A Multi-Molecular Fusion to Detect Transcriptomic Signature 
in Tissue-Specific Cancer

MALLIK, Saurav; SAHA, Suparna; BANDYOPADHYAY, Sanghamitra

doi:10.14744/ejmo.2022.53376

A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer

Suparna SAHA, (SyMeC Data Center, Indian Statistical Institute, Kolkata, India)

Saurav MALLIK, (Department of Environmental Epigenetics, Harvard T H Chan School of Public Health, Boston, USA)

Sanghamitra BANDYOPADHYAY (Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India)

Eurasian Journal of Medicine and Oncology

2 0

Yıl: 2022 Cilt: 6 Sayı: 2 Sayfa Aralığı: 156 - 171 Metin Dili: İngilizce DOI: 10.14744/ejmo.2022.53376 İndeks Tarihi: 06-07-2022

A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer

Öz:

Objectives: Analysis of multi-molecular interactions and detection of combinatorial transcriptomic signatures are emerging as important research topics in disease analytics. Currently, a combination of gene and miRNA expression profiling in bioinformatic analysis enables us to comprehensively detect molecular changes in cancer and thereafter to identify integrated signatures and pathways that exist in the miRNA and gene interaction networks. Although many methodologies and applications have been suggested in recent literature, efficient techniques that can integrate the complex gene as well as miRNA expression profiles, and identify the most relevant signatures are required. Methods: In this article, we presented a new framework of multi-molecular data integration to identify combinatorial transcriptomic signatures through the strategy of unsupervised learning and target detection. Later, we evaluated their utility in survival analysis through a multi-variate Cox regression study. We used a cervical cancer data repository to conduct our experiment. To construct the miRNA-mRNA interaction network, we selected the downregulated mRNAs that were negatively correlated with the upregulated miRNAs. Thereafter, we identified dense modules by using an unsupervised learning technique. The silhouette index value was computed for each cluster. Results: By considering the network centrality of each molecule belonging to each cluster we identified top 3 combined signatures We also highlighted cluster-2 (hsa-mir-944, CFTR, GABRB2, HNF4G, TAC1, and C7orf57) for its high cohesiveness and contained a combined signature. We then applied three well-known classifiers (viz., SVM, KNN, and random forest) using 10-fold cross-validation, and obtained a high AUC score for cluster-2. Finally, we conducted a survival study with each molecule of the same cluster. Conclusion: Finally, we conducted a survival study with each molecule of the same cluster. Our proposed combined signature detection strategy can determine the signature(s) for any microarray or RNA-Seq profile. The code is available at https://github.com/sahasuparna/DeMoS

Anahtar Kelime:

