Combination of hsa-miR-375 and hsa-miR-142-5p as a predictor for recurrence risk in gastric cancer patients following surgical resection

Zhang, X.; Yan, Zhiqiang; Zhang, J.; Gong, Linxia; Li, W.; Cui, Jiantao; Liu, Yiqiang; Gao, Zhiqiang; Li, Jianbing; Шен, Ли; Lu, Yuhan

doi:10.1093/annonc/mdq758

Cited by 146 publications

(92 citation statements)

References 33 publications

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“…Although several studies did not notice an upregulation of miR-375 in prostate carcinoma samples, various studies observed increased levels of this miRNA in prostate cancer serum samples (44,45). Publications confirm that miR-375 is downregulated in other cancer types such as cervical, lung, or head and neck carcinoma, as well as glioma (46)(47)(48)(49)(50) but upregulated in breast carcinoma (51).…”

Section: Discussionmentioning

confidence: 88%

Comparative microRNA Profiling of Prostate Carcinomas with Increasing Tumor Stage by Deep Sequencing

Hart

Nolte

Wach

et al. 2014

Molecular Cancer Research

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MicroRNAs (miRNA) posttranscriptionally regulate gene expression and are important in tumorigenesis. Previous deep sequencing identified the miRNA profile of prostate carcinoma versus nonmalignant prostate tissue. Here, we generated miRNA expression profiles of prostate carcinoma by deep sequencing, with increasing tumor stage relative to corresponding nonmalignant and healthy prostate tissue, and detected clearly changed miRNA expression patterns. The miRNA profiles of the healthy and nonmalignant tissues were consistent with our previous findings, indicating a high fidelity of the method employed. In the tumors, quantitative real-time PCR (qRT-PCR) analysis of 40 paired samples of prostate carcinoma versus normal tissue revealed significant upregulation of miR-20a, miR-148a, miR-200b, and miR-375 and downregulation of miR-143 and miR-145. Hereby, miR-375 increased from normal to organ-confined tumors (pT2 pN0), slightly decreased in tumors with extracapsular growth (pT3 pN0), but was then expressed again at higher levels in lymph node metastasizing (pN1) tumors. The sequencing data for miR-375 were confirmed by Northern blotting and qRT-PCR. The regulation for other selected miRNAs could, however, not be confirmed by qRT-PCR in individual tumor stages. MiR-200b, in addition to miR-200c and miR-375 reduced the expression of SEC23A. Interestingly, miR-375, found by sequencing in pT2 upregulated by us and others in tumor versus normal tissue, and miR-15a, found by sequencing in pT2 and pT3 and in the metastasizing tumors, target the phosphatases PHLPP1 and PHLPP2, respectively. PHLPP1 and PHLPP2 dephosphorylate members of the AKT family of signal transducers, thereby inhibiting cell growth. Coexpression of miR-15a and miR-375 resulted in downregulation of PHLPP1/2 and strongly increased prostate carcinoma cell growth.Implications: These genomic data reveal relevant miRNAs in prostate cancer that may have biomarker and therapeutic potential. Mol Cancer Res; 12(2); 250-63. Ó2013 AACR. IntroductionProstate carcinoma is the second most frequently diagnosed malignancy and a leading cause of cancer-related death in men worldwide (1). The underlying mechanisms resulting in invasive growth after dissemination of the primary tumor from the initial site in the prostate are not completely understood. MicroRNAs (miRNAs) are now recognized as contributing factors to the induction, growth, and metastasis

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Section: Discussionmentioning

confidence: 88%

Comparative microRNA Profiling of Prostate Carcinomas with Increasing Tumor Stage by Deep Sequencing

Hart

Nolte

Wach

et al. 2014

Molecular Cancer Research

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“…Other genes involved in Wnt pathway were revealed as potential target of miR30a (Runx2, CDH1, Wnt5A) and miR-103 (Axin2) in expression profiling studies in gastric cancer [160] . Moreover, hsa-miR-375 upregulation and hsa-miR-142-5p downregulation have been indicated as predictor for recurrence risk in gastric cancer patients following surgical resection, probably by regulating several Wnt/ b-catenin target genes [162] . In Chinese patients with gastric cancer other miRNA genes that regulate Wnt/b-catenin target genes, such as miR-144, hsa-miR-148a and miR-193b were found downregulated [163][164][165] .…”

Section: Role Of Micrornas In Wnt/b-catenin Signaling In the Gastric mentioning

confidence: 99%

Role of the Wnt/β-catenin pathway in gastric cancer: An in-depth literature review

Chiurillo

2015

WJEM

270

219

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Gastric cancer remains one of the most common cancers worldwide and one of the leading cause for cancerrelated deaths. Gastric adenocarcinoma is a multifactorial disease that is genetically, cytologically and architecturally more heterogeneous than other gastrointestinal carcinomas. The identification of specific membrane, intracellular, and extracellular components of the Wnt pathway has revealed potential targets for gastric cancer therapy. High-throughput "omics" approaches will help in the search for Wnt pathway antagonist in the near future.

