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Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

Spitzoid neoplasms are a challenging group of cutaneous melanocytic proliferations. They are characterized by epithelioid and/or spindle‐shaped melanocytes and classified as benign Spitz nevi (SN), atypical Spitz tumors (AST), or malignant Spitz tumors (MST). The intermediate AST category represents a diagnostically challenging group since on purely histopathological grounds, their benign or malignant character remains unpredictable. This results in uncertainties in patient treatment and prognosis. The molecular properties of Spitzoid lesions, especially their transcriptomic landscape, remain poorly understood, and genomic alterations in melanoma‐associated oncogenes are typically absent. The aim of this study was to characterize their transcriptome with digital mRNA expression profiling. Formalin‐fixed paraffin‐embedded samples (including 27 SN, 10 AST, and 14 MST) were analyzed using the NanoString nCounter PanCancer Pathways Panel. The number of significantly differentially expressed genes in SN vs. MST, SN vs. AST, and AST vs. MST was 68, 167, and 18, respectively. Gene set enrichment analysis revealed upregulation of pathways related to epithelial–mesenchymal transition and immunomodulatory‐, angiogenesis‐, hormonal‐, and myogenesis‐associated processes in AST and MST. A molecular signature of SN vs. MST was discovered based on the top‐ranked most informative genes: NRAS, NF1, BMP2, EIF2B4, IFNA17, and FZD9. The AST samples showed intermediate levels of the identified signature. This implies that the gene signature can potentially be used to distinguish high‐grade from low‐grade AST with a larger study cohort in the future. This combined histopathological and transcriptomic methodology is promising for prospective diagnostics of Spitzoid neoplasms and patient management in dermatological oncology., Spitzoid neoplasms are cutaneous melanocytic proliferations and classified as benign Spitz nevi (SN), atypical Spitz tumors (AST), or malignant Spitz tumors (MST). In particular, AST are challenging since on histopathological grounds, their benign or malignant potential is unpredictable. We characterized their transcriptome with digital mRNA expression profiling. A molecular signature was discovered, which might be promising for diagnostics in dermatological oncology.

FOLFOX (5‐fluorouracil, leucovorin and oxaliplatin) is one of the main chemotherapy regimens for colorectal cancer (CRC), but only half of CRC patients respond to this regimen. Using gene expression profiles of 96 metastatic CRC patients treated with FOLFOX, we first selected gene pairs whose within‐sample relative expression orderings (REO) were significantly associated with the response to FOLFOX using the exact binomial test. Then, from these gene pairs, we applied an optimization procedure to obtain a subset that achieved the largest F‐score in predicting pathological response of CRC to FOLFOX. The REO‐based qualitative transcriptional signature, consisting of five gene pairs, was developed in the training dataset consisting of 96 samples with an F‐score of 0.90. In an independent test dataset consisting of 25 samples with the response information, an F‐score of 0.82 was obtained. In three other independent survival datasets, the predicted responders showed significantly better progression‐free survival than the predicted non‐responders. In addition, the signature showed a better predictive performance than two published FOLFOX signatures across different datasets and is more suitable for CRC patients treated with FOLFOX than 5‐fluorouracil‐based signatures. In conclusion, the REO‐based qualitative transcriptional signature can accurately identify metastatic CRC patients who may benefit from the FOLFOX regimen., FOLFOX is one of the main chemotherapy regimens for colorectal cancer (CRC), but only half of CRC patients respond to this regimen. The REO‐based qualitative transcriptional signature can accurately identify CRC patients who may benefit from the FOLFOX regimen.

Background Breast cancer is the most common malignancy in female patients worldwide. Because of its heterogeneity in terms of prognosis and therapeutic response, biomarkers with the potential to predict survival or assist in making treatment decisions in breast cancer patients are essential for an individualised therapy. Epigenetic alterations in the genome of the cancer cells, such as changes in DNA methylation pattern, could be a novel marker with an important role in the initiation and progression of breast cancer. Method DNA methylation and RNA-seq datasets from The Cancer Genome Atlas (TCGA) were analysed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox model. Applying gene ontology (GO) and single sample gene set enrichment analysis (ssGSEA) an epigenetic signature associated with the survival of breast cancer patients was constructed that yields the best discrimination between tumour and normal breast tissue. A predictive nomogram was built for the optimal strategy to distinguish between high- and low-risk cases. Results The combination of mRNA-expression and of DNA methylation datasets yielded a 13-gene epigenetic signature that identified subset of breast cancer patients with low overall survival. This high-risk group of tumor cases was marked by upregulation of known cancer-related pathways (e.g. mTOR signalling). Subgroup analysis indicated that this epigenetic signature could distinguish high and low-risk patients also in different molecular or histological tumour subtypes (by Her2-, EGFR- or ER expression or different tumour grades). Using Gene Expression Omnibus (GEO) the 13-gene signature was confirmed in four external breast cancer cohorts. Conclusion An epigenetic signature was discovered that effectively stratifies breast cancer patients into low and high-risk groups. Since its efficiency appears independent of other known classifiers (such as staging, histology, metastasis status, receptor status), it has a high potential to further improve likely individualised therapy in breast cancer.

