Background:For patients with resected large-cell neuroendocrine carcinoma, the role of adjuvant chemotherapy remains uncertain, and factors associated with greater benefit are not well defined. This study aimed to evaluate the association between adjuvant chemotherapy and survival outcomes while accounting for treatment timing and to identify clinicopathologic subgroups that may derive greater benefit. Methods:This dual-center retrospective study included 185 patients with resected large-cell neuroendocrine carcinoma. To reduce treatment-timing bias, a time-dependent multivariable Cox proportional hazards model was used, in which adjuvant chemotherapy was treated as a time-dependent exposure. Overall survival and disease-free survival were evaluated. Sensitivity analyses, including time-fixed and landmark approaches, were performed to assess the robustness of the findings. Subgroup analyses were conducted in selected clinically relevant strata. Results:In the primary time-dependent multivariable Cox model, adjuvant chemotherapy was associated with improved overall survival (hazard ratio, 0.54; 95% confidence interval, 0.33-0.88; P=0.01). In contrast, adjuvant chemotherapy was not significantly associated with improved disease-free survival (hazard ratio, 0.88; 95% confidence interval, 0.57-1.38; P=0.59). Subgroup analyses suggested that the overall survival association was more pronounced in clinically higher-risk subgroups, particularly in patients with stage II-III disease (hazard ratio, 0.37; 95% confidence interval, 0.19-0.71; P=0.003) and node-positive disease (hazard ratio, 0.27; 95% confidence interval, 0.12-0.63; P=0.002). Sensitivity analyses showed generally consistent effect directions across different analytic strategies. Conclusions:In this dual-center retrospective cohort, postoperative adjuvant chemotherapy was associated with improved overall survival after resection for large-cell neuroendocrine carcinoma, whereas no significant association was observed for disease-free survival. The overall survival association appeared stronger in selected higher-risk subgroups, including patients with stage II-III disease and node-positive disease. Further studies are warranted to better define the patients most likely to benefit from adjuvant chemotherapy.

This study investigates the role of fatty acid metabolism (FAM)-related genes in lymph node metastasis (LNM) and prognosis of lung adenocarcinoma (LUAD) and elucidates the underlying mechanisms. Transcriptomic and single-cell RNA-seq data from TCGA and GEO were integrated to identify FAM-related genes. Non-negative matrix factorization clustering and univariate Cox regression were applied to develop a FAM-based prognostic risk model (FScore). Associations of FScore with gene mutations and tumor microenvironment features were analyzed. Immunohistochemistry, and functional assays were performed to alidate the role of ACSL3 in LUAD malignancy and lymphangiogenesis. A five-gene FAM-related risk signature (ACSL3, MCAT, NDUFAB1, OLAH, ACSL4) was identified. The derived FScore stratified patient prognosis across multiple independent cohorts, with high FScore linked to significantly worse overall survival. FScore increased progressively with nodal stage (N0 < N1 < N2) and correlated with an immunosuppressive “cold” tumor microenvironment and specific mutation patterns (e.g., low FLG mutation). Single-cell and spatial transcriptomics revealed cell-type–specific FAM activity, predominantly in epithelial, mast, and myeloid cells. ACSL3 was overexpressed in LUAD tissues and served as an independent poor prognostic factor. ACSL3 overexpression elevated intracellular triglyceride and phospholipid levels, upregulated key FAM enzymes (FASN, ACC, ACLY) and the c-Myc/VEGFC axis, promoted proliferation, migration, invasion, and lymphangiogenesis, while suppressing apoptosis. The FScore serves as a robust predictor of LNM and poor prognosis in LUAD. ACSL3 drives lymphatic metastasis via the c-Myc/VEGFC axis, positioning ACSL3 as a potential therapeutic target to suppress LNM in LUAD.

