Primary lung adenocarcinoma harboring upper mediastinal lymphatic skip metastasis of cervical squamous cell carcinoma: A case report and literature review.

The upper mediastinal lymph nodes are a rare site of metastasis in early-stage cervical cancer, but they are a common site of metastasis in lung cancer. Notably, standard approaches for identifying the source of metastasis and subsequent treatment are currently lacking. The present study describes the case of a patient with primary lung adenocarcinoma harboring upper mediastinal lymphatic skip metastasis from cervical squamous cell carcinoma 2 years after a radical hysterectomy. During video-assisted thoracoscopic surgery, it was indicated that the patient had a tendency for metastasis to the upper mediastinal lymph nodes from the lung tumor. Pathological examination confirmed the presence of metastasis; however, it was confirmed to originate from cervical carcinoma, rather than lung adenocarcinoma. In conclusion, for patients with lung cancer and concurrent malignancies, metastatic lymph nodes discovered during surgery may originate from the previous malignancy. Surgical management of oligometastatic lymph nodes in the mediastinum can be a potential treatment option, albeit one that may necessitate the integration of adjuvant treatment modalities as warranted by the individual case.

A 3D boundary-guided hybrid network with convolutions and Transformers for lung tumor segmentation in CT images.

-Accurate lung tumor segmentation from Computed Tomography (CT) scans is crucial for lung cancer diagnosis. Since the 2D methods lack the volumetric information of lung CT images, 3D convolution-based and Transformer-based methods have recently been applied in lung tumor segmentation tasks using CT imaging. However, most existing 3D methods cannot effectively collaborate the local patterns learned by convolutions with the global dependencies captured by Transformers, and widely ignore the important boundary information of lung tumors. To tackle these problems, we propose a 3D boundary-guided hybrid network using convolutions and Transformers for lung tumor segmentation, named BGHNet. In BGHNet, we first propose the Hybrid Local-Global Context Aggregation (HLGCA) module with parallel convolution and Transformer branches in the encoding phase. To aggregate local and global contexts in each branch of the HLGCA module, we not only design the Volumetric Cross-Stripe Window Transformer (VCSwin-Transformer) to build the Transformer branch with local inductive biases and large receptive fields, but also design the Volumetric Pyramid Convolution with transformer-based extensions (VPConvNeXt) to build the convolution branch with multi-scale global information. Then, we present a Boundary-Guided Feature Refinement (BGFR) module in the decoding phase, which explicitly leverages the boundary information to refine multi-stage decoding features for better performance. Extensive experiments were conducted on two lung tumor segmentation datasets, including a private dataset (HUST-Lung) and a public benchmark dataset (MSD-Lung). Results show that BGHNet outperforms other state-of-the-art 2D or 3D methods in our experiments, and it exhibits superior generalization performance in both non-contrast and contrast-enhanced CT scans.

Downregulation of DIP2B as a prognostic marker inhibited cancer proliferation and migration and was associated with immune infiltration in lung adenocarcinoma via CCND1 and MMP2.

Background: DIP2B is related to cancer progression. This study investigated the roles and pathways of DIP2B in lung adenocarcinoma (LUAD). Methods: DIP2B expression and the relationship between survival time of cancer patients and DIP2B expression were analyzed. The relationship between DIP2B expression and survival time in LUAD patients was evaluated by a meta-analysis. Cox and survival analyses were used to evaluate the prognostic factors and construct a prognostic nomogram. The mechanisms and effects of DIP2B and the relationship between DIP2B expression and the immune microenvironment were investigated using bioinformatics, CCK-8, western blotting, and transwell experiments. Results: DIP2B was overexpressed in LUAD tissues. DIP2B overexpression was associated with shorter prognosis and was an unfavorable risk factor for prognosis in LUAD patients. DIP2B co-expressed genes were involved in cell division, DNA repair, cell cycle, and others. Inhibition of DIP2B expression could downregulate the proliferation, migration, and invasion of LUAD A549 and H1299 cells, which was related to the decrease in CCND1 and MMP2 protein expression. BRCA1 overexpression was associated with short prognosis, and the nomogram formed by DIP2B and BRCA1 was associated with a poor prognosis in LUAD patients. DIP2B expression correlated with immune cells (such as CD8 T cells, Tcm, and iDCs) and cell markers. Conclusion: DIP2B is a potential biomarker of poor prognosis and the immune microenvironment in LUAD. Inhibition of DIP2B expression downregulated cancer cell proliferation, migration, and invasion, which might be related to the decrease in CCND1 and MMP2 protein expression. DIP2B-related nomograms might be useful tools for predicting the prognosis of LUAD patients.

