Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis
Abstract Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. But the effective biomarkers for HCC metastasis remain underexplored. Here we integrated GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) datasets to screen candidate genes for hepatocellular carcinom...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-12-01
|
| Series: | Discover Oncology |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s12672-024-01667-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846112388241162240 |
|---|---|
| author | Chen Chen Rui Peng Shengjie Jin Yuhong Tang Huanxiang Liu Daoyuan Tu Bingbing Su Shunyi Wang Guoqing Jiang Jun Cao Chi Zhang Dousheng Bai |
| author_facet | Chen Chen Rui Peng Shengjie Jin Yuhong Tang Huanxiang Liu Daoyuan Tu Bingbing Su Shunyi Wang Guoqing Jiang Jun Cao Chi Zhang Dousheng Bai |
| author_sort | Chen Chen |
| collection | DOAJ |
| description | Abstract Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. But the effective biomarkers for HCC metastasis remain underexplored. Here we integrated GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) datasets to screen candidate genes for hepatocellular carcinoma metastasis, a consensus metastasis-derived prognostic signature (MDPS) was constructed by machine learning. Based on the risk scores, HCC patients were stratified into high-risk and low-risk groups. Comprehensive analyses were conducted to investigate various aspects including survival outcomes, clinical characteristics, immune cell infiltration, as well as in vitro experiments. Together, we develop a comprehensive machine learning-based program for constructing a consensus MDPS including four genes (SPP1, TYMS, HMMR and MYCN). Our findings revealed that four genes could serve as efficient prognostic biomarkers and therapeutic targets in HCC. In addition, in vitro experiments showed that HMMR overregulation exacerbated tumor progression, including proliferation, migration and invasion. |
| format | Article |
| id | doaj-art-020a63ea15b3407780652a027f1fdaa4 |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-020a63ea15b3407780652a027f1fdaa42024-12-22T12:35:29ZengSpringerDiscover Oncology2730-60112024-12-0115112110.1007/s12672-024-01667-wIdentification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysisChen Chen0Rui Peng1Shengjie Jin2Yuhong Tang3Huanxiang Liu4Daoyuan Tu5Bingbing Su6Shunyi Wang7Guoqing Jiang8Jun Cao9Chi Zhang10Dousheng Bai11Department of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s HospitalDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityDepartment of Hepatobiliary Surgery, Northern Jiangsu People’s Hospital Affiliated to Yangzhou UniversityAbstract Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. But the effective biomarkers for HCC metastasis remain underexplored. Here we integrated GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) datasets to screen candidate genes for hepatocellular carcinoma metastasis, a consensus metastasis-derived prognostic signature (MDPS) was constructed by machine learning. Based on the risk scores, HCC patients were stratified into high-risk and low-risk groups. Comprehensive analyses were conducted to investigate various aspects including survival outcomes, clinical characteristics, immune cell infiltration, as well as in vitro experiments. Together, we develop a comprehensive machine learning-based program for constructing a consensus MDPS including four genes (SPP1, TYMS, HMMR and MYCN). Our findings revealed that four genes could serve as efficient prognostic biomarkers and therapeutic targets in HCC. In addition, in vitro experiments showed that HMMR overregulation exacerbated tumor progression, including proliferation, migration and invasion.https://doi.org/10.1007/s12672-024-01667-wHepatocellular carcinomaMetastasisMachine learningPrognosisBioinformatics |
| spellingShingle | Chen Chen Rui Peng Shengjie Jin Yuhong Tang Huanxiang Liu Daoyuan Tu Bingbing Su Shunyi Wang Guoqing Jiang Jun Cao Chi Zhang Dousheng Bai Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis Discover Oncology Hepatocellular carcinoma Metastasis Machine learning Prognosis Bioinformatics |
| title | Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis |
| title_full | Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis |
| title_fullStr | Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis |
| title_full_unstemmed | Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis |
| title_short | Identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis |
| title_sort | identification of potential biomarkers for hepatocellular carcinoma based on machine learning and bioinformatics analysis |
| topic | Hepatocellular carcinoma Metastasis Machine learning Prognosis Bioinformatics |
| url | https://doi.org/10.1007/s12672-024-01667-w |
| work_keys_str_mv | AT chenchen identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT ruipeng identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT shengjiejin identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT yuhongtang identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT huanxiangliu identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT daoyuantu identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT bingbingsu identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT shunyiwang identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT guoqingjiang identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT juncao identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT chizhang identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis AT doushengbai identificationofpotentialbiomarkersforhepatocellularcarcinomabasedonmachinelearningandbioinformaticsanalysis |