Article Contents
ARTICLE   Open Access     Cite

Pan-cancer analysis of DDX31 as a promising predictor for clinical prognosis and immunotherapy response

    Show all affliationsShow less
More Information
  • Corresponding author: caox@fudan.edu.cn
  • DownLoad: Full size image
    1. A systematic pan-cancer analysis of DEAD box polypeptide 31 (DDX31) was conducted by deep data mining.

      It was validated that DDX31 acted as an oncogene in Hepatocellular carcinoma (HCC).

      This study provided a theoretical basis for the development of drugs targeting DDX31 in the future.

  • DEAD box polypeptide 31 (DDX31), a member of the Asp-Glu-Ala-Asp (DEAD) box RNA helicase family, has been implicated in the progression of various malignancies, including muscle-invasive bladder cancer, renal cell carcinoma, and pancreatic ductal adenocarcinoma. To elucidate the multifaceted roles of DDX31 in oncogenesis, we conducted a comprehensive pan-cancer analysis to investigate its prognostic significance, functional implications, and immune-related characteristics through deep data mining. Our findings revealed that DDX31 was significantly upregulated in the majority of tumor types. Furthermore, DDX31 expression demonstrated strong prognostic value across multiple cancer types. Notably, DDX31 levels showed a significant positive correlation with immunoregulators and immune checkpoints in pan-cancer analysis. Conversely, DDX31 expression was inversely associated with immune cell infiltration in most malignancies. Importantly, integrated bulk-RNA and single-cell sequencing analyses revealed that DDX31 expression negatively correlated with immune regulatory systems while positively associating with oncogenic signaling pathways involved in tumor initiation and progression. Additionally, DDX31 demonstrated considerable predictive value for immunotherapy response. To validate these findings, we confirmed that DDX31 was upregulated in hepatocellular carcinoma (HCC) and that CRISPR/Cas9-mediated DDX31 knockout significantly inhibited HCC cell proliferation. Collectively, our pan-cancer analysis suggested that DDX31 may serve as a potential prognostic biomarker and provided a theoretical foundation for the development of targeted therapies against DDX31 in cancer treatment.
  • 加载中
  • [1] Bray F., Laversanne M., Sung H., et al. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 74:229−263. DOI:10.3322/caac.21834

    View in Article CrossRef Google Scholar

    [2] Siegel R. L., Miller K. D., Fuchs H. E., et al. (2022). Cancer statistics, 2022. CA Cancer J. Clin. 72:7−33. DOI:10.3322/caac.21708

    View in Article CrossRef Google Scholar

    [3] Du X., Yang B., An Q., et al. (2021). Acquired resistance to third-generation EGFR-TKIs and emerging next-generation EGFR inhibitors. The Innovation 2:100103. DOI:10.1016/j.xinn.2021.100103

    View in Article CrossRef Google Scholar

    [4] Siegel R. L., Miller K. D., Wagle N. S., et al. (2023). Cancer statistics, 2023. CA Cancer J. Clin. 73:17−48. DOI:10.3322/caac.21763

    View in Article CrossRef Google Scholar

    [5] Du X., Lu X. and Cao X. (2023). Gantt chart for updated OS and PFS after cancer targeted therapy. Innov. Med. 1:100008. DOI:10.59717/j.xinn-med.2023.100008

    View in Article Google Scholar

    [6] Barbee M. S., Ogunniyi A., Horvat T. Z., et al. (2015). Current status and future directions of the immune checkpoint inhibitors ipilimumab, pembrolizumab, and nivolumab in oncology. Ann. Pharmacother. 49:907−937. DOI:10.1177/1060028015586218

    View in Article CrossRef Google Scholar

    [7] Garon E. B., Rizvi N. A., Hui R., et al. (2015). Pembrolizumab for the treatment of non-small-cell lung cancer. N. Engl. J. Med. 372:2018−2028. DOI:10.1056/NEJMoa1501824

    View in Article CrossRef Google Scholar

    [8] Sharma P., Hu-Lieskovan S., Wargo J. A., et al. (2017). Primary, adaptive, and acquired resistance to cancer immunotherapy. Cell 168:707−723. DOI:10.1016/j.cell.2017.01.017

