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Cancer Cell International
Published in: Cancer Cell International 1/2022

Open Access 01-12-2022 | Biomarkers | Primary research

Switched alternative splicing events as attractive features in lung squamous cell carcinoma

Authors: Boxue He, Cong Wei, Qidong Cai, Pengfei Zhang, Shuai Shi, Xiong Peng, Zhenyu Zhao, Wei Yin, Guangxu Tu, Weilin Peng, Yongguang Tao, Xiang Wang

Published in: Cancer Cell International | Issue 1/2022

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Abstract

Background

Alternative splicing (AS) plays important roles in transcriptome and proteome diversity. Its dysregulation has a close affiliation with oncogenic processes. This study aimed to evaluate AS-based biomarkers by machine learning algorithms for lung squamous cell carcinoma (LUSC) patients.

Method

The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database were utilized. After data composition balancing, Boruta feature selection and Spearman correlation analysis were used for differentially expressed AS events. Random forests and a nested fivefold cross-validation were applied for lymph node metastasis (LNM) classifier building. Random survival forest combined with Cox regression model was performed for a prognostic model, based on which a nomogram was developed. Functional enrichment analysis and Spearman correlation analysis were also conducted to explore underlying mechanisms. The expression of some switch-involved AS events along with parent genes was verified by qRT-PCR with 20 pairs of normal and LUSC tissues.

Results

We found 16 pairs of splicing events from same parent genes which were strongly related to the splicing switch (intrapair correlation coefficient = − 1). Next, we built a reliable LNM classifier based on 13 AS events as well as a nice prognostic model, in which switched AS events behaved prominently. The qRT-PCR presented consistent results with previous bioinformatics analysis, and some AS events like ITIH5-10715-AT and QKI-78404-AT showed remarkable detection efficiency for LUSC.

