Drug compound prediction-based analysis of cigarette smoking to pancreatic cancer patients: A bioinformatics study

Tasnimul Alam Taz; Md Kawsar; Sinthia Siddique; Kawsar Ahmed; Mohammad Ali Moni; Bikash Kumar Paul

doi:10.1109/WIECON-ECE52138.2020.9397979

Drug compound prediction-based analysis of cigarette smoking to pancreatic cancer patients: A bioinformatics study

Tasnimul Alam Taz
, Md Kawsar
, Sinthia Siddique
, Kawsar Ahmed
, Mohammad Ali Moni
, Bikash Kumar Paul

Artificial Intelligence and Cyber Futures Institute

Daffodil International University
Jahangirnagar University
Mawlana Bhashani Science and Technology University
University of New South Wales, Sydney, Australia

Research output: Book chapter/Published conference paper › Conference paper › peer-review

1 Citation (Scopus)

Abstract

Considering the fact of survival rate, pancreatic cancer (PC) can be categorized among the most fatal cancer diseases as the survival rate is medium among most of the cases. Cigarette smoking is regarded as a significant risk factor for PC. In this study, therapeutic results are attempted to be found by the assist of a number of Bioinformatics tools. Two microarray datasets GSE144909 and GSE26307 are used for pancreatic cancer and active smoker lung cell samples respectively. Preprocessing and filtering of the datasets and common differentially expressed genes (DEGs) are identified with the assist of R programming language. Regulation of the DEGs are expressed with a Venn diagram. Then Protein-protein interactions (PPIs) network is designed based on the common DEGs and hub nodes are identified using topological analysis. RPA1, RPA2, BLM, FANCM and APITD1 genes are the top 5 mostly interconnected genes in PPIs network and visibility of RPA1 and RPA2 is found in inflammatory pancreatic cancer cell and smoker lung cell. Gene ontology (GO) and pathway identification is regarded as the future study of this research. Finally, a number of therapeutic targets have been identified based on the common DEGs.

Original language	English
Title of host publication	Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020
Subtitle of host publication	6th IEEE WIECON – ECE 2020
Place of Publication	Bangladesh
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	320-323
Number of pages	4
ISBN (Electronic)	9780738131443, 9781665419178
ISBN (Print)	9781665430272 (Print on demand)
DOIs	https://doi.org/10.1109/WIECON-ECE52138.2020.9397979
Publication status	Published - 26 Dec 2020
Event	6th IEEE WIECON-ECE 2020: 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) - Virtual, Bhubaneswar, India Duration: 26 Dec 2020 → 27 Dec 2020 https://web.archive.org/web/20201102021437/https://wiecon-ece.org/ (Conference website)

Publication series

Name	Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020

Conference

Conference	6th IEEE WIECON-ECE 2020
Country/Territory	India
City	Bhubaneswar
Period	26/12/20 → 27/12/20
Other	The conference aims to bring together leading academic researchers and research scholars to exchange and share their experiences and research results on all aspects of Electrical Engineering and Computer Science. It also provides an interdisciplinary platform for researchers, practitioners and educators to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Electrical and Computer Engineering. With the view to encourage participation of women scholars, each paper should have at least one female author.
Internet address	https://web.archive.org/web/20201102021437/https://wiecon-ece.org/ (Conference website)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1109/WIECON-ECE52138.2020.9397979

Cite this

Taz, T. A., Kawsar, M., Siddique, S., Ahmed, K., Moni, M. A., & Paul, B. K. (2020). Drug compound prediction-based analysis of cigarette smoking to pancreatic cancer patients: A bioinformatics study. In Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020: 6th IEEE WIECON – ECE 2020 (pp. 320-323). Article 9397979 (Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/WIECON-ECE52138.2020.9397979

Taz, Tasnimul Alam ; Kawsar, Md ; Siddique, Sinthia et al. / Drug compound prediction-based analysis of cigarette smoking to pancreatic cancer patients : A bioinformatics study. Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020: 6th IEEE WIECON – ECE 2020. Bangladesh : IEEE, Institute of Electrical and Electronics Engineers, 2020. pp. 320-323 (Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020).

@inproceedings{be6b96db6a644b3fb94e885ef9775682,

title = "Drug compound prediction-based analysis of cigarette smoking to pancreatic cancer patients: A bioinformatics study",

abstract = "Considering the fact of survival rate, pancreatic cancer (PC) can be categorized among the most fatal cancer diseases as the survival rate is medium among most of the cases. Cigarette smoking is regarded as a significant risk factor for PC. In this study, therapeutic results are attempted to be found by the assist of a number of Bioinformatics tools. Two microarray datasets GSE144909 and GSE26307 are used for pancreatic cancer and active smoker lung cell samples respectively. Preprocessing and filtering of the datasets and common differentially expressed genes (DEGs) are identified with the assist of R programming language. Regulation of the DEGs are expressed with a Venn diagram. Then Protein-protein interactions (PPIs) network is designed based on the common DEGs and hub nodes are identified using topological analysis. RPA1, RPA2, BLM, FANCM and APITD1 genes are the top 5 mostly interconnected genes in PPIs network and visibility of RPA1 and RPA2 is found in inflammatory pancreatic cancer cell and smoker lung cell. Gene ontology (GO) and pathway identification is regarded as the future study of this research. Finally, a number of therapeutic targets have been identified based on the common DEGs.",

keywords = "Differentially expressed genes, Hub gene, Pancreatic cancer, Protein-protein interactions",

