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Quant. Biol.    2023, Vol. 11 Issue (3) : 343-358    https://doi.org/10.15302/J-QB-023-0333
RESEARCH ARTICLE
Pattern discovery of long non-coding RNAs associated with the herbal treatments in breast and prostate cancers
Elham Dalalbashi Esfahani1(), Esmaeil Ebrahimie2,3,4, Ali Niazi1, Manijeh Mohammadi Dehcheshmeh2,3
1. Institute of Biotechnology, Shiraz University, Shiraz 7196484334, Iran
2. Genomics Research Platform, School of Agriculture, Biomedicine and Environment, La Trobe University, Melbourne, Victoria 3086, Australia
3. School of Animal and Veterinary Sciences, The University of Adelaide, South Australia 5005, Australia
4. School of BioSciences, The University of Melbourne, Victoria 3052, Australia
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Abstract

Background: Accumulating evidence shows that long non-coding RNAs (lncRNAs) play critical roles in cancer progression. The possible association between lncRNAs and herbal medicine is yet to be known. This study aims to identify medicinal herbs associated with lncRNAs by RNA-seq data for breast and prostate cancer.

Methods: To develop the optimal approach for identifying cancer-related lncRNAs, we implemented two steps: (1) applying protein–protein interaction (PPI), Gene Ontology (GO), and pathway analyses, and (2) applying attribute weighting and finding the efficient classification model of the machine learning approach.

Results: In the first step, GO terms and pathway analyses on differential co-expressed mRNAs revealed that lncRNAs were widely co-expressed with metabolic process genes. We identified two hub lncRNA-mRNA networks that implicate lncRNAs associated with breast and prostate cancer. In the second step, we implemented various machine learning-based prediction systems (Decision Tree, Random Forest, Deep Learning, and Gradient-Boosted Tree) on the non-transformed and Z-standardized differential co-expressed lncRNAs. Based on five-fold cross-validation, we obtained high accuracy (91.11%), high sensitivity (88.33%), and high specificity (93.33%) in Deep Learning which reinforces the biomarker power of identified lncRNAs in this study. As data originally came from different cell lines at different durations of herbal treatment intervention, we applied seven attribute weighting algorithms to check the effects of variables on identifying lncRNAs. Attribute weighting results showed that the cell line and time had little or no effect on the selected lncRNAs list. Besides, we identified one known lncRNAs, downregulated RNA in cancer (DRAIC), as an essential feature.

