Hypothyroidism reduces the risk of lung cancer through oxidative stress response and the PI3K/Akt signaling pathway: An RNA-seq and Mendelian randomization study.

Hypothyroidism has been suggested to play a role in tumor progression. However, the causal association between hypothyroidism and lung cancer remains unknow. To elucidate the potential association between hypothyroidism and lung cancer risk, we employ a Mendelian randomization (MR) approach. MR was performed to analyze pooled data from the International Lung Cancer Consortium (11,348 cases and 15,861 controls; European ancestry) to determine the causal relationship between hypothyroidism and lung cancer. We used 36, 83, and 14 single nucleotide polymorphisms as instrumental variables for hypothyroidism/myxoedema, hypothyroidism, and exercise, respectively. We further investigated the mechanisms involved in transcriptome analysis using data from The Cancer Genome Atlas and Genotype-Tissue Expression database. We conducted an initial validation of intermediary factor using a two-step MR analysis. Genetically predicted hypothyroidism was significantly related to the risk of overall lung cancer, specifically the risk of lung squamous cell cancer (LSCC) but not with the risk of lung adenocarcinoma (LUAD) as assessed using the inverse-variance weighted (IVM) method. A similar causal association was found between hypothyroidism/myxoedema and the risk of lung cancer, LSCC, and LUAD. Transcriptome analysis showed that genes associated with hypothyroidism, lung cancer, and LSCC were enriched in the PI3K/Akt signaling pathway and oxidative stress response. However, genes related to hypothyroidism and LUAD did not exhibit enrichment in these pathways. Hypothyroidism was significantly associated with strenuous sports or other exercises. Moreover, genetically predicted exercise was significantly related to the risk of overall lung cancer, and LSCC, but not LUAD. We detected no horizontal pleiotropy using the MR-PRESSO and MR Egger regression intercept. Hypothyroidism was causally associated with a lower risk of lung cancer, and these effects might be mediated by the oxidative stress response and the PI3K/Akt signaling pathway. Therefore, our study suggests that the potential factors and viable etiologies of hypothyroidism that contributed to lung cancer risk deserve further investigation.

A pairwise radiomics algorithm-lesion pair relation estimation model for distinguishing multiple primary lung cancer from intrapulmonary metastasis.

Background: Distinguishing multiple primary lung cancer (MPLC) from intrapulmonary metastasis (IPM) is critical for their disparate treatment strategy and prognosis. This study aimed to establish a non-invasive model to make the differentiation pre-operatively. Methods: We retrospectively studied 168 patients with multiple lung cancers (307 pairs of lesions) including 118 cases for modeling and internal validation, and 50 cases for independent external validation. Radiomic features on computed tomography (CT) were extracted to calculate the absolute deviation of paired lesions. Features were then selected by correlation coefficients and random forest classifier 5-fold cross-validation, based on which the lesion pair relation estimation (PRE) model was developed. A major voting strategy was used to decide diagnosis for cases with multiple pairs of lesions. Cases from another institute were included as the external validation set for the PRE model to compete with two experienced clinicians. Results: Seven radiomic features were selected for the PRE model construction. With major voting strategy, the mean area under receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity of the training versus internal validation versus external validation cohort to distinguish MPLC were 0.983 versus 0.844 versus 0.793, 0.942 versus 0.846 versus 0.760, 0.905 versus 0.728 versus 0.727, and 0.962 versus 0.910 versus 0.769, respectively. AUCs of the two clinicians were 0.619 and 0.580. Conclusions: The CT radiomic feature-based lesion PRE model is potentially an accurate diagnostic tool for the differentiation of MPLC and IPM, which could help with clinical decision making.

Macrophage migration inhibitory factor may contribute to the occurrence of multiple primary lung adenocarcinomas.