CT-based quantification of intratumoral heterogeneity for predicting pathologic complete response to neoadjuvant immunochemotherapy in non-small cell lung cancer.

Objectives: To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image. Methods: This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV). Results: In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686. Conclusion: The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

An advanced nomogram model using deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.

PURPOSE: To evaluate the effectiveness of deep learning radiomics nomogram in distinguishing the occult lymph node metastasis (OLNM) status in clinical stage IA lung adenocarcinoma. METHODS: A cohort of 473 cases of lung adenocarcinomas from two hospitals was included, with 404 cases allocated to the training cohort and 69 cases to the testing cohort. Clinical characteristics and semantic features were collected, and radiomics features were extracted from the computed tomography (CT) images. Additionally, deep transfer learning (DTL) features were generated using RseNet50. Predictive models were developed using the logistic regression (LR) machine learning algorithm. Moreover, gene analysis was conducted on RNA sequencing data from 14 patients to explore the underlying biological basis of deep learning radiomics scores. RESULT: The training and testing cohorts achieved AUC values of 0.826 and 0.775 for the clinical model, 0.865 and 0.801 for the radiomics model, 0.927 and 0.885 for the DTL-radiomics model, and 0.928 and 0.898 for the nomogram model. The nomogram model demonstrated superiority over the clinical model. The decision curve analysis (DCA) revealed a net benefit in predicting OLNM for all models. The investigation into the biological basis of deep learning radiomics scores identified an association between high scores and pathways related to tumor proliferation and immune cell infiltration in the microenvironment. CONCLUSIONS: The nomogram model, incorporating clinical-semantic features, radiomics, and DTL features, exhibited promising performance in predicting OLNM. It has the potential to provide valuable information for non-invasive lymph node staging and individualized therapeutic approaches.

Development and Validation of a Deep Learning Radiomics Model to Predict High-Risk Pathologic Pulmonary Nodules Using Preoperative Computed Tomography.

RATIONALE AND OBJECTIVES: To accurately identify the high-risk pathological factors of pulmonary nodules, our study constructed a model combined with clinical features, radiomics features, and deep transfer learning features to predict high-risk pathological pulmonary nodules. MATERIALS AND METHODS: The study cohort consisted of 469 cases of lung adenocarcinoma patients, divided into a training cohort (n = 400) and an external validation cohort (n = 69). We obtained computed tomography (CT) semantic features and clinical characteristics, as well as extracted radiomics and deep transfer learning (DTL) features from the CT images. Selected features were used for constructing prediction models using the logistic regression (LR) algorithm. The performance of the models was evaluated through metrics including the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, calibration curve, and decision curve analysis. RESULTS: The clinical model achieved an AUC of 0.774 (95% CI: 0.728-0.821) in the training cohort and 0.762 (95% confidence interval [CI]: 0.650-0.873) in the external validation cohort. The radiomics model demonstrated an AUC of 0.847 (95% CI: 0.810-0.884) in the training cohort and 0.800 (95% CI: 0.693-0.907) in the external validation cohort. The radiomics-DTL (Rad-DTL) model showed an AUC of 0.871 (95% CI: 0.838-0.905) in the training cohort and 0.806 (95% CI: 0.698-0.914) in the external validation cohort. The proposed combined model yielded AUC values of 0.872 and 0.814 in the training and external validation cohorts, respectively. The combined model demonstrated superiority over both the clinical model and the Rad-DTL model. There were no statistically significant differences observed in the comparison between the combined model incorporating clinical features and the Rad-DTL model. Decision curve analysis (DCA) indicated that the models provided a net benefit in predicting high-risk pathologic pulmonary nodules. CONCLUSION: Rad-DTL signature is a potential biomarker for predicting high-risk pathologic pulmonary nodules using preoperative CT, determining the appropriate surgical strategy, and guiding the extent of resection.