    View in Article CrossRef Google Scholar

    [9] Rao Q. W., Zhang S. L., Guo M. Z., et al. (2020). Sulfiredoxin-1 is a promising novel prognostic biomarker for hepatocellular carcinoma. Cancer Med. 9:8318−8332. DOI:10.1002/cam4.3430

    View in Article CrossRef Google Scholar

    [10] Xin Yu J., Hodge J. P., Oliva C., et al. (2020). Trends in clinical development for PD-1/PD-L1 inhibitors. Nat. Rev. Drug Discov. 19:163−164. DOI:10.1038/d41573-019-00182-w

    View in Article CrossRef Google Scholar

    [11] Zheng H., Wang M., Zhang S., et al. (2023). Comprehensive pan-cancer analysis reveals NUSAP1 is a novel predictive biomarker for prognosis and immunotherapy response. Int. J. Biol. Sci. 19:4689−4708. DOI:10.7150/ijbs.80017

    View in Article CrossRef Google Scholar

    [12] Chung K. Y., Gore I., Fong L., et al. (2010). Phase II study of the anti-cytotoxic T-lymphocyte-associated antigen 4 monoclonal antibody, tremelimumab, in patients with refractory metastatic colorectal cancer. J. Clin. Oncol. 28:3485−3490. DOI:10.1200/JCO.2010.28.3994

    View in Article CrossRef Google Scholar

    [13] Heerma van Voss M. R., van Diest P. J. and Raman V. (2017). Targeting RNA helicases in cancer: The translation trap. Biochim. Biophys. Acta Rev. Cancer 1868:510−520. DOI:10.1016/j.bbcan.2017.09.006

    View in Article CrossRef Google Scholar

    [14] Toyama Y. and Shimada I. (2024). NMR characterization of RNA binding property of the DEAD-box RNA helicase DDX3X and its implications for helicase activity. Nat. Commun. 15:3303. DOI:10.1038/s41467-024-47659-w

    View in Article CrossRef Google Scholar

    [15] Bourgeois C. F., Mortreux F. and Auboeuf D. (2016). The multiple functions of RNA helicases as drivers and regulators of gene expression. Nat. Rev. Mol. Cell Biol. 17:426–438. DOI:10.1038/nrm.2016.50

    View in Article Google Scholar

    [16] Truitt M. L. and Ruggero D. (2016). New frontiers in translational control of the cancer genome. Nat. Rev. Cancer 16:288−304. DOI:10.1038/nrc.2016.27

    View in Article CrossRef Google Scholar

    [17] Devasahayam Arokia Balaya R., Kanekar S., Kumar S., et al. (2025). Role of DEAD/DEAH-box helicases in immunity, infection and cancers. CCS 23:292. DOI:10.1186/s12964-025-02225-9

    View in Article CrossRef Google Scholar

    [18] Wang Y., Yang Q., Lin F., et al. (2025). RNA binding protein DDX3X drives pancreatic cancer progression via the TLE2-MYL9 axis. Sci. Adv. 11:eadw9519. DOI:10.1126/sciadv.adw9519

    View in Article CrossRef Google Scholar

    [19] Dian M., Yun L., Meng Q., et al. (2025). Targeting DDX3X suppresses progression of KRAS-driven lung cancer by disrupting antioxidative homeostasis and inducing ferroptosis. Cell Death Dis. 16:660. DOI:10.1038/s41419-025-07980-8

    View in Article CrossRef Google Scholar

    [20] Chen H. H., Yu H. I., Yang M. H., et al. (2018). DDX3 activates CBC-eIF3-mediated translation of uORF-containing oncogenic mRNAs to promote metastasis in HNSCC. Cancer Res. 78:4512−4523. DOI:10.1158/0008-5472.Can-18-0282

    View in Article CrossRef Google Scholar

    [21] Xie Y., Liu Y., Ding J., et al. (2022). Identification of DDX31 as a potential oncogene of invasive metastasis and proliferation in PDAC. Front. Cell Dev. Biol. 10:762372. DOI:10.3389/fcell.2022.762372

    View in Article CrossRef Google Scholar

    [22] Daizumoto K., Yoshimaru T., Matsushita Y., et al. (2018). A DDX31/Mutant-p53/EGFR axis promotes multistep progression of muscle-invasive bladder cancer. Cancer Res. 78:2233−2247. DOI:10.1158/0008-5472.CAN-17-2528