BACKGROUND: This study aimed to identify the key genes involved in the development of multiple primary lung cancers. METHODS: Differential expression analysis was performed, followed by comparing the infiltration levels of 22 immune cell types between multiple and single primary lung adenocarcinomas. Marker genes for epithelial cells with different proportions between the two types of lung adenocarcinomas were identified. The common genes between the marker genes and differentially expressed genes were identified. Finally, the effects of the key genes were tested on the in vitro proliferation, migration and morphology. RESULTS: The infiltration levels of helper follicular T cells, resting NK cells, activated NK cells, M2 macrophages and resting mast cells were higher in the patients with multiple than in those with single primary lung adenocarcinomas. A total of 1553 differentially expressed genes and 4414 marker genes of epithelial cells were identified. Logistic regression analysis was performed on the 164 resulting genes. The macrophage migration inhibitory factor expression was positively associated with the occurrence of multiple primary lung adenocarcinomas. Moreover, its signalling pathway was the key pathway among the epithelial cells and multiple and single primary lung adenocarcinoma cells, and it was upregulated in lung adenocarcinoma cells. It also increased the expression of lung cancer markers, including NES and CA125, induced morphological changes in alveolar epithelial type II cells, and promoted their proliferation, migration and invasion. CONCLUSIONS: Multiple and single primary lung adenocarcinomas have different tumour immune microenvironments, and migration inhibitory factor may be a key factor in the occurrence of multiple primary lung adenocarcinomas.

lncRNA VIM-AS1 acts as a prognostic biomarker and promotes apoptosis in lung adenocarcinoma.

Background: Long non-coding RNA VIM-antisense 1 (VIM-AS1) has been reported that it is involved in the progression of several cancers. However, the aberrant expression profile, clinical significance, and biological function of VIM-AS1in lung adenocarcinoma (LUAD) have not been fully described. We tend to perform a comprehensive analysis to identify the clinical prognostic value of VIM-AS1 for LUAD patients and explore its potential molecular mechanisms in LUAD development. Methods: The expression features of VIM-AS1 in LUAD were identified based on Cancer Genome Atlas database (TCGA) and genotypic tissue expression (GTEx). The LUAD patients' lung tissues were collected to testify above expression features. Survival analysis and COX regression analysis were performed to evaluate the prognostic value of VIM-AS1 in LUAD patients. Then Correlation analysis was performed to filter VIM-AS1 co-expression genes, and their molecular functions were constructed. Furtherly, we constructed the lung carcinoma A549 cell line with VIM-AS1 overexpression to test its effect on cell function. Results: VIM-AS1 expression levels were significantly downregulated in LUAD tissues. VIM-AS1 with low expression is significantly associated with short overall survival (OS), disease-specific survival (DSS), progress free interval (PFI), late T pathological stage, and lymph node metastasis for LUAD patients. The low expression level of VIM-AS1 was an independent risk factor for poor prognosis for LUAD patients. The biological functions of co-expressed genes indicated that VIM-AS1 regulating the apoptosis process may be the potential mechanism for LUAD. Specifically, we testified VIM-AS1 can promote apoptosis in A549 cells. Conclusion: VIM-AS1 was significantly downregulated in LUAD tissues, and it can be a promising prognostic index for LUAD development. VIM-AS1 regulating apoptotic effects may play important roles in LUAD progression.

A radiomics nomogram prediction for survival of patients with "driver gene-negative" lung adenocarcinomas (LUAD).

BACKGROUND: To study the role of computed tomography (CT)-derived radiomics features and clinical characteristics on the prognosis of "driver gene-negative" lung adenocarcinoma (LUAD) and to explore the potential molecular biological which may be helpful for patients' individual postoperative care. METHODS: A total of 180 patients with stage I-III "driver gene-negative" LUAD in the First Affiliated Hospital of Sun Yat-Sen University from September 2003 to June 2015 were retrospectively collected. The Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model was used to screen radiomics features and calculated the Rad-score. The prediction performance of the nomogram model based on radiomics features and clinical characteristics was validated and then assessed with respect to calibration. Gene set enrichment analysis (GSEA) was used to explore the relevant biological pathways. RESULTS: The radiomics and the clinicopathological characteristics were combined to construct a nomogram resulted in better performance for the estimation of OS (C-index: 0.815; 95% confidence interval [CI]: 0.756-0.874) than the clinicopathological nomogram (C-index: 0.765; 95% CI: 0.692-0.837). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics nomogram outperformed the traditional staging system and the clinicopathological nomogram. The clinical prognostic risk score of each patient was calculated based on the radiomics nomogram and divided by X-tile into high-risk (> 65.28) and low-risk (≤ 65.28) groups. GSEA results showed that the low-risk score group was directly related to amino acid metabolism, and the high-risk score group was related to immune and metabolism pathways. CONCLUSIONS: The radiomics nomogram was promising to predict the prognosis of patients with "driver gene-negative" LUAD. The metabolism and immune-related pathways may provide new treatment orientation for this genetically unique subset of patients, which may serve as a potential tool to guide individual postoperative care for those patients.

