Interferon (alpha, beta and omega) receptor 2 is a prognostic biomarker for lung cancer

Emerging role of competing endogenous RNA in lung cancer drug resistance

Lung cancer remains one of the most common malignant cancers worldwide, and its survival rate is extremely low. Chemotherapy, the mainstay of lung cancer treatment, is not as effective as it could be due to the development of cellular resistance. The molecular mechanisms of drug resistance in lung cancer remain to be elucidated. Accumulating evidence suggests that ceRNAs are involved in various carcinogenesis and development. CeRNA is a transcript that regulates each other through competition with miRNA. However, the relationship between ceRNAs and chemoresistance in lung cancer remains unclear. In this narrative review, we provided a summary of treatment approaches that focus on ceRNA networks to overcome drug resistance.

Long non-coding RNA prostate cancer-associated transcript 6 inhibited gefitinib sensitivity of non-small cell lung cancer by serving as a competing endogenous RNA of miR-326 to up-regulate interferon-alpha receptor 2

The critical roles of lncRNAs in drug resistance of malignancies have been widely recognized. This investigation aims to study the function of lncRNA PCAT6 in the resistance of non-small cell lung cancer (NSCLC) to gefitinib. In our study, we demonstrated that prostate cancer-associated transcript 6 (PCAT6) was upregulated in gefitinib-resistant NSCLC. PCAT6 knockdown inhibited gefitinib resistance of NSCLC, as indicated by decreased IC₅₀ value, proliferation, and metastasis, and increased cell apoptosis. Besides, PCAT6 could directly target miR-326 in gefitinib-resistant NSCLC cells and augment NSCLC resistance to gefitinib by serving as ceRNA of miR-326. Furthermore, interferon-alpha receptor 2 (IFNAR2) was validated as a downstream target of miR-326 and miR-326 reduced resistance to gefitinib by inhibiting IFNAR2 expression. Our investigation identified that PCAT6 enhanced gefitinib resistance of NSCLC via miR-326/IFNAR2 axis, which might offer a new therapeutic strategy against gefitinib resistance of NSCLC patients.

Interferons: role in cancer therapy

Interferons (IFNs) are a group of signaling cytokines, secreted by host cells to induce protection against various disorders. IFNs can directly impact on tumor cells or indirectly induce the immune system to protect host cells. The expression levels of IFNs and its functions of are excellently modulated in a way to protect host cells from probable toxicities caused by extreme responses. The efficacy of anticancer therapies is correlated to IFNs signaling. Although IFN signaling is involved in induction of antitumor responses, chronic stimulation of the IFN signaling pathway can induce resistance to various antineoplasm therapies. Hence, IFNs are expressed by both cancer and immune cells, and modulate their biological function. Understanding this mechanism of action might be a key target of combination therapies.

HIV infection does not alter interferon α/β receptor 2 expression on mucosal immune cells

The innate immune response induced by type I interferons (IFNs) plays a critical role in the establishment of HIV infection. IFNs are induced early in HIV infection and trigger an antiviral defense program by signaling through the IFNα/β receptor (IFNAR), which consists of two subunits, IFNAR1 and IFNAR2. Changes in IFNAR expression in HIV target cells, as well as other immune cells, could therefore have important consequences for initial HIV spread. It was previously reported that IFNAR2 expression is increased in peripheral blood CD4+ CXCR4+ T cells of HIV+ patients compared to HIV uninfected controls, suggesting that HIV infection may alter the IFN responsiveness of target cells. However, the earliest immune cells affected by HIV in vivo reside in the gut-associated lymphoid tissue (GALT). To date, it remains unknown if IFNAR expression is altered in GALT immune cells in the context of HIV infection and exposure to IFNs, including the 12 IFNα subtypes. Here, we analyzed the expression of surface bound and soluble IFNAR2 on Lamina propria mononuclear cells (LPMCs) isolated from the GALT of HIV- individuals and in plasma samples of HIV+ patients. IFNAR2 expression varied between different T cells, B cells and natural killer cells, but was not altered following HIV infection. Furthermore, expression of the soluble IFNAR2a isoform was not changed in HIV+ patients compared to healthy donors, nor in LPMCs after HIV-1 infection ex vivo. Even though the 12 human IFNα subtypes trigger different biological responses and vary in their affinity to both receptor subunits, stimulation of LPMCs with different recombinant IFNα subtypes did not result in any significant changes in IFNAR2 surface expression. Our data suggests that potential changes in the IFN responsiveness of mucosal immune cells during HIV infection are unlikely dictated by changes in IFNAR2 expression.

How unique is interferon-β within the type I interferon family?

All type I interferons share structural homology and bind to a common heterodimeric receptor consisting of the IFNAR1 and IFNAR2 subunits, which are expressed on most cell types. Although binding to the same receptor pair, they evoke a broad range of activities within the cell affecting the expression of numerous genes and resulting in profound cellular changes. Differential activation results from multiple levels of cellular and molecular events including binding affinity, receptor density, cell type-specific variations, and post-translational modification of signaling molecules downstream. Within the type I interferon family the Asn-Gly-Arg (NGR) sequence motif is unique to interferon-β and, together with its deamidated variants Asp-Gly-Arg (DGR) and iso-Asp-Gly-Arg (iso-DGR), imparts additional binding specificities that go beyond that of the canonical IFNAR1/IFNAR2. These warrant further investigations and functional studies and may eventually shed new light on differential effects observed for this molecule in oncology and autoimmune diseases.

