BRB-ArrayTools Data Archive for human cancer gene expression: a unique and efficient data sharing resource

Genomic analysis uncovers that cold-inducible RNA binding protein is associated with estrogen receptor in breast cancer

BACKGROUND

RNA-binding proteins (RBPs) perform various biological functions in humans and are associated with several diseases, including cancer. Therefore, RBPs have emerged as novel therapeutic targets. Although recent investigations have shown that RBPs have crucial functions in breast cancer (BC), detailed research is underway to determine the RBPs that are closely related to cancers.

OBJECTIVE

To provide an insight into estrogen receptor (ER) regulation by cold-inducible RNA binding protein (CIRBP) as a novel therapeutic target.

RESULTS

By analyzing the genomic data, we identified a potential RBP in BC. We found that CIRBP is highly correlated with ER function and influences clinical outcomes, such as patient survival and endocrine therapy responsiveness. In addition, CIRBP influences the proliferation of BC cells by directly binding to ER-RNA.

CONCLUSION

Our results suggest that CIRBP is a novel upstream regulator of ER and that the interplay between CIRBP and ER may be associated with the clinical relevance of BC.

ESRRG-PKM2 axis reprograms metabolism to suppress esophageal squamous carcinoma progression and enhance anti-PD-1 therapy efficacy

BACKGROUND

Glycolysis under normoxic conditions, known as the Warburg effect, confers a selective advantage for the survival and proliferation of many tumors. In this study, we investigated the role of estrogen-related receptor gamma (ESRRG) in metabolic reprogramming in esophageal squamous cell carcinoma (ESCC).

METHODS

Bioinformatics analysis indicated that ESRRG expression was decreased in ESCC tissue and associated with poor clinical outcomes. We also examined the effects of altered ESRRG expression on the proliferation and metabolic reprogramming of ESCC cells. We explored the impact of ESRRG on Pyruvate kinase M2 (PKM2) expression and malignant behavior in ESCC.

RESULTS

Our study revealed the inhibitory effects of ESRRG on the growth, tumorigenesis, and glycolysis activity of ESCC cells, which were mediated by the downregulation of PKM2 expression. We further demonstrated that ESRRG directly interacts with the PKM2 promoter to inhibit its activity in ESCC. Notably, the ESRRG-specific agonist, DY131, inhibited ESCC cell proliferation and glycolysis activity by modulating genes in the glycolysis pathway. Moreover, we verified that DY131 exhibits enhanced activity as an immune checkpoint inhibitor, considering the significance of the ESRRG-PKM2 axis in the lactate regulation of ESCC cells.

CONCLUSION

Our findings provide novel insights into the role of ESRRG-PKM2 signaling in regulating ESCC cell metabolism and immune checkpoint regulation. Additionally, we suggest that DY131 holds promise as a promising therapeutic agent for ESCC treatment.

Integrated Bioinformatics Analysis Identifies Crucial Biochemical Processes Shared between Pancreatitis and Pancreatic Ductal Adenocarcinoma

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy associated with rapid progression and an abysmal prognosis. Previous research has shown that chronic pancreatitis can significantly increase the risk of developing PDAC. The overarching hypothesis is that some of the biological processes disrupted during the inflammatory stage tend to show significant dysregulation, even in cancer. This might explain why chronic inflammation increases the risk of carcinogenesis and uncontrolled proliferation. Here, we try to pinpoint such complex processes by comparing the expression profiles of pancreatitis and PDAC tissues.

METHODS

We analyzed a total of six gene expression datasets retrieved from the EMBL-EBI ArrayExpress and NCBI GEO databases, which included 306 PDAC, 68 pancreatitis and 172 normal pancreatic samples. The disrupted genes identified were used to perform downstream analysis for ontology, interaction, enriched pathways, potential druggability, promoter methylation, and the associated prognostic value. Further, we performed expression analysis based on gender, patient's drinking habit, race, and pancreatitis status.

RESULTS

Our study identified 45 genes with altered expression levels shared between PDAC and pancreatitis. Over-representation analysis revealed that protein digestion and absorption, ECM-receptor interaction, PI3k-Akt signaling, and proteoglycans in cancer pathways as significantly enriched. Module analysis identified 15 hub genes, of which 14 were found to be in the druggable genome category.

CONCLUSION

In summary, we have identified critical genes and various biochemical processes disrupted at a molecular level. These results can provide valuable insights into certain events leading to carcinogenesis, and therefore help identify novel therapeutic targets to improve PDAC treatment in the future.

RNA-binding protein NONO contributes to cancer cell growth and confers drug resistance as a theranostic target in TNBC

Breast cancer (BC) is one of the most common cancers in women. TNBC (Triple-negative breast cancer) has limited treatment options and still lacks viable molecular targets, leading to poor outcomes. Recently, RNA-binding proteins (RBPs) have been shown to play crucial roles in human cancers, including BC, by modulating a number of oncogenic phenotypes. This suggests that RBPs represent potential molecular targets for BC therapy.

Methods: We employed genomic data to identify RBPs specifically expressed in TNBC. NONO was silenced in TNBC cell lines to examine cell growth, colony formation, invasion, and migration. Gene expression profiles in NONO-silenced cells were generated and analyzed. A high-throughput screening for NONO-targeted drugs was performed using an FDA-approved library.

Results: We found that the NONO RBP is highly expressed in TNBC and is associated with poor patient outcomes. NONO binds to STAT3 mRNA, increasing STAT3 mRNA levels in TNBC. Surprisingly, NONO directly interacts with STAT3 protein increasing its stability and transcriptional activity, thus contributing to its oncogenic function. Importantly, high-throughput drug screening revealed that auranofin is a potential NONO inhibitor and inhibits cell growth in TNBC.

Conclusions: NONO is an RBP upstream regulator of both STAT3 RNA and protein levels and function. It represents an important and clinically relevant promoter of growth and resistance of TNBCs. NONO is also therefore a potential therapeutic target in TNBC.

Transcriptome hallmarks in Helicobacter pylori infection influence gastric cancer and MALT lymphoma

Aim: Altered long noncoding RNA (lncRNA) and mRNA is vital in the progression from Helicobacter pylori (H. pylori, HP) infection to gastric cancer (GC) and mucosa-associated lymphoid tissue (MALT) lymphoma. Materials & methods: Five independent Gene Expression Omnibus datasets (GSE5081, GSE84433, GSE15459, GSE66229 and GSE25638) were included in our study. Results: Differentially expressed lncRNAs and mRNAs in both H. pylori-positive gastritis and GC tissues were identified. Using two GC cohorts, the H. pylori-related mRNA DYNC1I1 and MMP7 were independent predictors of overall survival. Moreover, the expressions of lncRNA GHRLOS and 44 mRNAs were significantly changed in gastric MALT lymphoma patients. Conclusion: The lncRNA/mRNA response to H. pylori infection in gastritis and GC influence the outcome of GC and progression of MALT lymphoma.

Aggressive Mammary Cancers Lacking Lymphocytic Infiltration Arise in Irradiated Mice and Can Be Prevented by Dietary Intervention

Because the incidence of breast cancer increases decades after ionizing radiation exposure, aging has been implicated in the evolution of the tumor microenvironment and tumor progression. Here, we investigated radiation-induced carcinogenesis using a model in which the mammary glands of 10-month-old BALB/c mice were transplanted with Trp53-null mammary tissue 3 days after exposure to low doses of sparsely ionizing γ-radiation or densely ionizing particle radiation. Mammary transplants in aged, irradiated hosts gave rise to significantly more tumors that grew more rapidly than those in sham-irradiated mice, with the most pronounced effects seen in mice irradiated with densely ionizing particle radiation. Tumor transcriptomes identified a characteristic immune signature of these aggressive cancers. Consistent with this, fast-growing tumors exhibited an immunosuppressive tumor microenvironment with few infiltrating lymphocytes, abundant immunosuppressive myeloid cells, and high COX-2 and TGFβ. Only irradiated hosts gave rise to tumors lacking cytotoxic CD8+ lymphocytes (defined here as immune desert), which also occurred in younger irradiated hosts. These data suggest that host irradiation may promote immunosuppression. To test this, young chimera mice were fed chow containing a honeybee-derived compound with anti-inflammatory and immunomodulatory properties, caffeic acid phenethyl ester (CAPE). CAPE prevented the detrimental effects of host irradiation on tumor growth rate, immune signature, and immunosuppression. These data indicated that low-dose radiation, particularly densely ionizing exposure of aged mice, promoted more aggressive cancers by suppressing antitumor immunity. Dietary intervention with a nontoxic immunomodulatory agent could prevent systemic effects of radiation that fuel carcinogenesis, supporting the potential of this strategy for cancer prevention.