Simple Summary Survivin, coded by the BIRC5 gene, is the cell death preventing protein, which is important for cell division in normal and cancer cells. It is intensively studied as a cancer biomarker and target for antitumor therapy. In this study we asked if we could get clinically helpful information on how active BIRC5 is in breast cancer patients? We studied the BIRC5 protein level in tumor samples for breast cancer patients from a West Swedish cohort and its mRNA level in two different public gene expression databases. Survival analysis demonstrated that a higher BIRC5 protein or mRNA level was associated with poor survival in all cohorts and for different cancer subtypes. We show that BIRC5 is a promising independent cancer survival marker. Abstract Breast cancer (BC) histological and molecular classifications significantly improved the treatment strategy and prognosis. Inhibitor of apoptosis BIRC5/survivin is often overexpressed in cancers, however, indications of its importance in BC are inconsistent. We integrate BIRC5 protein and mRNA measures with clinical associates and long-term outcome in three independent cohorts Protein levels of BIRC5 were measured in primary lysates of 845 patients of the West Swedish BC cohort (VGR-BC) and linked to 5- and 27-years survival. The results were externally validated in transcriptomic data from METABRIC and SCAN-B cohorts. Survival analysis showed that high levels of BIRC5 were consistently associated with a poor probability of 5-year overall survival. High BIRC5 in VGR-BC contributed negatively to the disease-specific survival at 5 and 27 years. Subsets with different status by ER (estrogen receptor) expression and presence of nodal metastasis supported independent association of high BIRC5 with poor prognosis in all cohorts. In METABRIC and SCAN-B cohorts, high levels of BIRC5 mRNA were associated with the basal-like and luminal B molecular BC subtypes and with increasing histologic grade. BIRC5 is a sensitive survival marker that acts independent of ER and nodal status, and its levels need to be considered when making treatment decisions.

Cancer genomes have been explored from the early 2000s through massive exome sequencing efforts, leading to the publication of The Cancer Genome Atlas in 2013. Sequencing techniques have been developed alongside this project and have allowed scientists to bypass the limitation of costs for whole‐genome sequencing (WGS) of single specimens by developing more accurate and extensive cancer sequencing projects, such as deep sequencing of whole genomes and transcriptomic analysis. The Pan‐Cancer Analysis of Whole Genomes recently published WGS data from more than 2600 human cancers together with almost 1200 related transcriptomes. The application of WGS on a large database allowed, for the first time in history, a global analysis of features such as molecular signatures, large structural variations and noncoding regions of the genome, as well as the evaluation of RNA alterations in the absence of underlying DNA mutations. The vast amount of data generated still needs to be thoroughly deciphered, and the advent of machine‐learning approaches will be the next step towards the generation of personalized approaches for cancer medicine. The present manuscript wants to give a broad perspective on some of the biological evidence derived from the largest sequencing attempts on human cancers so far, discussing advantages and limitations of this approach and its power in the era of machine learning., Since the publication of The Cancer Genome Atlas data in 2013, the advances in the sequencing techniques allowed us to study cancer through whole‐genome sequencing and multiomics approaches. The vast amount of data generated still needs to be thoroughly deciphered, and the advent of machine learning approaches will be the next step towards personalized approaches for cancer medicine.

