Gene Expression Profiling Of Cancer Research Articles

Gene Expression Profiling of Carcinoma Breast and its, Prognostic Signature: A Review

Breast cancer research and clinical care have reached a new era due to the development of modern genetic technologies. In the past, breast cancer diagnosis, prognosis and treatment decisions were based on, clinical and pathological analysis of the breast cancer tissues and axillary lymph nodes. It has been observed that the prognosis and recommendation about treatment based on these features, imperfectly predict the outcome and results in excessive treatment and chemotherapy, with marginal benefits. Hence new methods are needed to understand breast cancer properly, to optimize and individualize the breast cancer treatment and prognosis. The development of the gene microarray techniques has enabled scientists to detect the different gene expression array of thousands of gene simultaneously and thus create a gene expression profile for different types of breast cancers. Gene expression profiling of the breast cancers has improved our understanding of the heterogeneity of breast cancer on the genomic level; challenged the clinical classification of breast cancer; served as an important prognostic indicator and most importantly, begin to guide our treatment in women with early breast cancer. Incorporation of molecular assays into the treatment and planning strategy of breast cancer continues to be a work in progress. This approach is evolving quickly due to strong scientific pieces of evidence to become a standard of practice in the near future. This article provides an overview of the development and application of molecular assays as applied to breast cancer.

The effects of Wnt inhibition on tumor progression and the tumor microenvironment in a syngeneic mouse model of ovarian cancer.

e17078 Background: Alterations in the Wnt/β-catenin pathway have been associated with tumor progression in multiple cancers. Gene expression profiling in ovarian cancer has correlated Wnt upregulation to patterns of immune evasion in the tumor microenvironment (TME). We investigated effects of in vitro Wnt inhibition in a murine ovarian cancer cell line with p53 knocked out as a model to mimic human ovarian carcinoma. Methods: We used a small molecule inhibitor, CGX-1321, in C57Bl6 mice, an immunocompetent mouse model, injected intraperitoneally with either ID8 cells, a murine ovarian cancer cell line, or ID8-p53-/- cells. Mouse omentums were collected and weighed for evaluation of tumor growth. Representative histology slides were stained for H&E. Western blots for β-catenin baseline levels were performed on the cell lines. TopFlash Assays with WNT3A stimulation were used for analysis of cell line nuclear β-catenin levels. Flow cytometry was performed on mouse omentums for TME evaluation. Results: Treatment with CGX-1321 decreased tumor size via omentum weight with both cell lines, p = 0.0098 with ID8 cells and p = 0.0855 with ID8-p53-/- cells. H&E slides revealed a decreased tumor progression. Western blots confirmed a difference in baseline β-catenin levels between these two cell lines. However, TopFlash assays showed a response to stimulation. Changes in the TME between treated samples with ID8-p53-/- cells did now show significant differences via flow cytometry. Conclusions: In the syngeneic mouse model with a murine ovarian cancer cell line, with p53 knocked out, tumor size and proliferation were decreased with treatment with Wnt inhibition. Surprisingly, the TME changes were inconclusive via flow cytometry. Perhaps these cells do not rely upon the Wnt/ β-catenin pathway as heavily, as seen in changes to baseline β-catenin levels, or perhaps tumors progress quicker and the critical time point of TME change is being overlooked. More investigation needs to be performed to further elucidate these differences.

Analysis of the autophagy gene expression profile of pancreatic cancer based on autophagy-related protein microtubule-associated protein 1A/1B-light chain 3.

