Biswas Biplab, Sugimoto Masahiro, Hoque Md Aminul
Department of Statistics, Faculty of Science, Gopalganj Science & Technology University, Gopalganj 8100, Bangladesh.
Department of Statistics, Faculty of Science, University of Rajshahi, Rajshahi 6205, Bangladesh.
Biology (Basel). 2025 Apr 17;14(4):431. doi: 10.3390/biology14040431.
Liver cancer is one of the most common malignancies and the second leading cause of cancer-related deaths worldwide, particularly in developing countries, where it poses a significant financial burden. Early detection and timely treatment remain challenging due to the complex mechanisms underlying the initiation and progression of liver cancer. This study aims to uncover key genomic features, analyze their functional roles, and propose potential therapeutic drugs identified through molecular docking, utilizing single-cell RNA sequencing (scRNA-seq) data from liver cancer studies. We applied two advanced hybrid methods known for their robust identification of differentially expressed genes (DEGs) regardless of sample size, along with four top-performing individual methods. These approaches were used to analyze four scRNA-seq datasets, leading to the identification of essential DEGs. Through a protein-protein-interaction (PPI) network, we identified 25 hub-of-hub genes (hHubGs) and 20 additional hHubGs from two naturally occurring gene clusters, ultimately validating a total of 36 hHubGs. Functional, pathway, and survival analyses revealed that these hHubGs are strongly linked to liver cancer. Based on molecular docking and binding-affinity scores with 36 receptor proteins, we proposed 10 potential therapeutic drugs, which we selected from a pool of 300 cancer meta-drugs. The choice of these drugs was further validated using 14 top-ranked published receptor proteins from a set of 42. The proposed candidates include Adozelesin, Tivozanib, NVP-BHG712, Nilotinib, Entrectinib, Irinotecan, Ponatinib, and YM201636. This study provides critical insights into the genomic landscape of liver cancer and identifies promising therapeutic candidates, serving as a valuable resource for advancing liver cancer research and treatment strategies.
肝癌是最常见的恶性肿瘤之一,也是全球癌症相关死亡的第二大主要原因,在发展中国家尤为如此,给这些国家带来了沉重的经济负担。由于肝癌发生和发展的潜在机制复杂,早期检测和及时治疗仍然具有挑战性。本研究旨在利用肝癌研究中的单细胞RNA测序(scRNA-seq)数据,揭示关键的基因组特征,分析其功能作用,并通过分子对接提出潜在的治疗药物。我们应用了两种先进的混合方法,这两种方法以能够可靠地识别差异表达基因(DEG)而闻名,无论样本量大小,同时还应用了四种表现最佳的单独方法。这些方法用于分析四个scRNA-seq数据集,从而识别出必需的DEG。通过蛋白质-蛋白质相互作用(PPI)网络,我们从两个天然存在的基因簇中识别出25个中心枢纽基因(hHubG)和另外20个hHubG,最终总共验证了36个hHubG。功能、通路和生存分析表明,这些hHubG与肝癌密切相关。基于与36种受体蛋白的分子对接和结合亲和力得分,我们从300种癌症元药物中提出了10种潜在的治疗药物。使用一组42种中排名前14的已发表受体蛋白进一步验证了这些药物的选择。提出的候选药物包括阿多佐米、替沃扎尼、NVP-BHG712、尼洛替尼、恩曲替尼、伊立替康、波纳替尼和YM201636。本研究为肝癌的基因组格局提供了关键见解,并识别出有前景的治疗候选药物,为推进肝癌研究和治疗策略提供了宝贵资源。