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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Identification of Six Novel Prognostic Gene Signatures as Potential Biomarkers in Small Cell Lung Cancer

Author(s): Shicheng Feng, Xiuxiu Zhang, Xuyu Gu, Min Zhou, Yan Chen and Cailian Wang*

Volume 26, Issue 5, 2023

Published on: 20 December, 2022

Page: [938 - 949] Pages: 12

DOI: 10.2174/1386207325666220427121619

Price: $65

Abstract

Objective: As a subgroup of lung cancer, small cell lung cancer (SCLC) is characterized by a short tumor doubling time, high rates of early occurred distant cancer spread and poor outcomes. Our study aimed to identify novel molecular markers associated with SCLC prognosis.

Methods: Microarray data from the Gene Expression Omnibus (GEO) database of SCLC tumors and paired normal tissues were obtained. In the dataset, Differentially expressed genes (DEGs) which were identified by comparing gene expression between normal lung and SCLC samples, were screened using the R language. The STRING database was used to map protein-protein interaction (PPI) networks, and these were visualized with the Cytoscape software. Go enrichment analysis and prediction were performed using the Metascape database and the results were visualized. Autophagy-related prognostic genes were identified by univariate COX regression analysis. Subsequently, stepwise model selection using the Akaike information criterion (AIC) and multivariate COX regression model was performed to construct DEGs signature. Survival receiver operating characteristic (ROC) analysis was used to assess the performance of survival prediction. At last, we evaluated the differences in drug sensitivity of the two groups of patients to common chemotherapeutic drugs and small-molecule targeted drugs.

Results: A total of 441 identified DE genes, including 412 downregulated and 29 upregulated genes were identified. GO enrichment analyses showed that DEGs were significantly enriched in the collagen-containing extracellular matrix and extracellular matrix organization. 16 genes were individually associated with OS in univariate analyses. The high expression of 6 genes (HIST1H4L, RP11-16O9.2, SNORA71A, SELV, FAM66A and BRWD1-AS1)) was associated with the poor prognosis of SCLC patients. To predict patients’ outcomes, we developed an individual’s risk score model based on the 6 genes. We found that SCLC patients with a low-risk score had significantly better survival than those with a high-risk score. What’s more, association analysis between clinicopathological factors and gene signature showed the risk score was higher in patients with higher clinical stage or T stage. What’s more, the patients in the high-risk score group had better treatment effects for etoposide and docetaxel. This suggests that our model can guide clinical treatment decisions.

Conclusion: A novel six-gene signature was determined for prognostic prediction in SCLC. Our findings may provide new insights into the precise treatment and prognosis prediction of SCLC.

Keywords: SCLC, gene signature, biomarker, risk score, prognostic model, metastatic tumor.

