Multi-Omics Biomarkers for Tuberculosis Risk in Type 2 Diabetes Patients

A 198-subject prospective cohort study from an unspecified time period, profiling whole-transcriptome and plasma-proteome data to identify immune-related biomarkers for early tuberculosis detection in type 2 diabetes patients. The dataset, authored by Zhaoyang Ye and last updated in March 2026, includes 13 cross-platform biomarkers and diagnostic models validated on external GEO datasets. It was published on figshare under a CC-BY-4.0 license.

Use Cases

• Training diagnostic models based on the 13 identified immune-related biomarkers (e.g., IRF1, IFN-γ, hsa-miR-4726-5p).
• Analyzing immune-metabolic regulatory networks, such as the ceRNA interactions and pathways like Toll-like receptors and NF-κB, described in the results.
• Validating prognostic models using the 11 logistic regression models with reported AUC values between 0.93 and 0.99.
• Studying immune cell infiltration patterns, including shifts in M1/M2 macrophages and CD4+/CD8+ T cells, as characterized in the analysis.

Strengths

• Includes data from 198 subjects across three cohorts (Healthy, T2DM, T2DM-TB).
• Identifies 13 specific biomarkers across mRNA, miRNA, lncRNA, and protein platforms.
• Reports high diagnostic efficacy with model AUC values ranging from 0.93 to 0.99 and includes external validation using GEO datasets.
• Provides functional annotation through GO and KEGG analyses and confirms interactions via dual-luciferase assays.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is very small at 15.7 KB, indicating limited raw data or a summary document.
• Geographic and temporal coverage of the cohort study is not specified in the provided metadata.

Provenance

Source

Zhaoyang Ye via figshare

Collection Method

Prospective cohort study involving whole-transcriptome and plasma-proteome profiling.

Freshness

Last updated 2026-03-18 07:43:07