Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma
Abstract Background Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melanoma prognosis by employing multi-omics ana...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
|
| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12885-025-14012-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849737706840522752 |
|---|---|
| author | Songyun Zhao Zihao Li Kaibo Liu Gaoyi Wang Quanqiang Wang Hua Yu Wanying Chen Hao Dai Yijun Li Jiaheng Xie Yucang He Liqun Li |
| author_facet | Songyun Zhao Zihao Li Kaibo Liu Gaoyi Wang Quanqiang Wang Hua Yu Wanying Chen Hao Dai Yijun Li Jiaheng Xie Yucang He Liqun Li |
| author_sort | Songyun Zhao |
| collection | DOAJ |
| description | Abstract Background Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melanoma prognosis by employing multi-omics analysis and machine learning techniques, ultimately leading to the development of novel therapeutic strategies. Methods We obtained and processed transcriptomic data, including RNA expression profiles, methylation microarray data, gene mutation data, and clinical information, from the TCGA dataset using multi-omics analysis and machine learning techniques. We comprehensively evaluated the molecular subtypes of melanoma, the characteristics of the tumor microenvironment (TME), and their effects on patient outcomes. By analyzing the TCGA-SKCM and GEO cohorts, we identified three melanoma subtypes with distinct prognostic features. Additionally, we developed a machine learning-driven signature (MLDS) based on marker genes for different molecular subtypes to significantly improve the prognostic prediction accuracy for melanoma patients. We also extensively examined differences in clinical features, immune cell infiltration, mutational landscapes, and drug treatment effects between high- and low-scoring subgroups. The predictive reliability of MLDS was further explored by knocking down the key signature gene AGPAT2 in melanoma cells using small interfering RNA. Results The MLDS demonstrated high C-index values in both the training and validation cohorts, indicating its potential for clinical decision-making. The study also found that MLDS scores were associated with reduced immune cell infiltration and lower expression levels of immune checkpoints. Patients in the low MLDS group may be more responsive to chemotherapeutic agents and more likely to benefit from immune checkpoint inhibitors (ICIs). Single-cell sequencing analysis revealed that the MAPK signaling pathway between AGPAT2 + melanoma cells and fibroblasts/myeloid cells promotes tumor survival in the TME. Finally, the oncogenic role of AGPAT2 in melanoma cell lines was successfully confirmed through cell function assays and subcutaneous tumor formation assays in nude mice. Conclusion This study not only uncovers the diversity and complexity of melanoma molecular subtypes but also underscores the crucial role of the TME in melanoma progression. It provides new insights and tools for personalized treatment and prognostic assessment of SKCM. |
| format | Article |
| id | doaj-art-53c133f75d484a8480a5da5b375fa1e2 |
| institution | DOAJ |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-53c133f75d484a8480a5da5b375fa1e22025-08-20T03:06:51ZengBMCBMC Cancer1471-24072025-04-0125112110.1186/s12885-025-14012-3Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanomaSongyun Zhao0Zihao Li1Kaibo Liu2Gaoyi Wang3Quanqiang Wang4Hua Yu5Wanying Chen6Hao Dai7Yijun Li8Jiaheng Xie9Yucang He10Liqun Li11Department of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Oncology, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Plastic Surgery, Xiangya Hospital, Central South UniversityDepartment of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityDepartment of Plastic Surgery, The First Affiliated Hospital of Wenzhou Medical UniversityAbstract Background Melanoma (SKCM) is an extremely aggressive form of cancer, characterized by high mortality rates, frequent metastasis, and limited treatment options. Our study aims to identify key target genes and enhance the diagnostic accuracy of melanoma prognosis by employing multi-omics analysis and machine learning techniques, ultimately leading to the development of novel therapeutic strategies. Methods We obtained and processed transcriptomic data, including RNA expression profiles, methylation microarray data, gene mutation data, and clinical information, from the TCGA dataset using multi-omics analysis and machine learning techniques. We comprehensively evaluated the molecular subtypes of melanoma, the characteristics of the tumor microenvironment (TME), and their effects on patient outcomes. By analyzing the TCGA-SKCM and GEO cohorts, we identified three melanoma subtypes with distinct prognostic features. Additionally, we developed a machine learning-driven signature (MLDS) based on marker genes for different molecular subtypes to significantly improve the prognostic prediction accuracy for melanoma patients. We also extensively examined differences in clinical features, immune cell infiltration, mutational landscapes, and drug treatment effects between high- and low-scoring subgroups. The predictive reliability of MLDS was further explored by knocking down the key signature gene AGPAT2 in melanoma cells using small interfering RNA. Results The MLDS demonstrated high C-index values in both the training and validation cohorts, indicating its potential for clinical decision-making. The study also found that MLDS scores were associated with reduced immune cell infiltration and lower expression levels of immune checkpoints. Patients in the low MLDS group may be more responsive to chemotherapeutic agents and more likely to benefit from immune checkpoint inhibitors (ICIs). Single-cell sequencing analysis revealed that the MAPK signaling pathway between AGPAT2 + melanoma cells and fibroblasts/myeloid cells promotes tumor survival in the TME. Finally, the oncogenic role of AGPAT2 in melanoma cell lines was successfully confirmed through cell function assays and subcutaneous tumor formation assays in nude mice. Conclusion This study not only uncovers the diversity and complexity of melanoma molecular subtypes but also underscores the crucial role of the TME in melanoma progression. It provides new insights and tools for personalized treatment and prognostic assessment of SKCM.https://doi.org/10.1186/s12885-025-14012-3MelanomaMulti-omics analysisMachine learningMolecular subtypingMLDSAGPAT2 |
| spellingShingle | Songyun Zhao Zihao Li Kaibo Liu Gaoyi Wang Quanqiang Wang Hua Yu Wanying Chen Hao Dai Yijun Li Jiaheng Xie Yucang He Liqun Li Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma BMC Cancer Melanoma Multi-omics analysis Machine learning Molecular subtyping MLDS AGPAT2 |
| title | Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma |
| title_full | Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma |
| title_fullStr | Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma |
| title_full_unstemmed | Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma |
| title_short | Combining multi-omics analysis with machine learning to uncover novel molecular subtypes, prognostic markers, and insights into immunotherapy for melanoma |
| title_sort | combining multi omics analysis with machine learning to uncover novel molecular subtypes prognostic markers and insights into immunotherapy for melanoma |
| topic | Melanoma Multi-omics analysis Machine learning Molecular subtyping MLDS AGPAT2 |
| url | https://doi.org/10.1186/s12885-025-14012-3 |
| work_keys_str_mv | AT songyunzhao combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT zihaoli combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT kaiboliu combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT gaoyiwang combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT quanqiangwang combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT huayu combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT wanyingchen combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT haodai combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT yijunli combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT jiahengxie combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT yucanghe combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma AT liqunli combiningmultiomicsanalysiswithmachinelearningtouncovernovelmolecularsubtypesprognosticmarkersandinsightsintoimmunotherapyformelanoma |