BACKGROUNDPancreatic cancer is a highly invasive malignant tumor. Expression levels of the autophagy-related protein microtubule-associated protein 1A/1B-light chain 3 (LC3) and perineural invasion (PNI) are closely related to its occurrence and development. Our previous results showed that the high expression of LC3 was positively correlated with PNI in the patients with pancreatic cancer. In this study, we further searched for differential genes involved in autophagy of pancreatic cancer by gene expression profiling and analyzed their biological functions in pancreatic cancer, which provides a theoretical basis for elucidating the pathophysiological mechanism of autophagy in pancreatic cancer and PNI.AIMTo identify differentially expressed genes involved in pancreatic cancer autophagy and explore the pathogenesis at the molecular level.METHODSTwo sets of gene expression profiles of pancreatic cancer/normal tissue (GSE16515 and GSE15471) were collected from the Gene Expression Omnibus. Significance analysis of microarrays algorithm was used to screen differentially expressed genes related to pancreatic cancer. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to analyze the functional enrichment of the differentially expressed genes. Protein interaction data containing only differentially expressed genes was downloaded from String database and screened. Module mining was carried out by Cytoscape software and ClusterOne plug-in. The interaction relationship between the modules was analyzed and the pivot nodes between the functional modules were determined according to the information of the functional modules and the data of reliable protein interaction network.RESULTSBased on the above two data sets of pancreatic tissue total gene expression, 6098 and 12928 differentially expressed genes were obtained by analysis of genes with higher phenotypic correlation. After extracting the intersection of the two differential gene sets, 4870 genes were determined. GO analysis showed that 14 significant functional items including negative regulation of protein ubiquitination were closely related to autophagy. A total of 986 differentially expressed genes were enriched in these functional items. After eliminating the autophagy related genes of human cancer cells which had been defined, 347 differentially expressed genes were obtained. KEGG pathway analysis showed that the pathways hsa04144 and hsa04020 were related to autophagy. In addition, 65 clustering modules were screened after the protein interaction network was constructed based on String database, and module 32 contains the LC3 gene, which interacts with multiple autophagy-related genes. Moreover, ubiquitin C acts as a pivot node in functional modules to connect multiple modules related to pancreatic cancer and autophagy.CONCLUSIONThree hundred and forty-seven genes associated with autophagy in human pancreatic cancer were concentrated, and a key gene ubiquitin C which is closely related to the occurrence of PNI was determined, suggesting that LC3 may influence the PNI and prognosis of pancreatic cancer through ubiquitin C.

Gene expression profile of cancer stem‑like cells in the SW480 colon adenocarcinoma cell line.

Cancer stem cells (CSCs) serve an important role in tumorigenesis, tolerance to treatment, relapse and metastasis. Although the signaling pathways involved in cancer are well known, the molecular mechanisms underlying CSC actions require further investigation. In the present study, a population of colon CSCs with a cluster of differentiation 133 (CD133) surface‑expression phenotype from the human SW480 colon adenocarcinoma cell line were isolated by flow cytometry. The CD133+ cells exhibited increased tumor sphere‑forming efficiency in vitro and increased tumorigenic potential in vivo. Furthermore, the gene expression profile of colon CSCs was investigated using gene chip technology. The results demonstrated differential gene expression between the CD133+ and CD133‑ subpopulations, including in relation to a number of important genes with functions in transcription control, cell cycle, karyomitosis and protein phosphorylation, including cyclin dependent kinase inhibitor 1A, cyclin B1, checkpoint kinase 1, cyclin dependent kinase 1 and transforming growth factor β receptor 2. Pathway analysis revealed that the mitogen‑activated protein kinase, p53 and calcium signaling pathways, as well as other important signaling pathways, were differentially activated between CD133+ and CD133‑ cells. To the best of our knowledge, these results provide the first evidence for the gene expression profile of colon CSCs in the SW480 colon adenocarcinoma cell line. Although further studies will be required to identify the functional roles of these genes in the CSC phenotype, these observations provide the basis for future studies to elucidate the pathogenesis of colorectal cancer and to develop novel tumor‑targeting therapies.

Stimulative role of ST6GALNAC1 in proliferation, migration and invasion of ovarian cancer stem cells via the Akt signaling pathway

