Objective:To search for potential protein biomarkers of papillary thyroid carcinoma (PTC) and thyroid borderline lesion. Dif-ferentially expressed proteins between the two were analyzed and identified. Methods:A total of 118 cases of thyroid resection sam-ples were obtained from patients who underwent surgery at the First People's Hospital of Yunnan Province from April 2013 to Febru-ary 2015. Experimental groups included 43 PTCs (40 classic and 3 follicular variants) and 33 thyroid borderline lesions (with equivocal PTC type nuclear features and papillary structure, but without metastasis, and lacking capsular or vascular invasion;8 cases with atypi-cal adenoma), respectively. The control group included 42 normal thyroid tissues adjacent to carcinoma. The total protein extracts from frozen thyroid samples of 10 cases in each group were profiled with 2D electrophoresis. The differential protein spots were then revealed by PDQUEST 7.3 software and identified by matrix-assisted laser desorption ionization time-of-fight/time-of-fight mass spec-trometry and Swiss-Prot database search. Six differentially expressed proteins of these spots were further validated using 118 samples through immunohistochemistry. Results:A set of 24 differentially expressed spots significant in discriminating between the sample groups were found, and 18 proteins were identified. Immunohistochemistry revealed the following six proteins located in the cyto-plasm:keratin, type II cytoskeletal 8 (CK8);keratin, type I cytoskeletal 18 (CK18);60 kDa heat shock protein (HSP60);actin, cytoplasmic 2 (γ-actin);14-3-3 protein beta/alpha (14-3-3β/α);and 14-3-3 protein epsilon (14-3-3ε). All six proteins were overexpressed in PTC compared with normal tissues (P<0.001). Meanwhile, CK8, CK18, HSP60, andγ-actin were overexpressed in PTC compared with bor-derline lesions (P<0.01). Except for CK8, the five other proteins were overexpressed in borderline lesions compared with normal tis-sues (P<0.001). Conclusion:Proteomic analysis is useful in searching for new biomarkers of PTC and thyroid borderline lesion. The ex-pression patterns of these differentially expressed proteins can be further validated using immunohistochemistry. The newly identified protein biomarkers can positively contribute to early PTC diagnosis.

The increasing number of pulmonary nodules being detected by computed tomography scans significantly increase the workload of the radiologists for scan interpretation. Limitations of traditional methods for differential diagnosis of pulmonary nodules have been increasingly prominent. Artificial intelligence (AI) has the potential to increase the efficiency of discrimination and invasiveness classification for pulmonary nodules and lead to effective nodule management. Chinese Experts Consensus on Artificial Intelligence Assisted Management for Pulmonary Nodule (2022 Version) has been officially released recently. This article closely follows the context, significance, core implications, and the impact of future AI-assisted management on the diagnosis and treatment of pulmonary nodules. It is hoped that through our joint efforts, we can promote the standardization of management for pulmonary nodules and strive to improve the long-term survival and postoperative life quality of patients with lung cancer.