Dataset Information

Integrated multi-omics characterization of SMAD4 mutant colorectal cancer.

ABSTRACT: Colorectal cancer is one of the most common cancers around the world, which is a severe threat to people's health. SMAD4 belongs to the dwarfin/SMAD family, which plays a crucial role in TGF-β and BMP signal pathways. As the molecular characterization of colon cancer patients following SMAD4 mutations remains unclear, we integrated multi-omics data of SMAD4 mutant patients to reveal the profile of molecular characterization of SMAD4 mutation. A missense mutation is the most common mutant type of SMAD4. Patients with SMAD4 mutation had worse survival. Tumor tissues from patients carrying the SMAD4 mutation showed a reduction in various immune cells, such as CD4 + memory T cells and memory B cells. Many differential genes were identified compared to the SMAD4 mutation-free group and could be significantly enriched for tumor- and immune-related signaling pathways. In addition, the mutant group had different drug sensitivities than the non-mutant group.

SUBMITTER: Zhao D

PROVIDER: S-EPMC11362418 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

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Integrated multi-omics characterization of SMAD4 mutant colorectal cancer.

Zhao Danyi D Qu Yanjun Y Gao Na N Wu Tao T

Discover oncology 20240829 1

Colorectal cancer is one of the most common cancers around the world, which is a severe threat to people's health. SMAD4 belongs to the dwarfin/SMAD family, which plays a crucial role in TGF-β and BMP signal pathways. As the molecular characterization of colon cancer patients following SMAD4 mutations remains unclear, we integrated multi-omics data of SMAD4 mutant patients to reveal the profile of molecular characterization of SMAD4 mutation. A missense mutation is the most common mutant type of ...[more]

PMID: 39210191

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Project description:ObjectivesCervical Squamous Cell Carcinoma (CESC) is one of the most fatal female malignancies, and the underlying molecular mechanisms governing this disease have not been fully explored. In this research, we planned to conduct the analysis of Gene Expression Omnibus (GEO) cervical squamous cell carcinoma microarray datasets by a detailed in silico approach and to explore some novel biomarkers of CESC.MethodsThe top commonly differentially expressed genes (DEGs) from the GSE138080 and GSE113942 datasets were analyzed by Limma package-based GEO2R tool. The protein-protein interaction (PPI) network of the DEGs was drawn through Search Tool for the Retrieval of Interacting Genes (STRING), and top 6 hub genes were obtained from Cytoscape. Expression analysis and validation of hub genes expression in CESC samples and cell lines were done using UALCAN, OncoDB, GENT2, and HPA. Additionally, cBioPortal, Gene set enrichment analysis (GSEA) tool, Kaplan-Meier (KM) plotter, ShinyGO, and DGIdb databases were also used to check some important values of hub genes in CESC.ResultsOut of 79 DEGs, the minichromosome maintenance complex component 4 (MCM4), nucleolar and spindle-associated protein 1 (NUSAP1), cell division cycle associated 5 (CDCA5), cell division cycle 45 (CDC45), denticleless E3 ubiquitin protein ligase homolog (DTL), and chromatin licensing and DNA replication factor 1 (CDT1) genes were regarded as hub genes in CESC. Further analysis revealed that the expressions of all these hub genes were significantly elevated in CESC cell lines and samples of diverse clinical attributes. In this study, we also documented some important correlations between hub genes and some other diverse measures, including DNA methylation, genetic alterations, and Overall Survival (OS). Last, we also identify hub genes associated ceRNA network and 31 important chemotherapeutic drugs.ConclusionThrough detailed in silico methodology, we identified 6 hub genes, including MCM4, NUSAP1, CDCA5, CDC45, DTL, and CDT1, which are likely to be associated with CESC development and diagnosis.

Project description:Cancer is a complex disease that deregulates cellular functions at various molecular levels (e.g., DNA, RNA, and proteins). Integrated multi-omics analysis of data from these levels is necessary to understand the aberrant cellular functions accountable for cancer and its development. In recent years, Deep Learning (DL) approaches have become a useful tool in integrated multi-omics analysis of cancer data. However, high dimensional multi-omics data are generally imbalanced with too many molecular features and relatively few patient samples. This imbalance makes a DL based integrated multi-omics analysis difficult. DL-based dimensionality reduction technique, including variational autoencoder (VAE), is a potential solution to balance high dimensional multi-omics data. However, there are few VAE-based integrated multi-omics analyses, and they are limited to pancancer. In this work, we did an integrated multi-omics analysis of ovarian cancer using the compressed features learned through VAE and an improved version of VAE, namely Maximum Mean Discrepancy VAE (MMD-VAE). First, we designed and developed a DL architecture for VAE and MMD-VAE. Then we used the architecture for mono-omics, integrated di-omics and tri-omics data analysis of ovarian cancer through cancer samples identification, molecular subtypes clustering and classification, and survival analysis. The results show that MMD-VAE and VAE-based compressed features can respectively classify the transcriptional subtypes of the TCGA datasets with an accuracy in the range of 93.2-95.5% and 87.1-95.7%. Also, survival analysis results show that VAE and MMD-VAE based compressed representation of omics data can be used in cancer prognosis. Based on the results, we can conclude that (i) VAE and MMD-VAE outperform existing dimensionality reduction techniques, (ii) integrated multi-omics analyses perform better or similar compared to their mono-omics counterparts, and (iii) MMD-VAE performs better than VAE in most omics dataset.

Dataset Information

Integrated multi-omics characterization of SMAD4 mutant colorectal cancer.

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Integrated multi-omics characterization of SMAD4 mutant colorectal cancer.

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