« Previous Next »
[1]
Yang, S.; Zhang, Z.; Wang, Q. Emerging therapies for small cell lung cancer. J. Hematol. Oncol., 2019, 12(1), 47.
[http://dx.doi.org/10.1186/s13045-019-0736-3] [PMID: 31046803]
[2]
Waqar, S.N.; Morgensztern, D. Treatment advances in small cell lung cancer (SCLC). Pharmacol. Ther., 2017, 180, 16-23.
[http://dx.doi.org/10.1016/j.pharmthera.2017.06.002] [PMID: 28579387]
[3]
Uddin, A.; Chakraborty, S. Role of miRNAs in lung cancer. J. Cell. Physiol., 2018.
[http://dx.doi.org/10.1002/jcp.26607] [PMID: 29676470]
[4]
Du, L.; Pertsemlidis, A. microRNA regulation of cell viability and drug sensitivity in lung cancer. Expert Opin. Biol. Ther., 2012, 12(9), 1221-1239.
[http://dx.doi.org/10.1517/14712598.2012.697149] [PMID: 22731874]
[5]
Barrett, T.; Wilhite, S.E.; Ledoux, P.; Evangelista, C.; Kim, I.F.; Tomashevsky, M.; Marshall, K.A.; Phillippy, K.H.; Sherman, P.M.; Holko, M.; Yefanov, A.; Lee, H.; Zhang, N.; Robertson, C.L.; Serova, N.; Davis, S.; Soboleva, A. NCBI GEO: Archive for functional genomics data sets--update. Nucleic Acids Res., 2013, 41(Database issue), D991-D995.
[PMID: 23193258]
[6]
Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P.; Jensen, L.J.; von Mering, C. The STRING database in 2021: Customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res., 2021, 49(D1), D605-D612.
[http://dx.doi.org/10.1093/nar/gkaa1074] [PMID: 33237311]
[7]
Zhou, Y.; Zhou, B.; Pache, L.; Chang, M.; Khodabakhshi, A.H.; Tanaseichuk, O.; Benner, C.; Chanda, S.K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun., 2019, 10(1), 1523.
[http://dx.doi.org/10.1038/s41467-019-09234-6] [PMID: 30944313]
[8]
Gene Ontology, C. Gene Ontology Consortium. The Gene Ontology resource: Enriching a GOld mine. Nucleic Acids Res., 2021, 49(D1), D325-D334.
[http://dx.doi.org/10.1093/nar/gkaa1113] [PMID: 33290552]
[9]
Kanehisa, M.; Furumichi, M.; Sato, Y.; Ishiguro-Watanabe, M.; Tanabe, M. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res., 2021, 49(D1), D545-D551.
[http://dx.doi.org/10.1093/nar/gkaa970] [PMID: 33125081]
[10]
Gazdar, A.F.; Bunn, P.A.; Minna, J.D. Small-cell lung cancer: What we know, what we need to know and the path forward. Nat. Rev. Cancer, 2017, 17(12), 725-737.
[http://dx.doi.org/10.1038/nrc.2017.87] [PMID: 29077690]
[11]
Liao, Y.; Yin, G.; Wang, X.; Zhong, P.; Fan, X.; Huang, C. Identification of candidate genes associated with the pathogenesis of small cell lung cancer via integrated bioinformatics analysis. Oncol. Lett., 2019, 18(4), 3723-3733.
[http://dx.doi.org/10.3892/ol.2019.10685] [PMID: 31516585]
[12]
Paesmans, M.; Lafitte, J-J.; Lecomte, J-P.; Berghmans, T.; Efremidis, A.; Meert, A-P.; Leclercq, N.; Sculier, J-P.J.J.o.T.O. Validation and comparison of published prognostic classification for survival in patients (pts) with small cell lung cancer. Eur. Respir. J., 2010, 5, 265.
[13]
Kimura, K.; Wakamatsu, A.; Suzuki, Y.; Ota, T.; Nishikawa, T.; Yamashita, R.; Yamamoto, J.; Sekine, M.; Tsuritani, K.; Wakaguri, H.; Ishii, S.; Sugiyama, T.; Saito, K.; Isono, Y.; Irie, R.; Kushida, N.; Yoneyama, T.; Otsuka, R.; Kanda, K.; Yokoi, T.; Kondo, H.; Wagatsuma, M.; Murakawa, K.; Ishida, S.; Ishibashi, T.; Takahashi-Fujii, A.; Tanase, T.; Nagai, K.; Kikuchi, H.; Nakai, K.; Isogai, T.; Sugano, S. Diversification of transcriptional modulation: Large-scale identification and characterization of putative alternative promoters of human genes. Genome Res., 2006, 16(1), 55-65.
[http://dx.doi.org/10.1101/gr.4039406] [PMID: 16344560]
[14]
Luck, K.; Kim, D.K.; Lambourne, L.; Spirohn, K.; Begg, B.E.; Bian, W.; Brignall, R.; Cafarelli, T.; Campos-Laborie, F.J.; Charloteaux, B.; Choi, D.; Coté, A.G.; Daley, M.; Deimling, S.; Desbuleux, A.; Dricot, A.; Gebbia, M.; Hardy, M.F.; Kishore, N.; Knapp, J.J.; Kovács, I.A.; Lemmens, I.; Mee, M.W.; Mellor, J.C.; Pollis, C.; Pons, C.; Richardson, A.D.; Schlabach, S.; Teeking, B.; Yadav, A.; Babor, M.; Balcha, D.; Basha, O.; Bowman-Colin, C.; Chin, S.F.; Choi, S.G.; Colabella, C.; Coppin, G.; D’Amata, C.; De Ridder, D.; De Rouck, S.; Duran-Frigola, M.; Ennajdaoui, H.; Goebels, F.; Goehring, L.; Gopal, A.; Haddad, G.; Hatchi, E.; Helmy, M.; Jacob, Y.; Kassa, Y.; Landini, S.; Li, R.; van Lieshout, N.; MacWilliams, A.; Markey, D.; Paulson, J.N.; Rangarajan, S.; Rasla, J.; Rayhan, A.; Rolland, T.; San-Miguel, A.; Shen, Y.; Sheykhkarimli, D.; Sheynkman, G.M.; Simonovsky, E. Taşan, M.; Tejeda, A.; Tropepe, V.; Twizere, J.C.; Wang, Y.; Weatheritt, R.J.; Weile, J.; Xia, Y.; Yang, X.; Yeger-Lotem, E.; Zhong, Q.; Aloy, P.; Bader, G.D.; De Las Rivas, J.; Gaudet, S.; Hao, T.; Rak, J.; Tavernier, J.; Hill, D.E.; Vidal, M.; Roth, F.P.; Calderwood, M.A. A reference map of the human binary protein interactome. Nature, 2020, 580(7803), 402-408.