A systems medicine approach reveals disordered immune system and lipid metabolism in multiple sclerosis patients

Identification of autoimmune processes and introduction of new autoantigens involved in the pathogenesis of multiple sclerosis (MS) can be helpful in the design of new drugs to prevent unresponsiveness and side effects in patients. To find significant changes, we evaluated the autoantibody repertoires in newly diagnosed relapsing-remitting MS patients (NDP) and those receiving disease-modifying therapy (RP). Through a random peptide phage library, a panel of NDP- and RP-specific peptides was identified, producing two protein data sets visualized using Gephi, based on protein--protein interactions in the STRING database. The top modules of NDP and RP networks were assessed using Enrichr. Based on the findings, a set of proteins, including ATP binding cassette subfamily C member 1 (ABCC1), neurogenic locus notch homologue protein 1 (NOTCH1), hepatocyte growth factor receptor (MET), RAF proto-oncogene serine/threonine-protein kinase (RAF1) and proto-oncogene vav (VAV1) was found in NDP and was involved in over-represented terms correlated with cell-mediated immunity and cancer. In contrast, transcription factor RelB (RELB), histone acetyltransferase p300 (EP300), acetyl-CoA carboxylase 2 (ACACB), adiponectin (ADIPOQ) and phosphoenolpyruvate carboxykinase 2 mitochondrial (PCK2) had major contributions to viral infections and lipid metabolism as significant events in RP. According to these findings, further research is required to demonstrate the pathogenic roles of such proteins and autoantibodies targeting them in MS and to develop therapeutic agents which can ameliorate disease severity.

MUC1 in lung adenocarcinoma: cross-sectional genetic and serological study

Background

Mucin 1 (MUC1) contributes to the growth and metastasis of various cancers, including lung cancer, and MUC1 gene length polymorphisms are associated with susceptibility to lung cancer and its prognosis. In contrast, the association between rs4072037, a single nucleotide polymorphism in MUC1, and lung cancer has not been well studied.

Methods

In the present study, we determined the rs4072037 genotype and measured serum KL-6 levels to evaluate the association between lung adenocarcinoma (ADC) and rs4072037 or serum KL-6 levels. DNA samples were available for 172 patients and these were included in the genomic analyses. In addition, 304 patients were included in the serum analyses. Furthermore, 276 healthy volunteers were included in both genomic and serum analyses.

Results

The rs4072037 genotype was not associated with susceptibility to lung ADC or its prognosis. Interestingly, serum KL-6 levels significantly differed according to rs4072037 genotype in those with T1 or T2 (P < 0.001), N0 or N1 (P = 0.002) and M0 (P < 0.001), but not in those with T3 or T4 (P = 0.882), N2 or N3 (P = 0.616) and M1a or M1b (P = 0.501). Serum KL-6 levels were significantly associated with the presence of lung ADC, as well as with its progression and prognosis, indicating the crucial involvement of KL-6/MUC1 in the development of lung cancer and its progression.

Conclusion

Based on these findings, we conclude that rs4072037 does not have a significant impact on the pathogenesis or prognosis of lung ADC, whereas serum KL-6 levels, which might reflecting the molecular length of MUC1, are significantly associated with lung ADC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3272-y) contains supplementary material, which is available to authorized users.

Large-scale RNA-Seq Transcriptome Analysis of 4043 Cancers and 548 Normal Tissue Controls across 12 TCGA Cancer Types

The Cancer Genome Atlas (TCGA) has accrued RNA-Seq-based transcriptome data for more than 4000 cancer tissue samples across 12 cancer types, translating these data into biological insights remains a major challenge. We analyzed and compared the transcriptomes of 4043 cancer and 548 normal tissue samples from 21 TCGA cancer types, and created a comprehensive catalog of gene expression alterations for each cancer type. By clustering genes into co-regulated gene sets, we identified seven cross-cancer gene signatures altered across a diverse panel of primary human cancer samples. A 14-gene signature extracted from these seven cross-cancer gene signatures precisely differentiated between cancerous and normal samples, the predictive accuracy of leave-one-out cross-validation (LOOCV) were 92.04%, 96.23%, 91.76%, 90.05%, 88.17%, 94.29%, and 99.10% for BLCA, BRCA, COAD, HNSC, LIHC, LUAD, and LUSC, respectively. A lung cancer-specific gene signature, containing SFTPA1 and SFTPA2 genes, accurately distinguished lung cancer from other cancer samples, the predictive accuracy of LOOCV for TCGA and GSE5364 data were 95.68% and 100%, respectively. These gene signatures provide rich insights into the transcriptional programs that trigger tumorigenesis and metastasis, and many genes in the signature gene panels may be of significant value to the diagnosis and treatment of cancer.

Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer

In the last decade, optimized treatment for non-small cell lung cancer had lead to improved prognosis, but the overall survival is still very short. To further understand the molecular basis of the disease we have to identify biomarkers related to survival. Here we present the development of an online tool suitable for the real-time meta-analysis of published lung cancer microarray datasets to identify biomarkers related to survival. We searched the caBIG, GEO and TCGA repositories to identify samples with published gene expression data and survival information. Univariate and multivariate Cox regression analysis, Kaplan-Meier survival plot with hazard ratio and logrank P value are calculated and plotted in R. The complete analysis tool can be accessed online at: www.kmplot.com/lung. All together 1,715 samples of ten independent datasets were integrated into the system. As a demonstration, we used the tool to validate 21 previously published survival associated biomarkers. Of these, survival was best predicted by CDK1 (p<1E-16), CD24 (p<1E-16) and CADM1 (p = 7E-12) in adenocarcinomas and by CCNE1 (p = 2.3E-09) and VEGF (p = 3.3E-10) in all NSCLC patients. Additional genes significantly correlated to survival include RAD51, CDKN2A, OPN, EZH2, ANXA3, ADAM28 and ERCC1. In summary, we established an integrated database and an online tool capable of uni- and multivariate analysis for in silico validation of new biomarker candidates in non-small cell lung cancer.