CA : A Cancer Journal for Clinicians

// A. Marcell Szasz 1, 2 , Andras Lanczky 1 , Adam Nagy 1 , Susann Forster 3 , Kim Hark 4 , Jeffrey E. Green 4 , Alex Boussioutas 5, 6, 7 , Rita Busuttil 5, 6, 7 , Andras Szabo 8 , Balazs Győrffy 1, 8 1 MTA-TTK Lendulet Cancer Biomarker Research Group, Budapest, Hungary 2 2nd Department of Pathology, Semmelweis University, Budapest, Hungary 3 Max Delbruck Center for Molecular Medicine, Berlin, Germany 4 Transgenic Oncogenesis and Genomics Section, Laboratory of Cancer Biology and Genetics, National Cancer Institute, Bethesda, Maryland, USA 5 Cancer Genetics and Genomics Laboratory, Peter MacCallum Cancer Centre, East Melbourne, Australia 6 Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Australia 7 Department of Medicine, Royal Melbourne Hospital, The University of Melbourne, Melbourne, Australia 8 2nd Department of Pediatrics, Semmelweis University, Budapest, Hungary Correspondence to: Balazs Győrffy, email: gyorffy.balazs@ttk.mta.hu Keywords: gastric cancer, survival, meta-analysis Received: April 07, 2016      Accepted: June 13, 2016      Published: June 30, 2016 ABSTRACT Introduction: Multiple gene expression based prognostic biomarkers have been repeatedly identified in gastric carcinoma. However, without confirmation in an independent validation study, their clinical utility is limited. Our goal was to establish a robust database enabling the swift validation of previous and future gastric cancer survival biomarker candidates. Results: The entire database incorporates 1,065 gastric carcinoma samples, gene expression data. Out of 29 established markers, higher expression of BECN1 (HR = 0.68, p = 1.5E-05), CASP3 (HR = 0.5, p = 6E-14), COX2 (HR = 0.72, p = 0.0013), CTGF (HR = 0.72, p = 0.00051), CTNNB1 (HR = 0.47, p = 4.3E-15), MET (HR = 0.63, p = 1.3E-05), and SIRT1 (HR = 0.64, p = 2.2E-07) correlated to longer OS. Higher expression of BIRC5 (HR = 1.45, p = 1E-04), CNTN1 (HR = 1.44, p = 3.5E- 05), EGFR (HR = 1.86, p = 8.5E-11), ERCC1 (HR = 1.36, p = 0.0012), HER2 (HR = 1.41, p = 0.00011), MMP2 (HR = 1.78, p = 2.6E-09), PFKB4 (HR = 1.56, p = 3.2E-07), SPHK1 (HR = 1.61, p = 3.1E-06), SP1 (HR = 1.45, p = 1.6E-05), TIMP1 (HR = 1.92, p = 2.2E- 10) and VEGF (HR = 1.53, p = 5.7E-06) were predictive for poor OS. Materials and Methods: We integrated samples of three major cancer research centers (Berlin, Bethesda and Melbourne datasets) and publicly available datasets with available follow-up data to form a single integrated database. Subsequently, we performed a literature search for prognostic markers in gastric carcinomas (PubMed, 2012–2015) and re-validated their findings predicting first progression (FP) and overall survival (OS) using uni- and multivariate Cox proportional hazards regression analysis. Conclusions: The major advantage of our analysis is that we evaluated all genes in the same set of patients thereby making direct comparison of the markers feasible. The best performing genes include BIRC5, CASP3, CTNNB1, TIMP-1, MMP-2, SIRT, and VEGF.

/pdf/cross-validation-of-survival-associated-biomarkers-in-1boslqe3p5.pdf

Cross-validation of survival associated biomarkers in gastric cancer using transcriptomic data of 1,065 patients

MicroRNAs (miRNAs) are endogenous ∼23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.

MicroRNAs: Target Recognition and Regulatory Functions

MicroRNAs (miRNAs) represent a class of small, non-coding RNAs with the main roles of regulating mRNA through its degradation and adjusting protein levels. In recent years, extraordinary progress has been made in terms of identifying the origin and exact functions of miRNA, focusing on their potential use in both the research and the clinical field. This review aims at improving the current understanding of these molecules and their applicability in the medical field. A thorough analysis of the literature consulting resources available in online databases such as NCBI, PubMed, Medline, ScienceDirect, and UpToDate was performed. There is promising evidence that in spite of the lack of standardized protocols regarding the use of miRNAs in current clinical practice, they constitute a reliable tool for future use. These molecules meet most of the required criteria for being an ideal biomarker, such as accessibility, high specificity, and sensitivity. Despite present limitations, miRNAs as biomarkers for various conditions remain an impressive research field. As current techniques evolve, we anticipate that miRNAs will become a routine approach in the development of personalized patient profiles, thus permitting more specific therapeutic interventions.