BackgroundOvarian cancer is known as one of the most common cancers in the world among women. ST6GALNAC1 is highly expressed in cancer stem cells (CSCs), which correlates to high tumor-initiating, self-renewal and differentiation abilities. This present study aims to investigate how ST6GALNAC1 affects ovarian cancer stem cells (OCSCs).MethodsIn order to identify the differentially expressed genes related to ovarian cancer, microarray-based gene expression profiling of ovarian cancer was used, and ST6GALANC1 was one of the identified targets. After that, levels of ST6GALNAC1 in OCSCs and ovarian cancer cells were examined. Subsequently, an Akt signaling pathway inhibitor LY294002 was introduced into the cluster of differentiation 90+ (CD90+) stem cells, and cell proliferation, migration and invasion, levels of CXCL16, EGFR, CD44, Nanog and Oct4, as well as tumorigenicity of OCSCs were examined.ResultsBy using a comprehensive microarray analysis, it was determined that ST6GALNAC1 was highly expressed in ovarian cancer and it regulated the Akt signaling pathway. High levels of ST6GALNAC1 were observed in OCSCs and ovarian cancer cells. Silencing ST6GALNAC1 was shown to be able to reduce cell proliferation, migration, invasion, self-renewal ability, tumorigenicity of OCSCs. In accordance with these results, the effects of ST6GALNAC1 in OCSCs were dependent on the Akt signaling pathway.ConclusionsWhen taken together, our findings defined the potential stimulative roles of ST6GALNAC1 in ovarian cancer and OCSCs, which relied on the Akt signaling pathway.

Integrated Transcriptomic Analysis of Necrosis-related Gene in Diffuse Gliomas.

Glioblastoma multiforme (GBM), an aggressive brain tumor, is characterized histologically by the presence of a necrotic center surrounded by so-called pseudopalisading cells. Pseudopalisading necrosis has long been used as a prognostic feature. However, the underlying molecular mechanism regulating the progression of GBMs remains unclear. We hypothesized that the gene expression profiles of individual cancers, specifically necrosis-related genes, would provide objective information that would allow for the creation of a prognostic index. Gene expression profiles of necrotic and nonnecrotic areas were obtained from the Ivy Glioblastoma Atlas Project (IVY GAP) database to explore the differentially expressed genes.A robust signature of seven genes was identified as a predictor for glioblastoma and low-grade glioma (GBM/LGG) in patients from The Cancer Genome Atlas (TCGA) cohort. This set of genes was able to stratify GBM/LGG and GBM patients into high-risk and low-risk groups in the training set as well as the validation set. The TCGA, Repository for Molecular Brain Neoplasia Data (Rembrandt), and GSE16011 databases were then used to validate the expression level of these seven genes in GBMs and LGGs. Finally, the differentially expressed genes (DEGs) in the high-risk and low-risk groups were subjected to gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes pathway, and gene set enrichment analyses, and they revealed that these DEGs were associated with immune and inflammatory responses. In conclusion, our study identified a novel seven-gene signature that may guide the prognostic prediction and development of therapeutic applications.

Characterizing Basal‐like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach

Triple‐negative breast cancer (TNBC) constitutes approximately 20%–25% of all breast cancer cases with poor prognosis. It is unresponsive to targeted hormonal therapies, which limits treatment options to nonselective chemotherapeutic agents. Recently, gene expression analysis has shown promise to characterize triple negative breast cancers (TNBC). Although TNBCs have many basal‐like breast cancer characteristics, the relationship between clinically defined triple‐negative breast cancer and the gene expression profile of basal‐like breast cancer (BLBC) is not fully examined. The purpose of this study is to assemble publicly‐available TNBC gene expression datasets generated on Affymetrix gene chips and define a set of genes, or gene signature, that can classify BLBC and Non‐BLBC subtypes under TNBC type. We compiled over 3,500 breast cancer gene expression profiles from several individual publicly available datasets and extracted Affymetrix gene expression data for 580 TNBC cases. Several popular data mining along with variable reduction and feature selection techniques were applied to the resultant data sets to build predictive models to understand molecular characteristics and mechanisms associated with BLBCs and classify them more accurately according to important features extracted from Microarray data analysis of BLBC and Non‐BLBC cases. Our result can lead to proper identification and diagnosis of BLBCs, which can potentially direct clinical implications by dictating the most effective therapy.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