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

1. Bhadra T, Mallik S, Sohel A, Zhao Z. Unsupervised feature selection using an integrated strategy of hierarchical clustering with singular value decomposition: an integrative biomarker discovery method with application to acute myeloid leukemia. IEEE/ACM Trans Comput Biol Bioinform. 2021 Sep 8;PP. doi: 10.1109/TCBB.2021.3110989. [Epub ahead of print].
2. Kandimalla R, Shimura T, Mallik S, Sonohara F, Tsai S, Evans DB, et al. Identification of serum miRNA signature and establishment of a nomogram for risk stratification in patients with pancreatic ductal adenocarcinoma. Ann Surg 2022;275:229–37.
3. Mallik S, Zhao Z. Graph- and rule-based learning algorithms: a comprehensive review of their applications for cancer type classification and prognosis using genomic data. Brief Bioinform 2020;21:368–94. [CrossRef]
4. Bhadra T, Mallik S, Hasan N, Zhao Z. Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer. BMC Bioinformatics 2022;23:153. [CrossRef]
5. Maulik U, Sen S, Mallik S, Bandyopadhyay S. Detecting TF-miRNA-gene network based modules for 5hmC and 5mC brain samples: a intra- and inter-species case-study between human and rhesus. BMC Genet 2018;19:9. [CrossRef]
6. Cancer Genome Atlas Research Network, Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 2013;45:1113–20. [CrossRef]
7. Chanrion M, Negre V, Fontaine H, Salvetat N, Bibeau F, Mac Grogan G, et al. A gene expression signature that can predict the recurrence of tamoxifen-treated primary breast cancer. Clin Cancer Res 2008;14:1744–52. [CrossRef]
8. Liu R, Wang X, Chen GY, Dalerba P, Gurney A, Hoey T, et al. The prognostic role of a gene signature from tumorigenic breastcancer cells. N Engl J Med 2007;356:217–26.
9. Arranz EE, Vara JБ, Gбmez-Pozo A, Zamora P. Gene signatures in breast cancer: current and future uses. Transl Oncol 2012;5:398–403.
10. Sшrlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A 2001;98:10869–74.
11. Hou LK, Ma YS, Han Y, Lu GX, Luo P, Chang ZY, et al. Association of microRNA-33a molecular signature with non-small cell lung cancer diagnosis and prognosis after chemotherapy. PLoS One 2017;12:e0170431. [CrossRef]
12. Munding JB, Adai AT, Maghnouj A, Urbanik A, Zцllner H, Liffers ST, et al. Global microRNA expression profiling of microdissected tissues identifies miR-135b as a novel biomarker for pancreatic ductal adenocarcinoma. Int J Cancer 2012;131:E86–95.
13. Shenoy A, Blelloch RH. Regulation of microRNA function in somatic stem cell proliferation and differentiation. Nat Rev Mol Cell Biol 2014;15:565–76. [CrossRef]
14. Bartel DP. MicroRNAs: target recognition and regulatory functions. Cell 2009;136:215–33. [CrossRef]
15. Croce CM. Causes and consequences of microRNA dysregulation in cancer. Nat Rev Genet 2009;10:704–14. [CrossRef]
16. Dou C, Wang Y, Li C, Liu Z, Jia Y, Li Q, et al. MicroRNA-212 suppresses tumor growth of human hepatocellular carcinoma by targeting FOXA1. Oncotarget 2015;6:13216–28. [CrossRef]
17. Bandyopadhyay S, Mallik S, Mukhopadhyay A. A survey and comparative study of statistical tests for identifying differential expression from microarray data. IEEE/ACM transactions on computational biology and bioinformatics 2014;11:95– 115.
18. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res 2015;43:e47.
19. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004;3:Article3. [CrossRef]
20. Xiang Y, Zhang CQ, Huang K. Predicting glioblastoma prognosis networks using weighted gene co-expression network analysis on TCGA data. BMC Bioinformatics 2012;13:S12.
21. Venkatesan K, Rual JF, Vazquez A, Stelzl U, Lemmens I, Hirozane-Kishikawa T, et al. An empirical framework for binary interactome mapping. Nat Methods 2009;6:83–90. [CrossRef]
22. Rakyan VK, Down TA, Balding DJ, Beck S. Epigenome-wide association studies for common human diseases. Nat Rev Genet 2011;12:529–41. [CrossRef]
23. De Jager PL, Srivastava G, Lunnon K, Burgess J, Schalkwyk LC, Yu L, et al. Alzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci. Nat Neurosci 2014;17:1156–63. [CrossRef]
24. Mallik S, Odom GJ, Gao Z, Gomez L, Chen X, Wang L. An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays. Brief Bioinform 2019;20:2224–35. [CrossRef]
25. Hoshida Y, Villanueva A, Sangiovanni A, Sole M, Hur C, Andersson KL, et al. Prognostic gene expression signature for patients with hepatitis C-related early-stage cirrhosis. Gastroenterology 2013;144:1024–30.
26. Verhaak RG, Goudswaard CS, van Putten W, Bijl MA, Sanders MA, Hugens W, et al. Mutations in nucleophosmin (NPM1) in acute myeloid leukemia (AML): association with other gene abnormalities and previously established gene expression signatures and their favorable prognostic significance. Blood 2005;106:3747–54. [CrossRef]
27. Nielsen T, Wallden B, Schaper C, Ferree S, Liu S, Gao D, et al. Analytical validation of the PAM50-based Prosigna Breast Cancer Prognostic Gene Signature Assay and nCounter Analysis System using formalin-fixed paraffin-embedded breast tumor specimens. BMC Cancer 2014;14:177. [CrossRef]
28. Nguyen HG, Welty CJ, Cooperberg MR. Diagnostic associations of gene expression signatures in prostate cancer tissue. Curr Opin Urol 2015;25:65–70.
29. Baker SG, Kramer BS. Evaluating surrogate endpoints, prognostic markers, and predictive markers: Some simple themes. Clin Trials 2015;12:299–308. [CrossRef]
30. Zhang J, Lu K, Xiang Y, Islam M, Kotian S, Kais Z, et al. Weighted frequent gene co-expression network mining to identify genes involved in genome stability. PLoS Comput Biol 2012;8:e1002656.