https://www.mdpi.com/2073-4409/9/2/276/pdf

miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis

The incidence and mortality rate of cancer has been quickly increasing in the past decades. At present, cancer has become the leading cause of death worldwide. Most of the cancers cannot be effectively diagnosed at the early stage. Although there are multiple therapeutic treatments, including surgery, radiotherapy, chemotherapy, and targeted drugs, their effectiveness is still limited. The overall survival rate of malignant cancers is still low. It is necessary to further study the mechanisms for malignant cancers, and explore new biomarkers and targets that are more sensitive and effective for early diagnosis, treatment, and prognosis of cancers than traditional biomarkers and methods. Long non-coding RNAs (lncRNAs) are a class of RNA transcripts with a length greater than 200 nucleotides. Generally, lncRNAs are not capable of encoding proteins or peptides. LncRNAs exert diverse biological functions by regulating gene expressions and functions at transcriptional, translational, and post-translational levels. In the past decade, it has been demonstrated that the dysregulated lncRNA profile is widely involved in the pathogenesis of many diseases, including cancer, metabolic disorders, and cardiovascular diseases. In particular, lncRNAs have been revealed to play an important role in tumor growth and metastasis. Many lncRNAs have been shown to be potential biomarkers and targets for the diagnosis and treatment of cancers. This review aims to briefly discuss the latest findings regarding the roles and mechanisms of some important lncRNAs in the pathogenesis of certain malignant cancers, including lung, breast, liver, and colorectal cancers, as well as hematological malignancies and neuroblastoma.

https://www.mdpi.com/2073-4409/8/9/1015/pdf

Long Non-Coding RNA in the Pathogenesis of Cancers.

Circular RNAs (circRNAs) have received increasing attention in human tumor research. However, there are still a large number of unknown circRNAs that need to be deciphered. The aim of this study is to unearth novel circRNAs as well as their action mechanisms in hepatocellular carcinoma (HCC). A combinative strategy of big data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology was employed to dig HCC-related circRNAs and to explore their potential action mechanisms. A connectivity map (CMap) analysis was conducted to identify potential therapeutic agents for HCC. Six differently expressed circRNAs were obtained from three Gene Expression Omnibus microarray datasets (GSE78520, GSE94508 and GSE97332) using the RobustRankAggreg method. Following the RT-qPCR corroboration, three circRNAs (hsa_circRNA_102166, hsa_circRNA_100291 and hsa_circRNA_104515) were selected for further analysis. miRNA response elements of the three circRNAs were predicted. Five circRNA–miRNA interactions including two circRNAs (hsa_circRNA_104515 and hsa_circRNA_100291) and five miRNAs (hsa-miR-1303, hsa-miR-142-5p, hsa-miR-877-5p, hsa-miR-583 and hsa-miR-1276) were identified. Then, 1424 target genes of the above five miRNAs and 3278 differently expressed genes (DEGs) on HCC were collected. By intersecting the miRNA target genes and the DEGs, we acquired 172 overlapped genes. A protein–protein interaction network based on the 172 genes was established, with seven hubgenes (JUN, MYCN, AR, ESR1, FOXO1, IGF1 and CD34) determined from the network. The Gene Oncology, Kyoto Encyclopedia of Genes and Genomes and Reactome enrichment analyses revealed that the seven hubgenes were linked with some cancer-related biological functions and pathways. Additionally, three bioactive chemicals (decitabine, BW-B70C and gefitinib) based on the seven hubgenes were identified as therapeutic options for HCC by the CMap analysis. Our study provides a novel insight into the pathogenesis and therapy of HCC from the circRNA–miRNA–mRNA network view.

/pdf/a-circrna-mirna-mrna-network-identification-for-exploring-5e03xhsw2l.pdf

A circRNA–miRNA–mRNA network identification for exploring underlying pathogenesis and therapy strategy of hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a primary malignancy of the liver, is associated with high mortality rate and poor prognosis. Emerging evidence showed that novel biomarkers are required toward a better understanding of the biological mechanisms of HCC. NEAT1 (nuclear paraspeckle assembly transcript 1, also known as MENe/β), a novel long non-coding RNA (lncRNA), serves as a crucial regulator in several cancers. However, the correlation between NEAT1 expression with tumorigenesis and metastasis in HCC tissues remains out of the question so far. In the current study, the aim was to evaluate the potential role of NEAT1 expression in HCC tissues and its relationship with clinicopathological parameters. Method: The expression of NEAT1 was detected by qRT-PCR, in 95 cases of adjacent non-cancerous liver and their paired HCC tissues, respectively. The associations of NEAT1 with clinicopathological features and other biological factors were further analyzed. Result: Our results revealed that NEAT1 appeared to have higher expression in the HCC tissues, compared with the adjacent non-cancerous liver tissues. High levels of NEAT1 promoted the clinical features of HCC, including the number of tumor nodes, metastasis, clinical TNM stage, the status of portal vein tumor embolus, vaso-invasion and the infi ltration of tumor cells. Additionally, high NEAT1 expression levels were significantly associated with the expression level of MDTH, NM23 and MALAT1. Conclusion: Our study demonstrates that NEAT1 acts as a pivotal player in tumorigenesis and metastasis of hepatocellular carcinoma.

