Research Article
BibTex RIS Cite

MiR-3605, miR-511 and miR-6788: Potential diagnostic and prognostic biomarkers for squamous cell carcinoma of the lung

Year 2025, Issue: 060, 1 - 9, 25.03.2025
https://doi.org/10.59313/jsr-a.1581877

Abstract

Squamous cell carcinoma of the lung (LUSC) is the second most common subtype of lung cancer and lung cancer is responsible for most cancer-related deaths. It can therefore be assumed that there is still a large gap in reducing a significant proportion of the global cancer burden. To close this gap, new methods are needed that provide better diagnostic and prognostic approaches for LUSC. Given the advantages of miRNA biomolecules as potential biomarkers, a systems biology approach was used in this study to define diagnostic and/or prognostic miRNA biomarker candidates for LUSC. Accordingly, the differentially expressed genes (DEGs) of LUSC were identified by processing RNA-Seq expression data. After analyzing the DEGs, a reporter feature algorithm was applied, which yielded reporter miRNAs that have significant potential as biomarker candidates. Using miRNA-Seq data from LUSC, the potential diagnostic and prognostic performance of reporter miRNAs was investigated. Using this approach, miR-3605 and miR-6788 were found to have diagnostic capabilities in LUSC, while miR-511, which was found in serum, had diagnostic and prognostic properties. Overall, this study offers precious data for further experimental and clinical efforts to diagnose and predict LUSC, and the presented diagnostic and/or prognostic miRNAs were associated with LUSC for the first time in this study.

