Integrative Proteomic Characterization of Human Lung Adenocarcinoma.

Genomic studies of lung adenocarcinoma (LUAD) have advanced our understanding of the disease's biology and accelerated targeted therapy. However, the proteomic characteristics of LUAD remain poorly understood. We carried out a comprehensive proteomics analysis of 103 cases of LUAD in Chinese patients. Integrative analysis of proteome, phosphoproteome, transcriptome, and whole-exome sequencing data revealed cancer-associated characteristics, such as tumor-associated protein variants, distinct proteomics features, and clinical outcomes in patients at an early stage or with EGFR and TP53 mutations. Proteome-based stratification of LUAD revealed three subtypes (S-I, S-II, and S-III) related to different clinical and molecular features. Further, we nominated potential drug targets and validated the plasma protein level of HSP 90ß as a potential prognostic biomarker for LUAD in an independent cohort. Our integrative proteomics analysis enables a more comprehensive understanding of the molecular landscape of LUAD and offers an opportunity for more precise diagnosis and treatment.

N-glycosylation modulates enzymatic activity of Trypanosoma congolense trans-sialidase.

Trypanosomes cause the devastating disease trypanosomiasis, in which the action of trans-sialidase (TS) enzymes harbored on their surface is a key virulence factor. TS enzymes are N-glycosylated, but the biological functions of their glycans have remained elusive. In this study, we investigated the influence of N-glycans on the enzymatic activity and structural stability of TconTS1, a recombinant TS from the African parasite Trypanosoma congolense. We expressed the enzyme in Chinese hamster ovary Lec1 cells, which produce high-mannose type N-glycans similar to the TS N-glycosylation pattern in vivo. Our MALDI-TOF mass spectrometry data revealed that up to eight putative N-glycosylation sites were glycosylated. In addition, we determined that N-glycan removal via endoglycosidase Hf treatment of TconTS1 led to a decrease in substrate affinity relative to the untreated enzyme but had no impact on the conversion rate. Furthermore, we observed no changes in secondary structure elements of hypoglycosylated TconTS1 in CD experiments. Finally, our molecular dynamics simulations provided evidence for interactions between monosaccharide units of the highly flexible N-glycans and some conserved amino acids located at the catalytic site. These interactions led to conformational changes, possibly enhancing substrate accessibility and enzyme-substrate complex stability. The here-observed modulation of catalytic activity via N-glycans represents a so-far-unknown structure-function relationship potentially inherent in several members of the TS enzyme family.

Characterization of global fire activity and its spatiotemporal patterns for different land cover types from 2001 to 2020.

Fire is a widespread phenomenon that plays an important role in Earth's ecosystems. This study investigated the global spatiotemporal patterns of burned areas, daytime and nighttime fire counts, and fire radiative power (FRP) from 2001 to 2020. The month with the largest burned area, daytime fire count, and FRP presented a bimodal distribution worldwide, with dual peaks in early spring (April) and summer (July and August), while the month with the largest nighttime fire count and FRP showed a unimodal distribution, with a peak in July. Although the burned area showed decline at the global scale, a significant increase occurred in temperate and boreal forest regions, where nighttime fire occurrence and intensity have consistently increased in recent years. The relationships among burned area, fire count, and FRP were further quantified in 12 typical fire-prone regions. The burned area and fire count exhibited a humped relationship with FRP in most tropical regions, whereas the burned area and fire count constantly increased when the FRP was below approximately 220 MW in temperate and boreal forest regions. Meanwhile, the burned area and FRP generally increased with the fire count in most fire-prone regions, indicating an increased risk of more intense and larger fires as the fire count increased. The spatiotemporal dynamics of burned areas for different land cover types were also explored in this study. The results suggest that the burned areas in forest, grassland, and cropland showed dual peaks in April and from July to September while the burned areas in shrubland, bareland, and wetlands usually peaked in July or August. Significant increases in forest burned area were observed in temperate and boreal forest regions, especially in the western U.S. and Siberia, whereas significant increases in cropland burned area were found in India and northeastern China.

The Role of Primary Emission and Transboundary Transport in the Air Quality Changes During and After the COVID-19 Lockdown in China.