Conclusions: This study will provide further insights to investigate the potential therapeutic and prognostic targets for prostate cancer (PC) and breast cancer (BC) in common.
Keywords RNA-Seq      lncRNA      cancer      co-expression      machine learning      attribute weighting     
Corresponding Author(s): Elham Dalalbashi Esfahani   
Just Accepted Date: 14 July 2023   Online First Date: 24 August 2023    Issue Date: 08 October 2023
 Cite this article:   
Elham Dalalbashi Esfahani,Esmaeil Ebrahimie,Ali Niazi, et al. Pattern discovery of long non-coding RNAs associated with the herbal treatments in breast and prostate cancers[J]. Quant. Biol., 2023, 11(3): 343-358.
 URL:  
https://academic.hep.com.cn/qb/EN/10.15302/J-QB-023-0333
https://academic.hep.com.cn/qb/EN/Y2023/V11/I3/343
Fig.1  The flowchart of the analysis pipeline.
Fig.2  Pathway analysis figures used to predict the potential roles of lncRNAs.
GO IDGO namesGO termsCOUNTP-value
GO.0015175Neutral amino acid transmembrane transporter activityMF25.58E–6
GO.0015179L-amino acid transmembrane transporter activityMF29.80E–6
GO.0004181Metallocarboxypeptidase activityMF23.26E–4
GO.0001085RNA polymerase II transcription factor bindingMF28.02E–4
GO.0001228DNA-binding transcription activator activity, RNA polymerase II-specificMF51.97E–3
GO.1902475L-alpha-amino acid transmembrane transportBP27.50E–9
GO.0006560Proline metabolic processBP24.05E–8
GO.0010133Proline catabolic process to glutamateBP28.62E–8
GO.0048286Lung alveolus developmentBP48.86E–8
GO.00194704-hydroxyproline catabolic processBP22.90E–7
GO.0005886Plasma membraneCC274.43E–4
GO.0009898Cytoplasmic side of plasma membraneCC26.85E–4
GO.0010008Endosome membraneCC49.00E–4
GO.0031093Platelet alpha granule lumenCC21.77E–3
GO.0016324Apical plasma membraneCC44.26E–3
Tab.1  Only the top 15 enriched Gene Ontology terms of differentially expressed genes in BC and PC herbal treatment are presented
lncRNAEnsembl IDLncRNA chromosomeTranscript typemRNAs
RP4-536B24.2ENST00000563687.1Chr16Antisense19
FUT8-AS1ENST00000621019.2Chr14Antisense16
CTD-2140G10.4ENST00000534543.1Chr11Antisense8
APOBEC3B-AS1ENST00000513758.2Chr22Antisense8
AC008268.1ENST00000425887.2Chr2LincRNA7
Tab.2  The five top lncRNAs with the highest numbers of mRNA associations in differential co-expression in BC and PC herbal treatment
AttributeWeight_Info Gain RatioWeight_RuleWeight_Chi SquaredWeight_Gini IndexWeight_UncertaintyWeight_ReliefWeight_Info GainAverage
ENST00000498938.2 (DRAIC)1.00.70.81.01.00.81.00.9
ENST00000502514.50.70.41.00.70.81.00.60.7
ENST00000489011.10.71.00.60.40.60.90.50.7
ENST00000509144.20.60.60.60.50.60.80.60.6
ENST00000558107.10.70.90.70.60.60.40.50.6
ENST00000588466.10.60.90.80.50.60.50.40.6
ENST00000607600.10.40.40.60.50.50.90.40.5
ENST00000361558.70.60.90.20.40.40.60.30.5
ENST00000514752.10.60.30.50.40.50.60.40.5
ENST00000445997.10.60.80.50.40.40.10.40.5
ENST00000513758.20.70.50.40.50.30.00.60.4
ENST00000576302.10.61.00.20.40.20.20.40.4
ENST00000547946.10.50.30.30.30.40.60.30.4
ENST00000611475.20.60.70.30.30.50.20.30.4
ENST00000562814.10.50.50.40.30.50.20.30.4
ENST00000505030.50.60.30.60.30.60.00.30.4
ENST00000581457.10.40.80.50.10.60.00.10.4
ENST00000603875.10.60.40.40.30.60.10.30.4
ENST00000456526.10.50.30.60.20.60.10.20.4
ENST00000562038.10.30.20.40.50.30.30.40.3
ENST00000563687.10.50.40.20.30.30.20.30.3
ENST00000534543.10.50.40.20.20.20.10.30.3
ENST00000425887.20.40.40.30.10.60.00.10.3
ENST00000621019.20.30.20.40.30.40.00.20.3
Cell line0.00.00.00.00.00.70.00.1
Time0.00.10.00.00.00.20.00.1
Tab.3  Attribute weighting of the Z-standardized lncRNAs in BC and PC herbal treatment, regardless of herbal medicine type and concentration variable, according to the 7 applied attribute weighting algorithms
lncRNACancer geneFull nameCorrelation coefficientCorrelationCondition
RP4-536B24.2AKR1B10Aldo-Keto reductase family 1 member B100.880587PositiveControl
IGFBP4Insulin like growth factor binding protein 40.901975PositiveControl
PEG10Paternally expressed gene 10 protein0.857614PositiveControl
SUSD3Sushi domain containing protein 30.921415PositiveControl
TMEM92Transmembrane protein 920.862738PositiveControl
SLC3A2Solute carrier family 3 member 20.847517PositiveTreatment
SLC7A5Solute carrier family 7 member 50.863401PositiveTreatment
0.910333
FUT8-AS1CEBPBCCAAT enhancer binding protein beta0.929094PositiveTreatment
ACKR3Atypical chemokine receptor 30.894196PositiveControl
STRA6Stimulated by retinoic acid 60.845782PositiveControl
Tab.4  Co-expressed cancer-related genes in BC and PC herbal treatment with two hub lncRNAs and their high correlation in our data
Fig.3  DRAIC has a similar response pattern to herbal treatments in breast and prostate cancer cell lines.
Fig.4  Decision Tree induced by (A) Decision Tree Accuracy algorithm, (B) Decision Tree Gain Ratio algorithm on Z-standardized in distinguishing cancer cell line in BC and PC from under treatment ones. The model shows the importance of lncRNAs on sub-clinical cancer classification (control=cancer cell line, treated=healthy cell line). A significant cancer occurrence pattern was discovered where cell lines with a high level of ENST00000498938.2 (>?0.142) could be seen. However, with the low expression of this lncRNA (<?0.142), cancer could be suppressed in almost all cell lines
Fig.5  Comparing receiver operating characteristic (ROC) curves of machine learning models in BC and PC for predicting novel lncRNA-disease associations, run on Z-Standardized dataset
Simple sourcePlatformsTissue/cellHerbal medicineNo. of samplesAccession number
TreatmentControl
In vivoIllumina HiSeq 2000 (Homo sapiens)PcaDocetaxel66GSE51005
In vivoIllumina HiSeq 2000 (Homo sapiens)LNCaPSulforaphane33GSE48812
In vivoIllumina HiSeq 2000 (Homo sapiens)PC-3Sulforaphane33
In vivoIllumina Genome Analyzer IIx (Homo sapiens)MCF-7Phytoestrogen resveratrol22PRJDB1992
In vivoIllumina HiSeq 2000 (Homo sapiens)MCF-7S-equol22GSE56066
In vivoIllumina HiSeq 2000 (Homo sapiens)MCF-7Genistein22
In vivoIllumina HiSeq 2000 (Homo sapiens)MCF-7Liquiritigenin22
In vivoIllumina HiSeq 2000 (Homo sapiens)ECC-1Genistein22GSE38234
In vivoIllumina HiSeq 2000 (Homo sapiens)T-47DGenistein22
Tab.5  The selected original datasets of prostate and breast cancer herbal treatment
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