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Integrated multi-omics characterization of KRAS mutant colorectal cancer.

ABSTRACT: KRAS mutation is the most frequent oncogenic aberration in colorectal cancer (CRC). The molecular mechanism and clinical implications of KRAS mutation in CRC remain unclear and show high heterogeneity within these tumors. Methods: We harnessed the multi-omics data (genomic, transcriptomic, proteomic, and phosphoproteomic etc.) of KRAS-mutant CRC tumors and performed unsupervised clustering to identify proteomics-based subgroups and molecular characterization. Results: In-depth analysis of the tumor microenvironment by single-cell transcriptomic revealed the cellular landscape of KRAS-mutant CRC tumors and identified the specific cell subsets with KRAS mutation. Integrated multi-omics analyses separated the KRAS-mutant tumors into two distinct molecular subtypes, termed KRAS-M1 (KM1) and KRAS-M2 (KM2). The two subtypes had a similar distribution of mutated residues in KRAS (G12D/V/C etc.) but were characterized by distinct features in terms of prognosis, genetic alterations, microenvironment dysregulation, biological phenotype, and potential therapeutic approaches. Proteogenomic analyses revealed that the EMT, TGF-β and angiogenesis pathways were enriched in the KM2 subtype and that the KM2 subtype was associated with the mesenchymal phenotype-related CMS4 subtype, which indicated stromal invasion and worse prognosis. The KM1 subtype was characterized predominantly by activation of the cell cycle, E2F and RNA transcription and was associated with the chromosomal instability (CIN)-related ProS-E proteomic subtype, which suggested cyclin-dependent features and better survival outcomes. Moreover, drug sensitivity analyses based on three compound databases revealed subgroup-specific agents for KM1 and KM2 tumors. Conclusions: This study clarifies the molecular heterogeneity of KRAS-mutant CRC and reveals new biological subtypes and therapeutic possibilities for these tumors.

SUBMITTER: Chong W

PROVIDER: S-EPMC9274732 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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.

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Integrated multi-omics characterization of KRAS mutant colorectal cancer.

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