A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer

Esen Çakmak

doi:10.17776/csj.976510

EN

A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer

Abstract

Non-small cell lung cancer (NSCLC) is responsible for about 85% of lung cancer types. The molecular mechanism of NSCLC has not been completely elucidated. The current study aims to explore the potential biomarkers and targets for NSCLC. The gene and miRNA expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs (DEMs) and genes (DEGs) were determined and used for further analysis. Functional enrichment analyses were applied using the DAVID program. Moreover, the miRNA targets were predicted based on the miRWalk. The STRING software was constructed protein-protein interaction (PPI) and miRNA-mRNA networks and Cytoscape software was used to visualize PPI and miRNA-mRNA networks and to identify hub genes. As a result of bioinformatic analysis, a total of 159 DEGs and 22 DEMs were identified and DEGs were mostly enriched in the terms like ECM receptor interaction, signal transduction and leukocyte transendothelial migration. The identified hub genes were IL6, COL1A1, CLDN5, CAV1, CDH5, SPP1, GNG11, PPBP, CXCL2 and CXCR2. A total of 239 target genes were identified as potential mRNAs. The most significantly identified genes and miRNAs could serve as potential biomarkers for NSCLC.

Keywords

Non-small cell lung cancer, miRNA, mRNA, Bioinformatics analysis

References

[1] Li C., Yin Y., Liu X., Xi X., Xue W., Qu Y., Non-small cell lung cancer associated microRNA expression signature: Integrated bioinformatics analysis, validation and clinical significance, Oncotarget, 8 (15) (2017) 24564–24578.
[2] Shi K., Li N., Yang M., Li W., Identification of key genes and pathways in female lung cancer patients who never smoked by a bioinformatics analysis, J. Cancer, 10(1) (2019) 51–60.
[3] Chen Y.J., Guo Y.N., Shi K., Huang H.M., Huang S.P., Xu W.Q., Li Z.Y., Wei K.L., Gan T.Q., Chen G., Down-regulation of microRNA-144-3p and its clinical value in non-small cell lung cancer: A comprehensive analysis based on microarray, miRNA-sequencing, and quantitative real-time PCR data, Respir. Res., 20(1) (2019) 1–18.
[4] Yu H., Pang Z., Li G., Gu T., Bioinformatics analysis of differentially expressed miRNAs in non-small cell lung cancer, J. Clin. Lab. Anal., 35(2) (2021) 1–11.
[5] Chen Y., Min L, Ren C, Xu X., Yang J., Sun X., Wang T., Wang F., Sun C., Zhang X., MiRNA-148a serves as a prognostic factor and suppresses migration and invasion through Wnt1 in non-small cell Lung cancer, PLoS One, 12(2) (2017) 1–17.
[6] Si W., Shen J., Zheng H., Fan W., The role and mechanisms of action of microRNAs in cancer drug resistance, Clin. Epigenetics, 11 (1) (2019) 1–24.
[7] Cai X., Lin L., Zhang Q., Wu W., Su A., Bioinformatics analysis of the circRNA–miRNA–mRNA network for non-small cell lung cancer, J. Int. Med. Res., 48(6) (2020) 1-15.
[8] Mao Y., Xue P., Li L., Xu P., Cai Y., Chu X., Jiang P., Zhu S., Bioinformatics analysis of mRNA and miRNA microarray to identify the key miRNA-gene pairs in small-cell lung cancer, Mol. Med. Rep., 20(3) (2019) 2199–2208.
[9] Bao M., Jiang G., Differential expression and functional analysis of lung cancer gene expression datasets: A systems biology perspective, Oncol. Lett., 18(1) (2019) 776–782.
[10] Sticht C., De La Torre C., Parveen A., Gretz N., Mirwalk: An online resource for prediction of microrna binding sites, PLoS One, 13 (10) (2018) 1–6.

