ArticleDiscover oncology2023
Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma.
Article in Discover oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 8 citations in OpenAlex.
- Identification of an Anoikis-Related Gene Signature to Predict the Prognosis in Patients With Oral Squamous Cell Carcinoma.International dental journal · 2026Article
- Diagnostic Biomarkers and Targeted Drug Prediction for Acute Kidney Injury: A Computational ApproachEndocrine, metabolic & immune disorders drug targets · 2026Article
- HCV-related hepatocellular carcinoma: gene signatures associated with TERT promoter mutations and sex.Journal of translational medicine · 2025Article
- Causal associations between circulating protein ratios and drug resistance in papillary thyroid cancer: a Mendelian randomization study.Discover oncology · 2025Article
- Machine learning-based identification of diagnostic and prognostic mitotic cell cycle genes in hepatocellular carcinoma.PloS one · 2025Article
- Amplification of select autonomous HERV loci and surrounding host gene transcription in monocytes from patients with post-acute sequelae of COVID-19.Frontiers in immunology · 2025Article
- SPINK1 facilitates tumor progression via the EGFR/JAK/STAT3 axis in oral squamous cell carcinoma: insights from single-cell RNA sequencing.Frontiers in oncology · 2025Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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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