Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma
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 pr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2023-12-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-023-00849-2 |
| _version_ | 1797388274646384640 |
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| author | Shiming Yi Chunlei Zhang Ming Li Tianyi Qu Jiafeng Wang |
| author_facet | Shiming Yi Chunlei Zhang Ming Li Tianyi Qu Jiafeng Wang |
| author_sort | Shiming Yi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T22:37:26Z |
| format | Article |
| id | doaj.art-7b0893026de94b089566a2748dbd2c0e |
| institution | Directory Open Access Journal |
| issn | 2730-6011 |
| language | English |
| last_indexed | 2024-03-08T22:37:26Z |
| publishDate | 2023-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj.art-7b0893026de94b089566a2748dbd2c0e2023-12-17T12:21:33ZengSpringerDiscover Oncology2730-60112023-12-0114112010.1007/s12672-023-00849-2Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinomaShiming Yi0Chunlei Zhang1Ming Li2Tianyi Qu3Jiafeng Wang4Department of Hepatobiliary Surgery, Yantai Affiliated Hospital of Binzhou Medical UniversityDepartment of Colorectal and Anus Surgery, Yantai Affiliated Hospital of Binzhou Medical UniversityDepartment of Gastroenterology, Yantai Affiliated Hospital of Binzhou Medical UniversityEmergency Department, Yantai Affiliated Hospital of Binzhou Medical UniversityDepartment of Hepatobiliary Surgery, the Affiliated Taian City Central Hospital of Qingdao UniversityAbstract 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.https://doi.org/10.1007/s12672-023-00849-2Hepatocellular carcinomaImmune infiltrationBiomarkerSPINK1DiagnosisMachine learning |
| spellingShingle | Shiming Yi Chunlei Zhang Ming Li Tianyi Qu Jiafeng Wang Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma Discover Oncology Hepatocellular carcinoma Immune infiltration Biomarker SPINK1 Diagnosis Machine learning |
| title | Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma |
| title_full | Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma |
| title_fullStr | Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma |
| title_full_unstemmed | Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma |
| title_short | Machine learning and experiments identifies SPINK1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma |
| title_sort | machine learning and experiments identifies spink1 as a candidate diagnostic and prognostic biomarker for hepatocellular carcinoma |
| topic | Hepatocellular carcinoma Immune infiltration Biomarker SPINK1 Diagnosis Machine learning |
| url | https://doi.org/10.1007/s12672-023-00849-2 |
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