The analysis of prognosis factor in patients with non-small cell lung cancer receiving pneumonectomy

Machine learning and BP neural network revealed abnormal B cell infiltration predicts the survival of lung cancer patients

FAM83A gene is related to the invasion and metastasis of various tumors. However, the abnormal immune cell infiltration associated with the gene is poorly understood in the pathogenesis and prognosis of NSCLC. Based on the TCGA and GEO databases, we used COX regression and machine learning algorithms (CIBERSORT, random forest, and back propagation neural network) to study the prognostic value of FAM83A and immune infiltration characteristics in NSCLC. High FAM83A expression was significantly associated with poor prognosis of NSCLC patients (p = 0.00016), and had excellent prognostic independence. At the same time, the expression level of FAM83A is significantly related to the T, N, and Stage. Subsequently, based on machine learing strategies, we found that the infiltration level of naive B cells was negatively correlated with the expression of FAM83A. The low infiltration of naive B cells was significantly related to the poor overall survival rate of NSCLC (p = 0.0072). In addition, Cox regression confirmed that FAM83A and naive B cells are risk factors for the prognosis of NSCLC patients. The nomogram combining FAM83A and naive B cells (C-index = 0.748) has a more accurate prognostic ability than the Stage (C-index = 0.651) system. Our analysis shows that abnormal infiltration of naive B cells associated with FAM83A is a key factor in the prognostic prediction of NSCLC patients.

Clinical characteristics and survival outcomes of patients with pneumonectomies: A population-based study

Background

Prognostic factors in a pneumonectomy (PN) are not yet fully defined. This study sought to analyze and evaluate long-term survival after pneumonectomies (PNs) for patients with non-small cell lung cancer (NSCLC).

Methods

We obtained data from the Surveillance, Epidemiology, and End Results (SEER) database for patients who underwent PNs between 2004 and 2015. Propensity score matching (PSM) analysis and Kaplan–Meier curves were used to estimate overall survival (OS), while univariate and multivariable Cox proportional hazards regression analyses were applied to create a forest plot.

Results

In total, 1,376 patients were grouped according to right/left PNs. Before matching, OS was worse after a right PN [hazard ratio (HR): 1.459; 95% CI 1.254–1.697; P < 0.001] and after matching, survival differences between groups were not significant (HR: 1.060; 95% CI 0.906–1.240; P = 0.465). Regression analysis revealed that age, gender, grade, lymph node dissection, N-stage, and chemotherapy were independent predictors of OS (P < 0.05). Chemotherapy was associated with improved OS (P < 0.001).

Conclusions

Laterality was not a significant prognostic factor for long-term survival after a PN for NSCLC. Chemotherapy was a significant independent predictor of improved OS. Long-term survival and outcomes analyses should be conducted on larger numbers of patients.

A Multiple-Center Nomogram to Predict Pneumonectomy Complication Risk for Non-Small Cell Lung Cancer Patients

OBJECTIVE

This study aimed to construct a nomogram to quantitatively predict pneumonectomy complication risks for non-small cell lung cancer (NSCLC) patients.

METHODS

Data from 1052 NSCLC patients who underwent pneumonectomy were retrospectively retrieved from the databases of three thoracic centers. Multivariable logistic regression was used to investigate postoperative morbidity predictors. Clinical parameters and operative features were analyzed using univariable and multivariable logistic regression analyses, and a nomogram to predict the risk of postoperative complications was constructed using bootstrap resampling. A receiver operating characteristic (ROC) curve was used to estimate the discrimination power for the nomogram.

RESULTS

A total of 212 patients (20.2%) had major complications. After regression analysis, forced expiratory volume in 1 s, Charlson Comorbidity Index score, male sex, and right-sided pneumonectomy were identified and entered into the nomogram. The nomogram showed a robust discrimination, with an area under the ROC curve of 0.753 (95% confidence interval 0.604-0.818). The calibration curves for the probability of postoperative complications showed optimal agreement between the nomogram and the actual probability.

CONCLUSIONS

Based on preoperative data, we developed a nomogram for predicting complication risks after pneumonectomy. This model may be helpful for thoracic surgeons in selecting appropriate patients for adopting prophylactic measures after surgery.