Immune characteristics analysis and construction of a four-gene prognostic signature for lung adenocarcinoma based on estrogen reactivity.

Lung adenocarcinoma (LUAD) is a common type of malignant tumor with poor prognosis and high mortality. In our previous studies, we found that estrogen is an important risk factor for LUAD, and different estrogen statuses can predict different prognoses. Therefore, in this study, we constructed a prognostic signature related to estrogen reactivity to determine the relationship between different estrogen reactivities and prognosis. We downloaded the LUAD dataset from The Cancer Genome Atlas (TCGA) database, calculated the estrogen reactivity of each sample, and divided them into a high-estrogen reactivity group and a low-estrogen reactivity group. The difference in overall survival between the groups was significant. We also analyzed the status of immune cell infiltration and immune checkpoint expression between the groups. We analyzed the differential gene expression between the groups and screened four key prognostic factors by the least absolute shrinkage and selection operator (LASSO) regression and univariable and multivariable Cox regression. Based on the four genes, a risk signature was established. To a certain extent, the receiver operating characteristic (ROC) curve showed the predictive ability of the risk signature, which was further verified using the GSE31210 dataset. We also determined the role of estrogen in LUAD using an orthotopic mouse model. Additionally, we developed a predictive nomogram combining the risk signature with other clinical characteristics. In conclusion, our four-gene prognostic signature based on estrogen reactivity had prognostic value and can provide new insights into the development of treatment strategies for LUAD.

Comparison of bronchial methylene blue staining and modified inflation-deflation method in identifying the intersegmental plane during lung segmentectomy.

Background: Identification of the intersegmental plane (ISP) is the critical step in lung segmentectomy because of the complicated anatomic variations. Bronchial methylene blue staining was developed by our team in 2015 and is now commonly used at our center, it could rapidly and accurately identify the ISP. In this study, we aimed to compare bronchial methylene blue staining with the modified inflation-deflation method in terms of their perioperative characteristics and to present our experience of the methylene blue method. Methods: From June 2020 to September 2021, the data of 112 patients with pulmonary ground-glass nodules who underwent segmentectomy by video-assisted thoracoscopic surgery were retrospectively reviewed. Sixty-two patients underwent bronchial methylene blue staining, and 50 patients underwent the modified inflation-deflation method. Results: Both methods could accurately identify the ISP. The time taken to clearly display the ISP (82.94±28.08 vs. 868.20±145.89 seconds; P<0.001) and the surgical duration (131.69±32.05 vs. 146.08±28.11 minutes; P=0.014) were significantly shorter in the bronchial methylene blue staining group than in the modified inflation-deflation group. There were no significant differences between the two groups in the bleeding volume, drainage time, and length of postoperative hospital stay, as well as in most other perioperative characteristics. Conclusions: Compared with the modified inflation-deflation method, the bronchial methylene blue staining method can quickly display the ISP and shorten the surgical duration. This method is safe and feasible, can be widely applied during thoracoscopic anatomic segmentectomy.

A pan-cancer analysis of the oncogenic role of secreted phosphoprotein 1 (SPP1) in human cancers.

Background: Emerging evidence suggests that secreted phosphoprotein 1 (SPP1) is involved in tumor cell progression in multiple cancer types. However, the role of SPP1 in different cancers is still not clear. Methods: We used data from The Cancer Genome Atlas (TCGA) to analyze the multiomic roles of SPP1, including RNA expression, DNA methylation, protein phosphorylation, immune infiltration, and overall survival (OS) in 33 tumor types. Results: SPP1 is highly expressed in most cancer types, and its methylation variability and mRNA expression level are both correlated with prognosis in multiple cancer types. A higher S234 phosphorylation level was observed in 4 types of tumors, including colon adenocarcinoma (COAD) and lung adenocarcinoma (LUAD). SPP1 expression level was positively associated with the infiltration level of dendritic cells, neutrophils, and macrophages in multiple cancer types. It was also significantly positively correlated with hepatitis A virus cellular receptor 2 (HAVCR2), which was observed in most tumor types, including brain lower grade glioma (LGG) and ovarian serous cystadenocarcinoma (OV). Moreover, myeloid cell differentiation and leukocyte migration were observed in the enrichment analysis, suggesting that SPP1 might induce immune escape. Conclusions: Pan-cancer analysis using a multiomic approach offered a comprehensive overview of SPP1. This protein plays an important role in most of the analyzed tumor types and could be a valuable prognostic marker across different types of cancer.