[http://dx.doi.org/10.1038/s41586-020-2188-x] [PMID: 32296183]
[15]
Varlamova, E.G.; Goltyaev, M.V.; Fesenko, E.E. Expression of human selenoprotein genes selh, selk, selm, sels, selv, and gpx-6 in various tumor cell lines. Dokl. Biochem. Biophys., 2016, 468(1), 203-205.
[http://dx.doi.org/10.1134/S1607672916030121] [PMID: 27417721]
[16]
Varlamova, E.G.; Goltyaev, M.V.; Kuznetsova, J.P. Effect of sodium selenite on gene expression of SELF, SELW, and TGR selenoproteins in adenocarcinoma cells of the human prostate. Mol. Biol. (Mosk.), 2018, 52(3), 519-526.
[PMID: 29989584]
[17]
Wang, Y.; Li, X.; Yao, Y.; Zhao, X.; Shi, X.; Cai, Y. Selenium deficiency induces apoptosis and necroptosis through ROS/MAPK signal in human uterine smooth muscle cells. Biol. Trace Elem. Res., 2021.
[http://dx.doi.org/10.1007/s12011-021-02910-z] [PMID: 34480665]
[18]
Dieci, G.; Preti, M.; Montanini, B. Eukaryotic snoRNAs: A paradigm for gene expression flexibility. Genomics, 2009, 94(2), 83-88.
[http://dx.doi.org/10.1016/j.ygeno.2009.05.002] [PMID: 19446021]
[19]
Esteller, M. Non-coding RNAs in human disease. Nat. Rev. Genet., 2011, 12(12), 861-874.
[http://dx.doi.org/10.1038/nrg3074] [PMID: 22094949]
[20]
Zhang, Z.; Tao, Y.; Hua, Q.; Cai, J.; Ye, X.; Li, H. SNORA71A Promotes colorectal cancer cell proliferation, migration, and invasion. BioMed Res. Int., 2020, 2020, 8284576.
[http://dx.doi.org/10.1155/2020/8284576] [PMID: 33083486]
[21]
Mourksi, N.E.; Morin, C.; Fenouil, T.; Diaz, J.J.; Marcel, V. snoRNAs offer novel insight and promising perspectives for lung cancer understanding and management. Cells, 2020, 9(3), 541.
[http://dx.doi.org/10.3390/cells9030541] [PMID: 32111002]
[22]
Tang, G.; Zeng, Z.; Sun, W.; Li, S.; You, C.; Tang, F.; Peng, S.; Ma, S.; Luo, Y.; Xu, J.; Tian, X.; Zhang, N.; Gong, Y.; Xie, C. Small nucleolar RNA 71A promotes lung cancer cell proliferation, migration and invasion via MAPK/ERK pathway. J. Cancer, 2019, 10(10), 2261-2275.
[http://dx.doi.org/10.7150/jca.31077] [PMID: 31258730]
[23]
Lin, J.J.; Shaw, A.T. Resisting resistance: Targeted therapies in lung cancer. Trends Cancer, 2016, 2(7), 350-364.
[http://dx.doi.org/10.1016/j.trecan.2016.05.010] [PMID: 27819059]
[24]
Maxwell, E.S.; Fournier, M.J. The small nucleolar RNAs. Annu. Rev. Biochem., 1995, 64, 897-934.
[http://dx.doi.org/10.1146/annurev.bi.64.070195.004341] [PMID: 7574504]
[25]
Camões, M.J.; Paulo, P.; Ribeiro, F.R.; Barros-Silva, J.D.; Almeida, M.; Costa, V.L.; Cerveira, N.; Skotheim, R.I.; Lothe, R.A.; Henrique, R.; Jerónimo, C.; Teixeira, M.R. Potential downstream target genes of aberrant ETS transcription factors are differentially affected in Ewing’s sarcoma and prostate carcinoma. PLoS One, 2012, 7(11), e49819.
[http://dx.doi.org/10.1371/journal.pone.0049819] [PMID: 23185447]
[26]
Wu, Y.; Cao, H.; Baranova, A.; Huang, H.; Li, S.; Cai, L.; Rao, S.; Dai, M.; Xie, M.; Dou, Y.; Hao, Q.; Zhu, L.; Zhang, X.; Yao, Y.; Zhang, F.; Xu, M.; Wang, Q. Multi-trait analysis for genome-wide association study of five psychiatric disorders. Transl. Psychiatry, 2020, 10(1), 209.
[http://dx.doi.org/10.1038/s41398-020-00902-6] [PMID: 32606422]
[27]
Haidar, M.N.; Islam, M.B.; Chowdhury, U.N.; Rahman, M.R.; Huq, F.; Quinn, J.M.W.; Moni, M.A. Network-based computational approach to identify genetic links between cardiomyopathy and its risk factors. IET Syst. Biol., 2020, 14(2), 75-84.
[http://dx.doi.org/10.1049/iet-syb.2019.0074] [PMID: 32196466]
[28]
Taniwaki, M.; Daigo, Y.; Ishikawa, N.; Takano, A.; Tsunoda, T.; Yasui, W.; Inai, K.; Kohno, N.; Nakamura, Y. Gene expression profiles of small-cell lung cancers: Molecular signatures of lung cancer. Int. J. Oncol., 2006, 29(3), 567-575.
[http://dx.doi.org/10.3892/ijo.29.3.567] [PMID: 16865272]

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