Identification of key genes associated with papillary thyroid microcarcinoma characteristics by integrating transcriptome sequencing and weighted gene co-expression network analysis.
Fan Yang: Department of Otorhinolaryngology Head and Neck Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, China, 100029. Electronic address: azdryf@ccmu.edu.cn.
Meng Lian: Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China, 100730.
Hongzhi Ma: Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China, 100730.
Ling Feng: Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China, 100730.
Xixi Shen: Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China, 100730.
Jiaming Chen: Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China, 100730.
Jugao Fang: Department of Otorhinolaryngology Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China, 100730; Department of Thyroid Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China, 100730. Electronic address: fangjugao2@ccmu.edu.cn.
OBJECTIVE: Papillary thyroid microcarcinoma (PTMC) is the most prevalent histological type of thyroid carcinoma. Despite the overall favorable prognosis of PTMC, some cases exhibit aggressive phenotypes. The identification of robust biomarkers may improve early PTMC diagnosis. In this study, we integrated high-throughput transcriptome sequencing, bioinformatic analyses and experimental validation to identify key genes associated with the malignant characteristics of PTMC. METHODS: Total RNA was extracted from 24 PTMC samples and 7 non-malignant thyroid tissue samples, followed by RNA sequencing. The differentially expressed genes (DEGs) were identified and used to construct co-expression networks by weighted gene co-expression network analysis (WGCNA). Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed, and protein-protein interaction networks were constructed. Key modules and hub genes showing a strong correlation with the malignant characteristics of PTMC were identified and validated. RESULTS: The green-yellow and turquoise modules generated by WGCNA were strongly associated with the malignant characteristics of PTMC. Functional enrichment analysis revealed that genes in the green-yellow module participated in cell motility and metabolism, whereas those in the turquoise module participated in several oncogenic biological processes. Nine real hub genes (FHL1, NDRG2, NEXN, SYNM, COL1A1, FN1, LAMC2, POSTN, and TGFBI) were identified and validated at the transcriptional and translational levels. Our preliminary results indicated their diagnostic potentials in PTMC. CONCLUSIONS: In this study, we identified key co-expression modules and nine malignancy-related genes with potential diagnostic value in PTMC.