LncRNA and Gene expression profiling of human bladder cancer

Bladder cancer (BC) is the most common urologic neoplasms with a high rate of recurrence. The development of BC is a complex process, resulting from somke, drugs or bladder inflammation. However, the uinderlying mechanisms of BC tumorigenesis remained unclear. In this study, we performed RNA sequencing of three paired para-tumor and tumor tissuses from BC patients. Compared with para-tumor tissuses, BC tissues displayed 1136 upregulated and 1199 downregualted lncRNAs, and 1347 upregulated and 953 downregualted genes. Additionally, the common upregulated genes were mainly enriched in SNARE interactions, amino sugar and nucleotide sugar metabolism and antigen processing pathways. The common downregulated genes were mainly focused amino acid metabolism and drug meabolism signalings. More importantly, cytokine related genes were highly expressed in BC, which suggest that the dysregulation of immunoregulatory process and amino acid metabolism played a crucial role in the oncogenesis of BC.

BRAFV600E-mutant cancers display a variety of networks by SWIM analysis: prediction of vemurafenib clinical response.

Several studies have shown that different tumour types sharing a driver gene mutation do not respond uniformly to the same targeted agent. Our aim was to use an unbiased network-based approach to investigate this fundamental issue using BRAFV600E mutant tumours and the BRAF inhibitor vemurafenib. We applied SWIM, a software able to identify putative regulatory (switch) genes involved in drastic changes to the cell phenotype, to gene expression profiles of different BRAFV600E mutant cancers and their normal counterparts in order to identify the switch genes that could potentially explain the heterogeneity of these tumours' responses to vemurafenib. We identified lung adenocarcinoma as the tumour with the highest number of switch genes (298) compared to its normal counterpart. By looking for switch genes encoding for kinases with homology sequences similar to known vemurafenib targets, we found that thyroid cancer and lung adenocarcinoma have a similar number of putative targetable switch gene kinases (5 and 6, respectively) whereas colorectal cancer has just one. We are persuaded that our network analysis may aid in the comprehension of molecular mechanisms underlying the different responses to vemurafenib in BRAFV600E mutant tumours.

Application of multiple support vector machine recursive feature elimination model in cancer feature gene selection

Objective To analyze the cancergene expression profile data using multi-support vector machine recursive feature elimination algorithm (MSVM-RFE) and calculate the genetic ranking score to obtain the optimal feature gene subset. Methods Gene expression profiles of bladder cancer, breast cancer, colon cancer and lung cancer were downloaded from GEO (Gene Expression Omnibus) database. The differentially expressed genes were obtained by differential expression analysis. The differential gene expressions were sequenced by MSVM-RFE algorithm and the average test errors of each gene subset were calculated. Then the optimal gene subsetsof four kinds of cancer were obtained according to the minimum average test errors. Based on the datasets of four kinds of cancer characteristic genes before and after screening, linear SVM classifiers were constructed and the classification efficiencies of the optimal feature gene subsets were verified. Results Using the optimal feature gene subsetobtained by MSVM-RFE algorithm, the classification accuracy was improved from (96.77±1.28)% to (99.85±0.46)% for the bladder cancer data, improved from (83.77±4.93)% to (88.30±3.85)% for the breast cancer data, and improved from (72.69±2.41)% to (90.21±3.31)% for the lung cancer data.Besides, theoptimal feature gene subsetkept the classification accuracy of colon cancer classifierat a high level (>99.5%). Conclusions The feature gene extraction based on MSVM-RFE algorithm can improve the classification efficiency of cancer. Key words: Gene expression profile; Recursive feature elimination; Support vector machine; Feature gene

Abstract P4-08-07: Discordance between surrogated intrinsic subtypes defined by immunohistochemistry compared with PAM50 in ER positive / HER2 negative early breast cancer. Analysis of value of the status of the progesterone receptor and Ki67