In late January 2020, China's rapid and strict control measures to curb the COVID-19 spread led to a sharp halt in socio-economic activity and a significant reduction in emissions. Using the ground-based observational data, the authors synergistically quantify the nation-wide variations of major air pollutant as well as meteorology during and after the lockdown. Their concentrations (except O3) exhibited significant reduction during February and March 2020, by more than 24% during the lockdown compared with the earlier time period and by more than 17% compared with that in the same period in 2019. In contrast, ozone increased rapidly by about 60% across the country during the lockdown. Abnormal increases in carbon monoxide and particulate matter concentrations in southwest China are attributed to the severe wildfires in Southeast Asia. The concentration of air pollutants bounced back rapidly after the full-scale reopen in March 2020, indicating the decisive role of emissions in the pollution formation.

E-Jet 3D-Printed Scaffolds as Sustained Multi-Drug Delivery Vehicles in Breast Cancer Therapy.

PURPOSE: Combination chemotherapy is gradually receiving more attention because of its potential synergistic effect and reduced drug doses in clinical application. However, how to precisely control drug release dose and time using vehicles remains a challenge. This work developed an efficient drug delivery system to combat breast cancer, which can enhance drug effects despite reducing its concentration. METHODS: Controlled-release poly-lactic-co-glycolic acid (PLGA) scaffolds were fabricated by E-jet 3D printing to deliver doxorubicin (DOX) and cisplatin (CDDP) simultaneously. RESULTS: This drug delivery system allowed the use of a reduced drug dosage resulting in a better effect on the human breast cancer cell apoptosis and inhibiting tumor growth, compared with the effect of each drug and the two drugs administrated without PLGA scaffolds. Our study suggested that DOX-CDDP-PLGA scaffolds could efficiently destroy MDA-MB-231 cells and restrain tumor growth. CONCLUSIONS: The 3D printed PLGA scaffolds with their time-programmed drug release might be useful as a new multi-drug delivery vehicle in cancer therapy, which has a potential advantage in a long term tumor cure and prevention of tumor recurrence.

Regioselective and oxidant-free sulfinylation of indoles and pyrroles with sulfinamides.

An unexpected time-controlled highly selective C3- or C2-sulfinylation of pyrroles with sulfinamides is reported for the first time. The sulfinylation of indoles with sulfinamides using this protocol is oxidant-free and can be performed under obviously more feasible conditions (1.2 equiv. of indoles, 10 min) in comparison with the precedent procedure (3-20 equiv. of indoles, 16-18 h, ammonium persulfate as oxidant, hv). A variety of functional groups were tolerated, and various C2-thioindoles and C2/3-thiopyrroles were obtained in moderate to excellent yields.

Surface albedo regulates aerosol direct climate effect.

Aerosols and Surface Albedo (SA) are critical in balancing Earth's energy budget. With the changes of surface types and corresponding SA in recent years, an intriguing yet unresolved question emerges: how does Aerosol Direct Radiative Effect (ADRE) and its warming effect (AWE) change with varying SA? Here we investigate the critical SA marking ADRE shift from negative to positive under varying aerosol properties, along with the impact of SA on the ADRE. Results show that AWE often occurs in mid-high latitudes or regions with high-absorptivity aerosols, with critical SA ranging from 0.18 to 0.96. Thinner and/or more absorptive aerosols more readily cause AWE statistically. In regions where the SA trend is significant, SA has decreased at -0.012/decade, causing a -0.2 ± 0.17 W/m²/decade ADRE change, with the most pronounced changes in the Northern Hemisphere during June-July. As SA declines, we highlight enhanced ADRE cooling or reduced AWE, indicating aerosols' stronger cooling, partly countering the energy rise from SA reduction.

Observational evidence and mechanisms of aerosol effects on precipitation.