[11] Wang K., Chen M., Wu W., Analysis of microRNA (miRNA) expression profiles reveals 11 key biomarkers associated with non-small cell lung cancer, World J. Surg. Oncol., 15(1) (2017) 1–10.
[12] Sun M.Z., Guan Z., Liu S., Zhou X., Wang N., Shao S., Lin D., Caveolin-1 interferes cell growth of lung cancer NCI-H446 cell through the interactions with phospho-ERK1/2, estrogen receptor and progestin receptor, Biomed. Pharmacother., 66(4) (2012) 242–248.
[13] Yeh D., Chen C., Sun M.Z., Shao S., Hao L., Song Y., Gong L., Hu J., Wang Q., Caveolin-1 is an important factor for the metastasis and proliferation of human small cell lung cancer NCI-H446 cell, Anat. Rec., 292(10) (2009) 1584–1592.
[14] Luanpitpong S., Talbott S.J., Rojanasakul Y., Nimmannit U., Pongrakhananon V., Wang L., Chanvorachote P., Regulation of lung cancer cell migration and invasion by reactive oxygen species and caveolin-1, J. Biol. Chem., 285(50) (2010) 38832–38840.
[15] Chen H.L., Fan L.F., Gao J., Ouyang J.P., Zhang Y.X., Differential expression and function of the caveolin-1 gene in non-small cell lung carcinoma, Oncol. Rep., 25(2) (2011) 359–366.
[16] McKeown D.J., Brown D.J.F., Kelly A., Wallace A.M., McMillan D.C., The relationship between circulating concentrations of C-reactive protein, inflammatory cytokines and cytokine receptors in patients with non-small-cell lung cancer, Br. J. Cancer, 91 (12) (2004) 1993–1995.
[17] Hsu Y.L., Hung J.Y., Lee Y.L., Chen F.W., Chang K.F., Chang W.A., Tsai Y.M., Chong I.W., Kuo P.L., Identification of novel gene expression signature in lung adenocarcinoma by using next-generation sequencing data and bioinformatics analysis, Oncotarget, 8(62) (2017) 104831–104854.
[18] Lu Y., Wang L., Liu P., Yang P., You M., Gene-expression signature predicts postoperative recurrence in stage I non-small cell lung cancer patients, PLoS One, 7(1) (2012) 1-9.
[19] Piao J., Sun J., Yang Y., Jin T., Chen L., Lin Z., Target gene screening and evaluation of prognostic values in non-small cell lung cancers by bioinformatics analysis, Gene, 647 (2018) 306–311.
[20] Li T., Wang X., Jing L., Li Y., MiR-1-3p inhibits lung adenocarcinoma cell tumorigenesis via targeting protein regulator of cytokinesis 1, Front. Oncol., 9(120) (2019) 1–11.
[21] Li S.M., Wu H.L., Yu X., Tang K., Wang S.G., Ye Z.Q., Hu J., The putative tumour suppressor miR-1-3p modulates prostate cancer cell aggressiveness by repressing E2F5 and PFTK1, J. Exp. Clin. Cancer Res., 37(1) (2018) 1–15.
[22] Du G., Yu X., Chen Y., Cai W., MiR-1-3p Suppresses Colorectal Cancer Cell Proliferation and Metastasis by Inhibiting YWHAZ-Mediated Epithelial–Mesenchymal Transition, Front. Oncol., 11 (2) (2021)1–8.
[23] Tian F., Han Y., Yan X., Zhong D., Yang G., Lei J., Li X., Wang X., Upregulation of microrna-451 increases the sensitivity of A549 cells to radiotherapy through enhancement of apoptosis, Thorac. Cancer, 7(2) (2016) 226–231.
[24] Kim H., Kim T., Jaygal G., Woo J., Kim C.J., Baek M.J., Jeong D., Downregulation of miR-9 correlates with poor prognosis in colorectal cancer, Pathol. Res. Pract., 216(8), (2020) 1-7.
[25] Liu X.L., Xiao K., Xue B., Yang D., Lei Z., Shan Y., Zhang H.T., Dual role of TGFBR3 in bladder cancer, Oncol. Rep., 30(3) (2013) 1301–1308.
[26] Wu M., Pang J.S., Sun Q., Huang Y., Hou J.Y., Chen G., Zeng J.J., Feng Z.B., The clinical significance of CHEK1 in breast cancer: a high-throughput data analysis and immunohistochemical study., Int. J. Clin. Exp. Pathol., 12 (1) (2019) 1–20.
[27] Oshita H., Nishino R., Takano A., Fujitomo T., Aragaki M.., Kato T, Akiyama H.., Tsuchiya E., Kohno N., Nakamura Y., Daigo Y., RASEF is a novel diagnostic biomarker and a therapeutic target for lung cancer, Mol. Cancer Res., 11(8) (2013) 937–951.

Details

Primary Language

English

Subjects

Structural Biology

Journal Section

Research Article

Authors

Esen Çakmak ^*
0000-0001-8805-3315
Türkiye

Publication Date

March 30, 2022

Submission Date

July 30, 2021

Acceptance Date

January 9, 2022

Published in Issue

Year 2022 Volume: 43 Number: 1

DOI

https://doi.org/10.17776/csj.976510

IZ

https://izlik.org/JA42ZF68CA

APA

Çakmak, E. (2022). A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer. Cumhuriyet Science Journal, 43(1), 6-13. https://doi.org/10.17776/csj.976510

AMA

1.Çakmak E. A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer. CSJ. 2022;43(1):6-13. doi:10.17776/csj.976510

Chicago

Çakmak, Esen. 2022. “A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer”. Cumhuriyet Science Journal 43 (1): 6-13. https://doi.org/10.17776/csj.976510.

EndNote

Çakmak E (March 1, 2022) A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer. Cumhuriyet Science Journal 43 1 6–13.

IEEE

[1]E. Çakmak, “A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer”, CSJ, vol. 43, no. 1, pp. 6–13, Mar. 2022, doi: 10.17776/csj.976510.

ISNAD

Çakmak, Esen. “A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer”. Cumhuriyet Science Journal 43/1 (March 1, 2022): 6-13. https://doi.org/10.17776/csj.976510.

JAMA

1.Çakmak E. A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer. CSJ. 2022;43:6–13.

MLA

Çakmak, Esen. “A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer”. Cumhuriyet Science Journal, vol. 43, no. 1, Mar. 2022, pp. 6-13, doi:10.17776/csj.976510.

Vancouver

1.Esen Çakmak. A Bioinformatics Approach to Identify Potential Biomarkers in Non-Small Cell Lung Cancer. CSJ. 2022 Mar. 1;43(1):6-13. doi:10.17776/csj.976510

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