PRDM5 suppresses oesophageal squamous carcinoma cells and modulates 14-3-3zeta/Akt signalling pathway.

Dysregulation of PR (PRDI-BF1 and RIZ) domain protein 5 (PRDM5) expression has been shown to be associated with the progression of many malignancies. Nevertheless, the role and underlying mechanism of PRDM5 in oesophageal squamous cell carcinoma (ESCC) remain elusive. qRT-PCR was performed to analyze PRDM5 mRNA expression, and western blot was used to determine protein expression of PRDM5, MMP-2, MMP-9, 14-3-3zeta, pan-Akt and phosphorylated Akt expression. CCK-8 staining was employed to evaluate cell proliferation, while wound scratch assay and Transwell assay were carried out to detect cell migration. A tumour xenograft model of ESCC was also established to validate the effect of PRDM5. PRDM5 expression was downregulated in ESCC tissues and positively correlated with the overall survival of ESCC patients. Silencing PRDM5 expression promoted cell proliferation in ESCC cells, while overexpressing PRDM5 inhibited cell proliferation. Moreover, the migratory abilities of ESCC cells were promoted by PRDM5 knockdown but were attenuated by PRDM5 overexpression. Importantly, 14-3-3zeta expression, along with the phosphorylation of Akt, was suppressed by PRDM5 in ESCC cells. In the established tumour xenograft model, PRDM5 regulated ESCC tumour growth as well as the expression of 14-3-3zeta and phosphorylation of Akt protein. In conclusion, PRDM5 suppresses ESCC cell proliferation and migration and negatively regulates 14-3-3zeta/Akt signalling pathway in vitro and in vivo.

SPTBN2, a New Biomarker of Lung Adenocarcinoma.

OBJECTIVES: The roles played by ß-III-spectrin, also known as spectrin beta, non-erythrocytic 2 (SPTBN2), in the occurrence and development of lung adenocarcinoma (LUAD) have not been previously examined. Our study aimed to reveal the relationship between the SPTBN2 expression and LUAD. MATERIALS AND METHODS: Twenty pairs of LUAD tissues and adjacent tissues were collected from patients diagnosed and treated at the Thoracic Surgery Department of The First Affiliated Hospital of Zhengzhou University from July 2019 to September 2020. RNA sequencing (RNA-seq) analysis determined that the expression of SPTBN2 was higher in LUAD samples than in adjacent normal tissues. The expression levels of SPTBN2 were examined in various databases, including the Cancer Cell Line Encyclopedia (CCLE), Gene Expression Omnibus (GEO), and Human Protein Atlas (HPA). The Search Tool for the Retrieval of Interacting Genes (STRING) online website was used to examine protein-protein interactions involving SPTBN2, and the results were visualized by Cytoscape software. The Molecular Complex Detection (MCODE) plug-in for Cytoscape software was used to identify functional modules of the obtained protein-protein interaction (PPI) network. Gene enrichment analysis was performed, and survival analysis was conducted using the Kaplan-Meier plotter. The online prediction website TargetScan was used to predict SPTBN2-targeted miRNA sequences by searching for SPTBN2 sequences. Finally, we verified the expression of SPTBN2 in the obtained tissue samples using real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). The human lung cancer cell lines A549 and H1299 were selected for the transfection of small interfering RNA (siRNA) targeting SPTBN2 (si-SPTBN2), and the knockdown efficiency was evaluated by RT-qPCR. The cellular proliferation, migration, and invasion capacities of A549 and H1299 cells were determined using the cell counting kit-8 (CCK-8) proliferation assay; the wound-healing assay and the Transwell migration assay; and the Matrigel invasion assay, respectively. RESULTS: The expression of SPTBN2 in non-small cell lung cancer (NSCLC) ranked 13th among cancer cell lines based on the CCLE database. At the mRNA and protein levels, the expression levels of SPTBN2 were higher in LUAD tissues than in normal lung tissues. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that proteins related to SPTBN2 were enriched in apoptotic and phagosomal pathways. Kaplan-Meier survival analysis revealed that SPTBN2 expression was significantly related to the prognosis of patients with LUAD. The TargetScan database verified that miR-16 was a negative regulator of SPTBN2 mRNA expression. The results of the CCK-8 cell proliferation assay revealed that SPTBN2 knockdown significantly inhibited the cell proliferation abilities of A549 and H1299 cells. The wound-healing assay indicated that SPTBN2 knockdown resulted in reduced migration after 48 h compared with the control group. The Transwell migration and invasion test revealed that the migration and invasion abilities were greatly decreased by SPTBN2 knockdown compared with control conditions. CONCLUSION: We uncovered a novel gene, SPTBN2, that was significantly upregulated in LUAD tissues relative to normal tissue expression. SPTBN2 is highly expressed in LUAD, positively correlated with poor prognosis, and can promote the proliferation, migration, and invasion of LUAD cells.

