Pattern discovery of long non-coding RNAs associated with the herbal treatments in breast and prostate cancers

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.

Author summary

Functionally characterized lncRNAs play critical roles in cancer progression but the potential relationship between lncRNAs and herbal medicine is yet to be known. To identify this association by RNA-seq data for breast and prostate cancer, a co-expression network in response to herbal medicines was performed. GO terms and pathway analyses on differential co-expressed mRNAs revealed that lncRNAs were widely co-expressed with metabolic process genes. On the other hand, various machine learning-based prediction systems on the differential co-expressed lncRNAs were implemented. Results show that the Deep Learning model could accurately forecast cancer-related lncRNAs.

Graphical abstract

Keywords

RNA-Seq / lncRNA / cancer / co-expression / machine learning / attribute weighting

References

[1] Ekor, M. (2014). The growing use of herbal medicines: issues relating to adverse reactions and challenges in monitoring safety. Front. Pharmacol, 4: 177

[2] KuruppuA. I.,ParanagamaP.GoonasekaraC.. (2019) Medicinal plants commonly used against cancer in traditional medicine formulae in Sri Lanka. Saudi Pharm. J. 27, 565–573

[3] MachariaJ.MwangiR.RozmannN.,ZsoltK.,VarjasT.,UchechukwuP.WagaraI.RaposaB.. (2022) Medicinal plants with anti-colorectal cancer bioactive compounds: potential game-changers in colorectal cancer management. Biomed Pharmacother. 153, 113383

[4] Kooti, W., Servatyari, K., Behzadifar, M., Asadi-Samani, M., Sadeghi, F., Nouri, B. (2017). Effective medicinal plant in cancer treatment, part 2: review study. J. Evid. Based Complementary Altern. Med., 22: 982–995

[5] IqbalJ.,AbbasiB.MahmoodT.,KanwalS.,AliB.,ShahS.KhalilA.. (2017) Plant-derived anticancer agents: a green anticancer approach. Asian Pac. J. Trop. Med., 7, 1129–1150

[6] Diamandis, E. (1998). Breast and prostate cancer: an analysis of common epidemiological, genetic, and biochemical features. Endocr. Rev., 19: 365–396

[7] Wu, W., Wagner, E. Hao, Y., Rao, X., Dai, H., Han, J., Chen, J., Storniolo, A. Liu, Y. (2016). Tissue-specific co-expression of long non-coding and coding RNAs associated with breast cancer. Sci. Rep., 6: 32731

[8] Ren, Z. Cao, D. Zhang, Q., Ren, P. Liu, L. Wei, Q., Wei, W. (2019). First-degree family history of breast cancer is associated with prostate cancer risk: a systematic review and meta-analysis. BMC Cancer, 19: 871

[9] Zheng, Y., Xu, Q., Liu, M., Hu, H., Xie, Y., Zuo, Z. (2019). LnCAR: a comprehensive resource for lncRNAs from cancer arrays. Cancer Res., 79: 2076–83

[10] Beylerli, O., Gareev, I., Sufianov, A., Ilyasova, T. (2022). Long noncoding RNAs as promising biomarkers in cancer. Noncoding RNA Res., 7: 66–70

[11] Wang, J., Shen, Y. Chen, Z. Yuan, Z. Wang, H., Li, D. Liu, K. Wen, F. (2019). Microarray profiling of lung long non-coding RNAs and mRNAs in lipopolysaccharide-induced acute lung injury mouse model. Biosci. Rep., 39: BSR20181634

[12] Silva, A. M., Moura, S. R., Teixeira, J. H., Barbosa, M. A., Santos, S. G. Almeida, M. (2019). Long noncoding RNAs: a missing link in osteoporosis. Bone Res., 7: 10

[13] Zhou, S., He, Y., Yang, S., Hu, J., Zhang, Q., Chen, W., Xu, H., Zhang, H., Zhong, S., Zhao, J. . (2018). The regulatory roles of lncRNAs in the process of breast cancer invasion and metastasis. Biosci. Rep., 38: BSR20180772

[14] Cimadamore, A., Gasparrini, S., Mazzucchelli, R., Doria, A., Cheng, L., Lopez-Beltran, A., Santoni, M., Scarpelli, M. (2017). Long non-coding RNAs in prostate cancer with emphasis on second chromosome locus associated with prostate-1 expression. Front. Oncol., 7: 305

[15] Yang, W., Li, Y., Song, X., Xu, J. (2017). Genome-wide analysis of long noncoding RNA and mRNA co-expression profile in intrahepatic cholangiocarcinoma tissue by RNA sequencing. Oncotarget, 8: 26591–26599