High expression of citron kinase predicts poor prognosis of prostate cancer

Citron kinase (CIT) is a Rho-effector protein kinase that is associated with several types of cancer. However, the role of CIT in prostate cancer (PCa) is unclear. The current study utilized microarray data obtained from The Cancer Genome Atlas, which was analyzed via Biometric Research Program array tools. Additionally, reverse transcription-quantitative (RT-q)PCR was performed to compare the mRNA expression of CIT in PCa tissue and in benign prostatic hyperplasia. The protein expression of CIT was detected in a consecutive cohort via immunochemistry and CIT was screened as a potential oncogene in PCa. The results of RT-qPCR demonstrated that the mRNA expression of CIT was increased in PCa tissues. Furthermore, immunochemistry revealed that CIT protein expression was positively associated with age at diagnosis, Gleason grade, serum PSA, clinical T stage, risk group, lymph node invasion and metastasis. When compared with the low expression group, patients with a high CIT expression exhibited shorter survival rates, cancer specific mortalities (CSM) and biochemical recurrence (BCR). In addition, multivariate analysis revealed that CIT was a potential predictor of CSM and BCR. The results revealed that CIT is overexpressed during the malignant progression of PCa and may be a predictor of a poor patient prognosis.

Bioinformatics Tools and Resources for Cancer Immunotherapy Study

In recent years, cancer immunotherapy has emerged as a highly promising approach to treat patients with cancer, as the patient's own immune system is harnessed to attack cancer cells. However, the application of these approaches is still limited to a minority of patients with cancer and it is difficult to predict which patients will derive the greatest clinical benefit.One of the challenges faced by the biomedical community in the search of more effective biomarkers is the fact that translational research efforts involve collecting and accessing data at many different levels: from the type of material examined (e.g., cell line, animal models, clinical samples) to multiple data type (e.g., pharmacodynamic markers, genetic sequencing data) to the scale of a study (e.g., small preclinical study, moderate retrospective study on stored specimen sets, clinical trials with large cohorts).This chapter reviews several publicly available bioinformatics tools and data resources for high throughput molecular analyses applied to a range of data types, including those generated from microarray, whole-exome sequencing (WES), RNA-seq, DNA copy number, and DNA methylation assays, that are extensively used for integrative multidimensional data analysis and visualization.

Analysis of competing endogenous RNA network identifies a poorly differentiated cancer-specific RNA signature for hepatocellular carcinoma

Plenty of evidence has suggested that long noncoding RNAs (lncRNAs) play a vital role in competing endogenous RNA (ceRNA) networks. Poorly differentiated hepatocellular carcinoma (PDHCC) is a malignant phenotype. This paper aimed to explore the effect and the underlying regulatory mechanism of lncRNAs on PDHCC as a kind of ceRNA. Additionally, prognosis prediction was assessed. A total of 943 messenger RNAs (mRNAs), 86 miRNAs, and 468 lncRNAs that were differentially expressed between 137 PDHCCs and 235 well-differentiated HCCs were identified. Thereafter, a ceRNA network related to the dysregulated lncRNAs was established according to bioinformatic analysis and included 29 lncRNAs, 9 miRNAs, and 96 mRNAs. RNA-related overall survival (OS) curves were determined using the Kaplan-Meier method. The lncRNA ARHGEF7-AS2 was markedly correlated with OS in HCC (P = .041). Moreover, Cox regression analysis revealed that patients with low ARHGEF7-AS2 expression were associated with notably shorter survival time (P = .038). In addition, the area under the curve values of the lncRNA signature for 1-, 3-, and 5-year survival were 0.806, 0.741, and 0.701, respectively. Furthermore, a lncRNA nomogram was established, and the C-index of the internal validation was 0.717. In vitro experiments were performed to demonstrate that silencing ARHGEF7-AS2 expression significantly promoted HCC cell proliferation and migration. Taken together, our findings shed more light on the ceRNA network related to lncRNAs in PDHCC, and ARHGEF7-AS2 may be used as an independent biomarker to predict the prognosis of HCC.

An 11-lncRNA expression could be potential prognostic biomarkers in head and neck squamous cell carcinoma

The aim of our study is to construct the competing endogenous RNA (ceRNA) network of head and neck squamous cell carcinoma (HNSCC) and identify key long noncoding RNAs (lncRNAs) to predict prognosis. The genes whose expression were differentially in HNSCC and normal tissues were explored by the Cancer Genome Atlas database. The ceRNA network was constructed by the Cytoscape software. The lncRNAs which could estimate the overall survival were explored from Cox proportional hazards regression. There are 1997, 589, and 82 mRNAs, lncRNAs, and miRNAs whose expression were statistically significant different, respectively. Then, the network between miRNA and mRNA or miRNA and lncRNA was constructed by miRcode, miRDB, TargetScan, and miRanda. Five mRNAs, 10 lncRNAs, and 3 miRNAs were associated with overall survival. Then, 11-lncRNAs were found to be prognostic factors. Therefore, our research analyzed the potential signature of novel 11-lncRNA as candidate prognostic biomarker from the ceRNA network for patients with HNSCC.

The construction and analysis of the aberrant lncRNA-miRNA-mRNA network in non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) is the most common cancer and the pathogenesis remain unclear. According to the competing endogenous RNA (ceRNA) theory, long noncoding RNA (lncRNA) have a competition with mRNAs for the connecting with miRNAs that affecting the level of mRNA. In this work, the ceRNA network and the important genes to predict the survival prognosis were explored.

Methods

In the study, we recognized differently expressed genes (mRNAs, lncRNAs and miRNAs) between NSCLC and normal tissues from The Cancer Genome Atlas database (fold change >2, P<0.01) using edgeR. Then, the interaction between lncRNA and miRNA or mRNA and miRNA was explored by miRcode, miRDB, TargetScan, and miRanda. Furthermore, the functions and KEGG pathway were analyzed with DAVID and KOBAS. The connections of these mRNAs were explored by STRING online database. The relation between genes in the network and survival time were further explored by survival package in R.

Results

By bioinformatics tools, we explored 155 lncRNAs, 30 miRNAs and 68 mRNAs and constructed ceRNA network. The functions and KEGG pathway of 68 mRNAs were further analyzed. AQP2, EGF, SLC12A1, TRPV5 and AVPR2 was in the center of network and may play key roles in the development of NSCLC. And mRNA (CCNB1, COL1A1, E2F7, EGLN3, FOXG1 and PFKP), miRNA (miR-31, miR-144 and miR-192) and lncRNA (AC080129.1, AC100791.1, AL163952.1, AP000525.1, AP003064.2, C2orf48, C10orf91, FGF12-AS2, HOTAIR, LINC00518, LNX1-AS1, MED4-AS1, MIG31HG, MUC2, TTTY16 and UCA1) were closely related with overall survival (OS).

Conclusions

In summary, the present study provides a deeper understanding of the lncRNA-related ceRNA network in NSCLC and some genes may be new target to treat for NSCLC patients.

Integrated analysis of long noncoding RNA associated-competing endogenous RNA as prognostic biomarkers in clear cell renal carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant carcinomas and its molecular mechanisms remain unclear. Long noncoding RNA (lncRNA) could bind sites of miRNA which affect the expression of mRNA according to the competing endogenous (ceRNA) theory. The aim of the present study was to construct a ceRNA network and to identify key lncRNA to predict survival prognosis. We identified differentially expressed mRNA, lncRNA and miRNA between tumor tissues and normal tissues from The Cancer Genome Atlas database. Then, using bioinformatics tools, we explored the connection of 89 lncRNA, 10 miRNA and 22 mRNA, and we constructed the ceRNA network. Furthermore, we analyzed the functions and pathways of 22 differentially expressed mRNA. Then, univariate and multivariate Cox regression analyses of these 89 lncRNA and overall survival were explored. Nine lncRNA were finally screened out in the training group. The patients were divided into high‐risk and low‐risk groups according to the 9 lncRNA and low‐risk scores having better clinical overall survival (P < .01). Furthermore, the receiver operating characteristic curve demonstrates the predicted role of the 9 lncRNA. The 9‐lncRNA signature was successfully proved in the testing group and the entire group. Finally, multivariate Cox regression analysis and stratification analysis further proved that the 9‐lncRNA signature was an independent factor to predict survival. In summary, the present study provides a deeper understanding of the lncRNA‐related ceRNA network in ccRCC and suggests that the 9‐lncRNA signature could serve as an independent biomarker to predict survival in ccRCC patients.

Establishment and validation of a 7-microRNA prognostic signature for non-small cell lung cancer

Purpose

A series of microRNAs (miRNAs) have been identified as associated with the survival of patients with non-small-cell lung cancer (NSCLC). The aim of the present study was to explore whether combination of these experimentally validated individual miRNA biomarkers could be used to further increase their prognostic power in NSCLC.