Correlation of m6A methylation with immune infiltrates and poor prognosis in non-small cell lung cancer via a comprehensive analysis of RNA expression profiles.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a common type of lung cancer with a poor prognosis. N6-methyladenosine (m6A) methylation, which is a reversible ribonucleic acid (RNA) modification, plays an important role in the occurrence and development of NSCLC. However, the potential effect of m6A methylation on immune infiltrates and prognosis remains unclear. METHODS: In this study, a weighted gene co-expression network analysis was used to screen out messenger RNAs (mRNAs) and non-coding RNAs (ncRNAs) that were co-expressed with m6A regulators. Additionally, 2 molecular subtypes (Clusters 1 and 2) were determined via consensus clustering. Subsequently, a prognostic risk model was constructed using both co-expressed mRNAs and ncRNAs. Based on the risk scores calculated by the prognostic model, the patients were divided into the high-risk group or low-risk group. Finally, the altered patterns of the tumor immune microenvironments (TIMEs) between the 2 stratification methods were thoroughly investigated, and a gene set enrichment analysis was conducted to further examine the potential mechanism. RESULTS: Patients in Cluster 1 had lower immunoscores, higher programmed death-ligand 1 (PD-L1) expression, and shorter overall survival (OS) compared to patients in Cluster 2. A further investigation based on the prognostic model revealed that the PD-L1 expression levels of patients in the high-risk group were significantly upregulated, and the immunoscores were lower than those in the low-risk group. The immune cells with a high infiltration in Cluster 1 showed a significant positive correlation with the risk score; those with low infiltration showed a significant negative correlation. The hallmarks of the Myelocytomatosis viral oncogene (MYC) targets, the second Gap/Mitosis (G2/M) checkpoint, E2 transcription Factor (E2F) targets, glycolysis, deoxyribonucleic acid (DNA) repair, and unfolded protein response were significantly enriched in Cluster 1, the low-immunoscore group, and the high-risk group. CONCLUSIONS: This study revealed that m6A methylation is closely related to the poor prognosis of NSCLC patients via interference with the TIME, which suggests that m6A may play a role in optimizing individualized immunotherapy management and improving prognosis.

miRNA-218/FANCI is associated with metastasis and poor prognosis in lung adenocarcinoma: a bioinformatics analysis.