Aerosols greatly influence precipitation characteristics, thereby impacting the regional climate and human life. As an indispensable factor for cloud formation and a critical radiation budget regulator, aerosols can affect precipitation intensity, frequency, geographical distribution, area, and time. However, discrepancies exist among current studies due to aerosol properties, precipitation types, the vertical location of aerosols and meteorological conditions. The development of technology has driven advances in current research, but understanding the aerosol effects on precipitation remain complex and challenging. This paper revolves around the following topics from the two perspectives of Aerosol-Radiation Interaction (ARI) and Aerosol-Cloud Interaction (ACI): (1) the influence of different vertical locations of absorbing/scattering aerosols on the atmospheric thermal structure; (2) the fundamental theories of ARI reducing surface wind speed, redistributing water vapour and energy, and then modulating precipitation intensity; (3) different aerosol types (absorbing versus scattering) and aerosol concentrations causing different precipitation diurnal and weekly variations; (4) microphysical processes (cloud water competition, invigoration effect, and evaporation cooling) and observational evidence of different effects of aerosols on precipitation intensity, including enhancing, inhibiting, and transitional effects from enhancement to suppression; and (5) how meteorology, water vapor and dynamics influencing the effect of ACI and ARI on precipitation. In addition, this review lists the existing issues and future research directions for attaining a more comprehensive understanding of aerosol effects on precipitation. Overall, this review advances our understanding of aerosol effects on precipitation and could guide the improvement of weather and climate models to predict complex aerosol-precipitation interactions more accurately.

Emotion Recognition From Multimodal Physiological Signals via Discriminative Correlation Fusion With a Temporal Alignment Mechanism.

Modeling correlations between multimodal physiological signals [e.g., canonical correlation analysis (CCA)] for emotion recognition has attracted much attention. However, existing studies rarely consider the neural nature of emotional responses within physiological signals. Furthermore, during fusion space construction, the CCA method maximizes only the correlations between different modalities and neglects the discriminative information of different emotional states. Most importantly, temporal mismatches between different neural activities are often ignored; therefore, the theoretical assumptions that multimodal data should be aligned in time and space before fusion are not fulfilled. To address these issues, we propose a discriminative correlation fusion method coupled with a temporal alignment mechanism for multimodal physiological signals. We first use neural signal analysis techniques to construct neural representations of the central nervous system (CNS) and autonomic nervous system (ANS). respectively. Then, emotion class labels are introduced in CCA to obtain more discriminative fusion representations from multimodal neural responses, and the temporal alignment between the CNS and ANS is jointly optimized with a fusion procedure that applies the Bayesian algorithm. The experimental results demonstrate that our method significantly improves the emotion recognition performance. Additionally, we show that this fusion method can model the underlying mechanisms in human nervous systems during emotional responses, and our results are consistent with prior findings. This study may guide a new approach for exploring human cognitive function based on physiological signals at different time scales and promote the development of computational intelligence and harmonious human-computer interactions.

Few-shot short utterance speaker verification using meta-learning.

Short utterance speaker verification (SV) in the actual application is the task of accepting or rejecting the identity claim of a speaker based on a few enrollment utterances. Traditional methods have used deep neural networks to extract speaker representations for verification. Recently, several meta-learning approaches have learned a deep distance metric to distinguish speakers within meta-tasks. Among them, a prototypical network learns a metric space that may be used to compute the distance to the prototype center of speakers, in order to classify speaker identity. We use emphasized channel attention, propagation and aggregation in TDNN (ECAPA-TDNN) to implement the necessary function for the prototypical network, which is a nonlinear mapping from the input space to the metric space for either few-shot SV task. In addition, optimizing only for speakers in given meta-tasks cannot be sufficient to learn distinctive speaker features. Thus, we used an episodic training strategy, in which the classes of the support and query sets correspond to the classes of the entire training set, further improving the model performance. The proposed model outperforms comparison models on the VoxCeleb1 dataset and has a wide range of practical applications.

Screening microRNAs as potential prognostic biomarkers for lung adenocarcinoma.

OBJECTIVE: To screen and identify microRNAs (miRNAs) associated with the prognosis of lung adenocarcinoma (LUAD) using clinical samples and construct a prediction model for the prognosis of LUAD. METHODS: 160 patient samples were used to screen and identify miRNAs associated with the prognosis of LUAD. Differentially expressed miRNAs were analyzed using gene chip technology. The selected miRNAs were validated using samples from the validation sample group. Cox proportional hazards regression was used to construct the model and Kaplan-Meier was used to plot survival curves. Model power was assessed by testing the prognosis of the constructed model using real-time polymerase chain reaction (RT-PCR) data. RESULTS: The data showed that miR-1260b, miR-21-3p and miR-92a-3p were highly expressed in the early recurrence and metastasis group, while miR-2467-3p, miR-4659a-3p, miR-4514, miR-1471 and miR-3621 were lowly expressed. It was further confirmed that miR-21-3p was significantly highly expressed in the early recurrence and metastasis group (p = 0.02). Receiver operating characteristic (ROC) curve results showed cut-off point value of 0.0172, sensitivity of 88.2% and specificity of 100%. The predictive results of the constructed model were in good agreement with the actual prognosis of patients by using the validation sample test (Kappa = 0.426, p < 0.001), with a model sensitivity of 74.4%, a specificity of 68.3%, and an accuracy of 71.3%. CONCLUSION: miRNAs associated with the prognosis of patients with stage I LUAD were screened and validated, and a risk model for predicting the prognosis of patients was constructed. This model has good consistency with the actual prognosis of patients.