PURPOSE:This study aims to explore the potential causal relationship between gut microbiota and lung squamous cell carcinoma (LUSC). METHODS:A bidirectional two-sample Mendelian randomization analysis was conducted using genome-wide association study (GWAS) data from gut microbiota and LUSC. Gut microbiota served as the exposure factor, with instrumental variables selected from a GWAS involving 18 340 participants. LUSC data were drawn from a European cohort including 29 266 LUSC cases and 56 450 controls. Inverse-variance weighted (IVW) method was used as the primary method, with the Benjamini-Hochberg method applied to adjust for multiple comparisons. An independent dataset (ieu-a-967, containing 3275 LUSC cases and 15 038 controls) was used for replication analysis to ensure robustness. RESULTS:IVW analysis found that Butyricicoccus (OR = 0.79, 95% CI: 0.63-0.99, P = .042) and Coprobacter (OR = 0.85, 95% CI: 0.74-0.97, P = .018) were significantly protective against LUSC. In contrast, Victivallis (OR = 1.11, 95% CI: 1.00-1.23, P = .045) and Ruminococcus (OR = 1.28, 95% CI: 1.03-1.60, P = .028) increased LUSC risk. Replication analysis in the independent dataset confirmed significant associations for Ruminococcus and Coprobacter. No reverse causality or pleiotropy was detected. CONCLUSION:This study provides evidence of a causal relationship between specific gut microbiota and LUSC risk, highlighting new microbial targets for potential prevention and treatment strategies in lung cancer. Key messages What is already known on this topic?  Previous studies have suggested potential links between gut microbiota composition and the development of various cancers, including lung cancer. However, the exact causal relationship between specific gut microbiota and lung squamous cell carcinoma (LUSC) has remained unclear. Traditional observational studies have struggled to determine the direction of causality due to confounding factors, making further investigation necessary through more robust methods such as Mendelian randomization (MR). What this study adds?  This bidirectional MR study provides novel genetic evidence indicating that certain gut microbiotas are causally associated with LUSC risk. Specifically, Butyricicoccus appears to reduce the risk of LUSC, while Victivallis increases the risk. These findings highlight the role of the gut-lung axis in LUSC and open up new avenues for exploring gut microbiota as potential modulators of lung cancer risk. How this study might affect research, practice, or policy?  The implications of this study may significantly influence future research into cancer prevention strategies by targeting gut microbiota. Additionally, it could inform clinical practices aimed at modulating gut microbiota to lower the risk of LUSC, potentially influencing dietary or probiotic interventions to reduce cancer susceptibility. Furthermore, these results might shape public health policies that focus on the gut-lung axis as a novel avenue for cancer prevention and management.

AIM:To develop and validate a pathomics model that non-invasively predicts CD40LG expression from routine haematoxylin-eosin (HE) slides and clarifies its prognostic value in lung adenocarcinoma (LUAD). METHODS:HE whole-slide images from 327 TCGA-LUAD cases were randomly split into training (70 %) and internal-validation (30 %) sets; an external cohort of 89 patients from the Cancer Hospital Chinese Academy of Medical Sciences provided independent validation. From 1488 quantitative pathomic features, maximum-relevance minimum-redundancy and recursive feature elimination identified the most informative variables. A gradient-boosting machine (GBM) classified tumours as CD40LG-high or -low. Model performance was assessed with ROC curves, area under the curve (AUC), calibration plots and decision-curve analysis. Propensity-score matching (PSM) balanced baseline clinicopathologic factors in the TCGA cohort. Immune-cell deconvolution (CIBERSORTx) and gene-set variation analysis explored biological correlates of the pathomics score (PS). RESULTS:After PSM, high CD40LG expression remained an independent protective factor for overall survival (OS) in the TCGA cohort (HR = 0.601, p = 0.006) and in the external cohort. The GBM model achieved AUCs of 0.809 (training), 0.736 (internal validation) and 0.725 (external validation). The derived PS independently predicted improved OS and correlated with greater infiltration of naïve B cells and CD8⁺ T cells. Genes linked to epithelial-to-mesenchymal transition-including IL32 and TGM2-were up-regulated in tumours with high PS. CONCLUSIONS:This pathomics model accurately infers CD40LG expression from standard histology and stratifies LUAD patients by prognosis, offering a practical, low-cost tool for precision oncology while providing insight into immune-mediated disease mechanisms.