Conclusion

AS events, especially switched ones from the same parent genes, could provide new insights into the molecular diagnosis and therapeutic drug design of LUSC.
Appendix
Available only for authorised users
Literature
1.
2.
Sung H, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–49.PubMedCrossRef
3.
Pan D, et al. Preferential localization of MUC1 glycoprotein in exosomes secreted by non-small cell lung carcinoma cells. Int J Mol Sci. 2019;20:2.CrossRef
4.
Mohrherr J, et al. STAT3: Versatile Functions in Non-Small Cell Lung Cancer. Cancers (Basel). 2020;12:5.CrossRef
5.
Miller KD, et al. Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin. 2019;69(5):363–85.CrossRefPubMed
6.
Khozin S, et al. Real-world progression, treatment, and survival outcomes during rapid adoption of immunotherapy for advanced non-small cell lung cancer. Cancer. 2019;125(22):4019–32.PubMedCrossRef
7.
8.
Stark R, Grzelak M, Hadfield J. RNA sequencing: the teenage years. Nat Rev Genet. 2019;20(11):631–56.CrossRefPubMed
9.
Pan Q, et al. Deep surveying of alternative splicing complexity in the human transcriptome by high-throughput sequencing. Nat Genet. 2008;40(12):1413–5.PubMedCrossRef
10.
11.
Sammeth M, Foissac S, Guigó R. A general definition and nomenclature for alternative splicing events. PLoS Comput Biol. 2008;4(8):e1000147–e1000147.PubMedPubMedCentralCrossRef
12.
Iftikhar M, et al. Current and emerging therapies for Duchenne muscular dystrophy and spinal muscular atrophy. Pharmacol Ther. 2021;220:107719.PubMedCrossRef
13.
Matlin AJ, Clark F, Smith CWJ. Understanding alternative splicing: towards a cellular code. Nat Rev Mol Cell Biol. 2005;6(5):386–98.PubMedCrossRef
14.
Monteuuis G, et al. The changing paradigm of intron retention: regulation, ramifications and recipes. Nucleic Acids Res. 2019;47(22):11497–513.PubMedPubMedCentral
15.
16.
Bonnal SC, López-Oreja I, Valcárcel J. Roles and mechanisms of alternative splicing in cancer - implications for care. Nat Rev Clin Oncol. 2020;17(8):457–74.PubMedCrossRef
17.
Bessa C, et al. Alternative splicing: expanding the landscape of cancer biomarkers and therapeutics. Int J Mol Sci. 2020;21:23.CrossRef
18.
Urbanski LM, Leclair N, Anczuków O. Alternative-splicing defects in cancer: Splicing regulators and their downstream targets, guiding the way to novel cancer therapeutics. Wiley Interdiscip Rev RNA. 2018;9(4):e1476.PubMedPubMedCentralCrossRef
19.
20.
Camacho DM, et al. Next-generation machine learning for biological networks. Cell. 2018;173(7):1581–92.PubMedCrossRef
21.
Ryan M, et al. TCGASpliceSeq a compendium of alternative mRNA splicing in cancer. Nucleic Acids Res. 2016;44(D1):D1018–22.PubMedCrossRef
22.
Cai Q, et al. Exploration of predictive and prognostic alternative splicing signatures in lung adenocarcinoma using machine learning methods. J Transl Med. 2020;18(1):463.PubMedPubMedCentralCrossRef
23.
Ma B, et al. Identification of a sixteen-gene prognostic biomarker for lung adenocarcinoma using a machine learning method. J Cancer. 2020;11(5):1288–98.PubMedPubMedCentralCrossRef
24.
Kantidakis G, et al. Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques. BMC Med Res Methodol. 2020;20(1):277.PubMedPubMedCentralCrossRef
25.
Bindea G, et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics (Oxford, England). 2009;25(8):1091–3.
26.
Wu HY, et al. Prognostic index of aberrant mRNA splicing profiling acts as a predictive indicator for hepatocellular carcinoma based on TCGA SpliceSeq data. Int J Oncol. 2019;55(2):425–38.PubMedPubMedCentral
27.
Freitas AA. Investigating the role of Simpson’s paradox in the analysis of top-ranked features in high-dimensional bioinformatics datasets. Brief Bioinform. 2020;21(2):421–8.PubMedCrossRef
28.
Liu S, et al. lncRNA SNHG11 promotes lung cancer cell proliferation and migration via activation of Wnt/β-catenin signaling pathway. J Cell Physiol. 2020;235(10):7541–53.PubMedCrossRef
29.
Roskoski R Jr. Targeting ERK1/2 protein-serine/threonine kinases in human cancers. Pharmacol Res. 2019;142:151–68.PubMedCrossRef
30.
31.
Torres-Ayuso, P, et al., TNIK Is a Therapeutic Target in Lung Squamous Cell Carcinoma and Regulates FAK Activation through Merlin. 2021.
32.
Pezzella FJT. Early squamous cell lung carcinoma: prognostic biomarkers for the many. 2019;74(6):527–8.
33.
Kadota K, et al. Comprehensive pathological analyses in lung squamous cell carcinoma: single cell invasion, nuclear diameter, and tumor budding are independent prognostic factors for worse outcomes. J Thorac Oncol. 2014;9(8):1126–39.PubMedPubMedCentralCrossRef
34.
Noro R, et al. A two-gene prognostic classifier for early-stage lung squamous cell carcinoma in multiple large-scale and geographically diverse cohorts. J Thorac Oncol. 2017;12(1):65–76.PubMedCrossRef
35.
Martínez-Terroba E, et al. 5 protein-based signature for resectable lung squamous cell carcinoma improves the prognostic performance of the TNM staging. Thorax. 2019;74(4):371–9.PubMedCrossRef