STSNet: a novel spatio-temporal-spectral network for subject-independent EEG-based emotion recognition.

How to use the characteristics of EEG signals to obtain more complementary and discriminative data representation is an issue in EEG-based emotion recognition. Many studies have tried spatio-temporal or spatio-spectral feature fusion to obtain higher-level representations of EEG data. However, these studies ignored the complementarity between spatial, temporal and spectral domains of EEG signals, thus limiting the classification ability of models. This study proposed an end-to-end network based on ManifoldNet and BiLSTM networks, named STSNet. The STSNet first constructed a 4-D spatio-temporal-spectral data representation and a spatio-temporal data representation based on EEG signals in manifold space. After that, they were fed into the ManifoldNet network and the BiLSTM network respectively to calculate higher-level features and achieve spatio-temporal-spectral feature fusion. Finally, extensive comparative experiments were performed on two public datasets, DEAP and DREAMER, using the subject-independent leave-one-subject-out cross-validation strategy. On the DEAP dataset, the average accuracy of the valence and arousal are 69.38% and 71.88%, respectively; on the DREAMER dataset, the average accuracy of the valence and arousal are 78.26% and 82.37%, respectively. Experimental results show that the STSNet model has good emotion recognition performance.

CTRP6 protects against ferroptosis to drive lung cancer progression and metastasis by destabilizing SOCS2 and augmenting the xCT/GPX4 pathway.

Lung cancer is a highly heterogeneous malignancy, and despite the rapid development of chemotherapy and radiotherapy, acquired drug resistance and tumor progression still occur. Thus, it is urgent to identify novel therapeutic targets. Our research aims to screen novel biomarkers associated with the prognosis of lung carcinoma patients and explore the potential regulatory mechanisms. We obtained RNA sequencing (RNA-seq) data of lung cancer patients from public databases. Clinical signature analysis, weighted gene coexpression network analysis (WGCNA) and the random forest algorithm showed that C1q/tumor necrosis factor-related protein-6 (CTRP6) is a core gene related to lung cancer prognosis, and it was determined to promote tumor proliferation and metastasis both in vivo and in vitro. Mechanistically, silencing CTRP6 was determined to promote xCT/GPX4-involved ferroptosis through functional assays related to lipid peroxidation, Fe2+ concentration and mitochondrial ultrastructure. By performing interactive proteomics analyses in lung tumor cells, we identified the interaction between CTRP6 and suppressor of cytokine signaling 2 (SOCS2) leading to SOCS2 ubiquitination degradation, subsequently enhancing the downstream xCT/GPX4 signaling pathway. Moreover, significant correlations between CTRP6-mediated SOCS2 and ferroptosis were revealed in mouse models and clinical specimens of lung cancer. As inducing ferroptosis has been gradually regarded as an alternative strategy to treat tumors, targeting CTRP6-mediated ferroptosis could be a potential strategy for lung cancer therapy.

Machine learning-based estimation of ground-level NO2 concentrations over China.

Most current scientific research on NO2 remote sensing focuses on tropospheric NO2 column concentrations rather than ground-level NO2 concentrations; however, ground-level NO2 concentrations are more related to anthropogenic emissions and human health. This study proposes a machine learning estimation method for retrieving the ground-level NO2 concentrations throughout China based on the tropospheric NO2 column concentrations from the TROPOspheric Monitoring Instrument (TROPOMI) and multisource geographic data from 2018 to 2020. This method adopts the XGBoost machine learning model characterized by a strong fitting ability and complex model structure, which can explain the complex nonlinear and high-order relationships between ground-measured NO2 and its influencing factors. The R2 values between the retrievals and the validation and test datasets are 0.67 and 0.73, respectively, which suggests that the proposed method can reliably retrieve the ground-level NO2 concentrations across China. The distribution characteristics, seasonal variations and interannual differences in ground-level NO2 concentrations are further analyzed based on the retrieval results, demonstrating that the ground-level NO2 concentrations exhibit significant geographical and seasonal variations, with high concentrations in winter and low concentrations in summer, and the highly polluted regions are concentrated mainly in Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), Cheng-Yu District (CY) and other urban agglomerations. Finally, the interannual variation in the ground-level NO2 concentrations indicates that pollution decreased continuously from 2018 to 2020.

