Tag: TCGA

An integrated transcriptomic and experimental analysis revealed that elevated malignant cell abundance and MT2A expression correlate with poor prognosis in ...

A multi-omics and in vitro validation study demonstrates that elevated OIP5 expression drives cell cycle progression and promotes lung adenocarcinoma ...

Researchers developed a machine-learning pathomics model using patient data from TCGA to predict NRG1 expression in bladder cancer.

A pan-cancer investigation reveals that the endosomal antiporter gene SLC9A9 is upregulated in clear cell renal cell carcinoma and promotes ...

A study integrating single-cell sequencing and Mendelian randomization identified CTSH, MUC1, and SLBP as candidate genes associated with regulatory pathways ...

A new pathomics model predicts natural regulatory T cell infiltration in breast cancer from standard H&E images, offering a non-invasive ...

A study of 1,419 lung cancer patients found that the prognostic significance of four coagulation-related genes varied by mutation status, ...

A new self-supervised framework called Subtype-DIVIDE uses multi-omic data and random-walk-refined contrastive learning to identify robust cancer subtypes with significant ...

A computational model based on thirteen succinylation-related genes stratifies breast cancer survival outcomes and correlates with immune infiltration, mutation patterns, ...

A molecular stratification of endoplasmic reticulum stress in lung adenocarcinoma identifies DKK1 as a tumor-cell-biased prognostic regulator linked to ER-phagy-related ...