The American Joint Committee on Cancer has recently revised the tumor-node-metastasis (TNM) staging system on thyroid cancer, which illustrated that the cut-off age for predicting mortality has elevated from 45 to 55 years old. We aimed to investigate the inflammation index based on hematological parameters to predict the clinical characteristics of elderly papillary thyroid cancer (PTC) patients with an inferior prognosis. We retrospectively analyzed 558 patients newly diagnosed with PTC from January 2013 to December 2017, and 82 out of the 558 patients were over 55 years old. Receiver operating characteristic (ROC) study and univariate and multivariate logistic analysis was conducted to evaluate the diagnostic value of these preoperative inflammation indexes in PTC patients ≥ 55 years of age. Elevated neutrophils were prognostic of bilaterality (area under the ROC curve (AUC) = 0.673, p = 0.023) and lymph node metastasis (AUC = 0.649, p = 0·020). Decreased mean platelet volume (MPV) and platelet distribution width (PDW) were prognostic of coexistence with Hashimoto’s thyroiditis (AUC = 0.736, p = 0.016; AUC = 0.721, p = 0.024). Elevated lymphocyte and lymphocyte-to-monocyte ratio (LMR) were prognostic of advanced TNM stage (AUC = 0.691, p = 0.029; AUC = 0.680, p = 0.040). Meanwhile, the logistic regression model further revealed that LMR ≥ 5.45 was an independent risk factor for the advanced TNM stage (odds ratio (OR) = 7.306, p = 0.036). The preoperative neutrophils, lymphocytes, MPV, PDW, LMR were all prognostic. More importantly, the increased in LMR independently contributed to the advanced TNM stage of PTC patients ≥ 55 years.

Obesity is a complex condition that is affected by a variety of factors, including the environment, behavior, and genetics. However, the genetic mechanisms underlying obesity remains poorly elucidated. Therefore, our study aimed at identifying key genes for human obesity using bioinformatics analysis. The microarray datasets of adipose tissue in humans were downloaded from the Gene Expression Omnibus (GEO) database. After the selection of differentially expressed genes (DEGs), we used Lasso regression and Support Vector Machine (SVM) algorithm to further identify the feature genes. Moreover, immune cell infiltration analysis, gene set variation analysis (GSVA), GeneCards database and transcriptional regulation analysis were conducted to study the potential mechanisms by which the feature genes may impact obesity. We utilized receiveroperatingcharacteristic(ROC)curve to analysis the diagnostic efficacy of feature genes. Finally, we verified the feature genes in cell experiments and animal experiments. The statistical analyses in validation experiments were conducted using SPSS version 28.0, and the graph were generated using GraphPadPrism 9.0 software. The bioinformatics analyses were conducted using R language (version 4.2.2), with a significance threshold of p<0.05 used. 199 DEGs were selected using Limma package, and subsequently, 5 feature genes (EGR2, NPY1R, GREM1, BMP3 and COL8A1) were selected through Lasso regression and SVM algorithm. Through various bioinformatics analyses, we found some signaling pathways by which feature genes influence obesity and also revealed the crucial role of these genes in the immune microenvironment, as well as their strong correlations with obesity-related genes. Additionally, ROC curve showed that all the feature genes had good predictive and diagnostic efficiency in obesity. Finally, after validation through in vitro experiments, EGR2, NPY1R and GREM1 were identified as the key genes. This study identified EGR2, GREM1 and NPY1R as the potential key genes and potential diagnostic biomarkers for obesity in humans. Moreover, EGR2 was discovered as a key gene for obesity in human adipose tissue for the first time, which may provide novel targets for diagnosing and treating obesity.