A systems biology‐based approach to screen key splicing factors in hepatocellular carcinoma

A splicing factor is as an important upstream regulator of the alternative splicing process. Hence, it is considered to be a therapeutic target for hepatocellular carcinoma (HCC) tissues. In this study, a systems biology‐based methodology was used to screen the essential splicing factors precisely and efficiently. A more comprehensive set of alternative splicing events, which were linked to patient survival, was constructed by performing the bivariate Cox regression and receiver operating characteristic (ROC) analyses. Then, the expression data was obtained from The Cancer Genome Altas (TCGA) data set and the three Gene Expression Omnibus (GEO) datasets. It was used to obtain the survival‐related splicing factors, which showed a significantly differential expression in the tumor and normal tissues. Using the topological properties of the bipartite graph association network of the alternative splicing events and the splicing factors, we identified the five key splicing factors. Among them, four factors were found to play a prominent role in the development of HCC. The remaining factor was Survival Motor Neuron Domain Containing 1(SMNDC1), which showed a positive correlation with the immune cell infiltration, the biomarkers of immune cells, and the immune checkpoint genes. By performing quantitative real‐time polymerase chain reaction analyses, we proved that SMNDC1 was overexpressed in tumor cells. Following the knockdown of its expression, the proliferation and the migration of HCC cells could be suppressed. These results confirmed that the screening method of this study was reliable and accurate. It provided new insights into the mechanism through which splicing factors elicit tumor development.

[1]  P. Aguiar,et al.  Predictive genetic biomarkers in immune checkpoint inhibitors for non-small-cell lung cancer. , 2022, Immunotherapy.

[2]  Lizhi Lv,et al.  The diagnostic and prognostic significance of small nuclear ribonucleoprotein Sm D1 aberrantly high expression in hepatocellular carcinoma , 2022, Journal of Cancer.

[3]  Gongye Zhang,et al.  Deubiquitinase USP39 and E3 ligase TRIM26 balance the level of ZEB1 ubiquitination and thereby determine the progression of hepatocellular carcinoma , 2021, Cell Death & Differentiation.

[4]  X. Wang,et al.  Intratumoral γδ T‐Cell Infiltrates, Chemokine (C‐C Motif) Ligand 4/Chemokine (C‐C Motif) Ligand 5 Protein Expression and Survival in Patients With Hepatocellular Carcinoma , 2020, Hepatology.

[5]  Min Liu,et al.  Identification of DNA repair-related genes predicting pathogenesis and prognosis for liver cancer , 2020, Cancer Cell International.

[6]  Xiaole Shirley Liu,et al.  TIMER2.0 for analysis of tumor-infiltrating immune cells , 2020, Nucleic Acids Res..

[7]  P. Cui,et al.  Significance of Tumor-Infiltrating Immune Cells in the Prognosis of Colon Cancer , 2020, OncoTargets and therapy.

[8]  Qifeng Chen,et al.  Profiles of prognostic alternative splicing signature in hepatocellular carcinoma , 2020, Cancer medicine.

[9]  Xiao Li,et al.  Systematic profiling of alternative splicing signature reveals prognostic predictor for cervical cancer , 2019, Journal of Translational Medicine.

[10]  Liming Cheng,et al.  Identification of Prognostic and Metastatic Alternative Splicing Signatures in Kidney Renal Clear Cell Carcinoma , 2019, Front. Bioeng. Biotechnol..

[11]  Bo Hu,et al.  Far Upstream Element-Binding Protein 1 Facilitates Hepatocellular Carcinoma Invasion and Metastasis. , 2019, Carcinogenesis.

[12]  Dong Zhang,et al.  Systematic profiling of a novel prognostic alternative splicing signature in hepatocellular carcinoma , 2019, Oncology reports.

[13]  S. Pan,et al.  Prognostic index of aberrant mRNA splicing profiling acts as a predictive indicator for hepatocellular carcinoma based on TCGA SpliceSeq data , 2019, International journal of oncology.

[14]  Tianxin Lin,et al.  Polypyrimidine tract binding protein 1 promotes lymphatic metastasis and proliferation of bladder cancer via alternative splicing of MEIS2 and PKM. , 2019, Cancer letters.