[16] Marttila, S., Chatsirisupachai, K., Palmer, D. de Magalhaes, J. (2020). Ageing-associated changes in the expression of lncRNAs in human tissues reflect a transcriptional modulation in ageing pathways. Mech. Ageing. Dev., 185: 111177

[17] CogillS.. (2014) Co-expression network analysis of human lncRNAs and cancer genes. Cancer Inform. 13, 49–59

[18] Guttman, M. Rinn, J. (2012). Modular regulatory principles of large non-coding RNAs. Nature, 482: 339–346

[19] Sharma, A. (2022). Non-coding RNAs are brokers in breast cancer interactome networks and add discrimination power between subtypes. J. Clin. Med., 11: 2103

[20] Rinn, J. L. Chang, H. (2012). Genome regulation by long noncoding RNAs. Annu. Rev. Biochem., 81: 145–166

[21] Ruan, X., Chen, Y., Shi, Y., Pirooznia, M., Seifuddin, F., Suemizu, H., Ohnishi, Y., Yoneda, N., Nishiwaki, M. . (2020). In vivo functional analysis of nonconserved human lncRNAs associated with cardiometabolic traits. Nat. Commun., 11: 45

[22] HanS.,LiangY.,LiY.. (2016) Long noncoding RNA identification: comparing machine learning based tools for long noncoding transcripts discrimination. BioMed Res. Int. 2016, 1–14

[23] Wang, N., Khan, S. Elo, L. (2022). Deep learning tools are top performers in long non-coding RNA prediction. Brief. Funct. Genomics, 21: 230–241

[24] Zhu, B., Xu, M., Shi, H., Gao, X. (2017). Genome-wide identification of lncRNAs associated with chlorantraniliprole resistance in diamondback moth Plutella xylostella (L. ). BMC Genomics, 18: 380

[25] Zheng, H., Brennan, K., Hernaez, M. (2019). Benchmark of long non-coding RNA quantification for RNA sequencing of cancer samples. Gigascience, 8: giz145

[26] Sun, H., Huang, Z., Sheng, W. Xu, M. (2018). Emerging roles of long non-coding RNAs in tumor metabolism. J. Hematol. Oncol., 11: 106

[27] Lin, W., Zhou, Q., Wang, C. Q., Zhu, L., Bi, C., Zhang, S., Wang, X. (2020). LncRNAs regulate metabolism in cancer. Int. J. Biol. Sci., 16: 1194–1206

[28] WajantH.. (2009) The role of TNF in cancer. Results Probl. Cell Differ. 49,1–15

[29] Boroughs, L. K. DeBerardinis, R. (2015). Metabolic pathways promoting cancer cell survival and growth. Nat. Cell Biol., 17: 351–359

[30] Hao, Y., Wu, W., Shi, F., Dalmolin, R. J., Yan, M., Tian, F., Chen, X., Chen, G. (2015). Prediction of long noncoding RNA functions with co-expression network in esophageal squamous cell carcinoma. BMC Cancer, 15: 168

[31] EbrahimiM.,Mohammadi-DehcheshmehM.,EbrahimiE.PetrovskiK.. (2019) Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: deep learning and gradient-boosted trees outperform other models. Comp. Biol. Med., 114, 103456

[32] El Ansari, R., Craze, M. Miligy, I., Diez-Rodriguez, M., Nolan, C. Ellis, I. Rakha, E. Green, A. (2018). The amino acid transporter SLC7A5 confers a poor prognosis in the highly proliferative breast cancer subtypes and is a key therapeutic target in luminal B tumours. Breast Cancer Res., 20: 21

[33] Alfarsi, L. H., El-Ansari, R., Craze, M. L., Masisi, B. K., Mohammed, O. J., Ellis, I. O., Rakha, E. A. Green, A. (2020). Co-expression effect of SLC7A5/SLC3A2 to predict response to endocrine therapy in oestrogen-receptor-positive breast cancer. Int. J. Mol. Sci., 21: 1407

[34] Scalise, M., Galluccio, M., Console, L., Pochini, L. (2018). The human SLC7A5 (LAT1): the intriguing histidine/large neutral amino acid transporter and its relevance to human health. Front Chem., 6: 243–243

[35] Boque-Sastre, R., Moura, M. C., Gomez, A., Guil, S. (2017). Abstract 3483: genome-wide analysis of the antisense transcriptome in cancer. Cancer Res., 77: 3483

[36] WatanabeY.,NumataK.,MurataS.,OsadaY.,SaitoR.,NakaokaH.,YamamotoN.,WatanabeK.,KatoH.,AbeK.. (2010) Genome-wide analysis of expression modes and DNA methylation status at sense-antisense transcript loci in mouse. Genomics, 96, 333–341

