ISSN: 1837-9664Journal of Cancer
Global reach, higher impact
J Cancer 2021; 12(8):2440-2449. doi:10.7150/jca.53697 This issue Cite
Research Paper
Identification of tumor mutation burden-related hub genes and the underlying mechanism in melanoma
1. Department of Dermatology, China-Japan Union Hospital of Jilin University, Changchun 130033, People's Republic of China.
2. Department of Pediatric Surgery, the First Hospital of Jilin University, Changchun 130021, People's Republic of China.
3. Department of Neonatology, the First Hospital of Jilin University, Changchun 130021, People's Republic of China.
4. Department of Tissue Bank, China-Japan Union Hospital of Jilin University, Changchun, 130033, People's Republic of China.
5. Scientific Research Center, China-Japan Union Hospital of Jilin University, Changchun 130033, People's Republic of China.
Abstract
Background: Tumor mutation burden (TMB) has emerged as an important predictive factor for drug resistance in cancers; however, the specific mechanism underlying TMB function in melanoma remains elusive.
Methods: Data on somatic mutations, RNA sequencing (RNA-seq), miRNA sequencing (miRNA-seq), and clinical characteristics for 472 melanoma patients were extracted from the TCGA cohort. RNA-seq data of melanoma cell lines were obtained from the Cancer Cell Line Encyclopedia, and sensitivity of cell lines to therapeutic agents is available in the Cancer Therapeutics Response Portal. TMB was calculated based on somatic mutation data. Differentially expressed gene analysis, weighted gene co-expression network analysis, protein-protein interaction networks, Minimal Common Oncology Data Elements, and survival analysis were leveraged to determine TMB-related hub genes. Competing endogenous RNA (ceRNA) networks were constructed to explore the molecular mechanisms underlying hub gene function. The influence of key genes on drug sensitivity was analyzed to investigate their clinical significance.
Results: Elevated TMB levels were significantly correlated with improved survival outcomes. In addition, six tumor-infiltrating immune cells, including naive B cells, regulatory T cells, memory resting CD4 T cells, memory B cells, activated mast cells, and resting NK cells, were significantly overexpressed in the low-TMB group relative to the high-TMB group. Furthermore, we identified FLNC, NEXN, and TNNT3 as TMB-related hub genes, and constructed their ceRNA networks, including five miRNAs (has-miR-590-3p, has-miR-374b-5p, has-miR-3127-5p, has-miR-1913, and has-miR-1291) and 31 lncRNAs (FAM66C, MIAT, NR2F2AS1, etc.). Finally, we observed that TMB-related genes were associated with distinct therapeutic responses to AKT/mTOR pathway inhibitors.
Conclusions: We identified three TMB-associated key genes, established their ceRNA networks, and investigated their influence on therapeutic responses, which could provide insights into future precision medicine.
Keywords: tumor mutation burden, WGCNA, tumor-infiltrating immune cells, biomarker, ceRNA
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