Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) Is Associated With Recurrence and Survival of Resectable Non-Small Cell Lung Cancer (NSCLC): A Retrospective Study.

INTRODUCTION: Curative lung resection remains the key therapeutic strategy for early-stage non-small cell lung cancer (NSCLC). However, a proportion of patients still experience variable outcomes and eventually develop recurrence or die from their disease. Proprotein convertase subtilisin/kexin type 9 (PCSK9) has been identified as a deleterious factor that inhibits tumor cells apoptosis and leads to reduction of lymphocyte infiltration. However, there has been no research on the predicted role of PCSK9 as an immunohistochemical biomarker with survival in resectable NSCLC. METHODS: One hundred sixty-three patients with resectable NSCLC were retrospectively reviewed, and PCSK9 expression of resected NSCLC was analyzed by immunohistochemistry using tissue microarrays. RESULTS: PCSK9 was associated with recurrence (42.1% relapsed in the PCSK9lo group versus 57.9% relapsed in the PCSK9hi group, P = 0.006) and survival status (39.6% dead in PCSK9lo group versus 60.4% dead in PCSK9hi group, P = 0.004) in patients with resectable NSCLC. Moreover, resectable NSCLC patients with higher PCSK9 expression in tumor tissue experienced poorer disease-free survival (median disease-free survival: 10.5 versus 25.2 mo, hazard ratio = 1.620, 95% confidence interval: 1.124-2.334) and overall suvrival (median overall suvrival: 20.0 versus 54.1 mo, hazard ratio = 1.646, 95% confidence interval: 1.101-2.461) compared to those with lower PCSK9 expression. CONCLUSIONS: High PCSK9 expression of tumor was correlated with recurrence and worse survival status of resectable NSCLC in our retrospective study, which indicated that PCSK9 in NSCLC may be an immunohistochemical biomarker of poor prognosis for patients with resectable NSCLC. Further large-scale prospective studies are warranted to establish these results.

Prognostic role of C-reactive protein to albumin ratio in lung cancer: An updated systematic review and meta-analysis.

Background: C-reactive protein to albumin ratio (CRP/Alb ratio, CAR) has been suggested as a potential prognostic biomarker in lung cancer. This updated systematic review and meta-analysis aimed to assess the association between CAR and lung cancer prognosis in current literature. Methods: A systematic search of databases was conducted to identify relevant studies published up to April 2023. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to assess the association between CAR and overall survival (OS) and progression-free survival (PFS) and recurrence-free survival (RF) in lung cancer patients. Results: This meta-analysis includes 16 studies with a total of 5337 patients, indicating a significant association between higher CAR and poorer OS, PFS, and RFS in lung cancer patients, with a pooled HR of 1.78 (95% CI = 1.60-1.99), 1.57 (95% CI = 1.36-1.80), and 1.97 (95% CI = 1.40-2.77), respectively. Conclusions: This updated meta-analysis provides evidence for the potential prognostic role of CAR in lung cancer, suggesting its utility as an effective and noninvasive biomarker for identifying high-risk patients and informing treatment decisions in a cost-effective manner. However, further large-scale studies will be necessary to establish the optimal cut-off value for CAR in lung cancer and confirm the present findings.

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case-control study.

Objective: The purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling. Methods: A total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)>600 days n=152) noninert nodules (VDT<600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model >(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R2(determination coefficient). Results: The accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients' first examination and consultation. Conclusion: The INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules.

Research progress of extracellular vesicles as biomarkers in immunotherapy for non-small cell lung cancer.

Lung cancer is one of the most severe forms of malignancy and a leading cause of cancer-related death worldwide, of which non-small cell lung cancer (NSCLC) is the most primary type observed in the clinic. NSCLC is mainly treated with surgery, radiotherapy, and chemotherapy. Additionally, targeted therapy and immunotherapy have also shown promising results. Several immunotherapies, including immune checkpoint inhibitors, have been developed for clinical use and have benefited patients with NSCLC. However, immunotherapy faces several challenges like poor response and unknown effective population. It is essential to identify novel predictive markers to further advance precision immunotherapy for NSCLC. Extracellular vesicles (EVs) present an important research direction. In this review, we focus on the role of EVs as a biomarker in NSCLC immunotherapy considering various perspectives, including the definition and properties of EVs, their role as biomarkers in current NSCLC immunotherapy, and different EV components as biomarkers in NSCLC immunotherapy research. We describe the cross-talk between the role of EVs as biomarkers and novel technical approaches or research concepts in NSCLC immunotherapy, such as neoadjuvants, multi-omics analysis, and the tumour microenvironment. This review will provide a reference for future research to improve the benefits of immunotherapy for patients with NSCLC.

Immunotherapy efficacy predictive tool for lung adenocarcinoma based on neural network.