Patients and methods

Based on previously validated NSCLC prognostic miRNAs, gene signatures that could discriminate high-risk subgroups with poor clinical outcome in four NSCLC miRNA expression datasets (GSE13937, GSE16025, The Cancer Genome Atlas Lung Adenocarcinoma, and TCGA lung squamous cell carcinoma) were developed using the SurvMicro tool. The potential of the miRNA signature established was validated by quantitative real-time PCR analysis of clinical NSCLC samples, and its prognostic power evaluated using the survivalROC method.

Results

We developed two miRNA signatures with prognostic significance for NSCLC, comprising 12- and 7-miRNAs. The 7-miRNA signature (miR-148b, miR-365, miR-32, miR-375, miR-21, miR-125b, and miR-155) was a subset of a 12-miRNA set that retained prognostic power across NSCLC cohorts. Compared with previously established miRNA signatures, our 7-miRNA signature has similar potential, while comprising fewer miRNA components. The prognostic ability of the 7-miRNA signature was validated experimentally in an independent NSCLC cohort using real-time PCR (HR=3.4847, 95% CI=1.3693–8.8680, P=0.0092), and this signature, combined with tumor pathological stage, had superior prognostic ability compared with tumor stage alone.

Conclusion

Our data indicate that the established 7-miRNA signature is simple, robust, and may have greater clinical prognostic utility for patients with NSCLC.

E2F signature is predictive for the pancreatic adenocarcinoma clinical outcome and sensitivity to E2F inhibitors, but not for the response to cytotoxic-based treatments

The main goal of this study was to find out strategies of clinical relevance to classify patients with a pancreatic ductal adenocarcinoma (PDAC) for individualized treatments. In the present study a set of 55 patient-derived xenografts (PDX) were obtained and their transcriptome were analyzed by using an Affymetrix approach. A supervised bioinformatics-based analysis let us to classify these PDX in two main groups named E2F-highly dependent and E2F-lowly dependent. Afterwards their characterization by using a Kaplan-Meier analysis demonstrated that E2F high patients survived significantly less than E2F low patients (9.5 months vs. 16.8 months; p = 0.0066). Then we tried to establish if E2F transcriptional target levels were associated to the response to cytotoxic treatments by comparing the IC50 values of E2F high and E2F low cells after gemcitabine, 5-fluorouracil, oxaliplatin, docetaxel or irinotecan treatment, and no association was found. Then we identified an E2F inhibitor compound, named ly101-4B, and we observed that E2F-higly dependent cells were more sensitive to its treatment (IC50 of 19.4 ± 1.8 µM vs. 44.1 ± 4.4 µM; p = 0.0061). In conclusion, in this work we describe an E2F target expression-based classification that could be predictive for patient outcome, but more important, for the sensitivity of tumors to the E2F inhibitors as a treatment. Finally, we can assume that phenotypic characterization, essentially by an RNA expression analysis of the PDAC, can help to predict their clinical outcome and their response to some treatments when are rationally selected.

Estrogen-related receptor gamma functions as a tumor suppressor in gastric cancer

The principle factors underlying gastric cancer (GC) development and outcomes are not well characterized resulting in a paucity of validated therapeutic targets. To identify potential molecular targets, we analyze gene expression data from GC patients and identify the nuclear receptor ESRRG as a candidate tumor suppressor. ESRRG expression is decreased in GC and is a predictor of a poor clinical outcome. Importantly, ESRRG suppresses GC cell growth and tumorigenesis. Gene expression profiling suggests that ESRRG antagonizes Wnt signaling via the suppression of TCF4/LEF1 binding to the CCND1 promoter. Indeed, ESRRG levels are found to be inversely correlated with Wnt signaling-associated genes in GC patients. Strikingly, the ESRRG agonist DY131 suppresses cancer growth and represses the expression of Wnt signaling genes. Our present findings thus demonstrate that ESRRG functions as a negative regulator of the Wnt signaling pathway in GC and is a potential therapeutic target for this cancer.

Overexpression of α3, β3 and γ2 chains of laminin-332 is associated with poor prognosis in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDA) is a worldwide health problem. Early diagnosis and assessment may enhance the quality of life and survival of patients. The present study investigated the potential correlations between the gene and protein expression of laminin-332 (LM-332 or laminin-5) and clinicopathological factors as well as evaluating its influence on the survival of patients with PDA. The expression of LM-332 subunit mRNAs in pancreatic carcinoma specimens from 37 patients was investigated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis. Using immunohistochemical methods, the protein expressions of the three chains of LM-322 (LNα3, LNβ3 and LNγ2) were determined in 96 pancreatic carcinoma specimens, for association analysis with clinicopathological characteristics from patient data. The results of the prognosis analysis of three mRNAs expression datasets were validated in The Cancer Genome Atlas datasets. RT-qPCR results indicated that the overall relative values of LNα3 and LNγ2 mRNAs were increased in pancreatic carcinoma compared with the control. In immunostaining analyses LNα3 and LNγ2 expression was observed in all tumor tissues from the 96 patient samples. The expression levels of LNα3, LNβ3 and LNγ2 were associated with each other. LNα3 and LNγ2 positivity was significantly associated with differentiation, depth of invasion and advanced stage (P<0.05). The samples were classified into three groups: Basement membrane (B) type, cytoplasmic (C) type and mixed (M) type, according to their LNγ2 immunohistochemical expression patterns. The B type correlated significantly with differentiation (P=0.010) and the M type was significantly associated with hepatic metastasis (P=0.031). Patients with B-type LNγ2 demonstrated significantly better outcomes than patients with the C or M type (P=0.012 and P=0.003, respectively). Overexpression of the α3, β3 and γ2 chains of LM-332 may serve an important role in the progression and prognosis of PDA.

A six-microRNA signature predicts survival of patients with uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common female gynecological malignant tumors that threaten women health seriously. MicroRNAs (miRNAs) has been proved to play critical roles in tumor pathogenesis and malignant progression. In this study, we aimed to explore a novel signature of microRNA expression for predicting the overall survival (OS) of patients with UCEC. The genome-wide miRNA expression profiles and relevant clinical characteristics of 348 patients with UCEC were downloaded from the Cancer Genome Atlas (TCGA) data portal and analyzed comprehensively. A total of 144 miRNAs were confirmed to be expressed differentially in tumor tissues. Among them, 6 miRNAs (hsa-mir-15a.MIMAT0000068, hsa-mir-142.MIMAT0000433, hsa-mir-142.MIMAT0000434, hsa-mir-3170.MIMAT0015045, hsa-mir-1976.MIMAT0009451, and hsa-mir-146a.MIMAT0000449) were validated to be significantly correlated with the OS of patients with UCEC. The risk indictor established by the 6-microRNA signature was proved be an independent prognostic factor (Hazard ratio = 0.391; 95% CI: 0.195-0.783; P = 0.008). In conclusion, we identified miRNAs that were correlated with the occurrence and progression of UCEC and established a 6-microRNA expression signature as a predictor for the OS of patients with UCEC.

Crosstalk between prognostic long noncoding RNAs and messenger RNAs as transcriptional hallmarks in gastric cancer

AIM

Our study investigated the significance of the crosstalk between long noncoding RNAs (lncRNAs) and mRNAs in gastric cancer (GC).

METHODS

lncRNA and mRNA expression profiling data in 671 GC tumors and 77 nontumorous gastric tissues were retrieved from the gene expression omnibus database: GSE54129, GSE13911, GSE19826, GSE79973, GSE15459 and GSE66229. Differentially expressed analysis, RNA coexpression network construction, gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses were conducted in this study.

RESULTS

Using differentially expressed and prognostic lncRNAs or mRNAs in GC, we constructed the lncRNA-mRNA coexpression networks. This network involved with vital GO and KEGG pathways.

CONCLUSION

Our study reveals coexpressed lncRNAs and mRNAs as transcriptional hallmarks in GC patients which provide interesting information regarding the incidence and outcome of GC.

High expression of AMPD2 and obesity are associated with poor prognosis in colorectal cancer

The protein-coding gene adenosine monophosphate deaminase (AMPD) 2 plays a critical role in energy metabolism by converting adenosine-5-monophosphate (AMP) to iosine inosine-5-monophosphate (IMP). Obesity affects metabolic abnormalities in tumor cells and has been associated with high expression levels of AMPD2 and colorectal cancer (CRC). In this study, we performed immunohistochemical analysis of AMPD2 expression in 158 patients with CRC. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to determine AMPD2 mRNA expression levels, which were validated by The Cancer Genome Atlas (TCGA) datasets. Chi-square test and Fisher's exact test were used to evaluate the correlation between the expression of AMPD2 and clinicopathological parameters of CRC. Overall survival (OS) rates of the CRC patients were calculated using Kaplan-Meier survival analysis and a Cox proportional regression model was performed for univariate and multivariate analysis. A logistic regression model was used to plot the receiver operating characteristic (ROC) curve and to evaluate the predictive effect of multivariate studies on prognosis outcomes of CRC. We found a significant increase in AMPD2 expression in tumor tissue (91.8%, 146/158) compared to adjacent normal tissue (52.5%, 83/158, P < 0.01). The positive rate of AMPD2 expression was 72.7% (39/54) in overweight individuals versus 51.9% (54/104) in individuals with a normal weight (P = 0.014). AMPD2 mRNA levels as determined by qRT-PCR elevated levels of AMPD2 transcripts were higher in CRC samples compared to adjacent normal tissues (P < 0.05). In both the TCGA colon adenocarcinoma and rectal adenocarcinoma dataset, the number of CRC patients with increased levels of AMPD2 in tumor tissues was significantly higher compared to patients with adjacent normal tissue (P < 0.001). High expression of AMPD2 was associated with TNM stage, higher histological grade, obesity, and lower OS rates in patients with CRC. Obesity and high expression of AMPD2 in patients are with poor prognosis. Moreover, multivariate analysis indicated that AMPD2 levels and TNM stage were significant independent prognostic factors in CRC patients. The logistic regression predictive effect of the area under the curve (AUC) was 0.821 (P < 0.001). In conclusion, high levels of AMPD2 and obesity are associated with poor prognosis in patients with CRC.