31. Mallik S, S. Bandyopadhyay S. Wecomxp: Weighted connectivity measure integrating co-methylation, co-expression and protein-protein interactions for gene-module detection. IEEE/ ACM Transactions on Computational Biology and Bioinformatics 2018;17:690–703. [CrossRef]
32. Jin D, Lee H. FGMD: A novel approach for functional gene module detection in cancer. PLoS One 2017;12:e0188900.
33. Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the dynamic tree cut package for R. Bioinformatics 2007;24:719–20. [CrossRef]
34. Langfelder P, Horvath S. Wgcna: an R package for weighted correlation network analysis. BMC Bioinformatics 2008;9:559.
35. Jiang X, Zhang H, Quan X, Liu Z, Yin Y. Disease-related gene module detection based on a multi-label propagation clustering algorithm. PLoS One 2017;12:e0178006.
36. Seth S, Mallik S, Bhadra T, Zhao Z. Dimensionality reduction and louvain agglomerative hierarchical clustering for cluster-specified frequent biomarker discovery in single-cell sequencing data. Front Genet 2022;13:828479.
37. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási AL. Hierarchical organization of modularity in metabolic networks. Science 2002;297:1551–5. [CrossRef]
38. Mallik S, Zhao Z. ConGEMs: condensed gene co-expression module discovery through rule-based clustering and its application to carcinogenesis. Genes (Basel) 2017;9:7.
39. Cava C, Colaprico A, Bertoli G, Graudenzi A, Silva TC, Olsen C, et al. SpidermiR: An R/Bioconductor package for integrative analysis with miRNA data. Int J Mol Sci 2017;18:274. [CrossRef]
40. Srivastava P, Mangal M, Agarwal SM. Understanding the transcriptional regulation of cervix cancer using microarray gene expression data and promoter sequence analysis of a curated gene set. Gene 2014;535:233–8. [CrossRef]
41. Wang J, Lu M, Qiu C, Cui Q. TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Res 2010;38:D119–22.
42. Wang X, Tang S, Le SY, Lu R, Rader JS, Meyers C, et al. Aberrant expression of oncogenic and tumor-suppressive microRNAs in cervical cancer is required for cancer cell growth. PLoS One 2008;3:e2557.
43. Von Luxburg U. A tutorial on spectral clustering. Stat Comput 2007;17:395–416. [CrossRef]
44. Sommerová L, Fraňková H, Anton M, Jandáková E, Vojtěšek B, Hrstka R. Expression and functional characterization of miR34c in cervical cancer. Klin Onkol 2018;31:82–7.
45. Yuan SH, Qiu Z, Ghosh A. TOX3 regulates calcium-dependent transcription in neurons. Proc Natl Acad Sci U S A 2009;106:2909–14. [CrossRef]
46. Miao H, Wang N, Shi LX, Wang Z, Song WB. Overexpression of mircoRNA-137 inhibits cervical cancer cell invasion, migration and epithelial-mesenchymal transition by suppressing the TGF-β/smad pathway via binding to GREM1. Cancer Cell Int 2019;19:147.
47. Park S, Kim J, Eom K, Oh S, Kim S, Kim G, et al. microRNA-944 overexpression is a biomarker for poor prognosis of advanced cervical cancer. BMC Cancer 2019;19:419.
48. Pett MR, Herdman MT, Palmer RD, Yeo GS, Shivji MK, Stanley MA, et al. Selection of cervical keratinocytes containing integrated HPV16 associates with episome loss and an endogenous antiviral response. Proc Natl Acad Sci U S A 2006;103:3822–7. [CrossRef]
49. Wu X, Peng L, Zhang Y, Chen S, Lei Q, Li G, et al. Identification of key genes and pathways in cervical cancer by bioinformatics analysis. Int J Med Sci 2019;16:800–12.
50. International Collaboration of Epidemiological Studies of Cervical Cancer. Cervical carcinoma and reproductive factors: collaborative reanalysis of individual data on 16,563 women with cervical carcinoma and 33,542 women without cervical carcinoma from 25 epidemiological studies. Int J Cancer 2006;119:1108–24. [CrossRef]
51. Nelson EL, Wenzel LB, Osann K, Dogan-Ates A, Chantana N, Reina-Patton A, et al. Stress, immunity, and cervical cancer: biobehavioral outcomes of a randomized clinical trial [corrected]. Clin Cancer Res 2008;14:2111–8.
52. Brinton LA, Hamman RF, Huggins GR, Lehman HF, Levine RS, Mallin K, et al. Sexual and reproductive risk factors for invasive squamous cell cervical cancer. J Natl Cancer Inst 1987;79:23– 30.
53. Manzo-Merino J, Contreras-Paredes A, Vázquez-Ulloa E, Rocha-Zavaleta L, Fuentes-Gonzalez AM, Lizano M. The role of signaling pathways in cervical cancer and molecular therapeutic targets. Arch Med Res 2014;45:525–39. [CrossRef]
54. Ewald PW, Swain Ewald HA. Infection and cancer in multicellular organisms. Phil Trans R Soc B 2015;370:20140224.
55. Singh S, Kumar PU, Thakur S, Kiran S, Sen B, Sharma S, et al. Expression/localization patterns of sirtuins (SIRT1, SIRT2, and SIRT7) during progression of cervical cancer and effects of sirtuin inhibitors on growth of cervical cancer cells. Tumour Biol 2015;36:6159–71.
56. Sun L, Fang J. Macromolecular crowding effect is critical for maintaining SIRT1's nuclear localization in cancer cells. Cell Cycle 2016;15:2647–55.
57. Carter J, Auchincloss S, Sonoda Y, Krychman M. Cervical cancer: issues of sexuality and fertility. Oncology (Williston Park) 2003;17:1229–34.
58. Lin M, Ye M, Zhou J, Wang ZP, Zhu X. Recent advances on the molecular mechanism of cervical carcinogenesis based on systems biology technologies. Comput Struct Biotechnol J 2019;17:241–50. [CrossRef]
59. Therneau TM, Lumley T. Package ‘survival’. R Top Doc 2015;128:28–33.
60. Kuhn M. Building predictive models in R using the caret package. J Stat Softw 2008;28:1–26. [CrossRef

