Q-omics provides the consensus-scored INA profile across patient tissues and cancer cell-line models. INA expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, INA is differentially expressed in 13, with the highest sampling consensus in COAD. Additionally, INA protein abundance shows 18,940 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight HNSC, COAD, and GBM as cancer lineages where INA 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 INA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes INA survival associations across molecular data types. INA RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible INA RNA expression–survival associations across cancer types. High INA expression shows unfavorable associations in KIRP, UVM and MESO, but favorable associations in HNSC, LGG and CESC. The HNSC 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 HNSC as the clearest survival context for INA RNA expression.
This table summarizes INA 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 COAD for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for INA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. INA shows lower tumor expression in COAD and higher tumor expression in BLCA, LUSC, LUAD, THCA and UCEC. The COAD box plot shows higher INA RNA expression in normal versus tumor tissue (log2 FC = −1.386, t-test p < 0.001).
This table shows molecular features associated with INA in patient tissues and cancer cell lines. In patient samples, INA shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, INA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in STOMACH, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
