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

A 198-participant prospective cohort study from an unspecified time period, conducted by Zhaoyang Ye, profiling whole-transcriptome and plasma-proteome data. The dataset identifies 13 immune-related biomarkers for early tuberculosis detection in type 2 diabetes patients and includes 11 diagnostic models validated with external datasets. It was last updated on March 18, 2026.

Use Cases

• Developing logistic regression models for disease prediction based on the 13 identified cross-platform biomarkers.
• Analyzing immune-metabolic regulatory networks based on the described PPI and ceRNA interactions.
• Validating diagnostic models using the external GEO datasets (GSE181143, GSE114192) referenced in the study.
• Characterizing immune microenvironment shifts based on the xCell immune-infiltration analysis described.

Strengths

• Includes data from 198 participants across three cohorts (HC 71, T2DM 67, T2DM-TB 60).
• Identifies 13 specific biomarkers (4 mRNAs, 2 miRNAs, 3 lncRNAs, 4 proteins).
• Reports 11 diagnostic models with high AUC values ranging from 0.93 to 0.99.
• External validation performed using GEO datasets and RT-qPCR.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 14.9 KB file size suggests the dataset is a summary or supplementary table, not the full raw multi-omics data.

Provenance

Source

Zhaoyang Ye via figshare.

Collection Method

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

Freshness

Last updated 2026-03-18 07:43:08; freshness should be verified.