Analysis of relapse-associated alternative mRNA splicing and construction of a prognostic signature predicting relapse in I-III colon cancer

Exploring the mystery of colon cancer from the perspective of molecular subtypes and treatment

The molecular categorization of colon cancer patients remains elusive. Gene set enrichment analysis (GSEA), which investigates the dysregulated genes among tumor and normal samples, has revealed the pivotal role of epithelial-to-mesenchymal transition (EMT) in colon cancer pathogenesis. In this study, we employed multi-clustering method for grouping data, resulting in the identification of two clusters characterized by varying prognostic outcomes. These two subgroups not only displayed disparities in overall survival (OS) but also manifested variations in clinical variables, genetic mutation, and gene expression profiles. Using the nearest template prediction (NTP) method, we were able to replicate the molecular classification effectively within the original dataset and validated it across multiple independent datasets, underscoring its robust repeatability. Furthermore, we constructed two prognostic signatures tailored to each of these subgroups. Our molecular classification, centered on EMT, hold promise in offering fresh insights into the therapy strategies and prognosis assessment for colon cancer.

Identification of Key Prognostic Alternative Splicing Events of Costimulatory Molecule-Related Genes in Colon Cancer

OBJECTIVE

This study aimed to explore the key alternative splicing events in costimulatory molecule-related genes in colon cancer and to determine their correlation with prognosis.

METHODS

Gene expression RNA-sequencing data, clinical data, and SpliceSeq data of colon cancer were obtained from The Cancer Genome Atlas. Differentially expressed alternative splicing events in genes were identified, Followed by correlation analysis of genes corresponding to differentially expressed alternative splicing events with costimulatory molecule-related genes. Survival analysis was conducted using differentially expressed alternative splicing events in these genes and a prognostic model was constructed. Functional enrichment, proteinprotein interaction network, and splicing factor analyses were performed.

RESULTS

In total, 6504 differentially expressed alternative splicing events in 3949 genes were identified between tumor and normal tissues. Correlation analysis revealed 3499 differentially expressed alternative splicing events in 2168 costimulatory molecule-related genes. Moreover, 328 differentially expressed alternative splicing events in 288 costimulatory molecule-related genes were associated with overall survival. The prognostic models constructed using these showed considerable power in predicting survival. The ubiquitin A-52 residue ribosomal protein fusion product 1 and ribosomal protein S9 were the hub nodes in the protein-protein interaction network. Furthermore, one splicing factor, splicing factor proline and glutamine-rich, was significantly associated with patient prognosis. Four splicing factor-alternative splicing pairs were obtained from four alternative splicing events in three genes: TBC1 domain family member 8 B, complement factor H, and mitochondrial fission 1.

CONCLUSION

The identified differentially expressed alternative splicing events of costimulatory molecule-related genes may be used to predict patient prognosis and immunotherapy responses in colon cancer.

A novel 9-gene signature for the prediction of postoperative recurrence in stage II/III colorectal cancer

Background: Individualized recurrence risk prediction in patients with stage II/III colorectal cancer (CRC) is crucial for making postoperative treatment decisions. However, there is still a lack of effective approaches for identifying patients with stage II and III CRC at a high risk of recurrence. In this study, we aimed to establish a credible gene model for improving the risk assessment of patients with stage II/III CRC. Methods: Recurrence-free survival (RFS)-related genes were screened using Univariate Cox regression analysis in GSE17538, GSE39582, and GSE161158 cohorts. Common prognostic genes were identified by Venn diagram and subsequently subjected to least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis for signature construction. Kaplan-Meier (K-M), calibration, and receiver operating characteristic (ROC) curves were used to assess the predictive accuracy and superiority of our risk model. Single-sample gene set enrichment analysis (ssGSEA) was employed to investigate the relationship between the infiltrative abundances of immune cells and risk scores. Genes significantly associated with the risk scores were identified to explore the biological implications of the 9-gene signature. Results: Survival analysis identified 347 RFS-related genes. Using these genes, a 9-gene signature was constructed, which was composed of MRPL41, FGD3, RBM38, SPINK1, DKK1, GAL3ST4, INHBB, CTB-113P19.1, and FAM214B. K-M curves verified the survival differences between the low- and high-risk groups classified by the 9-gene signature. The area under the curve (AUC) values of this signature were close to or no less than the previously reported prognostic signatures and clinical factors, suggesting that this model could provide improved RFS prediction. The ssGSEA algorithm estimated that eight immune cells, including regulatory T cells, were aberrantly infiltrated in the high-risk group. Furthermore, the signature was associated with multiple oncogenic pathways, including cell adhesion and angiogenesis. Conclusion: A novel RFS prediction model for patients with stage II/III CRC was constructed using multicohort validation. The proposed signature may help clinicians better manage patients with stage II/III CRC.

Comprehensive Analysis Reveals the Potential Regulatory Mechanism Between Ub-Proteasome System and Cell Cycle in Colorectal Cancer

The ubiquitin (Ub)–proteasome system (UPS) is an important regulatory component in colorectal cancer (CRC), and the cell cycle is also characterized to play a significant role in CRC. In this present study, we firstly identified UPS-associated differentially expressed genes and all the differentially expressed protein-coding genes in CRC through three differential analyses. UPS-associated genes were also further analyzed via survival analysis. A weighted gene co-expression network analysis (WGCNA) was used to identify the cell cycle-associated genes. We used protein–protein interaction (PPI) network to comprehensively mine the potential mechanism of the UPS–cell cycle regulatory axis. Moreover, we constructed a signature based on UPS-associated genes to predict the overall survival of CRC patients. Our research provides a novel insight view of the UPS and cell cycle system in CRC.

