Misdiagnosis of primary mucosa-associated lymphoid tissue lymphoma of the pleura: case report and literature review.

Background: Mucosa-associated lymphoid tissue (MALT) lymphoma is an indolent B cell lymphoma. Its occurrence in the pleura is rare, with atypical clinical manifestations. MALT of the pleura is easily misdiagnosed. This is the first case report of pleural MALT lymphoma in China. Case Description: We report the case of a 54-year-old Chinese man with no notable medical history who complained of cough, sputum, and shortness of breath for 3 months. He had a positive purified protein derivative (PPD) test. An initial misdiagnosis of pleural tuberculosis was corrected, after 3 thoracoscopic biopsies and tests, to primary pleural MALT lymphoma. He received treatments of R-CHOP (rituximab, cyclophosphamide, epirubicin, vindesine and prednisolone) and traditional Chinese medicine. The patient was followed for 3 years until June 2022, with no obvious respiratory symptoms. Pleural MALT lymphoma is extremely rare, with only a few cases reported. This article describes our case, and includes an overview of 15 previously reported cases to summarize the characteristics, treatments, and prognosis of primary pleural MALT lymphoma. Conclusions: Pleural MALT lymphoma is rare, and a correct diagnosis depends on tissue biopsy, immunohistochemical staining, and detection of gene rearrangement. Thoracoscopy is important to diagnose this disease. Multiple thoracoscopic biopsies may be necessary.

Bioinformatics prediction of differential miRNAs in non-small cell lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) accounts for 85% of all lung cancers. The drug resistance of NSCLC has clinically increased. This study aimed to screen miRNAs associated with NSCLC using bioinformatics analysis. We hope that the screened miRNA can provide a research direction for the subsequent treatment of NSCLC. METHODS: We screened out the common miRNAs after compared the NSCLC-related genes in the TCGA database and GEO database. Selected miRNA was performed ROC analysis, survival analysis, and enrichment analysis (GO term and KEGG pathway). RESULTS: A total of 21 miRNAs were screened in the two databases. And they were all highly expressed in normal and low in cancerous tissues. Hsa-mir-30a was selected by ROC analysis and survival analysis. Enrichment analysis showed that the function of hsa-mir-30a is mainly related to cell cycle regulation and drug metabolism. CONCLUSION: Our study found that hsa-mir-30a was differentially expressed in NSCLC, and it mainly affected NSCLC by regulating the cell cycle and drug metabolism.

Construction of ceRNA network to identify the lncRNA and mRNA related to non-small cell lung cancer.

BACKGROUND: Non-small cell lung cancer (NSCLC) harms human health, but its pathogenesis remains unclear. We wish to provide more molecular therapeutic targets for NSCLC. METHODS: The NSCLC tissue and normal tissue samples were screened for genetic comparison in the TCGA database. The predicted lncRNA and mRNA in BEAS2B and A549 cells were detected. RESULTS: Volcano plot displayed differentially expressed lncRNAs and mRNAs in adjacent tissues and NSCLC tissues. The survival curve showed that the lncRNA and mRNA had a significant impact on the patient's survival. The results of GO term enrichment analysis indicated that mRNA functions were enriched in cell cycle-related pathways. In the ceRNA interaction network, 13 lncRNAs and 20 miRNAs were found to have an interactive relationship. Finally, 3 significantly different lncRNAs (LINC00968, lnc-FAM92A-9 and lnc-PTGFR-1) and 6 mRNAs (CTCFL, KRT5, LY6D, TMEM, GBP6, and TMEM179) with potential therapeutic significance were screened out. And the cell experiment verified our results. CONCLUSION: We screened out clinically significant 3 lncRNAs and 6 mRNAs involved in the ceRNA network, which were the key to our future research on the treatment of NSCLC.

Robust Prediction of Immune Checkpoint Inhibition Therapy for Non-Small Cell Lung Cancer.

Background: The development of immune checkpoint inhibitors (ICIs) is a revolutionary milestone in the field of immune-oncology. However, the low response rate is the major problem of ICI treatment. The recent studies showed that response rate to single-agent programmed cell death protein 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibition in unselected non-small cell lung cancer (NSCLC) patients is 25% so that researchers defined several biomarkers to predict the response of immunotherapy in ICIs treatment. Common biomarkers like tumor mutational burden (TMB) and PD-L1 expression have several limitations, such as low accuracy and inadequately validated cutoff value. Methods: Two published and an unpublished ICIs treatment NSCLC cohorts with 129 patients were collected and divided into a training cohort (n = 53), a validation cohort (n = 22), and two independent test cohorts (n = 34 and n = 20). We identified six immune-related pathways whose mutational status was significantly associated with overall survival after ICIs treatment. Then these pathways mutational status combined with TMB, PD-L1 expression and intratumor heterogeneity were incorporated to build a Bayesian-regularization neural networks (BRNN) model to predict the ICIs treatment response. Results: We firstly proved that TMB, PD-L1, and mutant-allele tumor heterogeneity (MATH) were independent biomarkers. The survival analysis of six immune-related pathways revealed the mutational status could distinguish overall survival after ICIs treatment. When predicting immunotherapy efficacy, the overall accuracy of area under curve (AUC) in validation cohort reaches 0.85, outperforming previous predictors in either sensitivity or specificity. And the AUC in two independent test cohorts reach 0.74 and 0.80. Conclusion: We developed a pathway-model that could predict the efficacy of ICIs in NSCLC patients. Our study made a significant contribution to solving the low prediction accuracy of immunotherapy of single biomarker. With the accumulation of larger data sets, further studies are warranted to refine the predictive performance of the approach.