Background Patient-derived xenograft (PDX) models with definite molecular signature are attractive preclinical models for development of novel targeted drugs. Here, we profiled and explored potential therapeutic targets based on characterized PDX models for advanced gastric cancer (AGC). Methods The genomic variation and molecular profile of 50 PDX models from AGC patients were analyzed by targeted next-generation sequencing, in situ hybridization, and immunohistochemistry. The antitumor activities of several targeted drugs were investigated in the PDX models. Furthermore, response biomarkers were explored. Results Each PDX model had individual histopathological and molecular features, and recurrent alterations in the MAPK, ErbB, VEGF, mTOR, and cell cycle signaling pathways were major events in these PDX models. Several potential drug targets, such as EGFR, MET, and CCNE1, were selected and validated in this study. Volitinib demonstrated strong antitumor activity in PDX models with MET and phosphorylated MET (pMET) overexpression. The EGFR monoclonal antibodies BK011 and cetuximab inhibited tumor growth in a PDX model with EGFR amplification. Afatinib inhibited tumor growth in the PDX models with EGFR amplification, EGFR overexpression, or HER2 amplification. Apatinib was more sensitive in the PDX models with high microvessel density. The CDK1/2/9 inhibitor AZD5438 had superior anti-tumor activity in two models with higher copy number of CCNE1. Conclusions PDX models with defined molecular signature are useful for preclinical studies with targeted drugs, and the results should be validated in larger studies with PDX models or in clinical trials. Electronic supplementary material The online version of this article (10.1186/s13045-018-0563-y) contains supplementary material, which is available to authorized users.

// Yang An 1, 2 , Haojie Wang 1, 2 , Jingyao Jie 1, 2 , Yitai Tang 3 , Weijuan Zhang 1, 2 , Shaoping Ji 1, 2, 4 , Xiangqian Guo 1, 2, 5, 6 1 Department of Biochemistry and Molecular Biology, Medical School, Henan University, Kaifeng 475004, China 2 Cell Signal Transduction Laboratory, Henan University, Kaifeng 475004, China 3 Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA 4 Department of Oncology, The First Affiliated Hospital of Henan University, Kaifeng, 475001, China 5 Department of Preventive Medicine, Medical School, Henan University, Kaifeng 475004, China 6 Department of Burn and Plastic Surgery, The Affiliated Nanshi Hospital of Henan University, Nanyang, 473003, China Correspondence to: Xiangqian Guo, email: xqguo@henu.edu.cn Shaoping Ji, email: shaopingji@163.com Keywords: uterine carcinosarcoma, molecular subtype, molecular signature, gene expression pattern, subtype-specific treatment Received: August 11, 2016 Accepted: January 06, 2017 Published: February 02, 2017 ABSTRACT Uterine carcinosarcoma (UCS) is a rare but lethal neoplasm with high metastasis and recurrence rate, and to date, no molecular classification of UCS has been defined to achieve targeted therapies. In this study, we identified two distinct molecular subtypes of UCS with distinct gene expression patterns and clinicopathologic characteristics. Subtype I UCS recapitulates low-grade UCS, in contrast subtype II UCS represents high-grade UCS with higher tumor invasion rate and tumor weight. Interestingly, subtype I UCS is characterized by cell adhesion and apoptosis pathways, whereas genes over-expressed in subtype II UCS are more involved in myogenesis/muscle development. We also proposed certain potential subtype specific therapeutic targets, such as SYK (spleen tyrosine kinase) for subtype I and cell-cycle proteins for subtype II. Our findings provide a better recognition of UCS molecular subtypes and subtype specific oncogenesis mechanisms, and can help develop more specific targeted treatment options for these tumors.

Background The concept of head and neck cancers (HNSCC) having unique molecular signatures is well accepted but relating this to clinical presentation and disease behaviour is essential for patient benefit. Currently the clinical significance of HNSCC molecular subtypes is uncertain therefore personalisation of HNSCC treatment is not yet possible. Methods We performed meta-analysis on 8 microarray studies and identified six significantly up- (PLAU, FN1, CDCA5) and down-regulated (CRNN, CLEC3B and DUOX1) genes which were subsequently quantified by RT-qPCR in 100 HNSCC patient margin and core tumour samples. Results Retrospective correlation with sociodemographic and clinicopathological patient details identified two subgroups of opposing molecular signature (+q6 and -q6) that correlated to two recognised high-risk HNSCC populations in the UK. The +q6 group were older, male, and excessive alcohol users whilst the –q6 group were younger, female, paan-chewers and predominantly Bangladeshi. Additionally, all patients with tumour recurrence were in the latter subgroup. Conclusions We provide the first evidence linking distinct molecular signatures in HNSCC with clinical presentations. Prospective trials are required to determine the correlation between these distinct genotypes and disease progression or treatment response. This is an important step towards the ultimate goal of improving outcomes by utilising personalised molecular-signature-guided treatments for HNSCC patients.