Verteporfin inhibits gastric cancer cell growth by suppressing adhesion molecule FAT1

Gastric cancer (GC) is a leading cause of death worldwide and in urgent need of targeted drug development. In the current, we investigated the ability of a repositioned drug verteporfin (VP), originally a treatment for macular degeneration, to inhibit GC cell growth. VP inhibited growth of various GC cell lines. Gene expression profiling of GC cell lines treated with VP revealed that migration-related genes and those with oncogenic potential were down-regulated. Of these genes, we found that FAT1, an adhesion molecule promoting cell invasion, was highly suppressed by VP. Silencing of FAT1 suppressed cell migration and invasion as VP did. FAT1 expression was up-regulated in tumors, and patients with high FAT1-expressing tumors had a worse prognosis. We propose that VP- targeting FAT1 to suppress metastatic potential is a promising therapeutic strategy against GC.

A BAP1 Mutation-specific MicroRNA Signature Predicts Clinical Outcomes in Clear Cell Renal Cell Carcinoma Patients with Wild-type BAP1

Background: Clear cell renal cell carcinoma (ccRCC) is the most prevalent histologic subtype of kidney cancers in adults, which could be divided into two distinct subgroups according to the BRCA1 associated protein-1 (BAP1) mutation status. In the current study, we comprehensively analyzed the genome-wide microRNA (miRNA) expression profiles in ccRCC, with the aim to identify the differentially expressed miRNAs between BAP1 mutant and wild-type tumors, and generate a BAP1 mutation-specific miRNA signature for ccRCC patients with wild-type BAP1.

Methods: The BAP1 mutation status and miRNA profiles in BAP1 mutant and wild-type tumors were analyzed. Subsequently, the association of the differentially expressed miRNAs with patient survival was examined, and a BAP1 mutation-specific miRNA signature was generated and examined with Kaplan-Meier survival, univariate and multivariate Cox regression analyses. Finally, the bioinformatics methods were adopted for the target prediction of selected miRNAs and functional annotation analyses.

Results: A total of 350 treatment-naïve primary ccRCC patients were selected from The Cancer Genome Atlas project, among which 35 (10.0%) subjects carried mutant BAP1 and had a shorter overall survival (OS) time. Furthermore, 33 miRNAs were found to be differentially expressed between BAP1 mutant and wild-type tumors, among which 11 (miR-149, miR-29b-2, miR-182, miR-183, miR-21, miR-365-2, miR-671, miR-365-1, miR-10b, miR-139, and miR-181a-2) were significantly associated with OS in ccRCC patients with wild-type BAP1. Finally, a BAP1 mutation-specific miRNA signature consisting of 11 miRNAs was generated and validated as an independent prognostic parameter.

Conclusions: In summary, our study identified a total of 33 miRNAs differentially expressed between BAP1 mutant and wild-type tumors, and generated a BAP1 mutation-specific miRNA signature including eleven miRNAs, which could serve as a novel prognostic biomarker for ccRCC patients with wild-type BAP1.

Integrated analysis of competing endogenous RNA network revealing lncRNAs as potential prognostic biomarkers in human lung squamous cell carcinoma

Accumulating evidence shows the important role of long non-coding RNAs (lncRNAs) in competing endogenous RNA (ceRNA) networks for predicting survival in tumor patients. However, prognostic biomarkers for lung squamous cell carcinoma (LUSC) are still lacking. The objective of this study is to identify a lncRNA signature for evaluation of overall survival (OS) in 474 LUSC patients from The Cancer Genome Atlas (TCGA) database. A total of 474 RNA sequencing profiles in LUSC patients with clinical data were obtained, providing a large sample of RNA sequencing data, and 83 LUSC-specific lncRNAs, 26 miRNAs, and 85 mRNAs were identified to construct the ceRNA network (fold change>2, P<0.05). Among these above 83 LUSC-specific lncRNAs, 22 were assessed as closely related to OS in LUSC patients using a univariate Cox proportional regression model. Meanwhile, two (FMO6P and PRR26) of the above 22 OS-related lncRNAs were identified using a multivariate Cox regression model to construct a risk score as an independent indicator of the prognostic value of the lncRNA signature in LUSC patients. LUSC patients with low-risk scores were more positively correlated with OS (P<0.001). The present study provides a deeper understanding of the lncRNA-related ceRNA network in LUSC and suggests that the two-lncRNA signature could serve as an independent biomarker for prognosis of LUSC.

Osteosarcoma enters a post genomic era with in silico opportunities: Generation of the High Dimensional Database for facilitating sarcoma biology research: A report from the Children's Oncology Group and the QuadW Foundation

The prospective banking of osteosarcoma tissue samples to promote research endeavors has been realized through the establishment of a nationally centralized biospecimen repository, the Children's Oncology Group (COG) biospecimen bank located at the Biopathology Center (BPC)/Nationwide Children's Hospital in Columbus, Ohio. Although the physical inventory of osteosarcoma biospecimens is substantive (>15,000 sample specimens), the nature of these resources remains exhaustible. Despite judicious allocation of these high-value biospecimens for conducting sarcoma-related research, a deeper understanding of osteosarcoma biology, in particular metastases, remains unrealized. In addition the identification and development of novel diagnostics and effective therapeutics remain elusive. The QuadW-COG Childhood Sarcoma Biostatistics and Annotation Office (CSBAO) has developed the High Dimensional Data (HDD) platform to complement the existing physical inventory and to promote in silico hypothesis testing in sarcoma biology. The HDD is a relational biologic database derived from matched osteosarcoma biospecimens in which diverse experimental readouts have been generated and digitally deposited. As proof-of-concept, we demonstrate that the HDD platform can be utilized to address previously unrealized biologic questions though the systematic juxtaposition of diverse datasets derived from shared biospecimens. The continued population of the HDD platform with high-value, high-throughput and mineable datasets allows a shared and reusable resource for researchers, both experimentalists and bioinformatics investigators, to propose and answer questions in silico that advance our understanding of osteosarcoma biology.

miR-130A as a diagnostic marker to differentiate malignant mesothelioma from lung adenocarcinoma in pleural effusion cytology

BACKGROUND

Malignant pleural mesothelioma is a rare tumor with a dismal prognosis, usually presenting with recurrent effusions. However, the majority of malignant pleural effusions are due to lung adenocarcinoma (AdC). The distinction between these tumors has considerable therapeutic and medicolegal implications and can be very challenging both histologically and cytologically. Appropriate immunohistochemistry (IHC) is required to support the diagnosis. MicroRNA (miRNA) expression analysis could be a viable diagnostic tool for distinguishing between these tumors. The purpose of the current study was to assess the reliability of miRNAs as diagnostic markers to differentiate epithelioid malignant mesothelioma (MM) from lung AdC.

METHODS

Bioinformatic analysis of publicly searchable data sets regarding miRNA expression profiling was performed to select the most significant differentially expressed miRNAs. These were analyzed by quantitative polymerase chain reaction on histologic (41 MM cases and 40 lung AdC cases) and cytological (26 MM cases and 27 lung AdC cases) specimens and the diagnostic performances were assessed.

RESULTS

miR-130a, miR-193a, miR-675, miR-141, miR-205, and miR-375 were found to be the best distinguishing markers. Of these, only miR-130a was significantly overexpressed in MM compared with lung AdC (P =.029 in histologic and P =.014 in cytological samples). miR-130a demonstrated a sensitivity of 77%, a specificity of 67%, a positive predictive value of 69%, a negative predictive value of 75%, and an accuracy of 72% in identifying MM.

CONCLUSIONS

The diagnostic performances of miR-130a expression analysis and IHC appear to be similar. miR-130a quantification could be used reliably as second-level diagnostic tool to differentiate MM from lung AdC in pleural effusion cytology, mainly in those cases with ambiguous or negative IHC. Further validation is needed. Cancer Cytopathol 2017;125:635-43. © 2017 American Cancer Society.

