RESUMO
Osteoarthritis (OA) is a prevalent degenerative joint disease that significantly impacts individuals and healthcare systems worldwide. However, the exploration of N6-methyladenosine (m6A)-related aging genes in OA pathogenesis remains largely underexplored. This study aimed to elucidate the role of m6A-related aging genes in OA and to develop a robust diagnostic model based on their expression profiles. Leveraging publicly available gene expression datasets, we conducted consensus clustering to categorize OA into distinct subtypes, guided by the expression patterns of m6A-related aging genes. Utilizing XGBoost, a cutting-edge machine learning approach, we identified key diagnostic genes and constructed a predictive model. Our investigation extended to the immune functions of these genes, shedding light on potential therapeutic targets and underlying regulatory mechanisms. Our analysis unveiled specific OA subtypes, each marked by unique expression profiles of m6A-related aging genes. We pinpointed a set of pivotal diagnostic genes, offering potential therapeutic avenues. The developed diagnostic model exhibited exceptional capability in distinguishing OA patients from healthy controls. To corroborate our computational findings, we performed quantitative real-time polymerase chain reaction analyses on two cell lines: HC-OA (representing adult osteoarthritis cells) and C-28/I2 (representative of normal human chondrocytes). The gene expression patterns observed were consistent with our bioinformatics predictions, further validating our initial results. In conclusion, this study underscores the significance of m6A-related aging genes as promising biomarkers for diagnosis and prognosis, as well as potential therapeutic targets in OA. Although these findings are encouraging, further validation and functional analyses are crucial for their clinical application.
Assuntos
Neoplasias , Osteoartrite , Adulto , Humanos , Adenina , Envelhecimento/genética , Osteoartrite/diagnóstico , Osteoartrite/genéticaRESUMO
The number of patients with lung cancer is difficultly diagnosed in the early stage. The purpose of the study was to investigate the effects of CT- and ultrasound-guided percutaneous transthoracic needle biopsy combined with serum CA125 and CEA on the diagnosis of lung cancer. 120 patients with suspected lung cancer admitted to our hospital from January 2019 to January 2020 were selected and divided into an ultrasound group (n = 60) and CT group (n = 60), according to different percutaneous transthoracic needle biopsy modalities. All patients received serum tumor markers detection, so as to compare the CT- and ultrasound-guided percutaneous transthoracic needle biopsy results and pathology results, levels of serum tumor markers among all patients and the patients with different lung cancer types, and diagnostic efficacy of tumor markers, as well as complication rate (CR) in patients. The sensitivity and specificity of ultrasound-guided percutaneous transthoracic needle biopsy were 0.880 and 0.800, respectively, while those of CT-guided percutaneous transthoracic needle biopsy were 0.909 and 0.625, respectively; the CA125 and CEA levels in the lung cancer group were higher than those in the benign group (P < 0.001); the CA125 and CEA levels of the patients with adenocarcinoma were higher than those with squamous carcinoma, and the CEA levels of the patients with small-cell carcinoma were lower than those with adenocarcinoma (P < 0.05); the sensitivity, specificity, and Youden indexes of CA125 were 0.638, 0.833, and 0.471, respectively, while those of CEA were 0.766, 0.778, and 0.544, respectively; there were no significant differences in CR between the two groups (P > 0.05). CT- and ultrasound-guided percutaneous transthoracic needle biopsy is a safe and feasible diagnostic modality for lung cancer, and its combination with serum CA125 and CEA can significantly improve the accuracy of the detection results, which is worthy of promotion and application in clinical practice.