Acute myeloid leukemia (AML) is a heterogeneous disease characterized by myeloid precursor proliferation in the bone marrow, apoptosis reduction and differentiation arrest. Although there are several studies in this field, events related to disease initiation and progression remain unknown. The malignant transformation of hematopoietic stem cells (HSC) is thought to generate leukemic stem cells, and this transformation could be related to changes in mesenchymal stromal cell (hMSC) signaling. Thus, the aim of this work was to analyze the gene expression profile of hMSC from AML patients (hMSC-AML) compared to healthy donors hMSCs (hMSC-HD). The results showed a common molecular signature for all hMSC-AML. Other assays were performed with a large number of patients and the results confirmed a molecular signature that is capable of distinguishing hMSC-AML from hMSC-HD. Moreover, CCL2 and BMP4 genes encode secreted proteins that could affect HSCs. To verify whether these proteins are differentially expressed in AML patients, ELISA was performed with plasma samples. CCL2 and BMP4 proteins are differentially expressed in AML patients, indicating changes in hMSC-AML signaling. Altogether, hMSCs-AML signaling alterations could be an important factor in the leukemic transformation process.

Aim Increased serum alpha-fetoprotein (AFP) levels are associated with specific molecular sub-classes of hepatocellular carcinoma (HCC), supporting AFP as a predictive or therapeutic biomarker for precision treatment of this disease. Considering recent efforts to validate HCC molecular classification systems across different populations, we applied existing signature-based classification templates to Hawaii cohorts and examined whether associations between HCC molecular sub-class, AFP levels, and clinical features found elsewhere can also be found in Hawaii, a region with a unique demographic and risk factor profile for HCC. Methods Whole-genome expression profiling was performed on HCC tumors collected from 40 patients following partial hepatectomy. Tumors underwent transcriptome-based categorization into 3 molecular sub-classes (S1, S2, and S3). Patient groups based on molecular sub-class and AFP level were then compared with regards to clinical features and survival. Differences associated with AFP level and other clinical parameters were also examined at the gene signature level by gene set enrichment analysis. Results Statistically confident (false discovery rate < 0.05) sub-classifications were made in 98% (39/40) of tumors. Patient sub-groups differed significantly with regards to serum AFP level, with significantly lower levels in the S3 sub-group as compared to S1 (P = 0.048) and S2 (P = 0.010). Serum AFP > 400 ng/mL predicted significant tumor enrichment for genes corresponding to MYC target activation, high cell proliferation, poor clinical prognosis, and the S2 sub-class. AFP > 400 ng/mL and non-S3 tumor classification were found to be significant predictors of overall survival. Conclusion Distinct sub-classes of HCC associated with different molecular features and survival outcomes can be detected with statistical confidence in a Pacific Island cohort. Molecular classification signatures and other predictive markers for HCC that are valid for all patient populations are needed to support multi-center efforts to develop targeted therapies for HCC.

Background Sessile serrated adenomas/polyps are distinguished from hyperplastic colonic polyps subjectively by their endoscopic appearance and histological morphology. However, hyperplastic and sessile serrated polyps can have overlapping morphological features resulting in sessile serrated polyps diagnosed as hyperplastic. While sessile serrated polyps can progress into colon cancer, hyperplastic polyps have virtually no risk for colon cancer. Objective measures, differentiating these types of polyps would improve cancer prevention and treatment outcome. Methods RNA-seq training data set and Affimetrix, Illumina testing data sets were obtained from Gene Expression Omnibus (GEO). RNA-seq single-end reads were filtered with FastX toolkit. Read mapping to the human genome, gene abundance estimation, and differential expression analysis were performed with Tophat-Cufflinks pipeline. Background correction, normalization, and probe summarization steps for Affimetrix arrays were performed using the robust multi-array method (RMA). For Illumina arrays, log2-scale expression data was obtained from GEO. Pathway analysis was implemented using Bioconductor package GSAR. To build a platform-independent molecular classifier that accurately differentiates sessile serrated and hyperplastic polyps we developed a new feature selection step. We also developed a simple procedure to classify new samples as either sessile serrated or hyperplastic with a class probability assigned to the decision, estimated using Cantelli’s inequality. Results The classifier trained on RNA-seq data and tested on two independent microarray data sets resulted in zero and three errors. The classifier was further tested using quantitative real-time PCR expression levels of 45 blinded independent formalin-fixed paraffin-embedded specimens and was highly accurate. Pathway analyses have shown that sessile serrated polyps are distinguished from hyperplastic polyps and normal controls by: up-regulation of pathways implicated in proliferation, inflammation, cell-cell adhesion and down-regulation of serine threonine kinase signaling pathway; differential co-expression of pathways regulating cell division, protein trafficking and kinase activities. Conclusions Most of the differentially expressed pathways are known as hallmarks of cancer and likely to explain why sessile serrated polyps are more prone to neoplastic transformation than hyperplastic. The new molecular classifier includes 13 genes and may facilitate objective differentiation between two polyps. Electronic supplementary material The online version of this article (10.1186/s12920-017-0317-7) contains supplementary material, which is available to authorized users.