BackgroundLung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer, with lymph node metastasis serving as a key prognostic factor. MUC5B, a member of the mucin family, has been implicated in the progression of various cancers, yet its specific role in LUAD metastasis remains underexplored. This study aimed to investigate the role of MUC5B in LUAD progression and its potential as a biomarker for lymph node metastasis.MethodsWe integrated TCGA data, single-cell RNA-seq, and machine learning (LASSO, SVM-RFE) to identify MUC5B and associated metastatic markers. A 13-gene predictive model was constructed and validated using ROC analysis. Immunohistochemical staining confirmed the expression of MUC5B in the clinical case samples (n=65). In vitro experiments were performed using MUC5B-knockdown LUAD cell lines (A549, H1975) to assess changes in proliferation, migration, invasion, and colony formation. RNA sequencing was conducted to explore downstream molecular changes following MUC5B depletion.ResultsMUC5B was significantly upregulated in LUAD with lymph node metastasis and associated with poor overall and progression-free survival. Knockdown of MUC5B suppressed LUAD cell proliferation, migration, and invasion. The 13-gene model showed high predictive accuracy (AUC > 0.9) for lymph node metastasis. GSVA analysis revealed most model genes correlated positively with Th2 cells and negatively with mast cells, type II interferons. Transcriptomic profiling revealed that MUC5B depletion led to significant downregulation of GINS1, GINS2, and GINS4—core components of the DNA replication GINS complex—suggesting a regulatory axis between MUC5B and cell cycle progression. Enrichment analyses further indicated that MUC5B promotes LUAD metastasis via pathways involved in DNA replication, cell cycle, and metabolic reprogramming.ConclusionMUC5B facilitates LUAD lymph node metastasis, potentially by regulating the GINS complex and promoting oncogenic signaling. These findings highlight MUC5B as a promising biomarker and therapeutic target for advanced LUAD.

We aimed to explore whether the genes associated with both platinum-based therapy and polyamine metabolism could predict the prognosis of LUAD. We searched for the differential expression genes (DEGs) associated with platinum-based therapy, then we interacted them with polyamine metabolism-related genes to obtain hub genes. Subsequently, we analysed the main immune cell populations in LUAD using the scRNA-seq data, and evaluated the activity of polyamine metabolism of different cell subpopulations. The DEGs between high and low activity groups were screened to identify key DEGs to establish prognostic risk score model. We further elucidated the landscape of immune cells, mutation and drug sensitivity analysis in different risk groups. Finally, we got 10 hub genes associated with both platinum-based chemotherapy and polyamine metabolism, and found that these hub genes mainly affected signalling transduction pathways. B cells and mast cells with highest polyamine metabolism activity, while NK cells were found with lowest polyamine metabolism activity based on scRNA-seq data. DEGs between high and low polyamine metabolism activity groups were identified, then 6 key genes were screened out to build risk score, which showed a good predictive power. The risk score showed a universal negative correlation with immunotherapy checkpoint genes and the cytotoxic T cells infiltration. The mutation rates of EGFR in low-risk group was significantly higher than that of high-risk group. In conclusion, we developed a risk score based on key genes associated with platinum-based therapy and polyamine metabolism, which provide a new perspective for prognosis prediction of LUAD.

Purpose FSTL3 expression is altered in various types of cancer. However, the role and mechanism of action of FSTL3 in lung adenocarcinoma development and tumor immunity are unknown. We investigated the association between FSTL3 expression and clinical characteristics and immune cell infiltration in lung adenocarcinoma samples from The Cancer Genome Atlas (TCGA) and a separate validation set from our hospital. Methods Data on immune system infiltration, gene expression, and relevant clinical information were obtained by analyzing lung adenocarcinoma sample data from TCGA database. Using online tools like GEPIA, the correlations between FSTL3 expression and prognosis, clinical stage, survival status, and tumor-infiltrating immune cells were examined. In a validation dataset, immunohistochemistry was performed to analyze FSTL3 expression and its related clinical characteristics. Results FSTL3 expression was markedly reduced in patients with lung adenocarcinoma. N stage, pathological stage, and overall survival were significantly correlated with FSTL3 expression. According to GSEA, FSTL3 is strongly linked to signaling pathways such as DNA replication and those involved in cell cycle regulation. Examination of TCGA database and TIMER online revealed a correlation between FSTL3 and B cell, T cell, NK cell, and neutrophil levels. The prognosis of patients with lung adenocarcinoma was significantly affected by six genes ( KRT6A , VEGFC , KRT14 , KRT17 , SNORA12 , and KRT81 ) related to FSTL3. Conclusion FSTL3 is significantly associated with the prognosis and progression of lung adenocarcinoma and the infiltration of immune cells. Thus, targeting FSTL3 and its associated genes in immunotherapy could be potentially beneficial for the treatment of lung adenocarcinoma.

