Identification of Hepatocellular Carcinoma Subtypes Based on Global Gene Expression Profiling to Predict the Prognosis and Potential Therapeutic Drugs.

IF 3.9 3区 工程技术 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY Biomedicines Pub Date : 2025-01-20 DOI:10.3390/biomedicines13010236
Cunzhen Zhang, Jiyao Wang, Lin Jia, Qiang Wen, Na Gao, Hailing Qiao
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Abstract

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous tumor, and distinguishing its subtypes holds significant value for diagnosis, treatment, and the prognosis. Methods: Unsupervised clustering analysis was conducted to classify HCC subtypes. Subtype signature genes were identified using LASSO, SVM, and logistic regression. Survival-related genes were identified using Cox regression, and their expression and function were validated via qPCR and gene interference. GO, KEGG, GSVA, and GSEA were used to determine enriched signaling pathways. ESTIMATE and CIBERSORT were used to calculate the stromal score, tumor purity, and immune cell infiltration. TIDE was employed to predict the patient response to immunotherapy. Finally, drug sensitivity was analyzed using the oncoPredict algorithm. Results: Two HCC subtypes with different gene expression profiles were identified, where subtype S1 exhibited a significantly shorter survival time. A subtype scoring formula and a nomogram were constructed, both of which showed an excellent predictive performance. COL11A1 and ACTL8 were identified as survival-related genes among the signature genes, and the downregulation of COL11A1 could suppress the invasion and migration of HepG2 cells. Subtype S1 was characterized by the upregulation of pathways related to collagen and the extracellular matrix, as well as downregulation associated with the xenobiotic metabolic process and fatty acid degradation. Subtype S1 showed higher stromal scores, immune scores, and ESTIMATE scores and infiltration of macrophages M0 and plasma cells, as well as lower tumor purity and infiltration of NK cells (resting/activated) and resting mast cells. Subtype S2 was more likely to benefit from immunotherapy. Subtype S1 appeared to be more sensitive to BMS-754807, JQ1, and Axitinib, while subtype S2 was more sensitive to SB505124, Pevonedistat, and Tamoxifen. Conclusions: HCC patients can be classified into two subtypes based on their gene expression profiles, which exhibit distinctions in terms of signaling pathways, the immune microenvironment, and drug sensitivity.

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基于全局基因表达谱的肝细胞癌亚型鉴定预测预后和潜在治疗药物。
背景:肝细胞癌(HCC)是一种高度异质性的肿瘤,区分其亚型对诊断、治疗和预后具有重要价值。方法:采用无监督聚类分析对HCC亚型进行分类。采用LASSO、SVM和logistic回归对亚型特征基因进行鉴定。采用Cox回归法鉴定生存相关基因,并通过qPCR和基因干扰验证其表达和功能。GO、KEGG、GSVA和GSEA用于检测富集的信号通路。使用ESTIMATE和CIBERSORT计算基质评分、肿瘤纯度和免疫细胞浸润。采用TIDE预测患者对免疫治疗的反应。最后,采用oncoppredict算法进行药敏分析。结果:鉴定出两种不同基因表达谱的HCC亚型,其中S1亚型的生存时间明显较短。构建了亚型评分公式和模态图,均具有较好的预测效果。在这些特征基因中,鉴定出COL11A1和ACTL8是与生存相关的基因,下调COL11A1可抑制HepG2细胞的侵袭和迁移。S1亚型的特征是与胶原蛋白和细胞外基质相关的通路上调,以及与外源代谢过程和脂肪酸降解相关的下调。S1亚型间质评分、免疫评分和ESTIMATE评分较高,巨噬细胞M0和浆细胞浸润较高,肿瘤纯度较低,NK细胞(静息/活化)和静息肥大细胞浸润较低。S2亚型更有可能从免疫治疗中获益。S1亚型对BMS-754807、JQ1和阿西替尼更敏感,而S2亚型对SB505124、Pevonedistat和他莫昔芬更敏感。结论:HCC患者根据基因表达谱可分为两种亚型,在信号通路、免疫微环境、药物敏感性等方面存在差异。
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来源期刊
Biomedicines
Biomedicines Biochemistry, Genetics and Molecular Biology-General Biochemistry,Genetics and Molecular Biology
CiteScore
5.20
自引率
8.50%
发文量
2823
审稿时长
8 weeks
期刊介绍: Biomedicines (ISSN 2227-9059; CODEN: BIOMID) is an international, scientific, open access journal on biomedicines published quarterly online by MDPI.
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