Health risk and disease burden attributable to long-term global fine-mode particles.

Particulate matter 2.5 (PM2.5) pollution has long been a global environmental problem and still poses a great threat to public health. This study investigates global spatiotemporal variations in PM2.5 using the newly developed satellite-derived PM2.5 dataset from 1998 to 2018. An integrated exposure-response (IER) model was employed to examine the characteristics of PM2.5-related deaths caused by chronic obstructive pulmonary disease (COPD), ischemic heart disease (IHD), lung cancer (LC), and stroke in adults (age≥25), as well as lower respiratory infection (LRI) in children (age≤5). The results showed that high annual PM2.5 concentrations were observed mainly in East Asia and South Asia. Over the 19-year period, PM2.5 concentrations constantly decreased in developed regions, but increased in most developing regions. Approximately 84% of the population lived in regions where PM2.5 concentrations exceeded 10 µg/m3. Meanwhile, the vast majority of the population (>60%) in East and South Asia was consistently exposed to PM2.5 levels above 35 µg/m3. PM2.5 exposure was linked to 3.38 (95% UI: 3.05-3.70) million premature deaths globally in 2000, a number that increased to 4.11 (95% UI: 3.55-4.69) million in 2018. Premature deaths related to PM2.5 accounted for 6.54%-7.79% of the total cause of deaths worldwide, with a peak in 2011. Furthermore, developing regions contributed to the majority (85.95%-95.06%) of PM2.5-related deaths worldwide, and the three highest-ranking regions were East Asia, South Asia, and Southeast Asia. Globally, IHD and stroke were the two main contributors to total PM2.5-related deaths, followed by COPD, LC, and LRI.

Microfluidic chip interfacing microdialysis and mass spectrometry for in vivo monitoring of nanomedicine pharmacokinetics in real time.

Although liposomes have demonstrated significant clinical success as drug delivery vehicles, pharmacokinetic (PK) profiling of liposomal nanomedicines remains difficult due to technical challenges accurately measuring low concentrations of free drug in complex biological matrices. Microdialysis (MD) is well established as a powerful in vivo sampling tool for PK studies, but non-volatile salts present in the microdialysate are incompatible with mass spectrometry (MS) analysis without tedious sample pre-treatment. To address this issue, a µSPE-based microfluidic chip was fabricated to interface MD with MS. By incorporating PEG 20,000 as an effective anti-foulant, the µSPE-based microfluidic chip demonstrated excellent efficiencies in drug extraction and de-salting of the microdialysate, providing a promising approach to real-time monitoring of nanomedicine PK profiles.

Zinc-finger protein 750 mitigates malignant biological behavior of oral CSC-like cells enriched from parental CAL-27 cells.

Oral squamous cell carcinoma (OSCC) is the most commonly occurring oral malignancy. Cancer stem cells (CSCs) are known to be responsible for cancer recurrence and metastasis. Zinc-finger protein 750 (ZNF750) has been reported to inhibit OSCC cell proliferation and invasion. The present study aimed to elucidate the role of ZNF750 in the inhibition of the renewal ability of CSCs derived from the OSCC cell line, CAL-27. The effects of ZNF750 on CSC-like properties were examined using aldehyde dehydrogenase (ALDH), tumor sphere formation and colony formation assays. Reverse transcription-quantitative PCR and western blotting were performed to detect the expression levels of octamer-binding transcription factor 4, sex-determining region Y-box 2, the enhancer of zeste homolog 2 (Ezh2), embryonic ectoderm development (EED) and SUZ12 polycomb repressive complex 2 subunit (SUZ12), and for the identification of genes associated with metastasis. ZNF750 effectively attenuated CSC-like cell self-renewal abilities; ZNF750 decreased the ALDH-positive cell population, tumor sphere and colony formation abilities, cell viability and stemness factors. Furthermore, the expression levels of Ezh2, EED and SUZ12 were decreased by ZNF750. ZNF750 inhibited MMP1, 3, 9 and 13 expression levels, and decreased the cell invasion and migratory abilities. Moreover, the expression of tissue inhibitors of matrix metalloproteinases-1 was increased by ZNF750. However, opposite effects were observed following the knockdown of the ZNF750 gene. Overall, the present study demonstrated that ZNF750 has the potential to inhibit the renewal of CSC-like cells enriched from parental CAL-27 cells.