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“…According to the United States' statistics released in 2010 the incidence rate of colon has decreased (2). Over the last decade, many studies have proposed various kinds of statistical methods to analyze gene expression patterns and identify new biomarkers for prognostic and/or predictive information in relation to human diseases (3,4). However, most of the early studies applied unsupervised approaches to data-mining and identification of differential gene expressed profiling of certain diseases, such as hierarchical clustering for class discovering, taking an unbiased approach to searching for subgroups in the data (5).…”

Section: Introductionmentioning

confidence: 99%

Identification of candidate colon cancer biomarkers by applying a random forest approach on microarray data

Yan

Xiong

et al. 2012

Oncology Reports

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Abstract. Colon cancer is the third most common cancer and one of the leading causes of cancer-related death in the world. Therefore, identification of biomarkers with potential in recognizing the biological characteristics is a key problem for early diagnosis of colon cancer patients. In this study, we used a random forest approach to discover biomarkers based on a set of oligonucleotide microarray data of colon cancer. Realtime PCR was used to validate the related expression levels of biomarkers selected by our approach. Furthermore, ROC curves were used to analyze the sensitivity and specificity of each biomarker in both training and test sample sets. Finally, we analyzed the clinical significance of each biomarker based on their differential expression. A single classifier consisting of 4 genes (IL8, WDR77, MYL9 and VIP) was selected by random forests with an average sensitivity and specificity of 83.75 and 76.15%. The differential expression levels of each biomarker was validated by real-time PCR in 48 test colon cancer samples compared to the matched normal tissues. Patients with high expression of IL8 and WDR77, and low expression of MYL9 and VIP had a significantly reduced median survival rate compared to colon cancer patients. The results indicate that our approach can be employed for biomarker identification based on microarray data. These 4 genes identified by our approach have the potential to act as clinical biomarkers for the early diagnosis of colon cancer. IntroductionColon cancer is the third most common cancer, and one of the leading causes of morbidity and mortality in the world (1). According to the United States' statistics released in 2010 the incidence rate of colon has decreased (2). Over the last decade, many studies have proposed various kinds of statistical methods to analyze gene expression patterns and identify new biomarkers for prognostic and/or predictive information in relation to human diseases (3,4). However, most of the early studies applied unsupervised approaches to data-mining and identification of differential gene expressed profiling of certain diseases, such as hierarchical clustering for class discovering, taking an unbiased approach to searching for subgroups in the data (5). Along with the statistical methods extensively penetrated into the field of biomedicine, many supervised clustering analysis and machine learning approaches were adopted to deal with gene expression profiling data and sieved feature genes which contained more information to classify different kinds of diseases or subclasses of the same disease.Various methods of statistics and machine learning, including clustering (6,7), Bayesian algorithms (8), and support vector machines (9), have been proposed to analyze microarray data generated through high-throughput experiments. Over the last few years, the technology of multiclassifier fusion developed substantially, and became very successful in improving the accuracy of certain classifiers. Random forests (RF) (10,11), a tree-based method of classificat...

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Combination of hsa-miR-375 and hsa-miR-142-5p as a predictor for recurrence risk in gastric cancer patients following surgical resection

Abstract: these results are the first to indicate that the KRAS 3'UTR polymorphism may predict for cetuximab responsiveness in KRASwt mCRC patients, which warrants validation in other clinical trials.

Cited by 146 publications

References 33 publications

Comparative microRNA Profiling of Prostate Carcinomas with Increasing Tumor Stage by Deep Sequencing

Comparative microRNA Profiling of Prostate Carcinomas with Increasing Tumor Stage by Deep Sequencing

Role of the Wnt/β-catenin pathway in gastric cancer: An in-depth literature review

Identification of candidate colon cancer biomarkers by applying a random forest approach on microarray data

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