[15]  H. Gautrey,et al.  Regulation of Mcl-1 alternative splicing by hnRNP F, H1 and K in breast cancer cells , 2018, RNA biology.

[16]  Hassan Badir,et al.  Identification of influential spreaders in complex networks using HybridRank algorithm , 2018, Scientific Reports.

[17]  S. Arii,et al.  Fatty Acid Binding Protein 4 (FABP4) Overexpression in Intratumoral Hepatic Stellate Cells within Hepatocellular Carcinoma with Metabolic Risk Factors. , 2018, The American journal of pathology.

[18]  Ping Zhu,et al.  Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types. , 2018, Cell reports.

[19]  Zuhua Chen,et al.  Systematic profiling of alternative splicing signature reveals prognostic predictor for ovarian cancer. , 2017, Gynecologic oncology.

[20]  Junjun Li,et al.  Snail Driving Alternative Splicing of CD44 by ESRP1 Enhances Invasion and Migration in Epithelial Ovarian Cancer , 2017, Cellular Physiology and Biochemistry.

[21]  Adam Godzik,et al.  The Functional Impact of Alternative Splicing in Cancer. , 2017, Cell reports.

[22]  Shuhan Sun,et al.  The MBNL3 splicing factor promotes hepatocellular carcinoma by increasing PXN expression through the alternative splicing of lncRNA-PXN-AS1 , 2017, Nature Cell Biology.

[23]  N. Gogtay,et al.  Biostatistics Series Module 9: Survival Analysis , 2017, Indian journal of dermatology.

[24]  N. Wei,et al.  SRSF2 Regulates Alternative Splicing to Drive Hepatocellular Carcinoma Development. , 2017, Cancer research.

[25]  Zefeng Wang,et al.  SPSB1-mediated HnRNP A1 ubiquitylation regulates alternative splicing and cell migration in EGF signaling , 2017, Cell Research.

[26]  Robert Brown,et al.  TCGASpliceSeq a compendium of alternative mRNA splicing in cancer , 2015, Nucleic Acids Res..

[27]  Kuen-Feng Chen,et al.  Treatment of Liver Cancer. , 2015, Cold Spring Harbor perspectives in medicine.

[28]  Yu Xue,et al.  Phosphoproteomic Analysis of the Highly-Metastatic Hepatocellular Carcinoma Cell Line, MHCC97-H , 2015, International journal of molecular sciences.

[29]  K. Tomczak,et al.  The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge , 2015, Contemporary oncology.

[30]  X. Wang,et al.  Genomic Predictors for Recurrence Patterns of Hepatocellular Carcinoma: Model Derivation and Validation , 2014, PLoS medicine.

[31]  Luciano da Fontoura Costa,et al.  The role of centrality for the identification of influential spreaders in complex networks , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[32]  K. Adams,et al.  Features of evolutionarily conserved alternative splicing events between Brassica and Arabidopsis. , 2013, The New phytologist.

[33]  Anthony J Bishara,et al.  Testing the significance of a correlation with nonnormal data: comparison of Pearson, Spearman, transformation, and resampling approaches. , 2012, Psychological methods.

[34]  F. Piva,et al.  SpliceAid 2: A database of human splicing factors expression data and RNA target motifs , 2012, Human mutation.

[35]  Fei Chen Molecular signature of hepatocellular carcinoma, hope or hype in prognosis and therapy. , 2011, Seminars in Cancer Biology.

[36]  Sandrine Dudoit,et al.  Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments , 2010, BMC Bioinformatics.

[37]  D. Anastassiou Computational analysis of the synergy among multiple interacting genes , 2007, Molecular systems biology.

[38]  Ariel Linden Measuring diagnostic and predictive accuracy in disease management: an introduction to receiver operating characteristic (ROC) analysis. , 2006, Journal of evaluation in clinical practice.

[39]  S. Love,et al.  Survival Analysis Part II: Multivariate data analysis – an introduction to concepts and methods , 2003, British Journal of Cancer.

[40]  J. Zucman‐Rossi,et al.  Hepatocellular carcinoma , 1998, Nature Reviews Disease Primers.

[41]  S W Lagakos,et al.  Nonparametric estimation of lifetime and disease onset distributions from incomplete observations. , 1982, Biometrics.