Molecular Classification Based on Prognostic and Cell Cycle-Associated Genes in Patients With Colon Cancer

The molecular classification of patients with colon cancer is inconclusive. The gene set enrichment analysis (GSEA) of dysregulated genes among normal and tumor tissues indicated that the cell cycle played a crucial role in colon cancer. We performed univariate Cox regression analysis to find out the prognostic-related genes, and these genes were then intersected with cell cycle-associated genes and were further recognized as prognostic and cell cycle-associated genes. Unsupervised non-negative matrix factorization (NMF) clustering was performed based on cell cycle-associated genes. Two subgroups were identified with different overall survival, clinical features, cell cycle enrichment profile, and mutation profile. Through nearest template prediction (NTP), the molecular classification could be effectively repeated in the original data set and validated in several independent data sets indicating that the classification is highly repeatable. Furthermore, we constructed two prognostic signatures in two subgroups, respectively. Our molecular classification based on cell cycle may provide novel insight into the treatment and the prognosis of colon cancer.

Construction and Validation of a Cell Cycle-Related Robust Prognostic Signature in Colon Cancer

Colon cancer is one of the most common cancers, great progress was taken place in the treatment of colon cancer, however, the prognostic assessment system remains lagging. Cell cycle plays a vital role in the whole procedure of cancers. In this study, we firstly identified cell cycle-related genes specific in colon cancer. Functional enrichment analysis proved our analysis reliable. Furthermore, we constructed a robust signature based on the cell cycle-related genes. The AUC of the signature to predict the overall survival was 0.808, 0.807, and 0.831 of AUC at 1, 3, and 5 years, respectively. Internal and external validation proved the signature efficient. The 9 genes involved in the signature also showed a great job in molecular subgrouping which indicated the significant value of the 9 genes for further experimental research. In conclusion, the present research provided a novel robust signature predicting the prognosis of colon cancer.

TCONS_00012883 promotes proliferation and metastasis via DDX3/YY1/MMP1/PI3K-AKT axis in colorectal cancer

Background

Long noncoding RNAs (lncRNAs) have emerged as key regulators in multiple cancers, including colorectal cancer (CRC). However, the biological functions and molecular mechanisms underlying most lncRNAs in CRC remain largely unknown.

Methods

A novel lncRNA (TCONS_00012883) was identified using RNA sequencing. The level of TCONS_00012883 expression in CRC was analyzed by qRT‐PCR. The biological functions of TCONS_00012883 in CRC were investigated by a series of in vitro and in vivo experiments: CCK8, colony formation, EdU, flow cytometric assays, transwell assays, and mouse xenograft. The molecular mechanisms of TCONS_00012883 were demonstrated by RNA pulldown, mass spectrometry analysis, RIP, coimmunoprecipitation, RNA sequencing, chromatin immunoprecipitation, and rescue experiments.

Results

Elevated expression of TCONS_00012883 was confirmed in CRC and positively associated with a poor prognosis. Functionally, gain‐ and loss‐of‐function assays indicated that TCONS_00012883 promoted proliferation and metastasis of CRC cell lines in vitro and in vivo. Mechanistically, RNA pulldown and mass spectrometry analysis showed that DEAD‐box helicase 3 (DDX3) was the protein partner of TCONS_00012883. Furthermore, RNA sequencing assay revealed that matrix metallopeptidase 1 (MMP1) was the downstream of TCONS_00012883. Intriguingly, we found that transcription factor (YY1) could serve as a bridge between TCONS_00012883, DDX3, and MMP1.

Conclusions

TCONS_00012883 significantly promoted CRC progression via the DDX3/YY1/MMP1 axis, and thus, may act as a major role in diagnosis and therapy of CRC.

Identification of an alternative splicing signature as an independent factor in colon cancer

BACKGROUND

Colon cancer is a common malignant tumor with a poor prognosis. Abnormal alternative splicing (AS) events played a part in the occurrence and metastasis of the tumor. We aimed to develop a survival-associated AS signature in colon cancer.

METHODS

The Percent Spliced In values of AS events were available in The Cancer Genome Atlas (TCGA) SpliceSeq database. Univariate Cox analysis was carried out to detect the prognosis-related AS events. We created a predictive model on account of the survival-associated AS events, which was further validated with a training-testing group design. Kaplan-Meier analysis was applied to assess patient survival. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of this model. Meanwhile, the clinical relevance of the signature and its regulatory relationship with splicing factors (SFs) were also evaluated.

RESULTS

In total, 2132 survival-related AS events were identified from colon cancer samples. We developed an eleven-AS signature, in which the 5-year AUC value was 0.911. Meanwhile, the AUC values at five years were 0.782 and 0.855 in the testing and entire cohort, respectively. Multivariate Cox regression displayed that the T category and the risk score of the signature were independent risk factors of colon cancer survival. Also, we constructed an SFs-AS network based on 11 SFs and 48 AS events.

CONCLUSIONS

We identified an eleven-AS signature of colon cancer. This signature could be treated as an independent prognostic factor.