Methylation of N6 adenosine-related long noncoding RNA: effects on prognosis and treatment in 'driver-gene-negative' lung adenocarcinoma.

The improvement of treatment for patients with 'driver-gene-negative' lung adenocarcinoma (LUAD) remains a critical problem to be solved. We aimed to explore the role of methylation of N6 adenosine (m6A)-related long noncoding RNA (lncRNA) in stratifying 'driver-gene-negative' LUAD risk. Patients negative for mutations in EGFR, KRAS, BRAF, HER2, MET, ALK, RET, and ROS1 were identified as 'driver-gene-negative' cases. RNA sequencing was performed in 46 paired tumors and adjacent normal tissues from patients with 'driver-gene-negative' LUAD. Twenty-three m6A regulators and relevant lncRNAs were identified using Pearson's correlation analysis. K-means cluster analysis was used to stratify patients, and a prognostic nomogram was developed. The CIBERSORT and pRRophetic algorithms were employed to quantify the immune microenvironment and chemosensitivity. We identified two clusters highly consistent with the prognosis based on their unique expression profiles for 46 m6AlncRNAs. A risk model constructed from nine m6A lncRNAs could stratify patients into high- and low-risk groups with promising predictive power (C-index = 0.824), and the risk score was an independent prognostic factor. The clusters and risk models were closely related to immune characteristics and chemosensitivity. Additional pan-cancer analysis using the nine m6AlncRNAs showed that the expression of DIO3 opposite strand upstream RNA (DIO3OS) is closely related to the immune/stromal score and tumor stemness in a variety of cancers. Our results show that m6AlncRNAs are a reliable prognostic tool and can aid treatment decision-making in 'driver-gene-negative' LUAD. DIO3OS is associated with the development of various cancers and has potential clinical applications.

Oxidative stress genes in patients with esophageal squamous cell carcinoma: construction of a novel prognostic signature and characterization of tumor microenvironment infiltration.

BACKGROUND: Oxidative stress plays an important role in the progression of various types of tumors. However, its role in esophageal squamous cell carcinoma (ESCC) has seldom been explored. This study aimed to discover prognostic markers associated with oxidative stress in ESCC to improve the prediction of prognosis and help in the selection of effective immunotherapy for patients. RESULTS: A consensus cluster was constructed using 14 prognostic differentially expressed oxidative stress-related genes (DEOSGs) that were remarkably related to the prognosis of patients with ESCC. The infiltration levels of neutrophils, plasma cells, and activated mast cells, along with immune score, stromal score, and estimated score, were higher in cluster 1 than in cluster 2. A prognostic signature based on 10 prognostic DEOSGs was devised that could evaluate the prognosis of patients with ESCC. Calculated risk score proved to be an independent clinical prognostic factor in the training, testing, and entire sets. P53 signaling pathway was highly enriched in the high-risk group. The calculated risk score was positively related to the infiltration levels of resting mast cells, memory B cells, and activated natural killer (NK) cells and negatively associated with the infiltration levels of M1 and M2 macrophages. The relationship between clinical characteristics and risk score has not been certified. The half-maximal inhibitory concentration (IC50) values for sorafenib and gefitinib were lower for patients in the low-risk group. CONCLUSION: Our prognostic signature based on 10 prognostic DEOSGs could predict the disease outcomes of patients with ESCC and had strong clinical value. Our study improves the understanding of oxidative stress in tumor immune microenvironment (TIME) and provides insights for developing improved and efficient immunotherapy strategies.