Identification of potential gene targets in systemic vasculitis using DNA microarray analysis

The present study aimed to identify the involvement of critical genes in systemic vasculitis, to gain an improved understanding of the molecular circuity and to investigate novel potential gene targets for systemic vasculitis treatment. The dual-color cDNA microarray data of GSE16945, consisting of peripheral mononuclear blood cell specimens from 13 patients with systemic vasculitis and 16 healthy controls, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened in systemic vasculitis compared with controls using BRB ArrayTools, followed by the construction of a protein-protein interaction (PPI) network using the clusterProfiler package, and significant functional interaction (FI) module selection. Furthermore, transcriptional factors (TFs) among the identified DEGs were predicted and a transcriptional regulation network was constructed. A total of 173 up- and 93 downregulated genes were identified, which were mainly associated with immune response pathways. FBJ murine osteosarcoma viral oncogene homolog (FOS), ubiquitin B (UBB), signal transducer and activator of transcription 1 (STAT1) and MX dynamin-like GTPase 1 (MX1) were identified as hub proteins in the PPI network. Furthermore, UBB, FOS, and STAT1 were hub proteins in the three identified FI modules, respectively. In total, nine TFs were predicted among the DEGs. Of the DEGs that were predicted to be TFs, STAT1, v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog B (MAFB) and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein Z (YWHAZ), which interacted with each other, were identified to regulate further DEGs as target genes. Various genes, including FOS, UBB, MX1, STAT1, MAFB, and YWHAZ may be potential targets useful for the treatment of systemic vasculitis.

Integrated analysis identifies microRNA-195 as a suppressor of Hippo-YAP pathway in colorectal cancer

Background

With persistent inconsistencies in colorectal cancer (CRC) miRNAs expression data, it is crucial to shift toward inclusion of a “pre-laboratory” integrated analysis to expedite effective precision medicine and translational research. Aberrant expression of hsa-miRNA-195 (miR-195) which is distinguished as a clinically noteworthy miRNA has previously been observed in multiple cancers, yet its role in CRC remains unclear.

Methods

In this study, we performed an integrated analysis of seven CRC miRNAs expression datasets. The expression of miR-195 was validated in The Cancer Genome Atlas (TCGA) datasets, and an independent validation sample cohort. Colon cancer cells were transfected with miR-195 mimic and inhibitor, after which cell proliferation, colony formation, migration, invasion, and dual luciferase reporter were assayed. Xenograft mouse models were used to determine the role of miR-195 in CRC tumorigenicity in vivo.

Results

Four downregulated miRNAs (hsa-let-7a, hsa-miR-125b, hsa-miR-145, and hsa-miR-195) were demonstrated to be potentially useful diagnostic markers in the clinical setting. CRC patients with a decreased level of miR-195-5p in tumor tissues had significantly shortened survival as revealed by the TCGA colon adenocarcinoma (COAD) dataset and our CRC cohort. Overexpression of miR-195-5p in DLD1 and HCT116 cells repressed cell growth, colony formation, invasion, and migration. Inhibition of miR-195-5p function contributed to aberrant cell proliferation, migration, invasion, and epithelial mesenchymal transition (EMT). We identified miR-195-5p binding sites within the 3’-untranslated region (3′-UTR) of the human yes-associated protein (YAP) mRNA. YAP1 expression was downregulated after miR-195-5p treatment by qRT-PCR analysis and western blot.

Conclusions

Four downregulated miRNAs were shown to be prime candidates for a panel of biomarkers with sufficient diagnostic accuracy for CRC in a clinical setting. Our integrated microRNA profiling approach identified miR-195-5p independently associated with prognosis in CRC. Our results demonstrated that miR-195-5p was a potent suppressor of YAP1, and miR-195-5p-mediated downregulation of YAP1 significantly reduced tumor development in a mouse CRC xenograft model. In the clinic, miR-195-5p can serve as a prognostic marker to predict the outcome of the CRC patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s13045-017-0445-8) contains supplementary material, which is available to authorized users.

EphA3 as a target for antibody immunotherapy in acute lymphoblastic leukemia

The human EphA3 gene was discovered in a pre-B acute lymphoblastic leukemia (pre-B-ALL) using the EphA3-specific monoclonal antibody (mAb), IIIA4, which binds and activates both human and mouse EphA3. We use two models of human pre-B-ALL to examine EphA3 function, demonstrating effects on pre-B-cell receptor signaling. In therapeutic targeting studies, we demonstrated antitumor effects of the IIIA4 mAb in EphA3-expressing leukemic xenografts and no antitumor effect in the xenografts with no EphA3 expression providing evidence that EphA3 is a functional therapeutic target in pre-B-ALL. Here we show that the therapeutic effect of the anti-EphA3 antibody was greatly enhanced by adding an α-particle-emitting ²¹³Bismuth payload.

Biomarker and competing endogenous RNA potential of tumor-specific long noncoding RNA in chromophobe renal cell carcinoma

Background

Accumulating evidence suggests long noncoding RNAs (lncRNAs) play important roles in the initiation and progression of cancers. However, their functions in chromophobe renal cell carcinoma (chRCC) are not fully understood.

Methods

We analyzed the expression profiles of lncRNA, microRNA, and protein-coding RNA, along with the clinical information of 59 primary chRCC patients collected from The Cancer Genome Atlas database to identify lncRNA biomarkers for prognosis. We also constructed an lncRNA–microRNA–mRNA coexpression network (competitive endogenous RNAs network) by bioinformational approach.

Results

One hundred and forty-two lncRNAs were found to be differentially expressed between the cancer and normal tissues (fold change ≥1.5, P<0.001). Among them, 12 lncRNAs were also differentially expressed with the corresponding clinical characteristics (fold change ≥1.5, P<0.01). Besides, 7 lncRNAs (COL18A1-AS, BRE-AS1, SNHG7, TMEM51-AS1, C21orf62-AS1, LINC00336, and LINC00882) were identified to be significantly correlated with overall survival (log-rank P<0.05). A competitive endogenous RNA network in chRCC containing 16 lncRNAs, 18 miRNAs, and 168 protein-coding RNAs was constructed.

Conclusion

Our results identified specific lncRNAs associated with chRCC progression and prognosis, and presented competing endogenous RNA potential of lncRNAs in the tumor.

MicroRNA expression profiles predict progression and clinical outcome in lung adenocarcinoma

Lung cancer is one of the leading causes of cancer death worldwide. Accumulating evidence has indicated that microRNAs (miRNAs) can be proposed as promising diagnostic and prognostic markers for various cancers. The current study analyzed the miRNA expression profiles of 418 lung adenocarcinoma (LUAD) cases obtained from The Cancer Genome Atlas dataset, with the aim to investigate the relationship of miRNAs with progression and prognosis of LUAD. A total of 185 miRNAs were found to be differentially expressed between LUAD tumor tissues and adjacent normal tissues. Among them, 13, 10, 0, and 10 miRNAs were discovered to be associated with pathologic T, N, M, and Stage, respectively. Interestingly, mir-200 family (mir-200a, mir-200b, and mir-429) was shown to play a critical role in the progression of LUAD. In the multivariate Cox regression analysis, mir-1468 (P=0.009), mir-212 (P=0.026), mir-3653 (P=0.012), and mir-31 (P=0.002) were significantly correlated with recurrence-free survival. With regard to overall survival, mir-551b (P=0.011), mir-3653 (P=0.016), and mir-31 (P=0.001) were proven as independent prognostic markers. In summary, this study identified the cancer-specific miRNAs that may predict the progression and prognosis of LUAD.

MicroRNA Profile of Lung Tumor Tissues Is Associated with a High Risk Plasma miRNA Signature

Lung cancer is the most common cause of cancer deaths worldwide. MicroRNAs (miRNAs) are short, non-coding RNAs that regulate gene expression. Many studies have reported that alterations in miRNA expression are involved in several human tumors. We have previously identified a circulating miRNA signature classifier (MSC) able to discriminate lung cancer with more aggressive features. In the present work, microarray miRNA profiling of tumor tissues collected from 19 lung cancer patients with an available MSC result were perform in order to find a possible association between miRNA expression and the MSC risk level. Eleven tissue mature miRNAs and six miRNA precursors were observed to be associated with the plasma MSC risk level of patients. Not one of these miRNAs was included in the MSC algorithm. A pathway enrichment analysis revealed a role of these miRNA in the main pathways determining lung cancer aggressiveness. Overall, these findings add to the knowledge that tissue and plasma miRNAs behave as excellent diagnostic and prognostic biomarkers, which may find rapid application in clinical settings.

Cholesterol biosynthesis pathway as a novel mechanism of resistance to estrogen deprivation in estrogen receptor-positive breast cancer

Background

Therapies targeting estrogenic stimulation in estrogen receptor-positive (ER+) breast cancer (BC) reduce mortality, but resistance remains a major clinical problem. Molecular studies have shown few high-frequency mutations to be associated with endocrine resistance. In contrast, expression profiling of primary ER+ BC samples has identified several promising signatures/networks for targeting.