    View in Article CrossRef Google Scholar

    [23] Linder P. and Jankowsky E. (2011). From unwinding to clamping - the DEAD box RNA helicase family. Nat. Rev. Mol. Cell Biol. 12:505−516. DOI:10.1038/nrm3154

    View in Article CrossRef Google Scholar

    [24] Madeira F., Park Y. M., Lee J., et al. (2019). The EMBL-EBI search and sequence analysis tools APIs in 2019. Nucleic Acids Res. 47:W636−w641. DOI:10.1093/nar/gkz268

    View in Article CrossRef Google Scholar

    [25] Li B. and Dewey C. N. (2011). RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12:323. DOI:10.1186/1471-2105-12-323

    View in Article CrossRef Google Scholar

    [26] Chandrashekar D. S., Karthikeyan S. K., Korla P. K., et al. (2022). UALCAN: An update to the integrated cancer data analysis platform. Neoplasia 25:18−27. DOI:10.1016/j.neo.2022.01.001

    View in Article CrossRef Google Scholar

    [27] Chandrashekar D. S., Bashel B., Balasubramanya S. A. H., et al. (2017). UALCAN: A portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia 19:649−658. DOI:10.1016/j.neo.2017.05.002

    View in Article CrossRef Google Scholar

    [28] Liu C. J., Hu F. F., Xie G. Y., et al. (2023). GSCA: An integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. Brief Bioinform. 24. DOI:10.1093/bib/bbac558.

    View in Article Google Scholar

    [29] Liu C. J., Hu F. F., Xia M. X., et al. (2018). GSCALite: A web server for gene set cancer analysis. Bioinformatics 34:3771−3772. DOI:10.1093/bioinformatics/bty411

    View in Article CrossRef Google Scholar

    [30] Chen D., Xu L., Xing H., et al. (2024). Sangerbox 2: Enhanced functionalities and update for a comprehensive clinical bioinformatics data analysis platform. iMeta 3:e238. DOI:10.1002/imt2.238

    View in Article CrossRef Google Scholar

    [31] Bou-Dargham M. J., Sha L., Sarker D. B., et al. (2023). TCGA RNA-seq and tumor-infiltrating lymphocyte imaging data reveal cold tumor signatures of invasive ductal carcinomas and estrogen receptor-positive human breast tumors. Int. J. Mol. Sci. 24:9355. DOI:10.3390/ijms24119355

    View in Article Google Scholar

    [32] Thorsson V., Gibbs D. L., Brown S. D., et al. (2018). The immune landscape of cancer. Immunity 48:812-830 e814. DOI:10.1016/j.immuni.2018.03.023

    View in Article Google Scholar

    [33] Gong L., Luo J., Yang E., et al. (2025). Cancer immunology data engine reveals secreted AOAH as a potential immunotherapy. Cell 188:5062-5080 e5032. DOI:10.1016/j.cell.2025.07.004

    View in Article Google Scholar

    [34] Rees M. G., Seashore-Ludlow B., Cheah J. H., et al. (2016). Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat. Chem. Biol. 12:109−116. DOI:10.1038/nchembio.1986

    View in Article CrossRef Google Scholar

    [35] Seashore-Ludlow B., Rees M. G., Cheah J. H., et al. (2015). Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 5:1210−1223. DOI:10.1158/2159-8290.CD-15-0235

    View in Article CrossRef Google Scholar

    [36] Basu A., Bodycombe N. E., Cheah J. H., et al. (2013). An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154:1151−1161. DOI:10.1016/j.cell.2013.08.003

    View in Article CrossRef Google Scholar

    [37] Meyers RM B. J., McFarland J. M., Weir B. A., et al. (2017). Computational correction of copy number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nat. Genet. 49:6. DOI. DOI:10.1038/ng.3984

    View in Article CrossRef Google Scholar

    [38] Orsolic I., Carrier A. and Esteller M. (2023). Genetic and epigenetic defects of the RNA modification machinery in cancer. Trends Genet. 39:74−88. DOI:10.1016/j.tig.2022.10.004

    View in Article CrossRef Google Scholar

    [39] Wen L., Li G., Huang T., et al. (2022). Single-cell technologies: From research to application. The Innovation 3:100342. DOI:10.1016/j.xinn.2022.100342

