International Journal of Chronic Diseases & Therapy (IJCDT)    IJCDT-01-003e

Systematic Strategy Opinion for Research and Clinical Practice of Chronic Diseases

Zhan X1,2,3,4*

1 Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, P. R. China.
2 Hunan Engineering Laboratory for Structural Biology and Drug Design, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, P. R. China.
3 State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, P. R. China.
4 The State Key Laboratory of Medical Genetics, Central South University, 88 Xiangya Road, Changsha, Hunan, P. R. China.

*Corresponding Author

Xianquan Zhan MD, PhD,
Key Laboratory of Cancer Proteomics of Chinese Ministry of Health,
Xiangya Hospital,
Central South University,
] 87 Xiangya Road, Changsha, Hunan 410008 P. R. China.
Tel: +86-731-84327905
Fax: +86-731-84327905

Artcle Type: Editorial
Received: September 28, 2015; Published: September 30, 2015

Citation: Zhan X (2015) Systematic Strategy Opinion for Research and Clinical Practice of Chronic Diseases. Int J Chronic Dis Ther. 1(3e) 1-2.

Copyright: Zhan X© 2015. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

Chronic diseases such as cancer, chronic obstructive pulmonary disease (COPD), diabetes mellitus, hypertension, epilepsy, chronic hepatitis, neurodegenerative disease, and obesity are a kind of whole-body diseases that are involved in multi-factors, multi-processes, and multi-consequences, that are involved in multiple molecules including genes, mRNAs, proteins, peptides, and metabolites, and that those molecules function in a mutually interacted pathway network system but not exert their roles individually [1-3]. Also, heterogeneity in between-individual and intra-disease is involved in each chronic disease [4, 5], and let each chronic disease more complicated, which is heavily challenging the efficacy of traditional therapeutic model – the same therapeutic strategy for the same type of disease [1]. It is very difficult or even impossible for one to use single-one factor such as one gene, protein, or metabolite to clarify occurrence, development, and consequence of a chronic disease. In fact, the traditional single-factor strategy is based on an unrealistic assumption that the increase in the amount of a single-molecule can unambiguously characterize a chronic disease [2]. The paradigm is being shifted from traditional single-factor strategy to multi-factor systematic strategy for research and clinical practice of a chronic disease.

From a systematic strategy viewpoint, multiple organs including tissues and body-fluids should be targeted for a chronic disease research [2]. The tissue research benefits clarification of molecular mechanisms and discovery of therapeutic targets. The body-fluid research benefits discovery of biomarkers for prediction, diagnosis, and prognosis. For example, one should consider multiple targeted organs such as pituitary tissues, cerebral spinal fluid (CSF), and blood for pituitary adenoma [2]; lung tissues and blood for lung cancer; lung tissues, blood, and airway secretions for COPD; brain, CSF, and blood for epilepsy, etc. With the rapid development of omics (genomics, transcriptomics, proteomics, peptidomics, and metabolomics), bioinformatics, computation biology, and systems biology [6, 7], it is possible for one to integrate multi-molecules at the different levels of gene (genome), mRNA (transcriptome), protein (protrome), peptide (peptidome), and metabolite (metabolome) together with clinical characteristics [8-10] for research and clinical practice of a chronic disease from an angle of multi-factor systematic strategy and molecular network to discover molecule-panel for accurate and effective molecular biomarker for prediction, diagnosis, prognosis, or therapeutic targets of a chronic disease towards personalized medicine [11] and precision medicine [12].


This work was supported by the grants from China “863” Plan Project (Grant No. 2014AA020610-1 to X. Z.), Xiangya Hospital Funds for Talent Introduction (to X.Z.), the National Natural Science Foundation of China (Grant No. 81272798 and 81572278 to X.Z.), and Hunan Provincial Natural Science Foundation of China (Grant No. 14JJ7008 to X. Z.).


  1. Hu R, Wang X, Zhan X (2013) Multi-parameter systematic strategies for predictive, preventive and personalised medicine in cancer. EPMA J 4(1): 2.
  2. Zhan X, Desiderio DM (2010) The use of variations in proteomes to predict, prevent, and personalize treatment for clinically nonfunctional pituitary adenomas. EPMA J 1(3): 439-459.
  3. Golubnitschaja O, Yeghiazaryan K, Costigliola V, Trog D, Braun M, et al. (2013) Risk assessment, disease prevention and personalised treatments in breast cancer: is clinically qualified integrative approach in the horizon? EPMA J 4(1): 6.
  4. Longo DL (2012) Tumor heterogeneity and personalized medicine. N Engl J Med 366(10): 956-957.
  5. Marusyk A, Almendro V, Polyak K (2012) Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer 12(5): 323-334.
  6. Hood L (2013) Systems biology and p4 medicine: past, present, and future. Rambam Maimonides Med J 4(2): e0012.
  7. Hood L, Balling R, Auffray C (2012) Revolutionizing medicine in the 21st century through systems approaches. Biotechnol J 7(8): 992-1001.
  8. Grech G, Zhan X, Yoo BC, Bubnov R, Hagan S, et al. (2015) EPMA position paper in cancer: Current overview and future perspectives. EPMA J 6(1): 9.
  9. Calimlioglu B, Karagoz K, Sevimoglu T, Kilic E, Gov E, et al. (2015) Tissue- Specific Molecular Biomarker Signatures of Type 2 Diabetes: An Integrative Analysis of Transcriptomics and Protein-Protein Interaction Data. OMICS 19(9): 563-573.
  10. Kwon MS, Kim Y, Lee S, Namkung J, Yun T, et al. (2015) Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer. BMC Genomics 16(Suppl 9): S4.
  11. Jackson SE, Chester JD (2015) Personalised cancer medicine. Int J Cancer 137(2): 262-266.
  12. Collins FS, Varmus H (2015) A new initiative on precision medicine. N Engl J Med 372(9): 793-795.

         Indexed in


       Total Visitors

SciDoc Counter

Get in Touch

SciDoc Publishers
16192 Coastal Highway
Lewes, Delaware 19958
Tel :+1-(302)-703-1005
Fax :+1-(302)-351-7355

Creative Commons License
SciDoc Publishers is licensed under a Creative Commons Attribution 4.0 International License.