Methods

To identify common adaptive mechanisms associated with resistance to aromatase inhibitors (AIs), we assessed changes in global gene expression during adaptation to long-term estrogen deprivation (LTED) in a panel of ER+ BC cell lines cultured in 2D on plastic (MCF7, T47D, HCC1428, SUM44 and ZR75.1) or in 3D on collagen (MCF7) to model the stromal compartment. Furthermore, dimethyl labelling followed by LC-MS/MS was used to assess global changes in protein abundance. The role of target genes/proteins on proliferation, ER-mediated transcription and recruitment of ER to target gene promoters was analysed.

Results

The cholesterol biosynthesis pathway was the common upregulated pathway in the ER+ LTED but not the ER– LTED cell lines, suggesting a potential mechanism dependent on continued ER expression. Targeting the individual genes of the cholesterol biosynthesis pathway with siRNAs caused a 30–50 % drop in proliferation. Further analysis showed increased expression of 25-hydroxycholesterol (HC) in the MCF7 LTED cells. Exogenous 25-HC or 27-HC increased ER-mediated transcription and expression of the endogenous estrogen-regulated gene TFF1 in ER+ LTED cells but not in the ER– LTED cells. Additionally, recruitment of the ER and CREB-binding protein (CBP) to the TFF1 and GREB1 promoters was increased upon treatment with 25-HC and 27-HC. In-silico analysis of two independent studies of primary ER+ BC patients treated with neoadjuvant AIs showed that increased expression of MSMO1, EBP, LBR and SQLE enzymes, required for cholesterol synthesis and increased in our in-vitro models, was significantly associated with poor response to endocrine therapy.

Conclusion

Taken together, these data provide support for the role of cholesterol biosynthesis enzymes and the cholesterol metabolites, 25-HC and 27-HC, in a novel mechanism of resistance to endocrine therapy in ER+ BC that has potential as a therapeutic target.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0713-5) contains supplementary material, which is available to authorized users.

Elucidation of molecular and functional heterogeneity through differential expression network analyses of discrete tumor subsets

Intratumor heterogeneity presents a major hurdle in cancer therapy. Most current research studies consider tumors as single entities and overlook molecular diversity between heterogeneous state(s) of different cells assumed to be homogenous. The present approach was designed for fluorescence-activated cell sorting-based resolution of heterogeneity arising from cancer stem cell (CSC) hierarchies and genetic instability in ovarian tumors, followed by microarray-based expression profiling of sorted fractions. Through weighted gene correlation network analyses, we could assign enriched modules of co-regulated genes to each fraction. Such gene modules often correlate with biological functions; one such specific association was the enrichment of CD53 expression in CSCs, functional validation indicated CD53 to be a tumor-initiating cell- rather than quiescent CSC-marker. Another association defined a state of poise for stress-induced metastases in aneuploid cells. Our results thus emphasize the need for studying cell-specific functionalities relevant to regeneration, drug resistance and disease progression in discrete tumor cell fractions.

Dynamic Contrast-Enhanced MRI Kinetic Parameters as Prognostic Biomarkers for Prediction of Survival of Patient with Advanced Hepatocellular Carcinoma: A Pilot Comparative Study

RATIONALE AND OBJECTIVES

Tracer kinetic model selection for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data analysis influences its use as a prognostic biomarker. Our aim was to find DCE-MRI parameters that predict 1-year survival (1YS) and overall survival (OS) among patients with advanced hepatocellular carcinoma (HCC) treated with antiangiogenic monotherapy by conducting a proof-of-concept comparative study of five different kinetic models.

MATERIALS AND METHODS

Twenty patients with advanced HCC underwent DCE-MRI and subsequently received sunitinib. Pretreatment DCE-MRI data were analyzed retrospectively by using the Tofts-Kety (TK), extended TK, two compartment exchange, adiabatic approximation to the tissue homogeneity (AATH), and distributed parameter (DP) models. Arterial flow fraction (γ), arterial blood flow (BFA), permeability-surface area product (PS), fractional interstitial volume (vI), and other five parameters were calculated for each model. Individual parameters were evaluated for 1YS prediction using cross-validated Kaplan-Meier analysis, and for association with OS using univariate Cox regression analysis, with additional permutation testing.

RESULTS

For 1YS prediction, the TK model-derived γ (P = .007) and vI (P = .029) and the AATH model-derived PS (P = .005) were significant; all these parameters were lower in the high-risk group. Increase in the AATH model-derived PS and the DP model-derived BFA was associated with significant increase in OS with hazard ratios of 0.766 (P = .023) and 0.809 (P = .025), respectively.

CONCLUSIONS

The AATH model-derived PS was an effective prognostic biomarker for both 1YS and OS.

Cancer Specific Long Noncoding RNAs Show Differential Expression Patterns and Competing Endogenous RNA Potential in Hepatocellular Carcinoma

Long noncoding RNAs (lncRNAs) regulate gene expression by acting with microRNAs (miRNAs). However, the roles of cancer specific lncRNA and its related competitive endogenous RNAs (ceRNA) network in hepatocellular cell carcinoma (HCC) are not fully understood. The lncRNA profiles in 372 HCC patients, including 372 tumor and 48 adjacent non-tumor liver tissues, from The Cancer Genome Atlas (TCGA) and NCBI GEO omnibus (GSE65485) were analyzed. Cancer specific lncRNAs (or HCC related lncRNAs) were identified and correlated with clinical features. Based on bioinformatics generated from miRcode, starBase, and miRTarBase, we constructed an lncRNA-miRNA-mRNA network (ceRNA network) in HCC. We found 177 cancer specific lncRNAs in HCC (fold change ≥ 1.5, P < 0.01), 41 of them were also discriminatively expressed with gender, race, tumor grade, AJCC tumor stage, and AJCC TNM staging system. Six lncRNAs (CECR7, LINC00346, MAPKAPK5-AS1, LOC338651, FLJ90757, and LOC283663) were found to be significantly associated with overall survival (OS, log-rank P < 0.05). Collectively, our results showed the lncRNA expression patterns and a complex ceRNA network in HCC, and identified a complex cancer specific ceRNA network, which includes 14 lncRNAs and 17 miRNAs in HCC.

Water-Exchange-Modified Kinetic Parameters from Dynamic Contrast-Enhanced MRI as Prognostic Biomarkers of Survival in Advanced Hepatocellular Carcinoma Treated with Antiangiogenic Monotherapy

BACKGROUND

To find prognostic biomarkers in pretreatment dynamic contrast-enhanced MRI (DCE-MRI) water-exchange-modified (WX) kinetic parameters for advanced hepatocellular carcinoma (HCC) treated with antiangiogenic monotherapy.

METHODS

Twenty patients with advanced HCC underwent DCE-MRI and were subsequently treated with sunitinib. Pretreatment DCE-MRI data on advanced HCC were analyzed using five different WX kinetic models: the Tofts-Kety (WX-TK), extended TK (WX-ETK), two compartment exchange, adiabatic approximation to tissue homogeneity (WX-AATH), and distributed parameter (WX-DP) models. The total hepatic blood flow, arterial flow fraction (γ), arterial blood flow (BFA), portal blood flow, blood volume, mean transit time, permeability-surface area product, fractional interstitial volume (vI), extraction fraction, mean intracellular water molecule lifetime (τC), and fractional intracellular volume (vC) were calculated. After receiver operating characteristic analysis with leave-one-out cross-validation, individual parameters for each model were assessed in terms of 1-year-survival (1YS) discrimination using Kaplan-Meier analysis, and association with overall survival (OS) using univariate Cox regression analysis with permutation testing.

RESULTS

The WX-TK-model-derived γ (P = 0.022) and vI (P = 0.010), and WX-ETK-model-derived τC (P = 0.023) and vC (P = 0.042) were statistically significant prognostic biomarkers for 1YS. Increase in the WX-DP-model-derived BFA (P = 0.025) and decrease in the WX-TK, WX-ETK, WX-AATH, and WX-DP-model-derived vC (P = 0.034, P = 0.038, P = 0.028, P = 0.041, respectively) were significantly associated with an increase in OS.

CONCLUSIONS

The WX-ETK-model-derived vC was an effective prognostic biomarker for advanced HCC treated with sunitinib.

The promise of omics-based approaches to cancer prevention

Cancer is a complex category of diseases caused in large part by genetic or genomic, transcriptomic, and epigenetic or epigenomic alterations in affected cells and the surrounding microenvironment. Carcinogenesis reflects the clonal expansion of cells that progressively acquire these genetic and epigenetic alterations-changes that, in turn, lead to modifications at the RNA level. Gradually advancing technology and most recently, the advent of next-generation sequencing (NGS), combined with bioinformatics analytic tools, have revolutionized our ability to interrogate cancer cells. The ultimate goal is to apply these high-throughput technologies to the various aspects of clinical cancer care: cancer-risk assessment, diagnosis, as well as target identification for treatment and prevention. In this article, we emphasize how the knowledge gained through large-scale omics-oriented approaches, with a focus on variations at the level of nucleic acids, can inform the field of chemoprevention.

