Soft Tissue Ewing Sarcoma Cell Drug Resistance Revisited: A Systems Biology Approach.

Ewing sarcoma is a rare type of cancer that develops in the bones and soft tissues. Drug therapy represents an extensively used modality for the treatment of sarcomas. However, cancer cells tend to develop resistance to antineoplastic agents, thereby posing a major barrier in treatment effectiveness. Thus, there is a need to uncover the molecular mechanisms underlying chemoresistance in sarcomas and, hence, to enhance the anticancer treatment outcome. In this study, a differential gene expression analysis was conducted on high-throughput transcriptomic data of chemoresistant versus chemoresponsive Ewing sarcoma cells. By applying functional enrichment analysis and protein-protein interactions on the differentially expressed genes and their corresponding products, we uncovered genes with a hub role in drug resistance. Granted that non-coding RNA epigenetic regulators play a pivotal role in chemotherapy by targeting genes associated with drug response, we investigated the non-coding RNA molecules that potentially regulate the expression of the detected chemoresistance genes. Of particular importance, some chemoresistance-relevant genes were associated with the autonomic nervous system, suggesting the involvement of the latter in the drug response. The findings of this study could be taken into consideration in the clinical setting for the accurate assessment of drug response in sarcoma patients and the application of tailored therapeutic strategies.

An in silico approach to the identification of diagnostic and prognostic markers in low-grade gliomas.

Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress they are becoming one of the deadliest brain tumors. There is still great need for timely and accurate diagnosis and prognosis of LGG. Herein, we aimed to identify diagnostic and prognostic biomarkers associated with LGG, by employing diverse computational approaches. For this purpose, differential gene expression analysis on high-throughput transcriptomics data of LGG versus corresponding healthy brain tissue, derived from TCGA and GTEx, respectively, was performed. Weighted gene co-expression network analysis of the detected differentially expressed genes was carried out in order to identify modules of co-expressed genes significantly correlated with LGG clinical traits. The genes comprising these modules were further used to construct gene co-expression and protein-protein interaction networks. Based on the network analyses, we derived a consensus of eighteen hub genes, namely, CD74, CD86, CDC25A, CYBB, HLA-DMA, ITGB2, KIF11, KIFC1, LAPTM5, LMNB1, MKI67, NCKAP1L, NUSAP1, SLC7A7, TBXAS1, TOP2A, TYROBP, and WDFY4. All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. The findings of this study could be applicable in the clinical setting for diagnosing and monitoring LGG.

Capsaicin suppresses ciliary function, while inducing permeability in bronchial epithelial cell cultures of COPD patients.

Take Home Message: Capsaicin modified inflammatory response and caused toxicity in bronchial epithelial cultures from patients with COPD. More importantly, capsaicin decreased ciliary beat frequency and induced epithelial permeability and these effects were partially prevented by formoterol and roflumilast. Tear gas is widely used to halt mass demonstrations. Studies have reported its adverse effects on multiple organ systems; however, its effect on individuals with chronic respiratory diseases and the underlying mechanisms of these effects are unclear. For the first time in the literature, we investigated the effects of capsaicin, the active ingredient of tear gas, on bronchial epithelial cell (BEC) cultures obtained from well-characterized groups of nonsmokers, smokers, and patients with chronic obstructive pulmonary disease (COPD). BEC cultures were incubated with 50-500 µM capsaicin in the absence and presence of formoterol (1µM) and roflumilast (0.1 µM) for 24 h. Ciliary beat frequency (CBF) and transepithelial electrical resistance (TEER) were assessed at T1/4, T1/2, T1, T2, T4, T6, and T24 h, whereas the release of granulocyte-macrophage colony-stimulating factor (GM-CSF), interleukin (IL)-8, and lactate dehydrogenase (LDH) was measured at T24 h. Capsaicin (250 µM) significantly decreased CBF of all BEC cultures from T1/4 h to T24 h (p<0.05). Formoterol significantly prevented decreases in CBF induced by capsaicin. Higher concentrations of capsaicin (250-500 µM) significantly reduced TEER of BECs from nonsmokers (T2-T24 h), smokers (T24 h) and COPD patients (T2 and T24 h), which was partially prevented by roflumilast. Capsaicin (500 µM) decreased release of IL-8 (p<0.0001) and GM-CSF (p<0.05) while inducing release of LDH in BECs (p<0.05), and this was more prominent in BEC from patients with COPD. In conclusion, our findings demonstrate that capsaicin can suppress ciliary activity and cytokine release from BECs, induce BEC culture permeability and cellular toxicity and that these effects can be partially prevented by formoterol and roflumilast.

