Background: Tumor heterogeneity imposes great challenges on cancer treatment.
Cancer stem cells (CSCs) are a leading factor contributing to tumor occurrence. However, the
mechanisms underlying the growth of thyroid cancer (TCHA) are still unclear.
Methods: Key genes regulating the characteristics of THCA, such as stemness were identified by
combining gene expressions of samples downloaded from the Cancer Genome Atlas (TCGA) and
were used to establish an mRNA expression stemness index (mRNAsi) through machine learningbased
methods. The relationships of mRNAsi, THCA clinical features and molecular subtypes
were analyzed. Weighted Gene Co-Expression Network Analysis (WGCNA) was performed to
obtain mRNAsi-related gene modules and determine mRNAsi-related differentially co-expressed
genes. Key genes related to mRNAsi were screened by protein interaction network. Functional
analysis was conducted and expressions of key genes were verified in multiple external data sets.
Results: The mRNAsi score, which was found to be lower in the TCHA tissues than that in
normal tissues (p<0.05), was positively correlated with a slow progression of tumor prognosis
(p=0.0085). We screened a total of 83 differentially co-expressed genes related to mRNAsi and
multiple tumor pathways such as apoptosis, tight junction, cytokine-cytokine receptor interaction, and
cAMP signaling pathway (p<0.05). Finally, 28 protein interaction networks incorporating 32 genes
were established, and 3 key genes were identified through network mining. 3 core genes were finally
determined, as their low expressions were strongly correlated with the progression of THCA.
Conclusion: The study found that NGF, FOS, and GRIA1 are closely related to the characteristics
of THCA stem cells. These genes, especially FOS, are highly indicative of the prognosis of THCA
patients. Thus, screening therapy could be used to inhibit the stemness of TCHA.