Primary pulmonary mucoepidermoid carcinoma (PMEC) is extremely rare. Herein, we report a case of a 71‐year‐old male patient with high‐grade PMEC involving the right upper lobe that was successfully resected via lobectomy. As a result of invasion into the pleural and paratracheal lymph nodes, four cycles of adjuvant chemotherapy with paclitaxel and carboplatin were administered. There were no signs of relapse during 10 months of follow‐up. Furthermore, we reviewed the literature and summarized the surgical approaches, prognostic factors, and underlying genetic mechanisms of PMEC, which will benefit clinical treatment.
Background Nowadays, the age of patients qualified for lung surgery has been lowered. These people have increasingly higher requirements for postoperative quality of life, which is closely related to pulmonary function (PF). Therefore, it’s meaningful to analyze the effects of different surgical methods on postoperative PF and postoperative recovery. Methods A total of 171 patients underwent thoracoscopic lung surgery were selected in our study: unilateral lobectomy (UL), unilateral sublobectomy (USL), and other surgical method (OSM). Other operations included unilateral/bilateral lobectomy and/or sublobectomy. Study indicators included patient general condition, PF and recovery condition. Results The USL was in the best condition during and after surgery. Then was OSM, USL was the worst. All the thoracoscopic operations significantly reduced PF and the loss of PF in the UL was significantly higher than USL. In the long-term observation, the loss of PF after lobectomy was almost the same. The compensatory capacity of the upper lobe was better than the lower lobe. The prediction models of PF cannot fully reflect the actual situation after operation. Our PF loss assessment table based on clinical data was constructed to correct existing models. Conclusions Surgeries had significant effects on PF, but these changes gradually disappeared after operation. The greatest affect were PEF and PEF%, the least were MVV and MVV%. The compensatory function of the upper lobe was better than lower one. The loss of PF in multiple lung tissue resection is equivalent to lobectomy. Our postoperative PF assessment table can better reflect the PF than existing prediction models.
Integrins are closely related to the occurrence and development of tumors. ITGA8 encodes the alpha 8 subunit of the heterodimeric integrin alpha8beta1. Studies on the role of this gene in the occurrence and development of lung cancer are scarce. The examination of public databases revealed that ITGA8 expression was significantly lower in tumor tissue than that in normal tissue, especially in lung cancer, renal carcinoma, and prostate cancer. Survival analysis of patients with lung adenocarcinoma revealed that higher ITGA8 expression had better prognosis. ITGA8 was positively related to immune checkpoints and immunomodulators, whereas B cell, CD4+ T cell, CD8+ T cell, neutrophil, macrophage, and dendritic cell infiltration had the same correlation. Moreover, ITGA8 was negatively related to cancer stemness. We used an online database to predict the miRNAs and lncRNAs that regulate ITGA8 and obtained the regulatory network of ITGA8 through correlation analysis and Kaplan–Meier survival analysis. Quantitative real-time PCR and western blot analyses showed that LINC01798 regulates ITGA8 expression through miR-17-5p. Therefore, the regulatory network of ITGA8 may serve as a new therapeutic target to improve the prognosis of patients with lung cancer.
BackgroundDetermining benign and malignant nodules before surgery is very difficult when managing patients with pulmonary nodules, which further makes it difficult to choose an appropriate treatment. This study aimed to develop a lung cancer risk prediction model for predicting the nature of the nodule in patients’ lungs and deciding whether to perform a surgical intervention.MethodsThis retrospective study included patients with pulmonary nodules who underwent lobectomy or sublobectomy at Tianjin Medical University General Hospital between 2017 and 2020. All subjects were further divided into training and validation sets. Multivariable logistic regression models with backward selection based on the Akaike information criterion were used to identify independent predictors and develop prediction models.ResultsTo build and validate the model, 503 and 260 malignant and benign nodules were used. Covariates predicting lung cancer in the current model included female sex, age, smoking history, nodule type (pure ground-glass and part-solid), nodule diameter, lobulation, margin (smooth, or spiculated), calcification, intranodular vascularity, pleural indentation, and carcinoembryonic antigen. The final model of this study showed excellent discrimination and calibration with a concordance index (C-index) of 0.914 (0.890–0.939). In an independent sample used for validation, the C-index for the current model was 0.876 (0.825–0.927) compared with 0.644 (0.559–0.728) and 0.681 (0.605–0.757) for the Mayo and Brock models. The decision curve analysis showed that the current model had higher discriminatory power for malignancy than the Mayo and the Brock models.ConclusionsThe current model can be used in estimating the probability of lung cancer in nodules requiring surgical intervention. It may reduce unnecessary procedures for benign nodules and prompt diagnosis and treatment of malignant nodules.
Background Patients with non‐small cell lung cancer (NSCLC) are diagnosed in advanced stages and with a poor 5‐year survival rate. There is a critical need to identify novel biomarkers to improve the therapy and overall prognosis of this disease. Methods Differentially expressed genes (DEGs) were identified from three profiles of GSE101586, GSE101684 and GSE112214 using Venn diagrams. hsa_circ_0043256 were validated using quantitative real‐time polymerase chain reaction (RT‐qPCR). The circular RNA–microRNA–messenger RNA (circRNA–miRNA–mRNA) regulatory network was constructed with Cytoscape 3.7.0. Hub genes were identified with protein interaction (PPI) and validated with the Gene Expression Profiling Interactive Analysis (GEPIA), Human Protein Atlas (HPA) databases, and immunohistochemistry. Survival analyses were also performed using a Kaplan–Meier (KM) plotter. The effects of hsa_circ_0043256 on cell proliferation and cell cycles were evaluated by EdU staining and flow cytometry, respectively. Results hsa_circ_0043256, hsa_circ_0029426 and hsa_circ_0049271 were obtained. Following RT‐qPCR validation, hsa_circ_0043256 was selected for further analysis. In addition, functional experiment results indicated that hsa_circ_0043256 could inhibit cell proliferation and cell‐cycle progression of NSCLC cells in vitro. Prediction by three online databases and combining with DEGs identified from The Cancer Genome Atlas (TCGA), a network containing one circRNAs, three miRNAs, and 209 mRNAs was developed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated DEGs might be associated with lung cancer onset and progression. A PPI network based on the 209 genes was established, and five hub genes (BIRC5, SHCBP1, CCNA2, SKA3, and GINS1) were determined. Following verification of five hub genes using GEPIA database, HPA database, and immunohistochemistry. High expression of all five hub genes led to poor overall survival. Conclusion Our study constructed a circRNA–miRNA–mRNA network of hsa_circ_0043256. hsa_circ_0043256 may be a potential therapeutic target for lung cancer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.