Advanced Hepatocellular Carcinoma: Perfusion Computed Tomography-Based Kinetic Parameter as a Prognostic Biomarker for Prediction of Patient Survival

OBJECTIVE

The aim of this study was to find prognostic biomarkers in perfusion computed tomography (PCT)-based kinetic parameters for advanced hepatocellular carcinoma (HCC) treated with antiangiogenic chemotherapy.

METHODS

Twenty-two patients with advanced HCC underwent PCT imaging and subsequently received bevacizumab in combination with gemcitabine and oxaliplatin. Pretreatment PCT data within advanced HCC were analyzed using the Tofts-Kety, 2-compartment exchange, adiabatic approximation to the tissue homogeneity (AATH), and distributed parameter models. Blood flow, blood volume, extraction fraction (E), and other 3 parameters were calculated. Kinetic parameters in each model were evaluated with 1-year survival discrimination using Kaplan-Meier analysis and with overall survival using univariate Cox regression analysis.

RESULTS

Only the AATH model-derived E was statistically significantly prognostic for 1-year survival. The increased AATH model-derived E was significantly associated with longer overall survival (P = 0.005).

CONCLUSIONS

The AATH model-derived E was an effective prognostic biomarker for advanced HCC.

Bioinformatics method to predict two regulation mechanism: TF-miRNA-mRNA and lncRNA-miRNA-mRNA in pancreatic cancer

Altered expressions of microRNAs (miRNAs) are reported in pancreatic cancer and associate with cancer pathogenesis, apoptosis, and cell growth, thereby functioning as either tumor suppressors or oncogenes. However, the majority of studies focus on defining the regulatory functions of miRNAs, whereas few investigations are directed toward assessing how the miRNA themselves are transcriptionally regulated. In this study, integration of published multi-level expression data and bioinformatics computational approach was used to predict two regulation mechanisms: transcription factors (TF)-miRNA-mRNA regulation and long non-coding RNA(lncRNA)-miRNA-mRNA regulation. To identify differentially expressed mRNAs, miRNAs, and lncRNAs, we integrated microarray expression data in pancreatic cancer tissues and normal tissues. Combination of differentially expressed mRNAs and miRNAs with miRNA-mRNA interactions based on crosslinking and immunoprecipitation followed by high-throughput sequencing (CLIP-Seq) data from StarBas, we constructed miRNA-mRNA regulatory network. Then we constructed two regulatory networks including TF-miRNA-mRNA and lncRNA-miRNA-mRNA based on chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-Seq) data from ChIPBase and CLIP-Seq data. A total of 4385 mRNAs, 500 miRNAs, and 21 lncRNAs were differentially expressed, of which, 18 mRNAs and 54 miRNAs are with high confidence. In miRNA-mRNA regulatory network, interrelated miRNAs target 1701 differentially regulated mRNAs. By constructing regulatory network, 19miRNAs including hsa-miR-137, hsa-miR-206, hsa-miR-429, hsa-miR-320d, and hsa-miR-320c are predicted to participate in lncRNA-miRNA-mRNA regulation. Furthermore, 8 miRNAs including hsa-mir-137, hsa-mir-206, hsa-mir-429, hsa-mir-375, hsa-mir-326, hsa-mir-217, hsa-mir-301b, and hsa-mir-184 are predicted to participate in TF-miRNA-mRNA regulation. In an integrated data analysis, we reveal large-scale effects of interrelated miRNAs and provide a model for predicting the mechanism of miRNAs disorder. Our study provides a new insight into understanding the transcriptional regulation of pancreatic cancer.

A tumor-specific microRNA signature predicts survival in clear cell renal cell carcinoma

PURPOSE

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancers in adults, and microRNAs (miRNAs) differentially expressed in ccRCC tumors have been identified and proposed to predict prognosis. In the present study, we comprehensively analyzed the genome-wide miRNA expression profiles in ccRCC, with the aim to generate a tumor-specific miRNA signature of prognostic values.

METHODS

The miRNA profiles in tumor and the adjacent normal tissue were analyzed, and the association of the differentially expressed miRNAs with patient survival was examined with univariate Cox regression analysis. Finally, a tumor-specific miRNA signature was generated and examined with Kaplan-Meier survival, univariate, and multivariate Cox regression analyses.

RESULTS

A total of 147 miRNAs were found differentially expressed between tumor and matched non-tumor tissues from 58 ccRCC patients. The prognostic values of these differentially expressed miRNAs were subsequently analyzed in the 411 ccRCC patients, and 22 miRNAs were found significantly correlated with patient survival. Finally, a tumor-specific miRNA signature of 22 miRNAs was generated and validated as an independent prognostic parameter.

CONCLUSIONS

A tumor-specific miRNA signature consisting of 22 miRNAs was identified and validated as an independent prognostic factor, which could serve as a novel biomarker for ccRCC prognostication and help in predicting treatment outcome.

Evaluation of Epigenetic Drug Targeting of Heterogenous Tumor Cell Fractions Using Potential Biomarkers of Response in Ovarian Cancer

PURPOSE

Resolution of aberrant epigenetic changes leading to altered gene expression during transformation and tumor progression is pertinent for mechanistic understanding of disrupted pathways in cancer. Such changes provide for biomarkers that can be applied in drug screening and improved disease management.

EXPERIMENTAL DESIGN

Genome-wide profiling and analyses of promoter DNA methylation, histone modifications, and gene expression of an in vitro progression model of serous ovarian adenocarcinoma were carried out. Similar in silico analyses and comparison of methylation and gene expression of early- and late-grade ovarian cancer samples in The Cancer Genome Atlas assigned a clinical relevance to our study. Candidate biomarkers were evaluated for epigenetic drug treatments in experimental animal models on a background of differing tumor cell responses arising from intratumor heterogeneity.

RESULTS

Differentially regulated genes during tumor progression were identified through the previously mentioned analyses as candidate biomarkers. In examining the tumor suppressor PTGIS as a potential biomarker for treatment with either 5-Aza-dC or TSA, 5-Aza-dC effectively stabilized cell cycling, restricted genetic instability, and derepressed PTGIS expression, while TSA led to emergence of drug-resistant progenitors lacking PTGIS expression. Profiling MEST and RXRγ for curcumin and CBB1007, respectively, indicated an inability of curcumin and CBB1007 in restricting residual tumor regenerative capabilities.

CONCLUSIONS

Our study provides novel insights into epigenetic regulation in ovarian cancer progression and potential biomarkers for evaluating efficacy of epigenetic drugs in restricting residual tumor regeneration. Such approaches may assign a new functional interpretation of drug efficacy and cell tumor responses in ovarian cancer.

A Seven-microRNA Expression Signature Predicts Survival in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the fifth common cancer. The differential expression of microRNAs (miRNAs) has been associated with the prognosis of various cancers. However, limited information is available regarding genome-wide miRNA expression profiles in HCC to generate a tumor-specific miRNA signature of prognostic values. In this study, the miRNA profiles in 327 HCC patients, including 327 tumor and 43 adjacent non-tumor tissues, from The Cancer Genome Atlas (TCGA) Liver hepatocellular carcinoma (LIHC) were analyzed. The associations of the differentially expressed miRNAs with patient survival and other clinical characteristics were examined with t-test and Cox proportional regression model. Finally, a tumor-specific miRNA signature was generated and examined with Kaplan-Meier survival, univariate\multivariate Cox regression analyses and KEGG pathway analysis. Results showed that a total of 207 miRNAs were found differentially expressed between tumor and adjacent non-tumor HCC tissues. 78 of them were also discriminatively expressed with gender, race, tumor grade and AJCC tumor stage. Seven miRNAs were significantly associated with survival (P value <0.001). Among the seven significant miRNAs, six (hsa-mir-326, hsa-mir-3677, hsa-mir-511-1, hsa-mir-511-2, hsa-mir-9-1, and hsa-mir-9-2) were negatively associated with overall survival (OS), while the remaining one (hsa-mir-30d) was positively correlated. A tumor-specific 7-miRNAs signature was generated and validated as an independent prognostic predictor. Collectively, we have identified and validated an independent prognostic model based on the expression of seven miRNAs, which can be used to assess patients' survival. Additional work is needed to translate our model into clinical practice.