Thymic epithelial tumors: examining the GTF2I mutation and developing a novel prognostic signature with LncRNA pairs to predict tumor recurrence.

BACKGROUND: General transcription factor IIi (GTF2I) mutations are very common in thymic epithelial tumors (TETs) and are related to a more favorable prognosis in TET patients. However, limited research has been conducted on the role of GTF2I in the tumor immune microenvironment (TIME). Further, long non-coding RNAs (lncRNAs) have been associated with the survival of patients with TETs. Therefore, this study aimed to explore the relationship between GTF2I mutations and TIME and build a new potential signature for predicting tumor recurrence in the TETs. Research data was downloaded from The Cancer Genome Atlas database and the CIBERSORT algorithm was used to evaluate TIME differences between GTF2I mutant and wild-type TETs. Relevant differentially expressed lncRNAs based on differentially expressed immune-related genes were identified to establish lncRNA pairs. We constructed a signature using univariate and multivariate Cox regression analyses. RESULTS: GTF2I is the most commonly mutated gene in TETs, and is associated with an increased number of early-stage pathological types, as well as no history of myasthenia gravis or radiotherapy treatment. In the GTF2I wild-type group, immune score and immune cell infiltrations with M2 macrophages, activated mast cells, neutrophils, plasma, T helper follicular cells, and activated memory CD4 T cells were higher than the GTF2I mutant group. A risk model was built using five lncRNA pairs, and the 1-, 3-, and 5-year area under the curves were 0.782, 0.873, and 0.895, respectively. A higher risk score was related to more advanced histologic type. CONCLUSION: We can define the GTF2I mutant-type TET as an immune stable type and the GTF2I wild-type as an immune stressed type. A signature based on lncRNA pairs was also constructed to effectively predict tumor recurrence.

miR-204-5p inhibits cell proliferation and induces cell apoptosis in esophageal squamous cell carcinoma by regulating Nestin.

Esophageal cancer (EC) is a highly malignant gastrointestinal tumor, and esophageal squamous cell carcinoma (ESCC) is one of the most common histological types of EC. MicroRNAs (miRNAs) are small noncoding RNAs closely related to tumorigenesis and tumor progression. In addition, Nestin is an intermediate filament protein (class VI) and contributes to the progression of numerous tumors. However, the correlation between miRNAs and Nestin in ESCC remains unclear. This study aimed to investigate the relationship between miR-204-5p and Nestin in ESCC. First, Nestin-related miRNAs in ESCC were explored using RNA sequencing. In ESCC tissues and cell lines, the expression of miR-204-5p was decreased detected by quantitative real-time polymerase chain reaction (qPCR), whereas Nestin protein level was upregulated identified by Western blotting (WB). Besides, Nestin was the direct target of miR-204-5p in ESCC determined via the luciferase reported assay. Moreover, miR-204-5p regulated Nestin to inhibit ESCC cell proliferation detected by the colony formation assay and promote ESCC cell apoptosis identified using the flow cytometry and TUNEL assay. Furthermore, miR-204-5p suppressed tumorigenesis in vivo evaluated by the murine xenograft tumor model. In conclusion, these results indicated that miR-204-5p inhibited cell proliferation and induced cell apoptosis in ESCC through targeting Nestin, which might provide novel therapeutic targets for ESCC therapy.

A Novel Ras--Related Signature Improves Prognostic Capacity in Oesophageal Squamous Cell Carcinoma.

