Release date: 2017-05-26

On May 20, 2017, at the "Biomarker and Liquid Biopsy Forum" hosted by the Chinese People's Liberation Army Naval General Hospital, Professor Chen Luonan of the Shanghai Institute of Biological Sciences of the Chinese Academy of Sciences shared the "not sick" based on dynamic network markers. Innovative theory, through the analysis of associations, builds a dynamic network, distinguishes between human health and different states before the disease, and contributes to the early screening and diagnosis of the disease. Shellhouse (iBio4P) participates in the forum as a media support.

Only 3 genes are diagnosed. Breast cancer diagnosis accuracy is up to 96%.

At present, in genomics, transcriptomics, proteomics and metabolomics, a large number of samples are sequenced and various genetic data and other high-latitude data are obtained. However, these sample values ​​are based on a time point and a state. The absolute value underneath, these values ​​may change with time and conditions, so it is not a good stable analysis value to characterize the feature.

The literature reports that the occurrence and development of disease is not the abnormal function of a cell, a gene or a protein, but the result of a group of related molecules or network interactions, such as the regulatory network of genes. When a gene is mutated, the regulatory relationship disappears. Therefore, it is necessary to study the interaction between this gene and other related biomarkers in the network , which proposes the concept of network biomarkers , which is more accurate than traditional disease detection methods because it takes into account the connection between biomolecules. In fact, this interactive network is not limited to gene regulatory networks, but also includes protein interaction networks, RNA networks, signaling networks, and metabolic networks.

Professor Chen Luonan suggested that detecting a sample, even a single cell, could also build a network to diagnose disease. For example, first measure data for N normal samples, and make a standard network containing several genes and several mapping relationships through bio-software. A similar method, together with the N normal samples, build a new correlation network for N+1 samples, compare the new network with the standard network, study the difference between the two, and find the network markers of the disease. . So on the basis of the original comparison, as long as there is a new sample, redo a reference map to it, you can get a new network. The correlation data is stable and does not change with state or condition. Professor Chen Luonan said, "Associating the Fourier transform, a thing that changes with time, once it changes to the frequency domain space, shows a very stable feature, and in the frequency domain space analysis, it gets very important information. ”

Taking breast cancer experiments as an example, a reference network was constructed for 50 normal samples, and 1000 experimental samples were mapped separately. Although they were all breast cancer diseases, each person's network characteristics were different, and finally 96% of the diseases were found. In the sample network, the expression of all three genes was up-regulated. This shows that only three gene expression levels are measured for the individual and transformed into a network reanalysis with a detection accuracy of 96%, so a stronger correlation can be found from the network level. This theory has experimental verification in kidney cancer, gastric cancer and brain cancer.

At the forum, Professor Chen Luonan elaborated on the significance of the theory. Firstly, the sequencing results of individual cells can be analyzed to realize network analysis of single samples. Without genetic differential expression analysis, key genes or driving genes of diseases can be found; Markers form new network markers in the field of precision medicine. "I can diagnose diseases from the network level. I want to replace all medical biomarkers with network markers. It is better for doctors to choose which type to use. I believe it is better than molecular markers."

Dynamic network marker early screening before disease outbreak

The above static network markers can diagnose diseases, and dynamic network biomarkers (DNB) can be used for early diagnosis and screening of diseases. In this regard, Professor Chen Luonan proposed a quantitative diagnosis of the disease precursor - not disease.

The occurrence and development of the disease is a process of slowly changing from a normal state to a rapid deterioration. The internal factors are caused by the participation of external factors, and the internal factors have the accumulation of random mutations. The external factors include PM2.5, food, and emotions.

He believes that traditional biomarkers can statically study the differences between disease samples and controls, but are no longer important in the development of nonlinear and dynamic diseases. There is a general critical phenomenon in many complex biological processes, that is, from a relatively stable state, after a critical point, quickly enter another stable state in a short period of time. Such as the malignant transformation of many complex diseases is such a common phenomenon, further development from the normal state, experiencing a critical node that is fast and irreversible, is also a critical point, and finally disease.

The "Yellow Emperor's Internal Classic" records that "the medical treatment is not ill, the Chinese medicine cures the disease, and the lower treatment cures the disease." The ancients proposed the concept of ill disease two thousand years ago, but it has been impossible to quantitatively judge. Professor Chen Luonan said that according to mathematical theory, one person can measure three times to find out whether it is in a critical state, but statistically, more than five samples need to be measured. Therefore, modern medicine is a one-time physical examination. The result of a physical examination is to tell you whether you are sick, but if you want to know if you are getting sick, you need to have a physical examination at least five times a year, and only three times in mathematics.

He further explained that the criteria for judging whether a person is at a critical state of disease must meet the following three conditions: First, in the correlation data network, the critical time relationship will change peculiarly, and some genes or protein correlations are extremely high. Second, compared with other molecules, the correlation of some molecules disappears at the critical point. Third, a group of molecules will be different, one will be high and low, and there will be great fluctuations; this indicates that the sample has reached a critical state. Further development will turn into disease.

This theory has been confirmed in experimental studies such as lung injury, precancerous metastasis of liver cancer, and diabetes. Both of them have found a critical state of strong signal, indicating that the critical point is indeed present.

Professor Chen Luonan pointed out that DNB has many advantages. First, it does not need to establish any model, it is a model-free method. Second, DNB can obtain early warning signals before disease mutation under small sample conditions. Third, it is a breakthrough state. First, the dominant molecular group or the sub-network of biomolecules entering the disease state can lead the disease development, rather than the molecular group affected by the disease, so it has important biological significance. “So it is DNB that drives the disease process and ultimately realizes biological functions by downstream differential genes.”

Professor Chen Luonan introduced that in fact, critical theory has a wide range of predictive applications in society, economics, and ecology. Well-known examples include the herding effect in the stock market; the hotspots in Internet communication; the collapse of the New York market through bank cash flow; the eutrophication of the Bohai Sea in Yunnan, and the prediction of algae breeding.

In April of this year, Nature published a paper titled "Hunt for cancer 'tipping point' heats up," indicating that the theory is increasingly recognized internationally. "In a defense last year, some experts questioned that this theory is fortune-telling. But this year has become a new hot spot. There is also the application of DNB in ​​the drug reaction. No one now says it is fortune-telling. The critical point research will become the next under cancer research. A general direction," said Professor Chen Luonan.

Source: Shell Society

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