Liu Qiang, Zhang Hua, Xiao Heng, Ren Ao, Cai Ying, Liao Rui, Yu Huarong, Wu Zhongjun, Huang Zuotian
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
School of Nursing, Chongqing Medical University, Chongqing, China.
Ann Med. 2025 Dec;57(1):2503645. doi: 10.1080/07853890.2025.2503645. Epub 2025 May 29.
Diagnosis of hepatocellular carcinoma (HCC) remains challenging for clinicians. Machine learning approaches and big data analyses are viable strategies for identifying HCC diagnostic markers.
In this study, we downloaded mRNA expression profiles of HCC from the GEO database and used random forest and machine learning algorithms, such as least absolute shrinkage and selection operator, to screen for reliable diagnostic genes. Disease Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis enrichment analyses were performed to explore differential gene functions and disease pathways. CIBERSORT was performed to calculate the immune cell infiltration of HCC and the correlation between diagnostic genes and immune cells. Cell experiments were performed to evaluate the function of R-spondin 3 (RSPO3) in HCC cells. Immunohistochemical staining was used to evaluate the protein expression of CD138, CD206 and iNOS.
The results indicated that extracellular matrix protein 1 (ECM1), Niemann-Pick C1-Like 1 (NPC1L1) and RSPO3 were down-regulated in HCC compared with the normal group ( < 0.05), which was validated in clinical tissue samples. Moreover, ECM1, NPC1L1 and RSPO3 had high diagnostic values (AUC > 0.75) for HCC in both training and test groups. Immuno-infiltration analysis revealed that ECM1 and RSPO3 were highly positively correlated with neutrophil and macrophage M2 levels, whereas they were negatively correlated with Tregs. RSPO3-si affected cell proliferation and apoptosis in HCC. Furthermore, RSPO3 exhibited a positive correlation with tumour progression, the proportion of plasma cells and M2 macrophages in mice, while showing a negative association with M1 macrophages.
The present study identified ECM1, NPC1L1 and RSPO3 as new diagnostic biomarkers for HCC based on normal and diseased samples from HCC, meanwhile the pro-oncogenic function of RSPO3 and its regulation on immune infiltration have been confirmed.
肝细胞癌（HCC）的诊断对临床医生来说仍然具有挑战性。机器学习方法和大数据分析是识别HCC诊断标志物的可行策略。
在本研究中，我们从基因表达综合数据库（GEO数据库）下载了HCC的mRNA表达谱，并使用随机森林和机器学习算法，如最小绝对收缩和选择算子，来筛选可靠的诊断基因。进行疾病本体论、京都基因与基因组百科全书（KEGG）和基因集富集分析富集分析，以探索差异基因功能和疾病途径。使用CIBERSORT计算HCC的免疫细胞浸润以及诊断基因与免疫细胞之间的相关性。进行细胞实验以评估R-spondin 3（RSPO3）在HCC细胞中的功能。采用免疫组织化学染色评估CD138、CD206和诱导型一氧化氮合酶（iNOS）的蛋白表达。
结果表明，与正常组相比，细胞外基质蛋白1（ECM1）、尼曼-匹克C1样1蛋白（NPC1L1）和RSPO3在HCC中表达下调（<0.05），这在临床组织样本中得到了验证。此外，ECM1、NPC1L1和RSPO3在训练组和测试组中对HCC均具有较高的诊断价值（曲线下面积>0.75）。免疫浸润分析显示，ECM1和RSPO3与中性粒细胞和巨噬细胞M2水平高度正相关，而与调节性T细胞（Tregs）负相关。RSPO3-si影响HCC细胞的增殖和凋亡。此外，RSPO3与肿瘤进展、小鼠浆细胞和M2巨噬细胞的比例呈正相关，而与M1巨噬细胞呈负相关。
本研究基于HCC的正常和患病样本，将ECM1, NPC1L1和RSPO3鉴定为HCC的新诊断生物标志物，同时证实了RSPO3的促癌功能及其对免疫浸润的调节作用。