Oesophageal squamous cell carcinoma (ESCC) remains a clinically challenging disease with high morbidity rates and poor prognosis. ESCC is also the most common pathological type of oesophageal cancer (EC) in China. Ras-related genes are one of the most frequently mutated gene families in cancer and regulate tumour development and progression. Given this, we investigated the Ras-related gene expression profiles and their values in ESCC prognosis, using data from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. We found that we could identify three distinct oesophageal cancer clusters based on their unique expression profile for 11 differentially expressed Ras-related genes with each of these demonstrating some prognostic value when, evaluated using univariate Cox analysis. We then used multivariate Cox analysis to identify relevant independent prognostic indicators and used these to build a new prognostic prediction model for oesophageal cancer patients using these three Ras-related genes. These evaluations produced an area under the curve (AUC) of 0.932. We found that our Ras-related signatures could also act as independent factors in ESCC prognosis and that patients with low Ras scores showed a higher overall expression levels of various immune checkpoint genes, including TNFSF4, TNFRSF8, TNFRSF9, NRP1, CD28, CD70, CD200, CD276, METTL16, METTL14, ZC3H13, YTHDF3, VIRMA, FTO, and RBM15, as well as a higher CSMD3, FLG, DNAH5, MUC4, PLCO, EYS, and ZNF804B mutation rates, and better sensitivity to drugs such as erlotinib, paclitaxel, and gefitinib. In conclusion, we were able to use the unique expression profiles of several Ras-related genes to produce a novel disease signature which might facilitate improved prognosis in ESCC, providing new insight into both diagnosis and treatment in these cancers.

A novel signature based on immune-related gene pairs and clinical features to predict prognosis and treatment effect in "driver gene negative" lung adenocarcinoma.

OBJECTIVE: Examining the role of immune-related genes (IRGs) in "driver gene negative" lung adenocarcinoma (LUAD) may provide new ideas for the treatment and study for this LUAD subgroup. We aimed to find the hub immune-related gene pairs can stratify the risk of "driver-gene-negative" LUAD. MATERIALS AND METHODS: IRGs were identified according to ImmPort database based on RNA sequencing results of tumors and normal tissues from 46 patients with "driver gene negative" LUAD at The First Affiliated Hospital of Sun Yat-sen University and cyclically singly paired as immune-related gene pairs (IRGPs). Multivariate Cox analysis was used to construct an immune risk model and a prognostic nomogram combining was also been developed. Immune microenvironment landscape described by CIBERSORT and drug sensitivity calculated by pRRophetic algorithm were used to explore possible treatment improvements. RESULTS: A novel immune risk model with 5-IRGPs (CD1A|CXCL135, CD1A|S100A7L2, IFNA7|CMTM2, IFNA7|CSF3, CAMP|TFR2) can accurately distinguish patients in the high- and low-risk groups. Risk score act as an independent prognostic factor and is related to clinical stage. There are significant differences in tumor immune microenvironment and PD-1/PD-L1/CTLA-4 expression between groups. The low-risk patient may benefit more from the commonly used chemotherapy regimens such as gemcitabine and paclitaxel. CONCLUSION: This study constructed 5-IRGPs as a reliable prognostic tool and may represent genes pairs that are potential rationale for choice of treatment for "driver gene negative" LUAD.

Neoadjuvant programmed cell death 1 blockade combined with chemotherapy for resectable esophageal squamous cell carcinoma.

BACKGROUND: Programmed cell death 1 (PD-1) blockade induces tumor regression in patients with advanced esophageal squamous cell carcinoma (ESCC); however, little is known about the efficacy of PD-1 blockade as neoadjuvant therapy in resectable ESCC. We aim to assess the safety and feasibility of using the combination of neoadjuvant PD-1 blockade with chemotherapy in patients with ESCC. METHODS: Patients with previously untreated, resectable (stage II or III) ESCC were enrolled. Each patient received two 21-day cycles of neoadjuvant treatment with camrelizumab, nab-paclitaxel, and carboplatin before undergoing surgical resection approximately 6-9 weeks after the first cycle. RESULTS: Between January 2020 and September 2020, 37 patients were screened, of whom 23 were enrolled. The neoadjuvant therapeutic regimen had an acceptable side effect profile, and no delays in surgery were observed. Severe (grade 3-4) treatment-related adverse events included neutropenia (9 of 23, 39.1%) and leukopenia (2 of 23, 8.7%). The objective response and disease control rates were 90.5% and 100%, respectively. Twenty patients received surgery, and R0 resection was achieved in all cases. Five (25%) patients had a pathological complete response (PCR) and 10 (50%) patients had a major pathological response. The proportion of patients with a high tumor mutation burden and a high expression of programmed death-ligand 1 (PD-L1) in primary tumor was significantly higher in the PCR group than in the non-PCR group (p=0.044). The number of infiltrating PD-L1+ CD163+ cells was significantly lower in the PCR group than in the non-PCR group after treatment (p=0.017). CONCLUSIONS: Neoadjuvant camrelizumab plus carboplatin and nab-paclitaxel had manageable treatment-related adverse effects and induced an objective response in 90.5% of patients, demonstrating its antitumor efficacy in resectable ESCC. TRIAL REGISTRATION NUMBER: ChiCTR2000028900.

