Ma Qiji, Wang Yun, Xing Jie, Wang Tielin, Wang Gang
Department of Breast and Thyroid Surgery, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China.
Department of Thoracic Surgery, The Affiliated Hospital to Changchun University of Chinese Medicine, Changchun, China.
Transl Gastroenterol Hepatol. 2025 Feb 23;10:28. doi: 10.21037/tgh-24-89. eCollection 2025.
Hepatocellular carcinoma (HCC), one of the most common malignant tumors worldwide, has a poor prognosis primarily due to its invasive and metastatic characteristics. Cancer invasion through basement membrane (BM) is the pivotal initial step in tumor dissemination and metastasis. This study aimed to identify gene signatures associated with the BM to enhance the overall prognosis of HCC.
In this study, we performed multiple bioinformatics analyses based on the RNA sequencing (RNA-seq) data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. An unsupervised consistent cluster analysis was conducted on 370 HCC patients, categorizing them into two distinct groups based on the expression profiles of 222 BM-related genes. Differentially expressed genes (DEGs) between these clusters were identified, followed by functional enrichment analysis. To explore the differences between the groups, the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data (ESTIMATE) and Cell type Identification By Estimating Relative Subsets Of known RNA Transcripts (CIBERSORT) algorithms were applied, along with the analysis of immune checkpoint molecules and human leukocyte antigen (HLA) expression levels. This helped in understanding the relationship between the HCC immune microenvironment and BM-related genes. A prognostic model was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses, with its performance subsequently estimated and validated. Additionally, hub biomarkers genes were identified using machine learning techniques, followed by an analysis of their functions and relationships with clinical characteristics. Finally, single-cell clustering analysis was employed to further investigate the expression distribution of these genes within the HCC immune microenvironment.
Following consistent cluster analysis, two groups were identified: the BM high group and the BM low group. Among the 6,221 DEGs between the two groups, 5,863 were upregulated and 358 were downregulated, with enrichment functions primarily associated with extracellular matrix (ECM) organization, cell adhesion, immune response, and metabolism. The expression levels of BM-related genes were found to regulate the HCC immune microenvironment. Using univariate Cox regression analysis, 60 prognostic BM-related genes were identified, leading to the establishment of an 11-gene prognostic model named BMscore to predict the overall survival (OS) of HCC patients. The high BMscore group indicated worse prognosis, and the model's predictive performance was validated using the GEO dataset. and were identified as hub biomarkers, playing roles in cell proliferation and ECM metabolism, with their expression distributions mapped respectively.
A prognostic model based on BM-related genes was successfully developed and shows promise for evaluating prognoses and offering personalized treatment recommendations.
肝细胞癌（HCC）是全球最常见的恶性肿瘤之一，其预后较差，主要归因于其侵袭和转移特性。肿瘤通过基底膜（BM）的侵袭是肿瘤播散和转移的关键起始步骤。本研究旨在识别与基底膜相关的基因特征，以改善HCC的总体预后。
在本研究中，我们基于来自癌症基因组图谱（TCGA）和基因表达综合数据库（GEO）数据集的RNA测序（RNA-seq）数据及临床信息进行了多项生物信息学分析。对370例HCC患者进行无监督一致性聚类分析，根据222个与基底膜相关基因的表达谱将他们分为两个不同的组。识别这些聚类之间的差异表达基因（DEG），随后进行功能富集分析。为探究两组之间的差异，应用了利用表达数据估计恶性肿瘤中的基质和免疫细胞（ESTIMATE）算法以及通过估计已知RNA转录本的相对子集进行细胞类型鉴定（CIBERSORT）算法，同时分析免疫检查点分子和人类白细胞抗原（HLA）的表达水平。这有助于了解HCC免疫微环境与基底膜相关基因之间的关系。使用单变量Cox回归和最小绝对收缩和选择算子（LASSO）回归分析构建预后模型，随后对其性能进行估计和验证。此外，利用机器学习技术识别枢纽生物标志物基因，接着分析它们的功能以及与临床特征的关系。最后，采用单细胞聚类分析进一步研究这些基因在HCC免疫微环境中的表达分布。
经过一致性聚类分析，识别出两组：基底膜高表达组和基底膜低表达组。两组之间的6221个差异表达基因中，5863个上调，358个下调，富集功能主要与细胞外基质（ECM）组织、细胞黏附、免疫反应和代谢相关。发现基底膜相关基因的表达水平调节HCC免疫微环境。通过单变量Cox回归分析，识别出60个与预后相关的基底膜相关基因，从而建立了一个名为BMscore的11基因预后模型，用于预测HCC患者的总生存期（OS）。BMscore高的组预后较差，该模型的预测性能在GEO数据集上得到验证。 和 被识别为枢纽生物标志物，在细胞增殖和ECM代谢中发挥作用，并分别绘制了它们的表达分布图。
成功开发了一种基于基底膜相关基因的预后模型，该模型在评估预后和提供个性化治疗建议方面显示出前景。
