Association Between Clinicopathological Parameters and S100A8/A9 Expression According to Smoking History in Patients With Non-small Cell Lung Cancer.

BACKGROUND/AIM: Lung cancer is a major cause of cancer-related deaths worldwide, and chronic inflammation caused by cigarette smoke plays a crucial role in the development and progression of this disease. S100A8/9 and RAGE are associated with chronic inflammatory diseases and cancer. This study aimed to investigate the expression of S100A8/9, HMBG1, and other related pro-inflammatory molecules and clinical characteristics in patients with non-small cell lung cancer (NSCLC). PATIENTS AND METHODS: We obtained serum and bronchoalveolar lavage (BAL) fluid samples from 107 patients and categorized them as never or ever-smokers. We measured the levels of S100A8/9, RAGE, and HMGB1 in the collected samples using enzyme-linked immunosorbent kits. Immunohistochemical staining was also performed to assess the expression of S100A8/9, CD11b, and CD8 in lung cancer tissues. The correlation between the expression of these proteins and the clinical characteristics of patients with NSCLC was also explored. RESULTS: The expression of S100A8/A9, RAGE, and HMGB was significantly correlated with smoking status and was higher in people with a history of smoking or who were currently smoking. There was a positive correlation between serum and BAL fluid S100A8/9 levels. The expression of S100A8/A9 and CD8 in lung tumor tissues was significantly correlated with smoking history in patients with NSCLC. Ever-smokers, non-adenocarcinoma histology, and high PD-L1 expression were significant factors predicting high serum S100A8/9 levels in multivariate analysis. CONCLUSION: The S100A8/9-RAGE pathway and CD8 expression were increased in smoking-related NSCLC patients. The S100A8/9-RAGE pathway could be a promising biomarker for chronic airway inflammation and carcinogenesis in smoking-related lung diseases.

Fuzzy-Rough Entropy Measure and Histogram Based Patient Selection for miRNA Ranking in Cancer.

MicroRNAs (miRNAs) are known as an important indicator of cancers. The presence of cancer can be detected by identifying the responsible miRNAs. A fuzzy-rough entropy measure (FREM) is developed which can rank the miRNAs and thereby identify the relevant ones. FREM is used to determine the relevance of a miRNA in terms of separability between normal and cancer classes. While computing the FREM for a miRNA, fuzziness takes care of the overlapping between normal and cancer expressions, whereas rough lower approximation determines their class sizes. MiRNAs are sorted according to the highest relevance (i.e., the capability of class separation) and a percentage among them is selected from the top ranked ones. FREM is also used to determine the redundancy between two miRNAs and the redundant ones are removed from the selected set, as per the necessity. A histogram based patient selection method is also developed which can help to reduce the number of patients to be dealt during the computation of FREM, while compromising very little with the performance of the selected miRNAs for most of the data sets. The superiority of the FREM as compared to some existing methods is demonstrated extensively on six data sets in terms of sensitivity, specificity, and score. While for these data sets the score of the miRNAs selected by our method varies from 0.70 to 0.91 using SVM, those results vary from 0.37 to 0.90 for some other methods. Moreover, all the selected miRNAs corroborate with the findings of biological investigations or pathway analysis tools. The source code of FREM is available at http://www.jayanta.droppages.com/FREM.html.

Serious Complications after Self-expandable Metallic Stent Insertion in a Patient with Malignant Lymphoma.

An 18-year-old woman was evaluated for a chronic productive cough and dyspnea. She was subsequently diagnosed with mediastinal non-Hodgkin lymphoma (NHL). A covered self-expandable metallic stent (SEMS) was implanted to relieve narrowing in for both main bronchi. The NHL went into complete remission after six chemotherapy cycles, but atelectasis developed in the left lower lobe 18 months after SEMS insertion. The left main bronchus was completely occluded by granulation tissue. However, the right main bronchus and intermedius bronchus were patent. Granulation tissue was observed adjacent to the SEMS. The granulation tissue and the SEMS were excised, and a silicone stent was successfully implanted using a rigid bronchoscope. SEMS is advantageous owing to its easy implantation, but there are considerable potential complications such as severe reactive granulation, stent rupture, and ventilation failure in serious cases. Therefore, SEMS should be avoided whenever possible in patients with benign airway disease. This case highlights that SEMS implantation should be avoided even in malignant airway obstruction cases if the underlying malignancy is curable.

Pneumatosis intestinalis complicated by pneumoperitoneum in a patient with asthma.

Pneumatosis intestinalis (PI) is a very rare condition that is defined as the presence of gas within the subserosal or submucosal layer of the bowel. PI has been described in association with a variety of conditions including gastrointestinal tract disorders, pulmonary diseases, connective tissue disorders, organ transplantation, leukemia, and various immunodeficiency states. We report a rare case of a 74-year-old woman who complained of dyspnea during the management of acute asthma exacerbation and developed PI; but, it improved without any treatment.

Entropy-based analysis of the non-linear relationship between gene expression profiles of amplified and non-amplified RNA.

Two critical issues in microarray-based gene expression profiling with amplified RNA are its reliability and reproducibility compared to the non-amplified RNA. In this study, the non-linear relationship between the two methods was evaluated with the entropy in addition to the linear relationship using correlation coefficients. The correlation coefficients within the amplification method and between the two methods were significantly high, 0.98 and 0.88, respectively. Comparing the entropy as increasing fold-change difference (k), the average entropy value was reduced to 0.02 in the cell line and 0.09 in the tissue samples, indicating that the number of different genes between the two methods was decreased. In addition, the threshold of k according to the percentage of p estimated from entropy values could be used to provide the cut-off line on gene selection. The quantity discordance rate of 0.3-5.47% and the common outlier proportion of 84.2-94.3% between the two methods were detected, according to the expression levels. In summary, we showed a high similarity between the two methods using non-linear as well as linear comparison. Furthermore, we proved that the entropy as the measure of non-linear relationship is useful for analyzing the similarity of replicated microarray data sets.

The classification of cancer based on DNA microarray data that uses diverse ensemble genetic programming.

OBJECT: The classification of cancer based on gene expression data is one of the most important procedures in bioinformatics. In order to obtain highly accurate results, ensemble approaches have been applied when classifying DNA microarray data. Diversity is very important in these ensemble approaches, but it is difficult to apply conventional diversity measures when there are only a few training samples available. Key issues that need to be addressed under such circumstances are the development of a new ensemble approach that can enhance the successful classification of these datasets. MATERIALS AND METHODS: An effective ensemble approach that does use diversity in genetic programming is proposed. This diversity is measured by comparing the structure of the classification rules instead of output-based diversity estimating. RESULTS: Experiments performed on common gene expression datasets (such as lymphoma cancer dataset, lung cancer dataset and ovarian cancer dataset) demonstrate the performance of the proposed method in relation to the conventional approaches. CONCLUSION: Diversity measured by comparing the structure of the classification rules obtained by genetic programming is useful to improve the performance of the ensemble classifier.