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

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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.", "claims": [{"id": "claim-method", "statement": "The DEGs were narrowed down by the use of the LASSO regression method, and the end result was the identification of 14 variables as potential diagnostic biomarkers for HCC (Fig. 3A). The SVM-RFE method was utilized so that the properties of the DEGs could be whittled down to a chosen group of five criteria (Fig. 3B).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38093163", "locator": "sections[19] \"Identification and validation of diagnostic genes\" paragraph 2", "quote": "The DEGs were narrowed down by the use of the LASSO regression method, and the end result was the identification of 14 variables as potential diagnostic biomarkers for HCC (Fig. 3A). The SVM-RFE method was utilized so that the properties of the DEGs could be whittled down to a chosen group of five criteria (Fig. 3B)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38093163", "locator": "sections[28] \"notes\" paragraph 1", "quote": "The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "LASSO and SVM-RFE analyses identified nine critical diagnostic genes for HCC.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38093163", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Based on LASSO and SVM-RFE analysis, we identified nine critical diagnostic genes, including C10orf113, SPINK1, CNTLN, NRG3, HIST1H2AI, GPRIN3, SCTR, C2orf40 and PITX1."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "LASSO and SVM-RFE analyses identified nine critical diagnostic genes for HCC.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:38093163"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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In this s", "btained from every patient. Cell culture and transfection HCC Cells (MHCC97H, HepG2, HCCLM3 and SMMC-7", "he Lipofectamine 2000 reagent kits, the cell transfection was carried out utilizing those kits. Statis", ": 37 degrees Celsius and 5% CO2. 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