A Counterintuitive Neutrophil-Mediated Pattern in COVID-19 Patients Revealed through Transcriptomics Analysis.

The COVID-19 pandemic has persisted for almost three years. However, the mechanisms linked to the SARS-CoV-2 effect on tissues and disease severity have not been fully elucidated. Since the onset of the pandemic, a plethora of high-throughput data related to the host transcriptional response to SARS-CoV-2 infections has been generated. To this end, the aim of this study was to assess the effect of SARS-CoV-2 infections on circulating and organ tissue immune responses. We profited from the publicly accessible gene expression data of the blood and soft tissues by employing an integrated computational methodology, including bioinformatics, machine learning, and natural language processing in the relevant transcriptomics data. COVID-19 pathophysiology and severity have mainly been associated with macrophage-elicited responses and a characteristic "cytokine storm". Our counterintuitive findings suggested that the COVID-19 pathogenesis could also be mediated through neutrophil abundance and an exacerbated suppression of the immune system, leading eventually to uncontrolled viral dissemination and host cytotoxicity. The findings of this study elucidated new physiological functions of neutrophils, as well as tentative pathways to be explored in asymptomatic-, ethnicity- and locality-, or staging-associated studies.

In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia.

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

Investigating Molecular Determinants of Cancer Cell Resistance to Ionizing Radiation Through an Integrative Bioinformatics Approach.

Eradication of cancer cells through exposure to high doses of ionizing radiation (IR) is a widely used therapeutic strategy in the clinical setting. However, in many cases, cancer cells can develop remarkable resistance to radiation. Radioresistance represents a prominent obstacle in the effective treatment of cancer. Therefore, elucidation of the molecular mechanisms and pathways related to radioresistance in cancer cells is of paramount importance. In the present study, an integrative bioinformatics approach was applied to three publicly available RNA sequencing and microarray transcriptome datasets of human cancer cells of different tissue origins treated with ionizing radiation. These data were investigated in order to identify genes with a significantly altered expression between radioresistant and corresponding radiosensitive cancer cells. Through rigorous statistical and biological analyses, 36 genes were identified as potential biomarkers of radioresistance. These genes, which are primarily implicated in DNA damage repair, oxidative stress, cell pro-survival, and apoptotic pathways, could serve as potential diagnostic/prognostic markers cancer cell resistance to radiation treatment, as well as for therapy outcome and cancer patient survival. In addition, our findings could be potentially utilized in the laboratory and clinical setting for enhancing cancer cell susceptibility to radiation therapy protocols.

HOTAIR as a Prognostic Predictor for Diverse Human Cancers: A Meta- and Bioinformatics Analysis.

Several studies suggest that upregulated expression of the long non-coding RNA HOX transcript antisense RNA (HOTAIR) is a negative predictive biomarker for numerous cancers. Herein, we performed a meta-analysis to further investigate the prognostic value of HOTAIR expression in diverse human cancers. To this end, a systematic literature review was conducted in order to select scientific studies relevant to the association between HOTAIR expression and clinical outcomes, including overall survival (OS), recurrence-free survival (RFS)/disease-free survival (DFS), and progression-free survival (PFS)/metastasis-free survival (MFS) of cancer patients. Collectively, 53 eligible studies including a total of 4873 patients were enrolled in the current meta-analysis. Pooled hazard ratios (HRs) with their corresponding 95% confidence intervals (CIs) were calculated to assess the relationship between HOTAIR and cancer patients' survival. Elevated HOTAIR expression was found to be significantly associated with OS, RFS/DFS and PFS/MFS in diverse types of cancers. These findings were also corroborated by the results of bioinformatics analysis on overall survival. Therefore, based on our findings, HOTAIR could serve as a potential biomarker for the prediction of cancer patient survival in many different types of human cancers.