Primary liver cancer gene classification and liver cancer tissue energy metabolism-based prognosis analysis method

Abstract

The invention discloses a prognosis analysis method based on primary liver cancer gene classification and liver cancer tissue energy metabolism. The prognosis analysis method comprises the following steps: acquiring clinical phenotype data, expression profile data and gene CNV and SNV mutation data of a liver cancer sample; calculating sample scores of four metabolic pathways of the tumor sample, and determining a plurality of optimal classifications; and analyzing differences of expression, gene mutation, clinical phenotypes and immune characteristics of a plurality of optimal classification metabolic pathways, and then comprehensively analyzing results to establish a primary liver cancer prognosis analysis model. The method has the advantages that the primary liver cancer gene classifier is constructed through a big data analysis technology, the molecular subtype of the primary liver cancer is determined, and the metabolic pathway expression difference, the gene mutation difference, the clinical manifestation difference and the immune difference of the molecular subtype of the primary liver cancer are analyzed, so that the primary liver cancer prognosis analysis model is established; the method is used for accurately predicting the prognosis of the primary liver cancer and provides a research basis for revealing an energy metabolism mode and a tumor microenvironment of the primary liver cancer.

Description

technical field [0001] The invention relates to the field of liver cancer research, in particular to a prognostic analysis method based on gene classification of primary liver cancer and energy metabolism of liver cancer tissue. Background technique [0002] For nearly a decade, metabolic reprogramming has been recognized as one of the top ten characteristics of cancer, that is, oncogenic tumors share a common phenotype that can efficiently generate energy and macromolecules for their own metabolism, making tumor cells uncontrolled. grow. Recent studies have shown plasticity and flexibility in cancer cell metabolism between and within tumors, leading to poor therapeutic efficacy. At the same time, the study also found that human cells have obvious differences in the metabolic characteristics of major energy substances such as glucose, fatty acids, and glutamine. This variability is related to a range of factors, including genetics, access to nutrients and oxygen. Therefor...

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Problems solved by technology

At present, the clinical prognosis of patients is mainly based on the staging of liver cancer, but this method cannot accurately determine the prognosis of patients with liver cancer

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