BACKGROUND: In this study, tumor microarray analysis was used to screen the key messenger RNAs (mRNAs) and microRNAs related to the progression of lung adenocarcinoma (LUAD), in order to provide a theoretical basis for early diagnosis, therapeutic targets, and prognosis evaluation of patients with LUAD. METHODS: The mRNA and miRNA expression datasets came from the Gene Expression Omnibus (GEO) project database. Differentially expressed genes (DEGs) and microRNAs (DEMs) between LUAD tissues and adjacent lung tissue were obtained using GEO2R. The Search Tool for the Retrieval of Interacting Genes website was also employed to construct and visualize the interactions of overlapped DEGs. The overall survival of DEMs was investigated using the Kaplan-Meier plotter. The TargetScan website (http://www.targetscan.org/) was used to verify the relationship between FA Complementation Group I (FANCI) and the expression of miRNA-218 (miR-218). The expression of FANCI was verified using the GEO and Human Protein Atlas databases, as well as Real Time Quantitative PCR using our own samples. Next, we analyzed the relationship between the expression of FANCI and the clinicopathological characteristics as well as the prognosis of patients with LUAD. We also explored whether the FANCI was related to immune cell infiltration in LUAD. RESULTS: FANCI was identified as a hub gene and associated with poor OS. We found that miR-218 negatively regulates FANCI mRNA expression. At the mRNA expression and protein level, FANCI was more highly expressed in LUAD tissues. The expression of FANCI in LUAD was related to tumor size (χ2=13.96, P<0.001), lymphatic metastasis (χ2=3.88, P<0.05), distant metastasis (χ2=45.39, P<0.001), and stage (χ2=11.03, P<0.05). In addition, the Cox regression model found that FANCI mRNA expression was an independent predictive factor of patient survival (P<0.05). FANCI expression was both weakly related to B cells and neutrophil infiltration in LUAD. CONCLUSIONS: miR-218 may negatively regulate FANCI, and FANCI could promote metastasis via extracellular matrix (ECM) receptor interaction, leading to poor prognosis of LUAD. FANCI may be a key gene to the determine metastasis and poor prognosis in patients with LUAD. Changes in the immune microenvironment may be the mechanism through which FANCI leads to poor prognosis of LUAD.

Catamenial pneumothorax with bubbling up on the diaphragmatic defects: a case report.

BACKGROUND: Catamenial pneumothorax is characterized by spontaneous recurring pneumothorax during menstruation, which is a common clinical manifestation of thoracic endometriosis syndrome. There are still controversies about its pathogenesis. CASE PRESENTATION: A 43-year-old woman with a history of endometriosis came to our hospital due to recurring pneumothorax during menstruation. Uniportal Video-assisted Thoracoscopic Surgery (VATS) exploration was performed on the eve of menstruating. We thoroughly explored the diaphragm, visceral and parietal pleura: The lung surface was scattered with yellowish-brown implants; no bullae were found; multiple diaphragmatic defects were found on the dome. And surprisingly, we caught a fascinating phenomenon: Bubbles were slipping into pleural cavity through diaphragmatic defects. We excised the diaphragmatic lesions and wedge resected the right upper lung lesion; cleared the deposits and flushed the thoracic cavity with pure iodophor. Diaphragmatic lesions confirmed the presence of endometriosis, and interestingly enough, microscopically, endometrial cells were shedding with impending menses. After a series of intraoperative operations and postoperative endocrine therapy, the disease did not recur after a period of follow-up. CONCLUSION: We have witnessed the typical signs of catamenial pneumothorax at the accurate timing: Not only observed the process of gas migration macroscopically, but also obtained pathological evidence of diaphragmatic periodic perforation microscopically, which is especially precious and confirms the existing theory that retrograde menstruation leads to diaphragmatic endometriosis, and the diaphragmatic fenestration is obtained due to the periodic activities of ectopic endometrium.

Key microRNAs and hub genes associated with poor prognosis in lung adenocarcinoma.

In the study, we obtained 36 pairs of lung adenocarcinoma (LUAD) tissues and adjacent non-tumorous tissues. Then, we chose a specific hub-target gene of miRNA and used qRT-PCR to evaluate the expression of PECAM1. We found that the expression level of PECAM1 mRNA in LUAD was significantly lower than that in adjacent nontumor tissues (P<0.0001). Univariate and multivariate analyses were conducted on 481 LUAD patients from The Cancer Genome Atlas (TCGA) according to the Cox proportional hazard regression model to evaluate the impact of PECAM1 expression and other clinicopathological factors on survival. The results showed that the low expression of PECAM1 was an important independent predictor of poor overall survival (HR, 0.704; 95% CI, 0.518-0.957; P = 0.025). Based on the Tumor Immune Estimation Resource (TIMER) database, the relationship between PECAM1 expression and B cell, CD8+ T cell, CD4+ T cell, macrophage, neutrophil, and dendritic cell infiltration was weak in LUAD (P<0.01). In particular, a more significant positive correlation between PECAM1 expression and HLA-complex members, CD1C, NRP1, and ITGAX expression in dendritic cell was detected in LUAD. The mechanism which PECAM1 involved in the development of LUAD may be closely related to changes in the immune microenvironment.

