Integrative single-cell and bulk transcriptomic analyses identify DRAM1 as a candidate gene from fibroblast-associated transcriptional programs in colorectal cancer - Scorecard - MDSpire

Integrative single-cell and bulk transcriptomic analyses identify DRAM1 as a candidate gene from fibroblast-associated transcriptional programs in colorectal cancer

  • By

  • Rudong Li

  • Zhipeng Zhao

  • Hengyu Liu

  • Chao Zhang

  • Zhicheng Wang

  • Siyi Wang

  • Xudong Wang

  • June 11, 2026

  • 0 min

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Clinical Scorecard: Combined Single-Cell and Bulk Transcriptomic Approaches Reveal DRAM1 as a Potential Gene Linked to Fibroblast-Related Transcriptional Networks in Colorectal Cancer

At a Glance

CategoryDetail
Condition
Key MechanismsFibroblast-associated transcriptional programs, intercellular interactions, and the role of DRAM1.
Target Population
Care Setting

Key Highlights

  • Identification of 17 cell clusters in CRC and adjacent normal tissues, highlighting the role of DRAM1.
  • DRAM1 selected for validation due to elevated expression in CRC tissues and its potential role in tumor progression.
  • Loss-of-function assays indicate DRAM1 silencing enhances CRC cell proliferation and invasion, suggesting its tumor-restraining role.
  • Integration of scRNA-seq and bulk transcriptomic data to characterize fibroblast roles, emphasizing the importance of DRAM1.
  • Machine learning prioritization identified five core genes linked to fibroblast functions, with DRAM1 being a key candidate.

Guideline-Based Recommendations

Diagnosis

    Management

    • Consider the role of fibroblast-associated genes, particularly DRAM1, in CRC progression and treatment response.

    Monitoring & Follow-up

      Risks

        Patient & Prescribing Data

        Further investigation needed on the role of DRAM1 in therapeutic responses, including potential targeted therapies.

        Clinical Best Practices

        • Incorporate high-dimensional gene co-expression analysis in CRC research, focusing on DRAM1.
        • Utilize integrative approaches combining single-cell and bulk transcriptomic data, with specific methodologies outlined.

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