Long non-coding RNA and RNA-binding protein interactions in cancer: Experimental and machine learning approaches

Shaath, Hibah; Vishnubalaji, Radhakrishnan; Elango, Ramesh; Kardousha, Ahmed; Islam, Zeyaul; Qureshi, Rizwan; Alam, Tanvir; Kolatkar, Prasanna R.; Alajez, Nehad M.

doi:10.1016/j.semcancer.2022.05.013

Cited by 67 publications

(34 citation statements)

References 225 publications

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“…Previously, a few studies were proposed for renal clear cell carcinoma or other cancers [ 47 ]. A few machine-learning-based and experimental approaches have been proposed to study multicellular complexity and tissue specificity [ 48 ], as well as to study molecular interactions in cancer [ 49 ]. The majority of the work conducted for the identification of RCCC driver genes uses experimental lab procedures.…”

Section: Discussionmentioning

confidence: 99%

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

et al. 2022

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To save lives from cancer, it is very crucial to diagnose it at its early stages. One solution to early diagnosis lies in the identification of the cancer driver genes and their mutations. Such diagnostics can substantially minimize the mortality rate of this deadly disease. However, concurrently, the identification of cancer driver gene mutation through experimental mechanisms could be an expensive, slow, and laborious job. The advancement of computational strategies that could help in the early prediction of cancer growth effectively and accurately is thus highly needed towards early diagnoses and a decrease in the mortality rates due to this disease. Herein, we aim to predict clear cell renal carcinoma (RCCC) at the level of the genes, using the genomic sequences. The dataset was taken from IntOgen Cancer Mutations Browser and all genes’ standard DNA sequences were taken from the NCBI database. Using cancer-associated information of mutation from INTOGEN, the benchmark dataset was generated by creating the mutations in original sequences. After extensive feature extraction, the dataset was used to train ANN+ Hist Gradient boosting that could perform the classification of RCCC genes, other cancer-associated genes, and non-cancerous/unknown (non-tumor driver) genes. Through an independent dataset test, the accuracy observed was 83%, whereas the 10-fold cross-validation and Jackknife validation yielded 98% and 100% accurate results, respectively. The proposed predictor RCCC_Pred is able to identify RCCC genes with high accuracy and efficiency and can help scientists/researchers easily predict and diagnose cancer at its early stages.

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Section: Discussionmentioning

confidence: 99%

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

et al. 2022

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“…LncRNAs, a diverse class of ncRNAs, are defined as untranslated RNA molecules greater than 200 nucleotides in length with low protein-coding potential. 40 Mounting evidence have suggested that lncRNAs play important roles in regulating gene expression at transcriptional, posttranscriptional and epigenetic levels in the development and progression of tumors. 41 , 42 More importantly, aberrant signatures of lncRNAs are developed as the potential diagnostic biomarkers and therapeutic targets of cancer due to their specific expression pattern in carcinomas.…”

Section: Lncrna Biomarkersmentioning

confidence: 99%

Circulating Non-Coding RNAs as Potential Diagnostic Biomarkers in Hepatocellular Carcinoma

Chen¹

2022

JHC

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Hepatocellular carcinoma (HCC) is currently the second leading cause of cancer-related deaths worldwide, with high morbidity and mortality. The clinical diagnosis of HCC mainly depends on imaging technology, such as ultrasound and computed tomography, and serum biomarkers, such as alpha-fetoprotein (AFP). However, HCC is still hard to diagnose at an early stage due to the low sensitivity of the above mentioned traditional methods. Typically, HCC is diagnosed at an advanced stage when limited treatment options are available. It is urgent to identify effective biomarkers for the early diagnosis of HCC. Increasing evidence uncovered ncRNAs, including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), could be used in HCC diagnosis. The aim of this review is to summarize our understanding of circulating miRNAs, lncRNAs and circRNAs as fluid-based non-invasive biomarkers, and aiming at providing new insights into the diagnosis of HCC.

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“…Their effects are made possible by networks that regulate lncRNA and RBP stability, ncRNA Metabolism including N6-methyladenosine (m6A) and alternative splicing, subcellular localization, and a wide variety of other cancer-related pathway processes. This review [22] investigated the reciprocal interaction that exists between long noncoding RNAs (lncRNAs) and RBPs, as well as their participation in epigenetic regulation through histone modifications and their essential role in cancer treatment resistance. Other properties of RBPs, such as the structural domains they include, give further insight into how lncRNAs and RBPs interact with one another and how they carry out their separate biological functions.…”

Section: Literature Reviewmentioning

confidence: 99%

Neural Network-Based Prediction of Potential Ribonucleic Acid Aptamers to Target Protein

Mahmood¹,

Malik²,

Khan³

et al. 2022

JCBI

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Aptamers are short strands of nucleic acid with a single strand that may unite to target a certain molecule in a selective and specific manner. SELEX experiments are the typical method used for identifying aptamers in vitro (systematic evolution of ligands by exponential enrichment). Several different computational methods have been developed to locate aptamers. The purpose of this research is to identify and make predictions on the possible RNA aptamers that may be used to target the protein. To do this, we propose the use of a multi-layer perceptron neural network with sixteen layers that are trained to locate possible aptamers of a protein target. This network is trained by extracting the main properties of RNA sequences. The outcome of our proposed model is compared to the output of two well-known machine learning classifiers, namely random forest (RF) and support vector machine (SVM). Additionally, we undertake the independent testing of our model on the benchmark dataset, which allows us to reach the highest accuracy possible. As a consequence of this, our model obtains an accuracy of 98.44% and an MCC of 0.9123 during the 15-fold cross-validation, and it achieves an accuracy of 98.10% and an MCC of 0.9354 when the leave-one-out cross-validation is performed. We are certain that our approach will contribute to a reduction in the amount of money and time spent on in vitro testing. Therefore, restricting the length of the initial pool of potential nucleic acid pattern combinations.

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Long non-coding RNA and RNA-binding protein interactions in cancer: Experimental and machine learning approaches

Cited by 67 publications

References 225 publications

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

Circulating Non-Coding RNAs as Potential Diagnostic Biomarkers in Hepatocellular Carcinoma

Neural Network-Based Prediction of Potential Ribonucleic Acid Aptamers to Target Protein

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