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Background <p>Under certain circumstances, nevi may undergo malignant transformation into skin cutaneous melanoma (SKCM), which is a highly aggressive and deadly malignant tumor originating from melanocytes. Early identification of important genes in the development of nevus into melanoma, timely treatment and blocking of the disease process can effectively reduce the occurrence of melanoma.</p> Methods <p>Six datasets were downloaded from Gene Expression Omnibus with the keywords “nevus and melanoma”. Differentially expressed genes (DEGs) in nevu vs. normal and melanoma vs. nevus were identified. WGCNA was used to identify disease status-related modules. Mfuzz clustering algorithm was used to select DEGs with different expression modes. PPI network was used for hub gene identification, followed by prognostic model construction, and immune microenvironment analysis. Finally, the expression of crucial genes in the process of SKCM was validated by RT-qPCR.</p> Results <p>Totally, 269 overlapped DEGs were filtered in two comparison groups. After comparing with module genes obtained from WGCNA, 159 overlapped genes were screened, which were clustered into 3 expression trend clusters. Through PPI network analysis, 26 hub genes were selected and then 5 signature genes (<i>CCND2</i>,<i> NFASC</i>,<i> ENPP2</i>,<i> EPHA4</i> and <i>SOX10</i>) were selected for prognostic model construction. The samples were divided into two risk groups. Thirteen immune cell types were found to have significant difference distribution between risk groups. The consistent rate of the identified crucial hub genes between RT-qPCR and bioinformatics analyses was 100%, which showed the relatively high reliability of our bioinformatic analyses.</p> Conclusion <p><i>CCND2</i>, <i>NFASC</i>, <i>ENPP2</i>, <i>EPHA4</i> and <i>SOX10</i> were identified to be candidate markers during melanoma progression. The proposed prognostic model had good predictive precision, which may be used for clinical prediction of melanoma development.</p>

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Identification of biomarkers related to the progression of cutaneous melanoma and construction of survival prognosis model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月3日

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  • Kana Chen,
  • Wei Ye,
  • Longjun Chi,
  • Shujie Xie

摘要

Background

Under certain circumstances, nevi may undergo malignant transformation into skin cutaneous melanoma (SKCM), which is a highly aggressive and deadly malignant tumor originating from melanocytes. Early identification of important genes in the development of nevus into melanoma, timely treatment and blocking of the disease process can effectively reduce the occurrence of melanoma.

Methods

Six datasets were downloaded from Gene Expression Omnibus with the keywords “nevus and melanoma”. Differentially expressed genes (DEGs) in nevu vs. normal and melanoma vs. nevus were identified. WGCNA was used to identify disease status-related modules. Mfuzz clustering algorithm was used to select DEGs with different expression modes. PPI network was used for hub gene identification, followed by prognostic model construction, and immune microenvironment analysis. Finally, the expression of crucial genes in the process of SKCM was validated by RT-qPCR.

Results

Totally, 269 overlapped DEGs were filtered in two comparison groups. After comparing with module genes obtained from WGCNA, 159 overlapped genes were screened, which were clustered into 3 expression trend clusters. Through PPI network analysis, 26 hub genes were selected and then 5 signature genes (CCND2, NFASC, ENPP2, EPHA4 and SOX10) were selected for prognostic model construction. The samples were divided into two risk groups. Thirteen immune cell types were found to have significant difference distribution between risk groups. The consistent rate of the identified crucial hub genes between RT-qPCR and bioinformatics analyses was 100%, which showed the relatively high reliability of our bioinformatic analyses.

Conclusion

CCND2, NFASC, ENPP2, EPHA4 and SOX10 were identified to be candidate markers during melanoma progression. The proposed prognostic model had good predictive precision, which may be used for clinical prediction of melanoma development.