Screening And Research Of Specific MiRNA Biomarkers In Plasma Of Lung Cancer

Background: Lung cancer is one of the most common malignancy. The new cases is about 1.61 million each year worldwide. Its mortality is the biggest in various cancers. Clinical data show that early detection of lung cancer can improve 5-year survival rate. Therefore, to achieve early diagnosis and effective treatment of cancer has become the key to improving patient survival. Including high specificity and high sensitivity of tumor markers play an important role in cancer diagnosis and treatment. mi RNA are a class of small non-coding RNA molecules, they are widely present in vivo. mi RNA have diverse biological function, They are widely involved in many physiological and pathological organisms development, such as proliferation, differentiation, senescence, apoptosis and other processes. Study found that the mi RNA has great relevance with the occurrence and development of lung cancer and its prognosis. In this paper, specific expression of mi RNA is screened in plasma, hoping to find specific mi RNA expression and be used as markers for early diagnosis of lung cancer.Methods: We use the cancer-related genes and lung cancer reported in the literature to find related mi RNA. We choose 50 cases of lung cancer patients for disease group and 50 healthy human controls. With the U6 as internal reference, via the extraction of total RNA in plasma, synthesis of mi RNA corresponding stem-loop reverse transcription primer, reverse transcribed into c DNA, use of real-time PCR instrument to quantitative PCR, and then using the SPSS software to analyze the mi RNA expression differences between lung cancer and the control group, the relationship between mi RNA expression and smoking or not, and then evaluating the value of mi RNA in diagnosis of lung cancer.Results: After extracting the plasma mi RNA and RT, fluorescence quantitative PCR and statistical analysis, we have confirmed the selected mi RNA have expression differences in lung cancer and control groups. Analyzing the correlation between the mi RNA and smoking or not, finally, we study the value of mi RNA in diagnosis of lung cancer.Conclusions: In this paper, we construct a system from the mi RNA extracted in plasma to the real-time PCR successfully, confirming that there are differences in mi RNA expression between lung cancer patients and the controls, exploring the relationship between mi RNA expression and smoking or not, and calculating the value of the mi RNA in diagnosis of lung cancer, provide an experimental basis for further clinical trials.