Common cancer biomarkers of breast and ovarian types identified through artificial intelligence.

Biomarkers can offer great promise for improving prevention and treatment of complex diseases such as cancer, cardiovascular diseases, and diabetes. These can be used as either diagnostic or predictive or as prognostic biomarkers. The revolution brought about in biological big data analytics by artificial intelligence (AI) has the potential to identify a broader range of genetic differences and support the generation of more robust biomarkers in medicine. AI is invigorating biomarker research on various fronts, right from the cataloguing of key mutations driving the complex diseases like cancer to the elucidation of molecular networks underlying diseases. In this study, we have explored the potential of AI through machine learning approaches to propose that these methods can act as recommendation systems to sort and prioritize important genes and finally predict the presence of specific biomarkers. Essentially, we have utilized microarray datasets from open-source databases, like GEO, for breast, lung, colon, and ovarian cancer. In this context, different clustering analyses like hierarchical and k-means along with random forest algorithm have been utilized to classify important genes from a pool of several thousand genes. To this end, network centrality and pathway analysis have been implemented to identify the most potential target as CREB1.

A Six-Gene-Based Prognostic Model Predicts Survival in Head and Neck Squamous Cell Carcinoma Patients.

BACKGROUND AND OBJECTIVE: Head and neck cancer is a malignant tumor that begins in the head and neck region, and has the sixth highest incidence worldwide. Previous studies have indicated several prognostic markers for head and neck squamous cell carcinoma (HNSCC), but due to poor accuracy and sensitivity of these clinical characteristic markers attention has been gradually switched to molecular biomarkers. This study aimed to sort out the mRNAs correlated with patient survival time to establish an mRNA combination prognostic biomarker model for HNSCC patient risk stratification, providing optimal therapeutic regimens and improving patient prognosis. METHODS: Clinical data and transcriptome sequencing data of HNSCC were retrieved from TCGA database and were allocated into training and validation datasets. The prognostic model was established using the mRNAs, which were sorted out from training dataset by a significant correlation with survival time. Eventually, the prediction property of the model was evaluated by Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve. RESULTS: An optimal prognostic model by the combination of six mRNAs was established. Kaplan-Meier survival analysis revealed effective risk stratification by this model for patients in the two datasets. The area under ROC curve (AUC) was > 0.65 for training and validation datasets, indicating good sensitivity and specificity of this model. Moreover, prominent superiority of this model to investigate prognostic biomarkers was demonstrated. CONCLUSION: Our model provided effective prognostication in terms of death risk stratification and evaluation in HNSCC patients. Combination of this prognostic model with current treatment measures is expected to greatly improve the patients' prognosis.

Interleukin-1 blockade overcomes erlotinib resistance in head and neck squamous cell carcinoma.

Erlotinib has demonstrated poor clinical response rates for head and neck squamous cell carcinoma (HNSCC) to date and the majority of respondents acquire resistance to erlotinib relatively quickly. To elucidate novel pathways involved in erlotinib resistance, we compared the gene expression profiles of erlotinib-resistant (ER) vs. erlotinib-sensitive (ES) HNSCC cell lines. Enrichment analysis of microarray data revealed a deregulation of the IL-1 signaling pathway in ER versus ES-HNSCC cells. Gene expression of interleukin-1 alpha (IL1A) and interleukin-1 beta (IL1B) were significantly upregulated by > 2 fold in ER-SQ20B and ER-CAL 27 cells compared to their respective ES-cells. Secretion of the IL-1 receptor antagonist (IL-1RA) was significantly reduced in ER-cells compared to ES-cells. Blockade of IL-1 signaling using a recombinant IL-1R antagonist (anakinra) was able to inhibit the growth of ER-SQ20B and ER-CAL 27 but not ES-SQ20B and ES-CAL 27 xenografts as a single agent and in combination with erlotinib. ER-SQ20B xenografts treated with anakinra ± erlotinib were found to be less vascularized than ER-SQ20B xenografts treated with water or erlotinib. Mice bearing ER-SQ20B xenografts had significantly lesser circulating levels of G-CSF and IL-1ß when treated with anakinra ± erlotinib compared to those treated with water or erlotinib alone. Furthermore, augmented mRNA levels of IL1A or interleukin-1 receptor accessory protein (IL1RAP) were associated with shortened survival in HNSCC patients. Altogether, blockade of the IL-1 pathway using anakinra overcame erlotinib resistance in HNSCC xenografts and may represent a novel strategy to overcome EGFR inhibitor resistance for treatment of HNSCC patients.