Background: Remarkably, the anti-cancer efficacy of immunotherapy in lung adenocarcinoma (LUAD) has been demonstrated. However, predicting the beneficiaries of this expensive treatment is still a challenge. Materials and methods: A group of patients (N = 250) diagnosed with LUAD and receiving immunotherapy were retrospectively studied. They were randomly divided into a training dataset (80%) and a test dataset (20%). The training dataset was utilized to train neural network models to predict patients' objective response rate (ORR), disease control rate (DCR), responders (progression-free survival time > 6 months), and overall survival (OS) possibility, which were validated by both the training and test datasets and packaged into a tool later. Results: In the training dataset, the tool scored 0.9016 area under the receiver operating characteristic (AUC) curve on ORR judgment, 0.8570 on DCR, and 0.8395 on responder prediction. In the test dataset, the tool scored 0.8173 AUC on ORR, 0.8244 on DCR, and 0.8214 on responder determination. As for OS prediction, the tool scored 0.6627 AUC in the training dataset and 0.6357 in the test dataset. Conclusions: This immunotherapy efficacy predictive tool for LUAD patients based on neural networks could predict their ORR, DCR, and responder well.

Association between response to anti-PD-1 treatment and blood soluble PD-L1 and IL-8 changes in patients with NSCLC.

In this study, we explored the dynamic changes in blood sPD-L1 and its clinical value during anti-PD-1 immunotherapy in non-small cell lung cancer (NSCLC) patients. First, we established a sandwich ELISA for functional sPD-L1 that can bind to PD-1 and has biological functions. By monitoring functional sPD-L1 in 39 NSCLC patients treated with anti-PD-1 antibodies, we found a positive correlation between baseline sPD-L1 and tissue PD-L1 (P = 0.0376, r = 0.3581), with patients with lymph node metastasis having higher sPD-L1 levels (P = 0.0037) than those without lymph node metastasis. Although baseline functional sPD-L1 and PFS did not correlate significantly in this study, changes in sPD-L1 in patients with different clinical responses showed different trends. Blood sPD-L1 increased in 93% of patients after two cycles of anti-PD-1 treatment (P = 0.0054); sPD-L1 in nonresponsive patients continued to increase (P = 0.0181), but sPD-L1 started to decline in responsive patients. Blood IL-8 levels were associated with tumor load, and when combined with IL-8, the evaluation accuracy of sPD-L1 improved to 86.4%. This study preliminarily shows that the combination of sPD-L1 and IL-8 is a convenient and effective method for monitoring and evaluating the effectiveness of anti-PD-1 immunotherapy in NSCLC patients.

Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.

Background: At present, immunotherapy is a very promising treatment method for lung cancer patients, while the factors affecting response are still controversial. It is crucial to predict the efficacy of lung squamous carcinoma patients who received immunotherapy. Methods: In our retrospective study, we enrolled lung squamous carcinoma patients who received immunotherapy at Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training cohort (80% of the total) and the test cohort (20% of the total). The training cohort was used to build neural network models to assess the efficacy and outcome of immunotherapy in lung squamous carcinoma based on clinical information. The main outcome was the disease control rate (DCR), and then the secondary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Results: A total of 289 patients were included in this study. The DCR model had area under the receiver operating characteristic curve (AUC) value of 0.9526 (95%CI, 0.9088-0.9879) in internal validation and 0.9491 (95%CI, 0.8704-1.0000) in external validation. The ORR model had AUC of 0.8030 (95%CI, 0.7437-0.8545) in internal validation and 0.7040 (95%CI, 0.5457-0.8379) in external validation. The PFS model had AUC of 0.8531 (95%CI, 0.8024-0.8975) in internal validation and 0.7602 (95%CI, 0.6236-0.8733) in external validation. The OS model had AUC of 0.8006 (95%CI, 0.7995-0.8017) in internal validation and 0.7382 (95%CI, 0.7366-0.7398) in external validation. Conclusions: The neural network models show benefits in the efficacy evaluation of immunotherapy to lung squamous carcinoma patients, especially the DCR and ORR models. In our retrospective study, we found that neoadjuvant and adjuvant immunotherapy may bring greater efficacy benefits to patients.

Development and validation of a prognostic predictive model of pulmonary spindle cell carcinoma from the surveillance, epidemiology and end results database.