BackgroundNatural killer (NK) cells are crucial for tumor prognosis; however, their role in non-small-cell lung cancer (NSCLC) remains unclear. The current detection methods for NSCLC are inefficient and costly. Therefore, radiomics represent a promising alternative.MethodsWe analyzed the radiogenomics datasets to extract clinical, radiological, and transcriptome data. The effect of NK cells on the prognosis of NSCLC was assessed. Tumors were delineated using a 3D Slicer, and features were extracted using pyradiomics. A radiomics model was developed and validated using five-fold cross-validation. A nomogram model was constructed using the selected clinical variables and a radiomic score (RS). The CIBERSORTx database and gene set enrichment analysis were used to explore the correlations of NK cell infiltration and molecular mechanisms.ResultsHigher infiltration of NK cells was correlated with better overall survival (OS) (P = 0.002). The radiomic model showed an area under the curve of 0.731, with 0.726 post-validation. The RS differed significantly between high and low infiltration of NK cells (P < 0.01). The nomogram, using RS and clinical variables, effectively predicted 3-year OS. NK cell infiltration was correlated with the ICOS and BTLA genes (P < 0.001) and macrophage M0/M2 levels. The key pathways included TNF-α signaling via NF-κB and Wnt/β-catenin signaling.ConclusionsOur radiomic model accurately predicted NK cell infiltration in NSCLC. Combined with clinical characteristics, it can predict the prognosis of patients with NSCLC. Bioinformatic analysis revealed the gene expression and pathways underlying NK cell infiltration in NSCLC.

Background: The tricarboxylic acid cycle (TCA cycle) is an important metabolic pathway and closely related to tumor development. However, its role in the development of esophageal squamous cell carcinoma (ESCC) has not been fully investigated.Methods: The RNA expression profiles of ESCC samples were retrieved from the TCGA database, and the GSE53624 dataset was additionally downloaded from the GEO database as the validation cohort. Furthermore, the single cell sequencing dataset GSE160269 was downloaded. TCA cycle-related genes were obtained from the MSigDB database. A risk score model for ESCC based on the key genes of the TCA cycle was built, and its predictive performance was evaluated. The association of the model with immune infiltration and chemoresistance were analyzed using the TIMER database, the R package “oncoPredict” score, TIDE score and so on. Finally, the role of the key gene CTTN was validated through gene knockdown and functional assays.Results: A total of 38 clusters of 8 cell types were identified using the single-cell sequencing data. The cells were divided into two groups according to the TCA cycle score, and 617 genes were identified that were most likely to influence the TCA cycle. By intersecting 976 key genes of the TCA cycle with the results of WGCNA, 57 genes significantly associated with the TCA cycle were further identified, of which 8 were screened through Cox regression and Lasso regression to construct the risk score model. The risk score was a good predictor of prognosis across subgroups of age, N, M classification and TNM stage. Furthermore, BI-2536, camptothecin and NU7441 were identified as possible drug candidates in the high-risk group. The high-risk score was associated with decreased immune infiltration in ESCC, and the low-risk group had better immunogenicity. In addition, we also evaluated the relationship between risk scores and immunotherapy response rates. Functional assays showed that CTTN may affect the proliferation and invasion of ESCC cells through the EMT pathway.Conclusion: We constructed a predictive model for ESCC based on TCA cycle-associated genes, which achieved good prognostic stratification. The model are likely associated with the regulation of tumor immunity in ESCC.