References

  • [1] R. L. Siegel, K. D. Miller, N.S. Wagle, and A. Jemal, “Cancer statistics, 2023,” CA Cancer J. Clin., vol. 73, no. 1, pp. 17-48, Jan. 2023, doi:10.3322/caac.21763.
  • [2] A. Lahiri, et al., “Lung cancer immunotherapy: progress, pitfalls, and promises,” Mol. Cancer, vol. 22, no. 1, Feb. 2023, doi:10.1186/s12943-023-01740-y.
  • [3] BEST (Biomarkers, EndpointS, and other Tools) Resource. Maryland, 2016.
  • [4] C.E. Condrat, et al., “miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis”, Cells, vol. 9, no. 2, Jan. 2020, doi:10.3390/cells9020276.
  • [5] K. Tomczak, P. Czerwińska, and M. Wiznerowicz, “The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge”, Contemp. Oncol. (Pozn), vol. 19, no. 1A, pp. A68-A77, 2023, doi:10.5114/wo.2014.47136.
  • [6] M. I. Love, W. Huber, and S. Anders, “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2”, Genome Biol., vol. 15, no. 12, 2014, doi:10.1186/s13059-014-0550-8.
  • [7] W. Huber, et al., “Orchestrating high-throughput genomic analysis with Bioconductor,” Nat. Methods, vol. 12, no. 2, pp. 115-121, Feb. 2015, doi:10.1038/nmeth.3252.
  • [8] K. R. Patil, and J. Nielsen, “Uncovering transcriptional regulation of metabolism by using metabolic network topology”, Proc. Natl. Acad. Sci. U S A, vol. 102, pp. 2685-9, Feb. 2005, doi:10.1073/pnas.0406811102.
  • [9] M. Kori, and K.Y. Arga, “Potential biomarkers and therapeutic targets in cervical cancer: Insights from the meta-analysis of transcriptomics data within network biomedicine perspective”, PLoS One, vol.13, Jul. 2018, doi: 10.1371/journal.pone.0200717.
  • [10] M. Kori, and E, Gov, “Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer”, Genes, vol. 13 no. 12, Jan. 2022, doi: 10.3390/genes13122233.
  • [11] N. Kelesoglu, M. Kori, B. Turanli, K. Y. Arga, B. K. Yilmaz, and O. A. Duru, “Acute Myeloid Leukemia: New Multiomics Molecular Signatures and Implications for Systems Medicine Diagnostics and Therapeutics Innovation”, OMICS, vol. 26, no. 7, p.p. 392-403, Jul. 2022, doi: 10.1089/omi.2022.0051.
  • [12] M. Kori, D. Cig, K. Y. Arga, and C. Kasavi, “Multiomics Data Integration Identifies New Molecular Signatures for Abdominal Aortic Aneurysm and Aortic Occlusive Disease: Implications for Early Diagnosis, Prognosis, and Therapeutic Targets,” OMICS, vol. 26, no. 5, p.p. 290-304, May 2022, doi: 10.1089/omi.2022.0021.
  • [13] E. Gov, and K.Y. Arga, “Interactive cooperation and hierarchical operation of microRNA and transcription factor crosstalk in human transcriptional regulatory network”, IET Syst. Biol., vol. 10, p.p. 219–228, Dec. 2016, doi:10.1049/iet-syb.2016.0001.
  • [14] C. H. Chou, et al., “miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database”, Nucleic Acids Res., vol. 44, no. D1, p.p. D239-D247, Jan. 2016, doi:10.1093/nar/gkv1258.
  • [15] J. N. Mandrekar. “Receiver operating characteristic curve in diagnostic test assessment”, J. Thorac. Oncol., vol. 5, no. 9, p.p. 1315-1316, Sep. 2010, doi: 10.1097/JTO.0b013e3181ec173d.
  • [16] A. Kozomara, M. Birgaoanu, and S. Griffiths-Jones, “miRBase: from microRNA sequences to function”, Nucleic Acids Res., vol. 47, no. D1, p.p. D155–D162, Jan 2019, doi: 10.1093/nar/gky1141.
  • [17] M. Kori, E. Gov, K. Y. Arga, R. Sinha, “Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer”, Biomark. Insights, vol. 3, no. 19, Oct. 2024, doi:10.1177/11772719241287400.
  • [18] F. Russo, et al., “miRandola: extracellular circulating microRNAs database”, PLoS One, vol. 7, no. 10, 2012, doi:10.1371/journal.pone.0047786.
  • [19] R. Karaismailoglu, and S. Marakli, “miRNAs as biomarkers in human diseases”, IJSL., , vol. 4, no. 1, p.p. 190-201, Feb. 2022, doi:10.38058/ijsl.1050036.
  • [20] B. Smolarz, A. Durczyński, H. Romanowicz, K. Szyłło, and P. Hogendorf, “miRNAs in Cancer (Review of Literature)”, Int. J. Mol. Sci., vol. 23, no. 5, Mar. 2022, doi:10.3390/ijms23052805.
  • [21] J. O'Brien, H. Hayder, Y. Zayed, and C. Peng, “Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation”, Front. Endocrinol. (Lausanne), vol. 3, no. 9, Aug. 2018, doi:10.3389/fendo.2018.00402.
  • [22] G.A.D. Metcalf, “MicroRNAs: circulating biomarkers for the early detection of imperceptible cancers via biosensor and machine-learning advances,” Oncogene, vol. 43, pp. 2135–2142, Jun. 2024, doi.org/10.1038/s41388-024-03076-3.
  • [23] A. Chakrabortty, D. J. Patton, B. F. Smith, and P. Agarwal, “miRNAs: Potential as Biomarkers and Therapeutic Targets for Cancer,” Genes, vol.14, no.7, Jun. 2023, doi:10.3390/genes14071375.
  • [24] L. Tan, et al., “Circulating miRNAs as Potential Biomarkers for Celiac Disease Development”, Front. Immunol., vol. 12, no. 734763, Dec. 2021, doi:10.3389/fimmu.2021.734763.
  • [25] F. Li, et al., “Long non-coding RNA CNALPTC1 promotes gastric cancer progression by regulating the miR-6788-5p/PAK1 pathway”, J. Gastrointest Oncol., vol.13, no.6, p.p. 2809-2822, Dec 2022, doi: 10.21037/jgo-22-1069.
  • [26] C. Wang, H. Q. Fan, and Y.W. Zhang, “MiR-511-5p functions as a tumor suppressor and a predictive of prognosis in colorectal cancer by directly targeting GPR116”, Eur. Rev. Med. Pharmacol. Sci., vol. 23, no. 14, p.p. 6119-6130, Jul. 2019, doi: 10.26355/eurrev_201907_18425.
  • [27] C. Zhang, T. Yang, and H. Jiang, “miR-511 inhibits proliferation and metastasis of breast cancer cells by targeting FGF4”, J. Gene Med., vol. 22, no.9, Sep. 2020, doi: 10.1002/jgm.3168.
  • [28] W. Yong, K. Zhang, Y. Deng, W. Tang, and R. Tao, “miR-511-5p Suppresses Cell Migration, Invasion and Epithelial-Mesenchymal Transition Through Targeting PAK2 in Gastric Cancer”, Biochem. Genet., vol. 60, no.3, p.p. 899-913, Jun. 2022, doi:10.1007/s10528-021-10126-y.
  • [29] Z. Fang, et al., “Regulation of TRIM24 by miR-511 modulates cell proliferation in gastric cancer”, J. Exp. Clin. Cancer Res., vol. 36, no. 1, Jan. 2017, doi:10.1186/s13046-017-0489-1.
  • [30] H. H. Zhang, et al., “miR-511 induces the apoptosis of radioresistant lung adenocarcinoma cells by triggering BAX”, Oncol. Rep., vol. 31, no.3, p.p. 1473–1479, Mar. 2014, doi:10.3892/or.2014.2973.
Year 2025, Issue: 060, 1 - 9, 25.03.2025
https://doi.org/10.59313/jsr-a.1581877