[37] Durai, R., Davies, M., Yang, W., Yang, S. Seifalian, A., Goldspink, G., (2006). Biology of insulin-like growth factor binding protein-4 and its role in cancer (review). Int. J. Oncol., 28: 1317–1325

[38] Li, X., Xiao, R., Tembo, K., Hao, L., Xiong, M., Pan, S., Yang, X., Yuan, W., Xiong, J. (2016). PEG10 promotes human breast cancer cell proliferation, migration and invasion. Int. J. Oncol., 48: 1933–1942

[39] Yu, Z., Jiang, E., Wang, X., Shi, Y., Shangguan, A. J., Zhang, L. (2015). Sushi domain-containing protein 3: a potential target for breast cancer. Cell Biochem. Biophys., 72: 321–324

[40] Yang, Y., Toy, W., Choong, L. Y., Hou, P., Ashktorab, H., Smoot, D. T., Yeoh, K. G. Lim, Y. (2012). Discovery of SLC3A2 cell membrane protein as a potential gastric cancer biomarker: implications in molecular imaging. J. Proteome Res., 11: 5736–5747

[41] SankpalN.MoskalukC.HamptonG.PowellS.. (2006) Overexpression of CEBPβ correlates with decreased TFF1 in gastric cancer. Oncogene. 7, 643–649

[42] Fukumoto, S., Yamauchi, N., Moriguchi, H., Hippo, Y., Watanabe, A., Shibahara, J., Taniguchi, H., Ishikawa, S., Ito, H., Yamamoto, S. . (2005). Overexpression of the aldo-keto reductase family protein AKR1B10 is highly correlated with smokers’ non-small cell lung carcinomas. Clin. Cancer Res., 11: 1776–1785

[43] Bhutia, Y. D., Babu, E., Ramachandran, S. (2015). Amino Acid transporters in cancer and their relevance to “glutamine addiction”: novel targets for the design of a new class of anticancer drugs. Cancer Res., 75: 1782–1788

[44] Morgan, R., Feng, G. Pandha, H. (2013). Abstract A193: transmembrane protein TMEM92 as a novel target in prostate cancer. Mol. Cancer Ther., 12: A193

[45] Szeto, W., Jiang, W., Tice, D. A., Rubinfeld, B., Hollingshead, P. G., Fong, S. E., Dugger, D. L., Pham, T., Yansura, D. G., Wong, T. A. . (2001). Overexpression of the retinoic acid-responsive gene Stra6 in human cancers and its synergistic induction by Wnt-1 and retinoic acid. Cancer Res., 61: 4197–4205

[46] Behnam Azad, B., Lisok, A., Chatterjee, S., Poirier, J. T., Pullambhatla, M., Luker, G. D., Pomper, M. G. (2016). Targeted imaging of the atypical chemokine receptor 3 (ACKR3/CXCR7) in human cancer xenografts. J. Nucl. Med., 57: 981–988

[47] FadakaA.,AjiboyeB.,OjoO.,AdewaleO.,OlayideI.,EmuowhochereR.. (2017) Biology of glucose metabolization in cancer cells. J. Oncol. Sci., 3, 45–51

[48] Kareva, I. (2022). Understanding metabolic alterations in cancer cachexia through the lens of exercise physiology. Cells, 11: 2317

[49] Govic, A., Nasser, H., Levay, E. A., Zelko, M., Ebrahimie, E., Mohammadi Dehcheshmeh, M., Kent, S., Penman, J. (2022). Long-term calorie restriction alters anxiety-like behaviour and the brain and adrenal gland transcriptomes of the ageing male rat. Nutrients, 14: 4670

[50] Zhao, D. Dong, J. (2018). Upregulation of long non-coding RNA DRAIC correlates with adverse features of breast cancer. Noncoding RNA, 4: 39

[51] Yao, Q., Zhang, X. (2022). The emerging potentials of lncRNA DRAIC in human cancers. Front. Oncol., 12: 867670

[52] Berry, D. C., Levi, L. (2014). Holo-retinol-binding protein and its receptor STRA6 drive oncogenic transformation. Cancer Res., 74: 6341–6351

[53] ndez, S., ndez, J. B., vez, J., n-Fonseca, O., n-Lobo, Y., Ortega, A., pez, M. (2020). STRA6 polymorphisms are associated with EGFR mutations in locally-advanced and metastatic non-small cell lung cancer patients. Front. Oncol., 10: 579561

