Literature DB >> 24807144

Prognostic and Predictive Biomarkers in Cancer.

Meera Nair, Sardul Singh Sandhu, Anil K Sharma1.   

Abstract

With the recent emergence of novel technologies, the field of biomarker discovery has been the subject of intense research and activity. Major advances in cancer control will be greatly aided by early detection for the diagnosis and treatment of cancer in its pre-invasive state. Cancer being a diverse disease involves alterations in three classes of genes viz. (proto) oncogenes, tumour suppressor genes and DNA repair genes offering a wide variety of opportunities for the development of biomarkers. The emergence of innovative technologies in genomics, proteomics, metabolomics and imaging allows researchers to facilitate inclusive analysis of cancer cells. These approaches have already demonstrated its power to discriminate cancer cells from normal cells and to identify specific genetic elements involved in cancer progression. Cancer evolves via manifold pathways and is a culmination of a variety of genetic, molecular and clinical events. In the past few years, several reports have led to identification of novel cancer signatures via high throughput biology. Current review gives an overview of the bioinformatics tools, cancer database and available software package tools and further summarizes about different strategies involved in Omics research (genomics, proteomics, metabolomics) for the development of cancer biomarkers. We also discuss about the current and emerging biomarkers in breast cancer with fundamental insight into different markers used in breast cancer detection. In addition, we focus upon the systematic integration of various omic data for accelerating cancer biomarker discovery with evidence based cancer management. The above strategies may lead to significant improvement in cancer screening, prognosis and management of therapeutic response in cancer patients.

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Year:  2014        PMID: 24807144     DOI: 10.2174/1568009614666140506111118

Source DB:  PubMed          Journal:  Curr Cancer Drug Targets        ISSN: 1568-0096            Impact factor:   3.428


  11 in total

1.  Text-mining in cancer research may help identify effective treatments.

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2.  Potential predictive plasma biomarkers for cervical cancer by 2D-DIGE proteomics and Ingenuity Pathway Analysis.

Authors:  Xia Guo; Yi Hao; Mayila Kamilijiang; Axiangu Hasimu; Jianlin Yuan; Guizhen Wu; Halidan Reyimu; Nafeisha Kadeer; Abulizi Abudula
Journal:  Tumour Biol       Date:  2014-11-27

3.  Exosomal miR-1298 and lncRNA-RP11-583F2.2 Expression in Hepato-cellular Carcinoma.

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Journal:  Curr Genomics       Date:  2020-01       Impact factor: 2.236

4.  Variations in Blood Platelet Proteome and Transcriptome Revealed Altered Expression of Transgelin-2 in Acute Coronary Syndrome Patients.

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Review 5.  Metabolomics insights into pathophysiological mechanisms of interstitial cystitis.

Authors:  Oliver Fiehn; Jayoung Kim
Journal:  Int Neurourol J       Date:  2014-09-24       Impact factor: 2.835

Review 6.  Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine.

Authors:  Hala Fawzy Mohamed Kamel; Hiba Saeed A Bagader Al-Amodi
Journal:  Genomics Proteomics Bioinformatics       Date:  2017-08-13       Impact factor: 7.691

7.  pyHIVE, a health-related image visualization and engineering system using Python.

Authors:  Ruochi Zhang; Ruixue Zhao; Xinyang Zhao; Di Wu; Weiwei Zheng; Xin Feng; Fengfeng Zhou
Journal:  BMC Bioinformatics       Date:  2018-11-26       Impact factor: 3.169

Review 8.  Natural Products for Drug Discovery in the 21st Century: Innovations for Novel Drug Discovery.

Authors:  Nicholas Ekow Thomford; Dimakatso Alice Senthebane; Arielle Rowe; Daniella Munro; Palesa Seele; Alfred Maroyi; Kevin Dzobo
Journal:  Int J Mol Sci       Date:  2018-05-25       Impact factor: 5.923

Review 9.  Deep learning in cancer diagnosis, prognosis and treatment selection.

Authors:  Khoa A Tran; Olga Kondrashova; Andrew Bradley; Elizabeth D Williams; John V Pearson; Nicola Waddell
Journal:  Genome Med       Date:  2021-09-27       Impact factor: 11.117

10.  Integrated profiling identifies ITGB3BP as prognostic biomarker for hepatocellular carcinoma.

Authors:  Qiuli Liang; Chao Tan; Feifei Xiao; Fuqiang Yin; Meiliang Liu; Lei Lei; Liuyu Wu; Yu Yang; Hui Juan Jennifer Tan; Shun Liu; Xiaoyun Zeng
Journal:  Bosn J Basic Med Sci       Date:  2021-12-01       Impact factor: 3.363

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