Abstract

References

  • [1] R. L. Siegel, K. D. Miller, N.S. Wagle, and A. Jemal, “Cancer statistics, 2023,” CA Cancer J. Clin., vol. 73, no. 1, pp. 17-48, Jan. 2023, doi:10.3322/caac.21763.
  • [2] A. Lahiri, et al., “Lung cancer immunotherapy: progress, pitfalls, and promises,” Mol. Cancer, vol. 22, no. 1, Feb. 2023, doi:10.1186/s12943-023-01740-y.
  • [3] BEST (Biomarkers, EndpointS, and other Tools) Resource. Maryland, 2016.
  • [4] C.E. Condrat, et al., “miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis”, Cells, vol. 9, no. 2, Jan. 2020, doi:10.3390/cells9020276.
  • [5] K. Tomczak, P. Czerwińska, and M. Wiznerowicz, “The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge”, Contemp. Oncol. (Pozn), vol. 19, no. 1A, pp. A68-A77, 2023, doi:10.5114/wo.2014.47136.
  • [6] M. I. Love, W. Huber, and S. Anders, “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2”, Genome Biol., vol. 15, no. 12, 2014, doi:10.1186/s13059-014-0550-8.
  • [7] W. Huber, et al., “Orchestrating high-throughput genomic analysis with Bioconductor,” Nat. Methods, vol. 12, no. 2, pp. 115-121, Feb. 2015, doi:10.1038/nmeth.3252.
  • [8] K. R. Patil, and J. Nielsen, “Uncovering transcriptional regulation of metabolism by using metabolic network topology”, Proc. Natl. Acad. Sci. U S A, vol. 102, pp. 2685-9, Feb. 2005, doi:10.1073/pnas.0406811102.
  • [9] M. Kori, and K.Y. Arga, “Potential biomarkers and therapeutic targets in cervical cancer: Insights from the meta-analysis of transcriptomics data within network biomedicine perspective”, PLoS One, vol.13, Jul. 2018, doi: 10.1371/journal.pone.0200717.
  • [10] M. Kori, and E, Gov, “Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer”, Genes, vol. 13 no. 12, Jan. 2022, doi: 10.3390/genes13122233.
  • [11] N. Kelesoglu, M. Kori, B. Turanli, K. Y. Arga, B. K. Yilmaz, and O. A. Duru, “Acute Myeloid Leukemia: New Multiomics Molecular Signatures and Implications for Systems Medicine Diagnostics and Therapeutics Innovation”, OMICS, vol. 26, no. 7, p.p. 392-403, Jul. 2022, doi: 10.1089/omi.2022.0051.
  • [12] M. Kori, D. Cig, K. Y. Arga, and C. Kasavi, “Multiomics Data Integration Identifies New Molecular Signatures for Abdominal Aortic Aneurysm and Aortic Occlusive Disease: Implications for Early Diagnosis, Prognosis, and Therapeutic Targets,” OMICS, vol. 26, no. 5, p.p. 290-304, May 2022, doi: 10.1089/omi.2022.0021.
  • [13] E. Gov, and K.Y. Arga, “Interactive cooperation and hierarchical operation of microRNA and transcription factor crosstalk in human transcriptional regulatory network”, IET Syst. Biol., vol. 10, p.p. 219–228, Dec. 2016, doi:10.1049/iet-syb.2016.0001.
  • [14] C. H. Chou, et al., “miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database”, Nucleic Acids Res., vol. 44, no. D1, p.p. D239-D247, Jan. 2016, doi:10.1093/nar/gkv1258.
  • [15] J. N. Mandrekar. “Receiver operating characteristic curve in diagnostic test assessment”, J. Thorac. Oncol., vol. 5, no. 9, p.p. 1315-1316, Sep. 2010, doi: 10.1097/JTO.0b013e3181ec173d.
  • [16] A. Kozomara, M. Birgaoanu, and S. Griffiths-Jones, “miRBase: from microRNA sequences to function”, Nucleic Acids Res., vol. 47, no. D1, p.p. D155–D162, Jan 2019, doi: 10.1093/nar/gky1141.
  • [17] M. Kori, E. Gov, K. Y. Arga, R. Sinha, “Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer”, Biomark. Insights, vol. 3, no. 19, Oct. 2024, doi:10.1177/11772719241287400.