Upregulated interleukin-6 expression contributes to erlotinib resistance in head and neck squamous cell carcinoma.

Despite the role of epidermal growth factor receptor (EGFR) signaling in head and neck squamous cell carcinoma (HNSCC) development and progression, clinical trials involving EGFR tyrosine kinase inhibitors (TKIs) have yielded poor results in HNSCC patients. Mechanisms of acquired resistance to the EGFR TKI erlotinib was investigated by developing erlotinib-resistant HNSCC cell lines and comparing their gene expression profiles with their parental erlotinib-sensitive HNSCC cell lines using microarray analyses and subsequent pathway and network analyses. Erlotinib-resistant HNSCC cells displayed a significant upregulation in immune response and inflammatory pathways compared to parental cells. Interleukin-6 (IL-6) was one of thirteen genes that was significantly differentially expressed in all erlotinib-resistant HNSCC cell lines, which was validated using RT-PCR and ELISA. Blockade of IL-6 signaling using the IL-6 receptor antagonist tocilizumab, was able to overcome erlotinib-resistance in erlotinib-resistant SQ20B tumors in vivo. Overall, erlotinib-resistant HNSCC cells display elevated IL-6 expression levels compared to erlotinib-sensitive HNSCC cells and blockade of the IL-6 signaling pathway may be an effective strategy to overcome resistance to erlotinib and possibly other EGFR TKIs for HNSCC therapy.

MyD88-Dependent Signaling Decreases the Antitumor Efficacy of Epidermal Growth Factor Receptor Inhibition in Head and Neck Cancer Cells.

EGFR is upregulated in the majority of head and neck squamous cell carcinomas (HNSCC). However, many patients with HNSCC respond poorly to the EGFR inhibitors (EGFRI) cetuximab and erlotinib, despite tumor expression of EGFR. Gene expression analysis of erlotinib-treated HNSCC cells revealed an upregulation of genes involved in MyD88-dependent signaling compared with their respective vehicle-treated cell lines. We therefore investigated whether MyD88-dependent signaling may reduce the antitumor efficacy of EGFRIs in HNSCC. Erlotinib significantly upregulated IL6 secretion in HNSCC cell lines, which our laboratory previously reported to result in reduced drug efficacy. Suppression of MyD88 expression blocked erlotinib-induced IL6 secretion in vitro and increased the antitumor activity of erlotinib in vivo. There was little evidence of Toll-like receptor or IL18 receptor involvement in erlotinib-induced IL6 secretion. However, suppression of IL1R signaling significantly reduced erlotinib-induced IL6 production. A time-dependent increase of IL1α but not IL1ß was observed in response to erlotinib treatment, and IL1α blockade significantly increased the antitumor activity of erlotinib and cetuximab in vivo. A pan-caspase inhibitor reduced erlotinib-induced IL1α secretion, suggesting that IL1α was released because of cell death. Human HNSCC tumors showed higher IL1α mRNA levels compared with matched normal tissue, and IL1α was found to be negatively correlated with survival in patients with HNSCC. Overall, the IL1α/IL1R/MYD88/IL6 pathway may be responsible for the reduced antitumor efficacy of erlotinib and other EGFRIs, and blockade of IL1 signaling may improve the efficacy of EGFRIs in the treatment of HNSCC.

Physicochemical Characterization of Simulated Welding Fume from a Spark Discharge System.

This study introduces spark discharge system (SDS) as a way to simulate welding fumes. The SDS was developed using welding rods as electrodes with an optional coagulation chamber. The size, morphology, composition, and concentration of the fume produced and the concentration of ozone (O3) and nitrogen oxides (NOX) were characterized. The number median diameter (NMD) and total number concentration (TNC) of fresh fume particles were ranged 10-23 nm and 3.1×107-6×107 particles/cm3, respectively. For fresh fume particles, the total mass concentration (TMC) measured gravimetrically ranged 85-760 µg/m3. The size distribution was stable over a period of 12 h. The NMD and TNC of aged fume particles were ranged 81-154 nm and 1.5×106-2.7×106 particles/cm3, respectively. The composition of the aged fume particles was dominated by Fe and O with an estimated stoichiometry between that of Fe2O3 and Fe3O4. Concentrations of O3 and NOX were ranged 0.07-2.2 ppm and 1-20 ppm, respectively. These results indicate that the SDS is capable of producing stable fumes over a long-period that are similar to actual welding fumes. This system may be useful in toxicological studies and evaluation of instrumentation.