The role of multiple metabolic genes in predicting the overall survival of colorectal cancer: A study based on TCGA and GEO databases

Development and validation of prognostic models for colon adenocarcinoma based on combined immune-and metabolism-related genes

Background

The heterogeneity of tumor tissue is one of the reasons for the poor effect of tumor treatment, which is mainly affected by the tumor immune microenvironment and metabolic reprogramming. But more research is needed to find out how the tumor microenvironment (TME) and metabolic features of colon adenocarcinoma (COAD) are related.

Methods

We obtained the transcriptomic and clinical data information of COAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Consensus clustering analysis was used to identify different molecular subtypes, identify differentially expressed genes (DEGs) associated with immune-and metabolism-related genes (IMRGs) prognosis. Univariate and multivariable Cox regression analysis and Lasso regression analysis were applied to construct the prognostic models based on the IMRG risk score. The correlations between risk scores and TME, immune cell infiltration, and immune checkpoint genes were investigated. Lastly, potential appropriate drugs related to the risk score were screened by drug sensitivity analysis.

Results

By consensus clustering analysis, we identified two distinct molecular subtypes. It was also found that the multilayered IMRG subtypes were associated with the patient's clinicopathological characteristics, prognosis, and TME cell infiltration characteristics. Meanwhile, a prognostic model based on the risk score of IMRGs was constructed and its predictive power was verified internally and externally. Clinicopathological analysis and nomogram give it better clinical guidance. The IMRG risk score plays a key role in immune microenvironment infiltration. Patients in the high-risk groups of microsatellite instability (MSI) and tumor mutational burden (TMB) were found to, although with poor prognosis, actively respond to immunotherapy. Furthermore, IMRG risk scores were significantly associated with immune checkpoint gene expression. The potential drug sensitivity study helps come up with and choose a chemotherapy treatment plan.

Conclusion

Our comprehensive analysis of IMRG signatures revealed a broad range of regulatory mechanisms affecting the tumor immune microenvironment (TIME), immune landscape, clinicopathological features, and prognosis. And to explore the potential drugs for immunotherapy. It will help to better understand the molecular mechanisms of COAD and provide new directions for disease treatment.

Identification of a 5-Nutrient Stress-Sensitive Gene Signature to Predict Survival for Colorectal Cancer

Background

The high heterogeneity and the complexity of the tumor microenvironment of colorectal cancer (CRC) have enhanced the difficulty of prognosis prediction based on conventional clinical indicators. Recent studies revealed that tumor cells could overcome various nutritional deficiencies by gene regulation and metabolic remodeling. However, whether differentially expressed genes (DEGs) in CRC cells under kinds of nutrient deficiency could be used to predict prognosis remained unveiled.

Methods

Three datasets (GSE70976, GSE13548, and GSE116087), in which colon cancer cells were, respectively, cultured in serum-free, glucose-free, or glutamine-free medium, were included to delineate the profiles of gene expression by nutrient stress. DEGs were figured out in three datasets, and gene functional analysis was performed. Survival analyses and Cox proportional hazards model were then used to identify nutrient stress sensitive genes in CRC datasets (GSE39582 and TCGA COAD). Then, a 5-gene signature was constructed and the risk scores were also calculated. Survival analyses, cox analyses, and nomogram were applied to predict the prognosis of patients with colorectal cancer. The effectiveness of the risk model was also tested.

Results

A total of 48 genes were found to be dysregulated in serum, glucose, or glutamine-deprived CRC cells, which were mainly enriched in cell cycle and endoplasmic reticulum stress pathways. After further analyses, 5 genes, MCM5, MCM6, CDCA2, GINS2, and SPC25, were identified to be differentially expressed in CRC and be related to prognosis of in CRC datasets. We used the above nutrient stress-sensitive genes to construct a risk scoring model. CRC samples in the datasets were divided into low-risk and high-risk groups. Data showed that higher risk scores were associated with better outcomes and risk scores decreased significantly with tumor procession. Moreover, the risk score could be used to predict the probability of survival based on nomogram.

Conclusions

The 5-nutrient stress-sensitive gene signature could act as an independent biomarker for survival prediction of CRC patients.