Background: Lung adenocarcinoma (LUAD) is the major cause of cancer mortality. Traditional prognostic factors have limited importance after including other parameters. Thus, developing a more credible prognostic model combined with genes and clinical parameters is necessary. Methods:The messenger RNA (mRNA) expression and clinical information from The Cancer Genome Atlas (TCGA)-LUAD datasets and microarray data from three Gene Expression Omnibus (GEO) databases were obtained. We identified differentially-expressed genes (DEGs) between lung tumor and normal tissues through integrated analysis of the three GEO datasets. Univariate and multivariate Cox regression analyses were conducted to select survival-associated DEGs and to establish a prognostic gene signature which was associated with overall survival (OS). The expression of gene proteins was assessed in 180 LUAD tissue microarrays (TMAs) by immunohistochemistry (IHC). We verified its predictive performance with a Kaplan-Meier (KM) curve, receiver operating characteristic (ROC) curve, and Harrell's concordance index (C-index) and validated it in external GEO databases. Multivariate Cox regression analysis was performed to identify the significant prognostic indicators in LUAD. Furthermore, we established a prognostic nomogram based on TCGA-LUAD dataset. Results: A three-gene signature was constructed to predict the OS of LUAD patients. The KM analysis, ROC curve, and C-index present a good predictive ability of the gene signature in TCGA dataset [P<0.0001; C-index 0.6375; 95% confidence interval (CI): 0.5632-0.7118; area under the ROC curve (AUC) 0.674] and the external GEO datasets (P=0.05, 0.004, and 0.04, respectively). Univariate and multivariate Cox regression analyses also verified that LUAD patients with low-risk scores had a decreased risk of death compared to those with a high-risk score in TCGA database [hazard ratio (HR) =0.3898; 95% CI: 0.1938-0.7842; P<0.05]. Finally, we constructed a nomogram integrating the gene signature and clinicopathological parameters (P<0.0001; C-index 0.762; 95% CI: 0.714-0.845; AUC 0.8136). Compared with conventional staging, a nomogram can effectively improve prognosis prediction. Conclusions: The nomogram is closely associated to the OS of LUAD patients. This consequence may be beneficial to individualized treatment and clinical decision-making.
Background: Postoperative fluid management plays a key role in providing adequate tissue perfusion, stabilizing hemodynamics, and reducing morbidities related to hemodynamics. This study evaluated the dose-response relationship between postoperative 24-hour intravenous fluid volume and postoperative outcomes in patients with non-small cell lung cancer (NSCLC) undergoing video-assisted thoracoscopic surgery (VATS) lobectomy. Methods: A retrospective analysis of adult patients with NSCLC undergoing VATS lobectomy betweenMay 2016 and April 2017 was performed. The primary exposure variable was total intravenous crystalloid infusion in the 24-hour postoperative period. The observation outcomes were postoperative pulmonary complications, acute kidney injury (AKI), in-hospital mortality, readmission within 30 days, prolonged hospital stay, postoperative length of stay, and total hospital care costs. Univariate and multivariate analyses were performed.Results: Of the 563 patients, 136 (24.2%) with pulmonary complications were observed. Binary logistics regression showed that, relative to the group with moderate postoperative 24-hour crystalloid infusion, the risk for postoperative pulmonary complications was significantly increased in the restrictive [odds ratio (OR) 1.815, 95% CI: 1.083-3.043; P=0.024] and liberal (OR 2.692, 95% CI: 1.684-4.305; P<0.001) groups. Conclusions:In patients with NSCLC undergoing VATS lobectomy, both restrictive and liberal 24-hour postoperative crystalloid infusions were related to adverse effects on postoperative outcomes and the optimal volume of 24-hour postoperative intravenous crystalloid infusion was 1,080-<1,410 mL.
Background: Patients who undergo lung resection are at risk of postoperative cerebral infarction, but the risk factors remain unclear, so the present study was a comprehensive investigation in patients who underwent lung resection for pulmonary nodules. Methods:The clinical characteristics of patients with postoperative cerebral infarction and patients who underwent lung resection on the same day but did not develop cerebral infarction were retrospectively compared. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for cerebral infarction after lung resection.Results: A total of 22 patients with postoperative cerebral infarction and 316 controls were included.Multivariate logistic regression analysis revealed that a history of cerebral infarction (odds ratio [OR], 7.289; P=0.030), activated partial thromboplastin time (APTT) <26.5 s (OR, 3.704; P=0.018), body mass index (BMI) ≥24.0 kg/m 2 (OR, 3.656; P=0.015), and surgical method (P=0.005) were independent risk factors for cerebral infarction after lung resection. Compared with patients undergoing lobectomy, the risk for postoperative cerebral infarction was significantly increased in patients undergoing segmentectomy (OR, 24.322; P=0.001), wedge resection (OR, 6.992; P=0.018), or combined surgical approach (OR, 29.921; P=0.028).Conclusions: A history of cerebral infarction, APTT <26.5 s, BMI ≥24.0 kg/m 2 , and surgical method were independent risk factors for cerebral infarction after lung resection. Strengthening thromboprophylaxis in patients with these risk factors may help to reduce the incidence of postoperative cerebral infarction.
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