// Hun-Jun Park 1, * , Ji Heon Noh 2, 3, * , Jung Woo Eun 2, 3, * , Yoon-Seok Koh 1 , Suk Min Seo 1 , Won Sang Park 2 , Jung Young Lee 2 , Kiyuk Chang 1 , Ki Bae Seung 1 , Pum-Joon Kim 1 , Suk Woo Nam 2, 3, 4 1 Department of Cardiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea 2 Department of Pathology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea 3 Functional RNomics Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea 4 Cancer Evolution Research Center, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea * These authors have contributed equally to this work Correspondence to: Suk Woo Nam, e-mail: swnam@catholic.ac.kr Pum-Joon Kim, e-mail: bjheart@catholic.ac.kr Keywords: ST-segment-elevation myocardial infarction, peripheral blood transcriptome, molecular signature, diagnostic biomarkers Received: March 25, 2015 Accepted: May 04, 2015 Published: May 18, 2015 ABSTRACT Blood transcriptome reflects the status of diseases, and characteristic molecular signature provides a novel window on gene expression preceding acute coronary events. We aim to determine blood transcriptome-based molecular signature of acute coronary syndrome (ACS), and to identify novel serum biomarkers for early stage ST-segment-elevation myocardial infarction (STEMI). We obtained peripheral blood from the patients with ACS who visited emergency department within 4 hours after the onset of chest pain: STEMI ( n = 10), Non-ST-segment-elevation MI (NSTEMI, n = 10) and unstable angina (UA, n = 11). Blood transcriptome scans revealed that a characteristic gene expression change exists in STEMI, resulting in 531 outlier genes as STEMI molecular signature (Welch’s t test, P < 0.05). Another analysis with a set of blood samples of patients with STEMI ( n = 7) before and 7 days after the primary percutaneous coronary intervention ( n = 7) and normal control ( n = 10) evidenced that STEMI molecular signature directly reflects the onset of STEMI pathogenesis. From the two sets of transcriptome-based STEMI signatures, we identified 10 genes encoding transmembrane or secretory proteins that are highly expressed in STEMI. We validated blood protein expression levels of these 10 putative biomarkers in 40 STEMI and 32 healthy subjects by ELISA. Data suggested that PGLYRP1, IRAK3 and VNN3 are more specific and sensitive diagnostic biomarkers for STEMI than traditional CK-MB or troponin. Blood transcriptome scans of ACS evidenced early stage molecular markers for STEMI. Here, we report novel biomarkers to diagnose STEMI at emergency department in hospitals by a simple ELISA method.

Infectious diseases represent the most serious threat to shrimp farming worldwide. Understanding the molecular mechanisms driving shrimp-pathogen interactions is necessary for developing strategies to control disease outbreaks in shrimp production systems. In the current study, we experimentally reproduced mortality events using standardized infections to characterize the hemocyte transcriptome response of the shrimp Litopenaeus vannamei succumbing to infectious diseases. By using a high-throughput microfluidic RT-qPCR approach, we identified molecular signatures in shrimp during lethal infections caused by the White Spot Syndrome Virus (WSSV) or the filamentous fungus Fusarium solani. We successfully identified gene expression signatures shared by both infections but also pathogen-specific gene responses. Interestingly, whereas lethal WSSV infection induced the expression of antiviral-related genes, the transcript abundance of many antimicrobial effectors was reduced by lethal F. solani infection. To our knowledge, this is the first report of the immune-gene repertoire of infected shrimp at the brink of death.

