Q-omics provides the consensus-scored TOR1AIP1 profile across patient tissues and cancer cell-line models. TOR1AIP1 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, TOR1AIP1 is differentially expressed in 15, with the highest sampling consensus in THCA. Additionally, TOR1AIP1 RNA expression shows 20,169 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, THCA, and UVM as cancer lineages where TOR1AIP1 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 TOR1AIP1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TOR1AIP1 survival associations across molecular data types. TOR1AIP1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (3) 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 TOR1AIP1 RNA expression–survival associations across cancer types. High TOR1AIP1 expression shows unfavorable associations in UVM, BLCA and CESC, but favorable associations in KIRC, SARC and PRAD. The KIRC 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 KIRC as the clearest survival context for TOR1AIP1 RNA expression.
This table summarizes TOR1AIP1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 4. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for TOR1AIP1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. TOR1AIP1 shows lower tumor expression in THCA, LUSC, KICH and UCEC and higher tumor expression in HNSC and LIHC. The THCA box plot shows higher TOR1AIP1 RNA expression in normal versus tumor tissue (log2 FC = −0.748, t-test p < 0.001).
This table shows molecular features associated with TOR1AIP1 in patient tissues and cancer cell lines. In patient samples, TOR1AIP1 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, TOR1AIP1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in LIVER and UPPER_AERODIGESTIVE_TRACT.
