Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes ASPM and KIF11 as Novel Biomarkers for Adrenocortical Carcinoma

Title:Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes ASPM and KIF11 as Novel Biomarkers for Adrenocortical Carcinoma

Volume: 26

Author(s): Jia-Yin Chen, Yu-Ting Xue, Shi-Wei Lin, Qi You, Bin Lin, Jiang-Bo Sun, Qing-Shui Zheng, Yong Wei, Shao-Hao Chen, Xue-Yi Xue, Xiao-Dong Li, Zhi-Bin Ke and Ning Xu*

Affiliation:

• Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China
• Department of Urology, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou 350212, China
• Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China

Keywords: Adrenocortical carcinoma, tumor mutation burden, prognostic genes, biomarker, bioinformatics.

Abstract:

Introduction: Adrenocortical Carcinoma (ACC) is regarded as an aggressive endocrine malignant tumor. The understanding of ACC tumorigenesis is still incomplete. This study aims to identify candidate tumor mutation burden (TMB)-related prognostic genes and explored the potential molecular mechanism of ACC based on comprehensive bioinformatic methods.

Methods: Single-nucleotide variations and transcriptome data were downloaded from the TCGA database. TMB scores were calculated using single-nucleotide variation data, and then, the correlation of TMB with tumor immune microenvironment, clinicopathologic characteristics, and PD-L1 expression level was explored. Differentially Expressed Genes (DEGs), ranging from high and low TMB scores, were identified. Weighted Gene Co-expression Network Analysis (WGCNA), Protein-Protein Interaction (PPI) networks, and Kaplan-Meier survival analysis were used to screen candidate TMB-related prognostic genes. Preliminary experimental verification of ASPM and KIF11 in ACC tumorigenesis was conducted.

Results: Patients with high TMB had worse OS, DSS, PFS, advanced pathological stage, lower PD-L1 expression level, lower stromal score, lower immune score, and higher tumor purity score. Seven ninety-seven DEGs in all between the high and low TMB groups were identified, including 203 downregulated DEGs and 594 upregulated DEGs. Functional enrichment analysis suggested that these DEGs might participate in cell division and cell cycle regulation. Furthermore, WGCNA analysis identified the turquoise module as the most significantly associated module with TMB. After screening with the PPI network and validating using survival analysis, a total of eight candidate TMB-related prognostic genes for ACC patients were finally identified, including ASPM, BIRC5, BUB1, CDC20, CDCA5, CEP55, KIF11, and TPX2. Preliminary experimental verification revealed that ASPM and KIF11 could promote the proliferation of ACC cells and the tumor growth of mice.

Discussion: ASPM and KIF11, identified as key TMB-related prognostic genes, promoted proliferation and inhibited apoptosis of ACC cells. This functional role revealed their significant potential as novel therapeutic targets for ACC.

Conclusion: A total of eight candidate TMB-related prognostic genes (including ASPM, BIRC5, BUB1, CDC20, CDCA5, CEP55, KIF11, and TPX2) for ACC patients were identified. Preliminary experimental verification revealed that ASPM and KIF11 could promote the proliferation of ACC cells and ACC tumor growth in vivo.

Cite this article as:

Chen Jia-Yin, Xue Yu-Ting, Lin Shi-Wei, You Qi, Lin Bin, Sun Jiang-Bo, Zheng Qing-Shui, Wei Yong, Chen Shao-Hao, Xue Xue-Yi, Li Xiao-Dong, Ke Zhi-Bin and Xu Ning*, Comprehensive Analysis Identifies Tumor Mutation Burden-associated Genes ASPM and KIF11 as Novel Biomarkers for Adrenocortical Carcinoma, Current Cancer Drug Targets 2026; 26 () . https://dx.doi.org/10.2174/0115680096396835251019170810