A signature predicting relapse based on integrated analysis on relapse-associated alternative mRNA splicing in I-III rectal 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.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Exploration of Alternative Splicing (AS) Events in MDV-Infected Chicken Spleens

Marek’s disease (MD) was an immunosuppression disease induced by Marek’s disease virus (MDV). MD caused huge economic loss to the global poultry industry, but it also provided an ideal model for studying diseases induced by the oncogenic virus. Alternative splicing (AS) simultaneously produced different isoform transcripts, which are involved in various diseases and individual development. To investigate AS events in MD, RNA-Seq was performed in tumorous spleens (TS), spleens from the survivors (SS) without any lesion after MDV infection, and non-infected chicken spleens (NS). In this study, 32,703 and 25,217 AS events were identified in TS and SS groups with NS group as the control group, and 1198, 1204, and 348 differently expressed (DE) AS events (p-value < 0.05 and FDR < 0.05) were identified in TS vs. NS, TS vs. SS, SS vs. NS, respectively. Additionally, Function enrichment analysis showed that ubiquitin-mediated proteolysis, p53 signaling pathway, and phosphatidylinositol signaling system were significantly enriched (p-value < 0.05). Small structural variations including SNP and indel were analyzed based on RNA-Seq data, and it showed that the TS group possessed more variants on the splice site region than those in SS and NS groups, which might cause more AS events in the TS group. Combined with previous circRNA data, we found that 287 genes could produce both circular and linear RNAs, which suggested these genes were more active in MD lymphoma transformation. This study has expanded the understanding of the MDV infection process and provided new insights for further analysis of resistance/susceptibility mechanisms.

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.

Tumor microenvironment derived signature predicting relapse-free survival in I-III cancer and preliminary experiment verification

The recurrence in colon cancer contributed to great difficulties in diagnostic and therapeutic treatment. Tumor microenvironment (TME) gains increasing attention recently. After univariate Cox analysis on relapse-free survival (RFS) and ESTIMATE analysis, WGCNA was further conducted to determine the TME and relapse-related genes in I-III colon cancer. Functional enrichment analyses were conducted. Furthermore, seven genes were screened to build a prognostic signature via LASSO and multivariate Cox analysis. Univariate followed multivariate Cox analysis all showed that the risk group calculated by the signature as a significant predictors. The ROC curves showed great prognostic in the internal training group, internal verification group, and independent external verification group. In the training group, the AUC at 1, 3, and 5 years was 0.737, 0.79, and 0.756. In addition, correlation analysis presented that the signature and genes involved in were significantly associated with the TME. Moreover, 3 of 7 genes (FAM78A, SGIP1, and MMP9) were validated to be associated with PDL1 through qRT-PCR.

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.