Safe pneumonectomy for locally advanced lung cancer after induction therapy

PURPOSE

To assess the safety and long-term outcomes of pneumonectomy after IT (IT-Pn) versus upfront pneumonectomy without IT (U-Pn) for locally advanced non-small-cell lung cancer (NSCLC).

METHODS

We reviewed the clinical records of 69 patients who underwent pneumonectomy as U-Pn (n = 30) or IT-Pn (n = 39) between 2000 and 2019 at our institution, RESULTS: U-Pn included patients with pathological N0 (n = 13), N1 (n = 11) and N2 (n = 6). Among the patients treated with IT-Pn, 18 had pathological N0 (including 7 with complete responses), 5 had N1, 14 had N2, and 2 had N3. It was suggested that 22 cases could be down-staged after IT. The 5-year overall survival (OS) was 28.1% in the U-Pn group and 43.1% in the IT-Pn group (p = 0.275), being 40.2% for IT-Pn with p-N2,3, but not reached for U-Pn with N2 (p = 0.307). The 90-day mortality was 6.7% for the U-Pn group and 5.1% for the IT-Pn group (p = 0.646). Major complications occurred in 25 patients (64.1%) treated with IT-Pn and 18 patients treated with U-Pn (60.0%; p = 0.602).

CONCLUSIONS

Pneumonectomy for NSCLC can be performed safely after IT with favorable results. For patients with N2 disease, induction therapy followed by surgery may warrant further study.

A Nomogram to Predict Long-Term Survival Outcomes of Patients Who Undergo Pneumonectomy for Non-small Cell Lung Cancer With Stage I-IIIB

Background: In this study, we aim to establish a nomogram to predict the prognosis of non-small cell lung cancer (NSCLC) patients with stage I-IIIB disease after pneumonectomy.

Methods: Patients selected from the Surveillance, Epidemiology, and End Results (SEER, N = 2,373) database were divided into two cohorts, namely a training cohort (SEER-T, N = 1,196) and an internal validation cohort (SEER-V, N = 1,177). Two cohorts were dichotomized into low- and high-risk subgroups by the optimal risk prognostic score (PS). The model was validated by indices of concordance (C-index) and calibration plots. Kaplan-Meier analysis and the log-rank tests were used to compare survival curves between the groups. The primary observational endpoint was cancer-specific survival (CSS).

Results: The nomogram comprised six factors as independent prognostic indictors; it significantly distinguished between low- and high-risk groups (all P < 0.05). The unadjusted 5-year CSS rates of high-risk and low-risk groups were 33 and 60% (SEER-T), 34 and 55% (SEER-V), respectively; the C-index of this nomogram in predicting CSS was higher than that in the 8th TNM staging system (SEER-T, 0.629 vs. 0.584, P < 0.001; SEER-V, 0.609 vs. 0.576, P < 0.001). In addition, the PS might be a significant negative indictor on CSS of patients with white patients [unadjusted hazard ration (HR) 1.008, P < 0.001], black patients (unadjusted HR 1.007, P < 0.001), and Asian or Pacific Islander (unadjusted HR 1.008, P = 0.008). In cases with squamous cell carcinoma (unadjusted HR 1.008, P < 0.001) or adenocarcinoma (unadjusted HR 1.008, P < 0.001), PS also might be a significant risk factor.

Conclusions: For post-pneumonectomy NSCLC patients, the nomogram may predict their survival with acceptable accuracy and further distinguish high-risk patients from low-risk patients.

Drug Regimen for Patients after a Pneumonectomy

Pneumonectomy is an entire lung removal and is indicated for both malignant and benign diseases. Due to its invasiveness and postoperative complications, pneumonectomy is still associated with high mortality and morbidity. Appropriate postoperative management is crucial in pneumonectomy patients to improve quality of life and overall survival rates. Diverse drug regimens are under development to be used in adjuvant chemotherapy or to improve respiratory health after a pneumonectomy. The most common causes for a pneumonectomy are non-small cell lung cancer, malignant pleural mesothelioma, and tuberculosis; thus, an appropriate drug regimen is necessary. The uncommon incidence of pneumonectomy cases remains the major obstacle in studies of postoperative drug regimens. As the majority of current studies include post-lobectomy and post-segmentectomy patients, it is highly recommended that further research of postoperative drug regimens be focused on post-pneumonectomy patients.