Identification of mRNA Signature for Predicting Prognosis Risk of Rectal Adenocarcinoma

Abstract

Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses. Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated. Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival. Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.

Keywords: immune; immune infiltrate; mRNA signature; prognosis; rectum adenocarcinoma.

FIGURE 1

Identification of immune-related DEGs. (A) DEGs volcano map. The horizontal axis depicted the effect size (log₂ FC), while the vertical axis depicted -log10 (FDR). The pink and blue dots represented DEGs that have been upregulated or downregulated, respectively. FDR <0.05 is indicated above the horizontal dashed line, and |log₂ FC|>1 is indicated outside the two vertical dashed lines. (B) Heatmap of DEGs. (C) Immune-related genes and DEGs Venn diagram. FDR stands for false discovery rate and DEGs stand for differentially expressed genes. Fold change, FC.

FIGURE 2

GO and KEGG analysis of DEGs. (A) Enriched GO terms with p values <0.05. (B) KEGG pathways were enriched with a p value of <0.05. The number of DEGs was represented by the horizontal axis, and the GO or KEGG items were represented by the vertical axis: the greater the significance, the closer the column color is to red.

FIGURE 3

Correlation heatmap between DEGs and immune infiltration cells. (A) Heatmap of the correlation between DEGs and immune infiltration cells. (B,C) Scatter plots of the correlation between immune infiltration cells and ADAMDEC1 and F13A1 expression levels, respectively.

FIGURE 4

Evaluation of the prognostic risk model in TCGA training data set and GSE56699 validation dataset. (A,C) Kaplan–Meier curve method was used to evaluate a prognostic risk model in the TCGA training and GSE56699 validation datasets. The ROC curve of the prognostic risk model prediction results (B,D). Numbers in parentheses in the figure represent the ROC curve’s specificity and sensitivity.

FIGURE 5

Stratified analysis on age and pathologic. (A) Age-related prognostic Kaplan–Meier curve. (B,C) Prognosis-related Kaplan–Meier curves in TCGA samples for patients aged 65 and younger. (D) Prognostic-related Kaplan–Meier curve of pathologic stage. The pathologic stages N0, N1, and N2 are represented in TCGA sample’s prognosis-related Kaplan–Meier curve chart (E–G). TCGA, The Cancer Genome Atlas.

FIGURE 6

Model comparison analyses. (A) Nomogram survival rate prediction model for independent prognostic factors. (B) A 3-year and 5-year survival rate prediction line graph and an actual survival rate consistency line graph. The horizontal axis shows the predicted OS rate, the vertical axis shows the actual OS rate, and the red and black lines show 3- and 5-year predicted line graphs, respectively.