Single-cell transcriptome analysis demonstrates inter-patient and intra-tumor heterogeneity in primary and metastatic lung adenocarcinoma.

In this study, we performed single-cell transcriptome data analysis of fifty primary and metastatic lung adenocarcinoma (LUAD) samples from the GSE123902 and GSE131907 datasets to determine the landscape of inter-patient and intra-tumoral heterogeneity. The gene expression profiles and copy number variations (CNV) showed significant heterogeneity in the primary and metastatic LUAD samples. We observed upregulation of pathways related to translational initiation, endoplasmic reticulum stress, exosomes, and unfolded protein response in the brain metastasis samples as compared to the primary tumor samples. Pathways related to exosomes, cell adhesion and metabolism were upregulated and the epithelial-to-mesenchymal-transition (EMT) pathway was downregulated in brain metastasis samples from chemotherapy-treated LUAD patients as compared to those from the untreated LUAD patients. Tumor cell subgroups in the brain metastasis samples showed differential expression of genes related to type II alveolar cells, chemoresistance, glycolysis and oxidative phosphorylation (metabolic reprogramming), and EMT. Thus, single-cell transcriptome analysis demonstrated intra-patient and intra-tumor heterogeneity in the regulation of pathways related to tumor progression, chemoresistance and metabolism in the primary and metastatic LUAD tissues. Moreover, our study demonstrates that single cell transcriptome analysis is a potentially useful tool for accurate diagnosis and personalized targeted treatment of LUAD patients.

A new technology for reducing anastomotic fistula in the neck after esophageal cancer surgery.

BACKGROUND: To explore the risk factors and prevention methods of cervical mechanical anastomotic fistula and stenosis after the radical resection of esophageal cancer. METHODS: From March 2018 to November 2018, 128 patients undergoing mechanical anastomosis of esophageal cancer were selected from the Department of Thoracic Surgery of The First Affiliated Hospital of Zhengzhou University. All the enrolled patients were operated on using the Mckeown method, and a retrospective study was conducted. Data for preoperative and postoperative test indices, intraoperative embedding materials, postoperative complications, and preoperative and postoperative treatment were collected, and the relationship between various factors and the incidence of cervical anastomotic fistula and stenosis was analysed. Univariate analysis was conducted using t tests or Fisher's exact probability method, and multivariate analysis was conducted using logistic regression models. RESULTS: All 128 patients successfully underwent surgery without dying. The enrolled patients were evaluated using the Stooler classification, with 28 patients having grade 0, 41 patients having grade 1, 34 patients having grade 2, 21 patients having grade 3, and 4 patients having grade 4 stenosis. Patients with stenosis of grade 3 or above had obvious choking sensation, which could only be relieved by balloon dilation. Symptoms in all patients with stenosis were relieved by balloon dilation. There were no significant differences between the two groups regarding embedding materials, preoperative choking history, history of alcohol consumption, history of hypertension, history of coronary heart disease, history of diabetes, postoperative calcium concentration, average albumin concentration, average platelet concentration, body mass index, anastomotic fistula, preoperative chemotherapy, postoperative chemotherapy, or postoperative cough (P>0.05). There were significant differences in postoperative reflux (χ2=11.338, P<0.05) and scar constitution (χ2=12.497, P<0.05). The effects of embedding materials in patients with anastomotic fistula were significantly different (χ2=4.372, P<0.05). CONCLUSIONS: Postoperative reflux and scar constitution may be risk factors for postoperative anastomotic stenosis after resection of esophageal cancer. There was almost no difference in the effects on esophageal anastomotic stenosis between embedding materials and the omentum majus, but Neoveil® may have certain advantages in preventing cervical anastomotic fistula, and thus may have certain clinical application value.