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. 2022 Jul 18:10:841034.
doi: 10.3389/fbioe.2022.841034. eCollection 2022.

Identification of the prognostic signature based on genomic instability-related alternative splicing in colorectal cancer and its regulatory network

Qiuying Ding  1 , Zhengping Hou  1 , Zhibo Zhao  2 , Yao Chen  1 , Lei Zhao  1 , Yue Xiang  1
Affiliations

Identification of the prognostic signature based on genomic instability-related alternative splicing in colorectal cancer and its regulatory network

Qiuying Ding et al. Front Bioeng Biotechnol. .

Abstract

Background: Colorectal cancer (CRC) is a heterogeneous disease with many somatic mutations defining its genomic instability. Alternative Splicing (AS) events, are essential for maintaining genomic instability. However, the role of genomic instability-related AS events in CRC has not been investigated. Methods: From The Cancer Genome Atlas (TCGA) program, we obtained the splicing profiles, the single nucleotide polymorphism, transcriptomics, and clinical information of CRC. Combining somatic mutation and AS events data, a genomic instability-related AS signature was constructed for CRC. Mutations analyses, clinical stratification analyses, and multivariate Cox regression analyses evaluated this signature in training set. Subsequently, we validated the sensitivity and specificity of this prognostic signature using a test set and the entire TCGA dataset. We constructed a nomogram for the prognosis prediction of CRC patients. Differentially infiltrating immune cells were screened by using CIBERSORT. Inmmunophenoscore (IPS) analysis was used to evaluate the response of immunotherapy. The AS events-related splicing factors (SF) were analyzed by Pearson's correlation. The effects of SF regulating the prognostic AS events in proliferation and migration were validated in Caco2 cells. Results: A prognostic signature consisting of seven AS events (PDHA1-88633-ES, KIAA1522-1632-AP, TATDN1-85088-ES, PRMT1-51042-ES, VEZT-23786-ES, AIG1-77972-AT, and PHF11-25891-AP) was constructed. Patients in the high-risk score group showed a higher somatic mutation. The genomic instability risk score was an independent variable associated with overall survival (OS), with a hazard ratio of a risk score of 1.537. The area under the curve of receiver operator characteristic curve of the genomic instability risk score in predicting the OS of CRC patients was 0.733. Furthermore, a nomogram was established and could be used clinically to stratify patients to predict prognosis. Patients defined as high-risk by this signature showed a lower proportion of eosinophils than the low-risk group. Patients with low risk were more sensitive to anti-CTLA4 immunotherapy. Additionally, HSPA1A and FAM50B were two SF regulating the OS-related AS. Downregulation of HSPA1A and FAM50B inhibited the proliferation and migration of Caco2 cells. Conclusion: We constructed an ideal prognostic signature reflecting the genomic instability and OS of CRC patients. HSPA1A and FAM50B were verified as two important SF regulating the OS-related AS.

Keywords: alternative splicing; colorectal cancer; genomic instability; overall survival; splicing factor.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Study flowchart.
FIGURE 2
FIGURE 2
Overview of AS events in TCGA CRC cohort. (A) Upset plot for all AS events. AS, alternative splicing; RI, retained intron; ME, mutually exclusive exons; ES, exon skipping; AT, alternative terminator; AP, alternative promoter; AD, alternative donor site; AA, alternative acceptor site. (B) Survival probability of different somatic mutation group. (C) Heat map of genomic instability-related AS events. TCGA, The Cancer Genome Atlas; CRC, Colorectal cancer.
FIGURE 3
FIGURE 3
Prognosis-related AS events in this study. (A) Upset plot for survival-related genes. (B) The Bubble plots of survival-associated AS events in CRC. (C,D) Optimal survival-related AS events selection in the LASSO regression model. (E) Heat map of the seven optimal survival-related AS events. LASSO, least absolute shrinkage and selection operator.
FIGURE 4
FIGURE 4
Relationship between the genomic instability-related AS signature and somatic mutation patterns of CRC patients. (A–C) The distribution of somatic mutation count and UBQLN4 expression of the training set (A), the test set (B), and the entire TCGA set (C). (D) Boxplots comparing the seven DNA mismatch repair genes expression between high- and low-risk groups. *p < 0.05 high-risk group vs. low-risk group. Statistical analysis was performed using the Mann-Whitney U test.
FIGURE 5
FIGURE 5
Kaplan-Meier curves and ROC curves of the prognostic AS models. (A,C,E) Kaplan-Meier plots of the genomic instability-related AS signature in the training set (A), the test set (C), and the entire TCGA set (E). (B,D,F) The ROC curves for overall survival of the genomic instability-related AS signature in three datasets, respectively. ROC, Receiver operating characteristic.
FIGURE 6
FIGURE 6
The independent prognostic analysis of the genomic instability-related AS signature. Construction of the nomograph model in patients with CRC. (A–C) Forest plots of univariate cox regression in the training set, test set, and the entire TCGA set. (D–F) Forest plots of multivariate cox regression in the three datasets. (G) The nomograph model predicting 1-, 2-, and 3-year survival in patients with CRC based on age, sex, TMN stage, and risk score.
FIGURE 7
FIGURE 7
Overview of the infiltrating immune cells in CRC. (A) Bar plot showing the proportion of the 22 types of immune cells. (B) Heat map of the immune cells proportion between the high- and low-risk groups. (C) Comparison of each immune cell type in the two risk groups. (D) Kaplan-Meier estimates of overall survival of patients with low or high eosinophils expression. (E) Violin plots of the IPS in two risk groups. IPS, immunophenoscore.
FIGURE 8
FIGURE 8
The splicing factors are associated with prognostic AS signature. (A) Splicing correlation network in CRC. The triangles represent the survival-related SF. The red and green ovals represent SREs that increase and decrease risk, respectively. Red and green lines represent the positive and negative correlations of connected triangles, respectively. SRE, alternative splicing events; SF, splicing factor. (B) ROC analysis of overall survival and disease-free survival for the AS signature-related splicing factors in patients with CRC. SF, splicing factor.
FIGURE 9
FIGURE 9
Downregulation of HSPA1A and FAM50B inhibits the proliferation and migration of Caco2 cells. Caco2 cells were transfected with control siRNA, HSPA1A siRNA, and FAM50B siRNA, respectively, or co-transfected with HSPA1A siRNA and FAM50B siRNA. (A) The mRNA levels of HSPA1A and FAM50B in Caco2 cells after transfection. (B) Cell proliferation were assessed by CCK8 assays (n ≥ 4). (C and D) The cell migration was detected by scratch (C) and transwell assays (D) (n = 3). * p < 0.05 vs. NCi group. #p < 0.05 vs. HSPA1Ai + FAM50Bi. Data are mean ± SEM.
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