Radiotherapy prognosis-associated gene GCNT3 promotes the proliferation, migration and invasion of lung adenocarcinoma cells

Zhao, Teng; Zhao, Xin; Qian, Kun; Shi, Kejian; Gu, Yanfei; Zhang, Yi

doi:10.1016/j.heliyon.2022.e12100

Cited by 3 publications

(1 citation statement)

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“…Due to LC being one of the malignancies with the poorest prognosis worldwide and having a high mortality rate, improving the prognosis of LC patients is a crucial topic in clinical research. 19 The prerequisite for enhancing the prognosis of LC patients is understanding the risk factors that influence patient outcomes. Wang et al 20 confirmed that higher age, advanced clinical TNM staging, increased expression of miRNA-21 and miRNA-155 in NSCLC tissues were independently associated with lower survival rates in NSCLC patients.…”

Section: Discussionmentioning

confidence: 99%

Prognostic Factors and Construction of Nomogram Prediction Model of Lung Cancer Patients Using Clinical and Blood Laboratory Parameters

Zhang,

Wan,

Shen

et al. 2024

OTT

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Objective This work aimed to explore the prognostic risk factors of lung cancer (LC) patients and establish a line chart prediction model. Methods A total of 322 LC patients were taken as the study subjects. They were randomly divided into a training set (n = 202) and a validation set (n = 120). Basic information and laboratory indicators were collected, and the progression-free survival (PFS) and overall survival (OS) were followed up. Single-factor and cyclooxygenase (COX) multivariate analyses were performed on the training set to construct a Nomogram prediction model, which was validated with 120 patients in the validation set, and Harrell’s consistency was analyzed. Results Single-factor analysis revealed significant differences in PFS ( P <0.05) between genders, body mass index (BMI), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), squamous cell carcinoma antigen (SCCA), treatment methods, treatment response evaluation, smoking status, presence of pericardial effusion, and programmed death ligand 1 (PD-L1) at 0 and 1–50%. Significant differences in OS ( P <0.05) were observed for age, tumor location, treatment methods, White blood cells (WBC), uric acid (UA), CA125, pro-gastrin-releasing peptide (ProGRP), SCCA, cytokeratin fragment 21 (CYFRA21), and smoking status. COX analysis identified male gender, progressive disease (PD) as treatment response, and SCCA > 1.6 as risk factors for LC PFS. The consistency indices of the line chart models for predicting PFS and OS were 0.782 and 0.772, respectively. Conclusion Male gender, treatment response of PD, and SCCA > 1.6 are independent risk factors affecting the survival of LC patients. The PFS line chart model demonstrates good concordance.

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Section: Discussionmentioning

confidence: 99%