Computers in biology and medicine · 2023
Identification and targeting of cancer-associated fibroblast signature genes for prognosis and therapy in Cutaneous melanoma.
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Zhang Guokun, Ji Pengfei, Xia Peng, Song Haoyun, Guo Zhao, Hu Xiaohui, Guo Yanan, Yuan Xinyi, Song Yanfeng, Shen Rong, Wang Degui
Аннотация
BACKGROUND: Cancer-associated fibroblasts (CAFs) play pivotal roles in tumor invasion and metastasis. However, studies on CAF biomarkers in Cutaneous Melanoma (CM) are still scarce. This study aimed to explore the potential CAF biomarkers in CM, propose the potential therapeutic targets, and provide new insights for targeted therapy of CAFs in CM.
METHODS: We utilized weighted gene co-expression network analysis to identify CAF signature genes in CM, and conducted comprehensive bioinformatics analysis on the CAF risk score established by these genes. Moreover, single-cell sequencing analysis, spatial transcriptome analysis, and cell experiments were utilized for verifying the expression and distribution pattern of signature genes. Furthermore, molecular docking was employed to screen potential target drugs.
RESULTS: FBLN1 and COL5A1, two crucial CAF signature genes, were screened to establish the CAF risk score. Subsequently, a comprehensive bioinformatic analysis of the CAF risk score revealed that high-risk score group was significantly enriched in pathways associated with tumor progression. Besides, CAF risk score was significantly negatively correlated with clinical prognosis, immunotherapy response, and tumor mutational burden in CM patients. In addition, FBLN1 and COL5A1 were further identified as CAF-specific biomarkers in CM by multi-omics analysis and experimental validation. Eventually, based on these two targets, Mifepristone and Dexamethasone were screened as potential anti-CAFs drugs.
CONCLUSION: The findings indicated that FBLN1 and COL5A1 were the CAF signature genes in CM, which were associated with the progression, treatment, and prognosis of CM. The comprehensive exploration of CAF signature genes is expected to provide new insight for clinical CM therapy.
METHODS: We utilized weighted gene co-expression network analysis to identify CAF signature genes in CM, and conducted comprehensive bioinformatics analysis on the CAF risk score established by these genes. Moreover, single-cell sequencing analysis, spatial transcriptome analysis, and cell experiments were utilized for verifying the expression and distribution pattern of signature genes. Furthermore, molecular docking was employed to screen potential target drugs.
RESULTS: FBLN1 and COL5A1, two crucial CAF signature genes, were screened to establish the CAF risk score. Subsequently, a comprehensive bioinformatic analysis of the CAF risk score revealed that high-risk score group was significantly enriched in pathways associated with tumor progression. Besides, CAF risk score was significantly negatively correlated with clinical prognosis, immunotherapy response, and tumor mutational burden in CM patients. In addition, FBLN1 and COL5A1 were further identified as CAF-specific biomarkers in CM by multi-omics analysis and experimental validation. Eventually, based on these two targets, Mifepristone and Dexamethasone were screened as potential anti-CAFs drugs.
CONCLUSION: The findings indicated that FBLN1 and COL5A1 were the CAF signature genes in CM, which were associated with the progression, treatment, and prognosis of CM. The comprehensive exploration of CAF signature genes is expected to provide new insight for clinical CM therapy.