Background: Pulmonary spindle cell carcinoma (PSCC) is a rare type of non-small cell lung cancer (NSCLC). The prognostic influent factors and therapeutic methods of PSCC are unclear, for there are only some case reports or small samples' analysis. This study aims to find prognosis related factors of PSCC, develop and validate a nomogram to predict their survival probability. Methods: The Surveillance, Epidemiology, and End Results (SEER) 18 Registries database (2000-2018) was searched to study PSCC. According to diagnosed time, data was divided into primary cohort (2000-2015) and validation cohort (2016-2018), both followed until December 31 2018. Chosen by Least Absolute Shrinkage and Selection Operator (LASSO) regression, age, sex, stage, surgery, chemotherapy, N, size and history of malignancy were taken out as predictive variables. The primary cohort was used to develop a nomogram to predict 1-, 3- and 5-year overall survival (OS) probability, and be validated by the validation cohort using concordance index (C-index) and calibration curves. Both cohorts were used to conduct a Cox regression to find the influential factors on OS of PSCC. Results: The nomogram shows a good concordance and discrimination on the prediction of OS, both internal (n=457 and C-index is 0.79) and external validation (n=100 and C-index is 0.76). The median survival time of PSCC is 4 months, with 20.1% OS possibility in 5 years. Multivariate analysis identified patients of older age [hazard ratio (HR), 1.02; 95% confidence interval (CI): 1.01-1.04], larger size of neoplasm (HR, 1.01; 95% CI: 1.01-1.01), M1 (HR, 2.96; 95% CI: 2.17-4.04), N2 (HR, 2.55; 95% CI: 1.81-3.59) or N3 (HR, 2.99; 95% CI: 1.58-5.66), regional stages (HR, 2.11; 95% CI: 1.29-3.44) and distant stages (HR, 6.17; 95% CI: 3.83-9.94) had a lower OS possibility, while surgery (HR, 0.39; 95% CI: 0.28-0.53) and history of malignancy (HR, 0.68; 95% CI: 0.48-0.98) was protective factors for PSCC. PSCC survived longer with surgery performed instead of chemotherapy or radiotherapy. Conclusions: Patients of PSCC have a poor prognosis, and using the nomogram developed by this study can predict their 1-, 3- and 5-year OS probability. Surgery is a better choice for PSCC and more studies are necessary to find potential treatment like targeted therapy, programmed death-1 (PD-1) and programmed death ligand 1 (PD-L1).

[Analysis of the Efficacy of Immunotherapy in Elderly Patients with Lung Cancer].

BACKGROUND: Immunotherapy represented by immune checkpoint inhibitors (ICIs) has become the standard treatment for patients with non-oncogenic advanced non-small cell lung cancer (NSCLC). While lung cancer is most prevalent in elderly patients, these patients are rarely included in pivotal clinical trial studies. We aimed to describe the efficacy and safety of immunotherapy for elderly patients in the "real-world". METHODS: The data of older NSCLC patients and younger patients who received immunotherapy between July 2018 to October 2021 were retrospectively analyzed and the objective response rate (ORR) and progression-free survival (PFS) in different age groups (less than 60 years old was defined as the young group, 60 years-74 years old was the young old group, 75 years old and above was the old old group) were compared. And the impact of different clinical characteristics on treatment response and prognosis were analyzed in each age subgroup. RESULTS: A total of 21 young patients, 70 young old patients and 15 old old patients were included in this study, with ORR of 33.3%, 52.8% and 53.3%, respectively, without statistically significant difference (P=0.284). The median PFS was 9.1 mon, 7.6 mon and 10.9 mon, respectively, without statistically significant difference (P=0.654). Further analysis of the predictors of immunotherapy in each subgroup revealed that patients in the young old group and young group who received immunotherapy in the first line had a longer PFS. The difference of the incidence of adverse events was not statistically significant among the three groups (P>0.05). CONCLUSIONS: The efficacy and safety of immunotherapy in elderly patients were similar to those in younger patients, and PFS was superior in the first-line immunotherapy. Further prospective studies are still needed to explore predictors of immunotherapy in elderly NSCLC patients.

Characteristics of Pulmonary Auscultation in Patients with 2019 Novel Coronavirus in China.

BACKGROUND: Effective auscultations are often hard to implement in isolation wards. To date, little is known about the characteristics of pulmonary auscultation in novel coronavirus (COVID-19) pneumonia. OBJECTIVES: The aim of this study was to explore the features and clinical significance of pulmonary auscultation in COVID-19 pneumonia using an electronic stethoscope in isolation wards. METHODS: This cross-sectional, observational study was conducted among patients with laboratory-confirmed COVID-19 at Wuhan Red-Cross Hospital during the period from January 27, 2020, to February 12, 2020. Standard auscultation with an electronic stethoscope was performed and electronic recordings of breath sounds were analyzed. RESULTS: Fifty-seven patients with average age of 60.6 years were enrolled. The most common symptoms were cough (73.7%) during auscultation. Most cases had bilateral lesions (96.4%) such as multiple ground-glass opacities (69.1%) and fibrous stripes (21.8%). High-quality auscultation recordings (98.8%) were obtained, and coarse breath sounds, wheezes, coarse crackles, fine crackles, and Velcro crackles were identified. Most cases had normal breath sounds in upper lungs, but the proportions of abnormal breath sounds increased in the basal fields where Velcro crackles were more commonly identified at the posterior chest. The presence of fine and coarse crackles detected 33/39 patients with ground-glass opacities (sensitivity 84.6% and specificity 12.5%) and 8/9 patients with consolidation (sensitivity 88.9% and specificity 15.2%), while the presence of Velcro crackles identified 16/39 patients with ground-glass opacities (sensitivity 41% and specificity 81.3%). CONCLUSIONS: The abnormal breath sounds in COVID-19 pneumonia had some consistent distributive characteristics and to some extent correlated with the radiologic features. Such evidence suggests that electronic auscultation is useful to aid diagnosis and timely management of the disease. Further studies are indicated to validate the accuracy and potential clinical benefit of auscultation in detecting pulmonary abnormalities in COVID-19 infection.