Undifferentiated and poorly differentiated thyroid tumors are responsible for more than half of thyroid cancer patient deaths in spite of their low incidence. Conventional treatments do not obtain substantial benefits, and the lack of alternative approaches limits patient survival. Additionally, the absence of prognostic markers for well-differentiated tumors complicates patient-specific treatments and favors the progression of recurrent forms. In order to recognize the molecular basis involved in tumor dedifferentiation and identify potential markers for thyroid cancer prognosis prediction, we analysed the expression profile of 44 thyroid primary tumors with different degrees of dedifferentiation and aggressiveness using cDNA microarrays. Transcriptome comparison of dedifferentiated and well-differentiated thyroid tumors identified 1031 genes with > 2-fold difference in absolute values and false discovery rate of

Background Several clinical and pathological factors have an impact on the prognosis of colorectal cancer (CRC), but they are not yet adequate for risk assessment. We aimed to identify a molecular signature that can reliably identify CRC patients at high risk for recurrence. Results Two hundred eighty-one CRC samples (stage II/III) were included in this study. A two-step gene expression profiling study was conducted. First, gene expression measurements from 81 fresh frozen CRC samples were obtained using Affymetrix Human Genome U133 Plus 2.0 Arrays. Second, a focused gene expression assay, including prognostic genes and genes of interest from literature reviews, was performed using 200 fresh frozen samples and a Taqman low-density array (TLDA) analysis. An optimal 31-gene expression classifier for the prediction of recurrence among patients with stage II/III CRC was developed using logistic regression analysis. This gene expression signature classified 58.5% of patients as low-risk and 41.5% as high-risk (P

The recent rapid development of high-throughput technology enables the study of molecular signatures for cancer diagnosis and prognosis at multiple levels, from genomic and epigenomic to transcriptomic. These unbiased large-scale scans provide important insights into the detection of cancer-related signatures. In addition to single-layer signatures, such as gene expression and somatic mutations, integrating data from multiple heterogeneous platforms using a systematic approach has been proven to be particularly effective for the identification of classification markers. This approach not only helps to uncover essential driver genes and pathways in the cancer network that are responsible for the mechanisms of cancer development, but will also lead us closer to the ultimate goal of personalized cancer therapy.

Background Ion channels play a critical role in a wide variety of biological processes, including the development of human cancer. However, the overall impact of ion channels on tumorigenicity in breast cancer remains controversial. Methods We conduct microarray meta-analysis on 280 ion channel genes. We identify candidate ion channels that are implicated in breast cancer based on gene expression profiling. We test the relationship between the expression of ion channel genes and p53 mutation status, ER status, and histological tumor grade in the discovery cohort. A molecular signature consisting of ion channel genes (IC30) is identified by Spearman’s rank correlation test conducted between tumor grade and gene expression. A risk scoring system is developed based on IC30. We test the prognostic power of IC30 in the discovery and seven validation cohorts by both Cox proportional hazard regression and log-rank test. Results 22, 24, and 30 ion channel genes are found to be differentially expressed with a change in p53 mutation status, ER status, and tumor histological grade in the discovery cohort. We assign the 30 tumor grade associated ion channel genes as the IC30 gene signature. We find that IC30 risk score predicts clinical outcome (P Conclusions We identified a molecular gene signature IC30, which represents a promising diagnostic and prognostic biomarker in breast cancer. Our results indicate that information regarding the expression of ion channels in tumor pathology could provide new targets for therapy in human cancers.

BackgroundPhyto-remedies for diabetic control are popular among patients with Type II Diabetes mellitus (DM), in addition to other diabetic control measures. A number of plant species are known to possess diabetic control properties.Costus pictusD. Don is popularly known as “Insulin Plant” in Southern India whose leaves have been reported to increase insulin pools in blood plasma. Next Generation Sequencing is employed as a powerful tool for identifying molecular signatures in the transcriptome related to physiological functions of plant tissues. We sequenced the leaf transcriptome ofC. pictususing Illumina reversible dye terminator sequencing technology and used combination of bioinformatics tools for identifying transcripts related to anti-diabetic properties ofC. pictus.ResultsA total of 55,006 transcripts were identified, of which 69.15% transcripts could be annotated. We identified transcripts related to pathways of bixin biosynthesis and geraniol and geranial biosynthesis as major transcripts from the class of isoprenoid secondary metabolites and validated the presence of putative norbixin methyltransferase, a precursor of Bixin. The transcripts encoding these terpenoids are known to be Peroxisome Proliferator-Activated Receptor (PPAR) agonists and anti-glycation agents. Sequential extraction and High Performance Liquid Chromatography (HPLC) confirmed the presence of bixin inC. pictusmethanolic extracts. Another significant transcript identified in relation to anti-diabetic, anti-obesity and immuno-modulation is of Abscisic Acid biosynthetic pathway. We also report many other transcripts for the biosynthesis of antitumor, anti-oxidant and antimicrobial metabolites ofC. pictusleaves.ConclusionSolid molecular signatures (transcripts related to bixin, abscisic acid, and geranial and geraniol biosynthesis) for the anti-diabetic properties ofC. pictusleaves and vital clues related to the other phytochemical functions like antitumor, anti-oxidant, immuno-modulatory, anti-microbial and anti-malarial properties through the secondary metabolite pathway annotations are reported. The data provided will be of immense help to researchers working in the treatment of DM using herbal therapies.