Abstract Introduction: Classification by intrinsic subtypes by gene expression profiles of early-stage breast cancer (EBC) provides information of prognostic value and constitutes a tool to help in making therapeutic decisions. Several authors have proposed surrogated classifications based on immunohistochemistry results (IHC) in order to facilitate a classification with identical prognostic and predictive value. However, there is evidence that suggests a lack of correlation between these classifications. The aim of this study was to evaluate the correlation between classification by intrinsic subtypes in patients with EBC ER+/ HER2neg, obtained by PAM50 and the surrogated classification proposed by St. Gallen 2013. Methods: Samples from 12 centers from the spanish region of Castilla y León were analyzed by PAM50 (nCounterTM / Nanostring) at the University Hospital of Salamanca. The results obtained were compared with the surrogate classification of St.Gallen'13 from local reports. Tumors of patients pre and post-menopausal tests T1-2, N0-N1mi, grade I-II that met criteria for inclusion of the regional evaluation program through ProsignaTM were included. Results: Between August'15 and December'17, 264 samples were analyzed. All patients were classified by IHC as lum-A or lum-B. In total 113 cases were reclassified by PAM50 (43%). The change of LumA by IHC to Lum-B by PAM50 was 18%, while Lum-B by IHC to Lum-A by PAM50 was 58% (n = 89). In those cases considered Lum-B by IHC based only in a value of Ki67>14% (n = 93), 54% was reclassified to Lum-A. Conversely, when low expression (negative or <20%) of Progesterone Receptor (PR) was used as the single criterion of Lum-B by IHC (n = 44), PAM50 reclassified 33 cases as Lum-A (75%). Applying the Kappa test to analyze the concordance between the 2 tests, a coefficient of 0.203 (low agreement) was obtained, statistically significant (0.000). Conclusion: The surrogated classification by IHC of intrinsic subtypes in EBC ER+/HER2neg shows a low concordance with PAM50 analysis, and cannot be considered adequate. In particular, the presence of negative or <20% PR as the only criterion seems the least appropriate and should not be recommended for a surrogated classification of a tumor as Lum-B, overestimating the real risk of numerous patients. PAM50 allowed the reclassification in more than 40% of cases, especially csaes considered Lum-B by IHC. Citation Format: Rodriguez CA, Guillen C, Garcia M, Sancho M, Gomez A, Ludena D, Cruz JJ. Discordance between surrogated intrinsic subtypes defined by immunohistochemistry compared with PAM50 in ER positive / HER2 negative early breast cancer. Analysis of value of the status of the progesterone receptor and Ki67 [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-08-07.

Quality control usage in high-density microarrays reveals differential gene expression profiles in ovarian cancer

There are several existing reports of microarray chip use for assessment of altered gene expression in different diseases. In fact, there have been over 1.5 million assays of this kind performed over the last twenty years, which have influenced clinical and translational research studies. The most commonly used DNA microarray platforms are Affymetrix GeneChip and Quality Control Software along with their GeneChip Probe Arrays. These chips are created using several quality controls to confirm the success of each assay, but their actual impact on gene expression profiles had not been previously analyzed until the appearance of several bioinformatics tools for this purpose. We here performed a data mining analysis, in this case specifically focused on ovarian cancer, as well as healthy ovarian tissue and ovarian cell lines, in order to confirm quality control results and associated variation in gene expression profiles. The microarray data used in our research were downloaded from ArrayExpress and Gene Expression Omnibus (GEO) and analyzed with Expression Console Software using RMA, MAS5 and Plier algorithms. The gene expression profiles were obtained using Partek Genomics Suite v6.6 and data were visualized using principal component analysis, heat map, and Venn diagrams. Microarray quality control analysis showed that roughly 40% of the microarray files were false negative, demonstrating over- and under-estimation of expressed genes. Additionally, we confirmed the results performing second analysis using independent samples. About 70% of the significant expressed genes were correlated in both analyses. These results demonstrate the importance of appropriate microarray processing to obtain a reliable gene expression profile.

