Q-omics provides the consensus-scored INAVA profile across patient tissues and cancer cell-line models. INAVA expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, INAVA is differentially expressed in 14, with the highest sampling consensus in KICH. Additionally, INAVA RNA expression shows 18,232 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight KIRC, KICH, and KIRP as cancer lineages where INAVA 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 INAVA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes INAVA survival associations across molecular data types. INAVA RNA expression shows survival associations in the most cancer types (26), followed by mutation status (6) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible INAVA RNA expression–survival associations across cancer types. High INAVA expression shows unfavorable associations in ACC, MESO, UCEC, LIHC and LUAD, but favorable associations in KIRC. 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 INAVA RNA expression.
This table summarizes INAVA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 4. The strongest signals are observed in KICH for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for INAVA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. INAVA shows lower tumor expression in KICH and higher tumor expression in LUAD, THCA, LUSC, HNSC and STAD. The KICH box plot shows higher INAVA RNA expression in normal versus tumor tissue (log2 FC = −3.310, t-test p < 0.001).
This table shows molecular features associated with INAVA in patient tissues and cancer cell lines. In patient samples, INAVA shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, INAVA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