author = "Taz, \{Tasnimul Alam\} and Md Kawsar and Sinthia Siddique and Kawsar Ahmed and Moni, \{Mohammad Ali\} and Paul, \{Bikash Kumar\}",

year = "2020",

month = dec,

day = "26",

doi = "10.1109/WIECON-ECE52138.2020.9397979",

language = "English",

isbn = "9781665430272 (Print on demand)",

series = "Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

pages = "320--323",

booktitle = "Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020",

address = "United States",

note = "6th IEEE WIECON-ECE 2020 : 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) ; Conference date: 26-12-2020 Through 27-12-2020",

url = "https://web.archive.org/web/20201102021437/https://wiecon-ece.org/",

}

Taz, TA, Kawsar, M, Siddique, S, Ahmed, K, Moni, MA & Paul, BK 2020, Drug compound prediction-based analysis of cigarette smoking to pancreatic cancer patients: A bioinformatics study. in Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020: 6th IEEE WIECON – ECE 2020., 9397979, Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020, IEEE, Institute of Electrical and Electronics Engineers, Bangladesh, pp. 320-323, 6th IEEE WIECON-ECE 2020, Bhubaneswar, India, 26/12/20. https://doi.org/10.1109/WIECON-ECE52138.2020.9397979

Drug compound prediction-based analysis of cigarette smoking to pancreatic cancer patients: A bioinformatics study. / Taz, Tasnimul Alam; Kawsar, Md; Siddique, Sinthia et al.
Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020: 6th IEEE WIECON – ECE 2020. Bangladesh: IEEE, Institute of Electrical and Electronics Engineers, 2020. p. 320-323 9397979 (Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020).

Research output: Book chapter/Published conference paper › Conference paper › peer-review

TY - GEN

T1 - Drug compound prediction-based analysis of cigarette smoking to pancreatic cancer patients

T2 - 6th IEEE WIECON-ECE 2020

AU - Taz, Tasnimul Alam

AU - Kawsar, Md

AU - Siddique, Sinthia

AU - Ahmed, Kawsar

AU - Moni, Mohammad Ali

AU - Paul, Bikash Kumar

PY - 2020/12/26

Y1 - 2020/12/26

N2 - Considering the fact of survival rate, pancreatic cancer (PC) can be categorized among the most fatal cancer diseases as the survival rate is medium among most of the cases. Cigarette smoking is regarded as a significant risk factor for PC. In this study, therapeutic results are attempted to be found by the assist of a number of Bioinformatics tools. Two microarray datasets GSE144909 and GSE26307 are used for pancreatic cancer and active smoker lung cell samples respectively. Preprocessing and filtering of the datasets and common differentially expressed genes (DEGs) are identified with the assist of R programming language. Regulation of the DEGs are expressed with a Venn diagram. Then Protein-protein interactions (PPIs) network is designed based on the common DEGs and hub nodes are identified using topological analysis. RPA1, RPA2, BLM, FANCM and APITD1 genes are the top 5 mostly interconnected genes in PPIs network and visibility of RPA1 and RPA2 is found in inflammatory pancreatic cancer cell and smoker lung cell. Gene ontology (GO) and pathway identification is regarded as the future study of this research. Finally, a number of therapeutic targets have been identified based on the common DEGs.

AB - Considering the fact of survival rate, pancreatic cancer (PC) can be categorized among the most fatal cancer diseases as the survival rate is medium among most of the cases. Cigarette smoking is regarded as a significant risk factor for PC. In this study, therapeutic results are attempted to be found by the assist of a number of Bioinformatics tools. Two microarray datasets GSE144909 and GSE26307 are used for pancreatic cancer and active smoker lung cell samples respectively. Preprocessing and filtering of the datasets and common differentially expressed genes (DEGs) are identified with the assist of R programming language. Regulation of the DEGs are expressed with a Venn diagram. Then Protein-protein interactions (PPIs) network is designed based on the common DEGs and hub nodes are identified using topological analysis. RPA1, RPA2, BLM, FANCM and APITD1 genes are the top 5 mostly interconnected genes in PPIs network and visibility of RPA1 and RPA2 is found in inflammatory pancreatic cancer cell and smoker lung cell. Gene ontology (GO) and pathway identification is regarded as the future study of this research. Finally, a number of therapeutic targets have been identified based on the common DEGs.

KW - Differentially expressed genes

KW - Hub gene

KW - Pancreatic cancer

KW - Protein-protein interactions

UR - https://www.scopus.com/pages/publications/85104627044

UR - https://www.scopus.com/pages/publications/85104627044#tab=citedBy

U2 - 10.1109/WIECON-ECE52138.2020.9397979

DO - 10.1109/WIECON-ECE52138.2020.9397979

M3 - Conference paper

AN - SCOPUS:85104627044

SN - 9781665430272 (Print on demand)

T3 - Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020

SP - 320

EP - 323

BT - Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - Bangladesh

Y2 - 26 December 2020 through 27 December 2020

ER -

Taz TA, Kawsar M, Siddique S, Ahmed K, Moni MA, Paul BK. Drug compound prediction-based analysis of cigarette smoking to pancreatic cancer patients: A bioinformatics study. In Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020: 6th IEEE WIECON – ECE 2020. Bangladesh: IEEE, Institute of Electrical and Electronics Engineers. 2020. p. 320-323. 9397979. (Proceedings of 2020 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON-ECE 2020). doi: 10.1109/WIECON-ECE52138.2020.9397979

Drug compound prediction-based analysis of cigarette smoking to pancreatic cancer patients: A bioinformatics study

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this