Background
Although significant progress has been made in surgery, radiation
therapy, chemotherapy, and targeted therapy, colorectal cancer (CRC) is
still one of the main types of cancer in terms of global morbidity and
cancer-related deaths. All the time, the TNM (tumor-nodes-matastasis)
staging system have been used as three prognostic indicators of the risk
of recurrence in CRC patients. But the TNM staging system only considers
the anatomical characteristics of the tumor, not the biological
characteristics of the tumor. The metabolic recoding of tumor cells
helps them adapt to the tumor microenvironment. The tumor
microenvironment can provide the energy needed to maintain the growth of
their malignant tumor cells, including accelerating proliferation,
anti-apoptosis, evading immune attack and maintaining cancer stem cell
status[1]. Certain genetic drivers of CRC, such as p53 [2]and
KRas[3], are well-known regulators of cancer metabolism. And
metabolic gene variants promote colorectal cancer[4]. It is
currently known that a single gene or molecular marker cannot provide a
good diagnosis or predict the progression of the disease. A single
biomolecular marker is usually unable to predict the survival of
patients with COAD, and more and more research institutions are using
multi-gene combination to build predictive models for disease diagnosis.
TCGA and GEO provide a lot of tumor-related information, such as gene
expression, methylation, mutations and clinical parameters[5], which
are of clinical significance cancer biology has created unprecedented
opportunities. In this study, we first screened the PRMG through
univariate COX regression based on the expression of metabolic genes,
and then used the LASSO to construct an important gene prognostic model.
In addition, ROC curve analysis of independent prognostic factors was
performed and a nomogram for predicting overall survival was
constructed. GSEA shows the way of KEGG enrichment.