Glycogen branching enzyme (GBE1) is a critical gene that participates in regulating glycogen metabolism. However, the correlations between GBE1 expression and the prognosis and tumor-associated macrophages in lung adenocarcinoma (LUAD) also remain unclear. Herein, we firstly analyzed the expression level of GBE1 in LUAD tissues and adjacent lung tissues via The Cancer Genome Atlas (TCGA) database. The effect of GBE1 on prognosis was estimated by utilizing TCGA database and the PrognoScan database. The relationships between the clinical characteristics and GBE1 expression were evaluated via TCGA database. We then investigated the relationships between GBE1 and infiltration of immune cells in LUAD by utilizing the CIBERSORT algorithm and Tumor Immune Estimation Resource (TIMER) database. In addition, we used a tissue microarray (TMA) containing 92 LUAD tissues and 88 adjacent lung tissues with immunohistochemistry staining to verify the association between GBE1 expression and clinical characteristics, as well as the immune cell infiltrations. We found the expression level of GBE1 was significantly higher in LUAD tissues. High expression of GBE1 was associated with poorer overall survival (OS) in LUAD. In addition, high expression of GBE1 was correlated with advanced T classification, N classification, M classification, TNM stage, and lower grade. Moreover, GBE1 was positively correlated with infiltrating levels of CD163+ tumor-associated macrophages in LUAD. In conclusion, the expression of GBE1 is associated with the prognosis and CD163+ tumor-associated macrophage infiltration in LUAD, suggesting that it has potential to be prognostic and immunological biomarkers in LUAD.

Background:The lung is one of the most common metastatic sites of malignant tumors. Early detection of pulmonary metastatic carcinoma can effectively reduce relative cancer mortality. Human metabolomics is a qualitative and quantitative study of low-molecular metabolites in the body. By studying the plasm metabolomics of patients with pulmonary metastatic carcinoma or other lung diseases, we can find the difference in plasm levels of low-molecular metabolites among them. These metabolites have the potential to become biomarkers of lung metastases.Methods:Patients with pulmonary nodules admitted to our department from February 1, 2019, to May 31, 2019, were collected. According to the postoperative pathological results, they were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Moreover, healthy people who underwent physical examination were enrolled as the healthy population group (HPG) during the same period. On the one hand, to study lung metastases screening in healthy people, PMC was compared with HPG. The multivariate statistical analysis method was used to find the significant low-molecular metabolites between the two groups, and their discriminating ability was verified by the ROC curve. On the other hand, from the perspective of differential diagnosis of lung metastases, three groups with different pulmonary lesions (PMC, BPN, and PLC) were compared as a whole, and then the other two groups were compared with PMC, respectively. The main low-molecular metabolites were selected, and their discriminating ability was verified.Results:In terms of lung metastases screening for healthy people, four significant low-molecular metabolites were found by comparison of PMC and HPG. They were O-arachidonoyl ethanolamine, adrenoyl ethanolamide, tricin 7-diglucuronoside, and p-coumaroyl vitisin A. In terms of the differential diagnosis of pulmonary nodules, the significant low-molecular metabolites selected by the comparison of the three groups as a whole were anabasine, octanoylcarnitine, 2-methoxyestrone, retinol, decanoylcarnitine, calcitroic acid, glycogen, and austalide L. For the comparison of PMC and BPN, L-tyrosine, indoleacrylic acid, and lysoPC (16 : 0) were selected, while L-octanoylcarnitine, retinol, and decanoylcarnitine were selected for the comparison of PMC and PLC. Their AUCs of ROC are all greater than 0.80. It indicates that these substances have a strong ability to differentiate between pulmonary metastatic carcinoma and other pulmonary nodule lesions.Conclusion:Through the research of plasm metabolomics, it is possible to effectively detect the changes in some low-molecular metabolites among primary lung cancer, pulmonary metastatic carcinoma, and benign pulmonary nodule patients and healthy people. These significant metabolites have the potential to be biomarkers for screening and differential diagnosis of lung metastases.

Despite the previous evidence showing that SHC adaptor protein 1 (SHC1) could encode three distinct isoforms (p46SHC, p52SHC and p66SHC) that function in different activities such as regulating life span and Ras activation, the precise underlying role of SHC1 in lung cancer also remains obscure. In this study, we firstly found that SHC1 expression was up-regulated both in lung adenocarcinoma (LUAD) and in lung squamous cell carcinoma (LUSC) tissues. Furthermore, compared to patients with lower SHC1 expression, LUAD patients with higher expression of SHC1 had poorer overall survival (OS). Moreover, higher expression of SHC1 was also associated with worse OS in patients with stages 1 and 2 but not stage 3 lung cancer. Significantly, the analysis showed that SHC1 methylation level was associated with OS in lung cancer patients. It seemed that the methylation level at specific probes within SHC1 showed negative correlations with SHC1 expression both in LUAD and in LUSC tissues. The LUAD and LUSC patients with hypermethylated SHC1 at cg12473916 and cg19356022 probes had a longer OS. Therefore, it is reasonable to conclude that SHC1 has a potential clinical significance in LUAD and LUSC patients.

