Background: Thermal ablation (TA), as an alternative to surgery, has shown some benefits in the treatment of papillary thyroid microcarcinoma (PTMC) patients, especially for those who are at high risk for surgery or refuse surgery. We performed a systematic review and meta-analysis to evaluate the efficiency, safety, and economy of TA, compared with those of routine surgery (RS), for the treatment of PTMC. Methods: PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang, and VIP databases were retrieved from inception to 10 January 2020 to identify relevant original studies on comparison of TA and RS for treatment of PTMC. The recurrence rate, recurrence-free survival (RFS), complication rate, operation time, postoperative length of stay, and cost during the perioperative period were extracted as main indices. The pooled standardized mean difference (SMD) or odds ratio (OR) with 95% confidence intervals (CI) were calculated and analyzed. Chi-square test and I 2 statistic were applied to determine the heterogeneity among studies. The sensitivity analysis was applied to explore the origin of heterogeneity, and the publication bias was evaluated by Egger's test. Results: Seven retrospective studies with a total of 867 patients met the eligibility criteria and were included in the final meta-analysis. Our study demonstrated that TA showed significant reduction in complication with a pooled OR 0.24 (95% CI 0.13 to 0.43), postoperative length of stay with a pooled SMD À3.14 (95% CI À4.77 to À1.51) and cost during the perioperative period with a pooled SMD of À1.69 (95% CI À3.18 to À0.20). It also demonstrated that both TA and RS had similar pooled proportion of recurrence of OR 0.93 (95% CI 0.38 to 2.30) and recurrence-free survive (RFS). The sensitivity analysis showed that each included study had no significant effect on the results and the results were stable and reliable. The Egger's test demonstrated publication bias was acceptable. Conclusions: TA may not be oncologically inferior to RS, and it is a relatively safe and economical alternative for the treatment of PTMC.

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Background: Melanoma is the most dangerous form of skin cancer because of its high metastatic potential.Potential-N6-methyladenosine (m6A)-related long noncoding RNAs (pMRlncRNAs) play a vital role in malignancy. The identification of prognostic-related pMRlncRNAs and development of risk signatures could improve the prognosis and promote the precise treatment of melanoma.Methods: Gene expression and relevant clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Prognostic-related pMRlncRNAs were selected using univariate Cox regression analysis. Patients with melanoma were classified into different subtypes using the "ConsensusClusterPlus" package, and the ESTIMATE algorithm was applied to depict their immune landscape. A pMRlncRNA risk signature was developed using least absolute shrinkage and selection operator regression analysis and verified using survival analysis and receiver operating characteristic curves. Gene set enrichment analysis (GSEA) was used to investigate the underlying biological pathways. The relationships between risk score and clinicopathological characteristics, as well as programmed cell death-ligand 1 (PD-L1) expression level, were investigated. A nomogram with calibration curves was established to comprehensively predict the outcome of melanoma.Results: Fifteen pMRlncRNAs were significantly associated with overall survival (OS). Two cluster subtypes were identified by consensus clustering. Patients in cluster 2 were associated with better OS, higher PD-L1 expression level, lower T stage, and higher ESTIMATEScore, ImmuneScore, and StromalScore than those in cluster 1. There were differences in immune cell infiltration between the 2 clusters. Ten pMRlncRNAs with prognostic value were selected to develop a risk signature, that functioned as an independent prognostic factor for melanoma. Patients with low-risk scores had a better prognosis in general. The area under the curve (AUC) value (0.720), as well as 1-, 3-, and 5-year calibration curves, revealed that the risk signature has suitable predictive power for prognosis. GSEA revealed 10 pathways that might play important roles in melanoma. Moreover, patients with high-risk scores were associated with advanced T stage, cluster 1, lower ImmuneScore, and higher PD-L1 expression level. Conclusions:We developed a novel 10-pMRlncRNA risk signature that could elucidate the crucial role of pMRlncRNAs in the immune landscape of melanoma and predict prognosis.