MicroRNA expression profiles in muscle-invasive bladder cancer: identification of a four-microRNA signature associated with patient survival

Bladder cancer ranks the second most common genitourinary tract cancer, and muscle-invasive bladder cancer (MIBC) accounts for approximately 25 % of all bladder cancer cases with high mortality. In the current study, with a total of 202 treatment-naïve primary MIBC patients identified from The Cancer Genome Atlas dataset, we comprehensively analyzed the genome-wide microRNA (miRNA) expression profiles in MIBC, with the aim to investigate the relationship of miRNA expression with the progression and prognosis of MIBC, and generate a miRNA signature of prognostic capabilities. In the progression-related miRNA profiles, a total of 47, 16, 3, and 84 miRNAs were selected for pathologic T, N, M, and histologic grade, respectively. Of the eight most important progression-related miRNAs, four (let-7c, mir-125b-1, mir-193a, and mir-99a) were significantly associated with survival of patients with MIBC. Finally, a four-miRNA signature was generated and proven as a promising prognostic parameter. In summary, this study identified the specific miRNAs associated with the progression and aggressiveness of MIBC and a four-miRNA signature as a promising prognostic parameter of MIBC.

Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists

Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform these data into biological knowledge, for example, how to use these data to answer questions such as: Which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network reconstruction, and network interrogation. Here we provide an overview of network analysis including a step-by-step guide on how to perform and use this approach to investigate a biological question. In this guide, we also include the software packages that we and others employ for each of the steps of a network analysis workflow.

Identification and profiling of microRNAs in goat endometrium during embryo implantation

Background

MicroRNAs (miRNAs) are short, highly conserved small noncoding RNAs that had fundamental roles in post-transcriptional gene expression, and they are crucial for proper control of biological processes and known to participate in embryo implantation. However, miRNA expression profiles in the pre-receptive and receptive phases of the goat endometrium during embryo implantation are unknown.

Results

A total of 1,069 and 847 miRNAs were expressed in receptive (R) and pre-receptive (P) goat endometrium, and 632 miRNAs were co-expressed in both phases. We identified 545 (50.98%) known miRNAs in the R library and 522 (61.63%) in the P library. There were 110 up-expressed miRNAs and 33 down-expressed miRNAs in receptive endometrium compared with the pre-receptive endometrium meeting the criteria of P-values< 0.05. Moreover, GO and KEGG analysis of the target genes of the differentially expressed miRNAs revealed some candidate miRNAs, genes and pathways that may involve in the formation of the receptive endometrium. Based on stem-loop RT-qPCR, 15 miRNAs were detected and the results suggested that the majority of the miRNA expression data measured by Solexa deep sequencing could represent actual miRNA expression levels.

Conclusions

Our data revealed the first miRNA profile related to the biology of the goat receptive endometrium during embryo implantation, and the results suggested that a subset of miRNAs might play important roles in the formation of endometrial receptivity. Thus, elucidating the physiological roles of endometrial miRNAs will help us better understand the genetic control of embryo implantation in goats.

Expression Profiles and Clinical Significance of MicroRNAs in Papillary Renal Cell Carcinoma: A STROBE-Compliant Observational Study

Papillary renal cell carcinoma (pRCC) is the second most prevalent subtype of kidney cancers. In the current study, we analyzed the global microRNA (miRNA) expression profiles in pRCC, with the aim to evaluate the relationship of miRNA expression with the progression and prognosis of pRCC.A total of 163 treatment-naïve primary pRCC patients were identified from the Cancer Genome Atlas dataset and included in this retrospective observational study. The miRNA expression profiles were graded by tumor-node-metastasis information, and compared between histologic subtypes. Furthermore, the training-validation approach was applied to identify miRNAs of prognostic values, with the aid of Kaplan-Meier survival, and univariate and multivariate Cox regression analyses. Finally, the online DAVID (Database for Annotation, Visualization, and Integrated Discover) program was applied for the pathway enrichment analysis with the target genes of prognosis-associated miRNAs, which were predicted by 3 computational algorithms (PicTar, TargetScan, and Miranda).In the progression-related miRNA profiles, 26 miRNAs were selected for pathologic stage, 28 for pathologic T, 16 for lymph node status, 3 for metastasis status, and 32 for histologic types, respectively. In the training stage, the expression levels of 12 miRNAs (mir-134, mir-379, mir-127, mir-452, mir-199a, mir-200c, mir-141, mir-3074, mir-1468, mir-181c, mir-1180, and mir-34a) were significantly associated with patient survival, whereas mir-200c, mir-127, mir-34a, and mir-181c were identified by multivariate Cox regression analyses as potential independent prognostic factors in pRCC. Subsequently, mir-200c, mir-127, and mir-34a were confirmed to be significantly correlated with patient survival in the validation stage. Finally, target gene prediction analysis identified a total of 113 target genes for mir-200c, 37 for mir-127, and 180 for mir-34a, which further generated 15 molecular pathways.Our results identified the specific miRNAs associated with the progression and aggressiveness of pRCC, and 3 miRNAs (mir-200c, mir-127, and mir-34a) as promising prognostic factors of pRCC.

Kinome profiling reveals breast cancer heterogeneity and identifies targeted therapeutic opportunities for triple negative breast cancer

Our understanding of breast cancer heterogeneity at the protein level is limited despite proteins being the ultimate effectors of cellular functions. We investigated the heterogeneity of breast cancer (41 primary tumors and 15 breast cancer cell lines) at the protein and phosphoprotein levels to identify activated oncogenic pathways and developing targeted therapeutic strategies. Heterogeneity was observed not only across histological subtypes, but also within subtypes. Tumors of the Triple negative breast cancer (TNBC) subtype distributed across four different clusters where one cluster (cluster ii) showed high deregulation of many proteins and phosphoproteins. The majority of TNBC cell lines, particularly mesenchymal lines, resembled the cluster ii TNBC tumors. Indeed, TNBC cell lines were more sensitive than non-TNBC cell lines when treated with targeted inhibitors selected based on upregulated pathways in cluster ii. In line with the enrichment of the upregulated pathways with onco-clients of Hsp90, we found synergy in combining Hsp90 inhibitors with several kinase inhibitors, particularly Erk5 inhibitors. The combination of Erk5 and Hsp90 inhibitors was effective in vitro and in vivo against TNBC leading to upregulation of pro-apoptotic effectors. Our studies contribute to proteomic profiling and improve our understanding of TNBC heterogeneity to provide therapeutic opportunities for this disease.

Supervised wavelet method to predict patient survival from gene expression data

In microarray studies, the number of samples is relatively small compared to the number of genes per sample. An important aspect of microarray studies is the prediction of patient survival based on their gene expression profile. This naturally calls for the use of a dimension reduction procedure together with the survival prediction model. In this study, a new method based on combining wavelet approximation coefficients and Cox regression was presented. The proposed method was compared with supervised principal component and supervised partial least squares methods. The different fitted Cox models based on supervised wavelet approximation coefficients, the top number of supervised principal components, and partial least squares components were applied to the data. The results showed that the prediction performance of the Cox model based on supervised wavelet feature extraction was superior to the supervised principal components and partial least squares components. The results suggested the possibility of developing new tools based on wavelets for the dimensionally reduction of microarray data sets in the context of survival analysis.

Meta-analysis of the global gene expression profile of triple-negative breast cancer identifies genes for the prognostication and treatment of aggressive breast cancer

Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype lacking expression of estrogen and progesterone receptors (ER/PR) and HER2, thus limiting therapy options. We hypothesized that meta-analysis of TNBC gene expression profiles would illuminate mechanisms underlying the aggressive nature of this disease and identify therapeutic targets. Meta-analysis in the Oncomine database identified 206 genes that were recurrently deregulated in TNBC compared with non-TNBC and in tumors that metastasized or led to death within 5 years. This ‘aggressiveness gene list' was enriched for two core functions/metagenes: chromosomal instability (CIN) and ER signaling metagenes. We calculated an ‘aggressiveness score' as the ratio of the CIN metagene to the ER metagene, which identified aggressive tumors in breast cancer data sets regardless of subtype or other clinico-pathological indicators. A score calculated from six genes from the CIN metagene and two genes from the ER metagene recapitulated the aggressiveness score. By multivariate survival analysis, we show that our aggressiveness scores (from 206 genes or the 8 representative genes) outperformed several published prognostic signatures. Small interfering RNA screen revealed that the CIN metagene holds therapeutic targets against TNBC. Particularly, the inhibition of TTK significantly reduced the survival of TNBC cells and synergized with docetaxel in vitro. Importantly, mitosis-independent expression of TTK protein was associated with aggressive subgroups, poor survival and further stratified outcome within grade 3, lymph node-positive, HER2-positive and TNBC patients. In conclusion, we identified the core components of CIN and ER metagenes that identify aggressive breast tumors and have therapeutic potential in TNBC and aggressive breast tumors. Prognostication from these metagenes at the mRNA level was limited to ER-positive tumors. However, we provide evidence that mitosis-independent expression of TTK protein was prognostic in TNBC and other aggressive breast cancer subgroups, suggesting that protection of CIN/aneuploidy drives aggressiveness and treatment resistance.