IntroductionAdvanced papillary thyroid cancer (PTC) has a poor prognosis, 60~70% of which become radio iodine refractory (RAI-R), but the molecular markers that assess PTC progress to advanced PTC remain unclear. Meanwhile, current targeted therapies are badly effective due to drug resistance and adverse side effects. Ligand-receptor pairs (L/R pairs) play an important role in the interactions between tumor cells and other cells in the tumor microenvironment (TME). Nowadays, therapies targeting ligand-receptor pairs in the TME are advancing rapidly in the treatment of advanced cancers. However, therapies targeting L/R pairs applied to advanced PTC remains challenging because of limited knowledge about L/R pairs in PTC.MethodsWe screened the critical L/R pair: CADM1-CADM1 using 65311 single-cell RNA sequencing (scRNA-seq) samples from 7 patients in different stage of PTC and bulk RNA-seq datasets containing data from 487 tumor samples and 58 para-carcinoma samples. Moreover, the expression levels of CADM1-CADM1 was assessed by quantitative real time polymerase chain reaction (qRT-PCR) and the function was analyzed using Transwell immigration assay.ResultsWe found that CADM1_CADM1 could be regarded as a biomarker representing a good prognosis of PTC. In addition, the high expression of CADM1_CADM1 can strongly increase the sensitivity of many targeted drugs, which can alleviate drug resistance. And the results of qRT-PCR showed us that the expression of CADM1_CADM1 in PTC was down-regulated and overexpression of CADM1 could suppresses tumor cell invasion migration.ConclusionOur study identified that CADM1_CADM1 played an essential role in the progression of PTC for the first time and our findings provide a new potential prognostic and therapeutic ligand-receptor pair for advanced PTC.

Background. The molecular classification of HCC premised on metabolic genes might give assistance for diagnosis, therapy, prognosis prediction, immune infiltration, and oxidative stress in addition to supplementing the limitations of the clinical staging system. This would help to better represent the deeper features of HCC. Methods. TCGA datasets combined with GSE14520 and HCCDB18 datasets were used to determine the metabolic subtype (MC) using ConsensusClusterPlus. ssGSEA method was used to calculate the IFNγ score, the oxidative stress pathway scores, and the score distribution of 22 distinct immune cells, and their differential expressions were assessed with the use of CIBERSORT. To generate a subtype classification feature index, LDA was utilized. Screening of the metabolic gene coexpression modules was done with the help of WGCNA. Results. Three MCs (MC1, MC2, and MC3) were identified and showed different prognoses (MC2-poor and MC1-better). Although MC2 had a high immune microenvironment infiltration, T cell exhaustion markers were expressed at a high level in MC2 in contrast with MC1. Most oxidative stress-related pathways are inhibited in the MC2 subtype and activated in the MC1 subtype. The immunophenotyping of pan-cancer showed that the C1 and C2 subtypes with poor prognosis accounted for significantly higher proportions of MC2 and MC3 subtypes than MC1, while the better prognostic C3 subtype accounted for significantly lower proportions of MC2 than MC1. As per the findings of the TIDE analysis, MC1 had a greater likelihood of benefiting from immunotherapeutic regimens. MC2 was found to have a greater sensitivity to traditional chemotherapy drugs. Finally, 7 potential gene markers indicate HCC prognosis. Conclusion. The difference (variation) in tumor microenvironment and oxidative stress among metabolic subtypes of HCC was compared from multiple angles and levels. A complete and thorough clarification of the molecular pathological properties of HCC, the exploration of reliable markers for diagnosis, the improvement of the cancer staging system, and the guiding of individualized treatment of HCC all gain benefit greatly from molecular classification associated with metabolism.