    View in Article CrossRef Google Scholar

    [40] Chen F., Zhang Y., Chandrashekar D. S., et al. (2023). Global impact of somatic structural variation on the cancer proteome. Nat. Commun. 14:5637. DOI:10.1038/s41467-023-41374-8

    View in Article CrossRef Google Scholar

    [41] Zhang Y., Chen F., Chandrashekar D. S., et al. (2022). Proteogenomic characterization of 2002 human cancers reveals pan-cancer molecular subtypes and associated pathways. Nat. Commun. 13:2669. DOI:10.1038/s41467-022-30342-3

    View in Article CrossRef Google Scholar

    [42] Gammall J. and Lai A. G. (2022). Pan-cancer prognostic genetic mutations and clinicopathological factors associated with survival outcomes: a systematic review. NPJ Precis. Oncol. 6:27. DOI:10.1038/s41698-022-00269-5

    View in Article CrossRef Google Scholar

    [43] Guo A., Zhang J., Tian Y., et al. (2022). Identify the immune characteristics and immunotherapy value of CD93 in the pan-cancer based on the public data sets. Front. Immunol. 13:907182. DOI:10.3389/fimmu.2022.907182

    View in Article CrossRef Google Scholar

    [44] Fukawa T., Ono M., Matsuo T., et al. (2012). DDX31 regulates the p53-HDM2 pathway and rRNA gene transcription through its interaction with NPM1 in renal cell carcinomas. Cancer Res. 72:5867−5877. DOI:10.1158/0008-5472.CAN-12-1645

    View in Article CrossRef Google Scholar

    [45] Yi M., Zheng X., Niu M., et al. (2022). Combination strategies with PD-1/PD-L1 blockade: Current advances and future directions. Mol. Cancer 21:28. DOI:10.1186/s12943-021-01489-2

    View in Article CrossRef Google Scholar

    [46] Pardoll D. M. (2012). The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12:252−264. DOI:10.1038/nrc3239

    View in Article CrossRef Google Scholar

    [47] Liu J., Yang T., Luo Y., et al. (2024). DEAD-box helicase 1 inhibited CD8(+) T cell antitumor activity by inducing PD-L1 expression in hepatocellular carcinoma. Cancer Sci. 115:763−776. DOI:10.1111/cas.16076

    View in Article CrossRef Google Scholar

    [48] Liu D., Wei B., Liang L., et al. (2024). The circadian clock component RORA increases immunosurveillance in melanoma by inhibiting PD-L1 expression. Cancer Res. 84:2265−2281. DOI:10.1158/0008-5472.Can-23-3942

    View in Article CrossRef Google Scholar

    [49] Liu Q., Sun Y., Long M., et al. (2023). DDX5 functions as a tumor suppressor in tongue cancer. Cancers 15. DOI:10.3390/cancers15245882

    View in Article Google Scholar

    [50] Li B., Cui H., Liu W., et al. (2025). DDX10 exacerbates exosomal PD-L1-dependent T cell exhaustion via phase separation of Rab27b in oral squamous cell carcinoma. Research (Washington, D.C.) 8:0697. DOI:10.34133/research.0697

    View in Article CrossRef Google Scholar

    [51] Thorsson V., Gibbs D. L., Brown S. D., et al. (2018). The immune landscape of cancer. Immunity 48:812−830.e814. DOI:10.1016/j.immuni.2018.03.023

    View in Article CrossRef Google Scholar

    [52] Li J., Qu Z., Zhu D., et al. (2025). Pan cancer research reveals the role of PTGDS in tumor suppression and immune regulation. NPJ Precis. Oncol. 9:319. DOI:10.1038/s41698-025-01097-z

    View in Article CrossRef Google Scholar

    [53] Zhu Y., Xu R., Wang L., et al. (2023). Pathological functions and therapeutic targets of post-translational modifications in pan-cancer. Innov. Med. 1:100045. DOI:10.59717/j.xinn-med.2023.100045

    View in Article CrossRef Google Scholar

    [54] Zhang H., Dai Z., Wu W., et al. (2021). Regulatory mechanisms of immune checkpoints PD-L1 and CTLA-4 in cancer. J. Exp. Clin. Cancer Res. 40:184. DOI:10.1186/s13046-021-01987-7

