Table 6_Combined single-cell transcriptome and Mendelian randomization to identify and val

Jiaxin Li's dataset, published on figshare in March 2026, contains results from a study integrating single-cell transcriptome analysis and Mendelian randomization to identify prognostic genes in lung adenocarcinoma. The data includes seven validated prognostic genes (VDAC1, TXNRD1, GDF15, TRIB3, LPL, KCNQ1, PKP2) and associated risk model, nomogram, and immune profiling results. The dataset is 11.4 KB in size and is licensed under CC-BY-4.0.

Use Cases

• Validating prognostic risk models for lung adenocarcinoma based on the seven identified genes.
• Analyzing immune microenvironment profiles based on the 13 differentially abundant immune cell types mentioned.
• Investigating pathway enrichment differences between high-risk and low-risk patient groups, such as olfactory transduction versus ribosome pathways.
• Exploring gene expression patterns in macrophage differentiation based on single-cell analysis findings for VDAC1, TXNRD1, and LPL.

Strengths

• Identifies seven specific prognostic genes (VDAC1, TXNRD1, GDF15, TRIB3, LPL, KCNQ1, PKP2) validated by RT-qPCR.
• Risk model and nomogram demonstrated predictive accuracy for 1-, 3-, and 5-year survival outcomes.
• Immune profiling revealed 13 differentially abundant immune cell types, including M1 macrophages.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is very small at 11.4 KB, indicating limited scope, likely containing summary results rather than raw data.
• Data may reflect bias inherent to the specific public datasets and analytical methods used in the source study.

Provenance

Source

Jiaxin Li via figshare.

Collection Method

Integrated analysis of public datasets using weighted gene co-expression network analysis, differential expression analysis, and Mendelian randomization.

Freshness

Last updated 2026-03-25 22:03:04; freshness should be verified.