Identifying primary site of lung-limited Cancer of unknown primary based on relative gene expression orderings

BackgroundPrecise diagnosis of the tissue origin for metastatic cancer of unknown primary (CUP) is essential for deciding the treatment scheme to improve patients’ prognoses, since the treatment for the metastases is the same as their primary counterparts. The purpose of this study is to identify a robust gene signature that can predict the origin for CUPs.MethodsThe within-sample relative gene expression orderings (REOs) of gene pairs within individual samples, which are insensitive to experimental batch effects and data normalizations, were exploited for identifying the prediction signature.ResultsUsing gene expression profiles of the lung-limited metastatic colorectal cancer (LmCRC), we firstly showed that the within-sample REOs in lung metastases of colorectal cancer (CRC) samples were concordant with the REOs in primary CRC samples rather than with the REOs in primary lung cancer. Based on this phenomenon, we selected five gene pairs with consistent REOs in 498 primary CRC and reversely consistent REOs in 509 lung cancer samples, which were used as a signature for predicting primary sites of metastatic CRC based on the majority voting rule. Applying the signature to 654 primary CRC and 204 primary lung cancer samples collected from multiple datasets, the prediction accuracy reached 99.36%. This signature was also applied to 24 LmCRC samples collected from three datasets produced by different laboratories and the accuracy reached 100%, suggesting that the within-sample REOs in the primary site could reveal the original tissue of metastatic cancers.ConclusionsThe result demonstrated that the signature based on within-sample REOs of five gene pairs could exactly and robustly identify the primary sites of CUPs.

A Novel Multi-Stage Fusion based Approach for Gene Expression Profiling in Non-Small Cell Lung Cancer

Background: Non-small cell lung cancer is defined at the molecular level by mutations and alterations to oncogenes, including AKT1, ALK, BRAF, EGFR, HER2, KRAS, MEK1, MET, NRAS, PIK3CA, RET, and ROS1. A better understanding of non-small cell lung cancer requires a thorough consideration of these oncogenes. However, the complexity of the problem arises from high-dimensional gene vector space, which complicates the identification of cluster boundaries, and hence gene expression cluster membership. This paper aims to analyze potential biological biomarkers for tumorigenesis in lung cancer based on different treatment solutions. Results: Genes BRAF, RET, and ROS1 show an overexpression transition by one cluster from non-treatment to treatment states, followed by a stabilization in the 3 treatment states at the same cluster. Genes MET, ALK, and PIK3CA show an overexpression transition by two clusters from non-treatment to treatment states, followed by a stabilization in the 3 treatment states at the same cluster. SME1 shows an under-expression transition by two clusters from non-treatment to the treatment states, a stabilization in the 3 treatment states at the same cluster. Conclusions: We present a novel fusion-based approach for gene expression profiling of non-small cell lung cancer under non-thermal plasma treatment. The main contribution of the proposed approach is to exploit Dempster-Shafer evidence theory-based data fusion to combine information from different samples in the considered dataset. This minimizes uncertainty and enhances the reliability and validity of decisions, leading to a better description of genes related to non-small cell lung cancer. We also propose use of fuzzy c-means-with-range clustering to track changes of genes' states under different non-thermal plasma treatments.

Immune Gene Signature Delineates a Subclass of Papillary Thyroid Cancer with Unfavorable Clinical Outcomes.

Papillary thyroid carcinoma (PTC) represents a heterogeneous disease with diverse clinical outcomes highlighting a need to identify robust biomarkers with clinical relevance. We applied non-negative matrix factorization-based deconvolution to publicly available gene expression profiles of thyroid cancers in the Cancer Genome Atlas (TCGA) consortium. Among three metagene signatures identified, two signatures were enriched in canonical BRAF-like and RAS-like thyroid cancers with up-regulation of genes involved in oxidative phosphorylation and cell adhesions, respectively. The third metagene signature representing up-regulation of immune-related genes further segregated BRAF-like and RAS-like PTCs into their respective subgroups of immunoreactive (IR) and immunodeficient (ID), respectively. BRAF-IR PTCs showed enrichment of tumor infiltrating immune cells, tall cell variant PTC, and shorter recurrence-free survival compared to BRAF-ID PTCs. RAS-IR and RAS-ID PTC subtypes included majority of normal thyroid tissues and follicular variant PTC, respectively. Immunopathological features of PTC subtypes such as immune cell fraction, repertoire of T cell receptors, cytolytic activity, and expression level of immune checkpoints such as and PD-L1 and CTLA-4 were consistently observed in two different cohorts. Taken together, an immune-related metagene signature can classify PTCs into four molecular subtypes, featuring the distinct histologic type, genetic and transcriptional alterations, and potential clinical significance.

