Blood serum metabolism biological marker for lung cancer patients

Abstract

The invention relates to a blood serum metabolism biological marker for lung cancer patients. The blood serum of lung cancer patients and controls is respectively mixed with D2O to form sample solution. A 1H NMR spectrum is collected. The hydrogen spectrum data of the sample is imported into a SIMCA-P software for pattern identification analysis. Compared with the blood serum of the ordinary people, the blood serum of the lung cancer patients have a plurality of metabolites the level of which is higher like lactic acid, dextrose, alanine, valine, leucine, isoleucine, glycine, glutamine, proline, acetacetate, glycoprotein, choline and 3-hydroxybutyric acid. The dextrose and the lactic acid are often influenced by personal factors and are not fit to be the marker. Other metabolites are more stable and almost not influenced by outside environment and can serve as the clinical diagnosis marker. Thereby, a model is established to discriminate the blood serum of the lung cancer patients from the blood serum of the ordinary people and divide into two types. According to the model, the blood serum of the suspected lung cancer case is put in the model to analyze. If the blood serum map of the patient belongs to one of the two types, the patient can be diagnosed as lung cancer or normal.

Description

technical field [0001] The present invention relates to a panel of serum metabolic biomarkers for lung cancer patients. The present invention relates to the identification and selection of biomarkers that exhibit unique advantages in identifying individuals with lung cancer. The present invention further relates to useful combinations of biomarkers for the diagnosis of lung cancer. Background technique [0002] Lung cancer is the most harmful malignant tumor in my country. With the progress of industrialization in my country, the incidence rate is still increasing year by year. The cure rate of the disease is low, and the prognosis is extremely poor. The lack of evidence for early diagnosis is one of the main reasons. Studies have used genomic and proteomic technologies to discover biomarkers of a variety of key intracellular signaling pathways (such as EGRF), which may be related to drug sensitivity and prognosis of lung cancer, but due to the following reasons: [0003...

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Problems solved by technology

[0004] Second, with the development of high-throughput and high-resolution proteomics technology in recent years, there have been a large number of reports on screening candidate biomarkers for tumors. However, due to the strong heterogeneity of lung cancer, the There are relatively few general proteomics studies, which also leads to the fact that none of the biomarkers currently approved by the US FDA that can be routinely used clinically is specific to lung cancer.

[0005] Third, most of the current anticancer drugs have severe side effects, and often face the problem of drug resistance of cancer cells when performing single chemotherapy or combination therapy; if proteins closely related to the mechanism of action of anticancer drugs can be found, it may be This is a new plan for designing a combination of drugs with this target

[0008] Existing tumor markers are mainly gene expression products and proteins, which have a low detection rate and cannot achieve the purpose of early diagnosis

At present, there have been research reports on the metabolite profile of lung cancer from the United States, but these studies are limited to the comparison of tumor tissue and non-tumor tissue, and there is no research on metabolic markers in serum

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