Q-omics provides the consensus-scored ITGA3 profile across patient tissues and cancer cell-line models. ITGA3 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in PAAD. Among the 18 cancer types available for tumor–normal comparison, ITGA3 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, ITGA3 protein abundance shows 25,856 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight PAAD, HNSC, and GBM as cancer lineages where ITGA3 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 ITGA3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ITGA3 survival associations across molecular data types. ITGA3 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (9) 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 ITGA3 RNA expression–survival associations across cancer types. High ITGA3 expression shows unfavorable associations in PAAD, LUSC, HNSC, LGG, MESO and GBM. The PAAD 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 PAAD as the clearest survival context for ITGA3 RNA expression.
This table summarizes ITGA3 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 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for ITGA3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ITGA3 shows lower tumor expression in KICH and higher tumor expression in HNSC, THCA, KIRP, KIRC and STAD. The HNSC box plot shows higher ITGA3 RNA expression in tumor versus normal tissue (log2 FC = +2.297, t-test p < 0.001).
This table shows molecular features associated with ITGA3 in patient tissues and cancer cell lines. In patient samples, ITGA3 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, ITGA3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and BONE.
