Competing Endogenous RNA Network in Newly Diagnosed Multiple Myeloma Revealed by Genetic Microarray

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Competing Endogenous RNA Network in Newly Diagnosed Multiple Myeloma Revealed by Genetic Microarray

Multiple myeloma (MM)—the second most common blood cancer—affects over 138,000 people globally each year. It begins when plasma cells (immune cells that make antibodies) multiply uncontrollably in the bone marrow, flooding the body with abnormal “monoclonal” proteins that damage kidneys, bones, and other organs. While treatments have improved, MM remains incurable for many—and scientists are still unlocking its genetic drivers. Now, a study using genetic microarrays offers fresh insight into how non-coding RNAs (molecules that regulate gene activity but don’t make proteins) fuel the disease through a “competing endogenous RNA” (ceRNA) network.

What Is a CeRNA Network?

Think of ceRNA as a molecular “sponge system”:

  • Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) act as sponges, soaking up tiny microRNAs (miRNAs).
  • MiRNAs normally bind to messenger RNAs (mRNAs)—the molecules that tell cells to make proteins—blocking them from producing harmful or excess proteins.
  • When lncRNAs/circRNAs “sponge” miRNAs, more mRNAs escape inhibition—throwing gene expression into chaos.

In cancer, this balance often breaks down. For MM, only one lncRNA (MEG3) had previously been linked to ceRNA activity. Now, researchers have mapped a far more complex network in newly diagnosed patients.

The Study: Tracking RNAs in Newly Diagnosed MM

A team from Beijing Chaoyang Hospital (Capital Medical University) and Tsinghua University’s Chuiyangliu Hospital analyzed bone marrow samples from 10 newly diagnosed, untreated MM patients and 10 healthy volunteers. The goal: identify which RNAs are over- or under-expressed in MM—and how they interact via ceRNA.

Who Participated?

  • MM Patients: Met 2014 International Myeloma Working Group (IMWG) criteria for diagnosis.
  • Controls: Healthy volunteers with no history of blood cancer.
  • Exclusions: Recurrent/refractory MM, severe illness, stem cell transplant candidates, or plasmacytoma (a related tumor).
  • Ethics: Approved by Beijing Chaoyang Hospital’s ethics committee; all participants gave informed consent.

How Did They Do It?

The team used Affymetrix Clariom™ D microarrays—a tool that tracks thousands of RNAs at once—to analyze bone marrow cells:

  1. RNA Isolation: Fresh bone marrow samples were processed within 2 hours to preserve RNA.
  2. Microarray Analysis: The chip measured levels of lncRNAs, circRNAs, miRNAs, and mRNAs.
  3. Data Crunching: They compared RNA levels between MM and control groups using:
    • t-tests (to check for statistical significance, P < 0.05).
    • Fold-change (FC) ≥ 2: RNAs that were twice as high or low in MM were labeled “differentially expressed.”
  4. CeRNA Network Building: Using Affymetrix’s GeneChip software, they linked RNAs that interact via the sponge effect.

Key Findings: A CeRNA Network Linked to Protein Overproduction

The microarray screened 135,731 genes and uncovered 234 lncRNAs, 557 circRNAs, 122 miRNAs, and 709 mRNAs that were abnormally expressed in MM. From these, a small subset formed a ceRNA network: 9 lncRNAs, 42 circRNAs, 8 miRNAs, and 51 mRNAs.

The Hub: hsa_miR-4772-3p

The most connected molecule in the network was hsa_miR-4772-3p, a miRNA linked to 24 other RNAs (its “degree value,” a measure of connections). Three lncRNAs (RPL4P4, RPSAP19, BMS1P5) and 13 circRNAs (e.g., hsa_circ_0004646, hsa_circ_0069826) all “sponged” this miRNA—and all pointed to one mRNA: RPL37A.

Why does this matter? RPL37A makes ribosomal protein L37—a building block of ribosomes, the cell’s protein-making factories. MM cells are notorious for overproducing monoclonal proteins—so a ceRNA network boosting ribosome activity aligns perfectly with the disease’s core feature.

Other Key Players

The network also highlighted two miRNAs with known roles in other cancers:

  • hsa_miR-618: Suppresses gastric cancer metastasis by blocking TGF-β2 (a growth factor that fuels cell division).
  • hsa_miR-1284: Slows breast cancer by targeting ZIC2 (a gene that drives tumor growth).

In MM, these miRNAs may act as “tumor suppressors” or “oncogenes” depending on which lncRNAs/circRNAs are sponging them—throwing gene expression off-balance.

What Does This Mean for MM Patients?

MM’s hallmark is too many abnormal proteins—and this study links that to a dysregulated ceRNA network. The hsa_miR-4772-3p/RPL37A axis suggests that when lncRNAs/circRNAs soak up this miRNA, more ribosomal protein is made—supercharging protein production in MM cells.

For researchers, this is a game-changer: ceRNA networks are targetable. If they can block the sponges that let RPL37A run wild—or restore miRNAs that silence harmful mRNAs—they could slow or stop MM cell growth.

Conclusion

This study is one of the first to map a comprehensive ceRNA network in newly diagnosed MM. By tracking thousands of RNAs, the team uncovered new drivers—like hsa_miR-4772-3p and RPL37A—that may fuel the disease. While more research is needed to confirm these links in larger groups, the findings offer a roadmap for new MM treatments targeting non-coding RNAs.

Study Authors & Funding

The research was led by:

  • Yu-Qin He (Department of Emergency, Beijing Chaoyang Hospital, Capital Medical University)
  • Zhi-Yao Zhang, Hui-Xing Zhou, Wen-Ming Chen (Department of Hematology, Beijing Chaoyang Hospital, Capital Medical University)
  • Fang Ye (Department of Hematology, Chuiyangliu Hospital, Tsinghua University)

Funding: Supported by China’s Special Medicine Innovation Scientific Special Project (2018ZX09733003).

References

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  2. Shen X, Bai H, Zhu H, Yan Q, Yang Y, Yu W, et al. Long non-coding RNA MEG3 functions as a competing endogenous RNA to regulate HOXA11 expression by sponging miR-181a in multiple myeloma. Cell Physiol Biochem 2018;49:87–100. doi: https://doi.org/10.1159/000492846
  3. Bi CL, Chng WJ. miRNA deregulation in multiple myeloma. Chin Med J 2011;124:3164–3169. doi: https://doi.org/10.3760/cma.j.issn.0366-6999.2011.19.037
  4. Santangelo GM, Tornow J, McLaughlin CS, Moldave K. Screening a yeast promoter library leads to the isolation of the RP29/L32 and SNR17B/RPL37A divergent promoters and the discovery of a gene encoding ribosomal protein L37. Gene 1991;105:137–138. doi: https://doi.org/10.1016/0378-1119(91)90526-h
  5. Shi J, Gong L, Chen L, Luo J, Song G, Xu J, et al. miR-618 suppresses metastasis in gastric cancer by downregulating the expression of TGF-beta2. Anat Rec (Hoboken) 2019;302:931–940. doi: https://doi.org/10.1002/ar.24083
  6. Zhang P, Yang F, Luo Q, Yan D, Sun S. miR-1284 inhibits the growth and invasion of breast cancer cells by targeting ZIC2. Oncol Res 2019;27:253–260. doi: https://doi.org/10.3727/096504018X15242763477504

Original Study Citation

He YQ, Zhang ZY, Zhou HX, Ye F, Chen WM. Competing endogenous RNA network in newly diagnosed multiple myeloma by genetic microarray. Chin Med J 2020;133:2619–2621. doi: https://doi.org/10.1097/CM9.0000000000001108

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