3D DenseNet Deep Learning Based Preoperative Computed Tomography for Detecting Myasthenia Gravis in Patients With Thymoma.

BACKGROUND: Myasthenia gravis (MG) is the most common paraneoplastic syndromes of thymoma and closely related to thymus abnormalities. Timely detecting of the risk of MG would benefit clinical management and treatment decision for patients with thymoma. Herein, we developed a 3D DenseNet deep learning (DL) model based on preoperative computed tomography (CT) as a non-invasive method to detect MG in thymoma patients. METHODS: A large cohort of 230 thymoma patients in a hospital affiliated with a medical school were enrolled. 182 thymoma patients (81 with MG, 101 without MG) were used for training and model building. 48 cases from another hospital were used for external validation. A 3D-DenseNet-DL model and five radiomic models were performed to detect MG in thymoma patients. A comprehensive analysis by integrating machine learning and semantic CT image features, named 3D-DenseNet-DL-based multi-model, was also performed to establish a more effective prediction model. FINDINGS: By elaborately comparing the prediction efficacy, the 3D-DenseNet-DL effectively identified MG patients and was superior to other five radiomic models, with a mean area under ROC curve (AUC), accuracy, sensitivity, and specificity of 0.734, 0.724, 0.787, and 0.672, respectively. The effectiveness of the 3D-DenseNet-DL-based multi-model was further improved as evidenced by the following metrics: AUC 0.766, accuracy 0.790, sensitivity 0.739, and specificity 0.801. External verification results confirmed the feasibility of this DL-based multi-model with metrics: AUC 0.730, accuracy 0.732, sensitivity 0.700, and specificity 0.690, respectively. INTERPRETATION: Our 3D-DenseNet-DL model can effectively detect MG in patients with thymoma based on preoperative CT imaging. This model may serve as a supplement to the conventional diagnostic criteria for identifying thymoma associated MG.

Machine learning predictive model for severe COVID-19.

To develop a modified predictive model for severe COVID-19 in people infected with Sars-Cov-2. We developed the predictive model for severe patients of COVID-19 based on the clinical date from the Tumor Center of Union Hospital affiliated with Tongji Medical College, China. A total of 151 cases from Jan. 26 to Mar. 20, 2020, were included. Then we followed 5 steps to predict and evaluate the model: data preprocessing, data splitting, feature selection, model building, prevention of overfitting, and Evaluation, and combined with artificial neural network algorithms. We processed the results in the 5 steps. In feature selection, ALB showed a strong negative correlation (r = 0.771, P < 0.001) whereas GLB (r = 0.661, P < 0.001) and BUN (r = 0.714, P < 0.001) showed a strong positive correlation with severity of COVID-19. TensorFlow was subsequently applied to develop a neural network model. The model achieved good prediction performance, with an area under the curve value of 0.953(0.889-0.982). Our results showed its outstanding performance in prediction. GLB and BUN may be two risk factors for severe COVID-19. Our findings could be of great benefit in the future treatment of patients with COVID-19 and will help to improve the quality of care in the long term. This model has great significance to rationalize early clinical interventions and improve the cure rate.