Clinical implication of long non-coding RNA NEAT1 expression in hepatocellular carcinoma patients

Long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), also known as nuclear-enriched transcript 2 (NEAT2), is highly conserved among mammals and highly expressed in the nucleus. It was first identified in lung cancer as a prognostic marker for metastasis but is also associated with several other solid tumors. In hepatocellular carcinoma (HCC), MALAT1 is a novel biomarker for predicting tumor recurrence after liver transplantation. The mechanism of overexpression in tumor progression remains unclear. In the present study, we investigated the role of specificity protein 1/3 (Sp1/3) in regulation of MALAT1 transcription in HCC cells. The results showed a high expression of Sp1, Sp3 and MALAT1 in HCC vs. paired non-tumor liver tissues, which was associated with the AFP level (Sp1, r=7.44, P=0.0064; MALAT1, r=12.37, P=0.0004). Co-silencing of Sp1 and Sp3 synergistically repressed MALAT1 expression. Sp1 binding inhibitor, mithramycin A (MIT), also inhibited MALAT1 expression in HCC cells. In conclusion, the upstream of MALAT1 contains five Sp1/3 binding sites, which may be responsible for MALAT1 transcription. Inhibitors, such as MIT, provide a potential therapeutic strategy for HCC patients with MALAT1 overexpression.

https://www.spandidos-publications.com/10.3892/or.2015.4259/download

Sp1 cooperates with Sp3 to upregulate MALAT1 expression in human hepatocellular carcinoma

Growing evidence indicates that microRNAs (miRNAs) play critical roles in the initiation and progression of breast carcinoma (BC) and are promising diagnostic biomarkers. In the present study, we aimed to identify a multi-marker miRNA pool with high diagnostic performance for BC. We collected miRNA expression profiles of BC samples and normal breast tissues from The Cancer Genome Atlas (TCGA) and screened differentially expressed miRNAs by conducting two‑sample t-tests and by calculating log2 fold-change (log2FC) ratios. Statistical significance was established at p 1. Then, we generated receiver operating characteristic (ROC) curves, calculated the area under the curve (AUC) with a 95% confidence interval (95% CI), and calculated the diagnostic sensitivity and specificity using MedCalc software. Additionally, we predicted the targets of candidate miRNAs using 10 online databases: TarBase, miRTarBase, TargetScan, TargetMiner, microRNA.org, RNA22, PicTar-vert, miRDB, PITA and PolymiRTS. Target genes that were predicted by at least four algorithms were chosen, and cooperative targets of multiple miRNAs were further selected for GO and KEGG pathway analyses through the DAVID online tool. Eventually, a total of 66 differentially expressed miRNAs were identified after miRNA expression profiles were analyzed in BC and normal breast samples. Of these, we selected nine dysregulated miRNAs as candidate diagnostic markers: seven upregulated miRNAs (hsa-miR-21, hsa-miR-96, hsa-miR-183, hsa-miR‑182, hsa-miR-141, hsa-miR-200a and hsa-miR-429) and two downregulated miRNAs (hsa-miR-139 and hsa-miR‑145). The ROC curve for the combination of these nine differently expressed miRNAs showed extremely high diagnostic accuracy, with an AUC of 0.995 (95% CI, 0.988‑0.999) and diagnostic sensitivity and specificity of 98.7 and 98.9%, respectively. In conclusion, the combination of these nine miRNAs significantly improved the accuracy of breast cancer diagnosis.

/pdf/a-nine-mirna-signature-as-a-potential-diagnostic-marker-for-uzaukkpt3q.pdf

A nine-miRNA signature as a potential diagnostic marker for breast carcinoma: An integrated study of 1,110 cases

Background
Recent reports have suggested that miR-30a plays a tumor-suppressive role in various cancers. However, miR-30a has not been completely studied in non-small lung cancer (NSCLC). Thus, the aim of the present study was to clarify the association between the expression of miR-30a and the clinicopathological features in NSCLC patients.

/pdf/downregulation-of-mir-30a-is-associated-with-poor-prognosis-k2cleybfua.pdf

Zhen-Bo Feng

Papers

A circRNA–miRNA–mRNA network identification for exploring underlying pathogenesis and therapy strategy of hepatocellular carcinoma

Clinical implication of long non-coding RNA NEAT1 expression in hepatocellular carcinoma patients

Sp1 cooperates with Sp3 to upregulate MALAT1 expression in human hepatocellular carcinoma

A nine-miRNA signature as a potential diagnostic marker for breast carcinoma: An integrated study of 1,110 cases

Downregulation of MiR-30a is Associated with Poor Prognosis in Lung Cancer