Dataset Information

Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.

ABSTRACT: Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common modules were evaluated by several biological metrics adapted to cancer. Then, a new prognostic scoring method was developed by weighting mRNA expression, methylation, and mutation status of genes. The survival analysis pointed out statistically significant results for GNG11, CBX2, CDKN3, ARHGEF10, CLN8, SEC61G and PTDSS1 genes. The literature search reveals that the identified biomarkers are associated with the same or different types of cancers. Our method does not only identify known cancer-specific biomarker genes, but also proposes new potential biomarkers. Thus, this study provides a rationale for identifying new gene targets and expanding treatment options across cancer types.

SUBMITTER: Demir Karaman E

PROVIDER: S-EPMC10366886 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.

Demir Karaman Ezgi E Işık Zerrin Z

Medical sciences (Basel, Switzerland) 20230627 3

Combining omics data from different layers using integrative methods provides a better understanding of the biology of a complex disease such as cancer. The discovery of biomarkers related to cancer development or prognosis helps to find more effective treatment options. This study integrates multi-omics data of different cancer types with a network-based approach to explore common gene modules among different tumors by running community detection methods on the integrated network. The common mo ...[more]

PMID: 37489460

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Project description:Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups. Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients. Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

Project description:BackgroundGastrointestinal cancers (GICs) continue to dominate in terms of both incidence and mortality worldwide. Due to the absence of efficient and accurate prognostic biomarkers, the prognosis and treatment outcomes of many GICs are poor. Identifying biomarkers to predict individual clinical outcomes efficiently is a fundamental challenge in clinical oncology. Although several biomarkers have been continually discovered, their predictive accuracy is relatively modest, and their therapeutic use is restricted. In light of this, the discovery of reliable biomarkers for predicting prognosis and outcome in GIC is urgently required.Materials and methodsWe evaluated the Human Protein Atlas dataset and identified NPC Intracellular Cholesterol Transporter 2 (NPC2) and Integrin Subunit Alpha V (ITGAV) as probable poor predictive genes for these cancers. In addition, we used the GEPIA2, cBioPortal, UALCAN, LinkedOmics, STRING, Enrichr, TISDB, TIMER2.0, hTFTarget, miRTarBase, circBank, and drug-gene interaction database databases to conduct a comprehensive and systematic analysis of the NPC2 and ITGAV genes.ResultOur results found high expression levels of NPC2 and ITGAV in most GICs. The aforementioned gene expressions were linked to several clinicopathological characteristics of GICs as well as poorer prognosis in LIHC and STAD. The most common alteration type of NPC2 was amplification, and for ITGAV was deep deletion. Significant promotor hypermethylation was also seen in NPC2 and ITGAV in PAAD and COAD, respectively. For the immunologic significance, NPC2 and ITGAV were positively correlated with the abundance of tumor-infiltrating lymphocytes and macrophages. Furthermore, various immunomodulators showed strong correlations with the expression of these genes. There were currently 10 small molecule drugs targeting ITGAV.ConclusionConsequently, our bioinformatics analysis showed that NPC2 and ITGAV might be used as potential biomarkers to determine the prognosis of various GICs and are also related to immune infiltration.

Project description:BackgroundUrinary bladder cancer (UBC) is a common malignancy of the urinary tract; however, the mechanism underlying its high recurrence and responses to immunotherapy remains unclear, making clinical outcome predictions difficult. Epigenetic alterations, especially DNA methylation, play important roles in bladder cancer development and are increasingly being investigated as biomarkers for diagnostic or prognostic predictions. However, little is known about hydroxymethylation since previous studies based on bisulfite-sequencing approaches could not differentiate between 5mC and 5hmC signals, resulting in entangled methylation results.MethodsTissue samples of bladder cancer patients who underwent laparoscopic radical cystectomy (LRC), partial cystectomy (PC), or transurethral resection of bladder tumor (TURBT) were collected. We utilized a multi-omics approach to analyze both primary and recurrent bladder cancer samples. By integrating various techniques including RNA sequencing, oxidative reduced-representation bisulfite sequencing (oxRRBS), reduced-representation bisulfite sequencing (RRBS), and whole exome sequencing, a comprehensive analysis of the genome, transcriptome, methylome, and hydroxymethylome landscape of these cancers was possible.ResultsBy whole exome sequencing, we identified driver mutations involved in the development of UBC, including those in FGFR3, KDMTA, and KDMT2C. However, few of these driver mutations were associated with the down-regulation of programmed death-ligand 1 (PD-L1) or recurrence in UBC. By integrating RRBS and oxRRBS data, we identified fatty acid oxidation-related genes significantly enriched in 5hmC-associated transcription alterations in recurrent bladder cancers. We also observed a series of 5mC hypo differentially methylated regions (DMRs) in the gene body of NFATC1, which is highly involved in T-cell immune responses in bladder cancer samples with high expression of PD-L1. Since 5mC and 5hmC alternations are globally anti-correlated, RRBS-seq-based markers that combine the 5mC and 5hmC signals, attenuate cancer-related signals, and therefore, are not optimal as clinical biomarkers.ConclusionsBy multi-omics profiling of UBC samples, we showed that epigenetic alternations are more involved compared to genetic mutations in the PD-L1 regulation and recurrence of UBC. As proof of principle, we demonstrated that the combined measurement of 5mC and 5hmC levels by the bisulfite-based method compromises the prediction accuracy of epigenetic biomarkers.

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Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.

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Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers.

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