable stage I and II HCC patients from The Cancer Genome Atlas (TCGA). After selecting 917 inflammation-related genes (IRGs), we identified 104 differentially expressed genes (DEGs) through differential expression analysis. Two significant prognostic DEGs, S100A9 and PBK, were identified using LASSO and Cox regression, forming the basis of a risk score model. We conducted functional enrichment and immune landscape analyses, validated our findings on 170 patients from the GSE14520 dataset, and performed mutational analysis using TCGA somatic mutation data.
We analyzed 296 samples (246 HCC, 50 normal liver), showing significant survival differences between high and low-risk groups based on our risk score model. Functional enrichment analysis unveiled inflammation-associated pathways. Validation using the GSE14520 dataset confirmed our risk score's predictive ability, and we explored clinical correlations.
Our study delineates inflammation-related genomic changes in HCC, unveiling prognostic biomarkers with potential therapeutic implications. These findings deepen our understanding of HCC molecular mechanisms and may guide personalized therapeutic approaches, ultimately improving patient outcomes.
肝细胞癌（HCC）是全球癌症相关死亡的主要原因，常与慢性炎症相关。我们的研究旨在从基因水平探究炎症途径，并确定与HCC患者生存相关的生物标志物。
我们分析了来自癌症基因组图谱（TCGA）的246例可切除的I期和II期HCC患者的基因转录组数据。在选择了917个炎症相关基因（IRGs）后，我们通过差异表达分析确定了104个差异表达基因（DEGs）。使用LASSO和Cox回归确定了两个显著的预后DEGs，即S100A9和PBK，它们构成了风险评分模型的基础。我们进行了功能富集和免疫景观分析，在来自GSE14520数据集的170例患者中验证了我们的发现，并使用TCGA体细胞突变数据进行了突变分析。
我们分析了296个样本（246例HCC，50例正常肝脏），根据我们的风险评分模型显示高风险组和低风险组之间存在显著的生存差异。功能富集分析揭示了与炎症相关的途径。使用GSE14520数据集进行的验证证实了我们风险评分的预测能力，并且我们探索了临床相关性。
我们的研究描绘了HCC中与炎症相关的基因组变化，揭示了具有潜在治疗意义的预后生物标志物。这些发现加深了我们对HCC分子机制的理解，并可能指导个性化治疗方法，最终改善患者的预后。