    View in Article CrossRef Google Scholar

    [55] Baas P., Scherpereel A., Nowak A. K., et al. (2021). First-line nivolumab plus ipilimumab in unresectable malignant pleural mesothelioma (CheckMate 743): A multicentre, randomised, open-label, phase 3 trial. Lancet 397:375−386. DOI:10.1016/S0140-6736(20)32714-8

    View in Article CrossRef Google Scholar

    [56] Carlino M. S., Larkin J. and Long G. V. (2021). Immune checkpoint inhibitors in melanoma. Lancet 398:1002−1014. DOI:10.1016/S0140-6736(21)01206-X

    View in Article CrossRef Google Scholar

    [57] Andrews L. P., Marciscano A. E., Drake C. G., et al. (2017). LAG3 (CD223) as a cancer immunotherapy target. Immunol. Rev. 276:80−96. DOI:10.1111/imr.12519

    View in Article CrossRef Google Scholar

    [58] Guo M., Qi F., Rao Q., et al. (2021). Serum LAG-3 predicts outcome and treatment response in hepatocellular carcinoma patients with transarterial chemoembolization. Front. Immunol. 12:754961. DOI:10.3389/fimmu.2021.754961

    View in Article CrossRef Google Scholar

    [59] Fu J., Li K., Zhang W., et al. (2020). Large-scale public data reuse to model immunotherapy response and resistance. Genome. Med. 12:21. DOI:10.1186/s13073-020-0721-z

    View in Article CrossRef Google Scholar

    [60] Ekins S., Bradford J., Dole K., et al. (2010). A collaborative database and computational models for tuberculosis drug discovery. Mol. Biosyst. 6:840−851. DOI:10.1039/b917766c

    View in Article CrossRef Google Scholar

    [61] Masoudi-Sobhanzadeh Y., Omidi Y., Amanlou M., et al. (2020). Drug databases and their contributions to drug repurposing. Genomics 112:1087−1095. DOI:10.1016/j.ygeno.2019.06.021

    View in Article CrossRef Google Scholar

    [62] Chen H., Guo G., Wu M., et al. (2024). ARID1A mutation: A new target for efficient cancer immunotherapy. Innov. Med. 2:100101. DOI:10.59717/j.xinn-med.2024.100101

    View in Article CrossRef Google Scholar

    [63] Huang X. and Cao X. (2023). Innovative drugs bring continuous benefits to cancer patients. Innov. Life 1:100043. DOI:10.59717/j.xinn-life.2023.100043

    View in Article Google Scholar

    [64] Swetha K. L., Sharma S., Chowdhury R., et al. (2020). Disulfiram potentiates docetaxel cytotoxicity in breast cancer cells through enhanced ROS and autophagy. Pharmacol. Rep. 72:1749−1765. DOI:10.1007/s43440-020-00122-1

    View in Article CrossRef Google Scholar

    [65] Davies A. M., Ho C., Lara P. N. Jr., et al. (2006). Pharmacodynamic separation of epidermal growth factor receptor tyrosine kinase inhibitors and chemotherapy in non-small-cell lung cancer. Clin. Lung Cancer 7:385−388. DOI:10.3816/CLC.2006.n.021

    View in Article CrossRef Google Scholar

    [66] Shiotsu Y., Kiyoi H., Ishikawa Y., et al. (2009). KW-2449, a novel multikinase inhibitor, suppresses the growth of leukemia cells with FLT3 mutations or T315I-mutated BCR/ABL translocation. Blood 114:1607−1617. DOI:10.1182/blood-2009-01-199307

    View in Article CrossRef Google Scholar

  • Cite this article:

    Rao Q., Dong J., Qi Z., et al. (2026). Pan-cancer analysis of DDX31 as a promising predictor for clinical prognosis and immunotherapy response. The Innovation Medicine 4:100211. https://doi.org/10.59717/j.xinn-med.2026.100211
    Rao Q., Dong J., Qi Z., et al. (2026). Pan-cancer analysis of DDX31 as a promising predictor for clinical prognosis and immunotherapy response. The Innovation Medicine 4:100211. https://doi.org/10.59717/j.xinn-med.2026.100211

Welcome!

To request copyright permission to republish or share portions of our works, please visit Copyright Clearance Center's (CCC) Marketplace website at marketplace.copyright.com.

Figures(7)    

Share

  • Share the QR code with wechat scanning code to friends and circle of friends.

Article Metrics

Article views(391) PDF downloads(219)

Relative Articles

Cited by

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint