Küçük Hücreli Akciğer Kanserinde Transkriptomik Veri Analizi İle Gen Ekspresyon Profili

Abstract

Küçük hücreli akciğer kanseri (SCLC), hızlı tümör gelişimi, erken metastatik yayılım ve genetik dengesizlik nedeniyle agresiftir. Array Express, MIAME uyumlu verileri MGED önerilerine uygun olarak depolamayı amaçlayan, transkriptomik ve ilgili veriler için halka açık bir depodur. Bu çalışmada, Array Express fonksiyonel genomik datası kullanarak, SCLC’nin akciğer dokusu örneklerinde gen ekspresyon analizine yönelik seçilen deneysel veri setinde RNA dizileme yöntemi uygulanmıştır. E-GEOD-60052 erişim numarası üzerinden NCBI Gene Ekspresyon Omnibus (GEO) kullanarak 7 sağlıklı kontrol ve 75 SCLC’li hastaların genomik dizi verilerini (GSE60052) GEO2R platformu aracılığıyla araştırmayı amaçladık. SCLC’de genlerin ekspresyon düzeylerinin belirlenmesi için 75 SCLC hastasından ve 7 kontrolden alınan akciğer dokusu örneklerinde Illumina HiSeq 2000 RNA dizileme yöntemi kullanılarak genomik ekspresyon çalışmasının GSE60052 veri seti GEO2R platformu üzerinden tespit edildi. SCLC’li hastalar ve kontrol grubunda, HOXD10, FAM83A, HOXB1, ECEL1, GATA4, DMRT3, TGM3, CHP2, PPP1R1A genlerinin aşağı regüle olduğu (log2(fold change) < -5), PGC, SFTPC, SLC6A4, CSF3 genlerinin (log2 (fold change > +5) ise yukarı regüle olduğu Volcano plot grafiği aracılığıyla tanımlandı. Yüksek maligniteye ve kötü prognoza sahip bir tür nöroendokrin tümör olan SCLC’nin, akciğer dokusu örneklerinde ekspresyon profillemesiyle anlamlı bulunan genlerin tanımlanmasının SCLC’nin altında yatan karmaşık mekanizmalarının aydınlatılmasında ve hastalığın prognozuna olan etkisinin belirlenmesinde etkili olabileceği görüşünü paylaşmaktayız. İlgili genlerin SCLC'de hedefe yönelik tedavide olası prognostik biyobelirteçler olarak kullanılması, tümör mikro ortamının bağışıklık hücreleri ve stromal hücreler üzerindeki etkilerinin belirlenmesini sağlar.

Keywords

• Küçük Hücreli Akciğer Kanseri
• Transkripsiyon Profil Array
• Gen Ekspresyonu

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

Abstract

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.

Keywords

• Biomarker
• Gene Expression
• RNA Sequencing
• Small Cell Lung Cancer
• Transcription Profile Array

References

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.

9. Li, Q., Wang, R., Yang, Z., et al. (2022). Molecular profiling of human non-small cell lung cancer by single-cell RNA-seq. Genome Med, 14(1), 87.
10. Tian, Y., Li, Q., Yang, Z., et al. (2022). Single-cell transcriptomic profiling reveals the tumor heterogeneity of small-cell lung cancer. Signal Transduct Target Ther, 7(1), 346.
11. Meijer, J. J., Leonetti, A., Airo, G., et al. (2022). Small cell lung cancer: Novel treatments beyond immunotherapy. Semin Cancer Biol, 86(Pt 2), 376-385.
12. Wang, Y., Zou, S., Zhao, Z., Liu, P., Ke, C., & Xu, S. (2020). New insights into small-cell lung cancer development and therapy. Cell Biol Int, 44(8), 1564-1576.
13. Agapito, G., Milano, M., Cannataro, M. (2022). A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies. BMC Bioinformatics, 23(6):393.
14. Hayashi, R., & Inomata, M. (2022). Small cell lung cancer; recent advances of its biology and therapeutic perspective. Respir Investig, 60(2), 197-204.
15. Yuan, M., Zhao, Y., Arkenau, H. T., Lao, T., Chu, L., & Xu, Q. (2022). Signal pathways and precision therapy of small-cell lung cancer. Signal Transduct Target Ther, 7(1), 187.
16. Li, S., Zhang, J., Zhao, Y., Wang, F., Chen, Y., & Fei, X. (2018). miR-224 enhances invasion and metastasis by targeting HOXD10 in non-small cell lung cancer cells. Oncol Lett, 15(5), 7069-7075.
17. Liu, H., Li, T., Ye, X., & Lyu, J. (2021). Identification of Key Biomarkers and Pathways in Small-Cell Lung Cancer Using Biological Analysis. Biomed Res Int, 2021, 5953386.
18. Yu, J., Hou, M., & Pei, T. (2020). FAM83A Is a Prognosis Signature and Potential Oncogene of Lung Adenocarcinoma. DNA Cell Biol, 39(5), 890-899.
19. Bai, S., Zhao, H., Zeng, X., et al. (2021). FAM83A-AS1 Promotes Tumor Progression Through MET Signaling in Lung Adenocarcinoma. Research Square. 1-19
20. Cui, F., Zhou, Q., Xiao, K., & Ma, S. (2020). The MicroRNA hsa-let-7g Promotes Proliferation and Inhibits Apoptosis in Lung Cancer by Targeting HOXB1. Yonsei Med J, 61(3), 210-217.
21. Gao, L., Hu, Y., Tian, Y., et al. (2019). Lung cancer deficient in the tumor suppressor GATA4 is sensitive to TGFBR1 inhibition. Nat Commun, 10(1), 1665.
22. Yang, D., Liu, M., Jiang, J., et al. (2022). Comprehensive Analysis of DMRT3 as a Potential Biomarker Associated with the Immune Infiltration in a Pan-Cancer Analysis and Validation in Lung Adenocarcinoma. Cancers (Basel), 14(24).
23. Zhang, S., Li, M., Ji, H., & Fang, Z. (2018). Landscape of transcriptional deregulation in lung cancer. BMC Genomics, 19(1), 435.
24. Zhang, W., Wu, C., Zhou, K., et al. (2022). Clinical and immunological characteristics of TGM3 in pan-cancer: A potential prognostic biomarker. Front Genet, 13, 993438.
25. Xu, L., Qin, Y., Sun, B., et al. (2020). Involvement of CHP2 in the Development of Non-Small Cell Lung Cancer and Patients' Poor Prognosis. Appl Immunohistochem Mol Morphol, 28(9), 678-686.
26. Takakura, S., Kohno, T., Manda, R., Okamoto, A., Tanaka, T., & Yokota, J. (2001). Genetic alterations and expression of the protein phosphatase 1 genes in human cancers. Int J Oncol, 18(4), 817-824.
27. Xia, D., Chen, Z., & Liu, Q. (2021). Circ-PGC increases the expression of FOXR2 by targeting miR-532-3p to promote the development of non-small cell lung cancer. Cell Cycle, 20(21), 2195-2209.
28. Li, B., Meng, Y. Q., Li, Z., et al. (2019). MiR-629-3p-induced downregulation of SFTPC promotes cell proliferation and predicts poor survival in lung adenocarcinoma. Artif Cells Nanomed Biotechnol, 47(1), 3286-3296.
29. Pappula, A. L., Gibson, L. N., Bouley, R. A., & Petreaca, R. C. (2022). In silico analysis of a SLC6A4 G100V mutation in lung cancers. MicroPubl Biol, 2022.
30. Huang, X., Hu, P., & Zhang, J. (2020). Genomic analysis of the prognostic value of colony-stimulating factors (CSFs) and colony-stimulating factor receptors (CSFRs) across 24 solid cancer types. Ann Transl Med, 8(16), 994.