Identification of hub genes associated with prognosis of lung cancer via integrated bioinformatics and
            <i>in vitro</i>
            approach

Yadav, Deep; Bhadresha, Kinjal; Rao, Priyashi; Shaikh, Shayma; Rawal, Rakesh

doi:10.1080/07391102.2022.2160816

Cited by 6 publications

(1 citation statement)

References 46 publications

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“… 2 Many clinical studies have found that LC patients must achieve “three early”, that is, early detection, early diagnosis, and early treatment, in order to achieve good therapeutic results. 3 , 4 However, the prognosis for most LC patients is still very poor. This is mainly because the early symptoms of LC are not obvious and have been overlooked by patients, while when patients enter the hospital, about 75% of patients are already in the late stage of LC, and the prognosis of the late stage is relatively poor.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%