Transcriptomics-based validation of the relatedness of heterogeneous nuclear ribonucleoproteins to chronic lymphocytic leukemia as potential biomarkers of the disease aggressiveness

Suliman A. Alsagaby


Objectives: To use independent transcriptomics data sets of cancer patients with prognostic information from public repositories to validate the relevance of our previously described chronic lymphocytic leukemia (CLL)-related proteins at the level of transcription (mRNA) to the prognosis of CLL. 

Methods: This is a validation study that was conducted at Majmaah University, Kingdom of Saudi Arabia between January-2017 and July-2018. Two independent data sets of CLL transcriptomics from Gene Expression Omnibus (GEO) with time-to-first treatment (TTFT) data (GSE39671; 130 patients) and information about overall survival (OS) (GSE22762; 107 patients) were used for the validation analyses. To further investigate the relatedness of a transcript of interest to other neoplasms, 6 independent data sets of cancer transcriptomics with prognostic information (1865 patients) from the cancer genomics atlas (TCGA) were used. Pathway-enrichment analyses were conducted using Reactome; and correlation analyses of gene expression were performed using Pearson score.

Results: Nine of the CLL-related proteins exhibited transcript expression that predicted TTFT and 7 of the CLL-related proteins showed mRNA levels that predicted OS in CLL patients (p≤0.05). Of these transcripts, 8 were different types of heterogeneous nuclear ribonucleoproteins (HNRNPs); and 2 (HNRNPUL2 and HIST1C1H) retained prognostic significance in the 2 independent data sets. Furthermore, genes that enriched CLL-related pathways (p≤0.05; false discovery rate [FDR] ≤0.05) were found to correlate with the expression of HNRNPUL2 (Pearson score: ≥0.50; p lessthan 0.00001). Finally, increased expression of HNRNPUL2 was indicative of poor prognosis of various types of cancer other than CLL (p less than 0.05).

Conclusion: The cognate transcripts of 14 of our CLL-related proteins significantly predicted CLL prognosis.


Saudi Med J 2019; Vol. 40 (4): 328-338
doi: 10.15537/smj.2019.4.23380

How to cite this article:
Alsagaby SA. Transcriptomics-based validation of the relatedness of heterogeneous nuclear ribonucleoproteins to chronic lymphocytic leukemia as potential biomarkers of the disease aggressiveness. Saudi Med J. 2019 Apr;40(4):328-338. doi: 10.15537/smj.2019.4.23380.


HNRNPs; CLL; Transcriptomics; Prognostic markers

Full Text:



Hallek M, Pflug N. Chronic lymphocytic leukemia. Ann Oncol 2010; 21: vii154-vii164.

Rozman C, Montserrat E. Chronic lymphocytic leukemia. N Engl J Med 1995; 333: 1052-1057.

Hallek M. Chronic lymphocytic leukemia: 2015 Update on diagnosis, risk stratification, and treatment. Am J Hematol 2015; 90: 446-460.

Nabhan C, Rosen ST. Chronic lymphocytic leukemia: a clinical review. JAMA 2014; 312: 2265-2276.

Alsagaby SA, Brennan P, Pepper C. Key Molecular Drivers of Chronic Lymphocytic Leukemia. Clin Lymphoma Myeloma Leuk 2016; 16: 593-606.

Hamblin TJ, Davis Z, Gardiner A, Oscier DG, Stevenson FK. Unmutated Ig V(H) genes are associated with a more aggressive form of chronic lymphocytic leukemia. Blood 1999; 94: 1848-1854.

Dürig J, Naschar M, Schmücker U, Renzing-Köhler K, Hölter T, Hüttmann A, et al. CD38 expression is an important prognostic marker in chronic lymphocytic leukaemia. Leukemia 2002; 16: 30-35.

Rassenti LZ, Huynh L, Toy TL, Chen L, Keating MJ, Gribben JG, et al. ZAP-70 compared with immunoglobulin heavy-chain gene mutation status as a predictor of disease progression in chronic lymphocytic leukemia. N Engl J Med 2004; 351: 893-901.

Döhner H, Stilgenbauer S, Benner A, Leupolt E, Kröber A, Bullinger L, et al. Genomic aberrations and survival in chronic lymphocytic leukemia. N Engl J Med 2000; 343: 1910-1916.

Mertens D, Stilgenbauer S. Prognostic and predictive factors in patients with chronic lymphocytic leukemia: relevant in the era of novel treatment approaches? J Clin Oncol 2014; 32: 869-872.

Alsagaby SA, Alhumaydhi FA. Proteomics insights into the pathology and prognosis of chronic lymphocytic leukemia. Saudi Med J 2019; 40: 179-189.

Alsagaby SA, Khanna S, Hart KW, Pratt G, Fegan C, Pepper C, et al. Proteomics-based strategies to identify proteins relevant to chronic lymphocytic leukemia. J Proteome Res 2014; 13: 5051-5062.

Alsagaby S, Brewis I, Pepper C, Fegan C, Brennan P. Analysis of human B-cells with quantitative and sub-cellular proteomics. Immunology 2010; 131: 115.

Gene Expression Omnibus [Internet]. National Centre for Biotechnology Information. NCBI; [Accessed 10 July 2018]. Available from:

National Cancer Institute. The Cancer Genome Atlas. NIH; [Accessed 2018 July 23]. Available from:

Chuang HY, Rassenti L, Salcedo M, Licon K, Kohlmann A, Haferlach T, et al. Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression. Blood 2012; 120: 2639-2649.

