Multidimensional transcriptomic and immune profiling of papillary thyroid cancer: bridging molecular networks, disease stage, survival outcomes, and drug discovery

Thyroid cancer ranks tenth among the deadliest cancers globally. Papillary thyroid cancer (PTC) represents the majority (> 80%) of all thyroid cancer types. Albeit radioactive iodine therapy achieves a great deal of success rate and efficacy, it is not easily available in all affected regions and its inappropriate use can drive the development of new malignancies. This motivated us to rigorously analyze the transcriptomes of PTC so as to infer novel prognostic markers and hotspot targets that are druggable using the approved drugs. We downloaded two datasets from gene expression omnibus and subsequently processed them via GEO2R tool. The mutual differentially expressed genes (DEGs) from the two datasets included 452 genes. This was followed by the prediction of the interacting network through NetworkAnalyst followed by cytoscape to shortlist them into four hub genes (FN1, MAPK13, KRT19 and EGFR) by applying four cytohubba filters (MCC, MNC, closeness and betweenness). The hub genes were then evaluated for their interaction with their neighbor genes, differential expression in tumor versus healthy control samples, stage correlation, immune infiltration, overall survival and availability of specific drugs. The four hub genes displayed higher level of expression in tumor samples, varied correlation with PTC stage, high immune cells infiltrate rate. Moreover, MAPK13 and KRT19 were strongly associated with overall survival of PTC, while FN1 and KRT19 showed significantly low tumor purity. In conclusion, FN1, MAPK13, KRT19 and EGFR can be considered both good prognostic markers and druggable targets for PTC.