Q-omics provides the consensus-scored ITGB4 profile across patient tissues and cancer cell-line models. ITGB4 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in LUAD. Among the 18 cancer types available for tumor–normal comparison, ITGB4 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, ITGB4 protein abundance shows 17,185 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight LUAD, and HNSC as cancer lineages where ITGB4 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 ITGB4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ITGB4 survival associations across molecular data types. ITGB4 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (10) 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 ITGB4 RNA expression–survival associations across cancer types. High ITGB4 expression shows unfavorable associations in LUAD, HNSC, LGG and PAAD, but favorable associations in LAML and SARC. The LUAD 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 LUAD as the clearest survival context for ITGB4 RNA expression.
This table summarizes ITGB4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ITGB4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ITGB4 shows lower tumor expression in KICH and higher tumor expression in HNSC, THCA, LUAD, COAD and LUSC. The HNSC box plot shows higher ITGB4 RNA expression in tumor versus normal tissue (log2 FC = +2.209, t-test p < 0.001).
This table shows molecular features associated with ITGB4 in patient tissues and cancer cell lines. In patient samples, ITGB4 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, ITGB4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BREAST and SKIN.