Hot water extract of Agaricus blazei Murrill specifically inhibits growth and induces apoptosis in human pancreatic cancer cells

BackgroundPancreatic cancer is one of the most aggressive human malignancies. The development of a novel drug to treat pancreatic cancer is imperative, and it is thought that complementary and alternative medicine (CAM) could yield such a candidate. Agaricus blazei Murrill is a CAM that has been tested as an anticancer drug, but its efficacy against pancreatic cancer is poorly understood. To study the potential of A. blazei in the treatment of pancreatic cancer, we examined the effects of its hot water extract on the proliferation and global gene expression profile of human pancreatic cancer cells.MethodsThree distinct human pancreatic cancer cell lines, MIAPaCa-2, PCI-35, and PK-8, and the immortalized human pancreatic duct-epithelial cell line, HPDE, were employed. The cells were incubated with the appropriate growth medium supplemented with the hot water extract of A. blazei at final concentrations of 0.005, 0.015%, or 0.045%, and cellular proliferation was assessed for five consecutive days using an MTT assay. Apoptosis was examined by using flow cytometry and the terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay. Caspase-dependent apoptosis was assayed using immunoblotting. Global gene expression profiles were examined using a whole human genome 44 K microarray, and the microarray results were validated by using real-time reverse transcription PCR.ResultsThe hot water extract of A. blazei significantly inhibited the proliferation of cultured pancreatic cancer cells through the induction of G0/G1 cell cycle arrest and caspase-dependent apoptosis; the effect was the smallest in HPDE cells. Furthermore, significant alterations in the global gene expression profiles of pancreatic cancer cells occurred following treatment with the hot water extract of A. blazei. Genes associated with kinetochore function, spindle formation, and centromere maintenance were particularly affected, as well as cyclins and cyclin-dependent kinases that are essential for cell cycle progression. In addition, proapoptotic genes were upregulated.ConclusionsThe hot water extract of A. blazei may be useful for the treatment of pancreatic cancer and is a potential candidate for the isolation of novel, active compounds specific for mitotic spindle dysfunction.

Identification of key genes in prostate cancer gene expression profile by bioinformatics.

The aim of this study was to identify key candidate genes in prostate cancer. The gene expression profiles of GSE32448, GSE45016, GSE46602 and GSE104749 were downloaded from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) between prostate cancer and normal samples were identified by R language. The gene ontology functional and pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery software followed by the construction of protein-protein interaction network. Hub gene identification was performed by the plug-in cytoHubba in Cytoscape software. The 217 DEGs were significantly enriched in biological processes including epithelial cell differentiation, response to estradiol and several pathways, mainly associated with protein digestion and absorption pathway in prostate cancer. Epithelial cell adhesion molecule, twist family basic helix-loop-helix transcription factor 1, CD38 molecule and vascular endothelial growth factor A were identified as hub genes. The expression levels of hub genes were consistent with data obtained in The Cancer Genome Atlas for prostate adenocarcinoma. These hub genes may be used as potential targets for prostate cancer diagnosis and treatment.

Establishment of five immortalized human ovarian surface epithelial cell lines via SV40 T antigen or HPV E6/E7 expression.

BackgroundHuman ovarian surface epithelial (HOSE) cells are a critical cell source for ovarian cancer research; however, they are difficult to obtain and maintain under standard laboratory conditions in large quantities. The aim of this study was to generate immortalized HOSE (IHOSE) cells with maintained properties to the original cell source, thereby guaranteeing a sufficiently large cell quantity for ovarian cancer research.MethodsHOSE cells isolated from four non-cancer patients and five IHOSE cell lines were established by induction of HPV-E6/E7 expression or SV40 large T antigen using a lenti-viral system. Each of IHOSE cells was confirmed to be distinct by STR profiling. RNA-sequencing was used to compare gene expression profiles in HOSE, IHOSE and ovarian cancer cells.ResultsRNA-sequencing results revealed a stronger linear correlation in gene expression between IHOSE and HOSE cells (R2 = 0.9288) than between IHOSE or HOSE cells and ovarian cancer cells (R2 = 0.8562 and R2 = 0.7982, respectively). The gene expression pattern of 319 differentially expressed genes revealed minimal differences between HOSE and IHOSE cells, while a strong difference between ovarian cancer cells and HOSE or IHOSE cells was observed. Furthermore, the five IHOSE cell lines displayed morphological characteristics typical of epithelial cells but showed a lower level of EpCAM, CD133 and E-cadherin, as cancer stem marker, than ovarian cancer cells. Moreover, unlike cancer cells, IHOSE cells could not form colonies in the anchorage-independent soft agar growth assay.ConclusionThese findings demonstrate that five newly established IHOSE cell lines have characteristics of progenitor HOSE cells while exhibiting continuous growth, and thus, should be highly useful as control cells for ovarian cancer research.