Herold T, Jurinovic V, Metzeler KH, Boulesteix AL, Bergmann M, Seiler T, et al. An eight-gene expression signature for the prediction of survival and time to treatment in chronic lymphocytic leukemia. Leukemia 2011; 25: 1639-1645.

Reimand J, Arak T, Adler P, Kolberg L, Reisberg S, Peterson H, et al. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res 2016; 44: W83-W89.

Pundir S, Martin MJ, O’Donovan C. UniProt Tools. Curr Protoc Bioinformatics 2016; 53: 1-15.

UniProt. The universal protein knowledgebase [Internet]. The UniProt Consortium. [Accessed 2018 July 1]. Available from:

Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal 2013; 6: pl1.

Croft D, Mundo AF, Haw R, Milacic M, Weiser J, Wu G, et al. The Reactome pathway knowledgebase. Nucleic Acids Res 2014; 42: D472-D477.

Babicki S, Arndt D, Marcu A, Liang Y, Grant JR, Maciejewski A, et al. Heatmapper: web-enabled heat mapping for all. Nucleic Acids Res 2016; 44: W147-W153.

Shilo A, Siegfried Z, Karni R. The role of splicing factors in deregulation of alternative splicing during oncogenesis and tumor progression. Mol Cell Oncol 2014; 2: e970955.

Barboro P, Ferrari N, Balbi C. Emerging roles of heterogeneous nuclear ribonucleoprotein K (hnRNP K) in cancer progression. Cancer Lett 2014; 352: 152-159.

Geng Y, Zhang L, Xu M, Sheng W, Dong A, Cao J, et al. [The expression and significance of hnRNPD in esophageal squamous cell carcinoma cells]. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi 2015; 31: 1659-1663. [Chinese]

Chen CY, Chuang YS, Pi WC, Wang TC. HnRNP A2/B1 regulates alternative splicing of Tid1 isoforms. The FASEB Journal 2014; 28: 742.

Revil T, Pelletier J, Toutant J, Cloutier A, Chabot B. Heterogeneous nuclear ribonucleoprotein K represses the production of pro-apoptotic Bcl-xS splice isoform. J Biol Chem 2009; 284: 21458-21467.

Izquierdo JM. Heterogeneous ribonucleoprotein C displays a repressor activity mediated by T-cell intracellular antigen-1-related/like protein to modulate Fas exon 6 splicing through a mechanism involving Hu antigen R. Nucleic Acids Res 2010; 38: 8001-8014.

Johnston HE, Carter MJ, Larrayoz M, Clarke J, Garbis SD, Oscier D, et al. Proteomics Profiling of CLL Versus Healthy B-cells Identifies Putative Therapeutic Targets and a Subtype-independent Signature of Spliceosome Dysregulation. Mol Cell Proteomics 2018; 17: 776-791.

Messmer BT, Messmer D, Allen SL, Kolitz JE, Kudalkar P, Cesar D, et al. In vivo measurements document the dynamic cellular kinetics of chronic lymphocytic leukemia B cells. J Clin Invest 2005; 115: 755-764.

Pepper C, Hewamana S, Brennan P, Fegan C. NF-kappaB as a prognostic marker and therapeutic target in chronic lymphocytic leukemia. Future Oncol 2009; 5: 1027-1037.

Stevenson FK, Krysov S, Davies AJ, Steele AJ, Packham G. B-cell receptor signaling in chronic lymphocytic leukemia. Blood 2011; 118: 4313-4320.

Shachar I, Cohen S, Marom A, Becker-Herman S. Regulation of CLL survival by hypoxia-inducible factor and its target genes. FEBS Lett 2012; 586: 2906-2910.

Krejci P, Pejchalova K, Rosenbloom BE, Rosenfelt FP, Tran EL, Laurell H, et al. The antiapoptotic protein Api5 and its partner, high molecular weight FGF2, are up-regulated in B cell chronic lymphoid leukemia. J Leukoc Biol 2007; 82: 1363-1364.

Del Gaizo Moore V, Brown JR, Certo M, Love TM, Novina CD, Letai A. Chronic lymphocytic leukemia requires BCL2 to sequester prodeath BIM, explaining sensitivity to BCL2 antagonist ABT-737. J Clin Invest 2007; 117: 112-121.

Secchiero P, Voltan R, di Iasio MG, Melloni E, Tiribelli M, Zauli G. The oncogene DEK promotes leukemic cell survival and is downregulated by both Nutlin-3 and chlorambucil in B-chronic lymphocytic leukemic cells. Clin Cancer Res 2010; 16: 1824-1833.

Geuens T, Bouhy D, Timmerman V. The hnRNP family: insights into their role in health and disease. Hum Genet 2016; 135: 851-867.

Alsagaby SA. Integration of proteomics and transcriptomics data sets identifies prognostic markers in chronic lymphocytic leukemia. Majmaah Journal of Health Sciences 2019; 7: 1-22.

Gry M, Rimini R, Strömberg S, Asplund A, Pontén F, Uhlén M, et al. Correlations between RNA and protein expression profiles in 23 human cell lines. BMC Genomics 2009; 10: 365.


  • There are currently no refbacks.

Saudi Medical Journal is copyright under the Berne Convention and the International Copyright Convention.  Saudi Medical Journal is an Open Access journal and articles published are distributed under the terms of the Creative Commons Attribution-NonCommercial License (CC BY-NC). Readers may copy, distribute, and display the work for non-commercial purposes with the proper citation of the original work. Electronic ISSN 1658-3175. Print ISSN 0379-5284.