  • [18] F. Russo, et al., “miRandola: extracellular circulating microRNAs database”, PLoS One, vol. 7, no. 10, 2012, doi:10.1371/journal.pone.0047786.
  • [19] R. Karaismailoglu, and S. Marakli, “miRNAs as biomarkers in human diseases”, IJSL., , vol. 4, no. 1, p.p. 190-201, Feb. 2022, doi:10.38058/ijsl.1050036.
  • [20] B. Smolarz, A. Durczyński, H. Romanowicz, K. Szyłło, and P. Hogendorf, “miRNAs in Cancer (Review of Literature)”, Int. J. Mol. Sci., vol. 23, no. 5, Mar. 2022, doi:10.3390/ijms23052805.
  • [21] J. O'Brien, H. Hayder, Y. Zayed, and C. Peng, “Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation”, Front. Endocrinol. (Lausanne), vol. 3, no. 9, Aug. 2018, doi:10.3389/fendo.2018.00402.
  • [22] G.A.D. Metcalf, “MicroRNAs: circulating biomarkers for the early detection of imperceptible cancers via biosensor and machine-learning advances,” Oncogene, vol. 43, pp. 2135–2142, Jun. 2024, doi.org/10.1038/s41388-024-03076-3.
  • [23] A. Chakrabortty, D. J. Patton, B. F. Smith, and P. Agarwal, “miRNAs: Potential as Biomarkers and Therapeutic Targets for Cancer,” Genes, vol.14, no.7, Jun. 2023, doi:10.3390/genes14071375.
  • [24] L. Tan, et al., “Circulating miRNAs as Potential Biomarkers for Celiac Disease Development”, Front. Immunol., vol. 12, no. 734763, Dec. 2021, doi:10.3389/fimmu.2021.734763.
  • [25] F. Li, et al., “Long non-coding RNA CNALPTC1 promotes gastric cancer progression by regulating the miR-6788-5p/PAK1 pathway”, J. Gastrointest Oncol., vol.13, no.6, p.p. 2809-2822, Dec 2022, doi: 10.21037/jgo-22-1069.
  • [26] C. Wang, H. Q. Fan, and Y.W. Zhang, “MiR-511-5p functions as a tumor suppressor and a predictive of prognosis in colorectal cancer by directly targeting GPR116”, Eur. Rev. Med. Pharmacol. Sci., vol. 23, no. 14, p.p. 6119-6130, Jul. 2019, doi: 10.26355/eurrev_201907_18425.
  • [27] C. Zhang, T. Yang, and H. Jiang, “miR-511 inhibits proliferation and metastasis of breast cancer cells by targeting FGF4”, J. Gene Med., vol. 22, no.9, Sep. 2020, doi: 10.1002/jgm.3168.
  • [28] W. Yong, K. Zhang, Y. Deng, W. Tang, and R. Tao, “miR-511-5p Suppresses Cell Migration, Invasion and Epithelial-Mesenchymal Transition Through Targeting PAK2 in Gastric Cancer”, Biochem. Genet., vol. 60, no.3, p.p. 899-913, Jun. 2022, doi:10.1007/s10528-021-10126-y.
  • [29] Z. Fang, et al., “Regulation of TRIM24 by miR-511 modulates cell proliferation in gastric cancer”, J. Exp. Clin. Cancer Res., vol. 36, no. 1, Jan. 2017, doi:10.1186/s13046-017-0489-1.
  • [30] H. H. Zhang, et al., “miR-511 induces the apoptosis of radioresistant lung adenocarcinoma cells by triggering BAX”, Oncol. Rep., vol. 31, no.3, p.p. 1473–1479, Mar. 2014, doi:10.3892/or.2014.2973.
There are 30 citations in total.

Details

Primary Language English
Subjects Genomics and Transcriptomics
Journal Section Research Articles
Authors

Medi Kori 0000-0002-4589-930X

Publication Date March 25, 2025
Submission Date November 8, 2024
Acceptance Date January 23, 2025
Published in Issue Year 2025 Issue: 060

Cite

IEEE M. Kori, “MiR-3605, miR-511 and miR-6788: Potential diagnostic and prognostic biomarkers for squamous cell carcinoma of the lung”, JSR-A, no. 060, pp. 1–9, March 2025, doi: 10.59313/jsr-a.1581877.