Multi-OMICS approaches in cancer biology: New era in cancer therapy

Chakraborty, Sohini; Sharma, Gaurav; Karmakar, Sricheta; Banerjee, Satarupa

doi:10.1016/j.bbadis.2024.167120

Cited by 21 publications

(2 citation statements)

References 105 publications

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“…Advancements in multi-omics studies, encompassing genomics, transcriptomics, proteomics, and metabolomics, have proven pivotal in advancing cancer research [14]. By leveraging these approaches, a ferroptosis and cuproptosis-associated signature was identi ed and it was used to develop a prognostic model for MIBC patients.…”

Section: Discussionmentioning

confidence: 99%

“…These mechanisms of cell death not only share common pathways in occurrence, cellular metabolism, signaling, and resistance to treatment but also present a potential area of signi cant impact on MIBC patient outcomes and treatment e cacy [13]. However, the intricate interplay and combined in uence of ferroptosis and cuproptosis on MIBC remain underexplored and highlight an urgent area for deeper investigation.Advancements in multi-omics studies, encompassing genomics, transcriptomics, proteomics, and metabolomics, have proven pivotal in advancing cancer research [14]. By leveraging these approaches, a ferroptosis and cuproptosis-associated signature was identi ed and it was used to develop a prognostic model for MIBC patients.…”

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confidence: 99%

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Muscle-invasive bladder cancer subtyping and prognosis: a multi-omics study based on a ferroptosis and cuproptosis-related signature

Hao,

Yang,

Wang

et al. 2024

Preprint

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Background: Muscle-invasive bladder cancer (MIBC), is a common, aggressive malignancy with significant mortality. Recent advancements have identified ferroptosis and cuproptosis as potential therapeutic targets. However, their interactions and impacts on MIBC overall survival (OS) and treatment outcomes are unclear. This study uses multi-omics approaches to explore these roles and their therapeutic potential, aiming to improve MIBC understanding and management. Methods: Using Cox’s regression and multiple machine learning (ML) techniques, a nomogram-based model incorporating ferroptosis and cuproptosis-related signature was developed based on the data of MIBC patients from The Cancer Genome Atlas (TCGA). Two Gene Expression Omnibus (GEO) datasets (GSE13507 and GSE32894) were utilised to externally validate this signature. Biological experimental validation was also performed in human bladder cancer cell lines. Results: The study identified a robust ferroptosis and cuproptosis-associated gene signature that significantly stratifies MIBC patients into distinct prognostic categories. This signature was not only predictive of overall survival but also correlated with distinct molecular subtypes of MIBC. Validation in independent datasets confirmed the prognostic value of the ferroptosis and cuproptosis-related signature and nomogram-based model. Conclusions: The established bioinformatics model based on ferroptosis and cuproptosis markers offers a novel framework for MIBC subtyping and could potentially guide personalised therapeutic strategies.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Muscle-invasive bladder cancer subtyping and prognosis: a multi-omics study based on a ferroptosis and cuproptosis-related signature

Hao,

Yang,

Wang

et al. 2024

Preprint

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Integrative multi‐omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma

Zhang,

Ren

et al. 2024

BioFactors

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The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi‐omic data obtained from multi‐level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi‐omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high‐ and low‐risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype‐specific machine learning model, LUSC multi‐omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward “cold” tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

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Cell death pathways: molecular mechanisms and therapeutic targets for cancer

Wang,

Guo,

Guo

et al. 2024

MedComm

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Cell death regulation is essential for tissue homeostasis and its dysregulation often underlies cancer development. Understanding the different pathways of cell death can provide novel therapeutic strategies for battling cancer. This review explores several key cell death mechanisms of apoptosis, necroptosis, autophagic cell death, ferroptosis, and pyroptosis. The research gap addressed involves a thorough analysis of how these cell death pathways can be precisely targeted for cancer therapy, considering tumor heterogeneity and adaptation. It delves into genetic and epigenetic factors and signaling cascades like the phosphatidylinositol 3‐kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) and mitogen‐activated protein kinase/extracellular signal‐regulated kinase (MAPK/ERK) pathways, which are critical for the regulation of cell death. Additionally, the interaction of the microenvironment with tumor cells, and particularly the influence of hypoxia, nutrient deprivation, and immune cellular interactions, are explored. Emphasizing therapeutic strategies, this review highlights emerging modulators and inducers such as B cell lymphoma 2 (BCL2) homology domain 3 (BH3) mimetics, tumour necrosis factor‐related apoptosis‐inducing ligand (TRAIL), chloroquine, and innovative approaches to induce ferroptosis and pyroptosis. This review provides insights into cancer therapy's future direction, focusing on multifaceted approaches to influence cell death pathways and circumvent drug resistance. This examination of evolving strategies underlines the considerable clinical potential and the continuous necessity for in‐depth exploration within this scientific domain.

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Multi-OMICS approaches in cancer biology: New era in cancer therapy

Cited by 21 publications

References 105 publications

Muscle-invasive bladder cancer subtyping and prognosis: a multi-omics study based on a ferroptosis and cuproptosis-related signature

Muscle-invasive bladder cancer subtyping and prognosis: a multi-omics study based on a ferroptosis and cuproptosis-related signature

Integrative multi‐omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma

Cell death pathways: molecular mechanisms and therapeutic targets for cancer

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