Perioperative risk factors for occurrence of myasthenia gravis after thymectomy in patients with thymoma.

OBJECTIVES: The goal of this study was to identify the relationship between clinical characteristics and the occurrence of postoperative myasthenia gravis (PMG) in patients with thymomas and to further identify the relationship between PMG and prognosis. METHODS: Thymoma patients who had surgery at the First Affiliated Hospital of Sun Yat-sen University between July 2004 and July 2016 were reviewed and those who had no previous symptoms of myasthenia gravis were selected for further investigation. In total, 229 patients were included in the study; their clinical characteristics were gathered and analysed. RESULTS: Among the 229 patients, 19 (8.3%) had PMG. The time between the operation and the onset of myasthenia gravis was 134 days on average (range 2-730 days). Patients experiencing PMG showed a lower rate of complete thymoma resection (73.7% vs 91.4%; P = 0.014) and total thymectomy (63.2% vs 82.9%; P = 0.035) compared with those who did not. Univariable and multivariable logistic regression revealed that thymomectomy [odds ratio (OR) 2.81, 95% confidence interval (CI) 1.02-7.77; P = 0.047] and incomplete tumour resection (OR 3.79, 95% CI 1.20-11.98; P = 0.023) were associated with the occurrence of PMG. Multivariable Cox regression showed that the PMG was not related to overall survival (P = 0.087). CONCLUSIONS: This study revealed that incomplete tumour resection and thymomectomy were independent risk factors for PMG in thymoma patients with no previous history of myasthenia gravis.

Pristimerin Exacerbates Cellular Injury in Conditionally Reprogrammed Patient-Derived Lung Adenocarcinoma Cells by Aggravating Mitochondrial Impairment and Endoplasmic Reticulum Stress through EphB4/CDC42/N-WASP Signaling.

Lung cancer is the most common and lethal malignant disease for which the development of efficacious chemotherapeutic agents remains an urgent need. Pristimerin (PRIS), a natural bioactive component isolated from various plant species in the Celastraceae and Hippocrateaceae families, has been reported to exhibit outstanding antitumor effects in several types of cells. However, the underlying mechanisms involved remain poorly understood. Here, we reported the novel finding that PRIS significantly suppressed lung cancer growth in conditionally reprogrammed patient-derived lung adenocarcinoma cells (CRLCs). We demonstrated that PRIS inhibited the cell viabilities, migrative and invaded abilities, and capillary structure formation of CRLCs. Furthermore, our results clarified that PRIS induced mitochondrial dysfunction through reactive oxygen species (ROS) generation, activation of caspase-9, caspase-3, and caspase-4, and expression of endoplasmic reticulum (ER) stress-associated proteins. Inhibition of ER stress by 4-PBA (4-phenylbutyric acid, a specific ER stress inhibitor) or CHOP siRNA transfection ameliorated PRIS-induced loss of mitochondrial membrane potential and intrinsic apoptosis. The present study also provides mechanistic evidence that PRIS suppressed the EphB4/CDC42/N-WASP signaling pathway, which is required for mitochondrial-mediated intrinsic apoptosis, activation of ER stress, and stimulation of caspase-4 induced by PRIS, and consequently resulting in suppressed cell viability, migration, and angiogenesis in CRLCs. Taken together, by providing a mechanistic insight into the modulation of ER stress-induced cell death in CRLCs by PRIS, we suggest that PRIS has a strong potential of being a new antitumor therapeutic agent with applications in the fields of human lung adenocarcinoma.

Development of a deep learning model for classifying thymoma as Masaoka-Koga stage I or II via preoperative CT images.

