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. 2022 Apr 29;12(1):7036.
doi: 10.1038/s41598-022-11089-9.

Alternative polyadenylation associated with prognosis and therapy in colorectal cancer

Yi Zhang #  1 , Yunfei Xu #  2 , Yuzhi Wang  3
Affiliations

Alternative polyadenylation associated with prognosis and therapy in colorectal cancer

Yi Zhang et al. Sci Rep. .

Abstract

Colorectal cancer (CRC) is among the most widely spread cancers globally. Aberrant alternative polyadenylation (APA) plays a role in cancer onset and its progression. Consequently, this study focused on highlighting the role of APA events and signals in the prognosis of patients with CRC. The APA events, RNA sequencing (RNA-seq), somatic mutations, copy number variants (CNVs), and clinical information of the CRC cohort were obtained from The Cancer Genome Atlas (TCGA) database and UCSC (University of California-Santa Cruz) Xena database. The whole set was sorted into two sets: a training set and a test set in a ratio of 7:3. 197 prognosis-related APA events were collected by performing univariate Cox regression signature in patients with CRC. Subsequently, a signature for APA events was established by least absolute shrinkage and selection operator (LASSO) and multivariate Cox analysis. The risk scores were measured for individual patients on the basis of the signature and patients were sorted into two groups; the high-risk group and the low-risk group as per their median risk scores. Kaplan-Meier curves, principal component analysis (PCA), and time-dependent receiver operator characteristic (ROC) curves revealed that the signature was able to predict patient prognosis effectively and further validation was provided in the test set and the whole set. The high-risk and low-risk groups displayed various distributions of mutations and CNVs. Tumor mutation burden (TMB) alone and in combination with the signature predicted the prognosis of CRC patients, but the gene frequencies of TMBs and CNVs did not change in the low- and high-risk groups. Moreover, immunotherapy and chemotherapy treatments showed different responses to PD-1 inhibitors and multiple chemotherapeutic agents in the low and high-risk groups based on the tumor immune dysfunction and exclusion (TIDE) and genomics of drugs sensitivity in cancer (GDSC) databases. This study may help in understanding the potential roles of APA in CRC, and the signature for prognosis-related APA events can work as a potential predictor for survival and treatment in patients with CRC.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Enrichment analysis of the corresponding genes of prognosis-related APA events and Construction of a survival-associated. Volcano plot of prognosis-related APA events. An overview of the GO annotations of the prognostic APA in three categories: BP (A), CC (B) and MF (C). (D) KEGG pathway analysis. (E) CRs-APAs network (blue triangles, purple triangles and green triangles were CRs, poor prognosis events and good prognosis events, respectively. Red/green lines represent positive/negative correlations between nodes).
Figure 2
Figure 2
Development and validation of a four-APA-based prognostic signature. The risk curves, survival state diagrams, and risk thermographies in the training (A–C) and test (D–F) sets based on the signature.
Figure 3
Figure 3
Prediction performances of the signature for CRC patients. (A,B) Survival curves in the training and test sets. (C,D) Time-dependent ROC curves for 1-, 3-, and 5-year OS predictions by the signature in the training and test sets. (E) PCA plot for CRC patients based on the risk groups.
Figure 4
Figure 4
Survival curves in different clinical subgroups.
Figure 5
Figure 5
Integrated comparisons of somatic mutation and CNVs between high-risk and low-risk groups in the whole set. (A,B) Waterfall plots showing the mutation information of top 10 genes with the highest mutation frequency in high-risk and low-risk groups. (B) Distribution of TMB in two groups. (C) Survival curves for the high‐and low‐TMB groups. (D) Gene fragments profiles with amplification (red) and deletion (blue) among the two groups. (E) Survival curves for patients stratified by both TMB and signature. (F,G) Comparison of the fraction of the genome altered, lost, and gained between the two groups.
Figure 6
Figure 6
Estimation of the immune status and response to immunotherapy based on the signature in the high-risk and low-risk groups for the whole set. (A) Heatmap of the immune scores, stromal scores, tumor purity, ESTIMATE scores and immune-infiltrating cells in the two groups. (B–E) Violin plots for the immune scores, stromal scores, ESTIMATE scores, and tumor purity. (F,G) Boxplots of immune cells and immune checkpoints expression. (H) TIDE prediction difference in the two groups. *P < 0.05; **P < 0.01; ***P < 0.001; ns: no significance.
Figure 7
Figure 7
Correlation of signature with clinical factors and identification of the composite prognostic nomogram in the whole set. (A) Heatmap presents the distribution of clinical features and corresponding risk score. (B) Nomogram prediction of 1-, 3-, 5-year OS. (C) Calibration curves of observed and predicted probabilities for the nomogram. (D) Concordance index plot for the nomogram. (E) Time-dependent ROC curves for the nomogram. (F–H) DCA curves for the nomogram in 1-, 3-, 5-year OS. *P < 0.05; **P < 0.01; ***P < 0.001.
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