The embryonic stem cell test (EST) predicts developmental toxicity based on the inhibition of cardiomyocyte differentiation of embryonic stem cells (ESC). The subjective endpoint, the long culture duration together with the undefined applicability domain and related predictivity need further improvement to facilitate implementation of the EST into regulatory strategies. These aspects may be improved by studying gene expression changes in the ESC differentiation cultures and their modulation by compound exposure using transcriptomics. Here, we tested the developmental toxicants monobutyl phthalate and 6-aminonicotinamide. ESC were allowed to differentiated, and cardiomyocyte differentiation was assessed after 10 days of culture. RNA of solvent controls was collected after 0, 24, 48, 72 and 96 hours of exposure, and RNA of developmental-toxicant-exposed cultures was collected after 24 and 96 hours. Samples were hybridized to DNA microarrays, and 1355 genes were found differentially expressed among the unexposed experimental groups. These regulated genes were involved in differentiation-related processes, and Principal Component Analysis (PCA) based on these genes showed that the unexposed experimental groups appeared in chronological order in the PCA, which can therefore be regarded as a continuous representation of the differentiation track. The developmental-toxicant-exposed cultures appeared to deviate significantly from this differentiation track, which confirms the compound-modulating effects on the differentiation process. The incorporation of transcriptomics in the EST is expected to provide a more informative and improved endpoint in the EST as compared with morphology, allowing early detection of differentiation modulation. Furthermore, this approach may improve the definition of the applicability domain and predictivity of the EST.

Senescence is a permanent proliferation arrest in response to cell stress such as DNA damage. It contributes strongly to tissue aging and serves as a major barrier against tumor development. Most tumor cells are believed to bypass the senescence barrier (become "immortal") by inactivating growth control genes such as TP53 and CDKN2A. They also reactivate telomerase reverse transcriptase. Senescence-to-immortality transition is accompanied by major phenotypic and biochemical changes mediated by genome-wide transcriptional modifications. This appears to happen during hepatocellular carcinoma (HCC) development in patients with liver cirrhosis, however, the accompanying transcriptional changes are virtually unknown. We investigated genome-wide transcriptional changes related to the senescence-to-immortality switch during hepatocellular carcinogenesis. Initially, we performed transcriptome analysis of senescent and immortal clones of Huh7 HCC cell line, and identified genes with significant differential expression to establish a senescence-related gene list. Through the analysis of senescence-related gene expression in different liver tissues we showed that cirrhosis and HCC display expression patterns compatible with senescent and immortal phenotypes, respectively; dysplasia being a transitional state. Gene set enrichment analysis revealed that cirrhosis/senescence-associated genes were preferentially expressed in non-tumor tissues, less malignant tumors, and differentiated or senescent cells. In contrast, HCC/immortality genes were up-regulated in tumor tissues, or more malignant tumors and progenitor cells. In HCC tumors and immortal cells genes involved in DNA repair, cell cycle, telomere extension and branched chain amino acid metabolism were up-regulated, whereas genes involved in cell signaling, as well as in drug, lipid, retinoid and glycolytic metabolism were down-regulated. Based on these distinctive gene expression features we developed a 15-gene hepatocellular immortality signature test that discriminated HCC from cirrhosis with high accuracy. Our findings demonstrate that senescence bypass plays a central role in hepatocellular carcinogenesis engendering systematic changes in the transcription of genes regulating DNA repair, proliferation, differentiation and metabolism. © 2013 Yildiz et al.