A universal monoclonal antibody-aptamer conjugation strategy for selective non-invasive bioparticle isolation from blood on a regenerative microfluidic platform.

Simultaneous isolation of various circulating tumor cell (CTC) subtypes from whole blood is useful in cancer diagnosis and prognosis. Microfluidic affinity separation devices are promising for CTC separation because of their high throughput capacity and automatability. However, current affinity agents, such as antibodies (mAbs) and aptamers (Apts) alone, are still suboptimal for efficient, consistent, and versatile cell analysis. By introducing a hybrid affinity agent, i.e., an aptamer-antibody (Apt-mAb) conjugate, we developed a universal and regenerative microchip with high efficiency and non-invasiveness in the separation and profiling of various CTCs from blood. The Apt-mAb conjugate consists of a monoclonal antibody that specifically binds the target cell receptor and a surface-bound aptamer that recognizes the conserved Fc region of the mAb. The aptamer then indirectly links the surface functionalization of the microfluidic channels to the mAbs. This hybrid affinity agent and the microchip platform may be widely useful for various bio-particle separations in different biological matrices. Further, the regeneration capability of the microchip improves data consistency between multiple uses and minimizes plastic waste while promoting environmental sustainability. STATEMENT OF SIGNIFICANCE: A hybrid affinity agent, Apt-mAb, consisting of a universal aptamer (Apt) that binds the conserved Fc region of monoclonal antibodies (mAbs) was developed. The invented nano-biomaterial combines the strengths and overcomes the weakness of both Apts and mAbs, thus changing the paradigm of affinity separation of cell subtypes. When Apt-mAb was used to fabricate microfluidic chips using a "universal screwdriver" approach, the microchip could be easily tuned to bind any cell type, exhibiting great universality. Besides high sensitivity and selectivity, the superior regenerative capacity of the microchips makes them reusable, which provides improved consistency and repeatability in cell profiling and opens a new approach towards in vitro diagnostic point-of-care testing devices with environmental sustainability and cost-effectiveness.

Survival After Lobectomy vs. Sublobar Resection for Stage IA Large-Cell Neuroendocrine Carcinoma of the Lung: A Population-Based Study.

Objective: Due to the low incidence of pulmonary large cell neuroendocrine carcinoma (LCNEC), the survival analysis for comparing lobectomy and sublobar resection (SLR) for stage IA LCNEC remains scarce. Methods: Patients diagnosed with pathological stage IA LCNEC between 1998 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The oncological outcomes were cancer-specific survival (CSS) and overall survival (OS). Kaplan-Meier analysis and Cox multivariate analysis were used to identify the independent prognostic factors for OS and CSS. Furthermore, propensity score matching (PSM) was performed between SLR and lobectomy to adjust the confounding factors. Results: A total of 308 patients with stage IA LCNEC met the inclusion criteria: 229 patients (74.4%) received lobectomy and 79 patients (25.6%) received SLR. Patients who underwent SLR were older (P < 0.001), had smaller tumor size (P = 0.010), and less lymph nodes dissection (P < 0.001). The 5-year CSS and OS rates were 56.5 and 42.9% for SLR, and 67.8 and 55.7% for lobectomy, respectively (P = 0.037 and 0.019, respectively). However, multivariate analysis did not identify any differences between the SLR group and lobectomy group in CSS (P = 0.135) and OS (P = 0.285); and the PSM also supported these results. In addition, the age at diagnosis and laterality of tumor were identified as significant predictors for CSS and OS, whereas the number of lymph nodes dissection was a significant predictor for CSS. Conclusions: Although SLR is not inferior to lobectomy in terms of oncological outcomes for patients with stage IA LCNEC, more lymph nodes can be dissected or sampled during lobectomy. Lobectomy should still be considered as a standard procedure for patients with early-stage LCNEC who are able to withstand lobectomy.