36.
37.
Li J, et al. Hsa_circ_0079530 promotes cell proliferation and invasion in non-small cell lung cancer. Gene. 2018;665:1–5.PubMedCrossRef
38.
39.
Ule J, Blencowe BJ. Alternative Splicing Regulatory Networks: Functions, Mechanisms, and Evolution. Mol Cell. 2019;76(2):329–45.PubMedCrossRef
40.
41.
42.
Ubels J, et al. RAINFOREST: a random forest approach to predict treatment benefit in data from (failed) clinical drug trials. Bioinformatics. 2020;36(Suppl_2):i601–9.PubMedCrossRef
43.
44.
Wang JZ et al. QKI-5 regulates the alternative splicing of cytoskeletal gene ADD3 in lung cancer. J Mol Cell Biol, 2020.
45.
46.
Pesson M, et al. A gene expression and pre-mRNA splicing signature that marks the adenoma-adenocarcinoma progression in colorectal cancer. PLoS ONE. 2014;9(2):e87761.PubMedPubMedCentralCrossRef
47.
Beghini A, et al. RNA hyperediting and alternative splicing of hematopoietic cell phosphatase (PTPN6) gene in acute myeloid leukemia. Hum Mol Genet. 2000;9(15):2297–304.PubMedCrossRef
48.
Zhao D, et al. Survival-associated alternative splicing signatures in non-small cell lung cancer. Aging (Albany NY). 2020;12(7):5878–93.CrossRef
49.
Lv Y, et al. SRSF1 inhibits autophagy through regulating Bcl-x splicing and interacting with PIK3C3 in lung cancer. Signal Transduct Target Ther. 2021;6(1):108.PubMedPubMedCentralCrossRef
50.
51.
Thomson SA, Wallace MR. RT-PCR splicing analysis of the NF1 open reading frame. Hum Genet. 2002;110(5):495–502.PubMedCrossRef
52.
Van Moerbeke M, Kasim A, Shkedy Z. The usage of exon-exon splice junctions for the detection of alternative splicing using the REIDS model. Sci Rep. 2018;8(1):8331.PubMedPubMedCentralCrossRef
53.
Oh, J., et al., Global Alternative Splicing Defects in Human Breast Cancer Cells. Cancers (Basel), 2021.
54.
Martin J, et al. Tumor Necrosis Factor-stimulated Gene 6 (TSG-6)-mediated Interactions with the Inter-α-inhibitor Heavy Chain 5 Facilitate Tumor Growth Factor β1 (TGFβ1)-dependent Fibroblast to Myofibroblast Differentiation. J Biol Chem. 2016;291(26):13789–801.PubMedPubMedCentralCrossRef
55.
56.
Rose M, et al. Epigenetic inactivation of ITIH5 promotes bladder cancer progression and predicts early relapse of pT1 high-grade urothelial tumours. Carcinogenesis. 2014;35(3):727–36.PubMedCrossRef
57.
Dötsch MM, et al. Low expression of ITIH5 in adenocarcinoma of the lung is associated with unfavorable patients’ outcome. Epigenetics. 2015;10(10):903–12.PubMedPubMedCentralCrossRef
58.
Veeck J, et al. The extracellular matrix protein ITIH5 is a novel prognostic marker in invasive node-negative breast cancer and its aberrant expression is caused by promoter hypermethylation. Oncogene. 2008;27(6):865–76.PubMedCrossRef
59.
Liu J, et al. ITIH5, a p53-responsive gene, inhibits the growth and metastasis of melanoma cells by downregulating the transcriptional activity of KLF4. Cell Death Dis. 2021;12(5):438.PubMedPubMedCentralCrossRef
60.
61.
Leclair NK, et al. Poison Exon Splicing Regulates a Coordinated Network of SR Protein Expression during Differentiation and Tumorigenesis. Mol Cell. 2020;80(4):648-665.e9.PubMedPubMedCentralCrossRef
62.
Dichmann DS, Walentek P, Harland RM. The alternative splicing regulator Tra2b is required for somitogenesis and regulates splicing of an inhibitory Wnt11b isoform. Cell Rep. 2015;10(4):527–36.PubMedPubMedCentralCrossRef
63.
64.
Liu J, et al. miR-335 inhibited cell proliferation of lung cancer cells by target Tra2β. Cancer Sci. 2018;109(2):289–96.PubMedCrossRef
65.
66.
Dong Y, Fasina OO, Pintel DJ. Minute Virus of Canines NP1 Protein Interacts with the Cellular Factor CPSF6 To Regulate Viral Alternative RNA Processing. J Virol. 2019;93:2.CrossRef
67.
Kasowitz SD, et al. Nuclear m6A reader YTHDC1 regulates alternative polyadenylation and splicing during mouse oocyte development. PLoS Genet. 2018;14(5):e1007412.PubMedPubMedCentralCrossRef
68.
van Roon AM, et al. 113Cd NMR experiments reveal an unusual metal cluster in the solution structure of the yeast splicing protein Bud31p. Angew Chem Int Ed Engl. 2015;54(16):4861–4.PubMedPubMedCentralCrossRef
69.
Koedoot E, et al. Uncovering the signaling landscape controlling breast cancer cell migration identifies novel metastasis driver genes. Nat Commun. 2019;10(1):2983.PubMedPubMedCentralCrossRef
70.
Li Y, et al. Prognostic alternative mRNA splicing signature in non-small cell lung cancer. Cancer Lett. 2017;393:40–51.PubMedCrossRef
Metadata
Title
Switched alternative splicing events as attractive features in lung squamous cell carcinoma
Authors
Boxue He
Cong Wei
Qidong Cai
Pengfei Zhang
Shuai Shi
Xiong Peng
Zhenyu Zhao
Wei Yin
Guangxu Tu
Weilin Peng
Yongguang Tao
Xiang Wang
Publication date
01-12-2022
Publisher
BioMed Central
Keyword
Biomarkers
Published in
Cancer Cell International / Issue 1/2022
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-021-02429-2

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