Q-omics provides the consensus-scored ESYT3 profile across patient tissues and cancer cell-line models. ESYT3 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in LUAD. Among the 18 cancer types available for tumor–normal comparison, ESYT3 is differentially expressed in 10, with the highest sampling consensus in KICH. Additionally, ESYT3 RNA expression shows 18,724 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight LUAD, KICH, and UVM as cancer lineages where ESYT3 shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for ESYT3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ESYT3 survival associations across molecular data types. ESYT3 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (11) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ESYT3 RNA expression–survival associations across cancer types. High ESYT3 expression shows unfavorable associations in MESO, LIHC and UCEC, but favorable associations in LUAD, KIRP and UCS. The LUAD Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify LUAD as the clearest survival context for ESYT3 RNA expression.
This table summarizes ESYT3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 3. The strongest signals are observed in KICH for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for ESYT3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ESYT3 shows lower tumor expression in KICH, LUSC, LUAD, KIRC and BRCA and higher tumor expression in LIHC. The KICH box plot shows higher ESYT3 RNA expression in normal versus tumor tissue (log2 FC = −1.040, t-test p < 0.001).
This table shows molecular features associated with ESYT3 in patient tissues and cancer cell lines. In patient samples, ESYT3 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, ESYT3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