APA	SAHA S, MALLIK S, BANDYOPADHYAY S (2022). A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer. , 156 - 171. 10.14744/ejmo.2022.53376
Chicago	SAHA Suparna,MALLIK Saurav,BANDYOPADHYAY Sanghamitra A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer. (2022): 156 - 171. 10.14744/ejmo.2022.53376
MLA	SAHA Suparna,MALLIK Saurav,BANDYOPADHYAY Sanghamitra A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer. , 2022, ss.156 - 171. 10.14744/ejmo.2022.53376
AMA	SAHA S,MALLIK S,BANDYOPADHYAY S A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer. . 2022; 156 - 171. 10.14744/ejmo.2022.53376
Vancouver	SAHA S,MALLIK S,BANDYOPADHYAY S A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer. . 2022; 156 - 171. 10.14744/ejmo.2022.53376
IEEE	SAHA S,MALLIK S,BANDYOPADHYAY S "A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer." , ss.156 - 171, 2022. 10.14744/ejmo.2022.53376
ISNAD	SAHA, Suparna vd. "A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer". (2022), 156-171. https://doi.org/10.14744/ejmo.2022.53376

APA	SAHA S, MALLIK S, BANDYOPADHYAY S (2022). A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer. Eurasian Journal of Medicine and Oncology, 6(2), 156 - 171. 10.14744/ejmo.2022.53376
Chicago	SAHA Suparna,MALLIK Saurav,BANDYOPADHYAY Sanghamitra A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer. Eurasian Journal of Medicine and Oncology 6, no.2 (2022): 156 - 171. 10.14744/ejmo.2022.53376
MLA	SAHA Suparna,MALLIK Saurav,BANDYOPADHYAY Sanghamitra A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer. Eurasian Journal of Medicine and Oncology, vol.6, no.2, 2022, ss.156 - 171. 10.14744/ejmo.2022.53376
AMA	SAHA S,MALLIK S,BANDYOPADHYAY S A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer. Eurasian Journal of Medicine and Oncology. 2022; 6(2): 156 - 171. 10.14744/ejmo.2022.53376
Vancouver	SAHA S,MALLIK S,BANDYOPADHYAY S A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer. Eurasian Journal of Medicine and Oncology. 2022; 6(2): 156 - 171. 10.14744/ejmo.2022.53376
IEEE	SAHA S,MALLIK S,BANDYOPADHYAY S "A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer." Eurasian Journal of Medicine and Oncology, 6, ss.156 - 171, 2022. 10.14744/ejmo.2022.53376
ISNAD	SAHA, Suparna vd. "A Multi-Molecular Fusion to Detect Transcriptomic Signature in Tissue-Specific Cancer". Eurasian Journal of Medicine and Oncology 6/2 (2022), 156-171. https://doi.org/10.14744/ejmo.2022.53376