Chenglixin research group of Shenzhen People's hospital proposed a new method of multi group data in the diagnosis and prognosis analysis of hepatocellular carcinoma megps

With the development of high flux technology , The integration and analysis of multiomics data provides a new direction for scientists to explore the mechanism of cancer occurrence and development , Including genomics 、 transcriptomics 、 Proteomics 、 Metabolomics, etc , However, the integration of multi group data has always been a difficult problem . At present , Many cancer researches use machine learning algorithms to model omics data , To identify molecular markers for the diagnosis and prognosis of cancer and its subtypes . However, in hepatocellular carcinoma （Hepatocellular Carcinoma,HCC） in , The research on the combination of multigroup analysis and machine learning algorithm is still very limited . If the two can be effectively combined , It will be helpful for the diagnosis of hepatocellular carcinoma 、 Prognosis and targeted therapy provide new directions .

In view of the negative correlation between promoter methylation and gene expression , Chenglixin research group of Shenzhen People's hospital has designed a new method to integrate methylomics and transcriptomics data ——meGPS, Combined with the core concept of personalized precision medicine , To reveal the role of epigenetic and transcriptomic markers in the early diagnosis and prognosis of hepatocellular carcinoma , So as to improve the adjuvant treatment of hepatocellular carcinoma （ chart 1）. The research results were recently published in Bioinformatics The magazine , Titled “meGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data”.

chart 1

meGPS Association between promoter region methylation and gene expression （methylation and expression,me）, The relative differences of paired genes are used as characteristic labels （gene pair signature, GPS） Extract stable gene features . In the application of hepatocellular carcinoma data , The team extracted a set of 4 To express paired genes and 1 The genetic characteristics of methylated paired genes （meGPS）. Multiple sets of data sets of blood and liver tissue samples were tested by multiple taxonomic test standards ,meGPS It can accurately classify patients with liver cancer （ chart 2）. meanwhile , This result reflects meGPS The classification robustness and stability of the method among data from different sequencing platforms . In order to verify meGPS The effectiveness of the method in other disease scenarios , The team further analyzed gender 、 The influence of age and different origins of liver cancer on classification . Results show ,meGPS It has good classification effect and strong robustness .

chart 2

in summary , Chenglixin's team put forward a new data integration scheme , It makes full use of the advantages of inter omics data , Improved classification effect , It expands the application of multiomics in taxonomy and tumor diagnosis , To provide systematic technical support from diagnosis to prognosis for patients with liver cancer .

reference ：

[1] Qiong Wu#, Xubin Zheng#, Kwong-Sak Leung, Man-Hon Wong, Stephen Kwok-Wing Tsui*, and Lixin Cheng*. meGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data. Bioinformatics (2022).

[2] Wang, R., Zheng, X., Wang, J., Wan, S., Song, F., Wong, M.H., Leung, K.S. and Cheng, L., Improving bulk RNA-seq classification by transferring gene signature from single cells in acute myeloid leukemia. Briefings in Bioinformatics (2022).

[3] Zheng, X., Leung, K.S., Wong, M.H. and Cheng, L., 2021. Long non-coding RNA pairs to assist in diagnosing sepsis. BMC Genomics (2021).