METHODS: We sourced data on clinical parameters and gene expressions for liver cancer patients from The Cancer Genome Atlas Program database and carried out all analyses using R software and its relevant codes.
RESULTS: In our research, we delved into the genes intertwined with NK cells in hepatocellular carcinoma (HCC). Leveraging the QUANTISEQ and MCPCOUNTER algorithms to quantify NK cells, we spotlighted genes vital to the recruitment of NK cells. Among these genes, GDE1, WDFY3, DNAJB14, PKD2, DGAT2, SGMS2 and MKNK2 showed a strong correlation with patient outcomes. We also mapped out the single-cell expression trajectories of these genes within the HCC milieu. From our findings, SGMS2 emerged as a key gene warranting further scrutiny. Our in-depth analysis of SGMS2 shed light on its influence over specific biological pathways, its contribution to the immune landscape and its role in genomic instability within HCC. Drawing from this, we formulated a predictive model rooted in SGMS2-associated genes. This model showcased remarkable precision across both training and validation cohorts.
CONCLUSIONS: Overall, our investigation underscored the profound implications of SGMS2, a gene pivotal to NK cell infiltration, in the landscape of HCC, thereby positioning it as a potential linchpin in oncological strategies.
方法:我们从癌症基因组图谱计划数据库中获取了肝癌患者的临床参数和基因表达数据,并使用R软件及其相关代码进行了所有分析。
结果:在我们的研究中,我们深入研究了与肝细胞癌(HCC)中NK细胞交织的基因。利用QUANTISEQ和MCPCOUNTER算法来量化NK细胞,我们聚焦了对NK细胞募集至关重要的基因。在这些基因中,GDE1,WDFY3,DNAJB14,PKD2,DGAT2,SGMS2和MKNK2与患者预后密切相关。我们还绘制了这些基因在HCC环境中的单细胞表达轨迹。根据我们的发现,SGMS2已成为需要进一步审查的关键基因。我们对SGMS2的深入分析揭示了它对特定生物学途径的影响,它对免疫景观的贡献及其在HCC基因组不稳定性中的作用。由此得出,我们建立了一个根植于SGMS2相关基因的预测模型。该模型在训练和验证队列中都展示了显着的准确性。
结论:总体而言,我们的调查强调了SGMS2的深远意义,SGMS2是NK细胞浸润的关键基因,在HCC的景观中,从而将其定位为肿瘤策略的潜在关键。