Q-omics provides the consensus-scored ITGAV profile across patient tissues and cancer cell-line models. ITGAV 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, ITGAV is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, ITGAV protein abundance shows 21,081 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, HNSC, and LSCC as cancer lineages where ITGAV 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 ITGAV — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ITGAV survival associations across molecular data types. ITGAV RNA expression shows survival associations in the most cancer types (26), followed by mutation status (10) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ITGAV RNA expression–survival associations across cancer types. High ITGAV expression shows unfavorable associations in MESO, STAD, LIHC, LGG and PAAD, 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 ITGAV RNA expression.
This table summarizes ITGAV 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 5. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ITGAV. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ITGAV shows lower tumor expression in UCEC and higher tumor expression in HNSC, LIHC, LUAD, BLCA and BRCA. The HNSC box plot shows higher ITGAV RNA expression in tumor versus normal tissue (log2 FC = +2.437, t-test p < 0.001).
This table shows molecular features associated with ITGAV in patient tissues and cancer cell lines. In patient samples, ITGAV shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ITGAV RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in CNS and BONE.