Abstract A22: Identification of gene expression differences between primary pediatric tumors and their PDX models

Abstract Molecularly targeted therapies inhibit specific cancer pathways and have fewer harmful side effects than cytotoxic chemotherapies; however, the development of targeted therapies for childhood cancers has lagged behind that of adult cancers. Although adult and childhood cancers have different molecular mechanisms, similar druggable cancer pathways may be activated. We propose using gene expression analysis to identify opportunities to reposition FDA-approved targeted therapies for childhood cancer patients. We developed an N-of-1 gene expression approach that leverages a compendium of ~11,000 cancer gene expression profiles. To evaluate the utility of this approach for identifying cancer driver pathways, we analyzed gene expression data for 9 patient-derived xenograft (PDX) tumors. PDXs were tested for sensitivity to selected targeted therapies. Our analysis correctly predicted drug sensitivity in 8 of the 9 PDXs based on overexpression of specific cancer pathways. We also compared gene expression between PDX and matched primary tumor samples (n=7). Using TumorMap analysis, we found that PDX tumors cluster with their matched primary tumor and other samples with a similar cancer diagnosis. We also ran differential expression analysis and found 260 overexpressed genes and 630 underexpressed genes in PDX tumors. Underexpressed genes were associated with immune functions, extracellular matrix proteins, and growth factor signaling. Overexpressed genes were involved in TNF-α; signaling. This analysis confirmed expected changes in gene expression while also identifying TNF-α; signaling as a potential biologic artifact of PDX tumors. These features may influence the results of PDX experiments and should be accounted for when using this model. Overall, the PDX model recapitulates gene expression features of pediatric cancer and is a powerful tool for evaluating novel precision medicine approaches. Citation Format: Jacob J. Pfeil, Alex G. Lee, Leanne C. Sayles, Ellen T. Kephart, Holly C. Beale, Lauren M. Sanders, Olena Morozova, Sofie R. Salama, Alejandro Sweet-Cordero, David Haussler. Identification of gene expression differences between primary pediatric tumors and their PDX models [abstract]. In: Proceedings of the AACR Special Conference: Pediatric Cancer Research: From Basic Science to the Clinic; 2017 Dec 3-6; Atlanta, Georgia. Philadelphia (PA): AACR; Cancer Res 2018;78(19 Suppl):Abstract nr A22.

TRAINING ANFIS SYSTEM WITH GENETIC ALGORITHM FOR DIAGNOSIS OF PROSTATE CANCER

Prostate cancer is one of the most common types of cancer among males as well as causing the most deaths. Early diagnosis of prostate cancer plays an important role in the treatment of the disease. Therefore, microarray technology is widely used in the diagnosis of inherited diseases such as prostate cancer. With this technology, it is possible to obtain more knowledge about cancer by analyzing thousands of gene expressions. However, it is quite difficult to analyze complex relationships among thousands of genes in microarray data. For this reason, high performance artificial intelligence-based classification methods are needed in recent years. In this study, a hybrid method has been proposed for optimizing the parameters of Adaptive Neuro Fuzzy Inference System (ANFIS) with Genetic Algorithm (GA) in order to classify prostate cancer gene expression profiles. The performance of the proposed method is compared with those of ANFIS models trained by different learning algorithms. According to obtained results, the proposed method is more successful than the other methods, with the accuracy of 90.32%.