Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma

Shiming Yi, Chunlei Zhang, Ming Li, Tianyi Qu, Jiafeng Wang
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Abstract

Machine learning techniques have been widely used in predicting disease prognosis, including cancer prognosis. One of the major challenges in cancer prognosis is to accurately classify cancer types and stages to optimize early screening and detection, and machine learning techniques have proven to be very useful in this regard. In this study, we aimed at identifying critical genes for diagnosis and outcomes of hepatocellular carcinoma (HCC) patients using machine learning. The HCC expression dataset was downloaded from GSE65372 datasets and TCGA datasets. Differentially expressed genes (DEGs) were identified between 39 HCC and 15 normal samples. For the purpose of locating potential biomarkers, the LASSO and the SVM-RFE assays were performed. The ssGSEA method was used to analyze the TCGA to determine whether there was an association between SPINK1 and tumor immune infiltrates. RT-PCR was applied to examine the expression of SPINK1 in HCC specimens and cells. A series of functional assays were applied to examine the function of SPINK1 knockdown on the proliferation of HCC cells. In this study, 103 DEGs were obtained. Based on LASSO and SVM-RFE analysis, we identified nine critical diagnostic genes, including C10orf113, SPINK1, CNTLN, NRG3, HIST1H2AI, GPRIN3, SCTR, C2orf40 and PITX1. Importantly, we confirmed SPINK1 as a prognostic gene in HCC. Multivariate analysis confirmed that SPINK1 was an independent prognostic factor for overall survivals of HCC patients. We also found that SPINK1 level was positively associated with Macrophages, B cells, TFH, T cells, Th2 cells, iDC, NK CD56bright cells, Th1 cells, aDC, while negatively associated with Tcm and Eosinophils. Finally, we demonstrated that SPINK1 expression was distinctly increased in HCC specimens and cells. Functionally, silence of SPINK1 distinctly suppressed the proliferation of HCC cells via regulating Wnt/β-catenin pathway. The evidence provided suggested that SPINK1 may possess oncogenic properties by inducing dysregulated immune infiltration in HCC. Additionally, SPINK1 was identified as a novel biomarker and therapeutic target for HCC.

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机器学习和实验确定 SPINK1 为肝细胞癌的候选诊断和预后生物标记物
机器学习技术已被广泛用于预测疾病预后,包括癌症预后。癌症预后的主要挑战之一是对癌症类型和分期进行准确分类,以优化早期筛查和检测,而机器学习技术已被证明在这方面非常有用。在这项研究中,我们的目标是利用机器学习识别肝细胞癌(HCC)患者诊断和预后的关键基因。我们从 GSE65372 数据集和 TCGA 数据集中下载了 HCC 表达数据集。在 39 个 HCC 样本和 15 个正常样本之间发现了差异表达基因(DEGs)。为了找到潜在的生物标记物,进行了 LASSO 和 SVM-RFE 分析。ssGSEA方法用于分析TCGA,以确定SPINK1与肿瘤免疫浸润之间是否存在关联。应用 RT-PCR 检测 SPINK1 在 HCC 标本和细胞中的表达。通过一系列功能检测,研究了SPINK1基因敲除对HCC细胞增殖的影响。本研究共获得 103 个 DEGs。基于LASSO和SVM-RFE分析,我们确定了9个关键诊断基因,包括C10orf113、SPINK1、CNTLN、NRG3、HIST1H2AI、GPRIN3、SCTR、C2orf40和PITX1。重要的是,我们证实 SPINK1 是 HCC 的预后基因。多变量分析证实,SPINK1是影响HCC患者总生存率的独立预后因素。我们还发现,SPINK1水平与巨噬细胞、B细胞、TFH、T细胞、Th2细胞、iDC、NK CD56bright细胞、Th1细胞、aDC呈正相关,而与Tcm和嗜酸性粒细胞呈负相关。最后,我们发现 SPINK1 在 HCC 标本和细胞中的表达明显增加。在功能上,沉默 SPINK1 可通过调节 Wnt/β-catenin 通路明显抑制 HCC 细胞的增殖。所提供的证据表明,SPINK1可能通过诱导HCC中失调的免疫浸润而具有致癌特性。此外,SPINK1 还被确定为 HCC 的新型生物标记物和治疗靶点。
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