Long Non-coding RNA MALAT1: A Key Player in Liver Diseases

Lü, Juan; Guo, Jing; Li, Jun; Mao, Xiaoming; Xu, Kaijin

doi:10.3389/fmed.2021.734643

Cited by 9 publications

(7 citation statements)

References 72 publications

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“…lncRNAs, including MALAT1, have been implicated in promoting aggressive phenotypes and tumor progression. Additionally, lncRNAs play a pivotal role in epigenetic regulation by modulating DNA methylation and histone modifications, thereby influencing gene expression patterns in HCC cells [ 77 ]. Understanding these lncRNA-driven epigenetic mechanisms offers insights into HCC pathogenesis and potential therapeutic interventions.…”

Section: Significance Of Studying Lncrnas In Hccmentioning

confidence: 99%

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HCC-Related lncRNAs: Roles and Mechanisms

Shah,

Sarkar

2024

IJMS

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Hepatocellular carcinoma (HCC) presents a significant global health threat, particularly in regions endemic to hepatitis B and C viruses, and because of the ongoing pandemic of obesity causing metabolic-dysfunction-related fatty liver disease (MAFLD), a precursor to HCC. The molecular intricacies of HCC, genetic and epigenetic alterations, and dysregulated signaling pathways facilitate personalized treatment strategies based on molecular profiling. Epigenetic regulation, encompassing DNA methyltion, histone modifications, and noncoding RNAs, functions as a critical layer influencing HCC development. Long noncoding RNAs (lncRNAs) are spotlighted for their diverse roles in gene regulation and their potential as diagnostic and therapeutic tools in cancer. In this review, we explore the pivotal role of lncRNAs in HCC, including MAFLD and viral hepatitis, the most prevalent risk factors for hepatocarcinogenesis. The dysregulation of lncRNAs is implicated in HCC progression by modulating chromatin regulation and transcription, sponging miRNAs, and influencing structural functions. The ongoing studies on lncRNAs contribute to a deeper comprehension of HCC pathogenesis and offer promising routes for precision medicine, highlighting the utility of lncRNAs as early biomarkers, prognostic indicators, and therapeutic targets.

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Section: Significance Of Studying Lncrnas In Hccmentioning

confidence: 99%

“…It is overexpressed in primary tumors and metastases and is prone to copy number changes in several cancer types, including HCC [ 114 ]. In HCC patients, it is associated with metastasis and poor prognosis [ 77 ].…”

Section: Lncrnas Regulating Hccmentioning

confidence: 99%

HCC-Related lncRNAs: Roles and Mechanisms

Shah,

Sarkar

2024

IJMS

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“…Besides, 17 ferroptosis-associated lncRNAs are identified as a prognostic and risk model for accurate decision making in clinical treatment and immunotherapy of HCC ( Lin and Yang, 2022 ). Importantly, as a key lncRNA that involved in HCC progression, MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) can affect cell proliferation, apoptosis, and migration ( Lu et al, 2021 ). It has been verified that MALAT1 is overexpressed in HCC and is associated with shorter overall survival, serving as a prognostic marker for patients with HCC ( Liao et al, 2022 ).…”

Section: Ferroptosis-related Ncrnas In Hccmentioning

confidence: 99%

Role of ferroptosis and its non-coding RNA regulation in hepatocellular carcinoma

2023

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Ferroptosis is a newly discovered form of programmed cell death that involves the accumulation of iron-dependent lipid peroxides and plays a vital role in the tumorigenesis, development, and drug resistance of various tumors such as hepatocellular carcinoma (HCC). As a hotspot in molecular biology, non-coding RNAs (ncRNAs) participate in the initiation and progression of HCC, either act as oncogenes or tumor suppressors. Recent studies have shown that ncRNAs can regulate ferroptosis in HCC cells, which would affect the tumor progression and drug resistance. Therefore, clarifying the underlying role of ferroptosis and the regulatory role of ncRNA on ferroptosis in HCC could develop new treatment interventions for this disease. This review briefly summarizes the role of ferroptosis and ferroptosis-related ncRNAs in HCC tumorigenesis, progression, treatment, drug resistance and prognosis, for the development of potential therapeutic strategies and prognostic markers in HCC patients.

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“…MALAT1 has been suggested as a potential indicator for the diagnosis and prognosis of cancer. [5][6][7][8][9][10] The epidermal growth factor receptor (EGFR) is a cellular protein that facilitates cell growth. A mutation in the gene for EGFR can make it grow too much, which can cause cancer.…”

Section: Introductionmentioning

confidence: 99%

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

Sadeghi,

Kiaei,

Boush

et al. 2024

Preprint

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Background: Adenocarcinoma of the lung is the most common type of lung cancer, and it is characterized by distinct cellular and molecular features. It occurs when abnormal lung cells multiply out of control and form a tumor in the outer region of the lungs. Adenocarcinoma of the lung is a serious and life-threatening condition that requires effective and timely management to improve the survival and quality of life of the patients. One of the challenges in this cancer treatment is finding the optimal combination of drugs that can target the genes or proteins that are involved in the disease process. Method: In this article, we propose a novel method to recommend combinations of trending drugs to target its associated proteins/genes, using a Graph Neural Network (GNN) under the RAIN protocol. The RAIN protocol is a three-step framework that consists of: 1) Applying graph neural networks to recommend drug combinations by passing messages between trending drugs for managing disease and genes that act as potential targets for disease; 2) Retrieving relevant articles with clinical trials that include those proposed drugs in previous step using Natural Language Processing (NLP); 3) Analyzing the network meta-analysis to measure the comparative efficacy of the drug combinations. Result: We applied our method to a dataset of nodes and edges that represent the network, where each node is a drug or a gene, and each edge is a p-value between them. We found that the graph neural network recommends combining Gefitinib, Paclitaxel, and Icotinib as the most effective drug combination to target this cancer associated proteins/genes. We reviewed the clinical trials and expert opinions on these medications and found that they support our claim. The network meta-analysis also confirmed the effectiveness of these drugs on associated genes. Conclusion: Our method is a novel and promising approach to recommend trending drugs combination to target cancer associated proteins/genes, using graph neural networks under the RAIN protocol. It can help clinicians and researchers to find the best treatment options for patients, and also provide insights into the underlying mechanisms of the disease.

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Long Non-coding RNA MALAT1: A Key Player in Liver Diseases

Cited by 9 publications

References 72 publications

HCC-Related lncRNAs: Roles and Mechanisms

HCC-Related lncRNAs: Roles and Mechanisms

Role of ferroptosis and its non-coding RNA regulation in hepatocellular carcinoma

A graphSAGE discovers synergistic combinations of Gefitinib, paclitaxel, and Icotinib for Lung adenocarcinoma management by targeting human genes and proteins: the RAIN protocol

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