[54] Rajan, P., Stockley, J., Sudbery, I. M., Fleming, J. T., Hedley, A., Kalna, G., Sims, D., Ponting, C. P., Heger, A., Robson, C. N. . (2014). Identification of a candidate prognostic gene signature by transcriptome analysis of matched pre- and post-treatment prostatic biopsies from patients with advanced prostate cancer. BMC Cancer, 14: 977

[55] Beaver, L. M., Buchanan, A., Sokolowski, E. I., Riscoe, A. N., Wong, C. P., Chang, J. H., hr, C. V., Williams, D. E., Dashwood, R. H. (2014). Transcriptome analysis reveals a dynamic and differential transcriptional response to sulforaphane in normal and prostate cancer cells and suggests a role for Sp1 in chemoprevention. Mol. Nutr. Food Res., 58: 2001–2013

[56] Gong, P., Madak-Erdogan, Z., Li, J., Cheng, J., Greenlief, C. M., Helferich, W., Katzenellenbogen, J. A. Katzenellenbogen, B. (2014). Transcriptomic analysis identifies gene networks regulated by estrogen receptor α (ERα) and ERβ that control distinct effects of different botanical estrogens. Nucl. Recept. Signal., 12: e001

[57] Gertz, J., Reddy, T. E., Varley, K. E., Garabedian, M. J. Myers, R. (2012). Genistein and bisphenol A exposure cause estrogen receptor 1 to bind thousands of sites in a cell type-specific manner. Genome Res., 22: 2153–2162

[58] Love, M. Huber, W. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biol., 15: 550

[59] HowardJ. T.,AshwellM. S.,BaynesR. E.,. (2017) Gene co-expression network analysis identifies porcine genes associated with variation in metabolizing fenbendazole and flunixin meglumine in the liver. Sci. Rep. 7, 1357

[60] JohnsonK. A.. (2022) Robust normalization and transformation techniques for constructing gene co-expression networks from RNA-seq data. Genome Biol. 23, 2022

[61] Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P. . (2019). STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res., 47: D607–D613

[62] Alanazi, I. O., Al Shehri, Z. S., Ebrahimie, E., Giahi, H. (2019). Non-coding and coding genomic variants distinguish prostate cancer, castration-resistant prostate cancer, familial prostate cancer, and metastatic castration-resistant prostate cancer from each other. Mol. Carcinog., 58: 862–874

[63] Alanazi, I. O., AlYahya, S. A., Ebrahimie, E. (2018). Computational systems biology analysis of biomarkers in lung cancer; unravelling genomic regions which frequently encode biomarkers, enriched pathways, and new candidates. Gene, 659: 29–36

[64] Fruzangohar, M., Ebrahimie, E., Ogunniyi, A. D., Mahdi, L. K., Paton, J. C. Adelson, D. (2013). Comparative GO: a web application for comparative Gene Ontology and Gene Ontology-based gene selection in bacteria. PLoS One, 8: e58759

[65] Ebrahimi, M., Lakizadeh, A., Agha-Golzadeh, P., Ebrahimie, E. (2011). Prediction of thermostability from amino acid attributes by combination of clustering with attribute weighting: a new vista in engineering enzymes. PLoS One, 6: e23146

[66] Ghasemi, E., Ebrahimi, M. (2022). Machine learning models effectively distinguish attention-deficit/hyperactivity disorder using event-related potentials. Cogn. Neurodynamics, 16: 1335–1349

[67] Ebrahimie, E., Zamansani, F., Alanazi, I. O., Sabi, E. M., Khazandi, M., Ebrahimi, F., Mohammadi-Dehcheshmeh, M. (2021). Advances in understanding the specificity function of transporters by machine learning. Comput. Biol. Med., 138: 104893

[68] MeredithM.KruschkeJ.. (2021) Bayesian Estimation Supersedes the t-test (computer software manual)

[69] Kruschke, J. (2013). Bayesian estimation supersedes the t test. J. Exp. Psychol. Gen., 142: 573–603

[70] HuJ.,GaoY.,LiJ.. (2019) Deep learning enables accurate prediction of interplay between lncRNA and disease. Front. Genet., 10

[71] YaoD.,ZhanX.,ZhanX.,KwohC. K.,LiP.. (2020) A random forest based computational model for predicting novel lncRNA-disease associations. BMC Bioinf. 21, 126

SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.15302/J-QB-023-0333.

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This research has received no external funding.

COMPLIANCE WITH ETHICS GUIDELINES

Conflicts of interest The authors Elham Dalalbashi Esfahani, Esmaeil Ebrahimie, Ali Niazi and Manijeh Mohammadi Dehcheshmeh declare that they have no competing interests.

The article does not contain any human or animal subjects performed by any of the authors.

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