Q-omics provides the consensus-scored ITGAE profile across patient tissues and cancer cell-line models. ITGAE expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ITGAE is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, ITGAE RNA expression shows 18,673 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, KICH, and UVM as cancer lineages where ITGAE 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 ITGAE — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ITGAE survival associations across molecular data types. ITGAE RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ITGAE RNA expression–survival associations across cancer types. High ITGAE expression shows unfavorable associations in KIRC, ACC, KICH and LGG, but favorable associations in BLCA and UCEC. The KIRC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for ITGAE RNA expression.
This table summarizes ITGAE tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 4. The strongest signals are observed in KICH for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for ITGAE. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ITGAE shows lower tumor expression in KICH, THCA, LUAD and LUSC and higher tumor expression in LIHC and KIRC. The KICH box plot shows higher ITGAE RNA expression in normal versus tumor tissue (log2 FC = −1.161, t-test p < 0.001).
This table shows molecular features associated with ITGAE in patient tissues and cancer cell lines. In patient samples, ITGAE 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, ITGAE RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