BACKGROUND: Accurate thymoma staging via computed tomography (CT) images is difficult even for experienced thoracic doctors. Here we developed a preoperative staging tool differentiating Masaoka-Koga (MK) stage I patients from stage II patients using CT images. METHODS: CT images of 174 thymoma patients were retrospectively selected. Two chest radiologists independently assessed the images. Variables with statistical differences in univariate analysis were adjusted for age, sex, and smoking history in multivariate logical regression to determine independent predictors of the thymoma stage. We established a deep learning (DL) 3D-DenseNet model to distinguish the MK stage I and stage II thymomas. Furthermore, we compared two different methods to label the regions of interest (ROI) in CT images. RESULTS: In routine CT images, there were statistical differences (P<0.05) in contour, necrosis, cystic components, and the degree of enhancement between stage I and II disease. Multivariate logical regression showed that only the degree of enhancement was an independent predictor of the thymoma stage. The area under the receiver operating characteristic curve (AUC) of routine CT images for classifying thymoma as MK stage I or II was low (AUC =0.639). The AUC of the 3D-DenseNet model showed better performance with a higher AUC (0.773). ROIs outlined by segmentation labels performed better (AUC =0.773) than those outlined by bounding box labels (AUC =0.722). CONCLUSIONS: Our DL 3D-DenseNet may aid thymoma stage classification, which may ultimately guide surgical treatment and improve outcomes. Compared with conventional methods, this approach provides improved staging accuracy. Moreover, ROIs labeled by segmentation is more recommendable when the sample size is limited.

Surgical treatment to multiple primary lung cancer patients: a systematic review and meta-analysis.

BACKGROUND: As there is no consensus on the optimal surgery strategy for multiple primary lung cancer (MPLC), we conducted this study to address this issue by comparing the prognosis of MPLC patients underwent different surgical strategies including sublobar resection and the standard resection through a systemic review and meta-analysis. METHODS: Relevant literature was obtained from three databases including PubMed, Embase and Web of Science. Inclusion and exclusion criteria were set for the screening of articles to be selected for further conduction of systemic review and meta-analysis. The HRs of OS of the sublobar group compared with standard resection group were extracted directly or calculated indirectly from included researches. RESULTS: Ten researches published from 2000 to 2017 were included in this study, with 468 and 445 MPLC cases for the standard resection group and sublobar resection group respectively. The result suggested that OS of MPLC patients underwent sublobar resection (segmentectomy or wedge resection for at least one lesion) was comparable with those underwent standard resection approach (lobectomy or pneumonectomy for all lesions), with HR 1.07, 95% CI 0.67-1.71, p = 0.784. Further analysis found no difference in subgroups of synchronous and metachronous (from second metachronous lesion), different population region and dominant sex type. CONCLUSIONS: This study may reveal that sublobar resection is acceptable for patients with MPLC at an early stage, because of the equivalent prognosis to the standard resection and better pulmonary function preservation. Further research is needed to validate these findings.

The role of DNMT1/hsa-miR-124-3p/BCAT1 pathway in regulating growth and invasion of esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is the major subtype of esophageal cancer with high aggressiveness and poor prognosis. There is an urgent need for understanding the molecular mechanism underlying the development and progression of ESCC. METHODS: ESCC tissues and corresponding non-neoplastic tissues were collected. The expression and function of miR-124-3p and BCAT1 in two cell lines KYSE-150 and Eca109 were determined. RESULTS: We show downregulation of miR-124-3p expression in ESCC tissues, which is highly correlated with proliferation and migration of ESCC cell lines KYSE-150 and Eca109. miR-124-3p show high correlation with TNM stage and differentiation grade. Furthermore, miR-124-3p directly targets mRNA 3'UTR region of BCAT1, which results in upregulation of BCAT1 expression as observed in ESCC tissues and cell lines. Also, our data indicates that BCAT1 high expression is strongly linked to the disease-free survival, tumor size, pathologic stage, T classification and differentiation grade. On the other hand, we clarified the upstream mechanism regulating miR-124-3p expression in ESCC, which involves in the hypermethylation-silencing regulation mediated by DNA methyltransferase 1(DNMT1), which is of high expression in ESCC tissues and cell lines in the present study. In addition, DNMT1 knockdown or inhibition of DNMT1 function contributes to downregulation of miR-124-3p and BCAT1 expression. CONCLUSIONS: Our study thus clarifies a new mechanism that DNMT1/miR-124/BCAT1 axis regulates the development and progression of ESCC.