[Objectives] By studying the plasma metabolomics of patients with different pulmonary nodules and healthy people, we can find the difference in plasma low-molecular metabolites among them. [Methods] Patients with pulmonary nodules admitted to our department were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Meanwhile healthy people were enrolled as healthy population group (HPG). PLC and HPG were equally divided into the Discovery Set and Validation set. [Results] Five significant low-molecular metabolites were found by comparison of four groups as a whole. Four to six metabolites were selected by comparison of the three pulmonary nodule groups with healthy people respectively. The AUC of ROC of these metabolites were all＞0.93. Each pairwise comparison within the three pulmonary nodule groups all found three metabolites, whose AUC of ROC were all＞0.83. From the comparison of PLC and HPG in the discovery set, six metabolites were selected. Their AUC of ROC were all greater than 0.95 in the validation set, indicating that they had a strong ability to differentiate between primary lung cancer and healthy people. [Conclusions] We can find the significant changes of some low-molecular metabolites among three pulmonary nodules and healthy people. These metabolites had the potential to be biomarkers for screening and differential diagnosis of lung cancer.

Background CD8+ T cells are one of the central effector cells in the immune microenvironment. CD8+ T cells play a vital role in the development and progression of lung adenocarcinoma (LUAD). This study aimed to explore the key genes related to CD8+ T-cell infiltration in LUAD and to develop a novel prognosis model based on these genes. Methods With the use of the LUAD dataset from The Cancer Genome Atlas (TCGA), the differentially expressed genes (DEGs) were analyzed, and a co-expression network was constructed by weighted gene co-expression network analysis (WGCNA). Combined with the CIBERSORT algorithm, the gene module in WGCNA, which was the most significantly correlated with CD8+ T cells, was selected for the subsequent analyses. Key genes were then identified by co-expression network analysis, protein–protein interactions network analysis, and least absolute shrinkage and selection operator (Lasso)-penalized Cox regression analysis. A risk assessment model was built based on these key genes and then validated by the dataset from the Gene Expression Omnibus (GEO) database and multiple fluorescence in situ hybridization experiments of a tissue microarray. Results Five key genes (MZT2A, ALG3, ATIC, GPI, and GAPDH) related to prognosis and CD8+ T-cell infiltration were identified, and a risk assessment model was established based on them. We found that the risk score could well predict the prognosis of LUAD, and the risk score was negatively related to CD8+ T-cell infiltration and correlated with the advanced tumor stage. The results of the GEO database and tissue microarray were consistent with those of TCGA. Furthermore, the risk score was higher significantly in tumor tissues than in adjacent lung tissues and was correlated with the advanced tumor stage. Conclusions This study may provide a novel risk assessment model for prognosis prediction and a new perspective to explore the mechanism of tumor immune microenvironment related to CD8+ T-cell infiltration in LUAD.

Chylothorax is a rare and challenging complication of thoracic surgery. Whereas most current studies focus on postoperative treatment and preventative measures for esophageal cancer surgery, the current study investigates the impact of prophylactic ligation of the thoracic duct branch on postoperative chylothorax after pulmonary resection for right lung cancer. The subjects of this retrospective study were 1165 patients who underwent right pulmonary resection and mediastinal lymph-node dissection in our department between January 2015 and August 2019. Those who underwent prophylactic ligation of the thoracic duct branch after 4R lymph-node dissection were assigned to group A (n = 475), and those who did not were assigned to group B (n = 690). The incidence of postoperative chylothorax, the success rate of conservative treatment, the postoperative hospital stay, and the chest drainage volume were recorded and compared statistically between the two groups. The incidence of postoperative chylothorax was significantly lower in group A than in group B (0.84% vs. 2.90%, p = 0.015). Patients who had a chylothorax in group A had a significantly shorter postoperative hospital stay, less mean drainage volume per day, and less total drainage than those in group B (7.25 ± 0.50 days vs. 11.00 ± 2.81 days, p = 0.003; 0.64 ± 0.04 L vs. 0.80 ± 0.09 L, p = 0.003; 4.64 ± 0.40 L vs. 8.82 ± 2.84 L; p = 0.002). The success rate of conservative treatment was higher in group A than in group B, but the difference was not significant (100% vs. 75.0%, p = 0.544). Performing prophylactic ligation of the thoracic duct branch during right pulmonary resection and mediastinal lymph-node dissection is an effective and safe method of preventing postoperative chylothorax.

