Araştırma Makalesi

Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer

Cilt: 11 Sayı: 2 29 Kasım 2024
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Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer

Öz

Small-cell lung cancer (SCLC) is aggressive due to fast tumor development, early metastatic dissemination, and genetic instability. In this study, the RNA sequencing method was applied to the selected experimental data set for gene expression analysis in lung tissue samples of SCLC using Array Express functional genomic data. Array Express is a public repository for transcriptomic and related data that aims to store MIAME-compliant data in accordance with MGED recommendations. We wanted to look into the genomic sequence data (GSE60052) of 7 healthy controls and 75 SCLC patients through the GEO2R platform and the NCBI Gene Expression Omnibus (GEO) using the accession number E-GEOD-60052. The GSE60052 dataset of the genomic expression study was found on the GEO2R platform using the Illumina HiSeq 2000 RNA sequencing method in lung tissue samples from 75 SCLC patients and 7 controls. This was done to find out how the gene profile in SCLC were being expressed. In patients both in the SCLC and the control group, it was identified through the Volcano plot graph that HOXD10, FAM83A, HOXB1, ECEL1, GATA4, DMRT3, TGM3, CHP2, and PPP1R1A genes were down-regulated (log2(fold change) < -5), while PGC, SFTPC, SLC6A4, and CSF3 genes were up-regulated (log2 (fold change > +5). We share the view that SCLC is a type of neuroendocrine tumor with high malignancy and a poor prognosis, and identifying significant genes through expression profiling in lung tissue samples may be effective in elucidating the complex mechanisms underlying SCLC and determining their effect on the prognosis of the disease. The use of related genes as possible prognostic biomarkers in targeted therapy in SCLC could be enables the determination of the effects of the tumor microenvironment on immune cells and stromal cells.

Anahtar Kelimeler

Kaynakça

  1. Liang, J., Guan, X., Bao, G., Yao, Y., & Zhong, X. (2022). Molecular subtyping of small cell lung cancer. Semin Cancer Biol, 86(Pt 2), 450-462.
  2. Li, C., Lei, S., Ding, L., Xu, Y., Wu, X., Wang, H., Zhang, Z., Gao, T., Zhang, Y., Li, L. (2023). Global burden and trends of lung cancer incidence and mortality. Chin Med J, 136(13), 1583-1590.
  3. Thandra, K.C., Barsouk, A., Saginala, K., Aluru, J.S., Barsouk, A. (2021). Epidemiology of lung cancer. Contemp Oncol, 25(1), 45-52.
  4. Siegel, R.L., Miller, K.D., Wagle, N.S., Jemal, A. (2023). Cancer statistics, 2023. CA Cancer J Clin, 73(1), 17-48.
  5. Rudin, C. M., Brambilla, E., Faivre-Finn, C., & Sage, J. (2021). Small-cell lung cancer. Nat Rev Dis Primers, 7(1), 3.
  6. Abe, Y., Tanaka, N. (2016). The Hedgehog Signaling Networks in Lung Cancer: The Mechanisms and Roles in Tumor Progression and Implications for Cancer Therapy. Biomed Res Int, 2016(7969286), 1-11.
  7. van Meerbeeck, J. P., Fennell, D. A., & De Ruysscher, D. K. (2011). Small-cell lung cancer. Lancet, 378(9804), 1741-1755.
  8. Gupta, S., Kass, G.E.N., Szegezdi, E., Joseph, B. (2009). The mitochondrial death pathway: a promising therapeutic target in diseases. J Cell Mol Med, 13(6), 1004-33.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Veri Madenciliği ve Bilgi Keşfi, Veritabanı Sistemleri

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

29 Kasım 2024

Gönderilme Tarihi

16 Eylül 2023

Kabul Tarihi

17 Aralık 2023

Yayımlandığı Sayı

Yıl 2024 Cilt: 11 Sayı: 2

Kaynak Göster

APA
Öztan, G. (2024). Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, 11(2), 276-284. https://doi.org/10.35193/bseufbd.1361618
AMA
1.Öztan G. Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi. 2024;11(2):276-284. doi:10.35193/bseufbd.1361618
Chicago
Öztan, Gözde. 2024. “Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer”. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi 11 (2): 276-84. https://doi.org/10.35193/bseufbd.1361618.
EndNote
Öztan G (01 Kasım 2024) Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi 11 2 276–284.
IEEE
[1]G. Öztan, “Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer”, Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, c. 11, sy 2, ss. 276–284, Kas. 2024, doi: 10.35193/bseufbd.1361618.
ISNAD
Öztan, Gözde. “Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer”. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi 11/2 (01 Kasım 2024): 276-284. https://doi.org/10.35193/bseufbd.1361618.
JAMA
1.Öztan G. Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi. 2024;11:276–284.
MLA
Öztan, Gözde. “Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer”. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi, c. 11, sy 2, Kasım 2024, ss. 276-84, doi:10.35193/bseufbd.1361618.
Vancouver
1.Gözde Öztan. Gene Expression Profiling with Transcriptomic Data Analysis In Small Cell Lung Cancer. Bilecik Şeyh Edebali Üniversitesi Fen Bilimleri Dergisi. 01 Kasım 2024;11(2):276-84. doi:10.35193/bseufbd.1361618

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