Functions of non-coding RNAs in regulating cancer drug targets

Chai, Binshu; Wang, Xianyi; Xu, Linfeng; Li, Yanli

doi:10.3724/abbs.2022006

Cited by 4 publications

(6 citation statements)

References 165 publications

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“…Although our understanding of CRC-related signaling pathways has advanced in recent years, chemotherapy and radiotherapy resistance due to specific molecules in these pathways continues to be a major obstacle to effective treatment ( 4 ). Here’s a possible revision: “Non-coding RNAs (ncRNAs) are known to have significant roles in regulating the chemical and radioresistance of CRC, making them promising targets for the development of new anti-drug and anti-radiation therapy strategies ( 6 , 7 ). These ncRNAs make up the majority (~90%) of the transcribed human genome and include RNAs of varying lengths ( 8 ).…”

Section: Introductionmentioning

confidence: 99%

Biological functions, mechanisms, and clinical significance of circular RNA in colorectal cancer

Fang

Zhang

et al. 2023

Front. Oncol.

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Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide due to the lack of effective diagnosis and prognosis biomarkers and therapeutic targets, resulting in poor patient survival rates. Circular RNA (circRNA) is a type of endogenous non-coding RNA (ncRNA) with a closed-loop structure that plays a crucial role in physiological processes and pathological diseases. Recent studies indicate that circRNAs are involved in the diagnosis, prognosis, drug resistance, and development of tumors, particularly in CRC. Therefore, circRNA could be a potential new target for improving CRC diagnosis, prognosis, and treatment. This review focuses on the origin and biological functions of circRNA, summarizes recent research on circRNA’s role in CRC, and discusses the potential use of circRNAs as clinical biomarkers for cancer diagnosis and prognosis, as well as therapeutic targets for CRC treatment.

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Section: Introductionmentioning

confidence: 99%

Biological functions, mechanisms, and clinical significance of circular RNA in colorectal cancer

Fang

Zhang

et al. 2023

Front. Oncol.

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“… 22 Additionally, lncRNAs are also associated with tumor metabolism, tumor microenvironment, and drug resistance, 23 consequently recognized as potential cancer drug targets. 24 , 25 …”

Section: Introductionmentioning

confidence: 99%

“…22 Additionally, ln-cRNAs are also associated with tumor metabolism, tumor microenvironment, and drug resistance, 23 consequently recognized as potential cancer drug targets. 24,25 Programmed cell death is an essential physiological and pathological process to remove superfluous or damaged cells to ensure healthy development and tissue homeostasis, 26 including apoptosis, autophagy, pyroptosis, ferroptosis, and necroptosis. [27][28][29] Recently, a novel cell death pathway was reported by Tsvetkov et al 30 Cuproptosis is defined as the copper-dependent death of the cells, where the copper binds directly to the lipoylated components of the tricarboxylic acid (TCA) cycle.…”

Section: Introductionmentioning

confidence: 99%

Construction of a cuproptosis‐related lncRNA signature for predicting prognosis and immune landscape in osteosarcoma patients

Hong

et al. 2022

Cancer Medicine

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Background: Long noncoding RNAs (lncRNAs) influence the onset of osteosarcoma. Cuproptosis is a novel cell death mechanism. We attempted to identify a cuproptosis-related lncRNA signature to predict the prognosis and immune landscape in osteosarcoma patients. Methods:Transcriptional and clinical data of 85 osteosarcoma patients were derived from the TARGET database and randomly categorized into the training and validation cohorts. We implemented the univariate and multivariate Cox regression, along with LASSO regression analyses for developing a cuproptosis-related lncRNA risk model. Kaplan-Meier curves, C-index, ROC curves, univariate and multivariate Cox regression, and nomogram were used to assess the capacity of this risk model to predict the osteosarcoma prognosis. Gene ontology, KEGG, and Gene Set Enrichment (GSEA) analyses were conducted for determining the potential functional differences existing between the high-risk and low-risk patients.We further conducted the ESTIMATE, single-smaple GSEA, and CIBERSORT analyses for identifying the different immune microenvironments and immune cells infiltrating both the risk groups. Results:We screened out four cuproptosis-related lncRNAs (AL033384.2, AL031775.1, AC110995.1, and LINC00565) to construct the risk model in the training cohort. This risk model displayed a good performance to predict the overall survival of osteosarcoma patients, which was confirmed by using the validation and the entire cohort. Further analyses showed that the low-risk patients have more immune activation and immune cells infiltrating as well as a good response to immunotherapy. Conclusions:We developed a novel cuproptosis-related lncRNA signature with high reliability and accuracy for predicting outcome and immunotherapy response in osteosarcoma patients, which provides new insights into the personalized treatment of osteosarcoma.

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“…Therefore, various computational models have been developed to detect possible CircRNA-Disease Associations (CDAs). For example, network embedding and subspace learning method (Xiao et al (2021)), knowledge attention network (Lan et al (2022)), multi-source feature fusion-based machine learning framework (Wang L. et al (2022)), and robust nonnegative matrix factorization model (Peng et al (2022c)) are widely used in CDA prediction.…”

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confidence: 99%

“…Particularly, noncoding RNAs have dense linkages with human diseases including cancers. Now, RNAs have been diagnostic or prognostic markers of complex diseases ( Hui et al (2011) ; Xu et al (2022) ; Peng et al (2022a) ; Shen et al (2022) ; Zhang T. et al (2022) ; Chai et al (2022) ). In this topic, we aim to analyze diverse RNA data to provide clues for the diagnosis and therapy of various diseases ( Dal Molin et al (2022) ; Wang S. et al (2022) ; Li J. et al (2019) ; Liu et al (2020) ).…”

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confidence: 99%

Editorial: Machine learning-based methods for RNA data analysis—Volume II

Peng

Yang²,

Wang³

et al. 2022

Front. Genet.

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Editorial on the Research Topic Machine learning-based methods for RNA data analysis-Volume II RNAs regulate multiple biological processes including RNA transcription, splicing, stability, and translation. They play significant roles in cell biology (Connelly et al. (2016); Licatalosi and Darnell (2010); Mukherjee et al. (2022); Chen et al. (2018b)). The Encyclopedia of DNA elements project reported that only 1.5% of human genome is translated into proteins, while approximately 70%-90% is transcribed to RNAs (Falese et al. (2021)). RNAs greatly expand the range of targets from proteins to RNAs by retargeting mutated targets (Yu et al. (2019); Chen et al. (2020); Li et al. (2022); Yang et al. (2022)). Particularly, noncoding RNAs have dense linkages with human diseases including cancers. Now, RNAs have been diagnostic or prognostic markers of complex diseases (Hui et al. (2011); Xu et al. (2022); Peng et al. (2022a); Shen et al. (2022); Zhang T. et al. (2022); Chai et al. (2022)). In this topic, we aim to analyze diverse RNA data to provide clues for the diagnosis and therapy of various diseases (Dal Molin et al. (2022); Wang S. et al. (2022); Li J. et al. (2019); Liu et al. ( 2020)). Long noncoding RNAs (lncRNAs) regulate many significant biological processes (such as immune response and embryonic stem cell pluripotency) by linking to RNA-binding proteins

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Functions of non-coding RNAs in regulating cancer drug targets

Cited by 4 publications

References 165 publications

Biological functions, mechanisms, and clinical significance of circular RNA in colorectal cancer

Biological functions, mechanisms, and clinical significance of circular RNA in colorectal cancer

Construction of a cuproptosis‐related lncRNA signature for predicting prognosis and immune landscape in osteosarcoma patients

Editorial: Machine learning-based methods for RNA data analysis—Volume II

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