Long noncoding RNAs (lncRNAs) have emerged as regulators of gene expression and play critical regulatory roles in diverse biological functions and diseases, including cancer. In this study, we report the downregulation of LINC01089 in non-small cell lung cancer (NSCLC) samples, relative to adjacent non-tumor tissues, and demonstrate its role in the inhibition of proliferation, migration, and epithelial–mesenchymal transition (EMT) of NSCLC cells. Mechanistic analysis indicates that LINC01089 acts as a sponge for miR-27a, regulating its expression in NSCLC. Interestingly, LINC01089 mediated the upregulation of SFRP1 expression by inhibiting the Wnt/β-catenin–EMT pathway and inhibiting the epithelial–mesenchymal transition of NSCLC via sponging miR-27a. Overall, our findings highlight LINC01089’s tumorigenic role and regulatory mechanism in NSCLC, thereby suggesting its potential as a therapeutic target for managing NSCLC.

[This corrects the article DOI: 10.2147/OTT.S245710.].

Objective Through the summary and analysis of large samples, the characteristic imaging manifestations of intrapulmonary lymph nodes (IPLNs) were quantified, and two corresponding rating tables were developed. These rating tables could be used to distinguish the IPLNs from primary lung cancer, so as to improve the diagnostic accuracy and help clinicians make correct judgments and decisions. Methods A total of 82 patients with 110 IPLNs and 35 patients with primary lung cancer lesions were collected from June 2017 to December 2018. All lesions were solid nodules of less than 12mm in diameter, which were confirmed by pathology. Observation indicators included location, size, shape, density, border and internal vacuoles of nodules, linear high-density shadow around the nodules, distance from the pleura, pleural indentation, and so on. Results There were statistically significant differences in the location, size, shape, internal vacuole of the nodules, and distance from the pleura ( p <0.05). The diagnostic scoring table of the nature of solid nodules and the malignant risk table were drawn. The nodule corresponding to Level A was most likely the primary lung cancer, and surgical resection was recommended. The nodule corresponding to Level C was most likely IPLNs, and it was better to receive no treatment currently. The positive predictive value was 81% (23/28), the negative predictive value was 97% (89/92), the sensitivity was 63% (23/35), and the specificity was 81% (89/110). Conclusion For the pulmonary solid nodules of less than 12mm in diameter and unknown nature, the evaluation in accordance with the Score Table and the Risk Level Table of this study can be more accurate and faster than the original judgment, which will help clinicians in diagnosis and treatment decisions.

Aims: Accumulating evidence indicates that aberrant expression of miR-107 plays a crucial role in cancers. This study aims to display the function of miR-107 and its novel target genes in the progression of lung cancer.Methods and Material: MiR-107 or miR-107 inhibitor was transfected into lung cancer cells A549. The levels of miR-107 and TP53 regulated inhibition of apoptosis 1 (TRIAP1) were examined by quantitative real-time Polymerase Chain Reaction (qRT-PCR) analysis and Western Blot. Functionally, MTT and colony formation assays were carried out to test the effect of miR-107 inhibitor and/or small interference RNA (siRNA) targeting TRIAP1 mRNA on proliferation of lung cancer cells. Levels of miR-107 or TRIAP1 were detected in clinical lung cancer samples by using qRT-PCR analysis.Results: QRT-PCR analysis revealed that miR-107 inhibitor or miR-107 was successfully transfected into A549 cells. Western Blot indicated that miR-107 decreased the expression of TRIAP1 protein in the cells. In contrast, miR-107 inhibitor augmented the levels of TRIAP1 protein. Functionally, miR-107 inhibitor remarkably suppressed A549 cell proliferation, whereas, TRIAP1 siRNAs could abrogate the miR-107 inhibitor-induced proliferation of cells. Then, we validated that TRIAP1 was increased in clinical lung cancer samples. MiR-107 expression was negatively related to TRIAP1 expression in clinical lung cancer samples.Conclusions: MiR-107 suppresses cell proliferation by targeting TRIAP1 in lung cancer. Our finding allows new insights into the mechanisms of lung cancer that is mediated by miR-107.